CN111492552A - Energy storage management and control method, system, computer equipment and storage medium - Google Patents

Abstract

The application provides an energy storage management method, an energy storage control method, systems, computer equipment and a storage medium. The energy storage management method is used for managing an energy storage device for providing reserve electric energy for a power consumer, and comprises the following steps: acquiring a power consumption prediction sequence of a power consumer in the remaining period of the current power consumption settlement period, acquiring current energy storage parameters of an energy storage device, and acquiring the current power consumption of the power consumer; generating an energy sequence of the energy storage device in the remaining period of the electricity consumption settlement period based on the acquired energy storage parameters, the electricity consumption and the electricity consumption prediction sequence; and repeatedly executing the steps based on the preset updating condition so as to manage the energy storage device by utilizing the energy sequence. According to the method and the device, the energy sequence is generated by using the acquired prediction sequence, the energy storage parameters of the energy storage device, the current power consumption of the power consumer and the like, and the energy storage device is correspondingly controlled by using the energy sequence, so that the purpose of effectively reducing the power consumption cost in one power consumption settlement period is achieved.

Description

Energy storage management and control method, system, computer equipment and storage medium Technical Field

The present disclosure relates to the field of power control, and in particular, to an energy storage management method, an energy storage control method, systems, a computer device, and a storage medium.

Background

The difference from residential electricity utilization is that in order to reduce the influence of sudden voltage change in a power grid on the load of a power supply system, power supply enterprises generally sign agreements with industrial and mining enterprises and enterprise parks on electricity utilization demand so as to limit the frequency of sudden electricity utilization peaks of the industrial and mining enterprises and the like. On one hand, power consumers of enterprises and the like reduce power consumption waste by controlling and improving management efficiency; on the other hand, along with energy storage device cost reduction, begin to set up energy storage device in some industrial and mining enterprises, enterprise's garden, the enterprise utilizes energy storage device to carry out the energy storage operation during power consumption valley, carries out the power supply operation during power consumption peak, comes the power consumption cost of cutting down during the power consumption peak.

The energy storage device is used in industrial and mining enterprises and the like, so that the industrial and mining enterprises and the like can select more compensation power consumption and compensation time, and the generation of sudden power consumption peak is effectively reduced. In the face of more complicated energy storage using modes, the problem of how to perform energy storage management quickly and accurately with predictability needs to be solved.

Disclosure of Invention

In view of the above, an object of the present application is to provide an energy storage management method, an energy storage control method, and systems, a computer device, and a storage medium, so as to solve the problems in the prior art that the energy storage management efficiency is low and the electricity cost of an electricity consumer cannot be effectively reduced.

To achieve the above and other related objects, a first aspect of the present application provides an energy storage management method for managing an energy storage device that provides a power consumer with reserve electric energy, comprising the steps of: acquiring a power consumption prediction sequence of the power consumer in the remaining period of the current power consumption settlement period, acquiring current energy storage parameters of the energy storage device, and acquiring the current power consumption of the power consumer; generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the acquired energy storage parameters, electricity consumption and electricity consumption prediction sequence; and repeatedly executing the steps based on a preset updating condition so as to manage the energy storage device by utilizing the energy sequence.

In certain embodiments of the first aspect, the energy storage management method further comprises: acquiring a self-powered system of the power consumer to provide a self-powered prediction sequence in the rest period of the current power consumption settlement period; the step of generating an energy sequence of the energy storage device in the remaining period of the electricity consumption settlement period based on the energy storage parameters, the electricity consumption and the electricity consumption prediction sequence comprises: and generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the energy storage parameters, the electricity consumption prediction sequence and a self-power prediction sequence.

In certain embodiments of the first aspect, the step of obtaining a self-powered system of the consumer providing a self-powered prediction sequence during a remaining period of the current power settlement period comprises: and predicting a self-power supply prediction sequence in the rest period of the current power utilization settlement period based on the acquired power generation related information of the self-power supply system.

In certain embodiments of the first aspect, the step of obtaining a sequence of power usage predictions for the power consumer over the remaining period of the current power usage settlement period comprises: acquiring power consumption related information according to the power consumption factors in the remaining period of the current power consumption settlement period; and predicting a power consumption prediction sequence in the remaining period of the current power consumption settlement period according to the power consumption related information.

In certain embodiments of the first aspect, the update condition comprises: the time interval between adjacent update operations is less than or equal to the metering time width used to determine the maximum demand; the step of executing the generation of the energy sequence according to the update condition includes: determining the maximum power consumption in the remaining duration of the metering time width according to the accumulated power consumption in the current metering time width and the predetermined maximum reference demand; and generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the energy storage parameters, the maximum electricity consumption and the electricity consumption prediction sequence.

In certain embodiments of the first aspect, the maximum reference demand comprises: the maximum value determined according to the counted electricity consumption within the historical metering time width of the current electricity consumption settlement period or a preset fixed value.

In certain embodiments of the first aspect, the step of generating the energy sequence of the energy storage device for the remainder of the electricity usage settlement period comprises: and under the constraint condition set based on the energy storage parameters, generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period by taking the minimum maximum demand of the electricity in the rest period of the current electricity consumption settlement period as an optimization target.

In certain embodiments of the first aspect, the step of generating the energy sequence of the energy storage device in the power utilization period with the minimum maximum demand of power in the remaining period of the current power utilization settlement period as an optimization target under the constraint condition set based on the energy storage parameter comprises: generating one or more candidate energy sequences within the remaining period of the current electricity usage settlement period under the constraint condition; and under the constraint condition and with the minimum maximum demand in the remaining period of the current electricity consumption settlement period as an optimization target, performing optimization processing on the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage devices in the electricity consumption period.

In certain embodiments of the first aspect, the step of optimizing the generated one or more candidate energy sequences comprises: determining a candidate energy sequence from the one or more candidate energy sequences according to a cutoff condition set for an optimization goal of minimizing a maximum demand in a remaining period of the current electricity consumption settlement period, and taking the candidate energy sequence as the energy sequence; and when the cutoff condition is not met, updating the generated at least one candidate energy sequence according to an updating strategy under the constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.

In certain embodiments of the first aspect, the energy storage parameters include at least two of: the detected or predicted energy stored by the energy storage device, the capacity of the energy storage device, the charge and discharge parameters of the energy storage device, and the loss parameters of the energy storage device.

In certain embodiments of the first aspect, the preset update condition comprises at least one of: the method comprises the steps of setting an updating condition based on an updating event of a power consumption prediction sequence, setting an updating condition according to a preset updating period, and setting an updating condition based on a triggering condition that the accumulated power consumption is larger than a preset threshold value in the current metering time width.

In certain embodiments of the first aspect, the preset update condition comprises at least one of: the method comprises the steps of setting an updating condition based on an updating event from a power consumption prediction sequence, setting an updating condition based on an updating event of a power consumption prediction sequence, setting an updating condition according to a preset updating period, and setting an updating condition based on a triggering condition that the accumulated power consumption is larger than a preset threshold value in the current metering time width.

In certain embodiments of the first aspect, the energy storage management method further comprises the step of outputting the energy sequence to a computer device.

A second aspect of the present application provides an energy storage control method for controlling an energy storage device that supplies stored electric energy to a consumer, including the steps of: acquiring an energy sequence of the energy storage device generated by the energy storage management method according to any one of the first aspect during the rest period of a power utilization settlement period; and determining control information used for controlling the operation of the energy storage device in the operation time interval by the energy storage device based on the energy value corresponding to the operation time interval in the acquired energy sequence.

In certain embodiments of the second aspect, the energy storage control method further comprises the step of controlling the operation of the energy storage device within respective operating time intervals based on the control information.

In certain embodiments of the second aspect, the energy storage control method further comprises: and acquiring and displaying at least one of the energy sequence, the self-power prediction sequence and the power consumption prediction sequence.

In certain embodiments of the second aspect, the energy storage control method further comprises the step of updating the control information based on a newly generated energy sequence.

In certain embodiments of the second aspect, the control information comprises at least one of: the charging and discharging control information of the energy storage device and the target energy storage value of the energy storage device in the operation time interval.

The application provides an energy storage management system for the management provides the energy memory of deposit electric energy for the consumer, includes: the acquisition module is used for acquiring a power consumption prediction sequence of the power consumer in the remaining period of the current power consumption settlement period, acquiring the current energy storage parameters of the energy storage device and acquiring the current power consumption of the power consumer; the generating module is used for generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the acquired energy storage parameters, the electricity consumption and the electricity consumption prediction sequence; restarting the acquisition module based on a preset update condition so that the energy storage device is managed by using the energy sequence.

In certain embodiments of the third aspect, the obtaining module is further configured to obtain that a self-powered system of the consumer provides a self-powered forecast sequence during a remaining period of a current power usage settlement period; the generation module is further configured to generate an energy sequence of the energy storage device during a remaining period of the electricity settlement period based on the energy storage parameter, the electricity usage prediction sequence, and a self-powered prediction sequence.

In certain embodiments of the third aspect, the obtaining module is configured to predict a self-powered prediction sequence during a remaining period of the current power usage settlement period based on the obtained power generation related information of the self-powered system.

In some embodiments of the third aspect, the obtaining module is configured to obtain the electricity-related information according to the electricity consumption factor in the remaining period of the current electricity consumption settlement period; and predicting a power consumption prediction sequence in the remaining period of the current power consumption settlement period according to the power consumption related information.

In certain embodiments of the third aspect, the update condition comprises: the time interval between adjacent update operations is less than or equal to the metering time width used to determine the maximum demand; the generating module is used for determining the maximum power consumption in the remaining duration of the measuring time width according to the accumulated power consumption in the current measuring time width and the predetermined predicted maximum demand; and generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the energy storage parameters, the maximum electricity consumption and the electricity consumption prediction sequence.

In certain embodiments of the third aspect, the maximum demand comprises: the maximum demand is determined by counting the historical electricity consumption of the measuring time width in the current electricity consumption settlement period, or is a preset fixed value.

In certain embodiments of the third aspect, the generating module is configured to generate the energy sequence of the energy storage device in the remaining period of the electricity usage settlement period with an optimization goal of minimizing the maximum demand of electricity in the remaining period of the current electricity usage settlement period under the constraint condition set based on the energy storage parameter.

In certain embodiments of the third aspect, the generation module is configured to generate, under the constraint, one or more candidate energy sequences for a remaining period of a current electricity usage settlement period; and under the constraint condition and with the minimum maximum demand in the remaining period of the current electricity consumption settlement period as an optimization target, performing optimization processing on the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage devices in the electricity consumption period.

In certain embodiments of the third aspect, the generation module is configured to determine a candidate energy sequence from the one or more candidate energy sequences as the energy sequence according to a cutoff condition set for an optimization goal of minimizing a maximum demand in a remaining period of the current electricity usage settlement period; and when the cutoff condition is not met, updating the generated at least one candidate energy sequence according to an updating strategy under the constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.

In certain embodiments of the third aspect, the energy storage parameters include at least two of: the detected or predicted energy stored by the energy storage device, a capacity of the energy storage device, a charge-discharge parameter of the energy storage device, a loss parameter of the energy storage device.

In certain embodiments of the third aspect, the preset update condition comprises at least one of: the method comprises the steps of setting an updating condition based on an updating event of a power consumption prediction sequence, setting an updating condition according to a preset updating period, and setting an updating condition based on a triggering condition that the accumulated power consumption is larger than a preset threshold value in the current metering time width.

In certain embodiments of the third aspect, the preset update condition comprises at least one of: the method comprises the steps of setting an updating condition based on an updating event from a power consumption prediction sequence, setting an updating condition based on an updating event of a power consumption prediction sequence, setting an updating condition according to a preset updating period, and setting an updating condition based on a triggering condition that the accumulated power consumption is larger than a preset threshold value in the current metering time width.

In certain embodiments of the third aspect, the energy storage management method further comprises an output module for outputting the energy sequence to a computer device.

The present application in a fourth aspect provides an energy storage control system for controlling an energy storage device for providing reserve electric energy for a power consumer, comprising: an obtaining module, configured to obtain an energy sequence of the energy storage device generated by the energy storage management system according to any one of the third aspects during a remaining period of a power utilization cycle; and the control module is used for determining control information used for controlling the operation of the energy storage device in the operation time interval on the basis of the energy value corresponding to the operation time interval in the acquired energy sequence.

In certain embodiments of the fourth aspect, the control module is configured to control operation of the energy storage device within respective operating time intervals based on the control information.

In certain embodiments of the fourth aspect, the obtaining module is further configured to obtain at least one of the energy sequence and a power usage prediction sequence; and the energy storage control system comprises a display module used for displaying at least one of the acquired energy sequence and the power consumption prediction sequence.

In certain embodiments of the fourth aspect, the control module is configured to update the control information based on a most recently generated energy sequence.

In certain embodiments of the fourth aspect, the control information comprises at least one of: charging and discharging control information of the energy storage device and a target energy storage value of the energy storage device in an operation time interval.

The fifth aspect of the present application provides a server for managing an energy storage device for providing reserve electric energy for a user, comprising: the interface unit is used for acquiring the current energy storage parameters of the energy storage device and acquiring the current power consumption of the power consumer; a storage unit for storing at least one program and a power consumption prediction sequence of the power consumer in the remaining period of the current power consumption settlement period; and a processing unit, configured to invoke the at least one program to coordinate the interface unit and the storage unit to execute the energy storage management method according to any one of the first aspect.

A sixth aspect of the present application provides a computer device comprising: an interface unit, configured to obtain an energy sequence provided by the server according to the fifth aspect; a storage unit for storing at least one program; and the processing unit is used for calling the at least one program to coordinate the interface unit and the storage unit to execute the energy storage control method in the second aspect.

A seventh aspect of the present application provides a computer-readable storage medium storing at least one program which, when invoked, performs the energy storage management method according to any one of the first aspects; or the energy storage control method according to any of the second aspects.

An eighth aspect of the present application provides an energy storage control system, comprising: the server according to the fifth aspect and the computer device according to the sixth aspect.

As described above, the energy storage management method, the energy storage control method, each system, the computer device, and the storage medium of the present application generate the energy sequence by using the pre-obtained prediction sequence, the current energy storage parameters of the energy storage device, and the current power consumption of the power consumer, and correspondingly control the energy storage device by using the energy sequence, thereby achieving the purpose of effectively reducing the power consumption cost in one power consumption settlement period.

Drawings

Fig. 1 is a schematic diagram showing the power transmission relationship among a power generation system, a self-powered system, a power utilization system and an energy storage device.

Fig. 2 shows a schematic structural diagram of a server according to an embodiment of the present application.

Fig. 3 is a flowchart illustrating an energy storage management method according to an embodiment of the present application.

Fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Fig. 5 is a flowchart illustrating an energy storage control method according to an embodiment of the present disclosure.

Fig. 6 is a schematic diagram of an energy storage control system according to an embodiment of the present disclosure.

Fig. 7 is a schematic diagram of an energy storage management system according to an embodiment of the present invention.

Fig. 8 is a schematic diagram of an energy storage control system according to an embodiment of the present disclosure.

Fig. 9 and 10 are schematic diagrams showing a plurality of sequences monitored after controlling the energy storage device with the energy sequences obtained according to the energy storage management strategy provided in the foregoing example.

Detailed Description

The following description of the embodiments of the present application is provided for illustrative purposes, and other advantages and capabilities of the present application will become apparent to those skilled in the art from the present disclosure.

As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some way.

Please refer to fig. 1, which is a schematic diagram illustrating a power transmission relationship among a power generation system, a self-powered system, a power utilization system and an energy storage device. The power generation system is managed by a power supplier, the self-power supply system, the power utilization system and the energy storage device are located on one side of power utilization parties such as power utilization enterprises, parks and buildings, the power generation system provides power for the power utilization system and the energy storage device through a power grid, and the self-power supply system is used for providing power for the power utilization system. The self-powered system comprises a solar power generation system, a wind power generation system, a transduction power generation system and the like. Furthermore, energy storage devices such as chemical energy storage devices and the like. Because there are constraints including the demand for power consumption and the daily unit price of power consumption between power supply and power consumption enterprises, the power consumption enterprises hope to reduce the power consumption cost by using the energy storage device.

In order to improve the utilization ratio of the energy storage device under the condition of ensuring the power utilization of enterprises, and effectively reduce the power utilization cost, the application provides an energy storage management method for managing the energy storage device which provides the stored electric energy for the power utilization party. The energy storage management method is mainly executed by an energy storage management system. The energy storage management system may be a software system configured at the server, and executes a corresponding program by using hardware of the configured server to provide an energy sequence of the energy storage device in an electricity consumption settlement period to be predicted for the electricity consumer, so that the electricity consumer can manage the energy storage device according to the energy sequence. Wherein, the electricity consumer and the power supplier use an electricity consumption settlement period as the settlement period of the electricity fee according to the convention. In a power consumption settlement period, on one hand, the power consumer pays the power fee to the power supplier according to the power consumption counted by the watt-hour meter, and on the other hand, the power supplier also requests the power consumer to pay the excess power fee by monitoring the power consumption peak value of the power consumer in the power consumption settlement period and monitoring the result. Wherein, one electricity consumption settlement period is usually the settlement period according to natural month, and can also be the settlement period according to bimonthus.

The application provides an energy storage management method for reducing the cost of electricity charges, particularly reducing the cost of excess electricity charges, in one electricity consumption settlement period for an electricity user. The energy storage management method is mainly executed by a server side.

Here, the server includes, but is not limited to, a single server, a server cluster, a distributed server group, a cloud server, and the like. The Cloud Service end comprises a Public Cloud (Public Cloud) Service end and a Private Cloud (Private Cloud) Service end, wherein the Public or Private Cloud Service end comprises Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), Infrastructure as a Service (IaaS), and Infrastructure as a Service (IaaS). The private cloud service end is used for example for an Aliskian cloud computing service platform, an Amazon cloud computing service platform, a Baidu cloud computing platform, a Tencent cloud computing platform and the like.

The server is in communication connection with the energy storage control system of the energy storage device, the power utilization control system of the power consumer, the production activity management system, the self-powered system and the like, and even can be in data connection with a third-party system, and the crawler technology is utilized to acquire internet data and the like related to power utilization of the power consumer in the internet. Wherein the energy storage control system includes, but is not limited to: the energy storage device comprises a detection device for detecting the energy stored by the energy storage device, a charge and discharge control system of the energy storage device and the like. The electricity utilization control system includes but is not limited to: metering devices (e.g., electrical meters), electrical equipment control systems, etc. installed within an enterprise. The management system of the production activity includes but is not limited to: a Manufacturing Execution System (MES), an Enterprise Resource Planning System (ERP), and the like. The self-powered systems include, but are not limited to: a detection device for detecting the amount of power generation from the power supply system, a power generation control system of the self-powered system, and the like. Examples of the third-party system include a self-contained server for storing historical electricity consumption data, a self-contained server for storing historical electricity price data, a self-contained WEB server for acquiring an enterprise electricity consumption plan and the like. Examples of the internet data include weather forecast data, which may be predicted based on historical contemporaneous weather data obtained from the internet, or weather forecast data obtained directly from a weather website or other websites.

Referring to fig. 2, which is a schematic structural diagram of a server according to an embodiment of the present disclosure, as shown in the figure, the server includes an interface unit 11, a storage unit 12, and a processing unit 13. The storage unit 12 includes a nonvolatile memory, a storage server, and the like. The nonvolatile memory is, for example, a solid state disk or a usb disk. The storage server is used for storing the acquired various electricity utilization related information and power supply related information. The interface unit 11 includes a network interface, a data line interface, and the like. Wherein the network interfaces include, but are not limited to: network interface devices based on ethernet, network interface devices based on mobile networks (3G, 4G, 5G, etc.), network interface devices based on near field communication (WiFi, bluetooth, etc.), and the like. The data line interface includes, but is not limited to: USB interface, RS232, etc. The interface unit is connected with data such as each system of a power supply party, each system of a power utilization party, a third party system, the Internet and the like. The processing unit 13 is connected to the interface unit 11 and the storage unit 12, and includes: a CPU or a chip integrated with a CPU, a programmable logic device (FPGA), and a multi-core processor. The processing unit 13 also includes memories, registers, etc. for temporarily storing data.

Please refer to fig. 3, which is a flowchart illustrating the energy storage management method. The energy storage management method is mainly executed by a processing unit 13 in a server, and data interaction is performed by reading at least one program stored in a storage unit 12 by the processing unit and according to hardware connection between the processing unit and hardware units such as the storage unit and an interface unit. In some practical applications, the server may perform the following steps at the beginning of the current electricity settlement period to obtain the energy sequence provided by the energy storage management method during the current electricity settlement period to manage the energy storage device. In still other practical applications, as the accumulated power consumption of the power consumer continuously increases in the current power consumption settlement period, the energy stored in the energy storage device needs to be adaptively adjusted in time during the remaining period of the current power consumption settlement period, so that the power consumption cost of the power consumer in the whole power consumption settlement period is as low as possible. For this purpose, the processing unit will repeatedly perform the following steps in order to adjust the energy in the energy storage device in time.

In step S110, a power consumption prediction sequence of a power consumer in a remaining period of a current power consumption settlement period is obtained, a current energy storage parameter of the energy storage device is obtained, and a current power consumption of the power consumer is obtained.

The electricity consumption prediction sequence refers to a set of a plurality of electricity consumptions predicted according to a time sequence in an electricity consumption settlement period. The electricity consumption is obtained by the electricity consumers and is related to the electricity consumption factors of the daily production activities of the electricity consumers. Wherein the electricity utilization factors include but are not limited to: human programs such as scheduling programs, store activity programs, programs summarized according to weather or social activity rules (e.g., weekdays, holidays). For example, for the electricity utilization situation of the product a produced in the factory, the electricity utilization related information may include historical electricity utilization data of the product a produced, equipment usage information determined based on the scheduling plan of the product a, electricity utilization information of the equipment, and the like. For another example, the power consumption related information may include information on use of manufacturing equipment such as machine tools and industrial control systems, information on use of air conditioners and power consumption, information on use of lighting lamps on working days and holidays, and information on use of low-power consumption equipment such as computers, which are set based on scheduling, for power consumption of large industrial and mining enterprises or factory parks. In some cases where air conditioner usage information is not set, the air conditioner usage information may also be determined based on weather forecast conditions. For example, the use of air conditioning is controlled in accordance with the forecasted air temperature. In summary, the electricity consumption prediction sequence can be obtained according to weather prediction information, scheduling information, electricity consumption of high-power consumption equipment in different operation states, holidays, and calculated electricity consumption graphs of low-power consumption equipment, historical electricity consumption data and the like. For the electricity consumers, in some examples, the sequence of predicted electricity usage within one electricity settlement period may be predicted before the corresponding electricity settlement period begins. In other examples, the sequence of power usage predictions for a power usage settlement period may be adjusted as the actual power usage is generated. For this, the step S110 includes steps S111 and S112.

In step S111, the electricity consumption related information is acquired according to the electricity consumption factor in the remaining period of the current electricity consumption settlement period. Here, in order to update the power consumption factor that affects the power consumption prediction sequence in the remaining period of the current power consumption settlement period in time, the power consumption related information is acquired according to the updated power consumption factor when the energy storage sequence in the remaining period is predicted.

In step S112, a power consumption prediction sequence in the remaining period of the current power consumption settlement period is predicted according to the power consumption related information.

The server-side system obtains a power consumption prediction sequence by establishing a prediction model, calculates at least one of prediction algorithms such as Random Forest (Random Forest), long and short term memory network (L STM), iterative decision tree (GBRT) and Convolutional Neural Network (CNN) by taking the power consumption related information as the input of the prediction model, and obtains the power consumption prediction sequence of the power consumption in the residual period of the power consumption settlement period.

The above-described method for predicting the amount of electricity used based on the electricity-related information is only an example, and is not a limitation of the present application. Those skilled in the art should understand that other electricity related information affecting the electricity consumption prediction sequence can also be used as an input of the prediction model to obtain the electricity consumption prediction sequence through a prediction algorithm, and details are not repeated here.

When step S110 is executed at any time from the start point of the current electricity consumption settlement period, the latest energy storage parameters of the energy storage device need to be acquired. The energy storage parameter includes, but is not limited to, a detected or predicted energy stored by the energy storage device, and at least one of a predetermined capacity of the energy storage device, a charge/discharge parameter of the energy storage device, and a loss parameter of the energy storage device. Wherein the capacity of the energy storage device comprises a maximum capacity and a minimum capacity of the energy storage device. The charge and discharge parameters of the energy storage device comprise the charge speed of the energy storage device, the discharge speed of the energy storage device, the upper limit and the lower limit of charge and discharge power and the like. The loss parameters of the energy storage device include a charge energy conversion rate of the energy storage device, a discharge energy conversion rate of the energy storage device, and a self-discharge amount of the energy storage device. The energy storage parameter may also be a set of parameters determined based on a temperature dependent variable.

It should be noted that, in practical engineering, the energy storage parameters of the energy storage device that can be obtained may only include two or more than two of the above, and not all of the energy storage parameters need to be obtained.

And the server also acquires the current power consumption of the power consumers in a synchronous or non-sequential execution sequence with the acquired power consumption prediction sequence and the acquired energy storage parameters. Wherein, the current electricity consumption refers to the used electricity consumption in the current electricity consumption settlement period. For example, at the beginning of the current electricity consumption settlement period, the accumulated electricity consumption of the enterprise is obtained from a metering device (such as a watt-hour meter) installed in the enterprise, and the accumulated electricity consumption is used as the beginning electricity consumption of the current electricity consumption settlement period.

In some practical applications, enterprises have not only energy storage devices, but also self-powered systems. To this end, in other embodiments, in a case where the power consumer is configured with a self-powered system, that is, the power available for the power consumer includes power provided by the self-powered system, the step S110 further includes: the self-powered system that acquires the electricity consumer provides a self-powered forecast sequence during the remainder of the current electricity usage settlement period.

The self-supply power amount prediction sequence is a set of a plurality of self-supply power amounts predicted in time sequence in a power consumption settlement period. The self-powered systems include, but are not limited to: photovoltaic power generation system, heat conversion system, trigeminy supplies system, wind power generation system etc..

According to the data provided by the self-power system used by the actual power consumer, in some examples, the service end predicts the self-power prediction sequence in the rest period of the current power consumption settlement period based on the acquired power generation related information of the self-power system. Wherein the power generation related information includes, but is not limited to: historical power generation data, and factors influencing power generation based on the working principle of the self-powered system. For example, in the case of a self-powered system employing photovoltaic power generation, factors affecting power generation mainly include solar irradiance and the like. For another example, in the case that the self-powered system employs wind power generation, the factors influencing the power generation mainly include wind speed, wind direction, and the like. For another example, in the case of a self-powered system that employs thermal conversion to generate electricity, the factors that affect the generation of electricity include mainly the thermal conversion efficiency of the system, the detected temperature, and the like.

The self-power-supply prediction sequence in the power utilization cycle is obtained by taking the power generation related information as the input of the prediction model and adopting a prediction algorithm such as Random Forest (Random Forest), long and short term memory network (L STM), iterative decision tree (GBRT), Convolutional Neural Network (CNN) and the like to calculate, and the self-power-supply prediction sequence in the power utilization cycle is obtained as the output.

It should be noted that the above embodiments of obtaining the self-power prediction sequence are only examples, and are not intended to limit the present application. One skilled in the art can construct a model for predicting the self-supply power prediction sequence in conjunction with the various embodiments mentioned in the foregoing power rate prediction sequence. For example, a self-powered electricity supply amount prediction sequence is calculated based on the input of the prediction model, the adopted prediction algorithm and the error range obtained through detection so as to improve the accuracy of subsequent prediction.

It should also be noted that the above manner of self-power prediction by using the self-power supply system is only an example, and not a limitation of the present application. Those skilled in the art will understand that the power generation related information based on the self-powered electricity quantity prediction sequence differs according to the power supply mode of the actual self-powered system, and the details are not repeated here.

In step S120, an energy sequence of the energy storage device for the remaining period of the electricity settlement period is generated based on the acquired energy storage parameter, the electricity usage amount, and the electricity usage amount prediction sequence.

Here, the server side can perform energy storage management on the energy storage device according to the actual management requirement of the power consumer, and further generate an energy sequence meeting the management requirement. Wherein the management requirements include, but are not limited to: the total price of the electricity consumption is reduced as much as possible, the electricity consumption of the peak value of the electricity consumption is reduced as much as possible, and the like. In order to provide continuous energy storage management for the energy storage device in the electricity consumption settlement period, the server side provides energy values with time intervals in the electricity consumption settlement period and forms an energy sequence. Wherein the time interval is related to the length of time it takes to control the energy storage, the time interval of updating the energy sequence, etc. For example, the time interval is longer than the length of time it takes to control the energy storage device. As another example, the time interval is shorter than or equal to the time interval of the update energy sequence.

For an enterprise provided with an autonomous power supply system, it is obvious that the step S120 should also add an autonomous power supply amount prediction sequence as an input parameter for obtaining an energy sequence, i.e. based on the energy storage parameter, the power consumption amount prediction sequence and the autonomous power amount prediction sequence, an energy sequence of the energy storage device during the remaining period of the power consumption settlement period is generated. For example, based on the energy storage parameter, the power consumption prediction sequence, and the self-powered prediction sequence, an energy sequence is generated with a maximum demand minimum for power during the remaining period of the current power consumption settlement period as an optimization target, or with a power consumption cost minimum during the remaining period of the current power consumption settlement period as an optimization target.

In some embodiments, the step S120 includes generating the energy sequence of the energy storage device in the remaining period of the electricity settlement period with an optimization goal of minimizing the maximum demand of electricity in the remaining period of the current electricity settlement period under the constraint condition set based on the energy storage parameter.

Wherein, for an energy storage device, the mathematical model can be described as:

wherein t is time (h); SOC (0) is the initial electric quantity (kWh) of the energy storage device; SOC (t) is the State of Charge (SOC, State of Charge,%); p is a radical of_iCharging and discharging power sequences (kW), (p) for consumer energy storage devices_iCharging power, p, for the energy storage means when greater than 0_iDischarge power, p, of the energy storage device when less than 0_iEqual to 0 indicates that the energy storage device is not operating); ess _ max is the maximum capacity of the energy storage device (kWh); e is the charging and discharging efficiency (%) of the energy storage device, e_cFor charging efficiency, e_dFor discharge efficiency, e_l△ t is the electrical load measurement time interval for calculating the fine agreed by the contract load mechanism.

In this case, at least one constraint condition is set according to the energy storage parameters of the energy storage device that can be actually obtained, which is intended to prevent an abnormality of the energy storage device when managing the energy storage device. For example, it is avoided that a certain energy value in the generated energy sequence exceeds the maximum capacity of the energy storage means, etc. Based on the optimization objective function and the model of the energy storage device, wherein the charging amount E of the energy storage device_G2BAnd energy storage device discharge capacity E_B2LControlled by a model of the energy storage device, constraints of the model including at least one of: constraints set for the energy storage device, and constraints set based on the relationship between the consumption of electric energy and the supply of electric energy.

Wherein the constraint condition set for the energy storage device includes at least one of:

SOC (t) is more than or equal to 0; and SOC (t) is less than or equal to 100 percent

In addition, according to other parameters such as the user energy situation, the establishment of relevant constraint conditions comprises the following steps:

p_i≤C_chr(ii) a And-p_i≤-C_dis

Wherein, C_chrCharging the energy storage device to an upper power limit; c_disThe upper discharge power limit of the energy storage device.

Here, the maximum demand may be represented by a maximum power consumption value determined by the power consumption reported by the power consumer to the power provider in a unit metering time width within one power consumption settlement period, according to a contract between the power provider and the power consumer. For example, in a power consumption settlement period, the metering device of the power consumer provides the accumulated power consumption once every 30 minutes, and the maximum demand is the maximum value of the power consumption in each 30 minutes in the power consumption settlement period; wherein the 30 minutes is a unit measurement time width. The maximum demand can also be expressed by counting the maximum average power consumption value of each power consumption peak value reported by the power consumers in a preset unit metering time width in a power consumption settlement period. For example, in a power consumption settlement period, the metering device of the power consumer determines the power consumption of the power consumer every 30 minutes according to the accumulated power consumption provided every 30 minutes, and traverses all the power consumption in the power consumption settlement period by taking the unit metering time width as a time window, wherein the maximum demand is the maximum value of the average power consumption in each unit metering time width based on the traversal. The maximum demand may also be expressed in terms of the number of times the consumer exceeds the contractual demand within a power settlement period. For example, in a power consumption settlement period, the metering device of the power consumer provides the accumulated power consumption once every 30 minutes, and the maximum demand is the number of times of exceeding the contractual demand in each 30 minutes in the power consumption settlement period. For this reason, the maximum power consumption minimum, the number of times of power consumption exceeding contractual demand, or the like may be set to be the minimum in such a manner that the maximum demand of power consumption in the remaining period of the current power consumption settlement period is the minimum as the optimization target.

Derived by the above example: the function of the optimization objective is:

wherein the content of the first and second substances,

wherein, L_iUsing electricity (except for energy storage devices) for customers in a power sequence (kW); f. of_UCCA penalty price (yuan/kW/month) outside the contract; days of the month is Days.

After determining the function and constraints that optimize the objective, the step of generating a sequence of energies for the energy storage device for the remainder of the power usage settlement period comprises: generating one or more candidate energy sequences within the remaining period of the current electricity usage settlement period under the constraint condition; and under the constraint condition and with the minimum maximum demand in the remaining period of the current electricity consumption settlement period as an optimization target, performing optimization processing on the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage devices in the electricity consumption period.

In certain embodiments, one or more candidate energy sequences for the remainder of the power usage settlement period are generated based on the predicted or detected electrical energy stored by the energy storage device and the constraints described above. Here, for the initialization candidate energy sequence (also referred to as an initialization candidate solution), one or more preset candidate energy sequences, that is, candidate solutions, may be generated in a random manner.

The generated candidate solution is optimized in the remaining period of the electricity settlement period under the constraint condition, for example, the generated candidate solution is optimized in the remaining period of the electricity settlement period by utilizing the variation trend of the maximum demand corresponding to the candidate solution in the △ t time period, so that an energy sequence with the minimum maximum demand in the remaining period of the electricity settlement period under the constraint condition is obtained.

In other examples, the method includes generating a plurality of candidate solutions, and filtering and/or adjusting the plurality of candidate solutions to obtain an energy sequence under at least one constraint condition and with a minimum maximum demand in the remaining period of the electricity settlement period as an optimization target, for example, calculating a maximum demand corresponding to each of the plurality of candidate solutions generated under the constraint condition, selecting a candidate solution with a minimum maximum demand as the generated energy sequence, calculating a maximum demand corresponding to each of the plurality of candidate solutions generated under the constraint condition, selecting a candidate solution with a minimum maximum demand, and performing optimization processing on the generated candidate solution by using a variation trend of the maximum demand corresponding to the candidate solution in a time period of △ t to obtain an energy sequence under at least one constraint condition and with a minimum maximum demand in the remaining period of the electricity settlement period as an optimization target.

In some embodiments, the step of performing optimization processing on the generated one or more candidate energy sequences includes: determining a candidate energy sequence from one or more candidate energy sequences according to a cutoff condition set for an optimization objective of minimizing a maximum demand in a remaining period of the electricity consumption settlement period, and taking the candidate energy sequence as the energy sequence; and when the cutoff condition is not met, updating the generated at least one candidate energy sequence according to an updating strategy under at least one constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.

The updating strategy comprises but is not limited to Lagrange Multiplier (L) method, sequence linear programming (S L P), Sequence Quadratic Programming (SQP), Interior Point method (Interior Point), Exterior Point method (Exterior Point), Active Set method (Active Set), Trust domain reflection algorithm (Trust Region reflection), Heuristic algorithm (Hearistic Algorithms), Meta-Heuristic algorithm (Meta-Gaussian), Evolutionary algorithm (evolution Algorithms), Swarm Intelligent algorithm (Swarm Intigrithms), Neural network algorithm (Neural network strategy), taboo search algorithm, simulated annealing algorithm, ant colony optimization algorithm, greedy optimization algorithm, self-adaptive search algorithm, random adaptive search algorithm, and other artificial immune system optimization or similar artificial selection strategies.

Taking the model constraint condition formula and the optimization target formula of the energy storage device as examples, and obtaining a specific example of the energy sequence by using a clonal selection algorithm, the specific example is as follows: based on the model constraint condition of the energy storage device and certain prior calculation, carrying out constraint limitation on a solution space, limiting the solution space in a local space range meeting the constraint condition, and obtaining a plurality of candidate solutions; all the candidate solutions are substituted into a preset optimization objective function to obtain an optimization objective value corresponding to each candidate solution (an evaluation step for short); then, sorting according to the optimized target values corresponding to the candidate solutions, screening and reserving a certain number of excellent solutions and eliminating the rest solutions (screening step for short); sorting the optimization target values in the order from small to large (namely the order from low to high of the total price of the electricity), screening out candidate solutions corresponding to the optimization target values of n (n is more than or equal to 1) before ranking, and eliminating the rest solutions. Next, a corresponding number of clones are performed on the n candidate solutions retained by the screening, and random variation with a certain probability (variation rate) is introduced in the cloning process, so as to generate new candidate solutions based on each retained candidate solution (referred to as a variant cloning step). Wherein the mutation rate is limited by the model constraint condition to ensure that the obtained new candidate solution is obtained based on the slight change of the candidate solution before the mutated clone. In this case, the variation rate may be introduced to all the clonal solutions of the retained candidate solutions, or only to the partial solutions. Repeating the steps of evaluating, screening and mutating cloning until the actual iteration times reach the cut-off condition of the preset iteration times; and all the finally obtained candidate solutions are substituted into the optimization objective function to obtain the optimization target value corresponding to each candidate solution, and the candidate solution corresponding to the minimum optimization target value is selected as the energy sequence of the energy storage device.

Taking the model constraint conditions and the optimization target of the energy storage device as examples, and obtaining a specific example of the energy sequence by using the SQP algorithm, the specific example is as follows: under the model constraint condition of the energy storage device and the constraint of certain prior calculation, converting an objective function and a constraint function by using Taylor expansion, and calculating by using the converted objective function and the constraint function to obtain a candidate solution and an error gradient; adjusting the candidate solution based on the obtained error gradient until a cutoff condition that the error gradient is smaller than a preset gradient threshold value is met; and taking the finally obtained candidate solution as an energy sequence of the energy storage device.

It should be noted that, during the evaluation, screening and iteration process of the candidate solution, besides the above two examples, the above steps may be adaptively adjusted and selected based on the aforementioned other algorithms, for this reason, the aforementioned other algorithms and other algorithms applicable to the technical idea described in the present application are used to determine the energy sequence of the energy storage device, and should not be considered as a specific example based on the technical idea described in the present application, and detailed description is omitted here.

It should be further noted that the cutoff conditions described in any of the above examples are not strictly in one-to-one correspondence with the used algorithms, and may also be set according to actual design requirements, for example, the change of the optimal target result of the latest iterations is smaller than a preset threshold, and the like, which is not described herein again.

It should also be noted that each stored energy value in the generated energy sequence is separated by an operation time interval. The operating time interval may be predetermined by the energy storage management system, typically having a duration greater than a maximum duration (or average duration) required for a controlled change in the energy storage device.

After the energy sequence of the energy storage device managed in the rest period of the electricity consumption settlement period is determined by using any one of the above examples, the above steps are repeatedly executed based on the preset updating condition, so that the energy sequence is timely adjusted according to the input information obtained by updating, and the energy storage control system can manage the energy storage device by using the energy sequence. Wherein, the input information is the aforementioned current power consumption, self-powered prediction sequence, power consumption prediction sequence, energy storage parameter, and the like.

Wherein the preset updating condition is related to the updating of the input parameters. In some examples, the enterprise is not supplementarily powered by a self-powered system, and the preset update condition includes at least one of: the method comprises the steps of setting an updating condition based on an updating event of a power consumption prediction sequence, setting an updating condition according to a preset updating period, and setting an updating condition based on a triggering condition that the accumulated power consumption is larger than a preset threshold value in the current metering time width. For example, when the server receives a power consumption prediction sequence or power consumption related information for calculating the power consumption prediction sequence, an update event of the power consumption prediction sequence is generated, the server executes an example including the foregoing steps according to the generated update event and the obtained updated power consumption prediction sequence or power consumption related information to obtain the latest power consumption prediction sequence, energy storage parameters, and the current power consumption of the power consumer in the current power consumption settlement period, and generates an energy sequence by using a pre-constructed algorithm with the latest obtained information as input. As another example, the server re-executes the above example including the foregoing steps at a fixed update period to generate an energy sequence. For another example, the server updates the generated energy sequence according to a preset update period, where the update period is less than a metering time width, and during the update execution, the server also monitors the power consumption accumulated in the current metering time width, and when the power consumption accumulated in the current metering time width is greater than a preset threshold, the update period is shortened so as to monitor the maximum demand that may occur in the current metering time width more closely.

In other examples, the enterprise supplies supplementary power by using a self-powered system, and the preset updating condition further includes, in addition to the above updating condition: an update condition set based on an update event from the power prediction sequence. For example, when the server receives a self-power prediction sequence or power generation related information for calculating the self-power prediction sequence, an update event indicating that a power consumption prediction sequence is generated, the server executes an example including the foregoing steps according to the generated update event, with the obtained updated power consumption prediction sequence or power consumption related information, to obtain the latest power consumption prediction sequence, energy storage parameters, and current power consumption of the power consumer in the current power consumption settlement period, and generates an energy sequence using a pre-constructed algorithm with the latest obtained information as input.

In still other examples, the update condition further includes: the time interval between adjacent update operations is less than or equal to the metering time width used to determine the maximum demand. For example, the update period from at least one of the power consumption prediction sequence, the power consumption prediction sequence is less than the metering time width. As another example, the predetermined update period is less than the metering time width.

The step of regenerating the energy sequence in the remaining period of the current electricity usage settlement period according to the updated condition further includes: determining the maximum power consumption in the remaining duration of the metering time width according to the accumulated power consumption in the current metering time width and the predetermined maximum reference demand; and generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the energy storage parameters, the maximum electricity consumption and the electricity consumption pre-sequencing sequence.

Taking the maximum demand as an example of the maximum power consumption value determined by the power consumption reported to the power supply side by the power consumer according to the unit metering time width in a power consumption settlement period, the server side repeatedly executes the steps at least twice within the unit metering time width. Therefore, according to the preset maximum reference demand Q, the server can obtain the maximum power consumption Q2 available for the power consumer to use for reference in the remaining duration of the current metering time width according to the power consumption Q1 consumed by the power consumer from the starting time of one metering time width to the updating time. Wherein, the maximum reference demand can be contractual demand of the contract, which is a fixed value in the current electricity consumption settlement period. For example, the initial value of the maximum reference demand is the contractual demand, or the maximum reference demand is always the contractual demand. The maximum reference demand may also be a maximum value determined according to the counted electricity usage within the historical metering time width of the current electricity settlement period. For example, the initial value of the maximum reference demand is the contract demand, the server side counts the electricity consumption in each metering time width of the history every metering time width, and determines the maximum value of each counted electricity consumption as the maximum reference demand in the current metering time width. By determining the maximum reference demand within the current metering time span, the server can predict whether a larger maximum demand will be generated within the current metering time span according to the predicted sequence of power consumptions and the maximum power consumption Q2, and obtain an energy sequence that minimizes the maximum demand within the remaining period by using the aforementioned input parameters. The input parameters comprise energy storage parameters, maximum power consumption, a power consumption prediction sequence, a self-powered prediction sequence and the like.

It should be noted that the above update examples are not mutually exclusive, and may be used in combination with each other in some practical applications, and are not described in detail herein.

It should be further noted that, as will be understood by those skilled in the art, the way of obtaining the energy sequence with the minimum maximum demand as the optimization goal by using the energy storage management method described in the present application can also be applied to the way of obtaining the corresponding energy sequence with the minimum electricity cost as the optimization goal. And will not be repeated here.

The generated energy sequence may be provided to a skilled person. For example, the server sends the generated energy sequence to a computer device with a display terminal used by a technician for display.

The energy sequence corresponding to the remaining period of the current electricity consumption settlement period obtained by the energy storage management method can be pushed to a control system built in the energy storage device, so that the energy storage control system can execute the energy storage control method according to the energy sequence. Therefore, the application also provides an energy storage control method. The energy storage control system may be a software system configured on the computer device, and controls the energy storage device by using the electric party based on the acquired energy sequence of the energy storage device, so as to achieve the purpose of achieving the preset optimization goal in the electricity consumption settlement period.

Here, the computer device may be a device located in a power utilization control room of an enterprise, or a service end in the internet. The server includes, but is not limited to, a single server, a server cluster, a distributed server cluster, a cloud server, and the like. The Cloud Service end comprises a Public Cloud (Public Cloud) Service end and a Private Cloud (Private Cloud) Service end, wherein the Public or Private Cloud Service end comprises Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), Infrastructure as a Service (IaaS), and Infrastructure as a Service (IaaS). The private cloud service end is used for example for an Aliskian cloud computing service platform, an Amazon cloud computing service platform, a Baidu cloud computing platform, a Tencent cloud computing platform and the like.

The computer device is in communication connection with an electricity price issuing system of an electricity supplier (or an electricity market manager, such as a government department), an energy storage control system of an energy storage device, an electricity utilization control system of an electricity consumer, a production activity management system, a self-powered system and the like, and can even be in data connection with a third party system, and internet data and the like related to electricity utilization of the electricity consumer in the internet are acquired by using a crawler technology. Wherein the electricity rate distribution system is a system in which an electricity provider distributes electricity rates. The energy storage control system includes, but is not limited to: the energy storage device comprises a detection device for detecting the energy stored by the energy storage device, a charge and discharge control system of the energy storage device and the like. The electricity utilization control system includes but is not limited to: metering devices (e.g., electrical meters), electrical equipment control systems, etc. installed within an enterprise. The management system of the production activity includes but is not limited to: a Manufacturing Execution System (MES), an Enterprise Resource Planning System (ERP), and the like. The self-powered systems include, but are not limited to: a detection device for detecting the amount of power generation from the power supply system, a power generation control system of the self-powered system, and the like. Examples of the third-party system include a self-owned server for storing historical electricity consumption data, a self-owned server for storing historical electricity price data, a self-owned WEB server for acquiring an enterprise electricity consumption plan and the like. Examples of the internet data include weather forecast data, which may be predicted based on historical contemporaneous weather data obtained from the internet, or weather forecast data obtained directly from a weather website or other websites.

Referring to fig. 4, which is a schematic structural diagram of a computer device according to an embodiment of the present application, as shown in the figure, the computer device includes an interface unit 61, a storage unit 62, and a processing unit 63. The storage unit 62 includes a nonvolatile memory, a storage server, and the like. The nonvolatile memory is, for example, a solid state disk or a usb disk. The storage server is used for storing the acquired various electricity utilization related information and power supply related information. The interface unit 61 includes a network interface, a data line interface, and the like. Wherein the network interfaces include, but are not limited to: network interface devices based on ethernet, network interface devices based on mobile networks (3G, 4G, 5G, etc.), network interface devices based on near field communication (WiFi, bluetooth, etc.), and the like. The data line interface includes, but is not limited to: USB interface, RS232, etc. The interface unit is connected with data such as each system of a power supply party, each system of a power utilization party, a third party system, the Internet and the like. The processing unit 63 connects the interface unit 61 and the storage unit 62, and includes: a CPU or a chip integrated with a CPU, a programmable logic device (FPGA), and a multi-core processor. The processing unit 63 also includes memories, registers, etc. for temporarily storing data.

Please refer to fig. 5, which is a flowchart illustrating the energy storage control method. The processing unit 63 reads at least one program stored in the storage unit to perform the energy storage control method as described below.

In step S210, the energy sequence of the energy storage device during the remaining period of the electricity consumption settlement period is acquired. Wherein, the energy sequence is provided by a server side executing any energy storage management method.

Here, according to a data communication mode between the server and the computer device, the server pushes the updated energy sequence to the computer device, or the computer device acquires the latest energy sequence from the server according to a preset acquisition period.

In step S220, control information used by the energy storage device to control the operation of the energy storage device in the operation time interval is determined based on the energy value corresponding to the operation time interval in the acquired energy sequence. Wherein the operation time interval is set according to the time interval of adjacent energy values in the energy sequence. For example, the time interval between adjacent energy values in the energy sequence is half an hour, and the corresponding operation time interval is half an hour.

And the energy storage control system determines control information used for controlling the operation of the energy storage device in the operation time interval based on the energy value corresponding to the operation time interval in the acquired energy sequence. Wherein the control information comprises at least one of: the charging and discharging control information of the energy storage device and the target energy storage value of the energy storage device in the operation time interval. Wherein, the target storage value includes: and determining a target energy storage threshold interval corresponding to the energy value in the energy sequence according to the self-loss coefficient of the preset energy storage device. The charge and discharge control information of the energy storage device includes, for example, a time period from charging (or discharging) of the energy value a1 to charging and discharging of the energy storage device correspondingly controlled by the energy value a2, which is set according to a preset charge and discharge speed; wherein the energy values a1 and a2 are adjacent energy values in the energy sequence.

In some embodiments, the energy storage management method further comprises the step of controlling the operation of the energy storage device within respective operation time intervals based on the control information. The energy storage management system automatically outputs a control command containing the control information to the energy storage device within a corresponding operation time interval according to the control information generated based on the energy sequence, so that the energy storage device correspondingly executes charging or discharging, and further realizes the purposes of storing electric energy during a period with a small amount of electricity consumption and releasing the electric energy during a period with a large amount of electricity consumption.

In still other embodiments, the energy storage control system is operated by a technician to perform control of the energy storage device, and for this purpose, the energy storage control method further includes a step of acquiring and displaying at least one of the energy sequence, the self-powered predicted sequence, and the power usage predicted sequence. Here, the server side where the energy storage management system is located sends at least one of the generated energy sequence, the obtained self-power prediction sequence and the power consumption prediction sequence to the energy storage control system, and the energy storage control system displays the obtained sequences to technicians in the form of a line graph, a histogram and the like so as to be referred by the technicians and execute control operation on the energy storage device in a corresponding operation time interval. The display unit may include the obtained control information and the like together with the sequences.

Please refer to fig. 9 and 10, which show schematic diagrams of a plurality of monitored sequences after controlling the energy storage device according to the energy sequence obtained by the energy storage management strategy provided by the foregoing example, wherein a graph 1 shown in fig. 9 is L_i+p_iThe actual power utilization sequence of the customer under the contract load management strategy is shown, and the sequence formed by the operation process of the energy storage device corresponding to the actual power utilization sequence comprises that a graph 2 in figure 9 is L_iIndicating a customer electricity power sequence; and soc (t) shown in fig. 10, which represents the energy storage device state of charge.

In summary, the energy storage management method and the energy storage control method provided by the application generate the energy sequence by using the pre-obtained prediction sequence, the current energy storage parameters of the energy storage device, the current power consumption of the power consumer and the like, and correspondingly control the energy storage device by using the energy sequence, thereby achieving the purpose of effectively reducing the power consumption cost in one power consumption settlement period.

Referring to fig. 6, the present application further provides an energy storage control system, which includes a server and a computer device. Wherein, the server and the computer device are in data communication. For example, the server and the computer device are separated into two different physical locations in a physical space and implement data communication through a data network such as the internet, a mobile network, and the like. For another example, the server and the computer device are the same entity server, and the server and the computer device perform data communication according to program scheduling. Furthermore, the server 41 can communicate with the self-powered system 45 of the consumer, the metering device 44, the energy storage device 43, and the like to obtain various data for generating the energy sequence. For this purpose, the server 41 may refer to the steps executed by the server shown in fig. 2 and described above, and the computer device 42 may refer to the steps executed by the computer device shown in fig. 4 and described above. And will not be described in detail herein.

The server side establishes a constraint condition according to the acquired energy storage parameters, establishes one or more candidate energy sequences from the candidate energy sequences according to the acquired energy consumption prediction sequence, the energy storage parameters, the current power consumption and other information, wherein the maximum demand is the minimum of the optimized target function, the server side generates a plurality of candidate energy sequences in the remaining period in a random mode under the established constraint condition, selects one or more candidate energy sequences from the candidate energy sequences according to a sorting rule which the maximum demand reaches from the minimum, acquires a plurality of candidate energy sequences according to a preset updating strategy, adjusts the selected candidate energy sequences until the selected candidate energy sequences meet the operation rule of the energy storage control device, and provides the selected candidate energy sequences for the control device according to the calculated control information, the obtained candidate energy sequences or the calculated control information of the energy storage control device.

The application also provides an energy storage management system. The energy storage management system is used for managing an energy storage device which provides reserve electric energy for a power consumer. The energy storage management system is a software system configured in the acquisition module. Please refer to fig. 7, which is a schematic diagram illustrating an architecture of the energy storage management system according to an embodiment. The energy storage management system 3 includes an obtaining module 31 and a generating module 32.

The obtaining module 31 is configured to obtain a power consumption prediction sequence of a power consumer in a remaining period of a current power consumption settlement period, obtain a current energy storage parameter of the energy storage device, and obtain a current power consumption of the power consumer.

The electricity consumption prediction sequence refers to a set of a plurality of electricity consumptions predicted according to a time sequence in an electricity consumption settlement period. The electricity consumption is obtained by the electricity consumers and is related to the electricity consumption factors of the daily production activities of the electricity consumers. Wherein the electricity utilization factors include but are not limited to: human programs such as scheduling programs, store activity programs, programs summarized according to weather or social activity rules (e.g., weekdays, holidays). For example, for the electricity utilization situation of the product a produced in the factory, the electricity utilization related information may include historical electricity utilization data of the product a produced, equipment usage information determined based on the scheduling plan of the product a, electricity utilization information of the equipment, and the like. For another example, the electricity consumption related information may include usage information of manufacturing equipment such as machine tools and industrial control systems, usage information of air conditioners and electricity consumption, usage information of lighting lamps on working days and holidays, computers, and the like, based on scheduling settings, for electricity consumption of large industrial and mining enterprises or factory parks. In some cases where air conditioner usage information is not set, the air conditioner usage information may also be determined based on weather forecast conditions. For example, the use of air conditioning is controlled in accordance with the forecasted air temperature. In summary, the electricity consumption prediction sequence can be obtained according to weather prediction information, scheduling information, electricity consumption of high-power consumption equipment in different operation states, holidays, and calculated electricity consumption graphs of low-power consumption equipment, historical electricity consumption data and the like. For the electricity consumers, in some examples, the sequence of predicted electricity usage within one electricity settlement period may be predicted before the corresponding electricity settlement period begins. In other examples, the sequence of power usage predictions for a power usage settlement period may be adjusted as the actual power usage is generated. For this purpose, the acquiring module 31 is configured to execute steps S111 and S112.

In step S111, the electricity consumption related information is acquired according to the electricity consumption factor in the remaining period of the current electricity consumption settlement period. Here, in order to update the power consumption factor that affects the power consumption prediction sequence in the remaining period of the current power consumption settlement period in time, the power consumption related information is acquired according to the updated power consumption factor when the energy storage sequence in the remaining period is predicted.

In step S112, a power consumption prediction sequence in the remaining period of the current power consumption settlement period is predicted according to the power consumption related information.

The obtaining module 31 obtains a power consumption prediction sequence by establishing a prediction model, and calculates at least one of a prediction algorithm such as Random Forest (Random Forest), long and short term memory network (L STM), iterative decision tree (GBRT), and Convolutional Neural Network (CNN) by using the power consumption related information as an input of the prediction model to obtain the power consumption prediction sequence of the power consumption party in the remaining period of the power consumption settlement period.

The above-described method for predicting the amount of electricity used based on the electricity-related information is only an example, and is not a limitation of the present application. Those skilled in the art should understand that other electricity related information affecting the electricity consumption prediction sequence can also be used as an input of the prediction model to obtain the electricity consumption prediction sequence through a prediction algorithm, and details are not repeated here.

When step S110 is executed at any time from the start point of the current electricity consumption settlement period, the latest energy storage parameters of the energy storage device need to be acquired. The energy storage parameter includes, but is not limited to, a detected or predicted energy stored by the energy storage device, and at least one of a predetermined capacity of the energy storage device, a charge/discharge parameter of the energy storage device, and a loss parameter of the energy storage device. Wherein the capacity of the energy storage device comprises a maximum capacity and a minimum capacity of the energy storage device. The charge and discharge parameters of the energy storage device comprise the charge speed of the energy storage device, the discharge speed of the energy storage device, the upper limit and the lower limit of charge and discharge power and the like. The loss parameters of the energy storage device include a charge energy conversion rate of the energy storage device, a discharge energy conversion rate of the energy storage device, and a self-discharge amount of the energy storage device. The energy storage parameter may also be a set of parameters determined based on a temperature dependent variable.

It should be noted that, in practical engineering, the energy storage parameters of the energy storage device that can be obtained may only include two or more than two of the above, and not all of the energy storage parameters need to be obtained.

The obtaining module 31 further obtains the current power consumption of the power consumer in synchronization with the obtaining of the power consumption prediction sequence and the energy storage parameter or in a sequence that is not necessarily executed in sequence. Wherein, the current electricity consumption refers to the used electricity consumption in the current electricity consumption settlement period. For example, at the beginning of the current electricity consumption settlement period, the accumulated electricity consumption of the enterprise is obtained from a metering device (such as a watt-hour meter) installed in the enterprise, and the accumulated electricity consumption is used as the beginning electricity consumption of the current electricity consumption settlement period.

In some practical applications, enterprises have not only energy storage devices, but also self-powered systems. To this end, in other embodiments, in the case that the consumer is configured with a self-powered system, i.e. the power available to the consumer includes the power provided by the self-powered system, the obtaining module 31 further obtains a self-powered amount prediction sequence provided by the self-powered system of the consumer during the remaining period of the current power settlement period.

The self-supply power amount prediction sequence is a set of a plurality of self-supply power amounts predicted in time sequence in a power consumption settlement period. The self-powered systems include, but are not limited to: photovoltaic power generation system, heat conversion system, trigeminy supplies system, wind power generation system etc..

In some examples, the obtaining module 31 predicts a self-powered prediction sequence in the remaining period of the current power utilization settlement period based on the obtained power generation related information of the self-powered system according to data provided by the self-powered system used by the actual power utilization party. Wherein the power generation related information includes, but is not limited to: historical power generation data, and factors influencing power generation based on the working principle of the self-powered system. For example, in the case of a self-powered system employing photovoltaic power generation, factors affecting power generation mainly include solar irradiance and the like. For another example, in the case that the self-powered system employs wind power generation, the factors influencing the power generation mainly include wind speed, wind direction, and the like. For another example, in the case of a self-powered system that employs thermal conversion to generate electricity, the factors that affect the generation of electricity include mainly the thermal conversion efficiency of the system, the detected temperature, and the like.

The obtaining module 31 may obtain a self-power prediction sequence by establishing a prediction model, and calculate the power generation related information as an input of the prediction model by using a prediction algorithm such as a Random Forest (Random Forest), a long-short term memory network (L STM), an iterative decision tree (GBRT), a Convolutional Neural Network (CNN), and obtain the self-power prediction sequence in the power utilization cycle as an output.

It should be noted that the above embodiments of obtaining the self-power prediction sequence are only examples, and are not intended to limit the present application. One skilled in the art can construct a model for predicting the self-supply power prediction sequence in conjunction with the various embodiments mentioned in the foregoing power rate prediction sequence. For example, a self-powered electricity supply amount prediction sequence is calculated based on the input of the prediction model, the adopted prediction algorithm and the error range obtained through detection so as to improve the accuracy of subsequent prediction.

It should also be noted that the above manner of self-power prediction by using the self-power supply system is only an example, and not a limitation of the present application. Those skilled in the art will understand that the power generation related information based on the self-powered electricity quantity prediction sequence differs according to the power supply mode of the actual self-powered system, and the details are not repeated here.

The generating module 32 is configured to generate an energy sequence of the energy storage device in the remaining period of the electricity consumption settlement period based on the acquired energy storage parameter, the electricity consumption amount and the electricity consumption amount prediction sequence.

Here, the generating module 32 may perform energy storage management on the energy storage device according to the actual management requirement of the power consumer, and further generate an energy sequence meeting the management requirement. Wherein the management requirements include, but are not limited to: the total price of the electricity consumption is reduced as much as possible, the electricity consumption of the peak value of the electricity consumption is reduced as much as possible, and the like. In order to provide continuous energy storage management for the energy storage device in the electricity consumption settlement period, the generation module 32 provides energy values with time intervals in the electricity consumption settlement period and forms an energy sequence. Wherein the time interval is related to the length of time it takes to control the energy storage, the time interval of updating the energy sequence, etc. For example, the time interval is longer than the length of time it takes to control the energy storage device. As another example, the time interval is shorter than or equal to the time interval of the update energy sequence.

For an enterprise provided with an autonomous power supply system, it is obvious that the generating module 32 should also add an autonomous power supply prediction sequence as an input parameter for obtaining an energy sequence, i.e. generate an energy sequence of the energy storage device during the remaining period of the electricity consumption settlement period based on the energy storage parameter, the electricity usage prediction sequence and the autonomous power prediction sequence. For example, based on the energy storage parameter, the power consumption prediction sequence, and the self-powered prediction sequence, an energy sequence is generated with a maximum demand minimum for power during the remaining period of the current power consumption settlement period as an optimization target, or with a power consumption cost minimum during the remaining period of the current power consumption settlement period as an optimization target.

In some embodiments, the generating module 32 generates the energy sequence of the energy storage device during the remaining period of the electricity settlement period with an optimization goal of minimizing the maximum demand of electricity during the remaining period of the current electricity settlement period under the constraint condition set based on the energy storage parameter.

Wherein, for an energy storage device, the mathematical model can be described as:

wherein t is time (h); SOC (0) is initial electric quantity (kWh) of the energy storage device; SOC (t) is the State of Charge (SOC, State of Charge,%); ess _ max is the maximum capacity of the energy storage device (kWh); p is a radical of_iCharging and discharging power sequences (kW), (p) for customer energy storage devices_iCharging power, p, for the energy storage means when greater than 0_iDischarge power, p, of the energy storage device when less than 0_iEqual to 0 indicates that the energy storage device is not operating); e is the charging and discharging efficiency (%) of the energy storage device, e_cFor charging efficiency, e_dFor discharge efficiency, e_l△ t is the electrical load measurement time interval for calculating the fine agreed by the contract load mechanism.

Yun, at least one constraint condition is set according to the energy storage parameter of the energy storage device which can be obtained actually, and the constraint condition aims to avoid the abnormality of the energy storage device when the energy storage device is managed. For example, it is avoided that a certain energy value in the generated energy sequence exceeds the maximum capacity of the energy storage means, etc. Based on the optimization objective function and the model of the energy storage device, wherein the charging amount E of the energy storage device_G2BAnd energy storage device discharge capacity E_B2LControlled by a model of the energy storage device, constraints of the model including at least one of: constraints set for the energy storage device, and constraints set based on the relationship between the consumption of electric energy and the supply of electric energy.

Wherein the constraint condition set for the energy storage device includes at least one of:

SOC (t) is more than or equal to 0; and SOC (t) is less than or equal to 100 percent

In addition, according to other parameters such as the user energy situation, the establishment of relevant constraint conditions comprises the following steps:

p_i≤C_chr(ii) a And-p_i≤-C_dis

Wherein, C_chrCharging the energy storage device to an upper power limit; c_disThe upper discharge power limit of the energy storage device.

Here, the maximum demand may be represented by a maximum power consumption value determined by the power consumption reported by the power consumer to the power provider in a unit metering time width within one power consumption settlement period, according to a contract between the power provider and the power consumer. For example, in a power consumption settlement period, the metering device of the power consumer provides the accumulated power consumption once every 30 minutes, and the maximum demand is the maximum value of the power consumption in each 30 minutes in the power consumption settlement period; wherein the 30 minutes is a unit measurement time width. The maximum demand can also be expressed by counting the maximum average power consumption value of each power consumption peak value reported by the power consumers in a preset unit metering time width in a power consumption settlement period. For example, in a power consumption settlement period, the metering device of the power consumer determines the power consumption of the power consumer every 30 minutes according to the accumulated power consumption provided every 30 minutes, and traverses all the power consumption in the power consumption settlement period by taking the unit metering time width as a time window, wherein the maximum demand is the maximum value of the average power consumption in each unit metering time width based on the traversal. The maximum demand may also be expressed in terms of the number of times the consumer exceeds the contractual demand within a power settlement period. For example, in a power consumption settlement period, the metering device of the power consumer provides the accumulated power consumption once every 30 minutes, and the maximum demand is the number of times of exceeding the contractual demand in each 30 minutes in the power consumption settlement period. For this reason, the maximum power consumption minimum, the number of times of power consumption exceeding contractual demand, or the like may be set to be the minimum in such a manner that the maximum demand of power consumption in the remaining period of the current power consumption settlement period is the minimum as the optimization target.

Derived by the above example: the function of the optimization objective is:

wherein the content of the first and second substances,

wherein, L_iTo be guestA power sequence (kW) for consumer electricity (excluding energy storage devices); f. of_UCCA penalty price (yuan/kW/month) outside the contract; days of the month is Days.

After determining the function and constraints that optimize the objective, the step of generating a sequence of energies for the energy storage device for the remainder of the power usage settlement period comprises: generating one or more candidate energy sequences within the remaining period of the current electricity usage settlement period under the constraint condition; and under the constraint condition and with the minimum maximum demand in the remaining period of the current electricity consumption settlement period as an optimization target, performing optimization processing on the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage devices in the electricity consumption period.

In certain embodiments, one or more candidate energy sequences for the remainder of the power usage settlement period are generated based on the predicted or detected electrical energy stored by the energy storage device and the constraints described above. Here, for the initialization candidate energy sequence (also referred to as an initialization candidate solution), one or more preset candidate energy sequences, that is, candidate solutions, may be generated in a random manner.

The generated candidate solution is optimized in the remaining period of the electricity settlement period under the constraint condition, for example, the generated candidate solution is optimized in the remaining period of the electricity settlement period by utilizing the variation trend of the maximum demand corresponding to the candidate solution in the △ t time period, so that an energy sequence with the minimum maximum demand in the remaining period of the electricity settlement period under the constraint condition is obtained.

In other examples, the method includes generating a plurality of candidate solutions, and filtering and/or adjusting the plurality of candidate solutions to obtain an energy sequence under at least one constraint condition and with a minimum maximum demand in the remaining period of the electricity settlement period as an optimization target, for example, calculating a maximum demand corresponding to each of the plurality of candidate solutions generated under the constraint condition, selecting a candidate solution with a minimum maximum demand as the generated energy sequence, calculating a maximum demand corresponding to each of the plurality of candidate solutions generated under the constraint condition, selecting a candidate solution with a minimum maximum demand, and performing optimization processing on the generated candidate solution by using a variation trend of the maximum demand corresponding to the candidate solution in a time period of △ t to obtain an energy sequence under at least one constraint condition and with a minimum maximum demand in the remaining period of the electricity settlement period as an optimization target.

In some embodiments, the step of performing optimization processing on the generated one or more candidate energy sequences includes: determining a candidate energy sequence from one or more candidate energy sequences according to a cutoff condition set for an optimization objective of minimizing a maximum demand in a remaining period of the electricity consumption settlement period, and taking the candidate energy sequence as the energy sequence; and when the cutoff condition is not met, updating the generated at least one candidate energy sequence according to an updating strategy under at least one constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.

The updating strategy comprises but is not limited to Lagrange Multiplier (L) method, sequence linear programming (S L P), Sequence Quadratic Programming (SQP), Interior Point method (Interior Point), Exterior Point method (Exterior Point), Active Set method (Active Set), Trust domain reflection algorithm (Trust Region reflection), Heuristic algorithm (Hearistic Algorithms), Meta-Heuristic algorithm (Meta-Gaussian), Evolutionary algorithm (evolution Algorithms), Swarm Intelligent algorithm (Swarm Intigrithms), Neural network algorithm (Neural network strategy), taboo search algorithm, simulated annealing algorithm, ant colony optimization algorithm, greedy optimization algorithm, self-adaptive search algorithm, random adaptive search algorithm, and other artificial immune system optimization or similar artificial selection strategies.

Taking the model constraint condition formula and the optimization target formula of the energy storage device as examples, and obtaining a specific example of the energy sequence by using a clonal selection algorithm, the specific example is as follows: based on the model constraint condition of the energy storage device and certain prior calculation, carrying out constraint limitation on a high-dimensional solution space, limiting the solution space in a local space range meeting the constraint condition, and obtaining a plurality of candidate solutions, wherein each candidate solution is 48-dimensional; all the candidate solutions are substituted into a preset optimization objective function to obtain an optimization objective value corresponding to each candidate solution (an evaluation step for short); then, sorting according to the optimized target values corresponding to the candidate solutions, screening and reserving a certain number of excellent solutions and eliminating the rest solutions (screening step for short); sorting the optimization target values in the order from small to large (namely the order from low to high of the total price of the electricity), screening out candidate solutions corresponding to the optimization target values of n (n is more than or equal to 1) before ranking, and eliminating the rest solutions. Next, a corresponding number of clones are performed on the n candidate solutions retained by the screening, and random variation with a certain probability (variation rate) is introduced in the cloning process, so as to generate new candidate solutions based on each retained candidate solution (referred to as a variant cloning step). Wherein the mutation rate is limited by the model constraint condition to ensure that the obtained new candidate solution is obtained based on the slight change of the candidate solution before the mutated clone. In this case, the variation rate may be introduced to all the clonal solutions of the retained candidate solutions, or only to the partial solutions. Repeating the steps of evaluating, screening and mutating cloning until the actual iteration times reach the cut-off condition of the preset iteration times; and all the finally obtained candidate solutions are substituted into the optimization objective function to obtain the optimization target value corresponding to each candidate solution, and the candidate solution corresponding to the minimum optimization target value is selected as the energy sequence of the energy storage device.

Taking the model constraint conditions and the optimization target of the energy storage device as examples, and obtaining a specific example of the energy sequence by using the SQP algorithm, the specific example is as follows: under the model constraint condition of the energy storage device and the constraint of certain prior calculation, converting an objective function and a constraint function by using Taylor expansion, and calculating by using the converted objective function and the constraint function to obtain a candidate solution and an error gradient; adjusting the candidate solution based on the obtained error gradient until a cutoff condition that the error gradient is smaller than a preset gradient threshold value is met; and taking the finally obtained candidate solution as an energy sequence of the energy storage device.

It should be noted that the cutoff conditions described in any of the above examples are not strictly in one-to-one correspondence with the algorithms used, and may also be set according to actual design requirements, for example, the change of the optimal target result of the latest iterations is smaller than a preset threshold, and the like, which is not described herein again.

It should also be noted that each stored energy value in the generated energy sequence is separated by an operation time interval. The operating time interval may be predetermined by the energy storage management system, typically having a duration greater than a maximum duration (or average duration) required for a controlled change in the energy storage device.

The generated energy sequence can be provided to a terminal device used by a technician through an output module of the server. For example, the server sends the generated energy sequence to a computer device with a display terminal used by a technician for display.

After determining the energy sequence of the energy storage device managed in the rest period of the electricity consumption settlement period by using any one of the above examples, the above modules are re-executed based on the preset updating condition, so that the energy storage device is managed by using the energy sequence.

Here, the preset update condition is related to the update of each input parameter. In some examples, the enterprise is not supplementarily powered by a self-powered system, and the preset update condition includes at least one of: the method comprises the steps of setting an updating condition based on an updating event of a power consumption prediction sequence, setting an updating condition according to a preset updating period, and setting an updating condition based on a triggering condition that the accumulated power consumption is larger than a preset threshold value in the current metering time width. For example, when the generating module 32 receives the power consumption prediction sequence or the power consumption related information for calculating the power consumption prediction sequence, it indicates that an update event of the power consumption prediction sequence is generated, and the generating module 32 executes an example including the foregoing steps according to the generated update event, with the obtained updated power consumption prediction sequence or power consumption related information, to obtain the latest power consumption prediction sequence, energy storage parameter, and current power consumption of the power consumer in the current power consumption settlement period, and generates the energy sequence using a pre-constructed algorithm with the latest obtained information as input. As another example, the generating module 32 re-executes the above example including the foregoing steps at a fixed updating period to generate the energy sequence. For another example, the generating module 32 updates the generated energy sequence according to a preset updating period, wherein the updating period is less than a metering time width, during the updating execution period, the generating module 32 further monitors the accumulated power consumption within the current metering time width, and when the accumulated power consumption within the current metering time width is greater than a preset threshold, the updating period is shortened so as to monitor the maximum demand which may occur within the current metering time width more closely.

In other examples, the enterprise supplies supplementary power by using a self-powered system, and the preset updating condition further includes, in addition to the above updating condition: an update condition set based on an update event from the power prediction sequence. For example, when the generating module 32 receives the self-power supply amount prediction sequence or the power generation related information for calculating the self-power supply amount prediction sequence, it indicates that an update event of a power consumption amount prediction sequence is generated, and the generating module 32 executes an example including the foregoing steps according to the generated update event, with the obtained updated power consumption amount prediction sequence or power consumption related information, to obtain the latest power consumption amount prediction sequence, energy storage parameter, and current power consumption amount of the power consumer in the current power consumption settlement period, and generates the energy sequence using a pre-constructed algorithm with the latest obtained information as input.

In still other examples, the update condition further includes: the time interval between adjacent update operations is less than or equal to the metering time width used to determine the maximum demand. For example, the update period from at least one of the power consumption prediction sequence, the power consumption prediction sequence is less than the metering time width. As another example, the predetermined update period is less than the metering time width.

The step of regenerating the energy sequence in the remaining period of the current electricity usage settlement period according to the updated condition further includes: determining the maximum power consumption in the remaining duration of the metering time width according to the accumulated power consumption in the current metering time width and the predetermined maximum reference demand; and generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the energy storage parameters, the maximum electricity consumption and the electricity consumption prediction sequence.

Taking the maximum demand as an example of the maximum power consumption determined by the power consumption reported to the power supplier by the power supplier according to the unit metering time width in a power consumption settlement period, the generating module 32 repeatedly executes the above steps at least twice within the unit metering time width. Therefore, according to the preset maximum reference demand Q, the generating module 32 can obtain the maximum power consumption Q2 available for the power consumer to reference within the remaining duration of the current metering time width according to the power consumption Q1 consumed by the power consumer from the starting time of the metering time width to the updating time. Wherein, the maximum reference demand can be contractual demand of the contract, which is a fixed value in the current electricity consumption settlement period. For example, the initial value of the maximum reference demand is the contractual demand, or the maximum reference demand is always the contractual demand. The maximum reference demand may also be a maximum value determined according to the counted electricity usage within the historical metering time width of the current electricity settlement period. For example, the initial value of the maximum reference demand is the contract demand, the generation module 32 counts the electricity consumption in each metering time width of the history every metering time width, and determines the maximum value of each counted electricity consumption as the maximum reference demand in the current metering time width. By determining the maximum reference demand over the current metering timeframe, the generation module 32 can predict from the predicted sequence of power usage and the maximum power usage Q2 whether a greater maximum demand will be generated over the current metering timeframe and, with the aforementioned input parameters, derive an energy sequence that minimizes the maximum demand over the remaining period. The input parameters comprise energy storage parameters, maximum power consumption, a power consumption prediction sequence, a self-powered prediction sequence and the like.

It should be noted that the above update examples are not mutually exclusive, and may be used in combination with each other in some practical applications, and are not described in detail herein.

It should be further noted that, as will be understood by those skilled in the art, the way of obtaining the energy sequence with the minimum maximum demand as the optimization goal by using the energy storage management method described in the present application can also be applied to the way of obtaining the corresponding energy sequence with the minimum electricity cost as the optimization goal. And will not be repeated here.

According to the energy sequence obtained by the energy storage management system corresponding to the remaining period of the current electricity consumption settlement period, the energy sequence can be pushed to the control system built in the energy storage device, so that the energy storage control system can start each module for controlling the built-in energy storage device. Therefore, the application also provides an energy storage control system. The energy storage control system may be a software system configured on the computer device, and controls the energy storage device by using the electric party based on the acquired energy sequence of the energy storage device, so as to achieve the purpose of achieving the preset optimization goal in the electricity consumption settlement period.

Referring to fig. 8, which is a schematic structural diagram of an energy storage control system according to an embodiment of the present disclosure, as shown in the figure, the energy storage control system 5 includes: an acquisition module 51 and a control module 52.

The obtaining module 51 is configured to obtain an energy sequence of the energy storage device generated by the energy storage management method provided in any of the foregoing examples during the remaining period of the electricity consumption settlement period.

Here, according to the data communication mode between the respective computer devices of the energy storage management system and the energy storage control system, the energy storage management system pushes the updated energy sequence to the obtaining module 51 of the energy storage management system, or the obtaining module 51 of the energy storage management system obtains the latest energy sequence from the energy storage management system according to a preset obtaining period.

The control module 52 is configured to determine control information used by the energy storage device to control the operation of the energy storage device in the operation time interval based on the energy value corresponding to the operation time interval in the acquired energy sequence. Wherein the operation time interval is set according to the time interval of adjacent energy values in the energy sequence. For example, the time interval between adjacent energy values in the energy sequence is half an hour, and the corresponding operation time interval is half an hour.

The control module 52 determines control information for controlling the operation of the energy storage device in the operation time interval based on the energy value corresponding to the operation time interval in the acquired energy sequence. Wherein the control information comprises at least one of: the charging and discharging control information of the energy storage device and the target energy storage value of the energy storage device in the operation time interval. Wherein, the target storage value includes: and determining a target energy storage threshold interval corresponding to the energy value in the energy sequence according to the self-loss coefficient of the preset energy storage device. The charge and discharge control information of the energy storage device includes, for example, a time period from charging (or discharging) of the energy value a1 to charging and discharging of the energy storage device correspondingly controlled by the energy value a2, which is set according to a preset charge and discharge speed; wherein the energy values a1 and a2 are adjacent energy values in the energy sequence.

In some embodiments, the control module 52 is further configured to control the operation of the energy storage device during the respective operation time interval based on the control information. Here, the control module 52 automatically outputs a control command including the control information to the energy storage device within a corresponding operation time interval according to the control information generated based on the energy sequence, so that the energy storage device correspondingly performs charging or discharging, thereby storing electric energy during a period of low power consumption and releasing electric energy during a period of high power consumption.

In still other embodiments, the control module 52 is required to control the energy storage device by a technician, for which purpose, the obtaining module 51 further obtains at least one of the energy sequence, the self-power prediction sequence and the power usage prediction sequence, and the energy storage control system further comprises a display module. Here, the energy storage management system sends at least one of the generated energy sequence, the obtained self-power prediction sequence and the power consumption prediction sequence to the obtaining module 51 in the energy storage control system, and the obtaining module 51 provides each obtained sequence to the display module so as to display the sequences to the technician in a line graph, a histogram and the like for the technician to refer to and execute the control operation on the energy storage device in the corresponding operation time interval. The display unit may include the obtained control information and the like together with the sequences.

Referring to fig. 9 and 10, a plurality of sequence diagrams monitored after controlling the energy storage device according to the energy sequence obtained by the energy storage management strategy provided by the foregoing example are shown, wherein a graph 1 shown in fig. 9 is L_i+p_iThe actual power utilization sequence of the customer under the contract load management strategy is shown, and the sequence formed by the operation process of the energy storage device corresponding to the actual power utilization sequence comprises that a graph 2 in figure 9 is L_iIndicating a customer electricity power sequence; and soc (t) shown in fig. 10, which represents the energy storage device state of charge.

To sum up, the energy storage management system and the energy storage control system provided by the application generate an energy sequence by utilizing a prediction sequence obtained in advance, the current energy storage parameters of the energy storage device, the current power consumption of a power consumer and the like, and correspondingly control the energy storage device by utilizing the energy sequence, so that the purpose of effectively reducing the power consumption cost in a power consumption settlement period is realized.

It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that part or all of the present application can be implemented by software and combined with necessary general hardware platform. Based on this understanding, the present application also provides a computer-readable storage medium storing at least one program which, when invoked, performs any of the energy storage management methods described above. In addition, the present application also provides a computer-readable storage medium, where the storage medium stores at least one program, and the at least one program executes any one of the foregoing energy storage control methods when being called.

With this understanding in mind, the technical solutions of the present application and/or portions thereof that contribute to the prior art may be embodied in the form of a software product that may include one or more machine-readable media having stored thereon machine-executable instructions that, when executed by one or more machines such as a computer, network of computers, or other electronic devices, may cause the one or more machines to perform operations in accordance with embodiments of the present application. For example, each step in the positioning method of the robot is performed. The machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (compact disc-read only memories), magneto-optical disks, ROMs (read only memories), RAMs (random access memories), EPROMs (erasable programmable read only memories), EEPROMs (electrically erasable programmable read only memories), magnetic or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing machine-executable instructions. Wherein the storage medium may be located in the robot or in a third party server, such as a server providing an application mall. The specific application mall is not limited, such as the millet application mall, the Huawei application mall, and the apple application mall.

The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical concepts disclosed in the present application shall be covered by the claims of the present application.

Claims (40)

1. An energy storage management method for managing an energy storage device for providing reserve electric energy to a consumer, comprising the steps of:

   acquiring a power consumption prediction sequence of the power consumer in the remaining period of the current power consumption settlement period, acquiring current energy storage parameters of the energy storage device, and acquiring the current power consumption of the power consumer;

   generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the acquired energy storage parameters, electricity consumption and electricity consumption prediction sequence;

   and repeatedly executing the steps based on a preset updating condition so as to manage the energy storage device by utilizing the energy sequence.
2. The energy storage management method according to claim 1, further comprising: acquiring a self-powered system of the power consumer to provide a self-powered prediction sequence in the rest period of the current power consumption settlement period;

   the step of generating an energy sequence of the energy storage device in the remaining period of the electricity consumption settlement period based on the energy storage parameters, the electricity consumption and the electricity consumption prediction sequence comprises: and generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the energy storage parameters, the electricity consumption prediction sequence and a self-power prediction sequence.
3. The energy storage management method according to claim 2, wherein the step of obtaining the self-powered system of the power consumer to provide the self-powered prediction sequence in the remaining period of the current power settlement period comprises: and predicting a self-power supply prediction sequence in the rest period of the current power utilization settlement period based on the acquired power generation related information of the self-power supply system.
4. The energy storage management method according to claim 1, wherein the step of obtaining the predicted sequence of power consumption of the power consumer in the remaining period of the current power consumption settlement period comprises:

   acquiring power consumption related information according to the power consumption factors in the remaining period of the current power consumption settlement period; and

   and predicting a power consumption prediction sequence in the remaining period of the current power consumption settlement period according to the power consumption related information.
5. The energy storage management method according to claim 1, wherein the update condition comprises: the time interval between adjacent update operations is less than or equal to the metering time width used to determine the maximum demand;

   the step of executing the generation of the energy sequence according to the update condition includes:

   determining the maximum power consumption in the remaining duration of the metering time width according to the accumulated power consumption in the current metering time width and the predetermined maximum reference demand;

   and generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the energy storage parameters, the maximum electricity consumption and the electricity consumption prediction sequence.
6. The energy storage management method according to claim 5, wherein the maximum reference demand comprises: the maximum value determined according to the counted electricity consumption within the historical metering time width of the current electricity consumption settlement period or a preset fixed value.
7. The energy storage management method according to claim 1, 2 or 5, wherein the step of generating the energy sequence of the energy storage device for the remaining period of the electricity usage settlement period comprises:

   and under the constraint condition set based on the energy storage parameters, generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period by taking the minimum maximum demand of the electricity in the rest period of the current electricity consumption settlement period as an optimization target.
8. The energy storage management method according to claim 7, wherein the step of generating the energy sequence of the energy storage device in the electricity utilization period with the minimum maximum demand of electricity in the remaining period of the current electricity utilization settlement period as an optimization target under the constraint condition set based on the energy storage parameters comprises:

   generating one or more candidate energy sequences within the remaining period of the current electricity usage settlement period under the constraint condition; and

   and under the constraint condition and with the minimum maximum demand in the rest period of the current electricity consumption settlement period as an optimization target, performing optimization processing on the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage devices in the electricity consumption period.
9. The energy storage management method according to claim 8, wherein the step of performing optimization processing on the generated one or more candidate energy sequences comprises:

   determining a candidate energy sequence from the one or more candidate energy sequences according to a cutoff condition set for an optimization goal of minimizing a maximum demand in a remaining period of the current electricity consumption settlement period, and taking the candidate energy sequence as the energy sequence; and

   and when the cutoff condition is not met, updating at least one generated candidate energy sequence according to an updating strategy under the constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.
10. The energy storage management method according to claim 1, wherein the energy storage parameters include at least two of: the detected or predicted energy stored by the energy storage device, a capacity of the energy storage device, a charge-discharge parameter of the energy storage device, a loss parameter of the energy storage device.
11. The energy storage management method according to claim 1, wherein the preset update condition comprises at least one of: the method comprises the steps of setting an updating condition based on an updating event of a power consumption prediction sequence, setting an updating condition according to a preset updating period, and setting an updating condition based on a triggering condition that the accumulated power consumption is larger than a preset threshold value in the current metering time width.
12. The energy storage management method according to claim 2, wherein the preset update condition comprises at least one of: the method comprises the steps of setting an updating condition based on an updating event from a power consumption prediction sequence, setting an updating condition based on an updating event of a power consumption prediction sequence, setting an updating condition according to a preset updating period, and setting an updating condition based on a triggering condition that the accumulated power consumption is larger than a preset threshold value in the current metering time width.
13. The energy storage management method according to claim 1, further comprising the step of outputting the energy sequence to a computer device.
14. An energy storage control method for controlling an energy storage device for supplying reserve electric energy to a consumer, comprising the steps of:

    acquiring an energy sequence of the energy storage device generated by the energy storage management method according to any one of claims 1 to 13 during a remaining period of a power usage settlement period;

    and determining control information used for controlling the operation of the energy storage device in the operation time interval by the energy storage device based on the energy value corresponding to the operation time interval in the acquired energy sequence.
15. The energy storage control method according to claim 14, further comprising the step of controlling the operation of the energy storage device within respective operation time intervals based on the control information.
16. The energy storage control method according to claim 14, characterized by further comprising: and acquiring and displaying at least one of the energy sequence, the self-power prediction sequence and the power consumption prediction sequence.
17. The energy storage control method according to any one of claims 14 to 16, further comprising the step of updating the control information based on a newly generated energy sequence.
18. The energy storage control method according to claim 14, wherein the control information includes at least one of: the charging and discharging control information of the energy storage device and the target energy storage value of the energy storage device in the operation time interval.
19. An energy storage management system for managing energy storage devices that provide reserve electrical energy to consumers, comprising:

    the acquisition module is used for acquiring a power consumption prediction sequence of the power consumer in the remaining period of the current power consumption settlement period, acquiring the current energy storage parameters of the energy storage device and acquiring the current power consumption of the power consumer;

    the generating module is used for generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the acquired energy storage parameters, the electricity consumption and the electricity consumption prediction sequence; restarting the acquisition module based on a preset update condition so that the energy storage device is managed by using the energy sequence.
20. The energy management system according to claim 19, wherein the obtaining module is further configured to obtain a self-powered system of the electricity consumer providing a self-powered prediction sequence during a remaining period of a current electricity settlement period;

    the generation module is further configured to generate an energy sequence of the energy storage device during a remaining period of the electricity settlement period based on the energy storage parameter, the electricity usage prediction sequence, and a self-powered prediction sequence.
21. The energy storage management system according to claim 20, wherein the obtaining module is configured to predict a self-powered prediction sequence during a remaining period of a current power usage settlement period based on the obtained power generation related information of the self-powered system.
22. The energy storage management system according to claim 19, wherein the obtaining module is configured to obtain the electricity consumption related information according to the electricity consumption factor in the remaining period of the current electricity consumption settlement period; and predicting a power consumption prediction sequence in the remaining period of the current power consumption settlement period according to the power consumption related information.
23. The energy storage management system of claim 19, wherein the update condition comprises: the time interval between adjacent update operations is less than or equal to the metering time width used to determine the maximum demand;

    the generating module is used for determining the maximum power consumption in the remaining duration of the measuring time width according to the accumulated power consumption in the current measuring time width and the predetermined predicted maximum demand; and generating an energy sequence of the energy storage device in the rest period of the electricity consumption settlement period based on the energy storage parameters, the maximum electricity consumption and the electricity consumption prediction sequence.
24. The energy storage management system of claim 23, wherein the maximum demand comprises: the maximum demand is determined by counting the historical electricity consumption of the measuring time width in the current electricity consumption settlement period, or is a preset fixed value.
25. The energy storage management system according to claim 10, 20 or 23, wherein the generating module is configured to generate the energy sequence of the energy storage device in the remaining period of the electricity settlement period with an optimization goal of minimizing the maximum demand of electricity in the remaining period of the current electricity settlement period under the constraint condition set based on the energy storage parameter.
26. The energy storage management system according to claim 25, wherein the generating module is configured to:

    generating one or more candidate energy sequences within the remaining period of the current electricity usage settlement period under the constraint condition; and

    and under the constraint condition and with the minimum maximum demand in the rest period of the current electricity consumption settlement period as an optimization target, performing optimization processing on the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage devices in the electricity consumption period.
27. The energy storage management system according to claim 28, wherein the generating module is configured to:

    determining a candidate energy sequence from the one or more candidate energy sequences according to a cutoff condition set for an optimization goal of minimizing a maximum demand in a remaining period of the current electricity consumption settlement period, and taking the candidate energy sequence as the energy sequence; and

    and when the cutoff condition is not met, updating at least one generated candidate energy sequence according to an updating strategy under the constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.
28. The energy storage management system of claim 19, wherein the energy storage parameters comprise at least two of: the detected or predicted energy stored by the energy storage device, a capacity of the energy storage device, a charge-discharge parameter of the energy storage device, a loss parameter of the energy storage device.
29. The energy storage management system according to claim 19, wherein the preset update condition comprises at least one of: the method comprises the steps of setting an updating condition based on an updating event of a power consumption prediction sequence, setting an updating condition according to a preset updating period, and setting an updating condition based on a triggering condition that the accumulated power consumption is larger than a preset threshold value in the current metering time width.
30. The energy storage management system according to claim 20, wherein the preset update condition comprises at least one of: the method comprises the steps of setting an updating condition based on an updating event from a power consumption prediction sequence, setting an updating condition based on an updating event of a power consumption prediction sequence, setting an updating condition according to a preset updating period, and setting an updating condition based on a triggering condition that the accumulated power consumption is larger than a preset threshold value in the current metering time width.
31. The energy storage management system of claim 19, further comprising an output module to output the energy sequence to a computer device.
32. An energy storage control system for controlling an energy storage device that supplies reserve electric energy to a consumer, comprising:

    an acquisition module for acquiring a sequence of energy of the energy storage device during a remainder of a power usage cycle generated by an energy storage management system according to any of claims 19-31; (ii) a

    And the control module is used for determining control information used for controlling the operation of the energy storage device in the operation time interval on the basis of the energy value corresponding to the operation time interval in the acquired energy sequence.
33. The energy storage control system of claim 32, wherein the control module is configured to control operation of the energy storage device during respective operational time intervals based on the control information.
34. The energy storage control system of claim 32, wherein the obtaining module is further configured to obtain at least one of the energy sequence and a power usage prediction sequence; and the energy storage control system comprises a display module used for displaying at least one of the acquired energy sequence and the power consumption prediction sequence.
35. The energy storage control system according to any one of claims 32-34, wherein the control module is configured to update the control information based on a most recently generated energy sequence.
36. The energy storage control system of claim 32, wherein the control information comprises at least one of: charging and discharging control information of the energy storage device and a target energy storage value of the energy storage device in an operation time interval.
37. A server for managing an energy storage device that provides reserve electrical energy to a consumer, comprising:

    the interface unit is used for acquiring the current energy storage parameters of the energy storage device and acquiring the current power consumption of the power consumer;

    a storage unit for storing at least one program and a power consumption prediction sequence of the power consumer in the remaining period of the current power consumption settlement period; and

    a processing unit for invoking the at least one program to coordinate the interface unit and the storage unit to perform the energy storage management method according to any one of claims 1-13.
38. A computer device, comprising:

    an interface unit for acquiring an energy sequence provided by the server according to claim 37;

    a storage unit for storing at least one program; and

    a processing unit for invoking the at least one program to coordinate the interface unit and the storage unit to perform the energy storage control method according to any one of claims 14-18.
39. A computer-readable storage medium, characterized by storing at least one program which, when invoked, performs the energy storage management method according to any one of claims 1-13; or an energy storage control method as claimed in any of claims 14 to 18.
40. An energy storage control system, comprising: the server according to claim 37 and the computer device according to claim 38.

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