CN111466063B - Energy storage management and control method, system, computer equipment and storage medium - Google Patents

Energy storage management and control method, system, computer equipment and storage medium Download PDF

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Publication number
CN111466063B
CN111466063B CN201880002440.7A CN201880002440A CN111466063B CN 111466063 B CN111466063 B CN 111466063B CN 201880002440 A CN201880002440 A CN 201880002440A CN 111466063 B CN111466063 B CN 111466063B
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energy storage
sequence
energy
power
electricity
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CN111466063A (en
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徐楠
苏明
王春光
陈光濠
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Equota Energy Technology (shanghai) Ltd
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Equota Energy Technology (shanghai) Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The application provides an energy storage management method, an energy storage control method, systems, computer equipment and storage media. The energy storage management method is used for managing an energy storage device for providing reserved electric energy for an electric power utilization party, and comprises the following steps: acquiring a power supply prediction sequence which can be used by the power consumer in a power utilization period, and a power consumption prediction sequence of the power consumer; and generating an energy sequence of the energy storage device in the electricity utilization period based on the energy storage parameters of the energy storage device acquired under preset acquisition conditions, and a power supply prediction sequence and an electricity consumption prediction sequence in the electricity utilization period, so that the energy storage device is managed based on the energy sequence. The application manages the energy storage device based on the energy sequence, thereby achieving the purpose of lowest total electricity consumption.

Description

Energy storage management and control method, system, computer equipment and storage medium
Technical Field
The present application relates to the field of industrial control technologies, and in particular, to an energy storage management method, an energy storage control method, and systems, computer devices, and storage media.
Background
Nowadays, as the cost of energy storage devices is reduced, energy storage devices are set up in some industrial and mining enterprises and enterprise parks, and enterprises utilize the energy storage devices to perform energy storage operation during low electricity prices and power supply operation during high electricity prices so as to reduce the cost of purchasing electricity from a power grid.
With this synchronous development, the calculation of the power grid to the power supply cost is more timely, and to the electric field scene such as industrial electricity, some places combine the power supply cost with the price of electricity more closely, have formed the mode that utilizes the floating price of electricity to charge the charges of electricity to the consumer. The floating electricity price refers to the electricity price purchased by the electricity consumer which changes with time. With the addition of floating electricity prices to existing electricity price mechanisms, how to better utilize energy storage devices to reduce electricity costs is a problem to be solved.
Disclosure of Invention
In view of the above, the present application is directed to an energy storage management method, an energy storage control method, systems, computer devices, and storage media for solving the problem of how to reduce the electricity cost by using an energy storage device in the prior art.
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 for providing reserve electric energy for an electric power consumer, the energy storage management method comprising the steps of: acquiring a power supply prediction sequence which can be used by the power consumer in a power utilization period, and a power consumption prediction sequence of the power consumer; and generating an energy sequence of the energy storage device in the electricity utilization period based on the energy storage parameters of the energy storage device acquired under preset acquisition conditions, and a power supply prediction sequence and an electricity consumption prediction sequence in the electricity utilization period, so that the energy storage device is managed based on the energy sequence.
In certain embodiments of the first aspect of the present application, the power supply prediction sequence includes a power rate prediction sequence, and the step of obtaining the power rate prediction sequence in the power utilization period includes any one of the following: acquiring an electricity price prediction sequence in the electricity utilization period; predicting a power rate prediction sequence within the power utilization period available to the power utilization party based on the obtained historical power rate prediction sequence and a deviation between corresponding historical actual power rates; and predicting a power price prediction sequence in the power utilization period based on the acquired power price related information.
In certain embodiments of the first aspect of the present application, the power supply prediction sequence includes a self-power prediction sequence of the self-power supply system, and the step of obtaining the self-power prediction sequence in the power utilization period includes: and predicting a self-powered quantity prediction sequence in the power utilization period based on the acquired power generation related information of the self-powered system.
In certain embodiments of the first aspect of the present application, the step of obtaining the power consumption prediction sequence of the power consumer includes: acquiring power consumption related information according to power consumption factors in the power consumption period; and predicting a power consumption prediction sequence in the power consumption period according to the power consumption related information.
In certain embodiments of the first aspect of the present application, the step of generating the energy sequence of the energy storage device during the power-up period based on the energy storage parameter of the energy storage device acquired under the preset acquisition condition, and the power supply prediction sequence and the power consumption prediction sequence during the power-up period includes: under at least one constraint condition, taking the total electricity consumption price in the electricity consumption period as an optimization target, and generating an energy sequence of the energy storage device in the electricity consumption period; wherein the constraints include constraints determined based on the energy storage parameters.
In certain embodiments of the first aspect of the present application, the step of generating an energy sequence of the energy storage device during the power cycle under at least one constraint with the overall power cost reduction during the power cycle as an optimization objective comprises: generating one or more candidate energy sequences within the powered cycle under at least one constraint; and optimizing the generated one or more candidate energy sequences under at least one constraint condition and with the total electricity consumption price in the electricity consumption period as an optimization target to obtain the energy sequence of the energy storage device in the electricity consumption period.
In certain embodiments of the first aspect of the present application, the step of optimizing the generated one or more candidate energy sequences comprises: determining one candidate energy sequence from the one or more candidate energy sequences according to a cut-off condition set by taking the total electricity consumption price in the electricity consumption period as an optimization target, and taking the one 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.
In certain embodiments of the first aspect of the present application, the energy storage parameter comprises 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, the loss parameters of the energy storage device.
In certain embodiments of the first aspect of the present application, the acquisition conditions include at least one of: updating an event of the power supply prediction sequence, an event of the power consumption prediction sequence and an update period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power consumption prediction sequence.
In certain embodiments of the first aspect of the present application, the energy storage management method further comprises the step of displaying at least one of the energy sequence, the power supply prediction sequence, and the power consumption prediction sequence.
A second aspect of the present application provides an energy storage control method for controlling an energy storage device that supplies reserve electric energy to an electricity consumer, the energy storage control method comprising the steps of: acquiring an energy sequence of the energy storage device in a power utilization period generated by the energy storage management method; and determining control information of the energy storage device 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.
In certain embodiments of the second aspect of the present application, the energy storage control method further comprises the step of controlling the operation of the energy storage device within a respective operation time interval based on the control information.
In certain embodiments of the second aspect of the present application, the energy storage control method further includes: and a step of acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence.
In certain embodiments of the second aspect of the present application, the energy storage control method further comprises the step of updating the control information based on the newly generated energy sequence.
In certain embodiments of the second aspect of the present application, 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.
A third aspect of the present application provides an energy storage management system for managing an energy storage device for providing reserve electrical energy for an electricity consumer, comprising: the power consumption prediction system comprises an acquisition module, a power consumption prediction module and a power consumption prediction module, wherein the acquisition module is used for acquiring a power supply prediction sequence which can be used by the power consumption party in a power consumption period and a power consumption prediction sequence of the power consumption party; and the generation module is used for generating an energy sequence of the energy storage device in the electricity utilization period based on the energy storage parameters of the energy storage device, the power supply prediction sequence and the electricity consumption prediction sequence in the electricity utilization period, which are acquired under preset acquisition conditions, so that the energy storage device is managed based on the energy sequence.
In certain embodiments of the third aspect of the present application, the power supply prediction sequence includes an electricity price prediction sequence, and the obtaining module includes at least one of the following: the first acquisition unit is used for acquiring an electricity price prediction sequence in the electricity utilization period; a second obtaining unit configured to predict a power rate prediction sequence in the power use period available to the power consumer based on the obtained historical power rate prediction sequence and a deviation between the corresponding historical actual power rates; and the third acquisition unit is used for predicting the electricity price prediction sequence in the electricity utilization period based on the acquired electricity price related information.
In certain embodiments of the third aspect of the present application, the power supply prediction sequence includes a self-power supply prediction sequence of the self-power supply system, and the acquisition module includes a fourth acquisition unit, configured to predict the self-power supply prediction sequence in the power utilization period based on the acquired power generation related information of the self-power supply system.
In certain embodiments of the third aspect of the present application, the obtaining module includes a fifth obtaining unit, configured to obtain electricity consumption related information according to electricity consumption factors in the electricity consumption period; and predicting a power consumption prediction sequence in the power consumption period according to the power consumption related information.
In certain embodiments of the third aspect of the present application, the generating module includes: a generating unit, configured to generate an energy sequence of the energy storage device in the electricity consumption period with a total electricity consumption price in the electricity consumption period as an optimization target under at least one constraint condition; wherein the constraints include constraints determined based on the energy storage parameters.
In certain embodiments of the third aspect of the present application, the generating unit is configured to generate one or more candidate energy sequences within the power usage period under at least one constraint; and optimizing the generated one or more candidate energy sequences under at least one constraint condition and with the total electricity consumption price in the electricity consumption period as an optimization target to obtain the energy sequence of the energy storage device in the electricity consumption period.
In certain embodiments of the third aspect of the present application, the generating unit is configured to determine one candidate energy sequence from the one or more candidate energy sequences according to a cutoff condition set for optimization of a total cost of electricity in the electricity usage period, and take the one 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.
In certain embodiments of the third aspect of the present application, the energy storage parameter comprises 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, the loss parameters of the energy storage device.
In certain embodiments of the third aspect of the present application, the acquisition conditions include at least one of: updating an event of the power supply prediction sequence, an event of the power consumption prediction sequence and an update period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power consumption prediction sequence.
In certain embodiments of the third aspect of the present application, the energy storage management system further comprises an output module for outputting at least one of the energy sequence, the power supply prediction sequence, and the power consumption prediction sequence for display.
A fourth aspect of the present application provides an energy storage control system for controlling an energy storage device for providing reserve electrical energy to an electricity consumer, comprising: an acquisition module for acquiring a sequence of energies of the energy storage devices during a power cycle generated by the energy storage management system as described above; the determining module is used for determining control information of the energy storage device 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.
In certain embodiments of the fourth aspect of the present application, the energy storage control system further comprises a control module for controlling the operation of the energy storage device within a respective operation time interval based on the control information.
In certain embodiments of the fourth aspect of the present application, the energy storage control system further comprises: and the display module is used for acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence.
In certain embodiments of the fourth aspect of the present application, the energy storage control system further comprises: and the updating module is used for updating the control information based on the latest generated energy sequence.
In certain embodiments of the fourth aspect of the present application, 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 a prediction time interval.
A fifth aspect of the present application provides a server, including: the interface unit is used for acquiring power supply related information which can be used by a power consumer in a power utilization period and power utilization related information of the power consumer; 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 management method as described above.
A sixth aspect of the application provides a computer device comprising: the interface unit is used for acquiring power supply related information which can be used by a power consumer in a power utilization period and power utilization related information of the power consumer; 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 as described above.
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 as described above.
An eighth aspect of the present application provides a computer-readable storage medium storing at least one program that when called performs the energy storage control method as described above.
A ninth aspect of the present application provides an energy storage control system comprising: a server as described above and a computer device as described above.
As described above, the energy storage management method, the energy storage control method, the systems, the computer device and the storage medium of the present application have the following beneficial effects: and generating an energy sequence of the energy storage device in a power utilization period based on the acquired power supply prediction sequence, the power consumption prediction sequence and the energy storage parameters of the energy storage device, so that the energy storage device can be managed based on the energy sequence, and the aim of lowest total power consumption price is fulfilled.
Drawings
FIG. 1 is a schematic diagram illustrating the power transfer relationship among a power generation system, a self-powered system, a power utilization system, and an energy storage device.
Fig. 2 is a schematic structural diagram of a server according to an embodiment of the application.
Fig. 3 is a flow chart of the energy storage management method of the present application.
Fig. 4a to 4d are schematic diagrams respectively showing a power price prediction sequence, a self-powered power consumption prediction sequence, a power consumption prediction sequence and an energy sequence of an energy storage device in a power utilization period according to the energy storage management method of the present application.
Fig. 5 is a schematic diagram showing a total electricity price obtained by an electricity consumer based on the energy storage management method of the present application and a total electricity price without an energy storage device.
Fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Fig. 7 is a flow chart of the energy storage control method of the present application.
Fig. 8 is a schematic structural diagram of an energy storage management system according to an embodiment of the present application.
Fig. 9 shows a schematic diagram of the energy storage control system according to the application, which is operated by means of a computer device, in an embodiment.
Fig. 10 is a schematic diagram of a network architecture of an energy storage control system according to an embodiment of the application.
Detailed Description
Further advantages and effects of the present application will become apparent to those skilled in the art from the disclosure of the present application, which is described by the following specific examples.
Furthermore, 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" specify the presence of stated features, steps, operations, elements, components, items, categories, and/or groups, but do not preclude the presence, presence or addition of one or more other features, steps, operations, elements, components, items, categories, and/or groups. 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, A is as follows; b, a step of preparing a composite material; c, performing operation; 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 in some way inherently mutually exclusive.
Referring to fig. 1, a schematic diagram of a power transmission relationship among a power generation system, a self-powered system, a power consumption system and an energy storage device is shown. The power generation system is managed by a power supply party, the self-powered system, the power utilization system and the energy storage device are located on one side of a power utilization party such as a power utilization enterprise, a park and a building, the power generation system provides power for the power utilization system and the energy storage device through a power grid, and the self-powered system is used for providing power for the power utilization system. Among them, self-powered systems such as solar power generation systems, wind power generation systems, transduction power generation systems, etc. In addition, energy storage devices such as chemical energy storage devices and the like. Under the existing two-section electricity price mechanism, the control mode of the energy storage device is easy to design, so that the energy storage device stores electric energy when the electricity price is low, and releases the electric energy when the electricity price is high, and the purpose of reducing the electricity fee is achieved. However, when the electricity price mechanism is changed from a two-stage type segmented electricity price to a multi-stage type segmented electricity price and the like, which is a charging mode floating with time, the control of the energy storage device becomes extremely complex.
In order to improve the utilization rate of the energy storage device under the condition that information such as electricity price, total electricity consumption and the like is in a change state so as to effectively reduce electricity consumption cost, the application provides an energy storage management method which is used for managing the energy storage device for providing reserved electric energy for an electricity consumer. 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 a server, and executes a corresponding program by using hardware of the configured server to provide an energy sequence of the energy storage device in a power utilization period to be predicted for a power utilization party, so that the power utilization party can manage the energy storage device based on the energy sequence. Wherein, the electricity utilization period is exemplified by natural days, natural months and the like. The energy sequence refers to a set of a plurality of energy values of the energy storage device to be managed in time sequence in the electricity utilization period. The generated energy sequence of the energy storage device can be used for helping the electricity utilization party manage the energy storage device so as to achieve the aim of reducing the electricity utilization cost as much as possible in each electricity utilization period by managing the energy storage device.
To this end, the present application provides an energy storage management method. The energy storage management method is mainly executed by a server. Here, 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 (Platform-as-Service), infrastructure-as-a-Service (IaaS) and the like. The private cloud service end is, for example, an ali cloud computing service platform, an Amazon (Amazon) cloud computing service platform, a hundred degree cloud computing platform, a Tencel cloud computing platform, and the like.
The service end is in communication connection with the electricity price issuing system of the power supplier, the energy storage control system of the energy storage device, the electricity utilization control system of the electricity consumer, the management system of production activities, the self-powered system and the like, and even can 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 can be obtained by utilizing a crawler technology. Wherein the electricity price distribution system is a system in which an electricity provider (or an electricity market manager, such as a government department) distributes electricity prices. For example, the electricity rate distribution system distributes the electricity rate prediction sequence 24 hours thereafter every 30 minutes. The energy storage control system includes, but is not limited to: the device comprises a detection device for detecting energy stored by the energy storage device, a charging and discharging control system of the energy storage device and the like. The power usage control system includes, but is not limited to: metering devices (e.g., electricity meters), electrical equipment control systems, etc. installed within an enterprise. The management system of the production campaign includes, but is not limited to: a production process execution system (MES, manufacturing Execution System), an enterprise resource planning system (ERP, enterprise Resource Planning), and the like. The self-powered system includes, but is not limited to: a detection device for detecting the power generation amount of the self-powered system, a power generation control system of the self-powered system, and the like. The third party system includes, for example, its own server for storing historical electricity consumption data, a server for storing historical electricity price data, a WEB server for acquiring enterprise electricity consumption plans, 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 site or other site.
Referring to fig. 2, a schematic structure of a server according to an embodiment of the application is shown, and 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 exemplified by a solid state disk or a USB flash disk. The storage server is used for storing the acquired various power consumption 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 interface includes, but is not limited to: an ethernet network interface device, a mobile network (3G, 4G, 5G, etc.) based network interface device, a near field communication (WiFi, bluetooth, etc.) based network interface device, etc. 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 connects the interface unit 11 and the storage unit 12, and includes: at least one of a CPU or a chip integrated with a CPU, a programmable logic device (FPGA), and a multi-core processor. The processing unit 13 further includes a memory, a register, or the like for temporarily storing data.
Referring to fig. 3, a flow chart of the energy storage management method is shown. The energy storage management method is mainly executed by the processing unit 13 in the server, reads at least one program stored in the storage unit 12 by the processing unit, and performs data interaction according to hardware connection between the processing unit and hardware units such as the storage unit and the interface unit. In some practical applications, the processing unit may perform the following steps at the beginning of the change of the electricity price, so as to obtain the management of the energy storage device according to the energy sequence provided by the energy storage management method during the current electricity utilization period. In still other practical applications, the energy stored by the energy storage device needs to be adapted in time according to the continuous change of the actual power consumption of the power consumer, so that the power consumption cost of the power consumer in the whole power 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 means in time.
In step S110, a power supply prediction available to the power consumer during the power utilization period and a power consumption prediction sequence of the power consumer are obtained. The electricity consumption period is the electricity consumption period to be predicted, and can be a preset electricity consumption period or an electricity consumption period set according to the obtained floating electricity price change period. Wherein the floating electricity price change period refers to an interval of electricity price change. For example, the floating power rate change period is a duration in which a single power rate is maintained. As another example, the floating power rate change period is an updated duration of a sequence of floating power rates. The power supply prediction sequence comprises a power supply party, a self-powered system or a third party, which predicts a plurality of power supply quantities in time sequence in a power utilization period. The third party comprises a plurality of sets of power supply quantities predicted in time sequence in a power utilization period according to parameter data, historical power supply data and the like which are acquired from the power utilization party and are related to power supply.
In some embodiments, where the power available to the utility includes a purchase from a power provider, the power supply forecast sequence includes a price of electricity forecast sequence. Accordingly, the step of acquiring the power supply prediction sequence in the power utilization period in step S110 includes the step of acquiring the power rate prediction sequence in the power utilization period. The electricity price prediction sequence refers to a set of a plurality of electricity prices predicted in time sequence in an electricity utilization period.
In practical applications, there are situations where a third party (e.g., a separate price prediction system, a power provider, or a separate price pricing system) provides a price prediction sequence. Based on this, in one embodiment, the step of acquiring the electricity rate prediction sequence in the electricity usage period may include directly acquiring the electricity rate prediction sequence from the third party and taking it as the electricity supply prediction sequence in step S110. Here, it should be noted that the electricity rate prediction sequence provided by the third party is typically an electricity rate prediction sequence spanning a certain period of time, and therefore, the electricity consumption period may also be set based on the period of time spanned by the electricity rate prediction sequence provided by the third party, for example, in the case where the period of time spanned by the electricity rate prediction sequence provided by the third party is 12 hours, the electricity consumption period may be set to 12 hours or less than 12 hours, so that the electricity rate prediction sequence obtained from the third party can be directly used in the subsequent processing.
In fact, the predicted sequence of electricity prices issued by the third party deviates from the actual electricity prices, and thus, in another embodiment, the step of obtaining the predicted sequence of electricity prices during the electricity utilization period may include: based on the obtained historical electricity price prediction sequence and the deviation between the corresponding historical actual electricity price, the electricity price prediction sequence in the electricity use period available for the electricity use party is predicted, so that the accuracy of the electricity price prediction sequence based on which the energy sequence is generated is improved, and the accuracy of the generated energy sequence is further improved.
In one example, a predicted sequence of historical electricity prices provided by a third party and corresponding historical actual electricity prices are first obtained. For example, a historical electricity price prediction sequence of a certain historical period (the last year) can be obtained from a third party or other data platforms, and a historical actual electricity price corresponding to at least one historical electricity price prediction value in the historical electricity price prediction sequence is obtained; then, counting the electricity price error between the historical electricity price predicted value and the corresponding historical actual electricity price to obtain an electricity price error range; and correcting the electricity price prediction sequence in the electricity utilization period acquired from a third party by taking the electricity price error range as a correction parameter so as to obtain an electricity price prediction sequence based on which an energy sequence is generated. It should be noted that, when the above-mentioned historical electricity price prediction sequence and the historical actual electricity price are counted, a plurality of electricity price error ranges may also be obtained according to the time length, and the electricity price prediction sequence of the corresponding time length in the electricity use period obtained from the third party may be corrected based on the plurality of electricity price error ranges.
In still other embodiments, the power provider does not provide a predicted sequence of electricity prices, and the step of obtaining the predicted sequence of electricity prices over the period of electricity usage may include: and predicting a power price prediction sequence in the power utilization period based on the acquired power price related information. Wherein the electricity rate related information includes, but is not limited to, at least one of: a historical actual electricity price sequence, electricity price rules of an electricity market, other factors influencing the change of electricity price, and the like. The historical actual electricity price sequence refers to a set of a plurality of actual electricity prices in time sequence in a certain historical time period. For example, a historical actual electricity rate sequence may be obtained from a third party or other data platform. The electricity price rules of the electricity market refer to electricity price rules set by local government or power suppliers for the jurisdiction, including but not limited to: and a penalty price set based on the electricity demand of the electricity consumer. Other factors that affect the change in electricity rates include, for example, weather, holidays, and the like. For example, a power rate prediction sequence within a power utilization period is predicted based on acquired weather forecast, published holiday schedule, and a historical actual power rate sequence, etc.
Based on the above, the electricity rate prediction sequence is obtained by building a prediction model. In an example, under the condition of comprehensively considering the above-mentioned electricity price related information, a historical actual electricity price sequence, weather forecast, holiday schedule and the like are taken as inputs of a prediction model, and a prediction algorithm such as Random Forest (Random), long-short-term memory network (LSTM), iterative decision tree (GBRT), convolutional Neural Network (CNN) and the like are adopted to calculate, so as to obtain an electricity price prediction sequence in an electricity utilization period as output. In addition, the result of the electricity price prediction sequence can be corrected according to the error range of the prediction model.
The above embodiments for obtaining the electricity rate prediction sequence are only examples, and are not limiting of the present application. Those skilled in the art can construct models for predicting electricity price prediction sequences in combination with the various embodiments described above. The electricity price prediction sequence is calculated, for example, based on the input of the above-described prediction model, the prediction algorithm employed, and the error range of the detected historical electricity price data, so as to improve the accuracy of the subsequent predictions.
In other embodiments, where the power consumer is configured with a self-powered system, the power available to the power consumer includes power provided by the self-powered system, the power supply prediction sequence further includes a self-powered amount prediction sequence. The self-powered quantity prediction sequence refers to a set of a plurality of self-powered quantities predicted in time sequence in a power utilization period. The self-powered system includes, but is not limited to: photovoltaic power generation system, heat conversion system, trigeminy supply system, wind power generation system etc..
Accordingly, the step of acquiring the power supply prediction sequence in the power utilization period in step S110 includes the step of acquiring the self-power supply prediction sequence in the power utilization period. According to the self-powered system used by the actual power consumer, the step S110 includes predicting a self-power prediction sequence in the power consumption period based on the acquired power generation related information of the self-powered system. Wherein the power generation related information includes, but is not limited to: historical power generation data, factors affecting power generation based on the operating principle of the self-powered system. For example, in the case where the self-powered system employs photovoltaic power generation, factors influencing the power generation mainly include solar irradiance and the like. In another example, in the case where the self-powered system uses wind energy to generate electricity, factors affecting the electricity generation mainly include wind speed, wind direction, and the like. For another example, in the case of self-powered systems that employ thermal conversion to generate electricity, factors that affect the generation of electricity mainly include the thermal conversion efficiency of the system, the detected temperature, and the like.
Based on the above, a self-powered prediction sequence may be obtained by building a prediction model. And taking the power generation related information as the input of a prediction model, and adopting a prediction algorithm such as Random Forest (Random Forest), long-term short-term memory network (LSTM), iterative decision tree (GBRT), convolutional Neural Network (CNN) and the like to calculate so as to obtain a self-powered quantity prediction sequence in a power utilization period as output. In addition, the result of the self-powered quantity prediction sequence can be corrected according to the error range of the prediction model.
It should be noted that the above embodiments for obtaining the self-power prediction sequence are only examples, and are not limiting of the present application. Those skilled in the art can construct models of the predicted self-supplied power amount prediction sequence in combination with the various embodiments mentioned in the foregoing electricity price prediction sequence. The self-supplied power amount prediction sequence is calculated based on, for example, the input of the prediction model, the prediction algorithm employed, and the error range obtained by detection, so as to improve the accuracy of the subsequent prediction.
It should also be noted that the above-described manners of self-powered prediction using a self-powered system are only examples, and are not limiting of the present application. Those skilled in the art will understand that the power generation related information based on the self-powered prediction sequence is different according to the power supply mode of the actual self-powered system, which is not described herein in detail.
It should be further noted that, according to the actual situation, the power supply prediction sequence obtained by executing the step S110 may include only the power price prediction sequence or the self-power supply amount prediction sequence; or both the power rate prediction sequence and the self-powered quantity prediction sequence. There may be no limitation in this regard.
Further, in step S110, the step of acquiring the electricity consumption prediction sequence of the electricity consumer includes: and acquiring power consumption related information according to the power consumption factors in the power consumption period, and predicting a power consumption prediction sequence in the power consumption period according to the power consumption related information. The electricity consumption prediction sequence refers to a set of a plurality of electricity consumption predicted in time sequence in an electricity consumption period. The electricity consumption obtained by the electricity consumer is related to the electricity consumption factors of the daily production activities. Wherein the electricity consumption factors include, but are not limited to: an artificial plan such as a scheduling plan, a market activity plan, a plan summarized according to weather or social activity laws (e.g., workdays, holidays). For example, for electricity usage of the factory production product a, the electricity usage related information may include historical electricity usage data of the production product a, equipment usage information determined based on the production schedule of the product a, electricity usage information of the equipment, and the like. For another example, for the electricity consumption situation of the office building, the electricity consumption related information may include air conditioner usage information set based on seasons, air conditioner usage information, usage information of illumination lamps, computers, etc. on weekdays and holidays. In some cases where the air conditioner use information is not set, the air conditioner use information may also be determined based on weather forecast conditions. For example, the use of an air conditioner is controlled in accordance with the predicted air temperature.
Based on the above, the electricity consumption amount prediction sequence may be obtained by building a prediction model. And taking the electricity consumption related information as the input of a prediction model, and adopting a prediction algorithm such as Random Forest (Random Forest), long-term and short-term memory network (LSTM), iterative decision tree (GBRT), convolutional Neural Network (CNN) and the like to calculate so as to obtain an electricity consumption prediction sequence of an electricity consumer in an electricity consumption period as output. In addition, the result of the electricity consumption prediction sequence can be corrected according to the error range of the prediction model.
It should be noted that the above power consumption prediction method based on the power consumption related information is only an example, and is not a limitation of the present application. Those skilled in the art will understand that other power consumption related information affecting the power consumption prediction sequence may also be used as an input of the prediction model to obtain the power consumption prediction sequence through a prediction algorithm, which is not described in detail herein.
In step S120, an energy sequence of the energy storage device in the electricity consumption period is generated based on the energy storage parameters of the energy storage device acquired under the preset acquisition conditions, and the power supply prediction sequence and the electricity consumption prediction sequence in the electricity consumption period, so that the energy storage device is managed based on the energy sequence.
Wherein the preset acquisition conditions include at least one of the following: updating an event of the power supply prediction sequence, an event of the power consumption prediction sequence and an update period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power consumption prediction sequence.
Here, the event of updating the power supply prediction sequence includes, but is not limited to: a third party electricity price prediction sequence update event, a change in a factor affecting the electricity price, and the like. Examples of the change of the factors influencing the electricity price include an event that the electricity consumption is increased due to the fact that the new activity day is increased and then the electricity price is changed, the change of the factors influencing the power generation of the self-powered system, and the like. Examples of the change of the factors affecting the power generation of the self-powered system include: and the photovoltaic power generation amount caused by the weather mutation is reduced, so that the self-power supply amount is changed. In addition, the event of updating the power consumption prediction sequence also includes, but is not limited to: events that change in factors affecting the amount of electricity used. Such as an event of an increase or decrease in electricity usage due to a change in scheduling.
Further, in some embodiments, the update period is determined based on an update period of a power supply prediction sequence. The update period of the power supply prediction sequence may be a preset update period or an update period set according to a floating electricity price change period. For example, in the case where the floating electricity price is changed every 30 minutes, the update period is set to be updated every 30 minutes. In other embodiments, the update period is determined based on an update period of a power usage prediction sequence. The update period of the electricity consumption prediction sequence may be a preset update period, or may be set according to adjustment of the electricity consumption plan. For example, when adjusting a scheduling plan, an update period is set according to the corresponding adjustment event. In still other embodiments, the update period is determined based on an update period of the power supply prediction sequence and an update period of the power usage prediction sequence. For example, the energy storage parameters of the energy storage device are obtained each time the electricity price prediction sequence changes, and the energy storage parameters of the energy storage device are obtained each time the electricity consumption plan is adjusted. In addition, the update cycle also includes updates that are not operated in accordance with the operations suggested by the energy storage management method. For example, when an operator is recommended to perform a charging operation on the energy storage device at a certain time according to the energy storage management method, but because the operator does not perform an operation according to the recommendation, when the operator performs an operation again, the energy storage device needs to be updated first, and then a corresponding operation is performed on the energy storage device based on the updated energy storage management recommendation.
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, the loss parameters 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 comprise the energy conversion rate of the energy storage process of the energy storage device, the energy conversion rate of the energy release process of the energy storage device and the energy loss rate of the idle process of the energy storage device. The energy storage parameter may also be a set of parameters determined based on temperature related variables.
When the service end reaches a preset acquisition condition during the working period, the power supply prediction sequence, the power consumption prediction sequence and the energy storage parameters of the energy storage device are updated. In some examples, either one of the power supply prediction sequence and the power consumption prediction sequence is updated, and then an energy sequence within a next power usage period is generated from the update time based on the updated power supply prediction sequence, the power consumption prediction sequence, and the acquired energy storage parameter. Taking an update period of 30 minutes and a power consumption period of 24 hours as an example, the server generates an energy sequence of the next 24 hours every 30 minutes, wherein the energy sequence may include energy values stored by energy storage devices predicted and ordered at 30 minute time intervals.
The server side can carry out energy storage management of the energy storage device according to the actual management requirement of the power utilization party, and further generates an energy sequence meeting the management requirement. Wherein the management requirements include, but are not limited to: the total cost of electricity consumption is reduced as much as possible, and the electricity consumption of electricity consumption peak value is reduced as much as possible. And the server manages energy of the energy storage device based on the energy sequence generated by the acquisition condition.
In certain embodiments, step S120 comprises: under at least one constraint, a sequence of energy storage device energies during the electricity usage period is generated with the overall electricity usage cost reduction during the electricity usage period as an optimization objective. Wherein the constraints include constraints determined based on the energy storage parameters. Under the condition that the lowest total electricity price in the electricity utilization period is taken as an optimization target, the optimization objective function is as follows:
wherein t represents the t time, E G2L Representing the amount of electricity purchased and directly used by the electricity consumer from the power grid; e (E) G2B Representing the amount of electricity purchased and stored by the electricity consumer from the grid; e (E) B2L The electric quantity released and used by the energy storage device of the power consumer is represented; p (P) G Representing a real-time electricity price for purchasing electricity from the grid; p (P) B Representing the cost of the charge and discharge cost, loss and other costs of the energy storage device.
Furthermore, for an energy storage device, its model mathematical description may be:
E btty (t)＝E btty (t-Δt)+ΔE
wherein E is btty (t) is the electric quantity stored in the energy storage device at the moment t, E btty And (t-deltat) is the electric quantity stored in the energy storage device at the moment (t-deltat), and deltaE is the electric quantity stored or released in the unit time deltat. Further, Δe expression is:
ΔE＝E G2B ×e charge ；E B2L ＝0
alternatively, Δe=e B2L ×e discharge ；E G2B ＝0
Alternatively, Δe=e loss ；E B2L ＝E G2B ＝0
Wherein e charge Representing an energy conversion rate of a charging process of the energy storage device; e, e discharge Representing an energy conversion rate of a discharging process of the energy storage device; e (E) loss The self-discharge amount of the energy storage device per unit time Δt is represented.
In this case, at least one constraint is set according to the actually available energy storage parameters of the energy storage device, which is intended to prevent an abnormality in the energy storage device during the management of the energy storage device. For example, to avoid that a certain energy value in the generated energy sequence exceeds the maximum capacity of the energy storage deviceEtc. Based on the optimized objective function and the model of the energy storage device, wherein the charge quantity E of the energy storage device G2B And the energy storage device stores electric quantity E B2L Controlled 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 a relationship between the consumption of electrical energy and the supply of electrical energy.
Wherein the constraints set for the energy storage device include at least one of:
1) Capacity of the energy storage device: e (E) btty_MIN ≤E btty ≤E btty_MAX
2) Charging and discharging speed of the energy storage device: 0.ltoreq.DELTA.E/Δt.ltoreq.CR charge Or CR discharge Delta E/delta t is more than or equal to 0; wherein E is btty_MIN Representing a minimum capacity of the energy storage device; e (E) btty_MAX Representing a maximum capacity of the energy storage device; CR (computed radiography) charge Indicating a charging speed of the energy storage device; CR (computed radiography) discharge Indicating the discharge rate of the energy storage device. Meanwhile, the related variables of the energy storage device are temperature related variables.
Wherein the constraint condition set based on the relation between the power consumption and the power supply means that the power consumed by the power consumer at a certain moment is the sum of at least one or more of the power purchased from the power grid, the power provided by discharging the energy storage device and the power corresponding to the self-supplied power amount generated by the self-powered system, namely (E) G2L +E B2L +E P2L ) Wherein E is P2L And the real-time self-power supply quantity of the power consumer is represented. In the case where the self-supplied power is used for the operation of the consumer device, the difference between the total power demand of the consumer and the predicted result of the self-supplied power is used as the sum of the power purchased from the power grid and directly used and the power released and used by the energy storage device (E G2L +E B2L ) Is a constraint on (c). That is, in a certain period of time, the upper limit of the discharge amount of the energy storage device is equal to the difference between the total required amount of electricity and the self-powered amount, and if the discharge amount of the energy storage device is insufficient, the electric quantity purchased from the power grid is used for supplementing.
It should be noted that, the self-powered amount of the self-powered system may sell the surplus portion to the power provider according to the actual situation, which does not affect the energy storage management scheme described in the present application, and will not be described in detail herein.
In one embodiment, the step of generating an energy sequence of the energy storage device during the electricity usage period with the total electricity usage level during the electricity usage period as an optimization target under at least one constraint in step S120 includes: generating one or more candidate energy sequences within the powered cycle under at least one constraint; and optimizing the generated one or more candidate energy sequences under at least one constraint condition and with the total electricity consumption price in the electricity consumption period as an optimization target to obtain the energy sequence of the energy storage device in the electricity consumption period.
In certain embodiments, one or more candidate energy sequences over the power usage period are generated based on the predicted or detected energy stored by the energy storage device, and all constraints described above. Here, the initialization candidate energy sequences (which may also be referred to as initialization candidate solutions) may be generated in a random manner, so as to generate one or more preset candidate energy sequences, i.e., candidate solutions.
Wherein in some examples the generated candidate solution is one, and the candidate solution is optimized under at least one constraint and with a total cost of electricity in the electricity usage period as an optimization target. For example, under the constraints described above, one candidate solution to the application power cycle is generated. And carrying out optimization processing on one generated candidate solution by utilizing the change trend of the total electricity price corresponding to the candidate solution within a delta t time length so as to obtain an energy sequence taking the total electricity price in the electricity utilization period as an optimization target under at least one constraint condition.
In other examples, the generated candidate solution is a plurality of, and the energy sequence is selected and/or adjusted from the plurality of candidate solutions under at least one constraint and with a total cost of electricity used in the electricity usage period as an optimization objective. For example, the total power consumption price corresponding to each of the plurality of candidate solutions generated under the constraint condition is calculated, and the candidate solution with the lowest total power consumption price is selected as the generated energy sequence. For another example, calculating the total price of electricity consumption corresponding to each of a plurality of candidate solutions generated under the constraint condition, and selecting the candidate solution with the lowest total price of electricity consumption; and carrying out optimization processing on one generated candidate solution by utilizing the change trend of the total electricity price corresponding to the candidate solution within a delta t time length so as to obtain an energy sequence taking the total electricity price in the electricity utilization period as an optimization target under at least one constraint condition.
Wherein in certain embodiments, the step of optimizing the generated one or more candidate energy sequences comprises: determining one candidate energy sequence from one or more candidate energy sequences according to a cut-off condition set by taking the total electricity consumption price in an electricity consumption period as an optimization target, and taking the 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 cutoff condition includes that the actual iteration number reaches the preset iteration number, or the change of the optimal target result of the latest several iterations is smaller than a preset threshold. The update strategy includes, but is not limited to, lagrangian multiplier method (Lagrange Multiplier), sequential Linear Programming (SLP), sequential Quadratic Programming (SQP), interior Point method (Intoror Point), exterior Point method (Exterior Point), active Set method (Active Set), trusted region reflection algorithm (Trust Region Reflective), heuristic algorithm (Heuristic Algorithms), meta-heuristic algorithm (Meta-heuristic Algorithms), evolutionary algorithm (Evolutionary Algorithms), swarm intelligence algorithm (Swarm Intelligence Algorithms), neural network algorithm (Neural Networks), tabu search algorithm, simulated annealing algorithm, ant colony optimization algorithm, particle swarm optimization algorithm, differential evolution, greedy random adaptive search, clonal selection algorithm, artificial immune system algorithm, and other similar conventional optimization strategies or intelligent optimization strategies.
Taking an update period of 30 minutes and an electricity consumption period of 24 hours as an example, based on the model constraint condition of the energy storage device and a certain priori calculation, carrying out constraint restriction on Gao Weijie space, and limiting a solution space in a local space range meeting the constraint condition to obtain a plurality of candidate solutions, wherein each candidate solution is 48 dimensions; substituting all the target values into the optimization objective function to obtain an optimization target value corresponding to each candidate solution (in short, an evaluation step); then sorting according to the optimized target value corresponding to the candidate solution, screening and retaining a certain number of excellent solutions and eliminating the rest solutions (short for screening step); and sequencing the optimization target values in the order from small to large (namely, the order of total electricity consumption price from low to high), 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 that are retained by the screening, while a random variation of a certain probability (variation rate) is introduced in the cloning process to generate new candidate solutions based on each of the retained candidate solutions (simply referred to as a variation cloning step). Wherein the mutation rate is limited by the model constraint conditions to ensure that the new candidate solution is obtained based on a slight change made to the candidate solution before mutation cloning. The mutation rate may be introduced into all solutions of the retained clone solutions of the candidate solutions, or may be introduced into only a part of 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 substituting all the finally obtained candidate solutions into the optimization objective function to obtain an optimization target value corresponding to each candidate solution, and selecting the candidate solution corresponding to the minimum optimization target value as an energy sequence of the energy storage device.
Taking the model constraint condition and the optimization target of the energy storage device as examples, and using the SQP algorithm to obtain the energy sequence, the specific examples are as follows: under the constraint of the model constraint condition of the energy storage device and a certain priori calculation, converting an objective function and a constraint function by utilizing Taylor expansion, and calculating by utilizing the converted objective function and constraint function to obtain a candidate solution and an error gradient; adjusting the candidate solution based on the obtained error gradient, and repeating the execution steps of calculating the candidate solution and adjusting until the error gradient is smaller than a cut-off condition of a preset gradient threshold; and taking the finally obtained candidate solution as an energy sequence of the energy storage device.
It should be noted that, the cutoff condition in any of the above examples is not strictly in one-to-one correspondence with the algorithm used, and may be set according to actual design requirements, for example, the change of the optimal target result for the most recent several iterations is less than a preset threshold, and the like, which is not described herein. Further, the above-mentioned values are merely examples, and are not limiting, and those skilled in the art can arbitrarily choose values to calculate based on the idea of the present application.
It should be further noted that, during the evaluation, screening, and iterative processing of the candidate solutions, the foregoing steps may be adaptively adjusted and selected based on the foregoing other algorithms, and therefore, the manner of determining the energy sequence of the energy storage device by using the foregoing other algorithms and other algorithms applicable to the technical ideas of the present application should be regarded as specific examples based on the technical ideas of the present application, which are not described in detail herein.
In addition, the energy storage management method further comprises the step of displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence. Fig. 4a to 4d are schematic diagrams showing a power price prediction sequence, a self-powered power consumption prediction sequence, a power consumption prediction sequence and an energy sequence of an energy storage device in a power utilization period according to the energy storage management method of the present application. As shown in the figure, in the present application, taking an example of setting the electricity consumption period to 24 hours, fig. 4a shows an electricity price prediction sequence obtained based on the energy storage management method of the present application; fig. 4b shows a self-power prediction sequence obtained based on the energy storage management method of the present application, wherein the curve 4b-1 is a self-power upper limit prediction sequence, the curve 4b-2 is a self-power prediction sequence, and the curve 4b-3 is a self-power lower limit prediction sequence. Fig. 4c shows a power consumption prediction sequence of a power consumer obtained based on the energy storage management method of the present application, wherein a curve 4c-1 is a power consumption upper limit prediction sequence, a curve 4c-2 is a power consumption prediction sequence, and a curve 4c-3 is a power consumption lower limit prediction sequence. Fig. 4d shows an energy sequence of an energy storage device obtained based on the energy storage management method of the present application, wherein a curve 1 is a total electricity price of a user when the user does not use the energy storage device, and a curve 2 is a total electricity price of the user obtained based on the energy storage management method of the present application.
In addition, referring to fig. 5, fig. 5 is a schematic diagram of a graph showing a total electricity price obtained by an electricity consumer based on the energy storage management method of the present application and a total electricity price obtained when the energy storage device is not used, and as shown in the graph, curve 1 shows a total electricity price obtained by an electricity consumer based on the energy storage management method of the present application, and curve 2 shows a total electricity price obtained when the energy storage device is not used by the electricity consumer, as compared with a case where the energy storage device is not used, the total electricity fee is saved by about 5% -20% due to the capacity of the energy storage system, the electricity consumption of the electricity consumer, and the like.
In summary, the energy storage management method of the present application generates the energy sequence of the energy storage device in the electricity utilization period based on the obtained power supply prediction sequence, the electricity consumption prediction sequence and the energy storage parameter of the energy storage device, so that the energy storage device can be managed based on the energy sequence, and the purpose of lowest total electricity consumption is achieved.
Under the floating electricity price mechanism, a user can purchase and store certain electric power through the self-owned energy storage device when the electricity price is low, and release the stored electric power for the user to use when the electricity price is high, so that the purpose of reducing the electricity fee to a certain extent is achieved. However, in practical applications, there is uncertainty about when the power consumer is controlling the energy storage device, whether the energy storage device is charging or discharging, and what amount of power needs to be charged or discharged. For this purpose, the application also provides an energy storage control method for controlling an energy storage device for providing reserve electric energy for the electricity consumer. The energy storage control method is mainly executed by an energy storage control system. The energy storage control system can be a software system configured on computer equipment, and is used for controlling the energy storage device by an electric party based on the acquired energy sequence of the energy storage device so as to achieve the aim of lowest total electricity consumption price in an electricity consumption period.
The computer equipment can be equipment located in an electric control machine room of an enterprise or a service end in the internet. The server side comprises, but is not limited to, a single server, a server cluster, a distributed server cluster, a cloud server side 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 (Platform-as-Service), infrastructure-as-a-Service (IaaS) and the like. The private cloud service end is, for example, an ali cloud computing service platform, an Amazon (Amazon) cloud computing service platform, a hundred degree cloud computing platform, a Tencel cloud computing platform, and the like.
The computer equipment is in communication connection with an electricity price issuing system of an electricity supplier, an energy storage control system of an energy storage device, an electricity utilization control system of an electricity consumer, a management system of production activities, a self-powered system and the like, and can be even in data connection with a third party system, and internet data related to electricity utilization of the electricity consumer in the internet can be obtained by utilizing a crawler technology. Wherein the electricity price distribution system is a system in which an electricity provider (or an electricity market manager, such as a government department) distributes electricity prices. The energy storage control system includes, but is not limited to: the device comprises a detection device for detecting energy stored by the energy storage device, a charging and discharging control system of the energy storage device and the like. The power usage control system includes, but is not limited to: metering devices (e.g., electricity meters), electrical equipment control systems, etc. installed within an enterprise. The management system of the production campaign includes, but is not limited to: a production process execution system (MES, manufacturing Execution System), an enterprise resource planning system (ERP, enterprise Resource Planning), and the like. The self-powered system includes, but is not limited to: a detection device for detecting the power generation amount of the self-powered system, a power generation control system of the self-powered system, and the like. The third party system includes, for example, its own server for storing historical electricity consumption data, a server for storing historical electricity price data, a WEB server for acquiring enterprise electricity consumption plans, 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 site or other site.
Referring to fig. 6, a schematic structural diagram of a computer device according to an embodiment of the present application is shown, and 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 exemplified by a solid state disk or a USB flash disk. The storage server is used for storing the acquired various power consumption 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 interface includes, but is not limited to: an ethernet network interface device, a mobile network (3G, 4G, 5G, etc.) based network interface device, a near field communication (WiFi, bluetooth, etc.) based network interface device, etc. 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: at least one of a CPU or a chip integrated with a CPU, a programmable logic device (FPGA), and a multi-core processor. The processing unit 63 further includes a memory, a register, or the like for temporarily storing data.
Referring to fig. 7, a flow chart of the energy storage control method is shown. The processing unit 63 reads at least one program, electricity consumption-related information, and power supply-related information stored in the storage unit to perform an energy storage control method as described below. Wherein the electricity consumption related information and the power supply related information are acquired from an interface unit in advance by a processing unit and stored in a storage unit.
In step S710, a sequence of energy stored in the energy storage device during a power cycle generated by the energy storage management method is acquired. The specific implementation manner of step S710 is as described in fig. 2 to 3 and the corresponding descriptions thereof, and will not be repeated here.
In step S720, control information for controlling the operation of the energy storage device during the operation time interval is determined based on the energy value corresponding to the operation time interval in the obtained energy sequence. The operation time interval may be customized by the electricity consumer, or may be set according to a time interval between adjacent energy values in the energy sequence of the energy storage device acquired in step S710. For example, when the operation time interval is defined by the user, the starting time defined by the user may be used as the update period in step S710 to obtain the energy sequence of the energy storage device in the latest power utilization period, and then the control information for controlling the operation of the energy storage device in the user-defined operation time interval is determined based on the corresponding energy value in the energy sequence. In case the operation time interval is set based on the energy sequence of the energy storage device acquired in step S710, for example, based on the energy sequence diagram as shown in fig. 4d, the operation time interval may be set to correspond to the time intervals of the charging and discharging phases of the energy storage device in the energy sequence diagram, respectively.
In addition, the control information comprises charge and discharge control information of the energy storage device and/or a target energy storage value of the energy storage device in an operation time interval. Wherein the charge and discharge control information includes, but is not limited to: charge-discharge speed, charge-discharge time, charge-discharge duration. The target energy storage value of the energy storage device in the operation time interval refers to the electric quantity of the energy storage device charged or discharged in a certain time period, and the charging and discharging speed of the energy storage device can be obtained based on the target energy storage value and the operation time interval.
In view of this, the energy storage control method of the present application further includes a step of controlling the operation of the energy storage device in the corresponding operation time interval based on the control information. For example, the energy storage device is controlled to perform a charging and discharging operation at a certain charging and discharging speed for a certain charging and discharging duration from a certain charging and discharging time based on the charging and discharging control information. For another example, the energy storage device is controlled to select different charge and discharge speeds based on the target energy storage value to achieve the target energy storage value within a certain operation time interval.
In addition, the energy storage control method further comprises the step of acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence, so that a user can intuitively observe the energy sequence of the energy storage device and each prediction sequence.
In addition, since the step S710 updates the power supply prediction sequence, the power consumption prediction sequence, and the energy storage parameter based on the preset acquisition condition, and further obtains a new energy sequence, the energy storage control method correspondingly further includes a step of updating the control information based on the latest generated energy sequence. For example, taking an electricity consumption period of 24 hours and an update period of 30 minutes as an example, firstly, according to step S710, the energy sequence of the energy storage device in 24 hours is obtained, according to step S720, control information of the energy storage device for controlling the operation of the energy storage device in an operation time interval is determined, and then, the user operates the energy storage device based on the control information. When the update period of 30 minutes is reached, a new energy sequence of the energy storage device is updated within 24 hours from the moment, and control information based on the new energy sequence is generated again, and then the user operates the energy storage device based on the new control information. It follows that while the energy sequence of the energy storage device is shown as an overall change for the next 24 hours (power cycle), in practice the user only has to pay attention to the operating information within 30 minutes (update cycle), with corresponding control of the energy storage device based on the new energy sequence every 30 minutes.
In summary, the energy storage control method controls the energy storage device to operate based on the obtained energy sequence of the energy storage device, so as to achieve the purpose of lowest total electricity consumption.
The application also provides an energy storage management system. The energy storage management system is a software system configured at the server side. Referring to fig. 8, a schematic diagram of the energy storage management system in an embodiment is shown. The energy storage management system 2 includes program modules such as an acquisition module 21 and a generation module 22.
The obtaining module 21 is configured to obtain a power supply prediction sequence available to the power consumer in a power utilization period and a power consumption prediction sequence of the power consumer. The electricity consumption period is the electricity consumption period to be predicted, and can be a preset electricity consumption period or an electricity consumption period set according to the obtained floating electricity price change period. Wherein the floating electricity price change period refers to an interval of electricity price change. For example, the floating power rate change period is a duration in which a single power rate is maintained. As another example, the floating power rate change period is an updated duration of a sequence of floating power rates. The power supply prediction sequence comprises a power supply party, a self-powered system or a third party, which predicts a plurality of power supply quantities in time sequence in a power utilization period. The third party comprises a plurality of sets of power supply quantities predicted in time sequence in a power utilization period according to parameter data, historical power supply data and the like which are acquired from the power utilization party and are related to power supply.
In some embodiments, where the power available to the utility includes a purchase from a power provider, the power supply forecast sequence includes a price of electricity forecast sequence. Accordingly, the acquisition module 21 includes at least one of: a first acquisition unit for acquiring a power price prediction sequence in the power utilization period; a second acquisition unit for predicting a power rate prediction sequence within the power use period available to the power use party based on the acquired historical power rate prediction sequence and a deviation between the corresponding historical actual power rates; and a third acquisition unit for predicting a power rate prediction sequence within the power usage period based on the acquired power rate related information. In some embodiments, where a third party (e.g., a separate electricity price prediction system, an electricity provider, or a separate electricity price pricing system) provides an electricity price prediction sequence, the electricity price prediction sequence over the electricity usage period may be directly acquired using the first acquisition unit. However, in reality, there is a deviation of the electricity rate prediction sequence issued by the third party from the actual electricity rate, and thus, in this case, the second acquisition unit may be used to predict the electricity rate prediction sequence in the electricity use period available to the electricity consumer based on the acquired historical electricity rate prediction sequence and the deviation between the corresponding historical actual electricity rates.
In still other embodiments, the power provider does not provide a predicted sequence of electricity prices, and the step of obtaining the predicted sequence of electricity prices over the period of electricity usage may include: and predicting a power price prediction sequence in the power utilization period based on the acquired power price related information. Wherein the electricity rate related information includes, but is not limited to, at least one of: a historical actual electricity price sequence, electricity price rules of an electricity market, other factors influencing the change of electricity price, and the like. The historical actual electricity price sequence refers to a set of a plurality of actual electricity prices in time sequence in a certain historical time period. For example, a historical actual electricity rate sequence may be obtained from a third party or other data platform. The electricity price rules of the electricity market refer to electricity price rules set by local government or power suppliers for the jurisdiction, including but not limited to: and a penalty price set based on the electricity demand of the electricity consumer. Other factors that affect the change in electricity rates include, for example, weather, holidays, and the like. For example, a power rate prediction sequence within a power utilization period is predicted based on acquired weather forecast, published holiday schedule, and a historical actual power rate sequence, etc.
Based on the above, the electricity rate prediction sequence is obtained by building a prediction model. In an example, under the condition of comprehensively considering the above-mentioned electricity price related information, a historical actual electricity price sequence, weather forecast, holiday schedule and the like are taken as inputs of a prediction model, and a prediction algorithm such as Random Forest (Random), long-short-term memory network (LSTM), iterative decision tree (GBRT), convolutional Neural Network (CNN) and the like are adopted to calculate, so as to obtain an electricity price prediction sequence in an electricity utilization period as output. In addition, the result of the electricity price prediction sequence can be corrected according to the error range of the prediction model.
The above embodiments for obtaining the electricity rate prediction sequence are only examples, and are not limiting of the present application. Those skilled in the art can construct models for predicting electricity price prediction sequences in combination with the various embodiments described above. The electricity price prediction sequence is calculated, for example, based on the input of the above-described prediction model, the prediction algorithm employed, and the error range of the detected historical electricity price data, so as to improve the accuracy of the subsequent predictions.
In other embodiments, where the power consumer is configured with a self-powered system, the power available to the power consumer includes power provided by the self-powered system, the power supply prediction sequence further includes a self-powered amount prediction sequence. The self-powered quantity prediction sequence refers to a set of a plurality of self-powered quantities predicted in time sequence in a power utilization period. The self-powered system includes, but is not limited to: photovoltaic power generation system, heat conversion system, trigeminy supply system, wind power generation system etc.. Accordingly, the acquisition module 21 includes a fourth acquisition unit for predicting a self-power supply amount prediction sequence in the power utilization period based on the acquired power generation related information of the self-power supply system. Wherein the power generation related information includes, but is not limited to: historical power generation data, factors affecting power generation based on the operating principle of the self-powered system. For example, in the case where the self-powered system employs photovoltaic power generation, factors influencing the power generation mainly include solar irradiance and the like. In another example, in the case where the self-powered system uses wind energy to generate electricity, factors affecting the electricity generation mainly include wind speed, wind direction, and the like. For another example, in the case of self-powered systems that employ thermal conversion to generate electricity, factors that affect the generation of electricity mainly include the thermal conversion efficiency of the system, the detected temperature, and the like.
Based on the above, the fourth acquisition unit may obtain the self-powered prediction sequence by building a prediction model. And taking the power generation related information as the input of a prediction model, and adopting a prediction algorithm such as Random Forest (Random Forest), long-term short-term memory network (LSTM), iterative decision tree (GBRT), convolutional Neural Network (CNN) and the like to calculate so as to obtain a self-powered quantity prediction sequence in a power utilization period as output. In addition, the fourth obtaining unit may further correct the result of the self-powered prediction sequence according to the error range of the prediction model.
It should be noted that the above embodiments for obtaining the self-power prediction sequence are only examples, and are not limiting of the present application. Those skilled in the art can construct models of the predicted self-supplied power amount prediction sequence in combination with the various embodiments mentioned in the foregoing electricity price prediction sequence. The self-supplied power amount prediction sequence is calculated based on, for example, the input of the prediction model, the prediction algorithm employed, and the error range obtained by detection, so as to improve the accuracy of the subsequent prediction.
It should also be noted that the above-described manners of self-powered prediction using a self-powered system are only examples, and are not limiting of the present application. Those skilled in the art will understand that the power generation related information based on the self-powered prediction sequence is different according to the power supply mode of the actual self-powered system, which is not described herein in detail.
It should be further noted that, according to practical situations, the power supply prediction sequence available to the acquisition module 21 may include only a power price prediction sequence or a self-powered quantity prediction sequence; or both the power rate prediction sequence and the self-powered quantity prediction sequence. There may be no limitation in this regard.
In addition, the obtaining module 21 is further configured to obtain power consumption related information according to power consumption factors in the power consumption period; and a fifth acquisition unit for predicting a power consumption prediction sequence in the power consumption period according to the power consumption related information. The electricity consumption prediction sequence refers to a set of a plurality of electricity consumption predicted in time sequence in an electricity consumption period. The electricity consumption obtained by the electricity consumer is related to the electricity consumption factors of the daily production activities. Wherein the electricity consumption factors include, but are not limited to: an artificial plan such as a scheduling plan, a market activity plan, a plan summarized according to weather or social activity laws (e.g., workdays, holidays). For example, for electricity usage of the factory production product a, the electricity usage related information may include historical electricity usage data of the production product a, equipment usage information determined based on the production schedule of the product a, electricity usage information of the equipment, and the like. For another example, for the electricity consumption situation of the office building, the electricity consumption related information may include air conditioner usage information set based on seasons, air conditioner usage information, usage information of illumination lamps, computers, etc. on weekdays and holidays. In some cases where the air conditioner use information is not set, the air conditioner use information may also be determined based on weather forecast conditions. For example, the use of an air conditioner is controlled in accordance with the predicted air temperature.
Based on the above, the fifth acquisition unit may obtain the electricity consumption amount prediction sequence by establishing a prediction model. And taking the electricity consumption related information as the input of a prediction model, and adopting a prediction algorithm such as Random Forest (Random Forest), long-term and short-term memory network (LSTM), iterative decision tree (GBRT), convolutional Neural Network (CNN) and the like to calculate so as to obtain an electricity consumption prediction sequence of an electricity consumer in an electricity consumption period as output. In addition, the fifth obtaining unit may further correct the result of the electricity consumption prediction sequence according to the error range of the prediction model.
It should be noted that the above power consumption prediction method based on the power consumption related information is only an example, and is not a limitation of the present application. Those skilled in the art will understand that other power consumption related information affecting the power consumption prediction sequence may also be used as an input of the prediction model to obtain the power consumption prediction sequence through a prediction algorithm, which is not described in detail herein.
The generating module 22 is configured to generate an energy sequence of the energy storage device in the electricity consumption period based on the energy storage parameters of the energy storage device acquired under the preset acquisition conditions, and the power supply prediction sequence and the electricity consumption prediction sequence in the electricity consumption period, so that the energy storage device is managed based on the energy sequence.
Wherein the preset acquisition conditions include at least one of the following: updating an event of the power supply prediction sequence, an event of the power consumption prediction sequence and an update period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power consumption prediction sequence.
Here, the event of updating the power supply prediction sequence includes, but is not limited to: a third party electricity price prediction sequence update event, a change in a factor affecting the electricity price, and the like. Examples of the change of the factors influencing the electricity price include an event that the electricity consumption is increased due to the fact that the new activity day is increased and then the electricity price is changed, the change of the factors influencing the power generation of the self-powered system, and the like. Examples of the change of the factors affecting the power generation of the self-powered system include: and the photovoltaic power generation amount caused by the weather mutation is reduced, so that the self-power supply amount is changed. In addition, the event of updating the power consumption prediction sequence also includes, but is not limited to: events that change in factors affecting the amount of electricity used. Such as an event of an increase or decrease in electricity usage due to a change in scheduling.
Further, in some embodiments, the update period is determined based on an update period of a power supply prediction sequence. The update period of the power supply prediction sequence may be a preset update period or an update period set according to a floating electricity price change period. For example, in the case where the floating electricity price is changed every 30 minutes, the update period is set to be updated every 30 minutes. In other embodiments, the update period is determined based on an update period of a power usage prediction sequence. The update period of the electricity consumption prediction sequence may be a preset update period, or may be set according to adjustment of the electricity consumption plan. For example, when adjusting a scheduling plan, an update period is set according to the corresponding adjustment event. In still other embodiments, the update period is determined based on an update period of the power supply prediction sequence and an update period of the power usage prediction sequence. For example, the energy storage parameters of the energy storage device are obtained each time the electricity price prediction sequence changes, and the energy storage parameters of the energy storage device are obtained each time the electricity consumption plan is adjusted. In addition, the update cycle also includes updates that are not operated in accordance with the operations suggested by the energy storage management method. For example, when an operator is recommended to perform a charging operation on the energy storage device at a certain time according to the energy storage management method, but because the operator does not perform an operation according to the recommendation, when the operator performs an operation again, the energy storage device needs to be updated first, and then a corresponding operation is performed on the energy storage device based on the updated energy storage management recommendation.
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, the loss parameters 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 comprise the energy conversion rate of the energy storage process of the energy storage device, the energy conversion rate of the energy release process of the energy storage device and the energy loss rate of the idle process of the energy storage device. The energy storage parameter may also be a set of parameters determined based on temperature related variables.
In some embodiments, the generation module 22 includes a generation unit for generating a sequence of energies of the energy storage devices during the electricity usage period under at least one constraint with a total cost of electricity usage during the electricity usage period as an optimization objective; wherein the constraints include constraints determined based on the energy storage parameters. Under the condition that the lowest total electricity price in the electricity utilization period is taken as an optimization target, the optimization objective function is as follows:
Wherein t represents the t time, E G2L Representing the amount of electricity purchased and directly used by the electricity consumer from the power grid; e (E) G2B Representing the amount of electricity purchased and stored by the electricity consumer from the grid; e (E) B2L The electric quantity released and used by the energy storage device of the power consumer is represented; p (P) G Representing a real-time electricity price for purchasing electricity from the grid; p (P) B Representing the cost of the charge and discharge cost, loss and other costs of the energy storage device.
Furthermore, for an energy storage device, its model mathematical description may be:
E btty (t)＝E btty (t-Δt)+ΔE
wherein E is btty (t) is the electric quantity stored in the energy storage device at the moment t, E btty And (t-deltat) is the electric quantity stored in the energy storage device at the moment (t-deltat), and deltaE is the electric quantity stored or released in the unit time deltat. Further, Δe expression is:
ΔE＝E G2B ×e charge ；E B2L ＝0
alternatively, Δe=e B2L ×e discharge ；E G2B ＝0
Alternatively, Δe=e loss ；E B2L ＝E G2B ＝0
Wherein e charge Representing an energy conversion rate of a charging process of the energy storage device; e, e discharge Representing an energy conversion rate of a discharging process of the energy storage device; e (E) loss The self-discharge amount of the energy storage device per unit time Δt is represented.
In this case, at least one constraint is set according to the actually available energy storage parameters of the energy storage device, which is intended to prevent an abnormality in the energy storage device during the management of the energy storage device. For example, avoiding that a certain energy value in the generated energy sequence exceeds the maximum capacity of the energy storage device, etc. Based on the optimized objective function and the model of the energy storage device, wherein the charge quantity E of the energy storage device G2B And the energy storage device stores electric quantity E B2L Controlled 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 a relationship between the consumption of electrical energy and the supply of electrical energy.
Wherein the constraints set for the energy storage device include at least one of:
1) Capacity of the energy storage device: e (E) btty_MIN ≤E btty ≤E btty_MAX
2) Charging and discharging speed of the energy storage device: 0.ltoreq.DELTA.E/Δt.ltoreq.CR charge Or CR discharge Delta E/delta t is more than or equal to 0; wherein E is btty_MIN Representing a minimum capacity of the energy storage device; e (E) btty_MAX Representing a maximum capacity of the energy storage device; CR (computed radiography) charge Indicating a charging speed of the energy storage device; CR (computed radiography) discharge Indicating the discharge rate of the energy storage device. Meanwhile, the related variables of the energy storage device are temperature related variables.
Wherein the constraint condition set based on the relation between the power consumption and the power supply means that the power consumed by the power consumer at a certain moment is the sum of at least one or more of the power purchased from the power grid, the power provided by discharging the energy storage device and the power corresponding to the self-supplied power amount generated by the self-powered system, namely (E) G2L +E B2L +E P2L ) Wherein E is P2L And the real-time self-power supply quantity of the power consumer is represented. Under the condition that the self-powered electricity consumption is used for running the electricity consumption equipment, the difference between the total electricity consumption of the electricity consumption and the prediction result of the self-powered electricity consumption is taken as the purchase from the power grid The sum of the purchased and directly used electric quantity and the electric quantity released and used by the energy storage device (E G2L +E B2L ) Is a constraint on (c). That is, in a certain period of time, the upper limit of the discharge amount of the energy storage device is equal to the difference between the total required amount of electricity and the self-powered amount, and if the discharge amount of the energy storage device is insufficient, the electric quantity purchased from the power grid is used for supplementing.
It should be noted that, the self-powered amount of the self-powered system may sell the surplus portion to the power provider according to the actual situation, which does not affect the energy storage management scheme described in the present application, and will not be described in detail herein.
In one embodiment, the generating unit is configured to generate one or more candidate energy sequences within the electricity usage period under at least one constraint; and optimizing the generated one or more candidate energy sequences under at least one constraint condition and with the total electricity consumption price in the electricity consumption period as an optimization target to obtain the energy sequence of the energy storage device in the electricity consumption period.
In certain embodiments, one or more candidate energy sequences over the power usage period are generated based on the predicted or detected energy stored by the energy storage device, and all constraints described above. Here, the initialization candidate energy sequences (which may also be referred to as initialization candidate solutions) may be generated in a random manner, so as to generate one or more preset candidate energy sequences, i.e., candidate solutions.
Wherein in some examples the generated candidate solution is one, and the candidate solution is optimized under at least one constraint and with a total cost of electricity in the electricity usage period as an optimization target. For example, under the constraints described above, one candidate solution to the application power cycle is generated. And carrying out optimization processing on one generated candidate solution by utilizing the change trend of the total electricity price corresponding to the candidate solution within a delta t time length so as to obtain an energy sequence taking the total electricity price in the electricity utilization period as an optimization target under at least one constraint condition.
In other examples, the generated candidate solution is a plurality of, and the energy sequence is selected and/or adjusted from the plurality of candidate solutions under at least one constraint and with a total cost of electricity used in the electricity usage period as an optimization objective. For example, the total power consumption price corresponding to each of the plurality of candidate solutions generated under the constraint condition is calculated, and the candidate solution with the lowest total power consumption price is selected as the generated energy sequence. For another example, calculating the total price of electricity consumption corresponding to each of a plurality of candidate solutions generated under the constraint condition, and selecting the candidate solution with the lowest total price of electricity consumption; and carrying out optimization processing on one generated candidate solution by utilizing the change trend of the total electricity price corresponding to the candidate solution within a delta t time length so as to obtain an energy sequence taking the total electricity price in the electricity utilization period as an optimization target under at least one constraint condition.
In some embodiments, the generating unit is configured to determine one candidate energy sequence from the one or more candidate energy sequences according to a cutoff condition set for optimization of total power consumption cost in the power consumption period, and take the one 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 cutoff condition includes that the actual iteration number reaches the preset iteration number, or the change of the optimal target result of the latest several iterations is smaller than a preset threshold. The update strategy includes, but is not limited to, lagrangian multiplier method (Lagrange Multiplier), sequential Linear Programming (SLP), sequential Quadratic Programming (SQP), interior Point method (Intoror Point), exterior Point method (Exterior Point), active Set method (Active Set), trusted region reflection algorithm (Trust Region Reflective), heuristic algorithm (Heuristic Algorithms), meta-heuristic algorithm (Meta-heuristic Algorithms), evolutionary algorithm (Evolutionary Algorithms), swarm intelligence algorithm (Swarm Intelligence Algorithms), neural network algorithm (Neural Networks), tabu search algorithm, simulated annealing algorithm, ant colony optimization algorithm, particle swarm optimization algorithm, differential evolution, greedy random adaptive search, clonal selection algorithm, artificial immune system algorithm, and other similar conventional optimization strategies or intelligent optimization strategies.
In addition, the energy storage management system further comprises an output module, wherein the output module is used for outputting at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence for display.
The working modes of the modules in the energy storage management system of the present application are the same as or similar to the corresponding steps in the energy storage management method, and are not described herein.
The application further provides an energy storage control system. The energy storage control system is a software system configured on a computer device. Referring to fig. 9, a schematic diagram of the energy storage control system in an embodiment is shown. The energy storage control system 3 comprises program modules such as an acquisition module 31, a determination module 32 and the like.
Wherein, the acquisition module 31 is used for acquiring the energy sequence of the energy storage device in the electricity utilization period generated by the energy storage management system. The determining module 32 is configured to determine control information for controlling an operation of the energy storage device during an operation time interval of the energy storage device based on an energy value corresponding to the operation time interval in the acquired energy sequence. The operation time interval may be customized by the electricity consumer, or may be set according to a time interval between adjacent energy values in the obtained energy sequence of the energy storage device.
The control information comprises charge and discharge control information of the energy storage device and/or a target energy storage value of the energy storage device in an operation time interval. Wherein the charge and discharge control information includes, but is not limited to: charge-discharge speed, charge-discharge time, charge-discharge duration. The target energy storage value of the energy storage device in the operation time interval refers to the electric quantity of the energy storage device charged or discharged in a certain time period, and the charging and discharging speed of the energy storage device can be obtained based on the target energy storage value and the operation time interval.
In addition, the energy storage control system further comprises a control module, wherein the control module is used for controlling the operation of the energy storage device in a corresponding operation time interval based on the control information.
In addition, the energy storage control system further comprises a display module, wherein the display module is used for acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence.
In addition, the energy storage control system further comprises an updating module for updating the control information based on the latest generated energy sequence, because the acquisition module can update the power supply prediction sequence, the power consumption prediction sequence and the energy storage parameter based on the preset acquisition condition so as to obtain a new energy sequence.
The operation mode of each module in the energy storage control system of the present application is the same as or similar to the corresponding steps in the energy storage control method, and will not be described herein.
It should be further noted that, from the above description of the embodiments, it will be apparent to those skilled in the art that part or all of the present application may be implemented by means of software in combination with a necessary general hardware platform. Based on such understanding, the present application also provides a computer readable storage medium storing at least one program that when invoked performs any of the aforementioned energy storage management methods. The present application also provides a computer-readable storage medium storing at least one program that, when called, performs any one of the foregoing energy storage control methods.
Based on such understanding, the present technology may, in essence or in part, contribute to the prior art, be embodied in the form of a software product, which may include one or more machine-readable media having stored thereon machine-executable instructions, which when executed by one or more machines, such as a computer, computer network, or other electronic device, 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 disk-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 in a server providing a certain application mall. Specific application malls are not limited herein, such as millet application malls, chinese application malls, apple application malls, etc.
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 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 application further provides an energy storage control system. The energy storage control system includes a server and a computer device as provided in any of the foregoing examples. Referring to fig. 10, a schematic diagram of a network architecture of the energy storage control system for controlling the energy storage device in one embodiment is shown. The server 41 and the computer device 42 may be located at the power consumer side, or located at any geographic location where data communication can be performed through a data transmission network such as the internet or a mobile network, or located at the power consumer side, or located at another geographic location where data communication can be performed. The computer device 42 may send control instructions to the energy storage device 43 via data communication, and collect energy storage parameters of the energy storage device 43. In some examples, the server 41 is further in data communication with the metering device 44 on the electricity consumer side, so as to obtain the electricity consumption of the electricity consumer detected by the metering device 44, so that the server 41 predicts the electricity consumption prediction sequence in the electricity consumption period according to the electricity consumption related information including the obtained electricity consumption. In still other examples, the power consumer further includes a self-powered system 45, and the server 41 obtains the power generation related information of the self-powered system 45 through a data communication manner according to the actual type of the self-powered system 45. For example, the self-powered system 45 generates power by thermal conversion, and the corresponding server 41 obtains temperature information of the self-powered system 45 as one of the power generation related information.
Taking fig. 10 as an example, the implementation process of the energy storage control system is as follows: the server 41 predicts a self-powered quantity prediction sequence in a power utilization period by acquiring power generation related information of the self-powered system 45; the power consumption prediction sequence in the same power consumption period is predicted by acquiring power consumption related information of a power consumer, production scheduling and the like; acquiring energy storage parameters of the energy storage device 43 at the starting moment of the power utilization period through the computer equipment 42; and acquiring a third party electricity price prediction sequence. The constraints determined by the server 41 based on the energy storage parameters include: 1) Capacity of the energy storage device 43: e (E) btty_MIN ≤E btty ≤E btty_MAX And 2) the charge-discharge rate of the energy storage device 43: 0.ltoreq.DELTA.E/Δt.ltoreq.CR charge Or CR discharge Delta E/delta t is more than or equal to 0; generating a plurality of candidate energy storage sequences in a random mode by taking the total electricity consumption cost in the electricity consumption period as an optimization target; selecting n candidate energy storage sequences with the lowest electricity consumption total price by calculating the electricity consumption total price corresponding to each candidate energy storage sequence; and (3) cloning the n reserved candidate energy storage sequences by a corresponding number, introducing random variation with a certain probability (variation rate) in the cloning process, and obtaining new candidate energy storage sequences. Wherein the variability is limited by the model constraint conditions, To ensure that the new candidate stored sequences obtained are based on minor changes made to the candidate solution prior to variant cloning. The mutation rate is introduced into all solutions of the reserved candidate energy storage sequence, or only a part of solutions. Screening and mutating cloning are carried out on the generated candidate energy sequence under the constraint of the constraint condition until the actual iteration times reach the cut-off condition of the preset iteration times; finally, the candidate energy storage sequence corresponding to the lowest total price of electricity is selected as the energy sequence of the energy storage device 43, and the obtained energy sequence is sent to the computer equipment 42.
The computer device 42 generates control information for controlling the energy storage device 43 to adjust from the currently stored energy value E0 to E1 according to the energy value E1 at the latest moment in the acquired energy sequence, and controls the energy storage device 43 to perform energy storage adjustment according to the control information.
Here, when any one of the electricity price prediction sequence, the electricity consumption prediction sequence, the self-powered electricity prediction sequence, or the energy storage parameter is updated, the server 41 generates an energy sequence according to the latest data, so that the computer device 42 timely controls the energy storage device 43 to perform energy storage adjustment. Therefore, the purpose that the electricity utilization party utilizes the energy storage to reduce the electricity utilization cost under the floating electricity price mechanism is achieved.
It should be noted that the above operation is merely an example, and is not a limitation of the present application, and in fact, the energy sequence generated by using any manner provided by the foregoing service end to generate the energy storage device may replace the generation manner in this example. And will not be described in detail herein.
In summary, the energy sequence of the energy storage device in the electricity utilization period is generated based on the obtained power supply prediction sequence, the electricity consumption prediction sequence and the energy storage parameters of the energy storage device, so that the energy storage device can be managed based on the energy sequence, and the purpose of lowest total electricity consumption is achieved.
The above embodiments are merely illustrative of the principles of the present application and its effectiveness, and are not intended to limit the application. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the application. Accordingly, it is intended that all equivalent modifications and variations of the application be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.

Claims (33)

1. An energy storage management method for managing an energy storage device for providing reserve electric energy for an electricity consumer, comprising the steps of:
Acquiring a power supply prediction sequence which can be used by the power consumer in a power utilization period and a power consumption prediction sequence of the power consumer, wherein the power supply prediction sequence comprises a power price prediction sequence and a self-powered power prediction sequence; and
generating an energy sequence of the energy storage device in the electricity utilization period based on the energy storage parameters of the energy storage device acquired under preset acquisition conditions, and a power supply prediction sequence and an electricity consumption prediction sequence in the electricity utilization period, so that the energy storage device is managed based on the energy sequence; wherein the preset acquisition conditions include at least one of the following: updating an event of the power supply prediction sequence, an event of the power consumption prediction sequence and an update period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power consumption prediction sequence.
2. The energy storage management method according to claim 1, wherein the step of obtaining a predicted sequence of electricity prices in the electricity utilization period includes any one of:
acquiring an electricity price prediction sequence in the electricity utilization period;
predicting a power rate prediction sequence within the power utilization period available to the power utilization party based on the obtained historical power rate prediction sequence and a deviation between corresponding historical actual power rates;
And predicting a power price prediction sequence in the power utilization period based on the acquired power price related information.
3. The energy storage management method of claim 1, wherein the step of obtaining a self-powered prediction sequence over a power utilization period comprises:
and predicting a self-powered quantity prediction sequence in the power utilization period based on the acquired power generation related information of the self-powered system.
4. The energy storage management method according to claim 1, wherein the step of obtaining a power consumption prediction sequence of the power consumer includes:
acquiring power consumption related information according to power consumption factors in the power consumption period; and
and predicting a power consumption prediction sequence in the power consumption period according to the power consumption related information.
5. The energy storage management method according to claim 1, wherein the step of generating the energy sequence of the energy storage device in the electricity usage period based on the energy storage parameter of the energy storage device acquired with the preset acquisition condition, and the power supply prediction sequence and the electricity usage prediction sequence in the electricity usage period includes:
under at least one constraint condition, taking the total electricity consumption price in the electricity consumption period as an optimization target, and generating an energy sequence of the energy storage device in the electricity consumption period; wherein the constraints include constraints determined based on the energy storage parameters.
6. The energy storage management method of claim 5, wherein said step of generating an energy sequence of said energy storage device during said electricity usage period under at least one constraint with an overall cost of electricity usage during said electricity usage period as an optimization objective comprises:
generating one or more candidate energy sequences within the powered cycle under at least one constraint; and
and under at least one constraint condition and with the total electricity consumption cost in the electricity consumption period as an optimization target, performing optimization processing on the generated one or more candidate energy sequences to obtain the energy sequence of the energy storage device in the electricity consumption period.
7. The energy storage management method of claim 6, wherein optimizing the generated one or more candidate energy sequences comprises:
determining one candidate energy sequence from the one or more candidate energy sequences according to a cut-off condition set by taking the total electricity consumption price in the electricity consumption period as an optimization target, and taking the one candidate energy sequence as the energy sequence; and
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.
8. The energy storage management method of claim 1 or 5, wherein 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, the loss parameters of the energy storage device.
9. The energy storage management method of claim 1, further comprising the step of displaying at least one of the energy sequence, the power supply prediction sequence, and the power usage prediction sequence.
10. An energy storage control method for controlling an energy storage device for providing reserve electric energy for an electricity consumer, comprising the steps of:
acquiring a sequence of energy of the energy storage device during a power cycle generated by the energy storage management method of any one of claims 1-8;
and determining control information of the energy storage device 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.
11. The energy storage control method of claim 10, further comprising the step of controlling operation of the energy storage device within a respective operation time interval based on the control information.
12. The energy storage control method of claim 10, further comprising: and a step of acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence.
13. The energy storage control method according to any one of claims 10-12, further comprising the step of updating the control information based on a newly generated energy sequence.
14. The energy storage control method of claim 10, wherein the control information includes 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.
15. An energy storage management system for managing an energy storage device that provides reserve electrical energy for an electricity consumer, comprising:
the power supply prediction system comprises an acquisition module, a power supply prediction module and a power supply control module, wherein the acquisition module is used for acquiring a power supply prediction sequence which can be used by the power consumer in a power utilization period and a power consumption prediction sequence of the power consumer, and the power supply prediction sequence comprises a power price prediction sequence and a self-power supply prediction sequence; and
the generation module is used for generating an energy sequence of the energy storage device in the electricity utilization period based on the energy storage parameters of the energy storage device, the power supply prediction sequence and the electricity consumption prediction sequence in the electricity utilization period, which are acquired under preset acquisition conditions, so that the energy storage device is managed based on the energy sequence; wherein the preset acquisition conditions include at least one of the following: updating an event of the power supply prediction sequence, an event of the power consumption prediction sequence and an update period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power consumption prediction sequence.
16. The energy storage management system of claim 15, wherein the acquisition module comprises at least one of:
the first acquisition unit is used for acquiring an electricity price prediction sequence in the electricity utilization period;
a second obtaining unit configured to predict a power rate prediction sequence in the power use period available to the power consumer based on the obtained historical power rate prediction sequence and a deviation between the corresponding historical actual power rates;
and the third acquisition unit is used for predicting the electricity price prediction sequence in the electricity utilization period based on the acquired electricity price related information.
17. The energy storage management system of claim 15, wherein the acquisition module includes a fourth acquisition unit for predicting a self-powered quantity prediction sequence within the power usage period based on the acquired power generation related information of the self-powered system.
18. The energy storage management system of claim 15, wherein the acquisition module includes a fifth acquisition unit for acquiring electricity related information according to electricity utilization factors in the electricity utilization period; and predicting a power consumption prediction sequence in the power consumption period according to the power consumption related information.
19. The energy storage management system of claim 15, wherein the generation module comprises:
a generating unit, configured to generate an energy sequence of the energy storage device in the electricity consumption period with a total electricity consumption price in the electricity consumption period as an optimization target under at least one constraint condition; wherein the constraints include constraints determined based on the energy storage parameters.
20. The energy storage management system of claim 19, wherein the generating unit is configured to generate one or more candidate energy sequences within the power usage period under at least one constraint; and optimizing the generated one or more candidate energy sequences under at least one constraint condition and with the total electricity consumption price in the electricity consumption period as an optimization target to obtain the energy sequence of the energy storage device in the electricity consumption period.
21. The energy storage management system of claim 20, wherein the generating unit is configured to determine one candidate energy sequence from the one or more candidate energy sequences as the energy sequence according to a cutoff condition set for an optimization objective of a total price of electricity used in the electricity usage period; 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.
22. The energy storage management system of claim 15 or 19, wherein 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, the loss parameters of the energy storage device.
23. The energy storage management system of claim 15, further comprising an output module for outputting at least one of the energy sequence, the power supply prediction sequence, and the power usage prediction sequence for display.
24. An energy storage control system for controlling an energy storage device that provides reserve electrical energy for a consumer, comprising:
an acquisition module for acquiring a sequence of energy of the energy storage device during a power cycle generated by the energy storage management system of any one of claims 15-23;
the determining module is used for determining control information of the energy storage device 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.
25. The energy storage control system of claim 24, further comprising a control module for controlling operation of the energy storage device within a respective operating time interval based on the control information.
26. The energy storage control system of claim 24, further comprising: and the display module is used for acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence.
27. The energy storage control system of any of claims 24-26, further comprising: and the updating module is used for updating the control information based on the latest generated energy sequence.
28. The energy storage control system of claim 24, wherein the control information includes 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 a prediction time interval.
29. A server, comprising:
the interface unit is used for acquiring power supply related information which can be used by a power consumer in a power utilization period and power utilization related information of the power consumer;
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 management method of any of claims 1-9.
30. A computer device, comprising:
The interface unit is used for acquiring power supply related information which can be used by a power consumer in a power utilization period and power utilization related information of the power consumer;
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 of claims 10-14.
31. A computer-readable storage medium, characterized in that at least one program is stored, which when called performs the energy storage management method according to any one of claims 1-9.
32. A computer-readable storage medium, characterized in that at least one program is stored, which when called performs the energy storage control method according to any one of claims 10-14.
33. An energy storage control system, comprising: a server as claimed in claim 29 and a computer device as claimed in claim 30.
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