CN117893104A - Intelligent park intelligent control method and device comprising price mechanism - Google Patents
Intelligent park intelligent control method and device comprising price mechanism Download PDFInfo
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Abstract
The embodiment of the invention discloses an intelligent control method and device for an intelligent park, which comprise a price mechanism, relate to the field of photovoltaic power generation, and can realize dynamic balance of local energy production and power consumption load, reduce power grid load in the peak period of a charging station and reduce the operation cost of the charging station. The invention comprises the following steps: collecting relevant parameters and running states of electrical equipment in a park; the decision module analyzes the collected intelligent park state data, parameters and communication data of the demand side management system by adopting a park maximum benefit strategy, and generates a decision scheme containing a price mechanism by adopting a master-slave game algorithm; the control module generates an instruction data stream according to a decision scheme; the communication module encodes according to the instruction stream, implements load regulation and control of the controlled electric equipment according to the communication protocol, and simultaneously realizes data transmission and communication with the management system at the demand side.
Description
Technical Field
The invention relates to the field of photovoltaic power generation in the power engineering technology, in particular to an intelligent control method and device for an intelligent park comprising a price mechanism.
Background
In recent years, with the rapid development of photovoltaic power generation technology and the reduction of the cost of photovoltaic modules and electrochemical energy storage systems, the photovoltaic energy storage technology is gradually accepted by the public. Because the battery of the charging station energy storage system has the upper limit of capacity, if the storage battery is fully charged in the valley period, and the photovoltaic power generation amount in other periods is larger than the power consumption amount of a user, the photovoltaic power is wasted, so that the power cost is increased; if the amount of electricity of the energy storage battery is reduced in the valley period, and the amount of electricity generated by the photovoltaic power generation in other periods is smaller than the amount of electricity used by the user, electricity may be purchased in the peak period, resulting in an increase in electricity cost. Therefore, the peak Gu Ping period, the photovoltaic power generation amount and the user power consumption are combined, an optimal power scheduling strategy is formulated, the photovoltaic utilization rate is maximum while the user power consumption is met, and the electricity purchasing cost is lowest.
Because solar energy has randomness and intermittence, the solar photovoltaic power generation system is required to realize continuous and stable power supply, and the energy storage device plays a very important role, so that the charge and discharge efficiency of the energy storage device is improved, the service life of the energy storage device is prolonged, and the charge and discharge of the energy storage device are required to be controlled and managed.
With the perfection of the electric power market, the user side is increasingly involved in demand response, and the influence of the response behavior of the user on the photovoltaic micro-grid is also becoming a research hotspot nowadays. The demand response refers to the way that users change the original power consumption mode and load using mode to achieve the mutual coordination of supply and demand benefits for the power market incentive mechanism and the electricity price information.
Therefore, how to improve the specific scheme of energy storage and optimal configuration and realize the dynamic balance of local energy production and electricity load, thereby reducing the power grid load in the peak period of the charging station and the operation cost of the charging station, and becoming the subject to be studied.
Disclosure of Invention
The embodiment of the invention provides an intelligent control method and device for an intelligent park, which comprise a price mechanism, can realize dynamic balance of local energy production and electricity load, reduce the power grid load in the peak period of a charging station and reduce the operation cost of the charging station.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method, including:
s1, periodically collecting working parameters and environmental parameters of park equipment;
s2, constructing a maximum benefit model corresponding to the photovoltaic park and a satisfaction degree function model of a user for charging electric charge;
and S3, generating an electricity price decision scheme according to the maximum benefit model and the satisfaction degree function model.
In the process of establishing the maximum benefit model corresponding to the photovoltaic park in S2, the method comprises the following steps: establishing an objective function of maximum benefit of the photovoltaic park established based on a game strategy; wherein the objective function is expressed as: maxf 1 ＝C new -(C sub +C esnew +C pv +C py ) F1 is the operation income of the intelligent park; c (C) new User receipt for consideration of compensation feeTaking electricity charge; c (C) sub The electricity purchasing expense is the upper layer; c (C) esnew To account for energy storage loss costs of the price mechanism; c (C) pv The operation cost of the photovoltaic power generation device in the park is the operation cost of the photovoltaic power generation device in the park; c (C) py For translatable load fees.
Establishing a cost model conforming to the maximum benefit of the photovoltaic park, wherein the cost model specifically comprises the following steps: for a user load regulation model:
basic fee C charged to user load The method comprises the following steps:t is the electricity consumption period: t is an optimization period; k (K) t Charging the electricity price to the user for each period; p (P) load(t) Using electric power for a user;
compensation fee C obtained by user in operation main body comp The method comprises the following steps:K comp compensating the unit compensation cost after the master-slave game of the user and the park; p (P) comp，t The load quantity of the operation main body is the t period;
fee C to be charged to the user in relation to the compensation fee new The method comprises the following steps: c (C) new ＝C load -C comp ；
Upper layer electricity purchasing expense C sub The method comprises the following steps:M t to purchase electricity price for the applied electricity period, P sub(t) Is the power purchased from the upper level.
The cost model conforming to the maximum benefit of the photovoltaic park further comprises a regulation and control model aiming at the energy storage system:
charging and discharging loss cost C of energy storage system ess The method comprises the following steps:c ess a cost coefficient for charge and discharge loss of the energy storage system; p (P) essc(t) Charging power for the energy storage system; p (P) essd(t) Discharging power of the energy storage system;
energy storage subsidy feeBy C es The method comprises the following steps:P es ＝P essd(t) -P essc(t) ，P essc(t) charging power for the energy storage system; p (P) essd(t) K is the discharge power of the energy storage system dr The energy storage subsidy price after the energy storage equipment manufacturer and the park master-slave game is carried out;
energy storage depletion charge C relating to price mechanism esnew The method comprises the following steps: c (C) esnew ＝C ess +C es 。
The cost model conforming to the maximum benefit of the photovoltaic park further comprises a regulation and control model aiming at a power generation system:
park photovoltaic power generation device operation cost C pv The method comprises the following steps:C pv electricity cost, P, for photovoltaic power generation device to account for operational, maintenance, depreciation factors pv(t) Active output of the photovoltaic power station in a t period;
translatable load cost C py The method comprises the following steps:C py compensation cost is unit; p (P) py(i) The i-th translatable load capacity is represented, K represents the number of translatable loads, and i is a positive integer.
The satisfaction degree function model for the user to charge the electric charge comprises the following steps:
f 2 to describe the satisfaction of the user to the electricity charge in the allowable variation range of the electricity price, the expenditure of the electricity charge is inversely related to the satisfaction, T is the total number of time periods, and P sell(t) To the electricity price at the t period, Q use(t) For the electricity consumption of the user in the period t, P init(t) Fixed electricity price for the t period without optimization initially, Q init(t) And the electricity consumption is used for the user in the t time period when the user is initially not optimized.
In a second aspect, an embodiment of the present invention provides an apparatus, including:
the system comprises an acquisition module, a storage module, a display module, a communication module, a power module, a decision module and a control module;
the collection module is used for periodically collecting working parameters and environment parameters of park equipment through the communication module;
the display module is used for displaying the data parameters acquired by the acquisition module, the running parameters before and after the execution of the scheduling command, the real-time electricity price and the system load demand;
the power supply module is used for supplying power to the acquisition module, the decision module, the control module, the display module and the communication module;
the decision module is used for constructing a maximum benefit model corresponding to the photovoltaic park and a satisfaction degree function model of a user for charging electric charge; generating an electricity price decision scheme according to the maximum benefit model and the satisfaction degree function model;
and the control module is used for generating an instruction data stream for regulating and controlling the controlled park equipment according to the output result of the model operated on the decision module.
According to the intelligent control method and device for the intelligent park comprising the price mechanism, provided by the embodiment of the invention, the electric equipment such as the photovoltaic, energy storage and charging piles and the like which are connected into the park are monitored in real time through the acquisition module, and the related parameters and the running state of the electric equipment of the park are acquired; the decision module analyzes the collected intelligent park state data, parameters and communication data of the demand side management system by adopting a park maximum benefit strategy, and generates a decision scheme containing a price mechanism by adopting a master-slave game algorithm; the control module generates an instruction data stream according to a decision scheme; the communication module encodes according to the instruction stream, implements load regulation and control of the controlled electric equipment according to the communication protocol, and simultaneously realizes data transmission and communication with the management system at the demand side. The intelligent park intelligent power generation system can monitor the conditions of load, power generation and energy storage equipment in the intelligent park, and optimally schedule according to the conditions of the demand side, so that the photovoltaic utilization rate is improved, and the waste of resources is avoided. The dynamic balance of local energy production and electricity load can be realized, the power grid load in the peak period of the charging station is reduced, and the operation cost of the charging station is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an intelligent control terminal for an intelligent campus including a price mechanism according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an intelligent control terminal for an intelligent park including a price mechanism according to an embodiment of the present invention;
fig. 3 is a schematic system diagram of an intelligent control terminal for an intelligent park according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail below with reference to the drawings and detailed description for the purpose of better understanding of the technical solution of the present invention to those skilled in the art. Embodiments of the present invention will hereinafter be described in detail, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention. As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The embodiment of the invention provides an intelligent control method for an intelligent park comprising a price mechanism, which comprises the following steps:
s1, periodically collecting working parameters and environmental parameters of park equipment;
wherein, the working parameters include: photovoltaic power generation, charging pile load power and energy storage conditions; the environmental parameters include: illumination intensity, ambient temperature and humidity, and wind speed.
S2, constructing a maximum benefit model corresponding to the photovoltaic park and a satisfaction degree function model of a user for charging electric charge;
and S3, generating an electricity price decision scheme according to the maximum benefit model and the satisfaction degree function model.
In this embodiment, in the process of establishing the maximum benefit model corresponding to the photovoltaic park in S2, the method includes: establishing an objective function of maximum benefit of the photovoltaic park established based on a game strategy; wherein the objective function is expressed as: maxf 1 ＝C new -(C sub +C esnew +C pv +C py )，f 1 The operation income of the intelligent park is obtained; c (C) new Charging the user with an electric charge for consideration of the compensation charge; c (C) sub The electricity purchasing expense is the upper layer; c (C) esnew To account for energy storage loss costs of the price mechanism; c (C) pv Is photovoltaic in parkThe running cost of the power generation device; c (C) py For translatable load fees.
In the process of establishing the maximum benefit model corresponding to the photovoltaic park in S2, the method further comprises the following steps: establishing a cost model conforming to the maximum benefit of the photovoltaic park, wherein the cost model specifically comprises the following steps: for a user load regulation model: basic fee C charged to user load The method comprises the following steps:t is the electricity consumption period; t is an optimization period; k (K) t Charging the electricity price to the user for each period; p (P) load(t) Using electric power for a user; compensation fee C obtained by user in operation main body comp The method comprises the following steps:K comp compensating the unit compensation cost after the master-slave game of the user and the park; p (P) comp，t The load quantity of the operation main body is the t period; fee C to be charged to the user in relation to the compensation fee new The method comprises the following steps: c (C) new ＝C load -C comp The method comprises the steps of carrying out a first treatment on the surface of the Upper layer electricity purchasing expense C sub The method comprises the following steps: /> M t To purchase electricity price for the applied electricity period, P sub(t) Is the power purchased from the upper level.
The cost model conforming to the maximum benefit of the photovoltaic park further comprises a regulation and control model aiming at the energy storage system: charging and discharging loss cost C of energy storage system ess The method comprises the following steps:the process is a cost coefficient of charge and discharge loss of the energy storage system; p (P) essc(t) Charging power for the energy storage system; p (P) essd(t) Discharging power of the energy storage system; energy storage subsidy expense C es The method comprises the following steps: />P essc(t) Is an energy storage systemIs set to the charging power of (a); p (P) essd(t) K is the discharge power of the energy storage system dr The energy storage subsidy price after the energy storage equipment manufacturer and the park master-slave game is carried out; energy storage depletion charge C relating to price mechanism esnew The method comprises the following steps: c (C) esnew ＝C ess +C es 。
The cost model conforming to the maximum benefit of the photovoltaic park further comprises a regulation and control model aiming at a power generation system: park photovoltaic power generation device operation cost C pv The method comprises the following steps:C pv electricity cost, P, for photovoltaic power generation device to account for operational, maintenance, depreciation factors pv(t) Active output of the photovoltaic power station in a t period; translatable load cost C py The method comprises the following steps:C py compensation cost is unit; p (P) py(i) The i-th translatable load capacity is represented, K represents the number of translatable loads, and i is a positive integer.
The satisfaction degree function model for the user to charge the electric charge comprises the following steps:
f 2 to describe the satisfaction of the user to the electricity charge in the allowable variation range of the electricity price, the expenditure of the electricity charge is inversely related to the satisfaction, T is the total number of time periods, and P sell(t) To the electricity price at the t period, Q use(t) For the electricity consumption of the user in the period t, P init(t) Fixed electricity price for the t period without optimization initially, Q init(t) And the electricity consumption is used for the user in the t time period when the user is initially not optimized.
The embodiment also provides an intelligent control device for an intelligent park including a price mechanism, the device can be arranged in a photovoltaic system of the park and comprises: the system comprises an acquisition module, a storage module, a display module, a communication module, a power module, a decision module and a control module;
the collection module is used for periodically collecting working parameters and environment parameters of park equipment through the communication module; the communication module is also used for realizing real-time communication and data interaction with the user terminal, and displaying data for monitoring the states of the photovoltaic cell panel, the energy storage battery and the charging pile for the display module; specifically, the acquisition module acquires environmental information and electric energy data through the sensor and transmission information, and the data acquisition module has a real-time communication function so as to transmit the data to the control module and receive a remote control command. In practical application, the acquisition module transmits working parameters of information acquisition park equipment including photovoltaic power, load power, energy storage condition and the like through the communication module at preset time intervals; and meanwhile, current environmental parameters including illumination intensity, environmental temperature and humidity, wind speed and the like are collected. The data acquisition module has a real-time communication function so as to transmit data to the control module and receive remote control commands.
The display module is used for displaying the data parameters acquired by the acquisition module, the running parameters before and after the execution of the scheduling command, the real-time electricity price and the system load demand; the display module is also used for displaying an interactive interface so that a user can interact with the system through a touch screen or buttons to change the setting and execute the command;
the power supply module is used for supplying power to the acquisition module, the decision module, the control module, the display module and the communication module;
the decision module is used for constructing a maximum benefit model corresponding to the photovoltaic park and a satisfaction degree function model of a user for charging electric charge; generating an electricity price decision scheme according to the maximum benefit model and the satisfaction degree function model;
and the control module is used for generating an instruction data stream for regulating and controlling the controlled park equipment according to the output result of the model operated on the decision module.
In this embodiment, the decision module runs a user load regulation model for regulating and controlling a user load, an energy storage system regulation model for regulating and controlling an energy storage system, and a power generation system regulation model for regulating and controlling a power generation system.The intelligent park maximum benefit model and the satisfaction degree function of the user for charging the electric charge are built through the decision-making module, and particularly the communication data of the collected data and the demand side management system can be analyzed by adopting the park maximum benefit strategy, and a decision-making scheme containing a price mechanism is generated by using a master-slave game algorithm. For example: the decision module adopts master-slave game algorithm modeling, takes a park as a game main body, and main body decision variables are unit compensation cost K for regulating and controlling user loads comp Energy storage subsidy price K regulated and controlled by energy storage equipment business dr And the price of the park is formulated. Objective function f with maximum benefit of park 1 The method is characterized in that the method is used as a main body objective function, the required response resources in the park are used as slave bodies, the required corresponding resources comprise charging piles, photovoltaics and energy storage equipment, the slave body objective function is a satisfaction degree function f2, and the decision variables of the slave bodies are the required resource load response quantity, so that a park load regulation and control metering mechanism is realized. The sales electricity price of the Jiangsu province power grid is taken as a reference, the initial electricity consumption value is set to 0.6068 yuan/kilowatt-hour, the price range is 0.5318-0.6068 yuan/kilowatt-hour, the decision step length is 0.02, and the master-slave game is participated after the decision is generated every 15 minutes to obtain the preferential electricity price. Specifically, the algorithm of the decision module includes: and constructing a photovoltaic park maximum benefit model and a satisfaction degree function of the user for charging electric charge to judge the satisfaction degree of the user for demand response. According to different time periods of peak Gu Ping, introducing master-slave game strategy to build objective function f of maximum benefits of park 1 The model is as follows: maxf 1 ＝C new -(C sub +C esnew +C pv +C py )，f 1 The operation income of the intelligent park is obtained; c (C) new Charging the user with an electric charge for consideration of the compensation charge; c (C) sub The electricity purchasing expense is the upper layer; c (C) esnew To account for energy storage loss costs of the price mechanism; c (C) pv The operation cost of the photovoltaic power generation device in the park is the operation cost of the photovoltaic power generation device in the park; c (C) py For translatable load fees.
Wherein the basic fee C is charged to the user load The method comprises the following steps:wherein: t is the electricity consumption period; t is the optimization weekA period; k (K) t Charging the electricity price to the user for each period; p (P) load(t) Using electric power for a user;
compensation fee C obtained by user in operation main body comp The method comprises the following steps:
wherein: k (K) comp Compensating the unit compensation cost after the master-slave game of the user and the park; p (P) comp，t And (5) operating the load quantity of the main body for the t-th period.
Fee C charged to the user taking account of the compensation fee new The method comprises the following steps: c (C) new ＝C load -C comp
Upper layer electricity purchasing expense C sub The method comprises the following steps:wherein: m is M t Electricity purchasing price for each electricity using period; p (P) sub(t) And purchasing power to the upper level.
Charging and discharging loss cost C of energy storage system ess The method comprises the following steps:wherein: c ess A cost coefficient for charge and discharge loss of the energy storage system; p (P) essc(t) Charging power for the energy storage system; p (P) essd(t) Is the discharge power of the energy storage system.
Energy storage subsidy expense C es The method comprises the following steps:wherein: p (P) es ＝P essd(t) -P essc(t) ，P essc(t) Charging power for the energy storage system; p (P) essd(t) K is the discharge power of the energy storage system dr And the price of the energy storage patch after the energy storage equipment manufacturer and the park master-slave game is increased.
Energy storage loss cost C considering price mechanism esnew The method comprises the following steps: c (C) esnew ＝C ess +C es
Park photovoltaic power generation device operation cost C pv The method comprises the following steps:wherein: c (C) pv The electricity cost of operation, maintenance and depreciation factors of the photovoltaic power generation device are considered; p (P) pv(t) Active output of the photovoltaic power station in a t period;
translatable load cost C py The method comprises the following steps:wherein: c (C) py Compensation cost is unit; p (P) py(i) Indicated is the ith translatable load capacity; k represents the number of translatable loads.
Establishing satisfaction degree function f of users for charging electric charge at different time periods of peak Gu Ping 2 The model is as follows:wherein f 2 The user satisfaction degree of the electricity consumption in the allowable change range of the electricity price is described, and the higher the electricity consumption expense is, the lower the satisfaction degree is. T is the total number of time periods; p (P) sell(t) Is the electricity price in the period t; q (Q) use(t) The electricity consumption of the user in the period t is calculated; p (P) init(t) Fixing electricity prices for a t period without optimization initially; q (Q) init(t) And the electricity consumption is used for the user in the t time period when the user is initially not optimized.
For example: the decision module comprises a decision scheme generated by three aspects of power generation system, energy storage system and user load regulation. Wherein, for the power generation system: based on the real-time electricity price data, the system may determine an optimal electricity generation period to increase the generated power during the high electricity price period, reducing the electricity cost. For an energy storage system: if the weather forecast shows the next few days of perineum rainy days, the system can store excess power in the battery at low electricity prices to provide a stable energy supply on overcast days. The energy storage battery can be used as a standby power supply, and is automatically powered when the electricity price is abnormally high or power failure occurs, so that continuous power supply is ensured. Regulation and control aiming at user load: according to the electricity price and the user demand, the system can delay the use of high-energy-consumption translatable load equipment such as a washing machine, a dryer and the like, so as to cut peaks and reduce valleys at high electricity price and reduce the electricity cost of the user. Real-time communications interact with the user to provide real-time electricity rate information and predictions to the user to encourage the user to reduce electricity usage during periods of low electricity rates. And meanwhile, the system can proxy the user to carry out load control according to the electricity consumption preference of the user. For example: the control method takes 24 hours in one day as a demand response period, divides one day into three time periods of peaks Gu Ping according to time-of-use electricity prices, and respectively sets an electricity peak period set S1= {9,10,11,12,19,20,21,22}, an electricity price level period set S2= {13,14,15,16,17,18,23,24}, and an electricity price valley period set S3= {1,2,3,4,5,6,7,8}.
At peak power consumption: the park load is reduced to draw power from the grid. When the photovoltaic output is insufficient to meet the load demand, the energy storage system discharges with maximum power, and if the photovoltaic output is insufficient, the power is transmitted by the power grid; and if the photovoltaic output is enough to meet the load demand, the photovoltaic power keeps the load running, more power is preferentially transmitted to the power grid, and the residual power stores energy.
At the time of level stabilization: if the photovoltaic output does not meet the requirement of the load on the requirement side, the photovoltaic and the residual energy storage power supply the power to the requirement side; and if the photovoltaic output meets the requirement of the load on the requirement side, the redundant power is sent to the power grid, and the energy storage system is free from charge and discharge.
At the time of electricity consumption valley: increasing the park load draws power from the grid. If the photovoltaic output does not meet the requirement of the load on the requirement side, the power grid charges the energy storage system; and if the photovoltaic output meets the requirement of the load on the requirement side, the photovoltaic power keeps the load running, the photovoltaic redundant power charges the energy storage system, and if the energy storage system is not full, the power grid continues to charge the energy storage system.
In this embodiment, the communication module includes: the communication interface is a wired communication method and a wireless communication method, the RS485 communication interface is adopted by the wired communication method, the WLAN is adopted by the wireless communication method, and the advantages of the wired communication interface and the wireless communication interface are fully exerted, so that the reliability and the flexibility of the system are improved. The photovoltaic power generation panel, the inverter, the energy storage battery system and the load control equipment are connected to the terminal by adopting serial communication. The external port of the communication module is used as a communication interface of the control terminal and is in communication connection with control terminals of other photovoltaic systems, and the internal port of the communication unit is connected with the algorithm decision module and the control execution module. The remote monitoring system and external communication adopt wireless communication so that technicians can monitor parameters at the mobile terminal. Real-time communications interact with the user to provide real-time electricity rate information and predictions to the user to encourage the user to reduce electricity usage during periods of low electricity rates. Meanwhile, the system can carry out load control according to the electricity utilization preference of the user instead of the user.
The control module is used for responding to the electricity consumption request of the load at the demand side, determining the instruction type of the dispatching instruction and matching the current intelligent park operation control strategy according to the instruction type of the dispatching instruction; the demand side load is then powered based on the current smart park operation control strategy. For example: responding to a power consumption request of a load at a demand side, judging whether a dispatching instruction of a power grid demand response is received, determining an instruction type of the dispatching instruction, and matching a current intelligent park operation control strategy according to the instruction type of the dispatching instruction; the demand side load is then powered based on the current smart park operation control strategy. In addition, in order to optimize the load of the park to the maximum, the control execution module can regulate and control the load, and can also control the rotation angle of the photovoltaic panel according to the illumination intensity and time, so that the photovoltaic power generation efficiency is improved.
The communication interface of the communication module adopts two communication methods of wire and wireless, the wire adopts an RS485 communication interface, and the wireless adopts WLAN, thereby fully playing the advantages of the two methods and increasing the reliability and the flexibility of the system. The photovoltaic power generation panel, the inverter, the energy storage battery system and the load control equipment are connected to the terminal by adopting wired communication. The external port of the communication module is used as a communication interface of the control terminal and is in communication connection with control terminals of other photovoltaic systems, and the internal port of the communication unit is connected with the algorithm decision module and the control execution module. The remote monitoring system and the external communication adopt wireless communication, so that a technician monitors parameters at a mobile terminal and a user checks electricity consumption conditions in real time. And if the control terminal needs to interact with an operator, the display module can be used as a part of a user interface, and a user can interact with the system through a touch screen or buttons so as to change the setting and execute the command. The display module monitors the states of the photovoltaic cell panel, the energy storage battery and the charging pile. In addition, the collected data parameters, the operation parameters before and after the execution of the scheduling command, the load control time, the system load demand execution result and the like are displayed. The intelligent control terminal is characterized in that the power module is arranged in the intelligent control terminal, and an independent power module supplies power to the data acquisition module, the decision module, the control execution module, the display module and the communication module. The control method comprises an electronic device, wherein the electronic device comprises a processor and a memory, and a computer program, user load data, weather data and the like are stored in the memory. The processor is configured to run the computer program to perform the control method of intelligent campus operation. Based on the intelligent park load control method and the terminal provided by the invention, the terminal is independently arranged in a photovoltaic system. The data acquired by the data acquisition module is processed by the algorithm decision module and then is delivered to the control execution module to be matched with a corresponding control method, and then a communication module sends a control instruction to each photovoltaic device for regulation and control. Therefore, the control terminal provided by the invention can realize dynamic balance of local energy production and electricity load through energy storage and optimal configuration, effectively reduce the power grid load in the peak period of the charging station, reduce the operation cost of the charging station and provide auxiliary service functions for the power grid.
For example, referring to fig. 1, the photovoltaic power generation device in the intelligent park in this embodiment mainly includes a photovoltaic array and a photovoltaic inverter; the main energy storage device is provided with an energy storage battery and an energy storage converter; the main user load equipment is provided with an alternating current-direct current charging pile. The control terminal comprises a data acquisition module, an algorithm module, a decision module, a control execution module, a display module, a communication module and a power module. And the system calculates the photovoltaic maximum output, the maximum energy storage and the maximum power consumption of the user at different periods of the peak Gu Ping according to the historical data collected in the memory. In the embodiment, the acquisition equipment is installed on site to acquire photovoltaic power generation capacity, energy storage condition and weather change of a photovoltaic park in real time; and installing a real-time monitoring device on the electric meter box to acquire the electricity consumption of the user and transmitting all data to the decision and control module. The decision and control module in this embodiment is responsible for processing the data from the various sensors, devices and communication modules. The method comprises the steps of calculating and analyzing real-time data such as photovoltaic power generation power, battery state, load demand and the like; the decision and control module executes an energy management algorithm to make decisions based on real-time data and system policies to maximize energy utilization. Such as adjusting photovoltaic power generation, energy storage, and load control strategies. In addition, the decision and control module cooperates with the communication module to realize data communication with other devices, external systems and cloud platforms. In this embodiment, the control execution module is responsible for actually executing the control operation according to the energy management policy provided by the decision and control module. This includes adjusting the output power of the photovoltaic power generation device, controlling the charging and discharging of the energy storage battery, and coordinating the operation of the load control device. Solar panels in a photovoltaic park can be affected by solar radiation and weather conditions, and their power generation can vary. The control execution module is responsible for adjusting the operation of the photovoltaic power generation device according to the real-time photovoltaic power generation power and the energy management strategy so as to capture available solar energy to the maximum extent and ensure the stable operation of the system. The control execution module monitors the state of the battery (e.g., state of charge, SOC) and controls the charging and discharging of the battery as needed. This helps balance energy supply and demand, store excess energy for demand from time to time, and participate in load regulation of the power market. The control execution module coordinates various load control devices such as intelligent electric meters, electric water heaters, electric vehicle charging piles and the like. The system can adjust the operation of the load devices according to the energy management strategy so as to meet energy requirements, participate in demand response and optimize power consumption. The communication module in this embodiment adopts a method of wired and wireless hybrid communication. To achieve full communication coverage and data exchange. Wired communication is used for stable data between remote control devices such as control terminals and photovoltaic power generation devices, battery systems and load control devices. The wireless communication is used for remote monitoring system and external communication, so that a technician monitors parameters at a mobile phone end or a PC end and a user checks electricity consumption conditions in real time. In the display module in this embodiment, if the control terminal needs to interact with an operator, the display module may be used as a part of a user interface, and a user may interact with the system through a touch screen or buttons to change settings and execute commands. In addition, the system is used for displaying the collected data parameters and the operation parameters before and after the execution of the scheduling command. The state of the photovoltaic cell panel, the energy storage battery and the charging pile can be monitored through the display module. The power module in this embodiment is an independent power source for supplying power to the data acquisition module, the decision algorithm module, the control execution module, the display module and the communication module, so as to ensure the reliable operation of the system.
Referring to fig. 2, an embodiment of the present invention provides an intelligent control terminal for an intelligent campus including a price mechanism, including the steps of: the system generates a photovoltaic park prediction model according to historical data in the memory, and predicts photovoltaic power generation power in a future period of time by using factors such as weather data, solar radiation, cloud cover and the like. The system is helped to plan the energy management strategy more accurately and make adjustments in advance to capture solar energy to the maximum extent and meet load demands. The data acquisition module is responsible for acquiring data from various devices and sensors such as a photovoltaic power generation device, an energy storage battery, load equipment and an environment sensor. Such data includes photovoltaic power generation power, battery status, load demand, environmental factors (e.g., temperature, radiation, etc.), and the like. The data acquisition module transmits the acquired data to the decision making and control module of the control terminal for processing. The decision and control module receives the data from the data acquisition module, processes and calculates the data in real time, executes an energy management algorithm, and makes decisions according to the data and the algorithm. The specific decision method comprises the following steps: dividing a day into three time periods of peak Gu Ping according to time-of-use electricity price by taking 24 hours of the day as a demand response period, and respectively setting an electricity price peak period set S1= {9,10,11,12,19,20,21,22}, an electricity price level period set S2= {13,14,15,16,17,18,23,24}, and an electricity price valley period set S3= {1,2,3,4,5,6,7,8}; the following strategy was adopted: at peak power consumption: the park load is reduced to draw power from the grid. When the photovoltaic output is insufficient to meet the load demand, the energy storage system discharges with maximum power, and if the photovoltaic output is insufficient, the power is transmitted by the power grid; and if the photovoltaic output is enough to meet the load demand, the photovoltaic power keeps the load running, more power is preferentially transmitted to the power grid, and the residual power stores energy. At the time of level stabilization: if the photovoltaic output does not meet the requirement of the load on the requirement side, the photovoltaic and the residual energy storage power supply the power to the requirement side; and if the photovoltaic output meets the requirement of the load on the requirement side, the redundant power is sent to the power grid, and the energy storage system is free from charge and discharge. At the time of electricity consumption valley: increasing the park load draws power from the grid. If the photovoltaic output does not meet the requirement of the load on the requirement side, the power grid charges the energy storage system; and if the photovoltaic output meets the requirement of the load on the requirement side, the photovoltaic power keeps the load running, the photovoltaic redundant power charges the energy storage system, and if the energy storage system is not full, the power grid continues to charge the energy storage system. The control execution module actually executes the control operation according to the decision and the energy management strategy provided by the control module. The communication module is responsible for data transmission and communication functions, transmitting the execution command to the remote device through wired transmission, and transmitting the data of the control terminal to external systems such as the power market and the user through wireless transmission.
The display module provides visualization of real-time data for operators to check the running state of the control terminal, and is provided with an interaction module, and a system administrator can interact with the system through a touch screen or buttons to change the setting and execute the command.
The embodiment comprises a data acquisition module, a display module, a communication module, a power module, a decision module and a control module; the system comprises an acquisition module, a control module and a control module, wherein the acquisition module monitors electric equipment such as photovoltaic, energy storage, charging piles and the like accessed in a park in real time and acquires relevant parameters and running states of the electric equipment in the park; the decision module analyzes the collected intelligent park state data, parameters and communication data of the demand side management system by adopting a park maximum benefit strategy, and generates a decision scheme containing a price mechanism by adopting a master-slave game algorithm; the control module generates an instruction data stream according to a decision scheme; the communication module encodes according to the instruction stream, implements load regulation and control of the controlled electric equipment according to the communication protocol, and simultaneously realizes data transmission and communication with the management system at the demand side. The intelligent park intelligent power generation system can monitor the conditions of load, power generation and energy storage equipment in the intelligent park, and optimally schedule according to the conditions of the demand side, so that the photovoltaic utilization rate is improved, and the waste of resources is avoided.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points. The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (10)
1. An intelligent control method for an intelligent park comprising a price mechanism, comprising the steps of:
s1, periodically collecting working parameters and environmental parameters of park equipment;
s2, constructing a maximum benefit model corresponding to the photovoltaic park and a satisfaction degree function model of a user for charging electric charge;
and S3, generating an electricity price decision scheme according to the maximum benefit model and the satisfaction degree function model.
2. The method of claim 1, wherein the operating parameters include: photovoltaic power generation, charging pile load power and energy storage conditions;
the environmental parameters include: illumination intensity, ambient temperature and humidity, and wind speed.
3. The method of claim 1, wherein in the step of S2 modeling the maximum benefit of the corresponding photovoltaic park, the method comprises:
establishing an objective function of maximum benefit of the photovoltaic park established based on a game strategy;
wherein the objective function is expressed as: maxf 1 ＝C new -(C sub +C esnew +C pv +C py )，f 1 The operation income of the intelligent park is obtained; c (C) new Charging the user with an electric charge for consideration of the compensation charge; c (C) sub The electricity purchasing expense is the upper layer; c (C) esnew To account for energy storage loss costs of the price mechanism; c (C) pv The operation cost of the photovoltaic power generation device in the park is the operation cost of the photovoltaic power generation device in the park; c (C) py For translatable load fees.
4. The method of claim 3, wherein in the step of S2 building the maximum benefit model corresponding to the photovoltaic park, further comprising:
establishing a cost model conforming to the maximum benefit of the photovoltaic park, wherein the cost model specifically comprises the following steps: for a user load regulation model:
basic fee C charged to user load The method comprises the following steps:t is the electricity consumption period: t is an optimization period; k (K) t Charging the electricity price to the user for each period; p (P) load(t) Using electric power for a user;
compensation fee C obtained by user in operation main body comp The method comprises the following steps:K comp compensating the unit compensation cost after the master-slave game of the user and the park; p (P) comp，t The load quantity of the operation main body is the t period;
fee C to be charged to the user in relation to the compensation fee new The method comprises the following steps: c (C) new ＝C load -C comp ；
Upper layer electricity purchasing expense C sub The method comprises the following steps:M t to purchase electricity price for the applied electricity period, P sub(t) Is the power purchased from the upper level.
5. The method of claim 4, wherein the cost model for maximum revenue for a photovoltaic park further comprises a regulatory model for an energy storage system:
charging and discharging loss cost C of energy storage system ess The method comprises the following steps:c ess a cost coefficient for charge and discharge loss of the energy storage system; p (P) essc(t) Charging power for the energy storage system; p (P) essd(t) Discharging power of the energy storage system;
energy storage subsidy expense C es The method comprises the following steps:P es ＝P essd(t) -P essc(t) ，P essc(t) charging power for the energy storage system; p (P) essd(t) K is the discharge power of the energy storage system dr The energy storage subsidy price after the energy storage equipment manufacturer and the park master-slave game is carried out;
energy storage depletion charge C relating to price mechanism esnew The method comprises the following steps: c (C) esnew ＝C ess +C es 。
6. The method of claim 5, wherein the cost model for maximum revenue for a photovoltaic park further comprises a regulatory model for a power generation system:
park photovoltaic power generation device operation cost C pv The method comprises the following steps:C pv electricity cost, P, for photovoltaic power generation device to account for operational, maintenance, depreciation factors pv(t) For photovoltaic power station in period tIs an active force of (a);
translatable load cost C py The method comprises the following steps:C py compensation cost is unit; p (P) py(i) The i-th translatable load capacity is represented, K represents the number of translatable loads, and i is a positive integer.
7. A method according to claim 3, wherein the user satisfaction function model for charging an electric fee comprises:
f 2 to describe the satisfaction of the user to the electricity charge in the allowable variation range of the electricity price, the expenditure of the electricity charge is inversely related to the satisfaction, T is the total number of time periods, and P sell(t) To the electricity price at the t period, Q use(t) For the electricity consumption of the user in the period t, P init(t) Fixed electricity price for the t period without optimization initially, Q init(t) And the electricity consumption is used for the user in the t time period when the user is initially not optimized.
8. An intelligent control device for an intelligent park comprising a price mechanism, comprising: the system comprises an acquisition module, a storage module, a display module, a communication module, a power module, a decision module and a control module;
the collection module is used for periodically collecting working parameters and environment parameters of park equipment through the communication module;
the display module is used for displaying the data parameters acquired by the acquisition module, the running parameters before and after the execution of the scheduling command, the real-time electricity price and the system load demand;
the power supply module is used for supplying power to the acquisition module, the decision module, the control module, the display module and the communication module;
the decision module is used for constructing a maximum benefit model corresponding to the photovoltaic park and a satisfaction degree function model of a user for charging electric charge; generating an electricity price decision scheme according to the maximum benefit model and the satisfaction degree function model;
and the control module is used for generating an instruction data stream for regulating and controlling the controlled park equipment according to the output result of the model operated on the decision module.
9. The method of claim 8, wherein the collection module is specifically configured to collect working parameters of the campus device by transmitting information through the communication module at intervals of a preset time, including load power and energy storage conditions such as photovoltaic power generation, charging pile, etc.; and meanwhile, current environmental parameters including illumination intensity, environmental temperature and humidity and wind speed are also collected.
10. The method of claim 9, wherein the decision module runs a user load regulation model for regulating user load, an energy storage system regulation model for regulating an energy storage system, and a power generation system regulation model for regulating a power generation system.
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