Operation state adjusting method, device and equipment of power system and storage medium

Abstract

The invention discloses a running state adjusting method, device and equipment of an electric power system and a storage medium, and the method comprises the steps: constructing a user decision model of the electric power system, the user decision model comprises an objective function with the maximization of expected benefits as an objective, and introducing demand response resources to participate in market transaction; combining constraint conditions of a market mechanism; acquiring demand response data of the power system; making a decision on the demand response data based on the user decision model to obtain a decision strategy; and adjusting the operation state of the power system based on the decision strategy. It can be seen that decision making is carried out and the decision strategy is generated by constructing the target function taking expected income maximization as the target function, introducing the demand response resources to participate in market transaction and combining the user decision model of the constraint condition of the market mechanism, so that the consumption and power generation process of distributed energy is adjusted and optimized, the operation state of the power system can be effectively adjusted, and the power generation efficiency is improved. And the consumption capability of the distributed energy and the operation stability of the power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, device, equipment and storage medium for regulating the operating state of a power system. Background Art

[0002] In recent years, distributed energy has achieved rapid development by virtue of its convenient installation, flexible switching, and low cost. However, with the large-scale access of distributed energy, the dispersion and diversity of distributed energy have also increased the complexity of grid management and dispatch, making it difficult for the flexible resources of distributed energy (such as energy storage and demand response) to play their full role, resulting in low absorption capacity and low operational stability of the power system. Summary of the invention

[0003] The present application proposes a method, device, computer equipment and storage medium for regulating the operating state of an electric power system to improve the absorption capacity of distributed energy and the operating stability of the electric power system.

[0004] In a first aspect, a method for adjusting the operating state of a power system is provided, comprising:

[0005] Constructing a user decision model for the power system, the user decision model includes an objective function with the goal of maximizing expected benefits and introducing demand response resources to participate in market transactions, combined with constraints of market mechanisms;

[0006] Obtain demand response data for power systems;

[0007] Making a decision on the demand response data based on the user decision model to obtain a decision strategy;

[0008] The operating state of the power system is adjusted based on the decision strategy.

[0009] In a second aspect, a device for adjusting the operating state of a power system is provided, comprising:

[0010] A construction module, used to construct a user decision model of the power system, wherein the user decision model includes an objective function with the goal of maximizing expected benefits and introducing demand response resources to participate in market transactions, combined with constraints of the market mechanism;

[0011] An acquisition module, used for acquiring demand response data of the power system;

[0012] A decision module, used to make a decision on the demand response data based on the user decision model to obtain a decision strategy;

[0013] A regulating module is used to regulate the operating state of the power system based on the decision strategy.

[0014] In one embodiment, the objective function is expressed as:

[0015] MaxR IN =R S2C,IN +R S2U,IN +R Cr,IN -C B

[0016] Among them, R RN ,R S2C,NN ,R S2U,NN ,R Cr,N ,C B They are the net income from users making electricity consumption and market participation decisions, the income from selling electricity to grid companies, the income from selling electricity to demand response resources, certificate income, and electricity costs.

[0017] In one embodiment, the revenue R of selling electricity to the power grid company is S2C,IN It is expressed as:

[0018]

[0019] The revenue R of selling electricity to demand response users S2U,IN It is expressed as:

[0020]

[0021] The electricity cost C B It is expressed as:

[0022]

[0023] The certificate benefits R Cr，IN It is expressed as:

[0024]

[0025] Among them, P s2c,t ,P s2u,t ,P d,t They are selling electricity to the grid, selling electricity to demand response users, and users buying electricity from the grid at time t; c s2c,t ,c s2u,t ,c r,t ,c cr They are the grid purchase rate, the market clearing price of demand response and renewable energy transactions at time t, the unit price of electricity purchased by users from the grid at time t, and the price of renewable energy green certificates.

[0026] In one embodiment, the decision module includes:

[0027] A prediction submodule, used to predict the demand response data to obtain market electricity consumption data;

[0028] The decision submodule is used to input the market electricity consumption data into the user decision model to make a decision and obtain the decision strategy.

[0029] In one embodiment, the adjustment module includes:

[0030] A control submodule, used for performing simulation control on the decision strategy based on a preset power system simulation model to obtain a simulation result;

[0031] An optimization submodule, used for optimizing the decision strategy based on the simulation results;

[0032] The regulating submodule is used to regulate the operating state of the power system according to the optimized decision strategy.

[0033] In one embodiment, the decision strategy includes an energy storage control strategy, and the control submodule includes:

[0034] A first control unit is used to write the energy storage control strategy into the power system simulation model for simulation control to obtain the simulation result; or

[0035] The second control unit is used to perform simulation control on the energy storage control strategy based on the load following control method of the power simulation system to obtain the simulation result.

[0036] In one embodiment, the second control unit is specifically configured to:

[0037] When the energy storage device and the renewable energy source are connected to the same node through an inverter, tracking the node power of the node;

[0038] The energy storage control strategy is simulated and controlled according to the node power to obtain a simulation result.

[0039] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for regulating the operating state of the power system when executing the computer program.

[0040] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for adjusting the operating state of the power system are implemented.

[0041] The present application provides a method, device, computer equipment and storage medium for regulating the operating state of an electric power system, by constructing a user decision model of the electric power system, the user decision model includes an objective function with the goal of maximizing expected benefits and introducing demand response resources to participate in market transactions, combined with the constraints of the market mechanism; obtaining demand response data of the electric power system; making decisions on the demand response data based on the user decision model to obtain a decision strategy; and regulating the operating state of the electric power system based on the decision strategy. In the operating state regulation scheme of the electric power system provided in the present application, by constructing a user decision model with the goal of maximizing expected benefits and introducing demand response resources to participate in market transactions, combined with the constraints of the market mechanism, making decisions on the demand response data of the electric power system to generate a decision strategy, and adjusting and optimizing the consumption and power generation process of distributed energy based on the decision strategy, the operating state of the electric power system can be effectively regulated, and the consumption capacity of distributed energy and the operating stability of the electric power system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 An application environment diagram of the method for adjusting the operating state of the power system provided in an embodiment of the present application;

[0044] Figure 2 A flow chart of a method for adjusting the operating state of an electric power system provided in an embodiment of the present application;

[0045] Figure 3 A line diagram of a clearing method for air-conditioning loads participating in the market provided in an embodiment of the present application;

[0046] Figure 4 A decision flow chart of a user decision model provided in an embodiment of the present application;

[0047] Figure 5 A discrete schematic diagram of the day-clearing results provided in the embodiments of the present application;

[0048] Figure 6 A discrete schematic diagram of the monthly clearing results provided in the embodiment of the present application;

[0049] Figure 7 A schematic diagram of a curve of daily energy storage power generation and consumption provided in an embodiment of the present application;

[0050] Figure 8A discrete schematic diagram of decision comparison provided for an embodiment of the present application;

[0051] Fig. 9 A curve diagram of the load following control method provided in an embodiment of the present application;

[0052] Fig.10 A structural block diagram of a device for adjusting the operating state of a power system provided in an embodiment of the present application;

[0053] Fig.11 A structural block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0056] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0057] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0058] The method for adjusting the operating state of a power system provided by the embodiment of the present invention can be applied in the following aspects: Figure 1In the application environment. Among them, the computer device 110 communicates with the server 120 through the network 130. The computer device 110 can construct a user decision model of the power system, the user decision model includes an objective function with the expected benefit maximization as the goal and the introduction of demand response resources to participate in market transactions, combined with the constraints of the market mechanism; obtain the demand response data of the power system; make decisions on the demand response data based on the user decision model to obtain a decision strategy; adjust the operating state of the power system based on the decision strategy, and display it through the computer device 110. In the present invention, by constructing a user decision model with the expected benefit maximization as the objective function and the introduction of demand response resources to participate in market transactions, combined with the constraints of the market mechanism, the demand response data of the power system is decided, and a decision strategy is generated. Based on the decision strategy, the consumption and power generation process of distributed energy are adjusted and optimized, which can effectively adjust the operating state of the power system, improve the consumption capacity of distributed energy and the operating stability of the power system. Among them, the computer device 110 can be, but not limited to, various smart phones 110-1, tablet computers 110-2 and laptop computers 110-3. The present invention is described in detail below through specific embodiments.

[0059] See also Figure 2 As shown, Figure 2 A flow chart of a method for adjusting the operating state of an electric power system provided in an embodiment of the present invention is provided. The method can be applied to both a terminal and a server. This embodiment is illustrated by applying it to a server. The method for adjusting the operating state of an electric power system includes the following steps:

[0060] S101: Construct a user decision model for the power system.

[0061] The user decision model includes an objective function aimed at maximizing expected benefits and the introduction of demand response resources to participate in market transactions, combined with constraints of market mechanisms.

[0062] The user decision model can be a decision model for users who have installed photovoltaics and energy storage to adjust their electricity consumption and market trading behaviors in order to maximize their own interests under the condition of promoting the free market trading of energy storage configuration and distribution network resources. Among them, the market mechanism can include price formation mechanism, transaction mechanism, and market access mechanism. Among them, the price formation mechanism is to reflect the real cost and market value of electricity through mechanisms such as real-time electricity prices and time-of-use electricity prices; the transaction value is to determine the transaction price and transaction volume through bilateral transactions, centralized bidding, etc.; the market access mechanism is to stipulate which types of power generation companies and users can enter the market to participate in market transactions.

[0063] The power system refers to a whole composed of power generation, transmission, transformation, distribution and consumption, which is a comprehensive system for producing, transmitting, distributing and using electric energy.

[0064] Avatar Grid Laboratory-Distributed (GridLab-D) couples power system models, terminal load models, market models, reliability modules, communication modules, and many other models through advanced algorithms, providing the latest end-user load modeling technology, suitable for detailed modeling of distribution networks. In this application, a user decision model of a power system (such as a distribution network) can be constructed through Avatar Grid Laboratory-Distributed (GridLab-D).

[0065] The constraints of the user decision model include power balance constraints, output characteristic constraints of demand response resources, maximum grid access power constraints, user bidding constraints, etc., which are conducive to ensuring the rationality and stability of the operation of the entire system.

[0066] Alternatively, the electric power balance constraint can be expressed as:

[0067] P load,t +P s2c,t +P s2u,t +P cut,t =P re,t +P d,t +P ba,t +P dr,t

[0068] P s2c,t ,P s2u,t ,P cut,t ,P d,t ≥0

[0069] Among them, P dr,t ,P load,t ,P re,t ,P ba,t ,P cut,t They are the output of users' own demand response resources, the natural electricity consumption when users do not use electricity and participate in market decision-making, the power generation of distributed renewable energy, the output of batteries, and the abandoned wind and solar power.

[0070] Alternatively, the renewable energy output characteristic constraint can be expressed as:

[0071] P re,t =p w,t n w-on,t +p pv,t n pv-on,t

[0072] Among them, p w,t ,p pv,t are the output of a single wind turbine and photovoltaic at time t, n w-on,t ,n pv-on,t They are the number of wind turbines and photovoltaics turned on.

[0073] Alternatively, the energy storage output constraint can be expressed as:

[0074] The relationship between the state of charge of energy storage and the charging and discharging power and efficiency is:

[0075] SOC(t)=SOC(t-1)+P ba Δtη ba / E ba

[0076] P ba-min ≤P ba ≤P ba_max

[0077] SOC min ≤SOC(t)≤SOC max

[0078] SOC(T)=SOC(1)≤σ

[0079] Among them, P ba is the energy storage charging and discharging power, SOC(t) is the state of charge of the energy storage at time t, η ba is the charge and discharge efficiency, Δt is the time interval, E ba is the rated capacity of energy storage, P ba-max ,P ba-min They are the maximum and minimum charging and discharging power of energy storage, SOC max ,SOC min are the maximum and minimum allowed states of charge, SOC(T) and SOC(1) are the states of charge at the beginning and end of the day, and σ is a very small value.

[0080] Demand response output constraints:

[0081] Taking air conditioner as the demand response object, considering that the user's decision mainly depends on the demand side resources they have, it is believed that the air conditioner responds to the power surplus signal P surphus Automatic demand response. surplus Represents the gap between the user's renewable energy generation power and natural load at a certain moment.

[0082] P surplus,t =P re,t -P load,t

[0083] The response strategy of the air conditioner can be described by a ramp function:

[0084]

[0085] Among them, P dr,t ,P hvac,t are the demand response power at time t and the air conditioning operating power without demand response, is the correlation coefficient between demand response and temperature adjustment, ΔT is the air conditioning temperature adjustment, and its value needs to meet the upper and lower limit constraints. Through simulation, we get load 0,t , are the original load at time t and the simulated load after changing the temperature setting, T set,t ,T desired,t are the temperature set point and the desired temperature set value at time t respectively.

[0086] Power constraints for access to the grid:

[0087]

[0088] in, is the amount of electricity purchased by the demand response user at the same node as the distributed renewable energy at time t. The formula represents the power injected by the user into the grid, which should not exceed the maximum value P specified by the grid company. in-max .

[0089] Market participation constraints, i.e. users cannot buy electricity from the grid and sell electricity to the grid company or trade with demand response resources at the same time, are expressed as:

[0090] P s2c,t +P s2u,t ≤P re,t +P ba,t +P dr,t -P load,t

[0091] The market price constraint, that is, the clearing price of distributed renewable energy and demand response transactions cannot be higher than the market electricity price, is expressed as:

[0092] c s2u,t ≤c r,t

[0093] For users who have installed photovoltaics and energy storage, maximizing economic benefits is the primary goal of electricity use and market transactions, with maximizing expected returns as the goal. That is, in one embodiment, the objective function is expressed as:

[0094] MaxR IN =R S2C,IN +R S2U,IN +R Cr,IN -CB

[0095] Among them, R RN ,R S2C,NN ,R S2U,NN ,R Cr,N ,C B They are the net income from users making electricity consumption and market participation decisions, the income from selling electricity to grid companies, the income from selling electricity to demand response resources, certificate income, and electricity costs.

[0096] In this embodiment, the objective function integrates multiple factors such as the revenue from selling electricity to the grid company, the revenue from selling electricity to demand response users, the green certificate revenue, and the electricity cost, so as to facilitate the decision-making of the optimal decision-making strategy.

[0097] In one embodiment, the revenue R of selling electricity to the power grid company is S2C,IN It is expressed as:

[0098]

[0099] The revenue R of selling electricity to demand response users S2U,IN It is expressed as:

[0100]

[0101] The electricity cost C B It is expressed as:

[0102]

[0103] The certificate benefits R Cr，IN It is expressed as:

[0104]

[0105] Among them, P s2c,t ,P s2u,t ,P d,t They are selling electricity to the grid, selling electricity to demand response users, and users buying electricity from the grid at time t; c s2c,t ,c s2u,t ,c r,t ,c cr They are the grid purchase rate, the market clearing price of demand response and renewable energy transactions at time t, the unit price of electricity purchased by users from the grid at time t, and the price of renewable energy green certificates.

[0106] S102: Obtain demand response data of the power system.

[0107] Weather information and retail electricity price forecast information of the power system can be obtained from GridLab-D, and combined with known certificate prices (due to the existence of the futures market, the price remains unchanged in the short term) and other data as demand response data for the power system.

[0108] S103: Making a decision on the demand response data based on the user decision model to obtain a decision strategy.

[0109] The decision-making strategy may include at least one of an energy storage charging and discharging strategy, a market bidding strategy, and a user electricity behavior adjustment strategy. The energy storage charging and discharging strategy is used to control when the energy storage device is charged and discharged, and the size of the charging and discharging power; the market bidding strategy is used to control how many cables are sold in the power market transaction and at what price; the user electricity behavior adjustment strategy is used to adjust the user's electricity consumption time and power consumption according to the electricity price signal and demand response incentives.

[0110] Decisions can be made on demand response data in GridLab-D to obtain decision strategies. The market module in GridLab-D conducts market transactions through auction objects. In GridLab-D, the auction object is the core component of the market module (Marketmodule), which is used to simulate the trading process of the electricity market. It determines the transaction price and volume by accepting bids from buyers and sellers in the system and clearing the market in a bilateral auction after a set time interval. The auction object accepts bids from sellers and buyers in the system and clears the market in a bilateral auction after a set time interval. The stub_bidder object is used to simulate buyers and sellers (i.e. bidders) in the market; users can bid in the market as buyers or sellers through the stub_bidder object.

[0111] In the demand response of air conditioning load, GridLab-D has a built-in module that automatically adjusts the temperature set point according to the input electricity price signal, thereby changing the operating conditions of the air conditioning load and realizing demand response. Therefore, the air conditioning load can directly participate in the specified market transaction in the form of bid_mode (used to specify the participation mode of demand response resources (such as air conditioners, water heaters, etc.) in the market). The bid_mode form of the air conditioning load determines the amount of electricity purchased according to the price, and the clearing mode of the air conditioning load participating in the market is shown in Figure 3.

[0112] Taking the user electricity behavior adjustment strategy of the air conditioner in heating mode or cooling mode as an example, in the case of heating mode, the temperature set point of the air conditioner is usually set to T (such as 20°C in heating mode and 24°C in cooling mode). desired ; When the electricity price signal O is greater than the average electricity price setting value O argWhen the device controller (such as a computer device) will actively move the temperature set point T set Lower than T desired , has achieved the effect of reducing the load; when the electricity price signal O is less than the average electricity price setting value O avg When the device controller moves the temperature set point T set Higher than T desired , increasing load energy consumption. In cooling mode, the opposite is true.

[0113] In one embodiment, making a decision on the demand response data based on the user decision model to obtain a decision strategy includes:

[0114] Predicting the demand response data to obtain market electricity consumption data;

[0115] The market electricity consumption data is input into the user decision model to make a decision and obtain the decision strategy.

[0116] MATLAB, neural network and other algorithms are used to analyze and predict demand response data to obtain prediction results, which may include key parameters such as the output of distributed renewable energy, market price, and natural power consumption. Based on the prediction results, the energy storage charging and discharging power, market bidding volume, and user power consumption are optimized to generate a decision-making strategy.

[0117] like Figure 4 As shown in the figure, the simulation decision-making process of the user decision model in the GridLab-D environment is provided: including starting GridLab-D; MATLAB predicts the output of renewable energy, load, and the price of transactions with demand response resources; Demand response (Demand Response, DR) refers to the user's active adjustment of electricity consumption behavior based on electricity price signals or incentive mechanisms to respond to the needs of the power grid. Distributed Energy Resources (Distributed Energy Resources, DER) refer to small-scale power generation and energy storage equipment distributed near users, such as solar photovoltaic, wind power generation, energy storage systems, etc. MATLAB solves the market participation model of distributed energy resources (Distributed Energy Resources, DER) power producers considering demand response (Demand Response, DR) to obtain information on energy storage charging and discharging, market bidding, and self-power consumption decision-making. Furthermore, the GridLab-D simulation is run, the decision results are written, and GridLab-D is paused. If the time meets the provisional time, the simulation is ended. Otherwise, it is determined whether the time meets the daily time. If not, the energy storage charging and discharging, market bidding, and its own electricity consumption decision information are solved, and the decision result writing is continued. If so, the energy storage charging and discharging, market bidding, and its own electricity consumption decision information are continued.

[0118] S104: Adjusting the operating state of the power system based on the decision-making strategy.

[0119] Apply decision-making strategies to the power system, and control energy storage equipment and user loads through intelligent devices (such as computer equipment) and communication technology to adjust the operating state of the power system.

[0120] For example, the decision strategy can be to track the node power through the energy storage system. When the node power is greater than the set value, the energy storage system discharges; when the node power is less than the set value, the energy storage system charges. When the node power is positive and exceeds the set value, the excess power is sold to the grid.

[0121] When the node power represents the power difference between the photovoltaic system and the user load in each time step, if the power difference is greater than the set value (such as 0), it means that the electric energy generated by the photovoltaic system exceeds the user load demand, and the excess electric energy can be used for charging or sold to the grid; if the power difference is less than or equal to the set value (such as 0), it means that the user load demand exceeds the electric energy generated by the photovoltaic system, and electric energy needs to be obtained from energy storage equipment or the grid.

[0122] The energy storage device power indicates the charge and discharge power of the energy storage device in each time step. A positive value indicates discharge, and a negative value indicates charge. If the energy storage device power is greater than the set value (such as 0), it means that the energy storage device is discharging; otherwise, it means that the energy storage device is charging.

[0123] The state of charge (SOC) indicates the remaining power of the energy storage device, usually expressed as a percentage. If the SOC is close to the preset maximum value, it means that the energy storage device is close to a full charge state, and the charging should be reduced or the discharging should be increased; if the SOC is close to the preset minimum value, it means that the energy storage device is close to an empty state, and the discharging should be reduced or the charging should be increased. The preset maximum value and the preset minimum value are the upper and lower limits of the state of charge.

[0124] The surplus power on-grid represents the power sold to the grid in each time step. If the surplus power on-grid is greater than the set value (such as 0), it means that there is surplus power sold to the grid, which can bring economic benefits.

[0125] The surplus power grid-connected income represents the economic benefits obtained by selling surplus power in each time step. If the surplus power grid-connected income is high, it means that the surplus power grid-connected strategy is effective and can be further optimized.

[0126] The decision strategy can be that if the node power is greater than the set value and the state of charge is less than the preset maximum value, charging is prioritized; if the node power is greater than the set value and the state of charge is greater than or equal to the preset maximum value, the surplus power is connected to the grid; if the node power is less than the set value and the state of charge is greater than the preset minimum value, the point is prioritized; if the node power is less than the set value and the state of charge is less than or equal to the preset minimum value, electricity is purchased from the grid. When the electricity price is high, the surplus power can be prioritized to be connected to the grid, and when the electricity price is low, charging or self-use can be prioritized.

[0127] In one embodiment, adjusting the operating state of the power system based on the decision strategy includes:

[0128] Performing simulation control on the decision strategy based on a preset power system simulation model to obtain simulation results;

[0129] Optimizing the decision strategy based on the simulation results;

[0130] The operating state of the power system is adjusted according to the optimized decision-making strategy.

[0131] You can refer to Figure 4 The preset power system simulation model can be GridLab-D, and the decision results can be written into GridLab-D for simulation control; GridLab-D simulates at each time step (step_time) of the simulation control (such as 1h) until the scheduled number of simulation days is reached, and accurate simulation results can be obtained. The simulation results can include the real market clearing volume and price, the real response volume of demand response, etc.; based on the simulation results, the demand response resource transaction and energy storage equipment configuration analysis are analyzed; and the decision strategy is optimized according to the analysis results.

[0132] For example, the impact of demand response resource trading on user benefits is analyzed:

[0133] Surplus power grid access refers to the process in which distributed energy (such as solar photovoltaic, wind power, etc.) power generation systems sell excess electricity to the grid after meeting the user's own electricity demand. Assume that the photovoltaic panel area of ​​the solar user with surplus power grid access is 50 square meters, the efficiency parameter is 0.2, the rated capacity is 10 kilowatts, and the maximum active power of the measured power generation is about 9.2 kilowatts, installed at 604 nodes. Most power sales companies will set higher electricity prices for users participating in the transaction, because the power sales company plays a backup role in it, so flexible loads such as air conditioners and water heaters are set to participate in market transactions, while the fixed load part is still set to buy electricity from the power sales company. The maximum power injected by distributed renewable energy into the grid can be set to 5kW. The quotation of distributed renewable energy power generators is calculated using max(0.4,0.9*LMP). 0.4 yuan / kwh is the guiding electricity price of the photovoltaic power station, which can be considered as the lowest grid-connected price that users can get. When the node injection power is limited, it will be based on the net injection power P inj Whether it exceeds the limit P inj-max Adjust the quotation, that is, lower the quotation by 0.1*(P inj -P inj-max ) / P inj-max , the reference value of the electricity price in the user auction module is set to 0.6 yuan / kWh, which will be used to provide the rolling average price for the auctioneer. The implementation method of this method refers to the market module of GridLab-D. The price of the green certificate is set to 0.1 yuan / kWh. Figure 5 to Figure 6 As shown, we can see two virtual quotation providers, providing guaranteed services for distributed power generators and power sales companies respectively. The two form a stepped supply curve (supplycurve) and a stepped demand curve (demandcurve) formed by users. The intersection of the two is the clearing point of the distributed power generation market.

[0134] The impact of whether the distributed renewable energy sources (RES) are traded with demand response loads under the condition that the grid power is limited to 5kW or less on the power sales. For example, by trading with DR loads, the amount of local consumption of distributed RES is increased, and the abandoned light part is significantly reduced. By analyzing the power sales and revenue of distributed RES in five scenarios: whether to trade with DR loads when there is no limit on the grid power (S1-A and S1-B), limited by grid power and not trading with DR loads (S1-C), trading with 5 DR loads when there is a grid power limit (S1-D), and trading with 7 DR loads when there is a grid power limit (S1-E), it is determined that the grid power limit leads to abandoned light (the abandoned light amount in the three restricted scenarios is not 0) and a reduction in power sales revenue; and users significantly reduce abandoned light by selling to demand response resources, and increase total power sales revenue, total equivalent revenue and net income. Comparing the S1-B and S1-D scenarios, we can see that although the grid power limit leads to a decrease in electricity sales revenue, users will increase their own electricity consumption, thereby increasing their equivalent benefits. It can be considered that the user's electricity experience has improved. Comparing S1-D and S1-E, if the DR load increases, the electricity sales revenue sold to the DR load will increase significantly and reduce the abandoned light. It can be seen that the expansion of the market scale is more conducive to the development of distributed RES.

[0135] Analysis of the impact of configuring energy storage equipment on user income: Energy storage equipment can charge during DER output peak hours and discharge during peak electricity consumption hours for user self-use, which not only reduces abandoned light, but also increases the self-use ratio of DER power generation in disguise, thereby improving the revenue of distributed DER power generation. Assume that the access capacity of 604 nodes is 10kW, the maximum charging and discharging power is 3kW, and the charging and discharging efficiency is 95%. The upper limit of the power injected into the grid is 5kW, and the impact of GridLab-D's built-in Loadfollowing control method (hereinafter referred to as LF control method) and optimization control method on user revenue are compared and analyzed. Among them, Loadfollowing is set to start charging when the net power consumption is 0, and to start discharging when the net power consumption is greater than 3kW. The load following control method of this application is not affected by the power limit of the network. The simulation duration is 1 month. Figure 7As shown in the figure, the power consumption curve is adjusted to store electricity (positive energy storage power) when photovoltaic power generation is large (net power without energy storage is negative), so as to play the role of energy storage in improving the user's self-use rate. However, it can also be seen that the optimization control method and the LF method have great differences in the scheduling of energy storage. For example, during the period of 12-17h, due to the continuous power generation of photovoltaic power, and the LF method has been continuously charging during the period of 8-11h, the charging and discharging power during 12-17h is very small; while the optimization control method (when there is no grid power limit) takes the electricity price into consideration, such as choosing to discharge in the 8th hour, and then choosing to charge when photovoltaic power generation is large (13-16h). Because the electricity price during these periods is higher, and the price sold to the grid remains unchanged, the distributed RES chooses to store electricity by itself, thereby increasing its own revenue. Comparing the charging and discharging power under the optimized control method with and without grid access power restrictions, it can be seen that when the power consumption is large, the energy storage is discharged in both scenarios to reduce the amount of electricity purchased from the grid (times 1, 20-23), but due to grid access power restrictions, the energy storage chooses to charge at times 11, 13, and 14 to reduce the power injection into the grid.

[0136] Figure 7 The adjustment effect of energy storage on the power generation and consumption of users equipped with distributed RES when the grid power is limited to 5kW is given. It can be seen that without energy storage, abandoned light will occur at moments 11, 13, and 14. The LF control method can reduce abandoned light, but the effect is limited (the effect is obvious at moments 9 and 11, but the amount of abandoned light remains almost unchanged at moments 13 and 14). However, under the load following control method of the present application, due to the absorption effect of energy storage, almost no abandoned light occurs.

[0137] Analysis of the impact of energy storage and demand response resource trading on user benefits: When there is a grid power limit, users participating in DR load trading also have energy storage equipment and use the proposed load following control method to compare the 24-hour decision-making in this scenario with the single demand response resource trading and the single configuration energy storage equipment. Figure 8 As shown. It can be seen that after connecting to energy storage, users can trade with demand response resources in a more flexible manner, which changes the market transaction clearing results, such as at moments 9 and 11-14; and trading with demand response resources will also affect the charging and discharging arrangements of energy storage. The clearing price advantage in the demand response trading market is very cheap. This is because the retail price given by the DSO is very cheap at this time, and no demand response resource is willing to trade with the distributed RES.

[0138] During the simulation control process, you can set the number of optimizations (or simulation days) and write the decision strategy to GridLab-D in a loop. For example, assuming that the decision strategy is an intraday decision strategy, that is, a one-day decision strategy, and the number of optimizations is 2, then write the decision results to GridLab-D in a loop; that is, write the decision strategy obtained by the user decision model into GridLab-D for optimization and simulation on the first day, and write the decision strategy obtained after the first day of optimization and simulation into GridLab-D for optimization and simulation on the second day; and get the final decision strategy.

[0139] In one embodiment, the decision strategy includes an energy storage control strategy, and the simulation control of the decision strategy based on a preset power system simulation model to obtain a simulation result includes:

[0140] Writing the energy storage control strategy into the power system simulation model for simulation control to obtain the simulation result; or,

[0141] The energy storage control strategy is simulated and controlled based on the load following control method of the power simulation system to obtain the simulation result.

[0142] The energy storage control strategy can be used to coordinate photovoltaic and energy storage power generation. GridLab-D has a built-in coordinated control method for photovoltaic and energy storage power generation. In one embodiment, the energy storage control strategy can be directly written into GridLab-D for simulation control; in one embodiment, the load following control method (Loadfollowing) in GridLab-D can be used to simulate the energy storage control strategy.

[0143] A TCP / IP connection is established through external software such as Matlab, and charging and discharging control is performed by directly controlling the power value of the inverter connected to the energy storage. Thus, simulation control of the energy storage control strategy is realized. In one embodiment, the load following control method based on the power simulation system simulates and controls the energy storage control strategy to obtain the simulation result, including:

[0144] When the energy storage device and the renewable energy source are connected to the same node through an inverter, tracking the node power of the node;

[0145] The energy storage control strategy is simulated and controlled according to the node power to obtain a simulation result.

[0146] like Fig. 9As shown, the energy storage is connected to the grid through an inverter, and the photovoltaic is also connected to the same node through an inverter; the energy storage inverter works in Loadfollowing mode, tracking the power of the node, and starts to act when the power is greater than or less than a certain value. For example, if the node power is greater than the set point, the energy storage device discharges; if the node power is less than the set point, the energy storage device charges; if the node strategy is in the dead zone, the energy storage device does not act, and finally the simulation results such as node power (node ​​power at each time step), energy storage device power (energy storage device charging and discharging power at each time step), and state of charge (energy storage device charge state at each time step) can be obtained, which can be used to analyze the operating status of the energy storage device, optimize the control strategy, and evaluate the absorption effect of distributed energy and the operation stability of the power grid; through the charging and discharging of the energy storage device, the power fluctuation of renewable energy is smoothed and the stability of the power grid is improved; when the node power is too high, the node is discharged, and when the node power is too low, the node is charged to optimize the absorption of distributed energy; reduce the power grid failure caused by power fluctuations and improve the reliability and safety of the power grid, that is, through the coordinated work of energy storage devices and renewable energy devices, and the Loadfollowing mode of the energy storage inverter, the simulation control of distributed energy can be effectively realized. This control method not only smoothes power fluctuations and optimizes energy utilization, but also improves the stability and reliability of the power grid.

[0147] In this application, by taking distributed renewable energy as the research object, in order to promote the development of distributed renewable energy, two common consumption mechanisms - energy storage and demand response loads (such as air conditioning, etc.) for the development of distributed renewable energy, a market participation decision model of distributed energy generators considering demand response resources under the consumption mechanism is established; the distribution network simulation software GridLab-D is used to finely model the distribution network, renewable energy and demand response resources to analyze the effectiveness of the consumption mechanism and the market participation decision model; and the decision strategy is optimized through simulation analysis, which has the following beneficial effects:

[0148] (1) Trading with demand response resources does not require additional equipment and can mobilize existing resources to consume distributed renewable energy, which is an effective means to promote the development of distributed renewable energy. Mobilizing more demand-side resources to participate in trading can better discover the value of renewable energy, increase the profits of power generators, and promote the growth of renewable energy installed capacity.

[0149] (2) When energy storage is used to absorb renewable energy, the load following control method proposed in this application can take into account the real-time electricity price better than load tracking, thereby bringing more benefits to users. However, when the energy storage cost is taken into account, the user's benefits are greatly affected. Considering that energy storage can bring reliability benefits to the system, the existing benefit sharing mechanism can be used to compensate the user's energy storage costs, prompting users to configure more energy storage facilities.

[0150] (3) Joint simulation can use the benefit indicators, power generation, self-use rate and other information of the microgrid obtained by GridLab-D simulation as the simulation input, so that the long-term simulation results are closer to the actual situation than a single simulation, which is conducive to the targeted optimization of the distributed RES consumption mechanism in various places.

[0151] The above is the operating status adjustment process of the power system of this application.

[0152] As mentioned above, the present application provides a method, device, computer equipment and storage medium for regulating the operating state of an electric power system, by constructing a user decision model of the electric power system, the user decision model includes an objective function with the goal of maximizing expected benefits and introducing demand response resources to participate in market transactions, combined with the constraints of the market mechanism; obtaining demand response data of the electric power system; making decisions on the demand response data based on the user decision model to obtain a decision strategy; and regulating the operating state of the electric power system based on the decision strategy. In the operating state regulation scheme of the electric power system provided in the present application, by constructing a user decision model with the objective function of maximizing expected benefits and introducing demand response resources to participate in market transactions, combined with the constraints of the market mechanism, making decisions on the demand response data of the electric power system, generating a decision strategy, based on which the user decision model is adjusted and optimized to optimize the consumption and power generation process of distributed energy, the operating state of the electric power system can be effectively regulated, and the consumption capacity of distributed energy and the operating stability of the electric power system can be improved.

[0153] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0154] In one embodiment, a power system operation state adjustment device is provided, and the power system operation state adjustment device corresponds to the power system operation state adjustment method in the above embodiment. Fig.10 As shown, the operation state regulating device of the power system includes:

[0155] A construction module 201 is used to construct a user decision model of the power system, wherein the user decision model includes an objective function with the goal of maximizing expected benefits and introducing demand response resources to participate in market transactions, combined with constraints of the market mechanism;

[0156] An acquisition module 202 is used to acquire demand response data of the power system;

[0157] A decision module 203, configured to make a decision on the demand response data based on the user decision model to obtain a decision strategy;

[0158] The adjustment module 204 is used to adjust the operating state of the power system based on the decision strategy.

[0159] In the operation status adjustment scheme of the power system provided in the present application, by constructing a user decision model with maximizing expected benefits as the objective function and introducing demand response resources to participate in market transactions, decisions are made on the demand response data of the power system in combination with the constraints of the market mechanism, and a decision strategy is generated. Based on the decision strategy, the consumption and power generation process of distributed energy is adjusted and optimized, which can effectively adjust the operation status of the power system, improve the consumption capacity of distributed energy and the operation stability of the power system.

[0160] In one embodiment, the objective function is expressed as:

[0161] MaxR IN =R S2C,IN +R S2U,IN +R Cr,IN -C B

[0162] Among them, R RN ,R S2C,NN ,R S2U,NN ,R Cr,N ,C B They are the net income from users making electricity consumption and market participation decisions, the income from selling electricity to grid companies, the income from selling electricity to demand response resources, certificate income, and electricity costs.

[0163] In one embodiment, the revenue R of selling electricity to the power grid company is S2C,IN It is expressed as:

[0164]

[0165] The revenue R of selling electricity to demand response users S2U,IN It is expressed as:

[0166]

[0167] The electricity cost C B It is expressed as:

[0168]

[0169] The certificate benefits R Cr，IN It is expressed as:

[0170]

[0171] Among them, P s2c,t ,P s2u,t ,P d,tThey are selling electricity to the grid, selling electricity to demand response users, and users buying electricity from the grid at time t; c s2c,t ,c s2u,t ,c r,t ,c cr They are the grid purchase rate, the market clearing price of demand response and renewable energy transactions at time t, the unit price of electricity purchased by users from the grid at time t, and the price of renewable energy green certificates.

[0172] In one embodiment, the decision module includes:

[0173] A prediction submodule, used to predict the demand response data to obtain market electricity consumption data;

[0174] The decision submodule is used to input the market electricity consumption data into the user decision model to make a decision and obtain the decision strategy.

[0175] In one embodiment, the adjustment module includes:

[0176] A control submodule, used for performing simulation control on the decision strategy based on a preset power system simulation model to obtain a simulation result;

[0177] An optimization submodule, used for optimizing the decision strategy based on the simulation results;

[0178] The regulating submodule is used to regulate the operating state of the power system according to the optimized decision strategy.

[0179] In one embodiment, the decision strategy includes an energy storage control strategy, and the control submodule includes:

[0180] A first control unit is used to write the energy storage control strategy into the power system simulation model for simulation control to obtain the simulation result; or

[0181] The second control unit is used to perform simulation control on the energy storage control strategy based on the load following control method of the power simulation system to obtain the simulation result.

[0182] In one embodiment, the second control unit is specifically configured to:

[0183] When the energy storage device and the renewable energy source are connected to the same node through an inverter, tracking the node power of the node;

[0184] The energy storage control strategy is simulated and controlled according to the node power to obtain a simulation result.

[0185] In one embodiment, a computer device is provided, the internal structure diagram of which can be as follows: Fig.11As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, the functions or steps of a method for adjusting the operating state of an electric power system are realized.

[0186] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0187] Construct a user decision model for the power system. The user decision model includes an objective function that maximizes expected revenue and introduces demand response resources to participate in market transactions, combined with the constraints of the market mechanism; obtain demand response data for the power system; make decisions on the demand response data based on the user decision model to obtain a decision strategy; and adjust the operating status of the power system based on the decision strategy.

[0188] In this embodiment, by constructing a user decision model with maximizing expected benefits as the objective function and introducing demand response resources to participate in market transactions, decisions are made on the demand response data of the power system in combination with the constraints of the market mechanism, and a decision strategy is generated. Based on the decision strategy, the consumption and power generation process of distributed energy is adjusted and optimized, which can effectively regulate the operating state of the power system, improve the consumption capacity of distributed energy and the operating stability of the power system.

[0189] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0190] Construct a user decision model for the power system. The user decision model includes an objective function that maximizes expected revenue and introduces demand response resources to participate in market transactions, combined with the constraints of the market mechanism; obtain demand response data for the power system; make decisions on the demand response data based on the user decision model to obtain a decision strategy; and adjust the operating status of the power system based on the decision strategy.

[0191] In this embodiment, by constructing a user decision model with maximizing expected benefits as the objective function and introducing demand response resources to participate in market transactions, decisions are made on the demand response data of the power system in combination with the constraints of the market mechanism, and a decision strategy is generated. Based on the decision strategy, the consumption and power generation process of distributed energy is adjusted and optimized, which can effectively regulate the operating state of the power system, improve the consumption capacity of distributed energy and the operating stability of the power system.

[0192] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0193] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0194] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0195] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for adjusting the operating state of an electric power system, characterized in that: include: Constructing a user decision model for the power system, the user decision model includes an objective function with the goal of maximizing expected benefits and introducing demand response resources to participate in market transactions, combined with constraints of market mechanisms; Obtain demand response data for power systems; Making a decision on the demand response data based on the user decision model to obtain a decision strategy; The operating state of the power system is adjusted based on the decision strategy.

2. The method for adjusting the operating state of the power system according to claim 1, characterized in that: The objective function is expressed as: MaxR IN =R S2C,IN +R S2U,IN +R Cr,IN -C B Among them, R RN ,R S2C,NN ,R S2U,NN ,R Cr,N ,C B They are the net income from users making electricity consumption and market participation decisions, the income from selling electricity to grid companies, the income from selling electricity to demand response resources, certificate income, and electricity costs.

3. The method for adjusting the operating state of the power system according to claim 2, characterized in that: The revenue from selling electricity to the grid company is R S2C,IN It is expressed as: The revenue R of selling electricity to demand response users S2U,IN It is expressed as: The electricity cost C B It is expressed as: The certificate benefits R Cr，IN It is expressed as: Among them, P s2c,t ,P s2u,t ,P d,t They are selling electricity to the grid, selling electricity to demand response users, and users buying electricity from the grid at time t; c s2c,t ,c s2u,t ,c r,t ,c cr They are the grid purchase rate, the market clearing price of demand response and renewable energy transactions at time t, the unit price of electricity purchased by users from the grid at time t, and the price of renewable energy green certificates.

4. The method for adjusting the operating state of the power system according to claim 1, characterized in that: The making a decision on the demand response data based on the user decision model to obtain a decision strategy includes: Predicting the demand response data to obtain market electricity consumption data; The market electricity consumption data is input into the user decision model to make a decision and obtain the decision strategy.

5. The method for adjusting the operating state of the power system according to claim 1, characterized in that: The step of adjusting the operating state of the power system based on the decision strategy includes: Performing simulation control on the decision strategy based on a preset power system simulation model to obtain simulation results; Optimizing the decision strategy based on the simulation results; The operating state of the power system is adjusted according to the optimized decision-making strategy.

6. The method for adjusting the operating state of the power system according to claim 5, characterized in that: The decision strategy includes an energy storage control strategy, and the decision strategy is simulated and controlled based on a preset power system simulation model to obtain a simulation result, including: Writing the energy storage control strategy into the power system simulation model for simulation control to obtain the simulation result; or, The energy storage control strategy is simulated and controlled based on the load following control method of the power simulation system to obtain the simulation result.

7. The method for adjusting the operating state of the power system according to claim 6, characterized in that: The load following control method based on the power simulation system performs simulation control on the energy storage control strategy to obtain the simulation result, including: When the energy storage device and the renewable energy source are connected to the same node through an inverter, tracking the node power of the node; The energy storage control strategy is simulated and controlled according to the node power to obtain a simulation result.

8. A device for regulating the operating state of an electric power system, characterized in that: include: A construction module, used to construct a user decision model of the power system, wherein the user decision model includes an objective function with the goal of maximizing expected benefits and introducing demand response resources to participate in market transactions, combined with constraints of the market mechanism; An acquisition module, used for acquiring demand response data of the power system; A decision module, used to make a decision on the demand response data based on the user decision model to obtain a decision strategy; A regulating module is used to regulate the operating state of the power system based on the decision strategy.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for adjusting the operating state of the power system as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for adjusting the operating state of the power system as claimed in any one of claims 1 to 7 are implemented.

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