A micro-grid dispatching method for independent energy storage and coordinated source-load accommodation

Abstract

The present application relates to a kind of independent energy storage collaborative source load accommodation microgrid scheduling method, belong to the technical field of integrated energy system collaborative regulation and interactive transaction.The method includes: considering the interest correlation of each subject, constructs multi-subject electric energy interaction mechanism;Based on the flexible adjustment of source-storage-load, a supply-demand double-response model is established;Considering the participation of independent energy storage in power transaction, an implementation method of microgrid electric energy transaction is proposed, a mathematical model of electric energy transaction of different subjects is constructed, and constraint conditions are determined;A hierarchical nested game model based on master-slave game is constructed to obtain the optimal operation strategy.The present application can effectively balance the interests of multiple subjects in microgrid, realize the collaborative optimal operation of source-storage-load, and promote the development and application of energy storage technology and microgrid technology.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of comprehensive energy system coordinated regulation and interactive transaction, and particularly relates to a micro-grid dispatching method for independent energy storage collaborative source-load consumption. BACKGROUND

[0002] Under the drive of the "double carbon" goal, developing new energy has become an objective requirement and inevitable choice for low-carbon transformation of energy structure. At the same time, under the background of energy internet technology, user side configuration and use of distributed new energy generation resources have become a new development trend. As an important carrier of distributed new energy, micro-grid can effectively integrate and manage various distributed energy, realize the interconnection of energy, and promote the reasonable consumption of new energy. However, the uncertainty and volatility of new energy output make the energy management and dispatching of micro-grid face great challenges. Therefore, how to ensure that the micro-grid has flexible regulation capacity to cope with the impact of continuously growing new energy and meet the needs of power supply and demand balance is an important direction for future energy development.

[0003] As an important regulation resource for peak shaving and valley cutting in micro-grid, the dispatching strategy of energy storage system has an important influence on improving the flexibility of micro-grid. With the promotion of power marketization reform, the independent subject status of energy storage participating in the power market is clear. Energy storage as an independent subject participating in the power market has become an inevitable trend, which can solve the problem of low efficiency of current energy storage operation mode and is conducive to the efficient use of energy storage resources. Research on energy storage system as an independent subject participating in micro-grid power dispatching can help to stimulate the enthusiasm of energy storage participating in coordinated dispatching, and through the integration of local distributed power and energy storage system, micro-grid can realize more efficient and more environmentally friendly energy utilization mode, providing a reference scheme for energy structure transformation. SUMMARY

[0004] The purpose of the present application is to provide a micro-grid dispatching method for independent energy storage collaborative source-load consumption, which can realize the collaborative optimization operation of source-storage-load and improve the ability of micro-grid to be self-sufficient in power supply.

[0005] To achieve the above purpose, the technical solution of the present application is as follows: a micro-grid dispatching method for independent energy storage collaborative source-load consumption, comprising:

[0006] Considering the interest correlation of each subject, a multi-subject power interaction mechanism is constructed;

[0007] A supply-demand double response model is established based on the flexible adjustment of source-storage-load;

[0008] Considering the participation of independent energy storage in power transaction, an implementation method of micro-grid power transaction is proposed, a mathematical model of power transaction of different subjects is constructed, and constraint conditions are determined;

[0009] A hierarchical nested game model based on principal-agent game is constructed to obtain an optimal operation strategy.

[0010] In an embodiment of the present application, the method further comprises:

[0011] Different scenarios are set to test the effectiveness of the established hierarchical nested game model based on principal-agent game.

[0012] In an embodiment of the present application, the specific implementation of the multi-agent electric energy interaction mechanism is as follows:

[0013] Step S11, the microgrid operator as a leader formulates an initial electricity selling price and a charging and discharging strategy according to an electric energy supply and demand condition;

[0014] Step S12, the photovoltaic user optimizes a load demand response condition according to the electricity selling price formulated by the microgrid operator, formulates an energy utilization strategy, and reports the energy utilization strategy to the microgrid operator; meanwhile, the independent energy storage operator formulates a charging and discharging price strategy for providing services to the microgrid operator;

[0015] Step S13, the microgrid operator optimizes and adjusts an internal energy conversion equipment output according to the energy utilization strategy set reported by the energy storage operator and the photovoltaic user, updates the electricity selling price and the charging and discharging strategy, and guides the independent energy storage operator and the photovoltaic user to adaptively adjust with the optimal economic benefit of the microgrid operator as a starting point until an optimal strategy combination is found.

[0016] In an embodiment of the present application, the specific implementation of the supply-demand double-response model established based on source-storage-load is as follows:

[0017] Step S21, a supply-side response model based on energy storage usage is established according to a composition structure of energy supply of the microgrid:

[0018]

[0019] In the formula, denotes an electric quantity provided by the supply side at a t period; and denote a wind power output and a traditional generator output at the t period respectively; denotes an interactive electric quantity of the microgrid operator and a superior grid at the t period, which is greater than 0 when purchasing electricity from the superior grid, and vice versa when selling electricity to the superior grid; denote charging and discharging quantities of the energy storage at the t period respectively;

[0020] Step S22, a demand-side photovoltaic user response model is established:

[0021]

[0022] In the formula, respectively represent the total power load, rigid load and flexible load of the user in the t period;

[0023] The flexible load can be adaptively adjusted, and the formula is:

[0024]

[0025] In the formula, represents the power load adjustment amount of the user in the t period; represents the flexible load after the user demand response; represents the power load of the user after adaptive adjustment in the t period; λ is the maximum proportion of the allowable adjustment power load in the t period; k is the total proportion of the adjustable power load in a day; the flexible load is required to be unchanged before and after the user demand response while improving the power consumption satisfaction of the user after adaptive adjustment of the flexible load;

[0026] Due to the photovoltaic power generation device on the user side, the user preferentially uses photovoltaic power generation to meet the power needs, and the net power load of the photovoltaic user after the demand response in the t period is:

[0027]

[0028] In the formula, represents the output of the photovoltaic device in the t period;

[0029] In step S23, the balance between power supply and demand in the microgrid is maintained during operation, and the formula is:

[0030]

[0031] In an embodiment of the present application, the constraint condition of the supply side response model is as follows:

[0032] In order to coordinate the volatility of new energy and improve the feasibility of the dispatching plan, the traditional generator set needs to meet the corresponding constraint during operation:

[0033]

[0034] In the formula, represents the maximum output power of the traditional generator set; respectively represent the minimum value and the maximum value of the climbing power of the traditional generator set;

[0035] In order to ensure the continuity of the charging and discharging service provided by the energy storage system, the corresponding constraint needs to be met during operation:

[0036]

[0037] In the formula, represents the energy storage capacity in the t period; η ES,ch , ηES,dis respectively represent the minimum and maximum capacity allowed by the energy storage; respectively represent the minimum and maximum capacity allowed by the energy storage; is a Boolean variable representing the charging and discharging state of the energy storage; P ch,max , P ch,min , P dis,max , P dis,min respectively represent the maximum charging power of the energy storage, the minimum charging power of the energy storage, the maximum discharging power of the energy storage, and the minimum discharging power of the energy storage;

[0038] The bidirectional power exchange between the microgrid operator and the upper grid should satisfy the upper and lower limit constraints:

[0039]

[0040] In the formula: μ net is a Boolean variable representing the state of the microgrid operator buying and selling electricity to the upper grid, and is 1 when buying electricity from the upper grid; P gs,max and P gb,max respectively represent the maximum power allowed to be transmitted by the microgrid when buying electricity from the upper grid, and the maximum power allowed to be transmitted by the microgrid when selling electricity to the upper grid.

[0041] In an embodiment of the present application, the specific implementation of the mathematical model for constructing different subject electric energy transactions is as follows:

[0042] Step S31, taking maximum benefit as the optimization objective, formulating the objective function of the microgrid operator

[0043]

[0044] In the formula: T represents the scheduling period; represents the revenue of the microgrid operator from selling electricity at period t; and respectively represent the electric energy transaction cost of the microgrid operator with the independent energy storage operator and the upper grid; and respectively represent the operation cost of the wind turbine and the traditional generator;

[0045] Among them:

[0046]

[0047] In the formula: represents the selling price of electricity at period t; represents the net load of the photovoltaic user at period t; respectively represent the charging and discharging price of the energy storage at period t; respectively represent the grid price and the on-grid price at period t; r WT is the wind power unit power operation cost coefficient; ae , b e , c e is the cost coefficient of the traditional generator set;

[0048] Constraint:

[0049]

[0050] In the formula: respectively represent the upper and lower limits of the dynamic electricity price inside the microgrid; represent the average dynamic electricity price upper limit of the t period;

[0051] Step S32, taking the maximum benefit as the optimization target, formulating the objective function of the independent energy storage operator

[0052]

[0053] In the formula: respectively represent the revenue of the t period independent energy storage operator in the electricity transaction with the microgrid operator and the photovoltaic user; represent the operation cost of the t period energy storage;

[0054] Wherein:

[0055]

[0056] In the formula: ε is the unit charge and discharge cost coefficient of the energy storage;

[0057] Constraint:

[0058]

[0059] Step S33, taking the maximum consumer surplus as the optimization target, formulating the objective function of the photovoltaic user:

[0060]

[0061] In the formula: I u represent the utility function of the user electricity consumption in the t period, measure the satisfaction of the user electricity purchase and consumption; represent the electricity purchase cost of the user in the t period; represent the operation cost of the photovoltaic power generation device in the t period;

[0062] Wherein:

[0063]

[0064] In the formula: is the electricity load of the photovoltaic user after demand response in the t period; α e , β e respectively represent the preference coefficient of the user to the electricity demand; r pvThe cost coefficient of unit power operation of a photovoltaic power generation device.

[0065] In an embodiment of the present application, the hierarchical nested game model based on the master-slave game is specifically represented as:

[0066] G={MGO∪ESO∪user;{δ MGO ,δ ESO ,δ user};{U MGO ,U ESO ,U user}}

[0067] wherein MGO, ESO and user represent the micro-grid operator, the energy storage operator and the photovoltaic user respectively; {δ MGO ,δ ESO ,δ user} represents the strategy set of each participant, wherein the strategy set δ MGO of the micro-grid operator is the determined electricity selling price and the charging and discharging amount of the energy storage; the strategy set δ ESO of the energy storage operator is the determined charging and discharging price in each period; and the strategy δ user of the photovoltaic user is the adjusted flexible load; and the specific representation is:

[0068]

[0069] When any participant cannot obtain a better benefit by unilaterally changing the strategy, the game reaches the Stackelberg equilibrium and obtains the optimal operation strategy, which is represented by the formula as:

[0070]

[0071] wherein is the equilibrium solution of Stackelberg.

[0072] In an embodiment of the present application, the specific implementation mode of setting different scenarios to verify the effectiveness of the established hierarchical nested game model based on the master-slave game is as follows:

[0073] Step S41, setting different operation scenarios;

[0074] Step S42: programming on the Matlab platform and solving the hierarchical nested game model by using the genetic algorithm combined with the CPLEX solver;

[0075] Step S43, simulating different scenarios to obtain the benefit optimization results and strategy set of each subject in different scenarios;

[0076] Step S44, the effectiveness of the method for improving the utilization rate of energy storage and promoting the cooperation of each subject in the micro-grid is verified by comparing the benefits and energy optimization scheduling results of each subject in different scenarios.

[0077] The application further provides a micro-grid scheduling system for independent energy storage and coordinated source-load accommodation, characterized by comprising a memory, a processor and computer program instructions stored in the memory and capable of being executed by the processor, and when the processor executes the computer program instructions, the method steps described above can be realized.

[0078] The application further provides a computer readable storage medium, which stores computer program instructions capable of being executed by a processor, and when the processor executes the computer program instructions, the method steps described above can be realized.

[0079] Compared with the prior art, the application has the following beneficial effects:

[0080] 1、The application introduces the autonomous pricing and scheduling mechanism of independent energy storage system, and the independent energy storage operator can flexibly adjust the charging and discharging price according to the market situation, thereby improving the utilization rate of energy storage resources and providing a profit space for the independent energy storage operator. The charging and discharging service of the energy storage is provided for the micro-grid operator, and the micro-grid operator can flexibly adjust the storage and release of electric energy according to the supply and demand situation, thereby reducing the dependence on the upper-level power grid, reducing the power purchase cost and improving the overall benefit.

[0081] 2、The application considers the mutual influence of different subject decisions, establishes a supply and demand double-response hierarchical nested game model considering the interests of multiple subjects, breaks through the limitation of the traditional single-level game model that is difficult to coordinate the interests of multiple subjects, and solves the problems of coordinated scheduling and benefit balance among multiple subjects in the micro-grid. BRIEF DESCRIPTION OF DRAWINGS

[0082] Figure 1 It is a micro-grid system architecture diagram of the embodiment of the application.

[0083] Figure 2 It is a multi-subject game interaction framework of the embodiment of the application.

[0084] Figure 3 It is a model solution flowchart of the embodiment of the application.

[0085] Figure 4 It is a wind and solar power generation and initial load prediction curve of the embodiment of the application.

[0086] Figure 5 It is an optimization result of dynamic electricity price of the micro-grid operator and the independent energy storage operator of the embodiment of the application.

[0087] Figure 6User load curve optimization result of the embodiment of the application.

[0088] Figure 7 Electricity supply and demand balance game result of the embodiment of the application.

[0089] Figure 8 Scenario 2 and 3 power and electricity balance result of the embodiment of the application.

[0090] Figure 9 Scenario 3 and 4 energy storage charging and discharging power and SOC value change graph of the embodiment of the application. DETAILED DESCRIPTION

[0091] The technical solutions of the application will be described in detail below with reference to the drawings.

[0092] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application pertains.

[0093] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and it should be understood that when the terms "comprise" and / or "include" are used in the specification, there is a presence of the features, steps, operations, devices, components and / or combinations thereof.

[0094] The application provides a micro-grid dispatching method for independent energy storage coordination source-load accommodation, comprising:

[0095] A multi-agent electric energy interaction mechanism is constructed by considering the interest relevance of each agent.

[0096] A supply-demand double-response model is established based on flexible adjustment of source-storage-load.

[0097] Considering that independent energy storage participates in power trading, an implementation method of micro-grid electric energy trading is proposed, a mathematical model of electric energy trading of different agents is constructed, and constraint conditions are determined.

[0098] A hierarchical nested game model based on principal-agent game is constructed to obtain an optimal operation strategy.

[0099] It also includes:

[0100] Different scenarios are set to test the effectiveness of the hierarchical nested game model based on principal-agent game.

[0101] The following is the specific implementation process of the application.

[0102] The embodiment provides a micro-grid scheduling method for independent energy storage and coordinated load accommodation, and specifically comprises the following steps.

[0103] Step S1: considering the interest correlation of each subject, a multi-subject electric energy interaction mechanism is constructed.

[0104] Step S2: a supply-demand double-response model is established based on flexible adjustment of a source-storage-load;

[0105] Step S3: considering that the independent energy storage participates in power transaction, an implementation method of micro-grid electric energy transaction is proposed, a mathematical model of electric energy transaction of different subjects is constructed, and constraint conditions are determined;

[0106] Step S4: a hierarchical nested game model based on master-slave game is constructed, and an optimal operation strategy is obtained.

[0107] In the embodiment, the following steps are further included.

[0108] Step S5: the effectiveness of the hierarchical nested game model based on master-slave game established is verified by setting different scenes.

[0109] In the embodiment, the construction of the electric energy interaction mechanism considering the interests of multiple subjects specifically comprises the following steps.

[0110] Step S11: the micro-grid operator serves as a leader, formulates an initial power selling price and a charging and discharging strategy according to an electric energy supply and demand situation;

[0111] Step S12: the photovoltaic user optimizes a load demand response situation according to the power selling price formulated by the micro-grid operator, formulates an energy utilization strategy, and reports the strategy to the micro-grid operator; meanwhile, the independent energy storage operator formulates a charging and discharging price strategy for providing services to the micro-grid operator;

[0112] Step S13: the micro-grid operator optimizes and adjusts the output of internal energy conversion equipment according to the strategy set reported by the energy storage operator and the user, updates the power selling price and the charging and discharging strategy of using energy storage, and guides the independent energy storage operator and the user to adaptively adjust until an optimal strategy combination is found, with the optimal economic benefit of the micro-grid operator as a starting point.

[0113] In the embodiment, the establishment of the supply-demand double-response model based on flexible adjustment of a source-storage-load specifically comprises the following steps.

[0114] Step S21: a supply-side response model based on energy storage utilization is established according to the composition structure of energy supply of the micro-grid.

[0115]

[0116] In the formula, P G represents the electric quantity provided by the supply side at the t period. and Pwind(t) and Pcon(t) represent the wind power output and the traditional generator output at time t, respectively; Pinter(t) represents the interactive power between the microgrid operator and the upper grid at time t, which is greater than 0 when the microgrid operator purchases power from the upper grid, and vice versa; Pcharge(t) and Pdischarge(t) represent the charging and discharging power of the energy storage at time t, respectively.

[0117] wherein:

[0118] To match the volatility of new energy and improve the feasibility of the dispatching plan, the traditional generator needs to meet the corresponding constraints when operating:

[0119]

[0120] wherein: Pmax represents the maximum output power of the traditional generator; Pmin and Pmax represent the minimum and maximum values of the climbing power of the traditional generator, respectively.

[0121] Secondly, to ensure the continuity of the energy storage system in providing charging and discharging services, the energy storage needs to meet the corresponding constraints when operating:

[0122]

[0123] wherein: C(t) represents the capacity of the energy storage at time t; η ES,ch and η ES,dis represent the charging and discharging efficiencies of the energy storage, respectively; Cmin and Cmax represent the minimum and maximum capacities allowed by the energy storage, respectively; Pcharge and Pdischarge represent the Boolean variables of the charging and discharging states of the energy storage, respectively; P ch,max (P ch,min ) and P dis,max (P dis,min ) represent the maximum (minimum) charging and discharging power of the energy storage, respectively.

[0124] In addition, the bidirectional power exchange power between the MGO and the upper grid should also meet the upper and lower limit constraints:

[0125]

[0126] wherein: μ net P represents the Boolean variable of the state of the microgrid operator purchasing or selling power to the upper grid, which is 1 when the microgrid operator purchases power from the upper grid; P gs,max and P gb,max represent the maximum power allowed to be transmitted by the microgrid when purchasing power from the upper grid and the maximum power allowed to be transmitted by the microgrid when selling power to the upper grid, respectively.

[0127] Step S22: Establishing a demand-side photovoltaic user response model:

[0128]

[0129] In the formula: respectively represent the total power load, rigid load and flexible load of the user in the t period.

[0130] The flexible load can be adaptively dynamically adjusted, and the formula is:

[0131]

[0132] In the formula: represents the power load adjustment amount of the user in the t period; represents the flexible load after the user demand response; represents the power load after adaptive adjustment of the user in the t period; λ is the maximum proportion of the allowable adjustment power load in the t period; k is the total proportion of the adjustable power load in a day. While the flexible load is adaptively adjusted to improve the power consumption satisfaction of the user, it is also necessary to ensure that the total amount of the flexible load before and after the user demand response is unchanged.

[0133] Since the user side is equipped with a photovoltaic power generation device, the user prefers to use photovoltaic power generation to meet their own power needs, and the net power load of the photovoltaic user after the demand response in the t period is:

[0134]

[0135] In the formula: represents the output of the photovoltaic device in the t period.

[0136] Step S23: maintaining the balance between power supply and demand in the microgrid during operation, which is expressed by the formula:

[0137]

[0138] In the embodiment, the implementation method of the microgrid electric energy transaction considering the participation of independent energy storage in electric power transaction includes the following steps of constructing a mathematical model of electric energy transaction of different subjects:

[0139] Step S31: taking the maximum benefit as the optimization target, formulating the objective function of the microgrid operator

[0140]

[0141] In the formula: T represents the scheduling period, and in this paper, T = 24h; represents the power selling income of the microgrid operator in the t period; and respectively represent the electric energy transaction cost of the microgrid operator with the independent energy storage operator and the superior power grid; and respectively represent the operation cost of wind turbine generator and traditional generator.

[0142] wherein:

[0143]

[0144] wherein: represents the electricity selling price of t period; represents the net electricity load of photovoltaic user in t period; respectively represent the charging and discharging price of energy storage in t period; respectively represent the grid electricity price and on-grid electricity price in t period; r WT is the wind power unit power operation cost coefficient; a e , b e , c e is the cost coefficient of traditional generator.

[0145] Constraint condition:

[0146]

[0147] wherein: respectively represent the upper and lower limits of dynamic electricity price inside microgrid; represents the average dynamic electricity price upper limit in t period

[0148] Step S32: Formulate the objective function of independent energy storage operator with the optimization target of maximum benefit

[0149]

[0150] wherein: respectively represent the income of independent energy storage operator in t period for electricity transaction with microgrid operator and photovoltaic user; represents the operation cost of energy storage in t period;

[0151] wherein:

[0152]

[0153] wherein: ε is the unit charging and discharging cost coefficient of energy storage.

[0154] Constraint condition:

[0155]

[0156] Step S33: Formulate the objective function of photovoltaic user with the optimization target of maximum consumer surplus:

[0157]

[0158] wherein: I uUtility function of user electricity consumption in t period, measuring the satisfaction of user electricity purchase and consumption; Purchase cost of user in t period; Operation cost of photovoltaic power generation device in t period;

[0159] Wherein:

[0160]

[0161] In the formula: is the electricity load of photovoltaic user after demand response in t period; α e , β e respectively represent the preference coefficient of user to electricity demand; r pv is the operation cost coefficient of photovoltaic power generation device per unit power.

[0162] In the embodiment, the construction of the hierarchical nested game model considering the interests of each subject can be specifically represented as:

[0163] G={MGO∪ESO∪user;{δ MGO ,δ ESO ,δ user};{U MGO ,U ESO ,U user}} (25)

[0164] In the formula: MGO, ESO and user represent the participants of microgrid operator, energy storage operator and photovoltaic user.{δ MGO ,δ ESO ,δ user} represent the strategy set of each participant, wherein the strategy set δ MGO of microgrid operator is the electricity selling price and the charge and discharge amount of using energy storage prepared; the strategy set δ ESO of energy storage operator is the charge and discharge price prepared in each period; the strategy δ user of photovoltaic user is the adjusted flexible load. The specific representation is:

[0165]

[0166] When any participant cannot obtain better benefits by changing the strategy unilaterally, the game reaches Stackelberg equilibrium and obtains the optimal operation strategy, which is represented by the formula:

[0167]

[0168] In the formula is the equilibrium solution of Stackelberg.

[0169] In the embodiment, the setting different scenarios tests the effectiveness of the established hierarchical nested game model based on the master-slave game, and specifically includes the following steps.

[0170] Step S41: setting different operation scenarios;

[0171] Step S42: programming on a Matlab platform, and solving the game model by using a genetic algorithm combined with a CPLEX solver;

[0172] Step S43: simulating different scenarios to obtain the benefit optimization results and strategy sets of each subject in different scenarios.

[0173] Step S44: verifying the effectiveness of the proposed method for improving the utilization rate of energy storage and promoting the win-win cooperation of each subject in the microgrid by comparing the benefits and the optimized scheduling results of each subject in different scenarios.

[0174] Next, the embodiment is further described in combination with specific parameters.

[0175] According to step S1, the electric energy interaction behaviors of multiple subjects are analyzed based on the microgrid structure Figure 1 The microgrid operator (MGO) as the manager of the microgrid, jointly with the wind turbine and the traditional generator, meets the electricity demand of the photovoltaic user based on the principle of maximizing the consumption of new energy. According to the output of the wind turbine and the electricity demand of the photovoltaic user, the internal electricity price of the microgrid is optimized, the internal controllable units are reasonably dispatched, the energy storage charging and discharging service is used, and the electric energy interaction with the upper-level power grid is performed to ensure the power balance in the microgrid. The photovoltaic user refers to the aggregation of small and medium-sized users assigned with photovoltaic power generation devices. By comprehensively considering the electricity satisfaction and the electricity cost, the photovoltaic user reasonably dispatches the adjustable load to maximize the consumer surplus. The photovoltaic user prioritizes the consumption of photovoltaic output to support the load, and the power surplus or deficit is the net electric load of the photovoltaic user. When the photovoltaic output cannot meet the electricity demand, the photovoltaic user purchases electric energy from the microgrid operator to smooth the net electric load fluctuation and meet the electricity demand. The energy storage system is configured in the microgrid and closely cooperates with the microgrid operator to provide the energy storage charging and discharging service for the microgrid operator to smooth the power fluctuation and coordinate the demand and supply balance in the microgrid. The energy storage system obtains benefits through the "low charging and high discharging" strategy.

[0176] According to step S2, the electric energy interaction behaviors of multiple subjects are analyzed based on the microgrid structure Figure 2A game-theoretic framework is used to construct a supply-demand dual-response model. The first level of the game involves photovoltaic (PV) users responding to demand based on dynamic electricity prices set by microgrid operators. Microgrid operators, as leaders, formulate dynamic electricity pricing strategies to maximize operational efficiency based on the microgrid's electricity supply and demand and market information. PV users, as followers, adaptively adjust their electricity load at different times based on the electricity prices set by the microgrid operators. The second level of the game involves independent energy storage operators (IESOs) and microgrid operators coordinating and optimizing the charging and discharging strategies of energy storage. IESOs, as leaders, set charging and discharging prices to maximize operational efficiency. Microgrid operators, as followers, optimize the scheduling of energy storage charging and discharging based on the energy storage operators' charging and discharging prices, wind turbine output, and the elastic load of PV users. The outcome of the first level of the game (i.e., the PV users' electricity consumption strategies) serves as one of the input conditions for the second level of the game (i.e., the microgrid's electricity supply and demand), influencing the microgrid operators' decisions in the second level of the game. The outcome of the second-level game (i.e., the charging and discharging strategies of microgrid operators using energy storage) influences the energy supply composition and electricity pricing strategies of microgrid operators in the first-level game, thus forming a closed-loop nested game structure. Through this hierarchical nested game, the decision-making of different stakeholders in the microgrid is optimized, promoting the efficient use of energy.

[0177] According to step S3, each participating entity conducts electricity trading with the goal of maximizing its own interests. The objective functions of each entity are equations (9), (16), and (21). During operation, the electricity needs to meet the constraints of equations (2), (3), (4), and (6), and the internal transaction electricity price needs to meet the constraints of equations (15) and (20).

[0178] According to step S4, this invention uses a distributed equilibrium solution method combining genetic algorithm and mixed integer programming to iteratively solve the hierarchical nested game model. The solution process is as follows: Figure 3 As shown in the figure. In this invention, photovoltaic power generation devices are installed on the user side of the microgrid, and a traditional generator set with a capacity of 600kW is installed on the microgrid operator side to ensure a reliable power supply for the microgrid. The relevant parameters of the energy storage system and microgrid are shown in Table 1. The unit power operation and maintenance coefficients of the photovoltaic generator set and the wind turbine are 0.0096 and 0.0296, respectively, and the user-side electricity consumption preference coefficient α... e β e The values ​​are 1.5 and 0.0009 respectively, and the maximum allowable adjustment ratio for flexible load is approximately 20%. The wind and solar power output forecast and user load forecast curves are shown below. Figure 4 As shown.

[0179] Table 1. Model-related parameters

[0180]

[0181] Based on the method, the micro-grid is optimized and dispatched, and dynamic electricity price strategies of the micro-grid operator and the energy storage operator after optimization are obtained, as shown in Figure 5 The user demand response optimization result is as shown in Figure 6

[0182] It can be seen from the analysis Figure 5 that the dynamic electricity prices formulated by the micro-grid operator and the energy storage operator are between the on-grid electricity price and the time-of-use electricity price of the power grid. Compared with the power grid, the power grid provides a more optimal price strategy for both supply and demand sides, encourages the micro-grid operator in the micro-grid to use the charge and discharge services of the energy storage system, reduces the energy interaction with the upper-level power grid, and further improves the ability of the micro-grid to autonomously operate and regulate the internal supply and demand. Figure 5 As can be seen from (b), the charge and discharge prices of the independent energy storage operator are affected by the real-time market price fluctuations and supply and demand changes. In most periods, the discharge price formulated by the energy storage operator is greater than the charge price, and the energy storage operator makes a profit through low charging and high discharging.

[0183] It can be seen from the analysis Figure 6 that the load curve before the user demand response appears peak values in two time periods of 11:00-13:00 and 18:00-20:00. At this time, the electricity price is high. After optimization, the user adaptively adjusts the electricity load, increases the electricity load in time periods of 00:00-10:00 and 23:00-24:00, and reduces the electricity load in a time period of 11:00-22:00. That is, the user tends to transfer the flexible load to the time period with a lower electricity price in response to the change trend of the market electricity price, and reduces the electricity load in the time period with a higher market electricity price. The user load curve before and after the demand response presents the characteristics of "peak load shifting", which shows that the optimization and dispatching method can effectively smooth the load fluctuation, reduce the electricity purchase cost while ensuring the electricity satisfaction, and improve the consumer surplus.

[0184] According to step S5, the effectiveness of the established multi-agent collaborative optimization and dispatching model is verified in different scenarios.

[0185] Scenario 1: The energy storage system does not participate in dispatching, and the micro-grid operator and the photovoltaic user do not perform master-slave game.

[0186] Scenario 2: The energy storage system does not participate in dispatching, and the micro-grid operator and the photovoltaic user respectively consider their own interests to perform master-slave game.

[0187] Scenario 3: The energy storage system participates in dispatching according to the electricity selling price formulated by the micro-grid operator, that is, the micro-grid operator is the leader, and the energy storage system and the user are the followers in a master-slave game operation strategy.

[0188] Scenario 4: The energy storage system participates in dispatching as an independent agent, and the multi-agent hierarchical nested master-slave game is performed for supply and demand side response, that is, the operation strategy proposed in the present application. ​

[0189] The benefit optimization results of each subject under different scenarios are shown in Table 2, and the electric energy optimization scheduling results are shown in Figure 7 .

[0190] Table 2 Optimization results of each subject under different scenarios

[0191]

[0192] From Table 2, it can be seen that the introduction of the game strategy (scenario 2) and the participation of the energy storage system in scheduling (scenario 3) significantly improve the benefits of the photovoltaic user. Compared with scenario 1, the consumer surplus of the photovoltaic user in scenario 2 increases by 890 yuan, because the photovoltaic user improves the flexibility of load demand through the master-slave game with the MGO. In scenario 3, the energy storage system participates in optimization and operation, and the MGO can schedule the charge and discharge power of the energy storage to meet the balance of power supply and demand, reducing the dependence of the microgrid on the upper grid. Compared with scenario 2, the photovoltaic user's income increases by 390 yuan, and IESO's profit is 699.8 yuan, because the discharge price set by IESO is lower than the grid price, and compared with purchasing electricity from the upper grid, the electric energy transaction between MGO and IESO reduces the electricity cost of the photovoltaic user.

[0193] In scenario 4, the combination of IESO's self-pricing and the supply-demand double-response game strategy realizes the optimal operation of the microgrid while taking into account the benefits of the energy storage system, so that the benefits of each subject reach the highest level. The energy storage system's income increases to 713.8 yuan, and compared with scenario 3, the MGO's income increases by 582 yuan, and the photovoltaic user's income increases by 80 yuan, because IESO's self-pricing strategy improves its flexibility in participating in scheduling, and the hierarchical nested game allows each independent subject to game according to its own interests, so as to maximize the benefits through two-way interaction and multi-level scheduling. From scenario 1 to scenario 4, the benefits of MGO, IESO and the photovoltaic user have all improved, indicating that through the introduction of the hierarchical nested mechanism and the optimization of the game strategy, the supply-demand bilateral collaborative optimization can be realized, and the overall economic benefits can be improved.

[0194] Figure 7 The electric energy supply and demand balance game results of the proposed strategy (scenario 4) are given, and it can be seen from Figure 7 that the independent energy storage system interacts with the MGO in most periods of a day, and the MGO also purchases electric energy and stores it in the energy storage system during the periods of 6:00-9:00 and 17:00-18:00 when the grid price is low; and releases it during the period of 10:00-12:00 when the market price is high to reduce the electricity cost of the microgrid, effectively utilizing the time-space shifting capability of the energy storage to meet the needs of the microgrid for electric energy supply and demand balance.

[0195] Compared with the one-way demand side response in scenario 2, the supply side in scenario 4 can also dynamically adjust the power supply strategy according to the load fluctuation of the demand side, thereby realizing two-way collaborative optimization. Figure 7 、 Figure 8 (a) It can be seen that, compared with scenario 2, in scenario 4, the energy storage discharges instead of the internal traditional generator set during the low price period of 1:00-5:00, and the amount of purchased power is significantly reduced during the load peak period of 10:00-14:00 and 18:00-22:00. The above results show that the global optimization strategy of supply and demand response improves the flexibility of microgrid power supply, and enhances the autonomous adjustment level and the ability to cope with power supply and demand fluctuations of the microgrid.

[0196] Comparative analysis Figure 7 and Figure 8 (b) It can be found that in the one master and multiple slave game strategy of scenario 3, the energy storage system cannot effectively balance the load peak demand, resulting in frequent use of traditional generator sets, and the system dispatching efficiency is low. In scenario 4, through the active dispatching of the independent energy storage system, the output of the traditional generator set during the load peak period is significantly reduced, and it no longer bears the main power supply load, but plays an auxiliary adjustment role during the peak period, the total output fluctuation is reduced, and the dispatching is more stable. The above results show that the strategy proposed in this paper can improve the energy interaction level of the microgrid, balance the supply and demand fluctuations, and optimize the composition structure of energy supply, verifying the superiority of the game strategy proposed in this paper in the dispatching of the supply side energy structure.

[0197] Analysis Figure 9 It can be seen that, compared with scenario 3, the total charging and discharging times of the energy storage in scenario 4 increase by 14 times, the total discharging power increases by 985.3kW, and the total charging power increases by 1091.39kW, greatly improving the utilization rate of energy storage resources. This is because in scenario 3, the energy storage system simply relies on the passive response of the decision made by the MGO, and the dispatching initiative is poor. In scenario 4, the energy storage system acts as an independent subject and participates in the game dispatching according to its own profit maximization target, which can effectively improve the flexibility and enthusiasm of the energy storage system in participating in the adjustment, fully tap the adjustment potential of microgrid power supply, and the MGO can more flexibly dispatch energy and more economically and reasonably meet the electricity demand of users in the microgrid through the double-level nested game.

[0198] The application also provides a microgrid dispatching system for independent energy storage collaborative source and load consumption, characterized by comprising a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor, when the processor executes the computer program instructions, the method steps as described above can be realized.

[0199] The application further provides a computer readable storage medium, which has computer program instructions capable of being run by a processor stored thereon, and the computer program instructions can realize the method steps as described above when the processor runs the computer program instructions.

[0200] Those skilled in the art should understand that embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code.

[0201] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that implements the functions specified in the flowcharts and / or block diagrams.

[0202] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that implements the functions specified in the flowcharts and / or block diagrams.

[0203] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that implements the functions specified in the flowcharts and / or block diagrams.

[0204] The above merely describes preferred embodiments of the present application, but is not intended to limit the present application to other forms, and any person skilled in the art can make changes or modifications to the above disclosed technical contents into equivalent embodiments with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application, and according to the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A microgrid dispatching method for independent energy storage and coordinated source-load consumption, characterized in that, include: Considering the stakes of all stakeholders, a multi-stakeholder power interaction mechanism is constructed; the specific implementation method is as follows: Step S11: As the leader, the microgrid operator formulates the initial electricity sales price and charging / discharging strategy based on the electricity supply and demand situation; Step S12: Photovoltaic users optimize their load demand response based on the electricity sales price set by the microgrid operator, formulate energy consumption strategies, and report these strategies to the microgrid operator; at the same time, independent energy storage operators formulate energy storage charging and discharging pricing strategies for providing services to the microgrid operator. Step S13: The microgrid operator optimizes and adjusts the output of its internal energy conversion equipment based on the energy consumption strategy set reported by the energy storage operator and photovoltaic user, updates the electricity sales price and charging and discharging strategy, and guides the independent energy storage operator and photovoltaic user to adaptively adjust based on their own economic benefits until the optimal strategy combination is found. A supply-demand dual-response model is established based on the flexible adjustment of source-storage-load; the specific implementation method is as follows: Step S21: Based on the energy supply structure of the microgrid, establish a supply-side response model based on energy storage usage: In the formula: This represents the amount of electricity supplied by the supply side during time period t; and These represent the wind power output and the traditional generator output during time period t, respectively. This represents the amount of electricity exchanged between the microgrid operator and the upstream power grid during time period t. When the value is greater than 0, the operator purchases electricity from the upstream power grid; otherwise, the operator sells electricity to the upstream power grid. These represent the charging and discharging amounts of energy stored during time period t, respectively. Step S22: Establish a demand-side photovoltaic user response model: In the formula: These represent the user's total electrical load, rigid load, and flexible load during time period t, respectively. The flexible load can adaptively and dynamically adjust, as expressed by the formula: In the formula: This represents the amount of electrical load adjustment by the user during time period t; This represents the flexible load after responding to user demand; λ represents the user's electricity load after adaptive adjustment during time period t; λ is the maximum allowable proportion of the electricity load to be adjusted during time period t; k is the total proportion of the electricity load that can be adjusted within a day; while improving user satisfaction with electricity consumption through flexible load adaptive adjustment, it is also necessary to ensure that the total amount of flexible load remains unchanged before and after responding to user demand. Since users are equipped with photovoltaic (PV) power generation devices, they prioritize using PV power to meet their own electricity needs. The net load of PV users after demand response in time period t is: In the formula: This indicates the output of the photovoltaic device during time period t; Step S23: Maintain a balance between power supply and demand within the microgrid during operation, expressed by the formula: Considering independent energy storage participating in electricity trading, this paper proposes a method for realizing microgrid electricity trading, constructs mathematical models for electricity trading by different entities, and determines the constraints. A hierarchical nested game model based on master-slave game theory is constructed to obtain the optimal operating strategy; the hierarchical nested game model based on master-slave game theory is specifically represented as follows: G={MGO∪ESO∪user;{δ MGO ,d ESO ,d user };{U MGO ,U ESO ,U user }} In the formula: MGO, ESO, and user represent microgrid operators, energy storage operators, and photovoltaic users, respectively; {δ MGO ,δ ESO ,δ user } represents the strategy set of each participating entity, where the strategy set δ of the microgrid operator is... MGO The electricity price and the charging and discharging capacity of energy storage; the strategy set δ of energy storage operators. ESO Charging and discharging prices set for different time periods; strategies for photovoltaic users. user This refers to the adjusted flexible load; specifically, it is expressed as: When no participant can gain a better benefit by unilaterally changing their strategy, the game reaches Stackelberg equilibrium, obtaining the optimal operating strategy, which can be expressed by the formula: In the formula This is the equilibrium solution for Stackelberg.

2. The microgrid dispatching method for independent energy storage and coordinated source-load consumption according to claim 1, characterized in that, Also includes: The effectiveness of the established hierarchical nested game model based on master-slave game was tested by setting up different scenarios.

3. The microgrid dispatching method for independent energy storage and coordinated source-load consumption according to claim 1, characterized in that, The constraints of the supply-side response model are as follows: To accommodate the volatility of new energy sources and improve the feasibility of dispatching plans, traditional generator sets must meet certain constraints during operation: In the formula: This indicates the maximum output power of a traditional generator set; These represent the minimum and maximum climbing power of a traditional generator set, respectively. To ensure the continuity of charging and discharging services provided by the energy storage system, certain constraints must be met during operation: In the formula: Indicates the energy storage capacity during time period t; η ES,ch η ES,dis These represent the energy storage charging and discharging efficiencies, respectively. These represent the minimum and maximum allowable capacity of energy storage, respectively. P is a Boolean variable representing the charging and discharging states of energy storage; ch,max P ch,min P dis,max P dis,min These represent the maximum charging power, minimum charging power, maximum discharging power, and minimum discharging power of energy storage, respectively. The bidirectional power exchange between the microgrid operator and the upper-level grid should meet upper and lower limits: Where: μ net A Boolean variable representing the microgrid operator's power purchase and sale status from the upstream grid; a value of 1 indicates power purchase from the upstream grid. gs,max and P gb,max These represent the maximum power that the microgrid is allowed to transmit to the power purchase station of the upper-level power grid and the maximum power that the microgrid is allowed to transmit to the power sales station of the upper-level power grid, respectively.

4. The microgrid dispatching method for independent energy storage and coordinated source-load consumption according to claim 1, characterized in that, The specific implementation method for constructing the mathematical model for electricity trading by different entities is as follows: Step S31: Formulate the objective function for the microgrid operator with the goal of maximizing benefits. In the formula: T represents the scheduling period; This represents the electricity sales revenue of the microgrid operator during time period t; and These represent the electricity transaction costs for microgrid operators, independent energy storage operators, and the upstream power grid, respectively. and These represent the operating costs of wind turbines and traditional generator sets, respectively. in: In the formula: This represents the electricity price during time period t; This represents the net electrical load of photovoltaic users during time period t; These represent the charging and discharging prices of energy storage during time period t, respectively. Represent the grid electricity price and the on-grid electricity price for time period t, respectively; r WT The operating cost coefficient per unit power of wind power; a e b e c e This represents the cost coefficient for traditional generator sets. Constraints: In the formula: These represent the upper and lower limits of the dynamic electricity price within the microgrid, respectively. This represents the average dynamic electricity price ceiling for time period t; Step S32: Formulate the objective function for independent energy storage operators with the goal of maximizing benefits. In the formula: These represent the revenue generated by independent energy storage operators in electricity trading with microgrid operators and photovoltaic users during time period t, respectively. This represents the operating cost of energy storage during time period t; in: Where: ε is the unit charge and discharge cost coefficient for energy storage; Constraints: Step S33: Formulate the objective function for photovoltaic users with the goal of maximizing consumer surplus: In the formula: This represents the utility function of a user's electricity consumption during time period t, measuring the user's satisfaction with electricity purchase and consumption. This represents the electricity purchase cost for users during time period t; This represents the operating cost of the photovoltaic power generation device during time period t; in: In the formula: For photovoltaic users, the electricity load after demand response in time period t; α e β e These represent the user's preference coefficients for electricity demand; r pv This is the unit power operating cost coefficient for photovoltaic power generation devices.

5. A microgrid dispatching method for independent energy storage and coordinated source-load consumption according to claim 2, characterized in that, The specific implementation method for testing the effectiveness of the established hierarchical nested game model based on master-slave game in different scenarios is as follows: Step S41: Set up different operating scenarios; Step S42: Program on the Matlab platform to solve the hierarchical nested game model using a genetic algorithm combined with the CPLEX solver; Step S43: Simulate different scenarios to obtain the benefit optimization results and strategy sets for each subject in different scenarios; Step S44: Verify the effectiveness of the proposed method in improving energy storage utilization and promoting win-win cooperation among various stakeholders in the microgrid by comparing the benefits of each entity in different scenarios and the results of power optimization scheduling.

6. A microgrid dispatching system for independent energy storage and coordinated source-load consumption, characterized in that, It includes a memory, a processor, and computer program instructions stored in the memory and executable by the processor, which, when executed by the processor, enable the implementation of the steps of the method as described in any one of claims 1-5.

7. A computer-readable storage medium having stored thereon computer program instructions executable by a processor, wherein when the processor executes the computer program instructions, it is able to implement the steps of the method as described in any one of claims 1-5.

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