Virtual power plant optimal scheduling and income distribution method and system considering newly-added adjustable load

Abstract

The invention discloses a virtual power plant optimal scheduling and income distribution method and system considering newly added adjustable loads, and the method comprehensively considers the minimum operation cost of a regional electric energy market and the minimum comprehensive operation cost of a virtual power plant, and constructs a spot market clearing bilevel planning model considering the participation of the virtual power plant. Meanwhile, on the basis of an aggregation strategy of newly-added adjustable loads, a plurality of virtual power plant alliances considering the newly-added adjustable loads are formed, the spot market clearing double-layer planning model is solved under different virtual power plant alliances, a virtual power plant scheduling strategy and a virtual power plant income condition corresponding to each virtual power plant alliance are obtained, and the virtual power plant scheduling strategy and the virtual power plant income condition of the spot market clearing double-layer planning model are obtained. And finally, virtual power plant internal benefit distribution considering member contribution is carried out. The method makes full use of the market mechanism, optimizes the energy structure, and has great significance in the aspects of energy conservation, consumption reduction and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plants, and in particular, relates to a method and system for optimizing scheduling and profit distribution of virtual power plants taking into account newly added adjustable loads. Background Art

[0002] Relying on the Internet and modern information and communication technologies, virtual power plants aggregate various resources scattered in the power grid, such as distributed power sources, energy storage, and loads, on the physical architecture of the traditional power grid, and carry out collaborative optimization, operation control, and market transactions. This can better tap the potential of distributed resources and is an important method to promote the organic interaction between sources, grids, loads, and storage and enhance system flexibility.

[0003] As a new type of market entity, virtual power plants can improve their overall revenue by participating in the electricity market and further promote the development of new energy and new power systems. Since virtual power plants include different stakeholders, the benefits generated by the optimized operation of virtual power plants need to be reasonably distributed among different entities. A good profit distribution mechanism should be able to accurately identify the contributions of different entities in the cooperation. At present, the virtual power plant optimization scheduling strategy and profit distribution of virtual power plants are mainly carried out for members within the plan. However, due to the development of energy and information power systems, more and more adjustable loads are connected to the power grid. Virtual power plants can achieve scale expansion by aggregating these adjustable loads. Therefore, it is of great engineering significance to study the scheduling strategy and profit distribution method of the original virtual power plant after aggregating the newly added adjustable loads. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention proposes a virtual power plant optimization scheduling and profit distribution method and system taking into account the newly added adjustable load.

[0005] The technical solution of the present invention is as follows:

[0006] A virtual power plant optimization scheduling and revenue distribution method taking into account newly added adjustable loads, comprising:

[0007] Construct a spot market clearing two-level planning model taking into account the participation of virtual power plants; the spot market clearing two-level planning model includes an upper model and a lower model; the upper model aims to minimize the spot market electricity purchase cost, and the constraints include power market clearing power balance constraints, thermal power unit bidding constraints, and virtual power plant power purchase and sale bidding constraints; the lower model aims to minimize the comprehensive cost of virtual power plants, and the constraints include virtual power plant internal power balance constraints, gas turbine cost constraints, energy storage constraints, wind power output constraints, gas turbine output constraints, and adjustable load response constraints;

[0008] On the basis of the original virtual power plant alliance, based on the aggregation strategy of the newly added adjustable load, several new virtual power plant alliances are formed considering the newly added adjustable load, each of the new virtual power plant alliances includes the original members of the original virtual power plant alliance and at least one new member corresponding to the newly added adjustable load;

[0009] Solve the spot market clearing two-level planning model under the original virtual power plant alliance and each newly added virtual power plant alliance respectively, and obtain the virtual power plant dispatching strategy and virtual power plant revenue corresponding to each virtual power plant alliance;

[0010] Based on the virtual power plant dispatching strategy corresponding to each virtual power plant alliance and the virtual power plant revenue situation, the internal benefit distribution of the virtual power plant is carried out taking into account the contributions of the members.

[0011] Furthermore, the objective function of the upper model is:

[0012]

[0013] in, is the winning bid price of thermal power generator i at time t; is the winning bid quantity of thermal power generator i at time t; is the winning bid price of the virtual power plant at time t; is the winning bid amount of electricity sold by the virtual power plant at time t.

[0014] Furthermore, the power balance constraint of the power market clearing in the constraint conditions of the upper model is:

[0015]

[0016] In the formula, is the winning bid quantity of thermal power generator i; is the winning bid amount of the virtual power plant at time t, is the amount of electricity purchased by the virtual power plant at time t, P L,t is the fixed load that cannot be aggregated by the virtual power plant during period t; is the load j that can be aggregated by the virtual power plant during period t, and after being aggregated by the virtual power plant, it can participate in the demand response within the virtual power plant;

[0017] The bidding constraints for the thermal power unit are:

[0018]

[0019] In the formula, is the winning bid status of thermal power unit i at time t, k is the bidding stage of the electricity seller in the market; P i max P is the maximum output of the thermal power sales entity;i min The minimum output of thermal power sales entities;

[0020] The virtual power plant power purchase and sale bidding constraints are

[0021]

[0022] In the formula, They are the power sales and power purchase bidding status of the virtual power plant at time t respectively; They are the electricity selling and purchasing quotations of the virtual power plant in stage k; They are the upper limits of electricity sales and purchases by virtual power plants; They are the lower limits of electricity sales and purchases by virtual power plants respectively.

[0023] Furthermore, the objective function of the lower model is:

[0024]

[0025] In the formula, The comprehensive energy cost of centralized energy storage aggregated by the virtual power plant; is the comprehensive energy cost of wind power aggregated by the virtual power plant; is the comprehensive energy cost of the gas turbines aggregated by the virtual power plant;

[0026] The expression of the comprehensive energy cost of the centralized energy storage aggregated by the virtual power plant is:

[0027]

[0028] In the formula, The unit energy cost of centralized energy storage for virtual power plants; They are the charging and discharging power of the centralized energy storage in the virtual power plant;

[0029] The expression of the comprehensive energy cost of wind power aggregated by the virtual power plant is:

[0030]

[0031] In the formula, is the unit energy cost of distributed wind power in the virtual power plant; P t WT is the grid-connected power of distributed wind power in the virtual power plant, The unit wind curtailment penalty cost for distributed wind power in virtual power plants;

[0032] The expression of the comprehensive energy cost of the gas turbine aggregated by the virtual power plant is:

[0033]

[0034] In the formula, a, b, and c are the fixed operating cost, startup cost, and shutdown cost of the gas turbine in the virtual power plant, respectively; are Boolean variables, indicating the working, starting and stopping status of the gas turbine, which is 1 if yes and 0 otherwise; n is the processing stage of the gas turbine; l g is the cost operation slope of the g-th section of the gas turbine; is the output of the g-th stage of the gas turbine at time t.

[0035] Furthermore, the internal power balance constraint of the virtual power plant in the constraint condition of the lower model is:

[0036]

[0037] Where P t G is the output of the gas turbine of the virtual power plant at time t; The controllable load demand after virtual power plant aggregation;

[0038] The wind power output constraints are as follows:

[0039]

[0040] The gas turbine output is as follows:

[0041]

[0042] The adjustable load response constraints are as follows:

[0043]

[0044] The energy storage constraints are as follows:

[0045]

[0046]

[0047] In the formula, is a Boolean variable, indicating the charging and discharging status of the centralized energy storage of the virtual power plant; Indicates the maximum charge and discharge power of the centralized energy storage of the virtual power plant; SOC t represents the amount of energy stored in the virtual power plant at time t; SOC min , SOC max They are respectively the maximum and minimum power allowed by the centralized energy storage of the virtual power plant; SOC0 is the initial power of the centralized energy storage of the virtual power plant, which is set to half of the maximum energy storage capacity; η is the charging and discharging efficiency of the centralized energy storage of the virtual power plant.

[0048] Furthermore, the specific steps of solving the spot market clearing two-level programming model under different virtual power plant alliances to obtain the virtual power plant dispatching strategy and virtual power plant revenue corresponding to each virtual power plant alliance include:

[0049] The spot market clearing two-level programming model is transformed into a single-level equilibrium constraint mathematical programming model through optimality conditions and linearized.

[0050] The linearized equilibrium constraint mathematical programming model is solved to obtain the virtual power plant dispatching strategy corresponding to different virtual power plant alliances, which consists of the virtual power plant's power purchase and sales volume, the power generation and consumption plans of its internal members, the market revenue of the virtual power plant, the market members' winning bids and winning bid prices.

[0051] Furthermore, the specific steps of distributing the internal benefits of the virtual power plant taking into account the contributions of the members based on the virtual power plant dispatching strategy corresponding to each virtual power plant alliance and the virtual power plant revenue include:

[0052] Based on the Shapley value method, the following formula is used to determine the profit distribution of virtual power plant members:

[0053] R VPP =-F VPP

[0054]

[0055] In the formula, R i is the revenue allocated to member i in the virtual power plant, R VPP is the income obtained by the virtual power plant from participating in the electricity market, x i is the Shapley value of member i in the virtual power plant, h is the total number of members in the virtual power plant, S is the sub-alliance under the virtual power plant, s is the number of members in the alliance S, V(S∪{i}) is the profit after removing member i from the alliance S, V(S) is the profit of the alliance S, and N is the virtual power plant alliance set.

[0056] A virtual power plant optimization scheduling and profit distribution system taking into account newly added adjustable loads, comprising: a model building module, an alliance building module, an optimization scheduling strategy solving module and a profit distribution module;

[0057] The model building module is used to build a spot market clearing two-level planning model taking into account the participation of virtual power plants; the spot market clearing two-level planning model includes an upper model and a lower model; the upper model aims to minimize the spot market electricity purchase cost, and the constraints include power market clearing power balance constraints, thermal power unit bid winning constraints, and virtual power plant power purchase and sale winning constraints; the lower model aims to minimize the comprehensive cost of the virtual power plant, and the constraints include virtual power plant internal power balance constraints, gas turbine cost constraints, energy storage constraints, wind power output constraints, gas turbine output constraints, and adjustable load response constraints;

[0058] The alliance building module is used to form several new virtual power plant alliances considering the newly added adjustable loads based on the aggregation strategy of the newly added adjustable loads on the basis of the original virtual power plant alliance, each of the new virtual power plant alliances includes the original members of the original virtual power plant alliance and at least one new member corresponding to the newly added adjustable load;

[0059] The optimization scheduling strategy solving module is used to solve the spot market clearing two-level programming model under the original virtual power plant alliance and each newly added virtual power plant alliance, and obtain the virtual power plant scheduling strategy and virtual power plant revenue corresponding to each virtual power plant alliance;

[0060] The profit distribution module is used to distribute the internal benefits of the virtual power plant taking into account the contributions of the members based on the virtual power plant scheduling strategy corresponding to each virtual power plant alliance and the virtual power plant profit situation.

[0061] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0062] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present invention proposes a virtual power plant optimization scheduling and revenue distribution method and system taking into account the newly added adjustable load. The method comprehensively considers the minimum operating cost of the regional electric energy market and the minimum comprehensive operating cost of the virtual power plant, constructs a spot market clearing two-layer planning model taking into account the participation of the virtual power plant, and at the same time, based on the aggregation strategy of the newly added adjustable load, forms several virtual power plant alliances taking into account the newly added adjustable load. By solving the spot market clearing two-layer planning model under different virtual power plant alliances, the virtual power plant scheduling strategy and virtual power plant revenue corresponding to each virtual power plant alliance are obtained, and finally the internal benefit distribution of the virtual power plant taking into account the contribution of members is performed. The method makes full use of the market mechanism, optimizes the energy structure, and is of great significance in terms of energy saving and consumption reduction.

[0065] In the method of the present invention, based on the aggregation strategy of the newly added adjustable load, several virtual power plant alliances taking into account the newly added adjustable load are formed, the potential of decentralized adjustable resources in the power system is explored, the development of the virtual power plant industry is promoted, and the construction of a new power system is promoted. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flow chart of a two-layer optimization method for a virtual power plant to participate in bidding in an electric energy market in an embodiment;

[0067] Figure 2 A schematic diagram of a market framework for the interaction between the electric energy market and the virtual power plant in an embodiment;

[0068] Figure 3 A schematic diagram of the power demand of load entities that cannot be aggregated by a virtual power plant in the electric energy market in an embodiment;

[0069] Figure 4 A schematic diagram of the power demand of the newly added adjustable load that can be aggregated by the virtual power plant in the embodiment;

[0070] Figure 5 Schematic diagram of the maximum output of wind turbines in a virtual power plant in the embodiment;

[0071] Figure 6 Schematic diagram of time-of-use price of load electricity in the electric energy market in the embodiment;

[0072] Figure 7 Schematic diagram of electricity market clearing price under different virtual power plant alliances in the embodiment;

[0073] Figure 8 Schematic diagram of the winning bid for electricity sold by virtual power plants under different virtual power plant alliances in the embodiments. DETAILED DESCRIPTION

[0074] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0075] Embodiment 1:

[0076] The present invention provides a virtual power plant optimization scheduling and revenue distribution method taking into account the newly added adjustable load, such as Figure 1 As shown, including:

[0077] S1. Construct a two-level planning model for spot market clearing that takes into account the participation of virtual power plants. The two-level planning model for spot market clearing includes an upper model and a lower model. The upper model aims to minimize the cost of electricity purchased in the spot market, and the constraints include power market clearing power balance constraints, thermal power unit bidding constraints, and virtual power plant power purchase and sales bidding constraints. The lower model aims to minimize the comprehensive cost of virtual power plants, and the constraints include virtual power plant internal power balance constraints, gas turbine cost constraints, energy storage constraints, wind power output constraints, gas turbine output constraints, and adjustable load response constraints.

[0078] S2. On the basis of the original virtual power plant alliance, based on the aggregation strategy of the newly added adjustable load, several new virtual power plant alliances are formed that take into account the newly added adjustable load, each of which includes the original members of the original virtual power plant alliance and at least one new member corresponding to the newly added adjustable load;

[0079] S3. Solve the spot market clearing two-level planning model under the original virtual power plant alliance and each newly added virtual power plant alliance, and obtain the virtual power plant dispatching strategy and virtual power plant revenue corresponding to each virtual power plant alliance;

[0080] S4. Based on the virtual power plant dispatching strategy corresponding to each virtual power plant alliance and the virtual power plant revenue, the internal interest distribution of the virtual power plant is carried out taking into account the contribution of members.

[0081] Embodiment 2:

[0082] This embodiment is further designed on the basis of the first embodiment in that the objective function of the upper layer model in this embodiment is:

[0083]

[0084] in, is the winning bid price of thermal power generator i at time t; is the winning bid quantity of thermal power generator i at time t; is the winning bid price of the virtual power plant at time t; is the winning bid amount of electricity sold by the virtual power plant at time t.

[0085] Embodiment three:

[0086] This embodiment is further designed on the basis of the second embodiment in that the power balance constraint of the power market clearing in the constraint condition of the upper model in this embodiment is

[0087]

[0088] In the formula, is the winning bid quantity of thermal power generator i; is the amount of electricity sold by the virtual power plant at time t, is the amount of electricity purchased by the virtual power plant at time t, P L,t is the fixed load that cannot be aggregated by the virtual power plant during period t; is the load j that can be aggregated by the virtual power plant during period t, and after being aggregated by the virtual power plant, it can participate in the demand response within the virtual power plant;

[0089] The constraints for winning the bid for thermal power units are

[0090]

[0091] In the formula, is the winning bid status of thermal power unit i at time t, k is the bidding stage of the electricity seller in the market; P i max P is the maximum output of the thermal power sales entity; i min It is the minimum output of the thermal power sales entity.

[0092] The constraints for winning bids for power purchase and sale by virtual power plants are

[0093]

[0094] In the formula, They are the power sales and power purchase bidding status of the virtual power plant at time t respectively; They are the electricity selling and purchasing quotations of the virtual power plant in stage k; They are the upper limits of electricity sales and purchases by virtual power plants; They are the lower limits of electricity sales and purchases by virtual power plants respectively.

[0095] Embodiment 4:

[0096] This embodiment is further designed on the basis of the first embodiment in that the objective function of the lower layer model in this embodiment is:

[0097]

[0098] In the formula, The comprehensive energy cost of centralized energy storage aggregated by the virtual power plant; is the comprehensive energy cost of wind power aggregated by the virtual power plant; is the comprehensive energy cost of the gas turbines aggregated by the virtual power plant;

[0099] The expression of the comprehensive energy cost of centralized energy storage aggregated by the virtual power plant is:

[0100]

[0101] In the formula, The unit energy cost of centralized energy storage for virtual power plants; They are the charging and discharging power of the centralized energy storage in the virtual power plant;

[0102] The expression of the comprehensive energy cost of wind power aggregated by the virtual power plant is:

[0103]

[0104] In the formula, is the unit energy cost of distributed wind power in the virtual power plant; P t WT is the grid-connected power of distributed wind power in the virtual power plant, The unit wind curtailment penalty cost for distributed wind power in virtual power plants;

[0105] The expression of the comprehensive energy cost of the gas turbines aggregated by the virtual power plant is:

[0106]

[0107] In the formula, a, b, and c are the fixed operating cost, startup cost, and shutdown cost of the gas turbine in the virtual power plant, respectively; are Boolean variables, indicating the working, starting and stopping status of the gas turbine, which is 1 if yes and 0 otherwise; n is the processing stage of the gas turbine; l g is the cost operation slope of the g-th section of the gas turbine; is the output of the g-th stage of the gas turbine at time t.

[0108] Embodiment five:

[0109] This embodiment is further designed on the basis of the fourth embodiment in that the internal power balance constraint of the virtual power plant in the constraint condition of the lower model in this embodiment is

[0110]

[0111] Where P t G is the output of the gas turbine of the virtual power plant at time t; The controllable load demand after virtual power plant aggregation;

[0112] Wind power output constraints are as follows:

[0113]

[0114] The gas turbine output is as follows:

[0115]

[0116] The adjustable load response constraints are as follows:

[0117]

[0118] The energy storage constraints are as follows:

[0119]

[0120] In the formula, is a Boolean variable, indicating the charging and discharging status of the centralized energy storage of the virtual power plant; Indicates the maximum charge and discharge power of the centralized energy storage of the virtual power plant; SOC t represents the amount of energy stored in the virtual power plant at time t; SOC min , SOC max They are respectively the maximum and minimum power allowed by the centralized energy storage of the virtual power plant; SOC0 is the initial power of the centralized energy storage of the virtual power plant, which is set to half of the maximum energy storage capacity; η is the charging and discharging efficiency of the centralized energy storage of the virtual power plant.

[0121] Embodiment six:

[0122] This embodiment is further designed on the basis of the first embodiment in that, in this example, on the basis of the original virtual power plant alliance, based on the aggregation strategy of the newly added adjustable load, several new virtual power plant alliances considering the newly added adjustable load are formed, and each new virtual power plant alliance includes the original members of the original virtual power plant alliance and at least one new member corresponding to the newly added adjustable load;

[0123] The following example illustrates the formation of a virtual power plant alliance considering newly added adjustable loads. In this example, the original member of the original virtual power plant alliance is denoted as V. There are 4 loads, 3 adjustable loads, denoted as M0, M1, and M2, and 1 rigid load (i.e., non-adjustable), denoted as N0. Among the 4 loads, there is 1 non-aggregatable load, denoted as N0, and the rest are newly added adjustable loads to be aggregated. The partial virtual power plant alliance formed can be expressed as {V}, {V, M0}, {V, M1}, {V, M2}, {V, M0, M1}, {V, M0, M2}, {V, M1, M2}, {V, M0, M1, M2}, etc.

[0124] The two-level planning model for spot market clearing is solved under different virtual power plant alliances. The specific steps for obtaining the virtual power plant dispatching strategy and virtual power plant revenue corresponding to each virtual power plant alliance include:

[0125] The spot market clearing two-level programming model is transformed into a single-level mathematical programming with equilibrium constraints (MPEC) model through the optimality (Karush-Kahn-Tucker, KKT) condition and linearized.

[0126] The linearized equilibrium constraint mathematical programming model is solved to obtain the virtual power plant dispatching strategy corresponding to different virtual power plant alliances, which consists of the virtual power plant's power purchase and sales volume, the power generation and consumption plans of its internal members, the market revenue of the virtual power plant, the market members' winning bids and winning bid prices.

[0127] Specifically, through the optimality (Karush-Kahn-Tucker, KKT) condition, the lower-level problem is transformed into optimality conditions and complementary relaxation conditions; for the nonlinear terms generated by the relaxation complementary conditions derived from the KKT conditions, the nonlinear terms in the equilibrium constraint mathematical programming model are linearized through linearization methods such as the big M method, so that the equilibrium constraint mathematical programming model can be simplified and convenient for the solver to solve.

[0128] The GUROBI solver is called on the MATLAB platform to solve the two-level programming model and obtain the dispatch strategies for different virtual power plant alliances and corresponding market environments, including: the amount of electricity purchased and sold by the virtual power plant, the power generation and consumption plans of the internal members of the virtual power plant, the market revenue of the virtual power plant, and the winning bids and prices of market members.

[0129] Embodiment seven:

[0130] This embodiment is further designed on the basis of the first embodiment in that, in this embodiment, based on the virtual power plant scheduling strategy corresponding to each virtual power plant alliance and the virtual power plant revenue situation, the specific steps of distributing the internal benefits of the virtual power plant taking into account the member contributions include:

[0131] Based on the Shapley value method, the following formula is used to determine the profit distribution of virtual power plant members:

[0132] R VPP =-F VPP

[0133]

[0134] In the formula, R iis the revenue allocated to member i in the virtual power plant, R VPP is the income obtained by the virtual power plant from participating in the electricity market, x i is the Shapley value of member i in the virtual power plant, h is the total number of members in the virtual power plant, S is the sub-alliance under the virtual power plant, s is the number of members in the alliance S, V(S∪{i}) is the profit after removing member i from the alliance S, V(S) is the profit of the alliance S, and N is the virtual power plant alliance set.

[0135] Embodiment eight:

[0136] A virtual power plant optimization scheduling and profit distribution system taking into account newly added adjustable loads, comprising: a model building module, an alliance building module, an optimization scheduling strategy solving module and a profit distribution module;

[0137] Model building module, used to build a spot market clearing two-level planning model taking into account the participation of virtual power plants; the spot market clearing two-level planning model includes an upper model and a lower model; the upper model aims to minimize the spot market electricity purchase cost, and the constraints include power market clearing power balance constraints, thermal power unit bidding constraints, and virtual power plant power purchase and sales bidding constraints; the lower model aims to minimize the comprehensive cost of virtual power plants, and the constraints include virtual power plant internal power balance constraints, gas turbine cost constraints, energy storage constraints, wind power output constraints, gas turbine output constraints, and adjustable load response constraints;

[0138] An alliance building module is used to form several new virtual power plant alliances taking into account the newly added adjustable loads based on the aggregation strategy of the newly added adjustable loads on the basis of the original virtual power plant alliance, each of which includes the original members of the original virtual power plant alliance and at least one new member corresponding to the newly added adjustable load;

[0139] The optimization scheduling strategy solving module is used to solve the spot market clearing two-level planning model under the original virtual power plant alliance and each newly added virtual power plant alliance, and obtain the virtual power plant scheduling strategy and virtual power plant revenue corresponding to each virtual power plant alliance;

[0140] The profit distribution module is used to distribute the internal benefits of the virtual power plant taking into account the contributions of members based on the virtual power plant scheduling strategy corresponding to each virtual power plant alliance and the virtual power plant profit situation.

[0141] Embodiment nine:

[0142] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0143] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0144] Application examples:

[0145] This example simulates the implementation of the virtual power plant optimization scheduling and profit distribution method taking into account the newly added adjustable load of the present invention. In this example, it is simulated that three thermal power units and one virtual power plant participate in the four-stage quotation in the spot market (hereinafter referred to as the "electricity energy market"). There is a large industrial user in the electricity energy market that cannot be aggregated by the virtual power plant and three small loads that can be aggregated by the virtual power plant. Before being aggregated by the virtual power plant, the load cannot participate in demand response alone. After being aggregated by the virtual power plant, the load can respond to the demand of the virtual power plant and become an adjustable load. The market framework for the interaction between the electricity energy market and the virtual power plant is as follows: Figure 2 For specific load requirements, see Figure 3 , Figure 4 Based on the aggregation strategy of virtual power plants for different adjustable loads, different virtual power plant alliances are obtained, and a test case of a two-level planning model is constructed. The parameters and quotations of thermal power units are shown in Table 1, and the parameters of virtual power plant members are shown in Tables 2 and Figure 5 As shown in the figure, the time-of-use price of load electricity in the electric energy market is as follows: Figure 6 shown.

[0146] Table 1 Thermal power unit parameters

[0147]

[0148] Table 2 Virtual power plant member parameters

[0149]

[0150] This embodiment is based on the MATLAB platform under the win11 operating system, R9CPU, 4.50GHz processor environment to carry out simulation and optimization analysis under different virtual power plant combination alliances, as follows:

[0151] VPP represents the original virtual power plant alliance, 1, 2, and 3 represent the new adjustable load entities P_load1, P_load2, and P_load3 that can be aggregated by the virtual power plant. The new virtual power plant alliance is composed of these four entities, so there are a total of 15 non-empty sub-alliances. The benefits of each alliance in the electric energy market are shown in Table 3. The clearing electricity price of the electric energy market and the winning bid of the virtual power plant electricity sales under different virtual power plant alliances are shown in Table 3. Figure 7 and Figure 8 shown.

[0152] Table 3 Profit distribution under different alliance conditions

[0153] alliance Revenue (Yuan) alliance Revenue (Yuan) alliance Revenue (Yuan) {VPP} 2039561.397 {VPP,2} 1137235.826 {VPP,1,2} -114824.694 {1} -1430725.595 {VPP,3} 948930.123 {VPP,1,3} -343097.867 {2} -839619.594 {1,2} -2270345.189 {VPP,2,3} 317481.641 {3} -1049538.655 {1,3} -2480264.250 {1,2,3} -3319883.844 {VPP,1} 493527.357 {2,3} -1889158.249 {VPP,1,2,3} -1132760.818

[0154] The profit results of each entity before and after the virtual power plant aggregates adjustable loads are shown in Table 4. After the virtual power plant aggregates adjustable load resources, the profit increased by 50,351.702 yuan, and after being aggregated by the virtual power plant, the electricity load cost of the adjustable load was reduced by 11,958.595, 43,883.594, and 41,367.655 yuan respectively. The Shapley value method is used to distribute the profits of the virtual power plant alliance, so that all members of the virtual power plant after aggregating the adjustable load have obtained profit growth, which is enough to maintain the cooperative relationship between the various entities in the virtual power plant and is conducive to the stable existence of the virtual power plant alliance.

[0155] Table 4 Comparison of benefits before and after virtual power plant aggregation adjustable load

[0156] item Virtual Power Plant P_load1 P_load2 P_load3 Before aggregation 2039561.397 -1430725.595 -839619.594 -1049538.655 After aggregation (Shapley value) 2089913.099 -1418767 -795736 -1008171 Revenue growth 50,351.702 11,958.595 43,883.594 41,367.655

[0157] According to the theoretically derived formula and simulation results, the following conclusions can be drawn:

[0158] The method of the present invention takes into account the process of virtual power plants aggregating new adjustable loads and exploring their flexibility, constructs a two-layer optimization model of the electricity spot market and virtual power plants, and realizes the profit distribution of the original virtual power plant alliance and the newly aggregated adjustable loads through the Shapley value method. The distribution results show that both virtual power plants and adjustable loads have achieved a certain profit increase, consolidating the stability of the new virtual power plant alliance.

[0159] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A virtual power plant optimization scheduling and revenue distribution method taking into account newly added adjustable loads, characterized in that: include: Construct a two-level planning model for spot market clearing with virtual power plant participation; The spot market clearing two-level planning model includes an upper model and a lower model; the upper model aims to minimize the spot market electricity purchase cost, and the constraints include power market clearing power balance constraints, thermal power unit bidding constraints, and virtual power plant power purchase and sale bidding constraints; the lower model aims to minimize the comprehensive cost of the virtual power plant, and the constraints include virtual power plant internal power balance constraints, gas turbine cost constraints, energy storage constraints, wind power output constraints, gas turbine output constraints, and adjustable load response constraints. On the basis of the original virtual power plant alliance, based on the aggregation strategy of the newly added adjustable load, several new virtual power plant alliances are formed considering the newly added adjustable load, each of the new virtual power plant alliances includes the original members of the original virtual power plant alliance and at least one new member corresponding to the newly added adjustable load; Solve the spot market clearing two-level planning model under the original virtual power plant alliance and each newly added virtual power plant alliance respectively, and obtain the virtual power plant dispatching strategy and virtual power plant revenue corresponding to each virtual power plant alliance; Based on the virtual power plant dispatching strategy corresponding to each virtual power plant alliance and the virtual power plant revenue situation, the internal benefit distribution of the virtual power plant is carried out taking into account the contributions of the members.

2. The virtual power plant optimization scheduling and profit distribution method taking into account the newly added adjustable load according to claim 1 is characterized in that: The objective function of the upper model is: in, is the winning bid price of thermal power generator i at time t; is the winning bid quantity of thermal power generator i at time t; is the winning bid price of the virtual power plant at time t; is the winning bid amount of electricity sold by the virtual power plant at time t.

3. The virtual power plant optimization scheduling and profit distribution method taking into account the newly added adjustable load according to claim 2 is characterized in that: The power balance constraint of the power market clearing in the constraint conditions of the upper model is: In the formula, is the winning bid quantity of thermal power generator i; is the winning bid amount of the virtual power plant for electricity sales at time t, is the amount of electricity purchased by the virtual power plant at time t, P L,t is the fixed load that cannot be aggregated by the virtual power plant during period t; is the load j that can be aggregated by the virtual power plant during period t, and after being aggregated by the virtual power plant, it can participate in the demand response within the virtual power plant; The bidding constraints for the thermal power unit are: In the formula, is the winning bid status of thermal power unit i at time t, and k is the bidding stage of the electricity seller in the market; It is the maximum output of the thermal power sales entity; The minimum output of thermal power sales entities; The virtual power plant power purchase and sale bidding constraints are In the formula, They are the power sales and power purchase bidding status of the virtual power plant at time t respectively; They are the electricity selling and purchasing quotations of the virtual power plant in stage k; They are the upper limits of electricity sales and purchases by virtual power plants; They are the lower limits of electricity sales and purchases by virtual power plants respectively.

4. The virtual power plant optimization scheduling and profit distribution method taking into account the newly added adjustable load according to claim 1 is characterized in that: The objective function of the lower model is: In the formula, The comprehensive energy cost of centralized energy storage aggregated by the virtual power plant; is the comprehensive energy cost of wind power aggregated by the virtual power plant; is the comprehensive energy cost of the gas turbines aggregated by the virtual power plant; The expression of the comprehensive energy cost of the centralized energy storage aggregated by the virtual power plant is: In the formula, The unit energy cost of centralized energy storage for virtual power plants; They are the charging and discharging power of the centralized energy storage in the virtual power plant; The expression of the comprehensive energy cost of wind power aggregated by the virtual power plant is: In the formula, The unit energy cost of distributed wind power in virtual power plants; is the grid-connected power of distributed wind power in the virtual power plant, The unit wind curtailment penalty cost for distributed wind power in virtual power plants; The expression of the comprehensive energy cost of the gas turbine aggregated by the virtual power plant is: In the formula, a, b, and c are the fixed operating cost, startup cost, and shutdown cost of the gas turbine in the virtual power plant, respectively; are Boolean variables, indicating the working, starting and stopping status of the gas turbine, which is 1 if yes and 0 otherwise; n is the processing stage of the gas turbine; l g is the cost operation slope of the g-th section of the gas turbine; is the output of the g-th stage of the gas turbine at time t.

5. The virtual power plant optimization scheduling and revenue distribution method taking into account newly added adjustable loads according to claim 4 is characterized in that: The internal power balance constraint of the virtual power plant in the constraint conditions of the lower model is: In the formula, is the output of the gas turbine of the virtual power plant at time t; The controllable load demand after virtual power plant aggregation; The wind power output constraints are as follows: The gas turbine output is as follows: The adjustable load response constraints are as follows: The energy storage constraints are as follows: In the formula, is a Boolean variable, indicating the charging and discharging status of the centralized energy storage of the virtual power plant; Indicates the maximum charge and discharge power of the centralized energy storage of the virtual power plant; SOC t represents the amount of energy stored in the virtual power plant at time t; SOC min , SOC max They are respectively the maximum and minimum power allowed by the centralized energy storage of the virtual power plant; SOC0 is the initial power of the centralized energy storage of the virtual power plant, which is set to half of the maximum energy storage capacity; η is the charging and discharging efficiency of the centralized energy storage of the virtual power plant.

6. The virtual power plant optimization scheduling and profit distribution method taking into account the newly added adjustable load according to claim 1 is characterized in that: The specific steps of solving the spot market clearing two-level programming model under different virtual power plant alliances to obtain the virtual power plant dispatching strategy and virtual power plant revenue corresponding to each virtual power plant alliance include: The spot market clearing two-level programming model is transformed into a single-level equilibrium constraint mathematical programming model through optimality conditions and linearized. The linearized equilibrium constraint mathematical programming model is solved to obtain the virtual power plant dispatching strategy corresponding to different virtual power plant alliances, which consists of the virtual power plant's power purchase and sales volume, the power generation and consumption plans of its internal members, the market revenue of the virtual power plant, the market members' winning bids and winning bid prices.

7. The virtual power plant optimization scheduling and profit distribution method taking into account the newly added adjustable load according to claim 1 is characterized in that: The specific steps of distributing the internal benefits of the virtual power plant taking into account the contributions of the members based on the virtual power plant dispatching strategy corresponding to each virtual power plant alliance and the virtual power plant revenue include: Based on the Shapley value method, the following formula is used to determine the profit distribution of virtual power plant members: R VPP =-F VPP In the formula, R i is the revenue allocated to member i in the virtual power plant, R VPP is the income obtained by the virtual power plant from participating in the electricity market, x i is the Shapley value of member i in the virtual power plant, h is the total number of members in the virtual power plant, S is the sub-alliance under the virtual power plant, s is the number of members in the alliance S, V(S∪{i}) is the profit after removing member i from the alliance S, V(S) is the profit of the alliance S, and N is the virtual power plant alliance set.

8. A virtual power plant optimization scheduling and revenue distribution system taking into account newly added adjustable loads, characterized in that: include: Model building module, alliance building module, optimization scheduling strategy solving module and profit distribution module; The model building module is used to build a spot market clearing two-level planning model taking into account the participation of virtual power plants; the spot market clearing two-level planning model includes an upper model and a lower model; the upper model aims to minimize the spot market electricity purchase cost, and the constraints include power market clearing power balance constraints, thermal power unit bid winning constraints, and virtual power plant power purchase and sale winning constraints; the lower model aims to minimize the comprehensive cost of the virtual power plant, and the constraints include virtual power plant internal power balance constraints, gas turbine cost constraints, energy storage constraints, wind power output constraints, gas turbine output constraints, and adjustable load response constraints; The alliance building module is used to form several new virtual power plant alliances considering the newly added adjustable loads based on the aggregation strategy of the newly added adjustable loads on the basis of the original virtual power plant alliance, each of the new virtual power plant alliances includes the original members of the original virtual power plant alliance and at least one new member corresponding to the newly added adjustable load; The optimization scheduling strategy solving module is used to solve the spot market clearing two-level programming model under the original virtual power plant alliance and each newly added virtual power plant alliance, and obtain the virtual power plant scheduling strategy and virtual power plant revenue corresponding to each virtual power plant alliance; The profit distribution module is used to distribute the internal benefits of the virtual power plant taking into account the contributions of the members based on the virtual power plant scheduling strategy corresponding to each virtual power plant alliance and the virtual power plant profit situation.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

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 as claimed in any one of claims 1 to 7 are implemented.

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