Cogeneration virtual power plant optimization scheduling method and device based on cooperative game

Abstract

The invention discloses a cogeneration virtual power plant optimization scheduling method and device based on a cooperative game, and relates to the field of energy optimization scheduling. The method comprises the following steps: acquiring resource data; constructing a combined heat and power generation virtual power plant cooperative game model according to the resource data; the combined heat and power generation virtual power plant cooperative game model comprises an objective function and constraint conditions; the objective function is constructed based on the day-ahead combined heat and power dispatching of the combined heat and power generation virtual power plant to maximize the day-ahead economic benefit; solving the objective function according to the constraint condition by adopting a differential evolution algorithm to obtain a solving result; and carrying out allocation processing according to a solving result by adopting an improved Shapley value method to obtain an allocation result. According to the method and the device, the resources of the distributed energy sources are configured, so that the reduction of renewable energy sources is reduced, and the economical efficiency and the stability of overall scheduling are improved.

Description

Technical Field

[0001] The present application relates to the field of energy optimization and scheduling, and in particular to a method and device for optimizing and scheduling a cogeneration virtual power plant based on cooperative game. Background Art

[0002] In the context of global energy transformation and sustainable development, the proportion of renewable energy in the supply side continues to increase, and building a new power system with new energy as the main body has become the development trend of the current power industry. However, the randomness and volatility of new energy such as wind power and photovoltaics have brought new challenges to the safe and stable operation of the power system. As a bridge connecting the power grid management center and distributed energy, the virtual power plant realizes the coordinated scheduling of the internal units of the system and ensures the stability and security of the power grid by organically combining distributed generators, distributed energy storage equipment, and controllable loads. However, in the actual scheduling scenario, the virtual power plant, as a profitable entity that aggregates multiple members, can obtain more benefits than members participating in the market individually after participating in the power market competition as a whole, but the internal distributed resource members are independent entities, so it is also necessary to distribute benefits to each member.

[0003] At present, the research on cogeneration virtual power plants mainly focuses on the comprehensive management of electric and thermal energy. According to the electric and thermal energy balance within the virtual power plant, a specific operation plan is formulated to achieve the optimal scheduling of the virtual power plant. However, the combined heat and power scheduling of the above-mentioned studies only stays at the level of satisfying energy balance, and does not consider the transmission characteristics and coupling characteristics of the electric and thermal coupling network. In addition, how to optimize resource allocation while satisfying all decentralized stakeholders (multiple investment entities in energy production, transmission, and consumption) has not been fully studied. Since the resources within the virtual power plant belong to different entities, the different interests of each entity must be considered when making a scheduling strategy. The traditional power system operation mode and profit distribution are not conducive to the consumption of renewable energy and the enthusiasm of various stakeholders. It is urgent to establish a set of practical operation strategies to determine the optimal decision of each entity and balance the interests of each entity. Summary of the invention

[0004] The purpose of this application is to provide a method and device for optimizing the scheduling of a cogeneration virtual power plant based on cooperative game, which can realize the allocation of distributed energy resources to reduce the reduction of renewable energy and improve the economy and stability of the overall scheduling.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for optimizing the scheduling of a combined heat and power virtual power plant based on cooperative game, comprising:

[0007] Obtain resource data; the resource data is aggregated data of distributed energy; the resource data includes: output data of renewable energy generators, storage data of energy storage systems, and controllable load data;

[0008] A cooperative game model of a cogeneration virtual power plant is constructed according to the resource data; the cooperative game model of the cogeneration virtual power plant includes: an objective function and constraints; the objective function is constructed based on the day-ahead heat and power joint dispatch of the cogeneration virtual power plant to maximize the day-ahead economic benefits;

[0009] Using a differential evolution algorithm, the objective function is solved according to the constraint conditions to obtain a solution result;

[0010] The improved Shapley value method is used to perform allocation processing according to the solution result to obtain an allocation result; the allocation result is used to configure the resources of distributed energy to reduce the reduction of renewable energy and improve the economy and stability of the overall scheduling.

[0011] Optionally, constructing a cogeneration virtual power plant cooperative game model based on the resource data specifically includes:

[0012] Constructing a cogeneration virtual power plant model; the cogeneration virtual power plant model is a physical model constructed based on the resource data; the cogeneration virtual power plant model includes: cogeneration units, wind and solar power generation units, energy storage units, electric heat pumps, and resource models corresponding to load demand responses; the wind and solar power generation units include: wind power and photovoltaics;

[0013] Based on the game strategy, the cooperative game model of the cogeneration virtual power plant is determined according to the cogeneration virtual power plant model; the game strategy is that when the sum of the lower limits of power generation of wind power, photovoltaic and cogeneration units is less than the load demand response, the cogeneration unit increases the power output by adjusting the heat-to-electricity ratio within the feasible operating area; when the sum of the lower limits of power generation of wind power, photovoltaic and cogeneration units exceeds the load demand response, wind power and photovoltaic must reduce part of the electricity according to their respective output ratios.

[0014] Optionally, the objective function is:

[0015]

[0016] Among them, V1 is the decision variable; I is the day-ahead economic benefit; I E I is the income from electricity sales; H is the revenue from heat sales; C is the total cost of each unit in the decision variables; ψ P is the penalty item when the power grid is unbalanced; T is the total number of time periods; The peak and valley time-of-use electricity price for conventional load; is the electricity bid amount submitted by the virtual power plant to the grid at time t; Dynamic electricity prices corresponding to demand response; is the power demand of the virtual power plant at time t after considering real-time pricing; Δt is the time interval; The unit price for hot sale; is the output corresponding to the heat storage equipment; is the thermal output of the cogeneration unit at time t; The heat generated by the electric heat pump after consuming electricity; is the cost of the combined heat and power unit; is the cost of the electricity storage unit; is the cost of the thermal storage unit; is the cost of the electric heat pump; The cost corresponding to demand response; is the price of electricity purchased from the power grid at time t; r penalty is the additional penalty unit price; is a binary variable indicating that the virtual power plant needs to purchase electricity from the grid; is the amount of electricity purchased from the power grid at time t; is the electricity price sold to the grid at time t; is a binary variable for the sale of electricity from the virtual power plant to the grid; is the amount of electricity sold to the grid at time t.

[0017] Optionally, the constraint conditions include: power grid balance constraint and heat network balance constraint.

[0018] Optionally, the expression of the power grid balance constraint is:

[0019] (P t PV +P t WPP +P t CHP +P t EESS -P t HP -P t DR +P t grid )=P t load ;

[0020]

[0021] P t sum =P t PV +P t WPP +P tCHP +P t EESS -P t HP -P t DR ;

[0022] P t bid =P t grid +P t sum ;

[0023] in, is the photovoltaic power generation power; is the wind power generation power; is the electrical output of the cogeneration unit at time t; The output of the power storage unit; is the output of the electric heat pump; The power demand after considering real-time pricing for the virtual power plant at time t; is the transaction volume between the virtual power plant and the grid; is the electrical load; is a binary variable indicating that the virtual power plant needs to purchase electricity from the grid; is the amount of electricity purchased from the power grid at time t; is a binary variable for the sale of electricity from the virtual power plant to the grid; is the amount of electricity sold to the grid at time t; is the sum of the power output of all power generation units in the virtual power plant; is the electricity bid amount submitted by the virtual power plant to the grid at time t.

[0024] Optionally, the expression of the heat network balance constraint is:

[0025]

[0026] in, is the heat flow; For pipeline The mass flow rate of water at the starting point; For pipeline The mass flow rate of the terminal water; is the unit conversion factor; is the specific heat of water; To pass through the pipeline Water mass flow rate; For pipeline The temperature of the water at the start; For pipeline The temperature of the end water.

[0027] Optionally, a differential evolution algorithm is used to solve the objective function according to the constraint condition to obtain a solution, which specifically includes:

[0028] Using a differential evolution algorithm to perform mutation, crossover and selection processing on the objective function to obtain a processed objective function;

[0029] The processed objective function is solved according to the constraint conditions to obtain a solution result.

[0030] Optionally, a differential evolution algorithm is used to perform mutation, crossover and selection processing on the objective function to obtain a processed objective function, specifically including:

[0031] Determine initial parameters; the initial parameters include: population size, variation factor, crossover probability and set number of iterations; the population is composed of multiple individuals; the individuals are output allocations corresponding to each resource model in the cogeneration virtual power plant cooperative game model;

[0032] Determine the test vector corresponding to each individual at the current iteration number according to the parameter combination; the parameter combination includes: a variation factor and a crossover probability; wherein the test vector is obtained by performing crossover based on the crossover probability after the individual determines the variation vector according to the variation factor;

[0033] Determine the objective function value at the current number of iterations according to the test vector;

[0034] Determine whether an iteration stop condition is met; the iteration stop condition is that the current iteration number reaches a set iteration number or the objective function value at the current iteration number is within a set objective function value interval;

[0035] If the iteration stop condition is met, the individual at the current iteration number is taken as the optimal individual;

[0036] If the iteration stop condition is not met, the individual at the current iteration is updated according to the objective function value at the current iteration, and the updated individual is used as the individual at the next iteration, and "determine the test vector corresponding to each individual at the current iteration according to the parameter combination" is returned;

[0037] Based on the objective function, a processed objective function is determined according to the optimal individual and the corresponding objective function value.

[0038] Optionally, an improved Shapley value method is used to perform allocation processing according to the solution result to obtain an allocation result, which specifically includes:

[0039] The total profit is determined according to the solution result; the calculation formula of the total profit is:

[0040] Π(S)=R sell (S)-C total (S)-P penalty (S);

[0041] The improved Shapley value method is used to determine the Shapley value based on the total profit; the calculation formula of the Shapley value is:

[0042] φ i '=ω i ·φ i ;

[0043]

[0044] According to the Shapley value, the formula P is used i =φ i '·Π(N) determines the allocation result;

[0045] Among them, Π(S) is the total profit; S is the solution result; R sell (S) is the revenue from electricity and heat sales; C total (S) is the operating cost of each resource in the solution; P penalty (S) is the penalty fee; φ i ′ is the Shapley value; ω i is the weight; φ i is the weighted average; N is the set of resource entities in the CHP virtual power plant; n is the number of resource entities; i is the sequence number of the resource entity; ! is the factorial; P i is the allocation result; Π(N) is the total profit of the set of resource entities in the cogeneration virtual power plant.

[0046] In a second aspect, the present application provides a cogeneration virtual power plant optimization scheduling device based on cooperative game, comprising:

[0047] A data acquisition module is used to acquire resource data; the resource data is the aggregated data of distributed energy; the resource data includes: output data of renewable energy generator sets, storage data of energy storage systems, and controllable load data;

[0048] A model building module is used to build a cooperative game model of a cogeneration virtual power plant according to the resource data; the cooperative game model of the cogeneration virtual power plant includes: an objective function and constraints; the objective function is based on the day-ahead heat and power joint dispatch of the cogeneration virtual power plant to maximize the day-ahead economic benefits; the constraints include: power grid balance constraints and heat network balance constraints;

[0049] A solution module, used for solving the objective function according to the constraint conditions by using a differential evolution algorithm to obtain a solution result;

[0050] The allocation module is used to adopt the improved Shapley value method to perform allocation processing according to the solution result to obtain the allocation result; the allocation result is used to configure the resources of distributed energy to reduce the reduction of renewable energy and improve the economy and stability of the overall scheduling.

[0051] According to the specific embodiments provided in this application, this application has the following technical effects:

[0052] The present application provides a method and device for optimizing the scheduling of a cogeneration virtual power plant based on cooperative game. Taking into account the uncertainty of renewable energy power generation, a cooperative game model of a cogeneration virtual power plant is established to achieve the optimal scheduling of the cogeneration virtual power plant. An improved Shapley value method is used to optimize resource allocation, reduce the reduction of renewable energy, improve the fairness of profit distribution, and thus improve the economy and stability of the overall scheduling while satisfying the interests of all stakeholders (i.e., each resource in the cogeneration virtual power plant) and the power and heat balance of the entire system on the electric-thermal coupling network. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A flowchart of the optimal dispatching method for combined heat and power virtual power plant based on cooperative game;

[0055] Figure 2 A schematic diagram of the operation steps of the optimal dispatching method of combined heat and power virtual power plant based on cooperative game in practical application;

[0056] Figure 3 This is a schematic diagram of the structure of the combined heat and power virtual power plant model;

[0057] Figure 4 This is a flowchart for solving the problem using the differential evolution algorithm. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] This application aims at the problem of optimizing the scheduling of virtual power plants considering multiple subjects, deeply explores the interest relationship between the energy supply side and the energy demand side, and proposes an optimized scheduling method for cogeneration virtual power plants based on cooperative game. By analyzing the interests of various subjects, a benefit model of each subject is established to improve the operating efficiency of the units, reduce the reduction of renewable energy, and fairly distribute the profits of various stakeholders in the virtual power plant.

[0060] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0061] In an exemplary embodiment, Figure 1 As shown, a method for optimizing the scheduling of a combined heat and power virtual power plant based on cooperative game is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to a server as an example for explanation, and includes the following steps.

[0062] like Figure 1 As shown in the figure, the optimal dispatching method of CHP virtual power plant based on cooperative game includes:

[0063] Step 100: Obtain resource data. Resource data is the aggregated data of distributed energy; resource data includes: output data of renewable energy generators, storage data of energy storage systems, and controllable load data.

[0064] Step 200: Construct a cooperative game model of a cogeneration virtual power plant according to resource data. The cooperative game model of a cogeneration virtual power plant includes: an objective function and constraints; the objective function is constructed based on the day-ahead heat and power joint dispatch of the cogeneration virtual power plant to maximize the day-ahead economic benefits.

[0065] Step 300: using a differential evolution algorithm to solve the objective function according to the constraint conditions to obtain a solution result.

[0066] Step 400: Using the improved Shapley value method, perform allocation processing according to the solution result to obtain the allocation result. The allocation result is used to configure the resources of distributed energy to reduce the reduction of renewable energy and improve the economy and stability of the overall scheduling.

[0067] In one embodiment, a cooperative game model of a cogeneration virtual power plant is constructed based on resource data, specifically including:

[0068] Construct a cogeneration virtual power plant model; the cogeneration virtual power plant model is a physical model constructed based on resource data; the cogeneration virtual power plant model includes: cogeneration units, wind and solar power generation units, energy storage units, electric heat pumps and resource models corresponding to load demand response; the wind and solar power generation units include: wind power and photovoltaics.

[0069] Based on the game strategy, the cooperative game model of the cogeneration virtual power plant is determined according to the cogeneration virtual power plant model; the game strategy is that when the sum of the lower limits of power generation of wind power, photovoltaic and cogeneration units is less than the load demand response, the cogeneration unit increases the power output by adjusting the heat-to-electricity ratio within the feasible operating area; when the sum of the lower limits of power generation of wind power, photovoltaic and cogeneration units exceeds the load demand response, wind power and photovoltaic must reduce part of the electricity according to their respective output ratios.

[0070] The objective function is:

[0071]

[0072] Among them, V1 is the decision variable; I is the day-ahead economic benefit; I E I is the income from electricity sales; H is the revenue from heat sales; C is the total cost of each unit in the decision variables; ψ P is the penalty item when the power grid is unbalanced; T is the total number of time periods; The peak and valley time-of-use electricity price for conventional load; is the electricity bid amount submitted by the virtual power plant to the grid at time t; Dynamic electricity prices corresponding to demand response; is the power demand of the virtual power plant at time t after considering real-time pricing; Δt is the time interval; The unit price for hot sale; is the output corresponding to the heat storage equipment; is the thermal output of the cogeneration unit at time t; The heat generated by the electric heat pump after consuming electricity; is the cost of the combined heat and power unit; is the cost of the electricity storage unit; is the cost of the thermal storage unit; is the cost of the electric heat pump; The cost corresponding to demand response; is the price of electricity purchased from the power grid at time t; r penalty is the additional penalty unit price; is a binary variable indicating that the virtual power plant needs to purchase electricity from the grid; is the amount of electricity purchased from the power grid at time t; is the electricity price sold to the grid at time t; is a binary variable for the sale of electricity from the virtual power plant to the grid; is the amount of electricity sold to the grid at time t.

[0073] The constraints include: grid balance constraints and heat network balance constraints.

[0074] The expression of the power grid balance constraint is:

[0075] (P t PV +P t WPP +P t CHP +P t EESS -P t HP -P t DR +P t grid )=P t load .

[0076]

[0077] P t sum =P t PV +P t WPP +P t CHP +P t EESS -P t HP -P t DR .

[0078] P t bid =P t grid +P t sum .

[0079] in, is the photovoltaic power generation power; is the wind power generation power; is the electrical output of the cogeneration unit at time t; The output of the power storage unit; is the output of the electric heat pump; The power demand after considering real-time pricing for the virtual power plant at time t; is the transaction volume between the virtual power plant and the grid; is the electrical load; is a binary variable indicating that the virtual power plant needs to purchase electricity from the grid; is the amount of electricity purchased from the power grid at time t; is a binary variable for the sale of electricity from the virtual power plant to the grid; is the amount of electricity sold to the grid at time t; is the sum of the power output of all power generation units in the virtual power plant; is the electricity bid amount submitted by the virtual power plant to the grid at time t.

[0080] The expression of heat network balance constraint is:

[0081]

[0082] in, is the heat flow; For pipeline The mass flow rate of water at the starting point; For pipeline The mass flow rate of the terminal water; is the unit conversion factor; is the specific heat of water; To pass through the pipeline Water mass flow rate; For pipeline The temperature of the water at the start; For pipeline The temperature of the end water.

[0083] In one embodiment, a differential evolution algorithm is used to solve the objective function according to the constraint conditions to obtain a solution, which specifically includes:

[0084] A differential evolution algorithm is used to perform mutation, crossover and selection processing on the objective function to obtain a processed objective function; the processed objective function is solved according to constraint conditions to obtain a solution result.

[0085] Among them, the differential evolution algorithm is used to perform mutation, crossover and selection on the objective function to obtain the processed objective function, which specifically includes:

[0086] Determine the initial parameters; the initial parameters include: population size, variation factor, crossover probability and set number of iterations; the population is composed of multiple individuals; the individuals are the output allocations corresponding to each resource model in the cogeneration virtual power plant cooperative game model.

[0087] According to the parameter combination, the test vector corresponding to each individual under the current number of iterations is determined; the parameter combination includes: mutation factor and crossover probability; wherein the test vector is obtained by crossover based on the crossover probability after the individual determines the mutation vector based on the mutation factor.

[0088] Determine the objective function value at the current iteration number based on the test vector.

[0089] Determine whether the iteration stop condition is met; the iteration stop condition is that the current iteration number reaches the set iteration number or the objective function value at the current iteration number is within the set objective function value range.

[0090] If the iteration stop condition is met, the individual at the current iteration number is taken as the optimal individual.

[0091] If the iteration stop condition is not met, the individual at the current iteration number is updated according to the objective function value at the current iteration number, and the updated individual is used as the individual at the next iteration number, and "determine the test vector corresponding to each individual at the current iteration number according to the parameter combination" is returned.

[0092] Based on the objective function, the processed objective function is determined according to the optimal individual and the corresponding objective function value.

[0093] As an optional implementation, an improved Shapley value method is used to perform allocation processing according to the solution result to obtain an allocation result, which specifically includes:

[0094] Determine the total profit based on the solution; the calculation formula for the total profit is:

[0095] Π(S)=R sell (S)-C total (S)-P penalty (S).

[0096] The improved Shapley value method is used to determine the Shapley value based on total profit; the calculation formula of the Shapley value is:

[0097] φ i '=ω i ·φ i .

[0098]

[0099] According to the Shapley value, the formula P i =φ i '·Π(N) determines the allocation result.

[0100] Among them, Π(S) is the total profit; S is the solution result; R sell (S) is the revenue from electricity and heat sales; C total (S) is the operating cost of each resource in the solution; P penalty (S) is the penalty fee; φ i ′ is the Shapley value; ω i is the weight; φ i is the weighted average; N is the set of resource entities in the CHP virtual power plant; n is the number of resource entities; i is the sequence number of the resource entity; ! is the factorial; Pi is the allocation result; Π(N) is the total profit of the set of resource entities in the cogeneration virtual power plant.

[0101] In practical applications, such as Figure 2 As shown, the method for optimizing the scheduling of a cogeneration virtual power plant based on cooperative game provided in this application may also include the following steps.

[0102] First, a cooperative game model of a cogeneration virtual power plant is constructed. The goal is to maximize the overall profit of the system, and the game parameters are initialized by combining the forecast data such as wind speed, solar irradiance, heat load and electric load, as well as the operating constraints of each unit. Secondly, the solution is obtained through the differential evolution algorithm. The optimal solution is selected and the population is updated according to the value of the objective function; it is iterated until the convergence condition is met or the Nash equilibrium solution is found. Finally, based on the game equilibrium results, the improved Shapley value method is used to fairly distribute the profits of each participant. This achieves a balanced distribution of interests among the stakeholders in the virtual power plant and improves the economy and stability of the overall system scheduling.

[0103] Step 1: Construct a cooperative game model of a cogeneration virtual power plant.

[0104] 1) Construct the basic model of cogeneration virtual power plant (cogeneration virtual power plant model).

[0105] Virtual power plants integrate geographically dispersed renewable energy generators, energy storage systems, and controllable loads through sophisticated control methods and energy management to achieve effective aggregation and management of distributed energy. In order to further tap the potential of distributed power sources and improve the flexibility of virtual power plants, cogeneration technology relies on the virtual power plant platform to form a cogeneration virtual power plant system with the power system as the core, achieving coordinated regulation of heat and electricity and improving the cascade utilization of energy. This application mainly studies the stochastic optimization scheduling problem of cogeneration virtual power plants. The model aggregates resources such as wind power, photovoltaics, energy storage units, cogeneration units, electric heat pumps, and load demand response, such as Figure 3 shown.

[0106] (1) Combined heat and power unit.

[0107] This application adopts an extraction condensing cogeneration unit, whose thermoelectric characteristics can be described by the allowable operating range of the electric and thermal output:

[0108]

[0109] Among them, P0 is the initial electric power of the cogeneration unit, that is, the power output of the unit at the beginning of the optimization model. It is a fixed reference value used to describe the initial state of the unit; H0 is the initial thermal power of the cogeneration unit, that is, the thermal power output of the unit at the beginning of the optimization model. Like P0, it is usually a reference value set based on initial operating conditions or system design requirements. t CHP and are the electric and thermal outputs of the cogeneration unit at time t respectively; P CHP as well as are the minimum and maximum electrical outputs allowed for the cogeneration unit, respectively; H CHP as well as are the minimum and maximum thermal outputs allowed by the cogeneration unit respectively; k0, k1, k2 are the absolute values ​​of the slopes of the boundaries of the allowed operating area of ​​the cogeneration unit’s thermal output; ε t It is the heat-to-electricity ratio of the cogeneration unit. The heat-to-electricity ratio of the extraction-condensing cogeneration unit is variable within the allowable operating range.

[0110] The ramping constraints and costs of the combined heat and power unit's electric and thermal output are:

[0111]

[0112] in, and They are the maximum changes per unit time when the electric load of the cogeneration unit is increased or decreased; and are the maximum changes per unit time when the heat load of the cogeneration unit is increased or decreased; c0, c1, c2, c3, c4 and c5 are all constants used to describe the cost of the cogeneration unit.

[0113] (2) Wind and solar power generation units.

[0114] The wind and solar power output constraints and their costs can be expressed as:

[0115]

[0116] Among them, P t WPP , P t PV are wind power generation and photovoltaic power generation respectively; P WPP and They are the upper and lower limits of wind power output respectively; P PV and are the upper and lower limits of photovoltaic power generation output respectively; κWPP , κ PV represent the cost coefficients of wind power and photovoltaic power respectively; are the costs of wind power generation and photovoltaic power generation respectively.

[0117] (3) Energy storage unit.

[0118] Energy storage units can be divided into two categories: heat storage and electricity storage. The heat storage and release, charging and discharging processes of these two types of energy storage devices are similar and meet the following constraints:

[0119]

[0120] Among them, taking energy storage equipment as an example, SoC is the state of charge; SoC i,t represents the charge state of the i-th energy storage unit at time t; and are the charge and discharge amounts of the i-th power storage device at time t respectively; and are the efficiency of charge and discharge respectively; The maximum energy value for charging and discharging the storage device. and It is a unitless quantity with a value of 0 or 1, which is used to indicate the state of charging and discharging, respectively. 1 indicates that charging and discharging are in progress, and 0 indicates that there is no charging or discharging state at present, and charging and discharging cannot be performed at the same time. SoC i and The lower and upper limits of charge and discharge; SoC i,0 represents the initial state of charge of the i-th energy storage unit at the initial time (t=0); SoC I is the state of charge at the initial moment.

[0121] The corresponding costs of the electricity storage unit and the heat storage unit are:

[0122]

[0123] in, and denote the costs of the electric storage unit and the thermal storage unit, respectively; represents the charge capacity of the storage device at the i-th node at time t, represents the discharge amount of the storage device at the i-th node at time t; represents the heat storage capacity of the heat storage device of the i-th node at time t, represents the heat released by the heat storage device of the i-th node at time t; VOM ch , VOM dis are the cost coefficients for variable operation and maintenance of energy storage and energy release of energy storage equipment respectively; A cost factor representing the variable operation and maintenance of the storage and discharge of electricity; A cost factor representing the variable operation and maintenance of the thermal storage device for storing and releasing heat.

[0124] (4) Electric heat pump.

[0125] An electric heat pump is a device that can convert electrical energy into thermal energy. In the model, it can absorb waste heat and quickly convert electrical energy into thermal energy to respond to the heat demand from users. Its electric heat output characteristics and costs are as follows:

[0126]

[0127] in, P is the heat generated by the electric heat pump after consuming electricity; t HP The power delivered to the electric heat pump by the system; η HP is the heating energy efficiency ratio of the electric heat pump; κ HP is the cost coefficient of the electric heat pump; The cost of an electric heat pump.

[0128] (5) Load demand response.

[0129] Load demand response refers to the demand that can be transferred to other time periods under the influence of real-time prices, etc. Its characteristics and costs are:

[0130]

[0131] Among them, P t DR0 and P t DR are the initial demand of the virtual power plant and the power demand after considering real-time pricing; and are the increased and decreased load demands after considering real-time pricing; F max and F min are the maximum and minimum values ​​of the load power factor respectively; and b t As a bilateral variable, the values ​​of 0 or 1 are used to represent the states of increasing and decreasing demand; is the proportion of demand that can be transferred to other time periods, is the maximum value of this ratio, The non-transferable part of the demand; is the cost coefficient corresponding to demand response; is the cost corresponding to demand response.

[0132] 2) Construct a cooperative game model of cogeneration virtual power plant.

[0133] (1) Objective function.

[0134] Since wind and photovoltaic power generation do not incur fuel costs, renewable energy generation is given priority when scheduling cogeneration virtual power plants. However, the intermittent nature of renewable energy generation poses a huge challenge to the stable and safe operation of the power system, so energy storage units are deployed to provide backup services. As the main heating unit, the cogeneration unit adopts an "electricity based on heat" operation mode. In order to reduce wind and solar power abandonment, the system is equipped with a heat storage device to achieve "thermal and electric decoupling" of the cogeneration unit. In addition, demand response is used to improve the flexibility of the system. The cogeneration virtual power plant is managed by the control center and conducts electricity transactions with the main power grid as a whole, settling at the price settled by the main power grid the day before. This application is aimed at the day-ahead heat and power combined scheduling problem of cogeneration virtual power plants, with the goal of maximizing the day-ahead economic benefits. Its objective function can be expressed as:

[0135]

[0136] in, are decision variables, representing the electric and thermal output of each unit in the day-ahead pre-dispatch. I is the day-ahead economic benefit, representing the net profit of the cogeneration virtual power plant in the day-ahead pre-dispatch stage, which is mainly composed of four parts: electricity sales income I E 、Hot sale income I H , the total cost of each unit, that is, the total cost of each unit in the decision variable C, the penalty term ψ when the power grid is unbalanced P Calculate the revenue from electricity sales I E When t ER represents the peak and valley time-of-use electricity price of conventional load, r t ED It represents the dynamic electricity price corresponding to the demand response; the heat sales revenue I H Use the selling price t H It is expressed by multiplying the actual heating supply; the total cost C of each unit in the decision variable includes the fuel cost, start-up and shutdown cost, and operation and maintenance cost of each unit, which is related to the actual output of the unit.

[0137] When there is an imbalance between supply and demand, it is necessary to purchase electricity from the grid or sell excess electricity to the grid. penalty It indicates the additional penalty unit price; is the price of electricity purchased from the power grid at time t; is the electricity price sold to the grid at time t; is a binary variable. When the value is 1, it means that the virtual power plant needs to purchase electricity from the grid (i.e., insufficient power); It is a binary variable indicating that the virtual power plant needs to purchase electricity from the grid. is a binary variable. When the value is 1, it means that the virtual power plant sells electricity to the grid (i.e., excess power). A binary variable representing the electricity sold by the virtual power plant to the grid. and It is impossible for both to be 1 at the same time, thus ensuring the uniqueness of power grid transaction behavior. is the amount of electricity purchased from the power grid at time t; is the amount of electricity sold to the grid at time t; Δt is the time interval in hours, which is a parameter used to discretize time and indicates the length of the optimization model in each time period; T is the total number of time periods, indicating the total number of time periods involved in the optimization problem. T and Δt together define the overall scheduling time range of the optimization model; is the power bid submitted by the virtual power plant to the grid at time t. In the optimization model, the virtual power plant adjusts and other unit outputs to ensure that load demands are met while maximizing revenue.

[0138] (2)Constraints.

[0139] Grid balance constraints:

[0140] (P t PV +P t WPP +P t CHP +P t EESS -P t HP -P t DR +P t grid )=P t load .

[0141]

[0142] P t sum =P t PV +P t WPP +P t CHP +P t EESS -P t HP -P t DR .

[0143] P t bid =P tgrid +P t sum .

[0144] in, is the electrical load; is the sum of the power output of all power generation units in the virtual power plant; It is the transaction volume between the virtual power plant and the power grid. When its value is positive, it indicates that the actual output of the virtual power plant is less than the electricity load, and it is necessary to purchase electricity from the power grid. When its value is negative, it indicates that the actual output of the virtual power plant is greater than the electricity load, and it is necessary to sell more electricity to the power grid.

[0145] Heat network balance constraints:

[0146] This application focuses only on the primary heating network, so the quality regulation method is used. Heat flow in the pipeline It is expressed as:

[0147]

[0148] in, is the heat flow; For pipeline The mass flow rate of water at the starting point; For pipeline The mass flow rate of the terminal water; is the unit conversion factor; is the specific heat of water; To pass through the pipeline Water mass flow rate; For pipeline The temperature of the water at the start; For pipeline The temperature of the water at the end. Considering the time delay τ and the pipe The heat transfer loss is:

[0149]

[0150] in, is the loss coefficient, It's a pipeline Characteristic factors determined by its length and cross section. To maintain thermal balance, we have:

[0151]

[0152] in, is the heat load and W is the total number of pipes.

[0153] 3) Game strategy.

[0154] In order to meet the thermal load requirements of users, cogeneration units adopt the "heat-to-electricity" operation mode. Normally, cogeneration units operate at the maximum heat-to-electricity ratio, which is the most efficient and also leaves more room for renewable energy generation. In other words, the power output of cogeneration units is usually at its lower limit. However, when the sum of the lower limits of wind power, photovoltaics, and cogeneration units is less than the load demand, the cogeneration unit needs to increase the power output by adjusting the heat-to-electricity ratio within its feasible operating area. On the contrary, when the sum of the lower limits of wind power, photovoltaics, and cogeneration units exceeds the load demand, wind power and photovoltaics need to reduce part of the power according to their respective output ratios.

[0155] Step 2: Solve the model through differential evolution algorithm.

[0156] This application uses the differential evolution algorithm to solve the optimization scheduling model of the combined heat and power virtual power plant. The differential evolution algorithm has simple parameters, fewer controlled parameters, and is relatively easy to operate. It has good generalization ability, good robustness and stability. The specific calculation process is as follows Figure 4 shown.

[0157] 1) Population initialization and algorithm parameter setting.

[0158] The objective function is to maximize the day-ahead economic benefits of the CHP virtual power plant cooperative game model, and the constraints have been introduced in step 1).

[0159] Then set the differential evolution algorithm parameters: population size NP, mutation factor F, crossover probability CR, maximum number of iterations G max .

[0160] Population initialization: Randomly generate NP initial solutions in the feasible solution space to form an initial population X0 = {x 1,0 ,x 2,0 ,···,x NP,0 Each solution vector represents a virtual power plant scheduling scheme, including the output allocation of each subsystem (heat and electricity).

[0161] 2) Mutation operation.

[0162] For each individual x in the population i,G (i-th individual, current generation is G), select three different individuals x r1,G 、x r2,G 、x r3,G , where the randomly selected individual numbers r1, r2, and r3 are required to be different from each other and cannot be the same as the target vector number i, so NP≥4 must be satisfied.

[0163] Generate mutation vector:

[0164] v i,G+1=x r1,G +F·(x r2,G -x r3,G ).

[0165] Among them, F is the variation factor, which controls the amplification of the difference vector.

[0166] 3) Crossover operation.

[0167] The mutation vector v i,G+1 With the current individual x i,G Perform crossover according to the crossover probability CR to generate the test vector u i,G+1 .

[0168]

[0169] Among them, j rand are randomly chosen indices, ensuring that at least one variable is from the mutation vector.

[0170] u i,G+1 It is composed of multiple u j,i,G+1 The vector composed of j,i,G+1 is u i,G+1 The crossover operation is performed on the current individual x. i,G and mutation vector v i,G+1 The components j are compared and updated one by one, and finally the test vector u is obtained i,G+1 .

[0171] 4) Select an operation.

[0172] For each individual, compare the test vector u according to the objective function value i,G+1 and the current individual x i,G Pros and cons:

[0173]

[0174] Where f(·) is the objective function.

[0175] 5) Iterative updates.

[0176] Repeat 2), 3), and 4) until the maximum number of iterations G is reached. max (i.e. setting the number of iterations) or satisfying the convergence conditions.

[0177] 6)Result output.

[0178] Output the optimal solution x * and the corresponding objective function value f(x * ).

[0179] Step 3: Allocate profits using the modified Shapley value method.

[0180] 1) Calculate the profit of each alliance.

[0181] Assume that there are n independent resource entities in the virtual power plant, denoted as set N = {1, 2, ···, n}. Any subset is the solution result Indicates an alliance.

[0182] The total profit Π(S) of each alliance is calculated as:

[0183] Π(S)=R sell (S)-C total (S)-P penalty (S).

[0184] Among them, R sell (S) is the revenue from electricity and heat sales; C total (S) is the operating cost of each resource in the alliance, including fuel, start-stop, operation and maintenance, etc.; P penalty (S) are penalty fees resulting from supply and demand imbalance or default.

[0185] Traverse all possible subsets Calculate the corresponding profits and form the alliance profit matrix for subsequent Shapley value calculation.

[0186] (2) Use the improved Shapley value method to calculate the profit distribution formula for each stakeholder.

[0187] 1) Traditional Shapley value formula:

[0188] For each resource subject i, the traditional Shapley value is:

[0189]

[0190] φ i represents the weighted average, that is, the weighted average of the marginal contribution of resource subject i; Π(S∪{i})-Π(S) is the contribution of resource subject i to the alliance.

[0191] 2) Improve the method.

[0192] According to the importance, response characteristics and reliability of different resources in the virtual power plant, the weight ω is defined i The modified Shapley value is:

[0193] φ i '=ω i ·φ i .

[0194] Among them, the weight ω i Can be adjusted dynamically based on the following factors:

[0195] a) Stability of resource operation (such as volatility of wind power and photovoltaic power).

[0196] b) The contribution of resources in global optimization (such as the role of energy storage in balance).

[0197] c) Response speed and scheduling flexibility (such as the ability to quickly adjust demand response).

[0198] According to the actual operation data, the weight ω is updated in real time i , ensuring that the allocation mechanism can adapt to changes in resource characteristics.

[0199] 3) Profit distribution.

[0200] Using the improved Shapley value φ i ', distribute the final profits of each entity, that is, the distribution result P i :

[0201] P i =φ i '·Π(N).

[0202] Among them, Π(N) is the total profit of the entire alliance, that is, the total profit of the collection of resource entities in the cogeneration virtual power plant.

[0203] Finally, check whether the allocation satisfies the following principles:

[0204] a) Efficiency: The sum of all entities’ distributions equals the total profit.

[0205] b) Symmetry: Subjects who contribute the same amount receive the same share.

[0206] c) Zero Contributors: Entities that do not contribute are assigned zero.

[0207] The random optimization scheduling method of the combined heat and power virtual power plant based on cooperative game proposed in this application achieves the following technical effects by comprehensively considering the characteristics and operation constraints of each distributed resource in the virtual power plant:

[0208] (1) By optimizing resource allocation, the phenomenon of wind and solar power abandonment caused by the intermittent nature of renewable energy (such as wind power and photovoltaics) is reduced, thereby improving the resource utilization efficiency and the grid's new energy absorption capacity.

[0209] (2) A cooperative game model was constructed with the goal of maximizing the overall profit of the virtual power plant, and the scheduling scheme was optimized by solving it through the differential evolution algorithm.

[0210] (3) The improved Shapley value method introduces dynamic weights such as the response characteristics, operating stability and global contribution of each resource entity on the basis of the traditional one, ensuring that the allocation results are more reasonable and fair and enhancing the willingness of each entity to cooperate.

[0211] Compared with traditional scheduling methods, this application not only solves the problem of electric-thermal coupling in the cogeneration system, but also takes into account the balance of interests among multiple stakeholders. While improving operational economy, flexibility and stability, it provides important support for the high proportion of new energy access.

[0212] Based on the same inventive concept, the embodiment of the present application also provides a cogeneration virtual power plant optimization scheduling device based on cooperative game for implementing the above-mentioned cogeneration virtual power plant optimization scheduling method based on cooperative game. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the cogeneration virtual power plant optimization scheduling device based on cooperative game provided below can refer to the limitations of the cogeneration virtual power plant optimization scheduling method based on cooperative game above, and will not be repeated here.

[0213] In an exemplary embodiment, a cogeneration virtual power plant optimization scheduling device based on cooperative game is provided, comprising:

[0214] The data acquisition module is used to acquire resource data; the resource data is the aggregated data of distributed energy; the resource data includes: output data of renewable energy generator sets, storage data of energy storage systems and controllable load data.

[0215] The model building module is used to build a cooperative game model of a cogeneration virtual power plant based on resource data; the cooperative game model of a cogeneration virtual power plant includes: objective function and constraints; the objective function is based on the day-ahead heat and power joint dispatch of the cogeneration virtual power plant to maximize the day-ahead economic benefits; the constraints include: power grid balance constraints and heat network balance constraints.

[0216] The solution module is used to adopt the differential evolution algorithm to solve the objective function according to the constraint conditions to obtain the solution result.

[0217] The allocation module is used to adopt the improved Shapley value method to perform allocation processing according to the solution results to obtain the allocation results; the allocation results are used to configure the resources of distributed energy to reduce the reduction of renewable energy and improve the economy and stability of the overall scheduling.

[0218] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0219] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing the scheduling of a combined heat and power virtual power plant based on cooperative game theory, characterized in that: The cooperative game-based cogeneration virtual power plant optimization scheduling method includes: Obtain resource data; the resource data is aggregated data of distributed energy; the resource data includes: output data of renewable energy generators, storage data of energy storage systems, and controllable load data; A cooperative game model of a cogeneration virtual power plant is constructed according to the resource data; the cooperative game model of the cogeneration virtual power plant includes: an objective function and constraints; the objective function is constructed based on the day-ahead heat and power joint dispatch of the cogeneration virtual power plant to maximize the day-ahead economic benefits; Using a differential evolution algorithm, the objective function is solved according to the constraint conditions to obtain a solution result; The improved Shapley value method is used to perform allocation processing according to the solution result to obtain an allocation result; the allocation result is used to configure the resources of distributed energy to reduce the reduction of renewable energy and improve the economy and stability of the overall scheduling.

2. The method for optimizing the scheduling of a combined heat and power virtual power plant based on cooperative game according to claim 1, characterized in that: A cooperative game model of a cogeneration virtual power plant is constructed based on the resource data, specifically including: Constructing a cogeneration virtual power plant model; the cogeneration virtual power plant model is a physical model constructed based on the resource data; the cogeneration virtual power plant model includes: cogeneration units, wind and solar power generation units, energy storage units, electric heat pumps, and resource models corresponding to load demand responses; the wind and solar power generation units include: wind power and photovoltaics; Based on the game strategy, the cooperative game model of the cogeneration virtual power plant is determined according to the cogeneration virtual power plant model; the game strategy is that when the sum of the lower limits of power generation of wind power, photovoltaic and cogeneration units is less than the load demand response, the cogeneration unit increases the power output by adjusting the heat-to-electricity ratio within the feasible operating area; when the sum of the lower limits of power generation of wind power, photovoltaic and cogeneration units exceeds the load demand response, wind power and photovoltaic must reduce part of the electricity according to their respective output ratios.

3. The method for optimizing the scheduling of a combined heat and power virtual power plant based on cooperative game according to claim 1, characterized in that: The objective function is: Among them, V1 is the decision variable; I is the day-ahead economic benefit; I E I is the income from electricity sales; H is the revenue from heat sales; C is the total cost of each unit in the decision variables; ψ P is the penalty item when the power grid is unbalanced; T is the total number of time periods; The peak and valley time-of-use electricity price for conventional load; is the electricity bid amount submitted by the virtual power plant to the grid at time t; Dynamic electricity prices corresponding to demand response; is the power demand of the virtual power plant at time t after considering real-time pricing; Δt is the time interval; The unit price for hot sale; is the output corresponding to the heat storage equipment; is the thermal output of the cogeneration unit at time t; The heat generated by the electric heat pump after consuming electricity; is the cost of the combined heat and power unit; is the cost of the electricity storage unit; is the cost of the thermal storage unit; is the cost of the electric heat pump; The cost corresponding to demand response; is the price of electricity purchased from the power grid at time t; r penalty is the additional penalty unit price; is a binary variable indicating that the virtual power plant needs to purchase electricity from the grid; is the amount of electricity purchased from the power grid at time t; is the electricity price sold to the grid at time t; is a binary variable for the sale of electricity from the virtual power plant to the grid; is the amount of electricity sold to the grid at time t.

4. The method for optimizing the scheduling of a combined heat and power virtual power plant based on cooperative game according to claim 1, characterized in that: The constraints include: power grid balance constraints and heat network balance constraints.

5. The method for optimizing the scheduling of a combined heat and power virtual power plant based on cooperative game according to claim 4, characterized in that: The expression of the power grid balance constraint is: in, is the photovoltaic power generation power; is the wind power generation power; is the electrical output of the cogeneration unit at time t; The output of the power storage unit; is the output of the electric heat pump; The power demand after considering real-time pricing for the virtual power plant at time t; is the transaction volume between the virtual power plant and the grid; is the electrical load; is a binary variable indicating that the virtual power plant needs to purchase electricity from the grid; is the amount of electricity purchased from the power grid at time t; is a binary variable for the sale of electricity from the virtual power plant to the grid; is the amount of electricity sold to the grid at time t; is the sum of the power output of all power generation units in the virtual power plant; is the electricity bid amount submitted by the virtual power plant to the grid at time t.

6. The method for optimizing the scheduling of a combined heat and power virtual power plant based on cooperative game according to claim 4, characterized in that: The expression of the heat network balance constraint is: in, is the heat flow; For pipeline The mass flow rate of water at the starting point; For pipeline The mass flow rate of the terminal water; is the unit conversion factor; is the specific heat of water; To pass through the pipeline Water mass flow rate; For pipeline The temperature of the water at the start; For pipeline The temperature of the end water.

7. The method for optimizing the scheduling of a combined heat and power virtual power plant based on cooperative game according to claim 1, characterized in that: The differential evolution algorithm is used to solve the objective function according to the constraint conditions to obtain a solution, which specifically includes: Using a differential evolution algorithm to perform mutation, crossover and selection processing on the objective function to obtain a processed objective function; The processed objective function is solved according to the constraint conditions to obtain a solution result.

8. The method for optimizing the scheduling of a combined heat and power virtual power plant based on cooperative game according to claim 7, characterized in that: The differential evolution algorithm is used to perform mutation, crossover and selection processing on the objective function to obtain the processed objective function, which specifically includes: Determine initial parameters; the initial parameters include: population size, variation factor, crossover probability and set number of iterations; the population is composed of multiple individuals; the individuals are output allocations corresponding to each resource model in the cogeneration virtual power plant cooperative game model; Determine the test vector corresponding to each individual at the current iteration number according to the parameter combination; the parameter combination includes: a variation factor and a crossover probability; wherein the test vector is obtained by performing crossover based on the crossover probability after the individual determines the variation vector according to the variation factor; Determine the objective function value at the current number of iterations according to the test vector; Determine whether an iteration stop condition is met; the iteration stop condition is that the current iteration number reaches a set iteration number or the objective function value at the current iteration number is within a set objective function value interval; If the iteration stop condition is met, the individual at the current iteration number is taken as the optimal individual; If the iteration stop condition is not met, the individual at the current iteration number is updated according to the objective function value at the current iteration number, and the updated individual is used as the individual at the next iteration number, and "determine the test vector corresponding to each individual at the current iteration number according to the parameter combination" is returned; Based on the objective function, a processed objective function is determined according to the optimal individual and the corresponding objective function value.

9. The method for optimizing the scheduling of a combined heat and power virtual power plant based on cooperative game according to claim 1, characterized in that: The improved Shapley value method is used to perform allocation processing according to the solution result to obtain the allocation result, which specifically includes: The total profit is determined according to the solution result; the calculation formula of the total profit is: Π(S)=R sell (S)-C total (S)-P penalty (S); The improved Shapley value method is used to determine the Shapley value based on the total profit; the calculation formula of the Shapley value is: According to the Shapley value, the formula P is used i =φ i '·Π(N) determines the allocation result; Among them, Π(S) is the total profit; S is the solution result; R sell (S) is the revenue from electricity and heat sales; C total (S) is the operating cost of each resource in the solution; P penalty (S) is the penalty fee; φ i ′ is the Shapley value; ω i is the weight; φ i is the weighted average; N is the set of resource entities in the CHP virtual power plant; n is the number of resource entities; i is the sequence number of the resource entity; ! is the factorial; P i is the allocation result; Π(N) is the total profit of the set of resource entities in the cogeneration virtual power plant.

10. A cogeneration virtual power plant optimization scheduling device based on cooperative game, characterized in that: The cogeneration virtual power plant optimization scheduling device based on cooperative game includes: A data acquisition module is used to acquire resource data; the resource data is the aggregated data of distributed energy; the resource data includes: output data of renewable energy generator sets, storage data of energy storage systems, and controllable load data; A model building module is used to build a cooperative game model of a cogeneration virtual power plant according to the resource data; the cooperative game model of the cogeneration virtual power plant includes: an objective function and constraints; the objective function is based on the day-ahead heat and power joint dispatch of the cogeneration virtual power plant to maximize the day-ahead economic benefits; the constraints include: power grid balance constraints and heat network balance constraints; A solution module, used for solving the objective function according to the constraint conditions by using a differential evolution algorithm to obtain a solution result; The allocation module is used to adopt the improved Shapley value method to perform allocation processing according to the solution result to obtain the allocation result; the allocation result is used to configure the resources of distributed energy to reduce the reduction of renewable energy and improve the economy and stability of the overall scheduling.

Images