Multi-type virtual power plant participation demand response optimization method comprehensively considering price-integral excitation

Abstract

The invention relates to a multi-type virtual power plant participation demand response optimization method comprehensively considering price-integral excitation, and belongs to the technical field of power demand response. The method comprises the following steps: firstly, determining a demand response mechanism, and constructing a master-slave game framework which takes a virtual power plant management and control platform (VPPC) as a leader and a load aggregation virtual power plant (VPP) as a follower; according to a master-slave game framework, taking the minimum operation cost of each VPP in the lower layer as a target, and constructing an optimization model of each VPP in the lower layer, including resistance smelting load aggregation VPP, air conditioning load aggregation VPP and energy storage load aggregation VPP, participating in demand response; constructing a VPPC optimization model considering two factors of economic benefit and integral benefit by taking maximum VPPC comprehensive benefit of the upper layer as a target; and solving the master-slave game model by using a heuristic intelligent algorithm. The method effectively solves the problem that income balance and production privacy protection of all subjects participating in demand response are difficult to take into account at the same time.

Description

Technical Field

[0001] The present invention relates to a method for optimizing the participation of multiple types of virtual power plants in demand response by comprehensively considering price-point incentives, and belongs to the technical field of power demand response. Background Art

[0002] In recent years, China's electricity demand has continued to grow. At the same time, the vigorous development of new energy has led to a shortage of grid flexibility resources. Carrying out high-proportion demand-side response is one of the important means to alleviate the contradiction between electricity supply and demand during peak hours and improve the flexibility of the power system. With the rapid development of new subject resources such as distributed new energy, new energy storage, and charging piles in China, it is of great significance to enhance the demand-side regulation capacity by building virtual power plants (VPPs) and fully calling on diversified adjustable resources on the load side. The joint participation of multiple types of virtual power plants in demand response can improve economy and robustness. However, different VPPs belong to different stakeholders, and traditional demand response optimization methods face the problem of lack of coordinated leadership and balancing the interests of various stakeholders. Therefore, this patent proposes a method for optimizing the participation of multiple types of virtual power plants in demand response that comprehensively considers price-point incentives. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract of the specification and the invention title of this application to avoid blurring the purpose of this section, the abstract of the specification and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above-mentioned problems, that is, the traditional demand response optimization method faces the technical problem of lack of coordination, leadership and balancing the interests of various entities, the present invention is proposed.

[0005] In order to solve the above problems, the present invention provides the following technical solutions: a method for optimizing the participation of multiple types of virtual power plants in demand response by comprehensively considering price-point incentives, comprising:

[0006] S1: Determine the demand response mechanism and build a master-slave game framework with the virtual power plant control platform VPPC as the leader and the load aggregation virtual power plant VPP as the follower;

[0007] S2: Based on the master-slave game framework, with the goal of minimizing the operating cost of each VPP in the lower layer, an optimization model for the participation of each VPP in the lower layer in demand response, including the resistance smelting load aggregation VPP, the air conditioning load aggregation VPP and the energy storage load aggregation VPP, is constructed. The optimization model includes the objective function and constraint conditions of each VPP;

[0008] S3: With the goal of maximizing the comprehensive income of the upper-layer VPPC, construct a VPPC optimization model based on two factors: economic benefits and integral benefits. The optimization model includes the objective function and constraint conditions of the VPPC comprehensive income;

[0009] S4: Use a heuristic intelligent algorithm to solve the master-slave game model.

[0010] The demand response mechanism is specifically as follows:

[0011] Each VPP adjusts its own power according to the demand response incentive price set by the VPPC to provide demand response electricity to the VPPC. The VPPC trades the demand response volume provided by the VPP with the power grid at the demand response price set by the power grid.

[0012] The master-slave game framework is specifically as follows:

[0013] According to the different status of both parties, the VPPC acts as the leader of the game, aggregates the demand response volume information reported by the VPPs, and combines the demand response incentive price issued by the power grid to set the demand response price for each VPP with the goal of maximizing its own income; the VPP acts as the follower and reports the demand response volume with the goal of minimizing its own operating cost according to the demand response price set by the VPPC.

[0014] The specific method for constructing an optimization model for the lower-layer VPPs including the resistance furnace smelting load aggregation VPP, air-conditioning load aggregation VPP, and energy storage load aggregation VPP to participate in demand response is as follows:

[0015] The resistance furnace smelting load aggregation VPP is specifically as follows: The optimization model of the resistance furnace smelting load aggregation VPP includes an objective function and constraint conditions. Among them, the objective function is to minimize the scheduling cost of the resistance furnace smelting load, including the power purchase cost of the load from the power grid, the loss cost of production inconvenience caused by power curtailment, and the economic compensation obtained from selling demand response electricity to the VPPC. The constraint conditions include the electrode position constraint for the submerged arc operation of the electric arc furnace load, the electric arc furnace smelting power constraint affected by the smelting time, the regulation ratio constraint of the furnace temperature and the cooling water inlet speed in the polysilicon reduction furnace, the power ramp constraint, the response duration constraint, the response times constraint of a single smelting load within the scheduling period, and considering that the response capabilities of different resistance furnace smelting loads are different under different working conditions, aggregating and equivalent multiple resistance furnace smelting loads;

[0016] The specific description of the air-conditioning load aggregated VPP is as follows: The optimization model of the air-conditioning load aggregated VPP includes an objective function and constraint conditions. Among them, the objective function is to minimize the energy consumption cost of the air-conditioning load aggregated VPP, including the electricity purchase cost of the air-conditioning load aggregated VPP from the power grid and the economic compensation obtained by the air-conditioning load aggregated VPP from selling the demand response electricity to the VPPC. The constraint conditions include the user comfort constraint, the cooling power generation constraint of the refrigerating machine in the air-conditioning system, the constraint of the cold storage and release power of the cold storage, and the constraint of the maximum cold energy storage capacity of the cold storage.

[0017] The specific description of the energy storage load aggregated VPP is as follows: The optimization model of the energy storage load aggregated VPP includes an objective function and constraint conditions. Among them, the objective function is to minimize the scheduling cost of the energy storage load aggregated VPP, including the electricity purchase cost of the energy storage load aggregated VPP from the power grid, the charge and discharge loss cost of the energy storage load, and the economic compensation obtained by selling the demand response electricity to the VPPC. The constraint conditions include the state of charge (SOC) constraint of the energy storage load, the charge and discharge power constraint of the energy storage load, the charge and discharge state constraint of the energy storage load, and the continuity constraint of the state of charge of the energy storage load.

[0018] The specific description of S3 is as follows:

[0019] The specific description of the integral benefit in the VPPC optimization model is as follows: The benefit of the VPPC obtaining integral is affected by two factors, namely the influence of its own historical cumulative integral amount and the average cumulative integral amount of other demand response trading entities. Combining the two influencing factors, an economic quantification model of integral incentive is obtained, as shown in the following formula:

[0020] h value (t) = a + b×ΔP DR (t)

[0021]

[0022] In the formula, h value (t) is the incentive integral obtained by the VPPC participating in the demand response, a is the basic reward integral obtained by the VPPC each time it participates in the demand response, b is the reward integral corresponding to the unit demand response volume, and ΔP DR (t) is the demand response volume. is the unit integral benefit of obtaining integral for this time affected by its own cumulative integral. is the historical cumulative integral amount of the VPPC itself. is the proportional preference coefficient. is the unit integral benefit of obtaining integral for this time affected by other VPPC entities. is the average cumulative integral amount of other VPPCs. is the proportional preference coefficient, h curObtain the total benefit of points for the current transaction. σ1 and σ2 are the preference coefficients of the influence of the subject's own historical points and the cumulative points of other VPPC subjects respectively.

[0023] The economic benefit in the VPPC optimization model is specifically as follows: The economic benefit of VPPC demand response is divided into three parts. The first part is the economic benefit obtained by VPPC participating in demand response from the power grid. The second part is the cost of VPPC purchasing demand response resources from each VPP. The third part is the control cost of VPPC for purchasing demand response resources during the transaction process.

[0024] The specific construction of the VPPC optimization model based on two factors of economic benefit and points benefit is as follows:

[0025] According to the preference ratio of price and points, construct the comprehensive utility model of the upper-layer VPPC as shown in the following formula:

[0026] max F = ω1C price +ω2kh cur

[0027] In the formula, F is the comprehensive utility of VPPC, k is the economic benefit coefficient of unit points, ω1 and ω2 are the preference coefficients of price and points incentives respectively, and C price is the economic benefit model of VPPC participating in demand response.

[0028] The specific content of S4 is as follows:

[0029] S4.1: Initialize the parameters of the particle swarm optimization algorithm, the demand response incentive price formulated by VPPC, and the incentive price update speed.

[0030] S4.2: Each lower-layer VPP calculates its own demand response volume according to the demand response incentive price.

[0031] S4.3: The upper-layer VPPC aggregates the demand response volumes reported by the lower layer, calculates its current own income, and updates the global optimal income.

[0032] S4.4: Update the demand response incentive price and the incentive price update speed.

[0033] S4.5: Repeat the above steps until the maximum number of iterations is reached, the solution is completed, and the optimal solution of the algorithm game is obtained.

[0034] The beneficial effects of the present invention are as follows: To solve the problem that it is difficult to balance the benefits of all parties participating in demand response and protect production privacy, the present invention constructs a master-slave game model with the upper-layer VPPC as the leader and each lower-layer load-aggregated VPP as the follower. To protect the production privacy data of all parties, the particle swarm optimization algorithm is used to solve the master-slave game model to obtain the equilibrium solution of the benefits of all parties. The integral mechanism is considered as a factor in the VPPC's benefits, making the constructed model more in line with the demand response rules. Description of the Drawings

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0036] Figure 1 It is the flowchart of an optimization method for multi-type virtual power plants participating in demand response considering price-integral incentives in Embodiment 1;

[0037] Figure 2 It is the trading mechanism diagram of VPPC and VPP participating in demand response in Embodiment 1;

[0038] Figure 3 It is the master-slave game architecture diagram of VPPC and VPP participating in demand response in Embodiment 1;

[0039] Figure 4 It is the flowchart of the particle swarm optimization algorithm in Embodiment 1;

[0040] Figure 5 It is an example diagram of the demand response quantity and demand response price of Task 1 VPPC and VPP in Embodiment 2;

[0041] Figure 6 It is an example diagram of the demand response quantity and demand response price of Task 2 VPPC and VPP in Embodiment 2;

[0042] Figure 7 It is an example diagram of the demand response quantity and demand response price of Task 3 VPPC and VPP in Embodiment 2. Detailed Embodiments

[0043] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0044] In the following description, many specific details are set forth to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0045] Secondly, as used herein, "an embodiment" or "embodiment" refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0046] Embodiment 1: Refer to Figures 1 to 4 , which is the first embodiment of the present invention. A method for optimizing the participation of multiple types of virtual power plants in demand response by comprehensively considering price-integral incentives is provided. The specific steps are as follows:

[0047] S1: Determine the demand response mechanism and construct a master-slave game framework with the virtual power plant control platform VPPC as the leader and the load aggregation virtual power plant VPP as the follower.

[0048] Specifically, the demand response mechanism is as follows: Each VPP adjusts its own power according to the demand response incentive price formulated by the VPPC to provide demand response electricity to the VPPC, and the VPPC trades the demand response volume provided by the VPP with the power grid at the demand response price formulated by the power grid.

[0049] Specifically, the master-slave game framework is as follows: According to the different statuses of both parties, the VPPC, as the leader of the game, aggregates the demand response volume information reported by the VPPs, combines the demand response incentive price issued by the power grid, and formulates the demand response price for each VPP with the goal of maximizing its own profit; the VPPs, as the followers, report the demand response volume with the goal of minimizing their own operating costs according to the demand response price formulated by the VPPC.

[0050] S2: According to the master-slave game framework, with the goal of minimizing the operating costs of each lower-layer VPP, construct an optimization model for each lower-layer VPP, including the resistance-type smelting load aggregation VPP, the air-conditioning load aggregation VPP, and the energy storage load aggregation VPP, participating in demand response. The optimization model includes the objective function and constraint conditions of each VPP.

[0051] Specifically, the optimization model for each lower-layer VPP participating in demand response includes:

[0052] (1) Resistance-type smelting load aggregation VPP

[0053] The optimization model of the resistive smelting load aggregation VPP includes an objective function and constraint conditions. Among them, the objective function is to minimize the scheduling cost of the resistive smelting load, including the cost of purchasing electricity from the power grid by the load, the cost of production inconvenience losses caused by power curtailment, and the economic compensation obtained from selling demand response electricity to the VPPC. The specific objective function is shown as follows.

[0054]

[0055] In the formula, is the scheduling cost of the resistive smelting load, is the electricity purchase price of the smelting load from the power grid, is the load power of the smelting load at time t, is the amount of load participating in demand response curtailment of the smelting load at time t, and ρ is the coefficient of production inconvenience loss cost caused by load curtailment, is the unit revenue obtained by the smelting load from selling demand response resources to the VPPC.

[0056] The constraint conditions are the electrode position constraint for the submerged arc operation of the electric arc furnace load, the electric arc furnace smelting power constraint affected by the smelting time, the constraint of the furnace temperature and the regulation ratio of the cooling water inlet speed in the polysilicon reduction furnace, the power ramp constraint, the response duration constraint, the constraint on the number of responses of a single smelting load within the scheduling period, and considering that the response capabilities of different resistive smelting loads are different under different working conditions, the aggregation and equivalence of multiple resistive smelting loads are carried out. The constraint of the resistive smelting load aggregation model is shown as follows.

[0057] L min ≤L arc ≤L max

[0058]

[0059] S i <=S max where i ∈ 1, 2…, N

[0060] In the above formula, L arc is the distance from the bottom of the electrode to the liquid level of the furnace charge. L min , L max are the allowable minimum and maximum values of the distance from the bottom of the electrode to the furnace charge smelting respectively. P is the power of the reduction furnace, η is the proportion of the heat absorbed by the reduction reaction in the sum of the heat absorbed by the reduction reaction and the heat radiated to the interlayer of the furnace shell and the cooling water at the bottom of the pan, r is the radius of the silicon rod, L is the equivalent total length of the silicon rod, K is the total heat transfer coefficient between the silicon rod and the mixed gas, with the unit of W / (m 2 ·K), T X is the surface temperature of the silicon rod, T outis the equivalent temperature of the furnace inner wall or the chassis surface, α is the adjustment ratio of the cooling water inlet speed. N is the polymerization load quantity, ΔP i DR,t is the power adjustment amount of the i-th load resource at time t, is the state quantity of the i-th load resource participating in the response at time t, are respectively the minimum and maximum response powers of the i-th load resource at time t, and Δt is the scheduling time interval. are respectively the total power adjustment amounts of the aggregated load at times t and t - 1, the total downward and upward ramp rates after load aggregation, T max is the maximum duration of a single response. The expression of is shown in the following formula:

[0061]

[0062] In the formula, are respectively the downward and upward ramp rates of the i-th load resource at time t.

[0063] Furthermore, as shown in Table 1, they are the model parameters of the resistance-type smelting load.

[0064] Table 1 Resistance-Type Smelting Load Model Parameter Table

[0065]

[0066] (2) Air-Conditioning Load Aggregation VPP

[0067] The air-conditioning load aggregation VPP optimization model includes an objective function and constraint conditions. Among them, the objective function is to minimize the energy consumption cost of the air-conditioning load aggregation VPP, including the power purchase cost of the air-conditioning load aggregation VPP from the power grid and the economic compensation obtained by the air-conditioning load aggregation VPP from selling the demand response electricity to the VPPC. The specific objective function is shown in the following formula.

[0068]

[0069] In the formula, is the energy consumption cost of the air-conditioning aggregated load, is the power purchase price of the air-conditioning load from the power grid.

[0070] The constraint conditions are user comfort constraints, the cooling power generation constraints of the air-conditioning system's chillers, the constraints on the storage and release of cooling power by the cold storage, and the maximum cold energy storage capacity constraints of the cold storage. The constraint conditions are shown in the following formula.

[0071]

[0072] In the formula, Q t is the total cooling capacity provided by the air-conditioning, Qt ch is the cooling energy generated by the air conditioner chiller, are the cooling powers stored and released by the air conditioner cold storage device respectively, is the maximum cooling power generated by the air conditioner chiller, is the maximum cooling power that the air conditioner cold storage device can store and release, are the cooling energy in the air conditioner cold storage device and the cold energy capacity of the cold storage device at time t respectively, is the cooling energy in the air conditioner cold storage device at time t - 1, η st 、η re are the efficiencies of storing and releasing cooling energy by the air conditioner respectively, μ ch 、μ st 、μ re are the energy value conversion efficiencies in the processes of refrigeration by the air conditioner chiller, storage of cooling energy by the cold storage device and release of cooling energy respectively, P t cold is the aggregated air conditioner load power before participating in demand response, ΔP t cold is the demand response amount provided by the aggregated air conditioner load.

[0073] Furthermore, as shown in Table 2, they are the parameters of the aggregated air conditioner load model.

[0074] Table 2 Aggregated Air Conditioner Load Model Parameter Table

[0075]

[0076] (3) Aggregated Energy Storage Load VPP

[0077] The optimization model of the aggregated energy storage load VPP includes an objective function and constraint conditions. Among them, the objective function is to minimize the scheduling cost of the aggregated energy storage load VPP, including the cost of purchasing electricity from the power grid by the aggregated energy storage load VPP, the cost of charging and discharging losses of the energy storage load, and the economic compensation obtained from selling demand response electricity to the VPPC. The specific objective function is shown as the following formula.

[0078]

[0079] In the formula, is the operating cost of the energy storage load, is the electricity purchase price of the energy storage power station from the power grid, P t cha 、P t dis are the charging and discharging powers of the energy storage power station at time t, and ε is the cost coefficient of the charging and discharging losses of the energy storage power station.

[0080] The constraint conditions are the state of charge (SOC) constraint of the energy storage load, the charge and discharge power constraint of the energy storage load, the charge and discharge state constraint of the energy storage load, and the continuity constraint of the state of charge of the energy storage load. The specific constraint conditions are shown in the following formula.

[0081] SOC min ≤SOC t ≤SOC max

[0082]

[0083] In the formula, SOC t is the state of charge of the energy storage at time t, SOC min , SOC max are the minimum and maximum values of the state of charge of the energy storage respectively, are the maximum charge and discharge powers of the energy storage respectively, is the charge and discharge state of the energy storage. Among them, being 1 represents the charging state, being 1 represents the discharging state, SOC t-1 is the state of charge of the energy storage at time t - 1, η cha , η dis are the charge and discharge efficiencies of the energy storage respectively.

[0084] Furthermore, as shown in Table 3, they are the model parameters of the energy storage load.

[0085] Table 3 Energy Storage Load Model Parameter Table

[0086]

[0087] S3: Taking the maximum comprehensive benefit of the upper-layer VPPC as the goal, a VPPC optimization model based on two factors of economic benefit and integral benefit is constructed. The optimization model includes the objective function and constraint conditions of the VPPC comprehensive benefit.

[0088] Specifically, the integral benefit in the VPPC optimization model is as follows: The benefit of the VPPC obtaining integral is affected by two factors, namely the influence of its own historical cumulative integral amount and the average cumulative integral amount of other demand response trading entities. Combining the two influencing factors, an economic quantification model of integral incentive is obtained, as shown in the following formula.

[0089] h value (t) = a + b×ΔP DR (t)

[0090]

[0091] In the formula, h value(t) is the incentive points obtained by the VPPC participating in demand response, a is the basic reward points obtained by the VPPC each time it participates in demand response, b is the reward points corresponding to the unit demand response volume, and ΔP DR (t) is the demand response volume, is the unit integral benefit of the current integral acquisition affected by its own cumulative points, is the historical cumulative points volume of the VPPC itself, is the proportional preference coefficient, is the unit integral benefit of the current integral acquisition affected by other VPPC entities, is the average cumulative points volume of other VPPCs, is the proportional preference coefficient, h cur is the total benefit of the current transaction for obtaining points. σ1 and σ2 are the preference coefficients of the influence of its own historical points and the cumulative points of other VPPC entities respectively. In the present invention, is taken as 0.5, σ1 is 0.6, and σ2 is 0.4.

[0092] The economic benefit in the VPPC optimization model is specifically as follows: The economic benefit of the VPPC demand response is divided into three parts. The first part is the economic benefit obtained by the VPPC participating in demand response from the power grid; the second part is the cost of the VPPC purchasing demand response resources from each VPP; the third part is the control cost of the VPPC for purchasing demand response resources during the transaction process. The specific objective function is shown as follows.

[0093]

[0094] In the formula, C price is the economic benefit of the VPPC participating in demand response. t1 and t2 are the start and end times of the demand response respectively, and z DR is the unit benefit obtained by the VPPC participating in demand response from the power grid, is the total response volume of the VPP participating in demand response, is the unit price of the VPPC purchasing demand response resources from each VPP, and υ is the economic coefficient of the VPPC control cost.

[0095] According to the preference ratio of price and points, the comprehensive utility model of the upper-layer VPPC is constructed as follows.

[0096] maxF = ω1C price +ω2kh cur

[0097] In the formula, F is the comprehensive utility of the VPPC, k is the economic benefit coefficient of the unit point, ω1 and ω2 are the preference coefficients of price and points incentives respectively. In the present invention, ω1 is taken as 0.7 and ω2 is taken as 0.3. C priceEconomic benefit model for VPPC to participate in demand response.

[0098] S4: Solve the master-slave game model using a heuristic intelligent algorithm.

[0099] Specifically, the present invention adopts the particle swarm optimization algorithm in the heuristic intelligent algorithm, and the detailed solution steps are as follows.

[0100] S4.1: Initialize the parameters of the particle swarm optimization algorithm, the demand response incentive price formulated by VPPC, and the incentive price update speed.

[0101] S4.2: Each VPP at the lower layer calculates its own demand response volume according to the demand response incentive price.

[0102] S4.3: The upper-layer VPPC aggregates the demand response volumes reported by the lower layer, calculates its current own revenue, and updates the global optimal revenue.

[0103] S4.4: Update the demand response incentive price and the incentive price update speed.

[0104] S4.5: Repeat the above steps until the maximum number of iterations is reached, the solution is completed, and the optimal solution of the algorithm game is obtained.

[0105] Example 2: Refer to Figures 5 to 7 , which is the second embodiment of the present invention, provides an optimization method for multi-type virtual power plants participating in demand response considering price-integral incentives. To verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0106] Construct a master-slave game model with VPPC as the leader and VPP as the follower.

[0107] The three task scenarios are as follows:

[0108] (1) Task 1: The dispatching center issues a 15MW demand response task, the response time is from 17:00 to 21:00 tomorrow, and the response duration is 4 hours.

[0109] (2) Task 2: The response task volume is also 15MW, the response time is from 19:00 to 22:00 tomorrow, and the response duration is 3 hours.

[0110] (3) Task 3: The response task volume is 18MW, the response time is from 16:00 to 18:00 tomorrow, and the response duration is 2 hours.

[0111] Heuristic intelligent algorithm:

[0112] Step 1: Initialize the parameters of the particle swarm optimization algorithm.

[0113] Step 2: Initialize the demand response incentive price formulated by VPPC and the incentive price update speed

[0114] Step 3: Each VPP at the lower layer determines the demand response incentive price Call the Gurobi 12.0 solver and the YAMLIP toolbox to calculate its own demand response volume

[0115] Step 4: The upper-layer VPPC aggregates the demand response volumes reported by the lower layer Calculate its current income and update the global optimal income F VPPC ;

[0116] Step 5: Update the demand response incentive price according to the update speed and update position formulas of particle m and the incentive price update speed The update speed and update position formulas of particle m are shown as follows:

[0117]

[0118] In the formula, d is the number of iterations, are the search speed and the current position of particle m at the (d + 1)-th iteration respectively, are the search speed and the current position of particle m at the d-th iteration respectively, gbest d are the current optimal position of particle m and the global optimal position of the particle after the d-th iteration respectively. c1 and c2 are the acceleration factors of the particle individual and the population respectively, r1 and r2 are random coefficients, and ω is the inertial acceleration factor of the particle.

[0119] Step 6: Repeat the above steps until the maximum number of iterations is reached, the solution is completed, and the optimal solution of the algorithm game is obtained.

[0120] Solve the strategies of VPPC and each VPP participating in demand response under the three tasks respectively according to the above steps. As shown in Table 4, it is the income of VPPC under different solution methods.

[0121] Table 4 Income of VPPC under different solution methods

[0122]

[0123] The optimization method proposed in the present invention further considers the integral incentive factor, can balance the income of multiple virtual power plants participating in the next virtual power plant, and stimulate the willingness of each virtual power plant to participate in demand response.

[0124] The specific embodiments of the present invention have been described in detail with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the scope of knowledge possessed by those of ordinary skill in the art.

Claims

1. An optimization method for multi-type virtual power plants participating in demand response by comprehensively considering price-integral incentives, characterized in that, Including: S1: Determine the demand response mechanism, and construct a master-slave game framework with the virtual power plant control platform VPPC as the leader and the load aggregation virtual power plant VPP as the follower; S2: According to the master-slave game framework, with the minimum operating cost of each VPP at the lower layer as the goal, construct an optimization model for each VPP at the lower layer, including the resistance smelting load aggregation VPP, the air-conditioning load aggregation VPP, and the energy storage load aggregation VPP, participating in demand response. The optimization model includes the objective function and constraint conditions of each VPP; S3: With the maximum comprehensive benefit of the upper-layer VPPC as the goal, construct a VPPC optimization model based on two factors: economic benefit and integral benefit. The optimization model includes the objective function and constraint conditions of the VPPC comprehensive benefit; S4: Use a heuristic intelligent algorithm to solve the master-slave game model.

2. The optimization method for multi-type virtual power plants participating in demand response by comprehensively considering price-integral incentives according to claim 1, characterized in that The specific demand response mechanism is as follows: Each VPP adjusts its own power according to the demand response incentive price set by the VPPC to provide demand response electricity to the VPPC. The VPPC trades the demand response volume provided by the VPP with the power grid at the demand response price set by the power grid.

3. A method for optimizing demand response participation of multi - type virtual power plants considering price - integral incentives according to claim 1, characterized in that, The specific master-slave game framework is as follows: According to the different status of both parties, the VPPC, as the leader of the game, aggregates the demand response volume information reported by the VPP, combines the demand response incentive price issued by the power grid, and sets the demand response price for each VPP with the goal of maximizing its own benefit; The VPP, as the follower, reports the demand response volume with the goal of minimizing its own operating cost according to the demand response price set by the VPPC.

4. A multi-type virtual power plant participation demand response optimization method considering price-integral incentives as claimed in claim 1, characterized in that The specific construction of the optimization model for each VPP at the lower layer, including the resistance smelting load aggregation VPP, the air-conditioning load aggregation VPP, and the energy storage load aggregation VPP, participating in demand response is as follows: The specific resistance smelting load aggregation VPP is as follows: The optimization model of the resistance smelting load aggregation VPP includes an objective function and constraint conditions. Among them, the objective function is to minimize the scheduling cost of the resistance smelting load, including the power purchase cost of the load from the power grid, the loss cost of production inconvenience caused by power curtailment, and the economic compensation obtained from selling demand response electricity to the VPPC. The constraint conditions include the electrode position constraint for the submerged arc operation of the electric arc furnace load, the electric arc furnace smelting power constraint affected by the smelting time, the regulation ratio constraint of the furnace temperature and the cooling water inlet speed in the polysilicon reduction furnace, the power ramp constraint, the response duration constraint, the response times constraint of a single smelting load within the scheduling period, and considering that the response capabilities of different resistance smelting loads are different under different working conditions, aggregating and equivalent multiple resistance smelting loads; The specific air-conditioning load aggregation VPP is as follows: The optimization model of the air-conditioning load aggregation VPP includes an objective function and constraint conditions. Among them, the objective function is to minimize the energy consumption cost of the air-conditioning load aggregation VPP, including the power purchase cost of the air-conditioning load aggregation VPP from the power grid and the economic compensation obtained from selling demand response electricity to the VPPC. The constraint conditions include the user comfort constraint, the cooling power generation constraint of the refrigerating machine in the air-conditioning system, the constraint of the cold storage storing and releasing cold power, and the maximum cold energy storage capacity constraint of the cold storage; The energy storage load aggregation VPP is specifically as follows: The optimization model of the energy storage load aggregation VPP includes an objective function and constraint conditions. Among them, the objective function is to minimize the scheduling cost of the energy storage load aggregation VPP, including the power purchase cost of the energy storage load aggregation VPP from the power grid, the charge and discharge loss cost of the energy storage load, and the economic compensation obtained from selling demand response electricity to the VPPC. The constraint conditions are the state of charge (SOC) constraint of the energy storage load, the charge and discharge power constraint of the energy storage load, the charge and discharge state constraint of the energy storage load, and the continuity constraint of the state of charge of the energy storage load.

5. The optimization method for multi-type virtual power plants considering price-integral incentives to participate in demand response according to claim 1, characterized in that The specific content of S3 is as follows: The integral benefit in the VPPC optimization model is specifically as follows: The benefit of the VPPC obtaining integral is affected by two factors, namely the influence of its own historical cumulative integral quantity and the average cumulative integral quantity of other demand response trading entities. Combining the two influencing factors, an economic quantification model for integral incentive is obtained, as shown in the following formula: h value h(t) = a + b×ΔP DR h(t) where h value (t) is the incentive points obtained by the VPPC participating in the demand response. a is the basic reward points obtained by the VPPC for each participation in the demand response. b is the reward points corresponding to the unit demand response volume. ΔP DR (t) is the demand response volume, is the unit integral benefit of the current integral acquisition affected by its own cumulative points, is the historical cumulative integral volume of the VPPC itself, is the proportional preference coefficient, is the unit integral benefit of the current integral acquisition affected by other VPPC entities, is the average cumulative integral volume of other VPPCs, is the proportional preference coefficient, h cur is the total benefit of the current transaction for obtaining points. σ1 and σ2 are the preference coefficients of the influence of its own historical points and the cumulative points of other VPPC entities respectively; The economic benefit in the VPPC optimization model is specifically as follows: The economic benefit of the VPPC demand response is divided into three parts. The first part is the economic benefit obtained by the VPPC participating in the demand response from the power grid; the second part is the cost of the VPPC purchasing demand response resources from each VPP; the third part is the control cost of the VPPC for purchasing demand response resources during the trading process.

6. The optimization method for multi-type virtual power plants participating in demand response by comprehensively considering price-integral incentives according to claim 1, characterized in that The specific content of constructing the VPPC optimization model based on two factors of economic benefit and integral benefit is as follows: According to the preference ratio of price and integral, construct the comprehensive utility model of the upper-layer VPPC as shown in the following formula: maxF = ω1C price + ω2kh cur where F is the comprehensive utility of the VPPC, k is the economic benefit coefficient per unit of points, ω1 and ω2 are the preference coefficients for price and points incentives respectively, and C price is the economic benefit model for the VPPC to participate in demand response.

7. A method for optimizing demand response participation of multi-type virtual power plants considering price-integral incentives according to claim 1, characterized in that The specific content of S4 is as follows: S4.1: Initialize the parameters of the particle swarm optimization algorithm, the demand response incentive price set by the VPPC, and the incentive price update speed; S4.2: Each lower-layer VPP calculates its own demand response volume according to the demand response incentive price; S4.3: The upper-layer VPPC aggregates the demand response volumes reported by the lower layer, calculates its current own benefit, and updates the global optimal benefit; S4.4: Update the demand response incentive price and the incentive price update speed; S4.5: Repeat the above steps until the maximum number of iterations is reached, the solution is completed, and the optimal solution of the algorithm game is obtained.

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