Virtual power plant optimization scheduling method based on block chain in electricity-carbon coupling market environment

Abstract

The invention relates to a virtual power plant optimization scheduling method based on a block chain in an electricity-carbon coupling market environment, and the method comprises the following steps: S1, building a committee based on all participants in a virtual power plant, and the committee comprises a main representative node and a plurality of other member nodes; s2, establishing a virtual power plant optimization scheduling model; s3, the main representative node solves the model outside the block chain by applying a KKT condition; s4, the main representative node encrypts the obtained optimal solution, stores the encrypted optimal solution to the block chain, and broadcasts the encrypted optimal solution to all other member nodes; and S5, each other member node verifies the received information through a verification mechanism on the block chain, and judges whether the optimal solution is correct or not through a voting mechanism, if yes, the virtual power plant executes the optimal solution for scheduling, and if not, a new main representative node is determined and the step S3 is returned. Compared with the prior art, efficient resource optimization can be realized in the peak regulation-carbon coupling market, and meanwhile, the fairness of market participation and the credibility of data are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of electricity market, and in particular to a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment. Background Art

[0002] As an important tool for regulating flexible resources, virtual power plants are gradually realizing their potential in the peak-shaving market and the carbon market. However, since virtual power plants often aggregate multiple adjustable loads and distributed photovoltaic and other heterogeneous resources, their operation and trading processes in different markets face multiple challenges: on the one hand, it is necessary to coordinate the coupling mechanism between different markets to achieve optimal resource allocation in the peak-shaving market and the carbon market; on the other hand, the transactions of virtual power plants in the peak-shaving-carbon coupling market require the submission of core data such as power regulation capacity and carbon emission reduction, and all market players have put forward high requirements for the transparency and fairness of the trading process.

[0003] Blockchain can improve market transparency and solve trust issues. As a safe, transparent and reliable technical tool, it has shown its potential to support virtual power plants to participate efficiently in the peak-shaving-carbon coupling market in some studies. Based on blockchain technology, the rules and resource allocation logic of the peak-shaving market and the carbon market can be deployed on the chain through smart contracts. All participants act as consensus nodes to jointly supervise the execution of transaction rules and the generation of results. The automatic execution and non-tamperability of smart contracts ensure the transparency of transactions and eliminate doubts about the "black box" of market operations. At the same time, blockchain uses a multilateral co-governance mechanism to achieve checks and balances between virtual power plants and other market players and enhance market trust.

[0004] However, as the market environment becomes increasingly complex, the existing blockchain application methods in the peak-shaving-carbon coupling market have some shortcomings: on the one hand, the clearing process in the peak-shaving-carbon coupling market needs to solve large-scale, multi-objective optimization problems, including load regulation plans, virtual power plant revenue distribution and other complex model calculations. Traditional blockchain technology can only handle simple calculations, which is difficult to meet the computing needs of virtual power plants that aggregate massive resources to participate in the peak-shaving-carbon coupling market. On the other hand, blockchain technology is prone to efficiency bottlenecks due to calculations in traditional architectures, making it difficult to meet the current virtual power plant scheduling efficiency requirements. Therefore, it is necessary to design a virtual power plant optimization scheduling method that can achieve efficient resource optimization in the peak-shaving-carbon coupling market while ensuring the fairness of market participation and the credibility of data. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment, which can achieve efficient resource optimization in the peak-shaving-carbon coupling market while ensuring the fairness of market participation and the credibility of data.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] The present invention provides a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment, comprising the following steps:

[0008] S1. Establish a committee based on all participating entities in the virtual power plant, wherein the committee includes a main representative node and multiple other member nodes;

[0009] S2. With the goal of maximizing the overall profit of the virtual power plant, a virtual power plant optimization scheduling model is established based on the benefit-cost factors, where the benefit-cost factors include load power utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, abandoned solar power cost, and peak-shaving deviation penalty;

[0010] S3. The master representative node applies KKT conditions to solve the virtual power plant optimization scheduling model outside the blockchain to obtain the optimal scheduling plan of the virtual power plant in each time period;

[0011] S4. The main representative node encrypts the optimal solution obtained in step S3 and stores it in the blockchain, and then broadcasts the encrypted optimal solution to all other member nodes in the committee;

[0012] S5. After receiving the encrypted information broadcast by the main representative node, the other member nodes exchange information through cross-communication and verify it through the verification mechanism on the blockchain. When all other member nodes complete the verification, they determine whether the optimal solution obtained by the main representative node is correct through the voting mechanism. If so, the virtual power plant executes the optimal solution for scheduling. Otherwise, a new main representative node is determined and returns to step S3.

[0013] Furthermore, in step S2, the expression of the virtual power plant optimization scheduling model is specifically as follows:

[0014] max P=R load +R F +P G +R CM -C qd -C ferror

[0015] Among them, P represents the overall profit of the virtual power plant, R load Represents the load power utility, R F represents the revenue of virtual power plants participating in the power peak load regulation market, P G represents the benefits of interaction between the virtual power plant and the grid, R CM represents the income of virtual power plants participating in the carbon market, C qd represents the cost of curtailment of virtual power plant, Cferror It represents the virtual power plant peak load deviation penalty. The specific calculation formulas of each part are as follows:

[0016]

[0017] in, represents the electricity utility of the i-th load in period t;

[0018]

[0019] in, It indicates the peak-shaving price for each period of the day before issued by the power trading center. Indicates the planned peak load of the virtual power plant a few days ago. represents the peak load of load i in time period t, represents the amount of abandoned light from distributed photovoltaic k in time period t;

[0020] P G =R G -C G

[0021] Among them, R G is the revenue from electricity sales of the virtual power plant, C G The cost of purchasing electricity for the virtual power plant;

[0022]

[0023] Among them, ρ ω is the probability of scenario ω occurring, is the kth distributed photovoltaic output in the scenario ω during period t, λ CM is the carbon quota price, x a1 Apply for CCER certificate for distributed photovoltaic to obtain carbon quota coefficient, x a2 The carbon emission coefficient of the virtual power plant load purchasing electricity from the grid, The power sold to the grid by the virtual power plant during period t;

[0024]

[0025] Among them, λ p The cost price of abandoned power from distributed photovoltaic power generation;

[0026]

[0027] Among them, λ pf Penalize price for virtual power plant deviations, is the baseline of the virtual power plant period t.

[0028] Furthermore, the electricity utility of the i-th load in period t is The calculation formula is as follows:

[0029]

[0030] in, is the load amount of load i in time period t, and are the secondary power utility parameters of the load, and are the minimum load and maximum load in period t, for The corresponding maximum value of the quadratic function.

[0031] Furthermore, the revenue from electricity sales of the virtual power plant is R G and the virtual power plant electricity purchase cost C G The calculation formula is as follows:

[0032]

[0033]

[0034] in, is the electricity price for tomorrow’s period t, is the wholesale electricity price, and They are the power purchased from the grid by the virtual power plant during period t and the power sold to the grid by the virtual power plant during period t. is the net power of interaction between the virtual power plant and the grid during period t, ρ ω is the probability of scenario ω occurring, For the distributed photovoltaic output in the scenario, is the load amount of load i in time period t.

[0035] Furthermore, the baseline of the virtual power plant period t The calculation formula is as follows:

[0036]

[0037] Among them, ρ ω is the probability of scenario ω occurring, For the distributed photovoltaic output in the scenario, is the load amount of load i in time period t.

[0038] Furthermore, in step S3, the specific process of applying the KKT condition to solve the virtual power plant optimization scheduling model is as follows:

[0039] S301. Construct the following Lagrangian function:

[0040]

[0041] in, and are the Lagrange coefficients of the lower and upper limits of load peak regulation, and are the Lagrangian coefficients of the lower and upper limits of photovoltaic peak regulation, is the load amount of load i in time period t, P loadimin and P loadimax are the minimum load and the maximum load, respectively, PVkmax The upper limit of photovoltaic peak regulation;

[0042] S302. Calculate the partial derivative of each variable in the Lagrangian function and set it to 0, and solve the virtual power plant optimization scheduling model under multiple constraints. The multiple constraints include load power consumption constraints, photovoltaic output constraints, virtual power plant aggregation subject peak load constraints, grid interaction constraints and carbon emission limit constraints.

[0043] Furthermore, the specific expression of the load power consumption constraint is as follows:

[0044]

[0045] Among them, P loadimin and P loadimax are the minimum load and the maximum load, respectively;

[0046] The specific expression of the peak load constraint of the virtual power plant aggregation subject is as follows:

[0047]

[0048] in, and are the minimum load and maximum load in period t respectively.

[0049] Furthermore, the optimal dispatching plan of the virtual power plant in each time period obtained in step S3 includes the load amount of each load in each time period, the output and abandoned light amount of each photovoltaic in each time period, the interactive power between the virtual power plant and the power grid, the revenue items of the virtual power plant in the peak-shaving market and the carbon market, and the interactive power purchase and sales plan of the power grid.

[0050] Further, in step S5, the verification mechanism is KKT condition verification, including first-order condition check, complementary relaxation condition check and non-negativity condition check.

[0051] Furthermore, in step S5, the specific process of judging whether the optimal solution obtained by the main representative node is correct through the voting mechanism is as follows: each other member node generates a voting result based on the verification result. If more than half of the voting results indicate that the optimal solution obtained by the main representative node is correct, then the optimal solution obtained by the main representative node is finally determined to be correct.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The present invention proposes a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment. First, a committee is established based on all participating entities in the virtual power plant, and the committee includes a main representative node and multiple other member nodes; then, with the goal of maximizing the overall profit of the virtual power plant, a virtual power plant optimization scheduling model is established based on benefit-cost factors. The benefit-cost factors include load electricity utility, peak-shaving market benefits, grid interaction benefits, carbon market benefits, abandoned light costs, and peak-shaving deviation penalties, which can improve the comprehensiveness of the model and the accuracy of the scheduling scheme; subsequently, the main representative node applies the KKT condition to solve the virtual power plant optimization scheduling model outside the blockchain to obtain the optimal scheduling plan for the virtual power plant in each time period. By placing complex optimization solution tasks off-chain, the redundant computing and communication burden caused by the blockchain consensus mechanism can be significantly reduced. , improve the scheduling and clearing efficiency of virtual power plants; finally, the main representative node encrypts the optimal solution obtained and stores it in the blockchain, and then broadcasts the encrypted optimal solution to all other member nodes in the committee. After receiving the encrypted information, the other member nodes exchange information through cross-communication and verify it through the verification mechanism on the blockchain. When all other member nodes complete the verification, the voting mechanism is used to determine whether the optimal solution obtained by the main representative node is correct. If so, the virtual power plant executes the optimal solution for scheduling, otherwise, a new main representative node is determined and the KKT condition is applied to solve it. That is, the encrypted optimal solution data stored on the chain is always verified and confirmed by multiple nodes and votes, which can ensure the transparency and fairness of the optimal solution, ensure the credibility and trustworthiness of virtual power plants in the peak-shaving-carbon coupling market, and effectively avoid the risk of unfairness or single node control. The above method uses the model of "off-chain KKT solution and on-chain verification and evidence storage" to enable virtual power plants to not only achieve efficient resource optimization in the peak-shaving-carbon market, but also ensure the fairness of market participation and the credibility of data through the transparency and immutability of blockchain technology, thereby achieving the effective integration of flexible loads and distributed photovoltaics, and making the scheduling scheme of virtual power plants more widely accepted and trusted in the market.

[0054] 2. The present invention introduces a cross-communication mechanism, which can exchange information between other member nodes and jointly verify the validity of the solution results of the main representative node, thereby ensuring that the representative nodes have a consistent understanding of the scheduling results and forming a mutual supervision mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0057] Example 1

[0058] This embodiment provides a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment. Figure 1 As shown, the following steps are included:

[0059] S1. A committee is established based on all participating entities in the virtual power plant. The committee includes a main representative node and multiple other member nodes.

[0060] First, all entities participating in the virtual power plant (including adjustable loads and distributed photovoltaic resources) aggregate to form a "consensus representative" set, namely the committee, to ensure that each entity has representatives participating in the scheduling decision. In the committee, a "main representative" is selected through voting or other election methods. This node is responsible for solving the optimization scheduling problem off-chain and publishing the results to all other member nodes. The published results include multiple scheduling information such as load distribution, photovoltaic output, grid interaction strategy and carbon market transactions.

[0061] S2. With the goal of maximizing the overall profit of the virtual power plant, an optimal scheduling model for the virtual power plant is established based on the revenue-cost factors.

[0062] The benefit-cost factors include load electricity utility, peak-shaving market benefits, grid interaction benefits, carbon market benefits, abandoned solar power costs and peak-shaving deviation penalties. The expression of the virtual power plant optimization scheduling model is as follows:

[0063] max P=R load +R F +P G +R CM -C qd -C ferror

[0064] Among them, P represents the overall profit of the virtual power plant, R load Represents the load power utility, R F represents the revenue of virtual power plants participating in the power peak load regulation market, P G represents the benefits of interaction between the virtual power plant and the grid, R CM represents the income of virtual power plants participating in the carbon market, C qd represents the cost of curtailment of virtual power plant, C ferror Represents the virtual power plant peak load deviation penalty. The specific calculation formulas of each part are as follows:

[0065] (1) Load power efficiency Rload

[0066]

[0067] in, It represents the electricity utility of the i-th load in the t period. The calculation formula is as follows:

[0068]

[0069] in, is the load amount of load i in time period t, and are the secondary power utility parameters of the load, and are the minimum load and maximum load in period t, for The corresponding maximum value of the quadratic function.

[0070] (2) The revenue of virtual power plants participating in the power peak load regulation market R F

[0071]

[0072] in, It indicates the peak-shaving price for each period of the day before issued by the power trading center. Indicates the planned peak load of the virtual power plant a few days ago. represents the peak load of load i in time period t, It represents the amount of abandoned light of distributed photovoltaic k in time period t.

[0073] (3) The benefits of interaction between virtual power plants and power grids P G

[0074] P G =R G -C G

[0075] Among them, R G is the revenue from electricity sales of the virtual power plant, C G is the electricity purchase cost of the virtual power plant, and the calculation formulas are as follows:

[0076]

[0077] in, is the electricity price for tomorrow’s period t, is the wholesale electricity price, and They are the power purchased from the grid by the virtual power plant during period t and the power sold to the grid by the virtual power plant during period t. is the net power of interaction between the virtual power plant and the grid during period t, ρω is the probability of scenario ω occurring, is the kth distributed photovoltaic output under scenario ω, which is predicted using the scenario generation method. is the load amount of load i in time period t.

[0078] (4) The benefits of virtual power plants participating in the carbon market R CM

[0079]

[0080] Among them, λ CM is the carbon quota price, x a1 Apply for CCER certificate for distributed photovoltaic to obtain carbon quota coefficient, x a2 The carbon emission coefficient of the virtual power plant load purchasing electricity from the grid, The power sold to the grid by the virtual power plant during period t.

[0081] (5) Virtual power plant curtailment cost C qd

[0082]

[0083] Among them, λ p It is the cost price of abandoned power from distributed photovoltaic power generation.

[0084] (6) Virtual power plant peak load deviation penalty C ferror

[0085]

[0086] Among them, λ pf Penalize price for virtual power plant deviations, is the baseline of the virtual power plant period t, and the calculation formula is as follows:

[0087]

[0088] S3. The master representative node applies the KKT condition to solve the virtual power plant optimization scheduling model outside the blockchain to obtain the optimal scheduling plan of the virtual power plant in each time period.

[0089] The specific process of solving the virtual power plant optimization scheduling model using KKT conditions is as follows:

[0090] S301. Construct the following Lagrangian function:

[0091]

[0092] in, and are the Lagrange coefficients of the lower and upper limits of load peak regulation, and are the Lagrangian coefficients of the lower and upper limits of photovoltaic peak regulation, P loadimin and P loadimax are the minimum load and the maximum load, respectively, PVkmax The upper limit of photovoltaic peak regulation;

[0093] S302. Calculate the partial derivative of each variable in the Lagrangian function and set it to 0, and solve the virtual power plant optimization scheduling model under multiple constraints. The multiple constraints include load power consumption constraints, photovoltaic output constraints, virtual power plant aggregation subject peak load constraints, grid interaction constraints and carbon emission limit constraints.

[0094] The specific expression of load power consumption constraint is as follows:

[0095]

[0096] Among them, P loadimm and P loadimax The minimum load and the maximum load.

[0097] The specific expression of photovoltaic output constraint is as follows:

[0098]

[0099] Among them, P PVkmax The maximum photovoltaic output.

[0100] The specific expression of the peak load constraint of the virtual power plant aggregation entity is as follows:

[0101]

[0102] in, and are the minimum load and maximum load in period t respectively.

[0103] The optimal dispatching plan of the virtual power plant obtained in each time period includes the load amount of each load in each time period, the output and abandoned light amount of each photovoltaic in each time period, the interactive power between the virtual power plant and the power grid, the income items of the virtual power plant in the peak-shaving market and the carbon market, and the interactive power purchase and sales plan of the power grid.

[0104] S4. The main representative node encrypts the optimal solution obtained in step S3 and stores it in the blockchain, and then broadcasts the encrypted optimal solution to all other member nodes in the committee.

[0105] S5. After receiving the encrypted information broadcast by the main representative node, other member nodes exchange information through cross-communication and verify it through the verification mechanism on the blockchain. When all other member nodes complete the verification, they use the voting mechanism to determine whether the optimal solution obtained by the main representative node is correct. If so, the virtual power plant executes the optimal solution for scheduling. Otherwise, a new main representative node is determined and returns to step S3.

[0106] By introducing a cross-communication mechanism, information can be exchanged between other member nodes, and the validity of the solution results of the main representative node can be verified collaboratively, ensuring that the representative nodes have a consistent understanding of the scheduling results and forming a mutual supervision mechanism.

[0107] The verification includes checking whether the scheduling plan calculated by the main representative meets the originally set objective function and constraints, and whether the scheduling result is logically reasonable. Each other consensus representative node can use certain verification logic (for example, the load, output, grid interaction power, etc. in the optimal solution are checked for rationality) to verify whether the solution of the main representative node meets expectations. In this embodiment, other consensus representative nodes use KKT conditions to quickly verify the optimization results of the main representative, and check whether the results meet all necessary constraints through KKT conditions, thereby determining the rationality and feasibility of the scheduling plan. KKT condition verification specifically includes:

[0108] (1) First-order condition check: Check whether the partial derivative of each variable is zero to ensure that the Lagrangian function meets the first-order optimality condition at the current solution.

[0109] (2) Complementary slack condition check: Verify whether the Lagrange multiplier of each constraint satisfies the complementary slack condition.

[0110] (3) Non-negativity condition check: Ensure that all Lagrange multipliers are non-negative.

[0111] The specific process of judging whether the optimal solution obtained by the main representative node is correct through the voting mechanism is as follows: each other member node generates a voting result based on the verification result. If more than half of the voting results indicate that the optimal solution obtained by the main representative node is correct, the optimal solution obtained by the main representative node is finally determined to be correct, and the optimized scheduling result is officially effective. The final confirmed optimal solution will be fully stored on the blockchain, including complete scheduling plan data for subsequent audit and verification. The stored content is open to all committee member nodes and market regulators to ensure the transparency of the entire scheduling process and prevent "black box operations" or abuse of power by a single node.

[0112] Traditional blockchain technology faces efficiency bottlenecks when processing large-scale and complex calculations, and it is difficult to support the needs of virtual power plants to participate in the coupling market. The above method moves complex calculations off-chain and concentrates the blockchain's responsibilities on data verification and evidence storage, avoiding blockchain nodes from processing complex calculations on the chain, significantly reducing the redundant calculations and communication burdens caused by the blockchain consensus mechanism, and improving the scheduling and clearing efficiency of virtual power plants. Especially in the high-frequency peak-shaving-carbon market, the calculation time can be greatly shortened, which can better meet the real-time requirements of virtual power plants. Through this "off-chain solution, on-chain verification" mechanism, the efficiency of scheduling calculations is guaranteed, and the blockchain technology is used to realize transparent data evidence storage and multi-node collaborative supervision, ensuring the fairness and credibility of the scheduling plan of virtual power plants in the peak-shaving-carbon market, and providing trust guarantee for the subsequent scheduling process.

[0113] Example 2

[0114] The difference between this embodiment and embodiment 1 is that in step S5 of this embodiment, if no consensus is reached in voting or some nodes disagree with the result, the committee will ask the main representative to re-solve or explain the stored result in detail. If no consensus can be reached, the main representative will be re-elected to solve the problem off-chain.

[0115] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment, characterized in that: The following steps are involved: S1. Establish a committee based on all participating entities in the virtual power plant, wherein the committee includes a main representative node and multiple other member nodes; S2. With the goal of maximizing the overall profit of the virtual power plant, a virtual power plant optimization scheduling model is established based on the benefit-cost factors, where the benefit-cost factors include load power utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, abandoned solar power cost, and peak-shaving deviation penalty; S3. The master representative node applies KKT conditions to solve the virtual power plant optimization scheduling model outside the blockchain to obtain the optimal scheduling plan of the virtual power plant in each time period; S4. The main representative node encrypts the optimal solution obtained in step S3 and stores it in the blockchain, and then broadcasts the encrypted optimal solution to all other member nodes in the committee; S5. After receiving the encrypted information broadcast by the main representative node, the other member nodes exchange information through cross-communication and verify it through the verification mechanism on the blockchain. When all other member nodes complete the verification, they determine whether the optimal solution obtained by the main representative node is correct through the voting mechanism. If so, the virtual power plant executes the optimal solution for scheduling. Otherwise, a new main representative node is determined and returns to step S3.

2. According to claim 1, a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment is characterized in that: In step S2, the expression of the virtual power plant optimization scheduling model is specifically as follows: maxP=R load +R F +P G +R CM -C qd -C ferror Among them, P represents the overall profit of the virtual power plant, R load Represents the load power utility, R F represents the revenue of virtual power plants participating in the power peak load regulation market, P G represents the benefits of interaction between the virtual power plant and the grid, R CM represents the benefits of virtual power plants participating in the carbon market, C qd represents the cost of curtailment of virtual power plant, C ferror It represents the virtual power plant peak load deviation penalty. The specific calculation formulas of each part are as follows: in, represents the electricity utility of the i-th load in period t; in, It indicates the peak-shaving price for each period of the day before issued by the power trading center. Indicates the planned peak load of the virtual power plant a few days ago. represents the peak load of load i in time period t, represents the amount of abandoned light from distributed photovoltaic k in time period t; P G =R G -C G Among them, R G is the revenue from electricity sales of the virtual power plant, C G The cost of purchasing electricity for the virtual power plant; Among them, ρ ω is the probability of scenario ω occurring, is the kth distributed photovoltaic output under scenario v during period t, λ CM is the carbon quota price, x a1 Apply for CCER certificate for distributed photovoltaic to obtain carbon quota coefficient, x a2 The carbon emission coefficient of the virtual power plant load purchasing electricity from the grid, The power sold to the grid by the virtual power plant during period t; Among them, λ p The cost price of abandoned power from distributed photovoltaic power generation; Among them, λ pf Penalize price for virtual power plant deviations, is the baseline of the virtual power plant period t.

3. According to claim 2, a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment is characterized in that: The electricity utility of the i-th load in period t The calculation formula is as follows: in, is the load amount of load i in time period t, and are the secondary power utility parameters of the load, and are the minimum load and maximum load in period t, for The corresponding maximum value of the quadratic function.

4. According to claim 2, a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment is characterized in that: Virtual power plant electricity sales revenue R G and the virtual power plant electricity purchase cost C G The calculation formula is as follows: in, is the electricity price for tomorrow’s period t, is the wholesale electricity price, and They are the power purchased from the grid by the virtual power plant during period t and the power sold to the grid by the virtual power plant during period t. is the net power of interaction between the virtual power plant and the grid during period t, ρ ω is the probability of scenario ω occurring, For the distributed photovoltaic output in the scenario, is the load amount of load i in time period t.

5. According to claim 2, a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment is characterized in that: Baseline of the virtual power plant period t The calculation formula is as follows: Among them, ρ ω is the probability of scenario ω occurring, For the distributed photovoltaic output in the scenario, is the load amount of load i in time period t.

6. According to claim 2, a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment is characterized in that: In step S3, the specific process of applying the KKT condition to solve the virtual power plant optimization scheduling model is as follows: S301. Construct the following Lagrangian function: in, and are the Lagrange coefficients of the lower and upper limits of load peak regulation, and are the Lagrangian coefficients of the lower and upper limits of photovoltaic peak regulation, is the load of load i in time period t, P loadimin and P loadimax are the minimum load and the maximum load, respectively, PVkmax The upper limit of photovoltaic peak regulation; S302. Calculate the partial derivative of each variable in the Lagrangian function and set it to 0, and solve the virtual power plant optimization scheduling model under multiple constraints. The multiple constraints include load power consumption constraints, photovoltaic output constraints, virtual power plant aggregation subject peak load constraints, grid interaction constraints and carbon emission limit constraints.

7. The method for optimizing the scheduling of virtual power plants based on blockchain in an electricity-carbon coupling market environment according to claim 6 is characterized in that: The specific expression of the load power consumption constraint is as follows: Among them, P loadimin and P loadimax are the minimum load and the maximum load, respectively; The specific expression of the peak load constraint of the virtual power plant aggregation subject is as follows: in, and are the minimum load and maximum load in period t respectively.

8. According to claim 1, a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment is characterized in that: The optimal dispatching plan of the virtual power plant in each time period obtained in step S3 includes the load amount of each load in each time period, the output and abandoned light amount of each photovoltaic in each time period, the interactive power between the virtual power plant and the power grid, the income items of the virtual power plant in the peak-shaving market and the carbon market, and the interactive power purchase and sales plan of the power grid.

9. According to claim 1, a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment is characterized in that: In step S5, the verification mechanism is KKT condition verification, including first-order condition check, complementary relaxation condition check and non-negativity condition check.

10. According to claim 1, a virtual power plant optimization scheduling method based on blockchain in an electricity-carbon coupling market environment is characterized in that: In step S5, the specific process of judging whether the optimal solution obtained by the main representative node is correct through the voting mechanism is as follows: each other member node generates a voting result based on the verification result. If more than half of the voting results indicate that the optimal solution obtained by the main representative node is correct, then the optimal solution obtained by the main representative node is finally determined to be correct.

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