Virtual power plant management system based on intelligent contract in electricity-carbon coupling market

Abstract

The invention relates to a virtual power plant management system based on a smart contract in an electricity-carbon coupling market. The virtual power plant management system comprises a peak regulation information reporting smart contract module, a virtual power plant day-ahead optimization scheduling smart contract module, a virtual power plant intra-day execution smart contract module and a virtual power plant future income settlement smart contract module. The peak regulation information reporting intelligent contract module is used for acquiring and storing peak regulation information; the virtual power plant day-ahead optimal scheduling intelligent contract module is used for substituting peak regulation information into the virtual power plant optimal scheduling model and solving outside a chain; the virtual power plant intra-day execution intelligent contract module is used for executing the optimal scheduling plan which is verified to be qualified; and the virtual power plant future revenue settlement intelligent contract module is used for solving a virtual power plant revenue distribution model based on generalized Nash bargaining according to the execution information, obtaining an optimal revenue distribution scheme of each resource main body of the virtual power plant and executing the optimal revenue distribution scheme. Compared with the prior art, fair income distribution can be automatically carried out, and meanwhile data safety is fully guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of energy blockchain technology, and in particular to a virtual power plant management system based on smart contracts in an electricity-carbon coupling market. Background Art

[0002] A virtual power plant is a carrier based on control technology and communication technology, which can participate in power market transactions and power grid operations as a special power plant. Virtual power plants are mainly used to aggregate distributed clean energy and adjustable loads to participate in power peak regulation. Existing technologies usually use a centralized approach to dispatch various entities aggregated by virtual power plants. This type of dispatching method has the following limitations: First, it is difficult to respond to large-scale data traffic in a timely manner, especially when faced with massive aggregated resources, it is difficult to ensure the authenticity and integrity of each piece of data; second, the virtual power plant has a large amount of data information of its aggregated entities, and the centralized management method is difficult to ensure the security and privacy of the data of each entity; third, the transaction involves many entities and the decision-making and data processing processes are not transparent, making it difficult to ensure the fairness of the dispatching method and the rationality of the distribution of benefits.

[0003] In addition, with the continuous development of the carbon market, distributed clean energy can obtain offset quotas by applying for CCER certificates and trade them in the carbon market. At the same time, the carbon market will also assess the indirect carbon emissions generated by the load purchasing electricity from the power grid. However, there are currently few plans for virtual power plants to participate in carbon market assessments as aggregation entities. In the process of virtual power plants participating in the carbon market as a whole, each entity participates in the carbon market in different ways, and has different contributions to the virtual power plant carbon quota assessment process. The carbon trading data is numerous and complex, and it is necessary to use scientific and reasonable methods to ensure the fairness of virtual power plant carbon trading.

[0004] To sum up, in the current electricity-carbon coupling market, virtual power plants can participate in power peak regulation and carbon trading as aggregation entities. It is necessary to design a corresponding virtual power plant management system that can dispatch each entity under the optimal plan and automatically distribute profits fairly and reasonably, while also fully ensuring the security, authenticity and integrity of the 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 management system based on smart contracts in the electricity-carbon coupling market, which can dispatch various entities under the optimal plan and automatically distribute profits fairly and reasonably, while also fully ensuring the security, authenticity and integrity of the 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 management system based on smart contracts in an electricity-carbon coupling market, including a peak-shaving information reporting smart contract module deployed on a blockchain, a virtual power plant day-ahead optimization scheduling smart contract module, a virtual power plant intraday execution smart contract module, and a virtual power plant future revenue settlement smart contract module;

[0008] The peak-shaving information reporting smart contract module is used to obtain and store peak-shaving information, and the peak-shaving information includes the day-ahead peak-shaving price of the virtual power plant from the power grid and the peak-shaving range generated by each resource subject of the virtual power plant based on the day-ahead peak-shaving price of the virtual power plant;

[0009] The virtual power plant's day-ahead optimization scheduling smart contract module is used to substitute the peak-shaving information into the virtual power plant optimization scheduling model, and use computing resources outside the blockchain to solve and obtain the optimal scheduling plan. The optimal scheduling plan is stored on the blockchain after being verified by the verification mechanism on the blockchain. The virtual power plant optimization scheduling model aims to maximize the overall profit of the virtual power plant and is established based on the benefit-cost factors. The benefit-cost factors include load electricity utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, abandoned light cost and peak-shaving deviation penalty;

[0010] The intraday execution smart contract module of the virtual power plant is used to execute the verified optimal dispatch plan, and the corresponding execution information is stored on the blockchain during the execution process;

[0011] The virtual power plant future profit settlement smart contract module is used to solve the virtual power plant profit distribution model based on generalized Nash bargaining according to the execution information, obtain the optimal profit distribution plan for each resource entity of the virtual power plant and execute it. The virtual power plant profit distribution model based on generalized Nash bargaining aims to maximize the generalized Nash bargaining utility function.

[0012] Furthermore, the expression of the virtual power plant optimization scheduling model is as follows:

[0013] max P=R load +R F +P G +R CM -C qd -C ferror

[0014] 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:

[0015]

[0016] in, represents the electricity utility of the i-th load in period t;

[0017]

[0018] 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 amount of abandoned light from distributed photovoltaic k in time period t, Represents the peak load of load i in time period t. The specific expression of the peak load constraint of the virtual power plant aggregation subject is as follows:

[0019]

[0020] in, and are the minimum load and maximum load in period t respectively;

[0021] P G =R G -C G

[0022] 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;

[0023]

[0024] 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;

[0025]

[0026] Among them, λ p The cost price of abandoned power from distributed photovoltaic power generation;

[0027]

[0028] Among them, λ pf Penalize price for virtual power plant deviations, is the baseline of the virtual power plant period t.

[0029] Furthermore, it is characterized in that the power utility of the i-th load in the t period The calculation formula is as follows:

[0030]

[0031] 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.

[0032] 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:

[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.

[0035] Furthermore, the KKT condition is applied to solve the virtual power plant optimization scheduling model. The specific process is as follows:

[0036] Construct the following Lagrangian function:

[0037]

[0038] 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, PVkfmax and P PVkfmin They are the upper and lower limits of photovoltaic peak regulation respectively;

[0039] The partial derivative of each variable in the above Lagrangian function is calculated and set to 0, and the virtual power plant optimization scheduling model is solved 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.

[0040] Furthermore, the verification mechanism on the blockchain is specifically KKT condition verification, including first-order condition check, complementary relaxation condition check and non-negative condition check.

[0041] Furthermore, the expression of the virtual power plant revenue distribution model based on generalized Nash bargaining is as follows:

[0042]

[0043] Among them, E PVk and E loadi They are the utility functions of distributed photovoltaic k and load i in generalized Nash bargaining, respectively.

[0044] Furthermore, the utility function E of distributed photovoltaic k and load i in generalized Nash bargaining is PVk and E loadi The specific expression is as follows:

[0045] E PVk =ln(P PVkA -P PVks )

[0046] E loadi =ln(P loadiA -P loadis )

[0047] P PVkA ≥P PVks

[0048] P loadiA ≥P loadis

[0049] Among them, P PVkA and P loadiA are the profits of distributed photovoltaic and load after participating in virtual power plant aggregation, P PVkS and P loadiS They are the profits of distributed photovoltaics and loads before participating in virtual power plant aggregation;

[0050] Profit P of distributed photovoltaics before participating in virtual power plant aggregation PVkS The specific expression is as follows:

[0051]

[0052] Among them, ρ ω is the probability of scenario ω occurring, is the kth distributed photovoltaic output in the scenario ω during period t, is the wholesale electricity price, λ CM is the carbon quota price, x a1 Apply for CCER certificates for distributed photovoltaics to obtain carbon quota coefficients. represents the amount of abandoned light from distributed photovoltaic k in time period t;

[0053] Profit P before load participation in virtual power plant aggregation loadiS The specific expression is as follows:

[0054]

[0055] in, represents the electricity utility of the i-th load in period t, is the load amount of load i in time period t, is the electricity price for tomorrow’s period t, λ CM is the carbon quota price, x a2 The carbon emission coefficient for purchasing electricity from the grid for the virtual power plant load.

[0056] Furthermore, the KKT condition is applied to solve the virtual power plant revenue distribution model based on generalized Nash bargaining. The specific process is as follows:

[0057] Construct the following Lagrangian function:

[0058]

[0059] 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, λ kmin and λ kmax are the Lagrangian coefficients of the lower and upper limits of the kth distributed photovoltaic revenue, λ imin and λ imax are the Lagrangian coefficients of the lower and upper limits of the peak load, is the load of load i in time period t, P loadimin and P loadimax are the minimum load and the maximum load, respectively, PVkfmax and P PVkfmin are the upper and lower limits of photovoltaic peak regulation, R PVkA and R loadiA are the benefits of distributed photovoltaic k and load i after aggregation, R PVkS and RloadiS are the benefits of distributed photovoltaic k and load i before aggregation;

[0060] Take the partial derivative of each variable in the above Lagrangian function and set it to 0.

[0061] Furthermore, after obtaining the optimal profit distribution plan, the verification mechanism on the blockchain is used to verify it, and it is executed after the verification is qualified.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The present invention proposes a virtual power plant management system based on smart contracts in the electricity-carbon coupling market, including a peak-shaving information reporting smart contract module deployed on the blockchain, a virtual power plant day-ahead optimization scheduling smart contract module, a virtual power plant intraday execution smart contract module and a virtual power plant future profit settlement smart contract module. The present invention utilizes the distributed characteristics of smart contracts to disperse the tasks of data processing and execution to each contract for processing, thereby reducing the pressure on the central system and improving the efficiency of data processing; the automatic execution and pre-programming functions of smart contracts enable the peak-shaving and carbon market scheduling processes to be automated according to rules, which not only reduces human operational errors, but also ensures the compliance of the process and the real-time response.

[0064] Specifically, the peak-shaving information reporting smart contract module is used to obtain and store peak-shaving information. Based on the immutability and transparency of blockchain technology, the present invention can ensure the authenticity, integrity and traceability of each piece of data, and at the same time reduce the dependence on the central server. The virtual power plant recently optimized scheduling smart contract module is used to substitute the peak-shaving information into the virtual power plant optimization scheduling model, and use computing resources outside the blockchain to solve and obtain the optimal scheduling plan. The optimal scheduling plan is stored on the blockchain after being verified by the verification mechanism on the blockchain. Through this "off-chain solution, on-chain verification" mode, the redundant calculation and communication burden caused by the blockchain consensus mechanism can be significantly reduced, and the scheduling and clearing efficiency of the virtual power plant can be improved; the virtual power plant optimization scheduling model aims to maximize the overall profit of the virtual power plant, and comprehensively considers the load power utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, abandoned light cost and peak-shaving deviation penalty and other revenue-cost factors to establish a virtual power plant optimization scheduling model, which can improve the accuracy of obtaining the scheduling plan. The virtual power plant's intraday execution smart contract module is used to execute the verified optimal scheduling plan. During the execution process, the corresponding execution information is stored on the blockchain. The virtual power plant's future profit settlement smart contract module is used to solve the virtual power plant profit distribution model based on generalized Nash bargaining according to the execution information, obtain the optimal profit distribution plan for each resource entity in the virtual power plant and execute it. The virtual power plant profit distribution model based on generalized Nash bargaining aims to maximize the generalized Nash bargaining utility function. The smart contract's distribution mechanism based on contribution makes the participation and benefits of various entities in the power peak regulation and carbon market more fair and reasonable.

[0065] In the above process, the scheduling and profit distribution rules can be pre-set in the smart contract and dynamically adjusted according to the needs of different entities and changes in market conditions, ensuring the transparency of each entity's contribution and benefits, and the scheduling decisions are open and traceable, thereby improving the fairness and flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the structure of the system of the present invention. DETAILED DESCRIPTION

[0067] 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.

[0068] Example 1

[0069] This embodiment provides a virtual power plant management system based on smart contracts in the electricity-carbon coupling market. Figure 1As shown, it includes the peak load information reporting smart contract module deployed on the blockchain, the virtual power plant day-ahead optimization dispatch smart contract module, the virtual power plant intraday execution smart contract module and the virtual power plant future revenue settlement smart contract module. The composition and function of each module are as follows:

[0070] A. Peak load information reporting smart contract module

[0071] The peak-shaving information reporting smart contract module is used to obtain and store peak-shaving information, which includes the day-ahead peak-shaving price of the virtual power plant from the power grid and the peak-shaving range generated by each resource entity of the virtual power plant based on the aforementioned day-ahead peak-shaving price of the virtual power plant.

[0072] First, each resource subject of the virtual power plant applies for registration on the blockchain. Each resource subject needs to report the organization to which the account belongs. Among them, load users need to report the load power usage and power usage range, and distributed photovoltaic resources need to report the installed capacity. The power usage utility of the i-th adjustable load in time period t The modeling is as follows:

[0073]

[0074] in, is the load amount of load i in time period t, and the load consumption power constraint is 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, P loadimin and P loadimax are the minimum load and the maximum load, respectively;

[0075] Output of the kth distributed photovoltaic system in the scenario ω during time period t The constraints of photovoltaic output are obtained through prediction by scenario generation method as follows:

[0076]

[0077] Among them, P PVkmax The maximum photovoltaic output.

[0078] Secondly, the virtual power plant organization administrator approves the entry of resources. If they are allowed to enter, their resource information will be stored in the blockchain database and the information will be broadcast. As the organization administrator, the virtual power plant aggregator receives the subject registration information and allocates the blockchain transaction account. The allocated blockchain transaction account information includes the blockchain transaction account address, account adjustable peak volume, account peak-shaving scheduling volume, account peak-shaving execution volume and account income. The virtual power plant aggregator and reliable nodes approve whether each resource meets the access standards. The access mechanism is: adjustable loads need to have a certain amount of adjustable capacity, and distributed photovoltaics need to have a certain amount of installed capacity. After the review is passed, the account information and resource characteristics of the resource subject are stored in the blockchain database, and the transaction account information is broadcast through the blockchain to ensure reliable records throughout the network.

[0079] After the virtual power plant aggregator sets the peak-shaving price for the power grid a day ago, each resource entity of the registered virtual power plant will report its peak-shaving range after receiving the price information. The aggregator will then broadcast the information after verifying the peak-shaving range.

[0080] In summary, the peak-shaving information reporting smart contract module supports the registration of virtual power plant resource entities and the storage of basic information, including the power usage utility and power usage range of the load, as well as the installed capacity of distributed photovoltaics as basic resource information stored in the blockchain to support subsequent scheduling transactions.

[0081] B. Virtual power plant day-ahead optimization dispatch smart contract module

[0082] The virtual power plant's recently optimized scheduling smart contract module is used to substitute peak-shaving information into the virtual power plant's optimized scheduling model, and use computing resources outside the blockchain to solve the optimal scheduling plan. The optimal scheduling plan is stored on the blockchain after being verified by the verification mechanism on the blockchain.

[0083] The virtual power plant optimization dispatch model aims to maximize the overall profit of the virtual power plant and is established based on the benefit-cost factors, which include load power utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, abandoned light cost and peak-shaving deviation penalty. The expression of the virtual power plant optimization dispatch model is as follows:

[0084] max P=R load +R F +P G +R CM -C qd -C ferror

[0085] 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, RCM 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:

[0086] (1) Load power efficiency R load

[0087]

[0088] (2) The revenue of virtual power plants participating in the power peak load regulation market R F

[0089]

[0090] 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 amount of abandoned light from distributed photovoltaic k in time period t, Represents the peak load of load i in time period t. The specific expression of the peak load constraint of the virtual power plant aggregation subject is as follows:

[0091]

[0092]

[0093] in, and are the minimum load and maximum load in period t respectively.

[0094] (3) The benefits of interaction between virtual power plants and power grids P G

[0095] P G =R G -C G

[0096] 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:

[0097]

[0098] 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.

[0099] (4) The benefits of virtual power plants participating in the carbon market R CM

[0100]

[0101] 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.

[0102] (5) Virtual power plant curtailment cost C qd

[0103]

[0104] Among them, λ p It is the cost price of abandoned power from distributed photovoltaic power generation.

[0105] (6) Virtual power plant peak load deviation penalty C ferror

[0106]

[0107] 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:

[0108]

[0109] 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.

[0110] Then, the KKT condition is applied to solve the above virtual power plant optimization scheduling model. The specific process is as follows:

[0111] S101. Construct the following Lagrangian function:

[0112]

[0113] 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, PVkfmax and P PVkfmin They are the upper and lower limits of photovoltaic peak regulation respectively.

[0114] S102. Calculate the partial derivative of each variable in the above-mentioned 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.

[0115] The specific expression of the peak load constraint of the virtual power plant aggregation entity is as follows:

[0116]

[0117] in, and are the minimum load and maximum load in period t respectively.

[0118] In this embodiment, the verification mechanism on the blockchain is specifically KKT condition verification, which checks whether the results of the KKT condition check meet all necessary constraints, thereby determining the rationality and feasibility of the scheduling plan. KKT condition verification specifically includes:

[0119] (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.

[0120] (2) Complementary slack condition check: Verify whether the Lagrange multiplier of each constraint satisfies the complementary slack condition.

[0121] (3) Non-negativity condition check: Ensure that all Lagrange multipliers are non-negative.

[0122] The virtual power plant recently optimized the scheduling smart contract module through the "off-chain solution, on-chain verification" mechanism, which not only ensured the efficiency of scheduling calculations, but also used blockchain technology to achieve transparent data storage and multi-node collaborative supervision, ensuring the fairness and credibility of the virtual power plant's scheduling plan in the peak-shaving-carbon market, and providing trust protection for subsequent scheduling processes.

[0123] C. Virtual power plant intraday execution smart contract module

[0124] The virtual power plant's intraday execution smart contract module is used to execute the verified optimal scheduling plan, and the corresponding execution information is stored on the blockchain during the execution process.

[0125] D. Virtual power plant future income settlement smart contract module

[0126] The virtual power plant's post-day profit settlement smart contract module is used to solve the virtual power plant profit distribution model based on generalized Nash bargaining according to the execution information in the virtual power plant's intraday execution smart contract module, obtain the optimal profit distribution plan for each resource entity in the virtual power plant and execute it.

[0127] The virtual power plant revenue distribution model based on generalized Nash bargaining aims to maximize the generalized Nash bargaining utility function, and the specific expression is as follows:

[0128]

[0129] Among them, E PVk and E loadi They are the utility functions of distributed photovoltaic k and load i in generalized Nash bargaining, and the specific expressions are as follows:

[0130] E PVk =ln(P PVkA -P PVks )

[0131] E loadi =ln(P loadiA -P loadis )

[0132] P PVkA ≥P PVkS

[0133] P loadiA ≥P loadis

[0134] Among them, P PVkA and P loadiA are the profits of distributed photovoltaic and load after participating in virtual power plant aggregation, P PVkS and P loadiS They are the profits of distributed photovoltaics and loads before they participate in virtual power plant aggregation.

[0135] Before the aggregation, the photovoltaic grid is connected, and the income of distributed photovoltaic k comes from the carbon quota obtained by grid connection and application of CCER certificates:

[0136]

[0137] Before the load i is aggregated, it does not meet the peak load access requirements. It only has the electricity utility, the cost of purchasing electricity from the grid, and the assessment by the carbon market:

[0138]

[0139] It is necessary to ensure that the benefits of each aggregation entity are higher after aggregation, so as to increase the enthusiasm of each entity to participate in the electricity-carbon coupling market transactions:

[0140] P PVkA ≥P PVkS

[0141] P loadiA ≥P loadiS

[0142] The above process refers to the income of each subject before participating in the aggregation. If the income of a subject is high after aggregation, then the subject has stronger bargaining power and should receive higher income. The above contribution-based allocation mechanism makes the participation and income of each subject in the power peak load regulation and carbon market more fair and reasonable.

[0143] Similarly, the KKT condition is applied to solve the above virtual power plant revenue distribution model based on generalized Nash bargaining. The specific process is as follows:

[0144] S201. Construct the following Lagrangian function:

[0145]

[0146] 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, λ kmin and λ kmax are the Lagrangian coefficients of the lower and upper limits of the kth distributed photovoltaic revenue, λ imin and λ imax are the Lagrangian coefficients of the lower and upper limits of the peak load, is the load of load i in time period t, P loadimin and P loadimax are the minimum load and the maximum load, respectively, PVkfmax and P PVkfmin are the upper and lower limits of photovoltaic peak regulation, R PVkA and R loadiA are the benefits of distributed photovoltaic k and load i after aggregation, R PVkS and R loadiS They are the benefits of distributed photovoltaic k and load i before aggregation.

[0147] S202. Calculate the partial derivative of each variable in the above Lagrangian function and set it to 0.

[0148] Based on the optimal allocation plan obtained, the virtual power plant aggregator distributes the profits of each entity's resources in the virtual power plant off-chain and records the results on-chain.

[0149] Compared with the prior art, the above method has at least the following advantages:

[0150] 1. Improve data processing efficiency and real-time response

[0151] Traditional virtual power plants process data in a centralized manner, which is prone to data flow bottlenecks when faced with massive resources, resulting in response delays. The present invention utilizes the distributed characteristics of smart contracts to distribute the scheduling and data processing tasks to various contracts for processing, thereby reducing the pressure on the central system and improving the efficiency of data processing. The automatic execution of smart contracts also ensures that peak-shaving instructions can be quickly communicated to various entities, achieving true real-time response.

[0152] 2. Ensure the authenticity, integrity and security of data

[0153] Traditional centralized methods make it difficult to ensure that data is not tampered with during transmission and processing. Based on the immutability and transparency of blockchain technology, smart contracts can ensure the authenticity and integrity of each piece of data, while ensuring the traceability of data. In addition, since data is stored in a decentralized manner on the blockchain, it reduces dependence on central servers, further improving data security and the data privacy of each subject.

[0154] 3. Ensure fairness and transparency in decision-making

[0155] Smart contracts provide an automated fair mechanism. In this invention, the dispatching and revenue distribution rules of virtual power plants can be pre-set in smart contracts to ensure the transparency of each subject's contribution and revenue, and the dispatching decision is open and traceable, eliminating human intervention and opaque operations, and improving overall fairness. The contribution-based distribution mechanism of smart contracts makes the participation and revenue of each subject in the power peaking and carbon markets more fair and reasonable.

[0156] 4. Improve system automation and compliance

[0157] The automatic execution and pre-programming functions of smart contracts enable the dispatching process of peak load regulation and carbon market to be automated according to the rules, which not only reduces the errors of human operation, but also ensures the compliance of the process. The transaction data, dispatching instructions and income distribution in the carbon market are recorded on the blockchain, which enhances traceability and provides reliable data support for market supervision, meeting the compliance requirements of the current carbon market.

[0158] 5. Improve the flexibility and adaptability of the system

[0159] In the power peaking and carbon markets, the demands and market conditions of different entities are constantly changing. Smart contracts can flexibly adjust the dispatching and allocation rules to adapt to market changes, while automatically updating the contribution records of each entity in transactions and dispatching. In this way, virtual power plants can participate in the peaking and carbon markets more flexibly and dynamically adjust strategies to adapt to real-time market needs.

[0160] In summary, the present invention combines the advantages of distributed data processing, data security, automated transparency and flexible adjustment of smart contracts, and solves the problems of slow response, data opacity, and unfair distribution of traditional virtual power plants. In the coupled scenario of power peak regulation and carbon market, the application of smart contracts improves the fairness, compliance and data security of virtual power plants, enabling them to participate in the market more efficiently, more transparently and more securely, and has broad application prospects.

[0161] Example 2

[0162] The difference between this implementation and Example 1 is that the virtual power plant aggregator first broadcasts the optimal allocation plan to the main resources of the virtual power plant and verifies it using the verification mechanism on the blockchain. After the verification is qualified, it is executed and the profit distribution is carried out. Otherwise, the solution is re-calculated, which can further ensure the fairness and rationality of the profit distribution.

[0163] 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 management system based on smart contracts in the electricity-carbon coupling market, characterized in that: It includes the peak load information reporting smart contract module deployed on the blockchain, the day-ahead optimization dispatching smart contract module for virtual power plants, the day-ahead execution smart contract module for virtual power plants, and the future revenue settlement smart contract module for virtual power plants; The peak-shaving information reporting smart contract module is used to obtain and store peak-shaving information, and the peak-shaving information includes the day-ahead peak-shaving price of the virtual power plant from the power grid and the peak-shaving range generated by each resource subject of the virtual power plant based on the day-ahead peak-shaving price of the virtual power plant; The virtual power plant's day-ahead optimization scheduling smart contract module is used to substitute the peak-shaving information into the virtual power plant optimization scheduling model, and use computing resources outside the blockchain to solve and obtain the optimal scheduling plan. The optimal scheduling plan is stored on the blockchain after being verified by the verification mechanism on the blockchain. The virtual power plant optimization scheduling model aims to maximize the overall profit of the virtual power plant and is established based on the benefit-cost factors. The benefit-cost factors include load electricity utility, peak-shaving market revenue, grid interaction revenue, carbon market revenue, abandoned light cost and peak-shaving deviation penalty; The intraday execution smart contract module of the virtual power plant is used to execute the verified optimal dispatch plan, and the corresponding execution information is stored on the blockchain during the execution process; The virtual power plant future profit settlement smart contract module is used to solve the virtual power plant profit distribution model based on generalized Nash bargaining according to the execution information, obtain the optimal profit distribution plan for each resource entity of the virtual power plant and execute it. The virtual power plant profit distribution model based on generalized Nash bargaining aims to maximize the generalized Nash bargaining utility function.

2. According to claim 1, a virtual power plant management system based on smart contracts in an electricity-carbon coupling market is characterized in that: The expression of the virtual power plant optimization scheduling model is as follows: max P=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 amount of abandoned light from distributed photovoltaic k in time period t, Represents the peak load of load i in time period t. 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; 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 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; 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 management system based on smart contracts in an electricity-carbon coupling market 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 right The maximum value of the corresponding quadratic function.

4. According to claim 2, a virtual power plant management system based on smart contracts in an electricity-carbon coupling market 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.

5. According to claim 2, a virtual power plant management system based on smart contracts in an electricity-carbon coupling market is characterized in that: The KKT condition is applied to solve the virtual power plant optimization scheduling model. The specific process is as follows: 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, P loadimin and P loadimax are the minimum load and the maximum load, respectively, PVkfmax and P PVkfmin They are the upper and lower limits of photovoltaic peak regulation respectively; The partial derivative of each variable in the above Lagrangian function is calculated and set to 0, and the virtual power plant optimization scheduling model is solved 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.

6. According to claim 1, a virtual power plant management system based on smart contracts in an electricity-carbon coupling market is characterized in that: The verification mechanism on the blockchain is specifically KKT condition verification, which includes first-order condition check, complementary relaxation condition check and non-negative condition check.

7. According to claim 1, a virtual power plant management system based on smart contracts in an electricity-carbon coupling market is characterized in that: The expression of the virtual power plant revenue distribution model based on generalized Nash bargaining is as follows: Among them, E PVk and E loadi They are the utility functions of distributed photovoltaic k and load i in generalized Nash bargaining, respectively.

8. The virtual power plant management system based on smart contracts in the electricity-carbon coupling market according to claim 7 is characterized in that: Utility function E of distributed photovoltaic k and load i in generalized Nash bargaining PVk and E loadi The specific expression is as follows: HAVE BEEN PVk =ln(P PVkA -P PVkS ) HAVE BEEN loadi =ln(P laadiA -P loadiS ) P PVkA ≥P PVkS P loadiA ≥P loadiS Among them, P PVkA and P loadiA are the profits of distributed photovoltaic and load after participating in virtual power plant aggregation, P PVkS and P loadis They are the profits of distributed photovoltaics and loads before participating in virtual power plant aggregation; Profit P of distributed photovoltaics before participating in virtual power plant aggregation PVks The specific expression is as follows: Among them, ρ ω is the probability of scenario ω occurring, is the kth distributed photovoltaic output in the scenario ω during period t, is the wholesale electricity price, λ CM is the carbon quota price, x a1 Apply for CCER certificates for distributed photovoltaics to obtain carbon quota coefficients. represents the amount of abandoned light from distributed photovoltaic k in time period t; Profit P before load participation in virtual power plant aggregation loaaiS The specific expression is as follows: in, represents the electricity utility of the i-th load in period t, is the load amount of load i in time period t, is the electricity price for tomorrow’s period t, λ CM is the carbon quota price, x a2 The carbon emission coefficient for purchasing electricity from the grid for the virtual power plant load.

9. The virtual power plant management system based on smart contracts in the electricity-carbon coupling market according to claim 8 is characterized in that: The KKT condition is applied to solve the virtual power plant revenue distribution model based on generalized Nash bargaining. The specific process is as follows: 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, λ kmin and λ kmax are the Lagrangian coefficients of the lower and upper limits of the kth distributed photovoltaic revenue, λ imin and λ imax are the Lagrangian coefficients of the lower and upper limits of the peak load, is the load of load i in time period t, P loadimin and P koadimax are the minimum load and the maximum load, respectively, PVkfmax and P PVkfmin are the upper and lower limits of photovoltaic peak regulation, R PVkA and R loadiA are the benefits of distributed photovoltaic k and load i after aggregation, R PVkS and R loadiS are the benefits of distributed photovoltaic k and load i before aggregation; Take the partial derivative of each variable in the above Lagrangian function and set it to 0.

10. The virtual power plant management system based on smart contracts in the electricity-carbon coupling market according to claim 1 is characterized in that: After obtaining the optimal profit distribution plan, the verification mechanism on the blockchain is used for verification, and it is executed only after the verification is qualified.

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