Optimization method and system for joint dispatching of optical storage based on blockchain credible game

Abstract

The present application relates to the technical field of distributed energy scheduling, in particular to a photovoltaic storage joint scheduling optimization method and system based on a blockchain trusted game. The present application first establishes utility functions of photovoltaic power suppliers, energy storage service providers and users, and the utility of the energy storage service providers includes network internal balance service income and chain storage rental income; then an asymmetric Nash bargaining model is constructed, with the weighted product of the difference between the utility and the reservation utility being maximized as the target; then constraint conditions such as power balance, energy storage state, charge-discharge mutual exclusion, price rationality and capacity occupation are set; further, the global problem is decomposed into sub-problems by using the alternating direction multiplier method, and each subject independently solves the local optimal decision; finally, the global variables are updated through the blockchain platform and iterated to convergence, and the scheduling scheme including power distribution, market price and income distribution is output. The present application effectively solves the trust and efficiency problem of distributed energy cooperation.

Description

Technical Field

[0001] This invention relates to the field of distributed energy dispatching technology, specifically to a method and system for optimizing the joint dispatching of photovoltaic and energy storage based on blockchain-based trusted game theory. Background Technology

[0002] With the rapid development of distributed photovoltaic (PV) power, its volatility and uncertainty have become increasingly prominent. Energy storage systems, due to their excellent power regulation capabilities, have become a key means to achieve stable integration of distributed renewable energy. Distributed energy cooperation network mechanisms are gradually becoming an important pathway for integrating PV, energy storage, and load resources.

[0003] However, traditional energy dispatch models have significant shortcomings: First, centralized platforms lack transparency in data collection and settlement processes, posing a risk of tampering and resulting in high trust costs among participating entities. Second, centralized dispatch algorithms struggle to simultaneously balance efficiency, fairness, and privacy protection. Third, existing shared energy storage models are open to the market, lacking dedicated guarantees for specific network members, making it difficult to achieve accurate and fair allocation of energy storage resources in dynamic scenarios and failing to establish a compensation mechanism directly linked to photovoltaic volatility absorption. Existing research is largely limited to single resources or single markets, and no technical solution has yet been found that can organically combine distributed photovoltaics with energy storage resources that possess network specificity, transaction credibility, and game-theoretic allocation within a unified framework. Summary of the Invention

[0004] This invention provides a method and system for joint scheduling optimization of photovoltaic and energy storage based on blockchain trusted game theory, which realizes multi-entity collaborative optimization and fair distribution of benefits.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] This invention relates to a photovoltaic-storage joint scheduling optimization method based on blockchain trusted game theory, comprising:

[0007] S100: Establish utility functions for photovoltaic power generators, energy storage service providers, and users; among which, the utility function of energy storage service providers includes revenue from network internal balancing services and revenue from chain-shared energy storage leasing.

[0008] S200: Based on the utility functions of the photovoltaic power generators, energy storage service providers, and users, an asymmetric Nash bargaining model is constructed. The objective function is to maximize the weighted product of the difference between the utility and the retention utility of the photovoltaic power generators, energy storage service providers, and users, and to establish a global optimization problem.

[0009] S300: Set constraints for the global optimization problem, including power demand balance constraints, energy storage state of charge constraints, charge and discharge mutual exclusion constraints, price rationality constraints, and capacity occupancy constraints;

[0010] S400: The global optimization problem is decomposed into energy storage service provider subproblems, photovoltaic power generator subproblems and user subproblems by using the alternating direction multiplier method. Each of the photovoltaic power generator, energy storage service provider and user solves the local optimal decision variables independently.

[0011] S500: The photovoltaic power generator, energy storage service provider and user will upload their respective locally optimal decision variables to the blockchain platform, and the blockchain platform will update the globally consistent variables and dual variables and iterate until convergence.

[0012] S600: The output includes a scheduling scheme that includes power allocation, market price, and revenue distribution, and is recorded on the blockchain.

[0013] As a preferred technical solution of the present invention, the revenue from the Chain-Shared Energy Storage Leasing includes revenue from the leasing of power usage rights, which consists of the discharge electricity fee charged by the energy storage service provider to the user, the charging electricity fee paid to the user, and the power usage right leasing fee.

[0014] As a preferred embodiment of the present invention, the network internal balancing service revenue includes charging response revenue and discharging response revenue;

[0015] The charging response revenue includes the electricity revenue from the energy storage service provider's purchase of electricity from the photovoltaic power generator and the consumption service compensation fee paid by the photovoltaic power generator;

[0016] The discharge response revenue includes the electricity revenue from the energy storage service provider's sale of electricity to the photovoltaic power generator and the performance guarantee premium paid by the photovoltaic power generator;

[0017] The compensation fee for grid connection services shall be shared by the photovoltaic power generation group according to their respective installed capacity proportions, and the performance guarantee premium shall be shared by the photovoltaic power generation group according to their respective day-ahead forecast error standard deviation proportions.

[0018] As a preferred embodiment of the present invention, the reserved utility is the lowest utility value that the photovoltaic power generator, the energy storage service provider, and the user can obtain when they do not participate in collaborative dispatch; the weighting coefficient in the weighted product is the bargaining weight of the photovoltaic power generator, the energy storage service provider, and the user.

[0019] As a preferred embodiment of the present invention, S300 includes:

[0020] The power demand balance constraint is the power balance equation between photovoltaic power generation, energy storage charging and discharging and user power demand in each time period.

[0021] The energy storage state of charge constraints include the energy storage state of charge dynamic equation, the energy storage state of charge upper limit constraint, and the energy storage state of charge lower limit constraint.

[0022] The charging and discharging mutual exclusion constraint is achieved by introducing charging behavior variables and discharging behavior variables, which constrain that the charging behavior variables and discharging behavior variables cannot be 1 at the same time during the same period.

[0023] The price reasonableness constraint limits the photovoltaic power sales price, energy storage charging and discharging price, and power usage right leasing unit price to a reasonable range;

[0024] The capacity occupancy constraint limits the chain-shared energy storage capacity leased by each user to no more than the allocation limit, and the total capacity leased by all users to no more than the upper limit of the energy storage system's dispatchable capacity.

[0025] As a preferred embodiment of the present invention, in step S400:

[0026] The energy storage service provider subproblem is further decomposed into a charge / discharge decision subproblem and a state of charge subproblem. The local optimal decision variables of the charge / discharge decision subproblem are the charging amount and discharging amount in each time period, and the local optimal decision variables of the state of charge subproblem are the energy storage state of charge in each time period. The energy storage state of charge dynamic equation is coupled with the charge / discharge decision subproblem.

[0027] The locally optimal decision variable for the photovoltaic power generation electronic problem is the power generation in each time period;

[0028] The locally optimal decision variable for the user subproblem is the amount of electricity purchased in each time period.

[0029] As a preferred embodiment of the present invention, the process of iterating to convergence includes:

[0030] The blockchain platform aggregates locally optimal decision variables uploaded by photovoltaic power generators, energy storage service providers, and users through smart contracts, calculates and updates globally consistent variables and dual variables, and then distributes them to each entity.

[0031] Each entity performs the next round of solving based on the updated global consistency variables and dual variables;

[0032] The blockchain platform repeats the steps of updating the global consistency variable and dual variable and distributing them to each subject, as well as each subject solving for the next round of local optimal decision variables, until the change in the global consistency variable is less than the preset convergence threshold.

[0033] This invention also proposes a photovoltaic-storage joint scheduling optimization system based on blockchain trusted game theory, comprising:

[0034] The utility function modeling module is used to establish the utility functions of photovoltaic power generators, energy storage service providers, and users; among which, the utility function of energy storage service providers includes the revenue from network internal balancing services and the revenue from chain-shared energy storage leasing.

[0035] The game modeling module is used to construct an asymmetric Nash bargaining model based on the utility functions of the photovoltaic power generator, energy storage service provider, and user. The objective function is to maximize the weighted product of the difference between the utility and the retention utility of the photovoltaic power generator, energy storage service provider, and user, and to establish a global optimization problem.

[0036] The constraint setting module is used to set constraints for the global optimization problem, including power demand balance constraints, energy storage state of charge constraints, charge and discharge mutual exclusion constraints, price rationality constraints, and capacity occupancy constraints.

[0037] The distributed solution module is used to decompose the global optimization problem into subproblems for energy storage service providers, photovoltaic power generators, and users using the alternating direction multiplier method. Each of the photovoltaic power generators, energy storage service providers, and users independently solves for the local optimal decision variables.

[0038] The blockchain coordination module is used by the photovoltaic power generator, energy storage service provider and user to upload their respective locally optimal decision variables to the blockchain platform, and the blockchain platform updates the globally consistent variables and dual variables and iterates until convergence.

[0039] The scheduling scheme output module is used to output a scheduling scheme that includes power allocation, market price and revenue allocation, and record it on the blockchain.

[0040] The present invention also proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned optical-storage joint scheduling optimization method based on blockchain trusted game theory.

[0041] The present invention also proposes a readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned optical-storage joint scheduling optimization method based on blockchain trusted game theory.

[0042] The beneficial effects of this invention are:

[0043] 1. This invention proposes the concept of chain-shared energy storage and establishes a differentiated cost-sharing mechanism based on risk contribution: the compensation fee for grid connection services is shared according to the proportion of installed capacity, and the performance guarantee premium is shared according to the proportion of the standard deviation of the prediction error. This mechanism precisely links the cost of energy storage balancing services with the volatility contribution of each photovoltaic power generator, realizing the fair principle of "whoever contributes the risk bears the cost," and effectively solving the problem of mismatch between cost sharing and risk responsibility under the traditional model.

[0044] 2. This invention embeds an asymmetric Nash bargaining model into a blockchain smart contract, using bargaining weights to characterize the differences in negotiation capabilities among different entities, thus overcoming the limitations of the symmetric game assumption. The blockchain platform automatically executes game solving and payout distribution, ensuring the credibility of the game process and the immutability of the distribution results. While reflecting the true balance of market power, it eliminates the trust costs and manipulation risks of centralized platforms.

[0045] 3. This invention employs the alternating direction multiplier method to decompose the energy storage service provider subproblem into a charge / discharge decision subproblem and a state of charge subproblem, which are then solved in a coupled manner using the SOC dynamic equation. This decomposition strategy ensures the accurate satisfaction of energy storage physical constraints while enabling independent decision-making and privacy protection for each entity, significantly improving the computational efficiency and practicality of distributed collaborative optimization. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0047] Figure 1 This is a flowchart illustrating the optical-storage joint scheduling optimization method based on blockchain trusted game theory, as described in this invention.

[0048] Figure 2 This is a schematic diagram of the structure of the photovoltaic-storage joint scheduling optimization system based on blockchain trusted game theory, as per the present invention.

[0049] Figure 3 This is a diagram of the optical-storage joint scheduling optimization system based on blockchain trusted game theory, as described in this invention.

[0050] Figure 4 This is a flowchart of the multi-agent distributed optimization interaction based on the alternating direction multiplier method in this invention. Detailed Implementation

[0051] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0052] Example 1: As Figure 1 As shown, this invention proposes a photovoltaic-storage joint scheduling optimization method based on blockchain trusted game theory. The core of this invention lies in constructing a trusted game execution environment through blockchain technology and embedding an asymmetric Nash bargaining mechanism into a blockchain smart contract to achieve fair bargaining, revenue distribution, and joint scheduling among photovoltaic power generators, energy storage service providers, and users.

[0053] The distributed energy collaboration network involved in this invention includes: a blockchain-shared energy storage resource pool, distributed photovoltaic units, a user group, an energy collaboration network platform operator, and a power grid dispatching agency. The photovoltaic units provide renewable electricity, the energy storage resource pool participates in market transactions through capacity or power usage rights leasing, the user group acts as both electricity purchaser and energy storage lessor, the operator is responsible for market matching and dispatching, and the power grid agency provides ancillary services. All parties achieve transparent recording of transaction information and automatic execution of smart contracts through a consortium blockchain.

[0054] This invention relates to a photovoltaic-storage joint scheduling optimization method based on blockchain trusted game theory, comprising:

[0055] S100: Establish utility functions for photovoltaic power generators, energy storage service providers, and users; among which, the utility function of energy storage service providers includes revenue from network internal balancing services and revenue from chain-shared energy storage leasing.

[0056] Furthermore, the revenue from the ChainShare energy storage leasing includes revenue from the leasing of power usage rights, which consists of the discharge electricity fees charged by the energy storage service provider to the user, the charging electricity fees paid to the user, and the power usage rights leasing fees.

[0057] Furthermore, the revenue from the network-internal balancing service includes charging response revenue and discharging response revenue;

[0058] The charging response revenue includes the electricity revenue from the energy storage service provider's purchase of electricity from the photovoltaic power generator and the consumption service compensation fee paid by the photovoltaic power generator;

[0059] The discharge response revenue includes the electricity revenue from the energy storage service provider's sale of electricity to the photovoltaic power generator and the performance guarantee premium paid by the photovoltaic power generator;

[0060] The compensation fee for grid connection services shall be shared by the photovoltaic power generation group according to their respective installed capacity proportions, and the performance guarantee premium shall be shared by the photovoltaic power generation group according to their respective day-ahead forecast error standard deviation proportions.

[0061] Specifically, this invention establishes utility functions for three types of entities: energy storage service providers, photovoltaic power generators, and users, providing a foundation for the subsequent construction of game theory models.

[0062] The utility function of an energy storage service provider consists of three parts: revenue from network-internal balancing services, revenue from chain-shared energy storage leasing, and operating costs. Its mathematical expression is:

[0063] ;

[0064] in, To balance service revenue within the network, To share the revenue from energy storage leasing, The operating cost of a distributed energy collaborative network.

[0065] The revenue from network-wide balancing services comprises two parts: charging response revenue and discharging response revenue. This revenue is borne by the photovoltaic power generation group and is used to compensate for the two-way service costs incurred by energy storage in mitigating photovoltaic fluctuations. The calculation formula is as follows:

[0066] ;

[0067] in, This represents the total number of scheduling periods.

[0068] Charging Response Benefits This includes: electricity fees paid by energy storage service providers to photovoltaic power generators. (Expenditure item, hence negative), and the compensation for grid connection services paid by photovoltaic power generators. (Revenue item). This compensation fee is used to compensate energy storage systems for providing services that enable immediate consumption and avoid curtailment of solar power.

[0069] Discharge response benefit portion This includes: electricity revenue generated by energy storage service providers from electricity sales to photovoltaic power generators. And the performance guarantee premium paid by photovoltaic power generators. This premium is used to hedge against the discrepancy between the day-ahead forecast and the actual output, ensuring the reliability of contract performance.

[0070] in, , Time periods The amount of charging and discharging requests (kWh) responded to by energy storage service providers in the collaborative network; , Time periods The purchase and sale price of electricity from photovoltaic power generation groups by energy storage service providers (RMB / kWh); , For energy storage service providers during time periods The charging and discharging behavior, if charging... , If discharge occurs , .

[0071] Disposal service compensation fee and performance guarantee premium Calculated separately as follows:

[0072] ;

[0073] ;

[0074] in, Compensation fees paid by photovoltaic power generators to energy storage systems / guaranteed premium unit price (RMB / kWh), For time period The power demand of the collaborative network needs to be balanced.

[0075] The innovation of this invention lies in the fact that the grid connection service compensation cost is shared by the photovoltaic power generator group according to their respective installed capacity proportions, and the performance guarantee premium is shared by the photovoltaic power generator group according to their respective day-ahead forecast error standard deviation proportions. Specifically, photovoltaic power generators Charging response cost amortization factor Based on its installed capacity Sure:

[0076] ;

[0077] This coefficient measures the photovoltaic power generation rate. Contribution to the potential risk of over-issuance.

[0078] Photovoltaic power generator Discharge response cost amortization factor Determined based on the standard deviation of its day-ahead forecast error: ;

[0079] in, For photovoltaic power generators The day-ahead forecast error standard deviation (kWh), this coefficient measures the performance of photovoltaic power generators. Contribution to forecast bias risk. This differentiated cost-sharing mechanism can reasonably allocate costs based on the actual contribution of each photovoltaic power generator to volatility, reflecting the principle of fairness.

[0080] ChainShare's energy storage leasing revenue includes revenue from power usage rights leasing. This revenue consists of three parts: the discharge fee charged by the energy storage service provider to the user, the charging fee paid to the user, and the power usage rights leasing fee. The calculation formula is as follows:

[0081] ;

[0082] ;

[0083] in, Total number of users; For time period Energy storage service providers participate in power usage rights trading from users Earnings (in yuan); , For time period Electricity prices (RMB / kWh) for energy storage service providers selling and buying electricity from users; , For time period Energy storage service providers to users Electricity sold and purchased (kWh); , For users During the period Behavioral variables related to the charging and discharging of leased energy storage, if the user Leasing energy storage and charging If the user Leasing energy storage and discharge ; The unit price for power usage rights leasing is (RMB / kWh).

[0084] The operating costs of distributed energy collaborative networks are borne by energy storage service providers, including both fixed input costs and variable compensation costs.

[0085] ;

[0086] Fixed input costs for:

[0087] ;

[0088] in, The fixed leasing cost coefficient (yuan / kWh) for the unit energy capacity of the chain-shared energy storage system per unit time. This represents the upper limit of dispatchable electricity (kWh) for the energy storage system.

[0089] Variable compensation cost for:

[0090] ;

[0091] in, , For time period The total charging and discharging power (kW) of the energy storage system. This is the primary cost coefficient for energy storage deployment. These are quadratic coefficients, reflecting the linear and nonlinear effects of energy storage call frequency and depth on lifespan loss, respectively.

[0092] Total charging and discharging power , It consists of two parts: network internal balancing service and chain-shared energy storage leasing.

[0093] ;

[0094] ;

[0095] The utility function of a photovoltaic power generator aims to subtract the power generation cost and its allocated network balancing service cost from the transaction revenue.

[0096] ;

[0097] in, For photovoltaics The trading price during the time period (RMB / kWh), For the corresponding electricity (kWh) and , for Forecasted power generation for different time periods; The marginal cost per unit of electricity generation (yuan / kWh); To absorb the service cost allocation coefficient, This serves as the cost-sharing coefficient for ensuring contract performance. For photovoltaic power generators The balancing cost (in yuan) arising from the deviation between actual output and predicted output is allocated according to the aforementioned allocation coefficients, thereby reflecting the differentiated cost-sharing of energy storage services by photovoltaic power generators.

[0098] The user's utility function aims to minimize the cost of purchasing electricity.

[0099] ;

[0100] in, The price for purchasing photovoltaic power for users (RMB / kWh), For users Purchased photovoltaic power (kWh); The electricity price for energy storage (yuan / kWh), For users Leased energy storage capacity (kWh).

[0101] The three types of utility functions mentioned above together form the basis of the multi-agent game model of this invention, providing an optimization objective for the subsequent establishment of an asymmetric Nash bargaining model.

[0102] S200: Based on the utility functions of the photovoltaic power generators, energy storage service providers, and users, an asymmetric Nash bargaining model is constructed. The objective function is to maximize the weighted product of the difference between the utility and the retention utility of the photovoltaic power generators, energy storage service providers, and users, and to establish a global optimization problem.

[0103] Furthermore, the reserved utility is the lowest utility value that each of the photovoltaic power generator, energy storage service provider, and user can obtain when they do not participate in collaborative dispatch; the weighting coefficient in the weighted product is the bargaining weight of each of the photovoltaic power generator, energy storage service provider, and user.

[0104] Specifically, based on the utility functions of each entity, this invention constructs an asymmetric Nash bargaining model to achieve equilibrium among photovoltaic power generators, energy storage service providers, and users in terms of power allocation, price formation, and revenue distribution.

[0105] The objective function of the asymmetric Nash bargaining model is to maximize the weighted product of the differences between the utility and retention utility of photovoltaic power generators, energy storage service providers, and users.

[0106] ;

[0107] in, Indicates the first The utility function of the square. This indicates its retained utility. This indicates the bargaining power of the participating parties. It represents the collection of all participating parties, including photovoltaic power generators, energy storage service providers, and users.

[0108] Preservative utility is the minimum utility value that photovoltaic power generators, energy storage service providers, and users can obtain without participating in collaborative dispatch, serving as a constraint on individual rationality in the game. Specifically:

[0109] Retention utility of photovoltaic power generators The revenue it can obtain when it sells electricity directly to the grid without participating in the distributed energy cooperation network:

[0110] ;

[0111] in, For time period Grid purchase price (RMB / kWh).

[0112] Retention utility of energy storage service providers The minimum return for which it does not provide network internal balancing services and chain-shared energy storage leasing services can be set to zero or its benchmark return in the external market.

[0113] User retention utility The cost it would have to pay if it purchased electricity entirely from the grid:

[0114] ;

[0115] in, For time period The price at which users purchase electricity from the grid (yuan / kWh), For user time period The electricity demand (kWh) is represented by the negative sign, indicating expenditure.

[0116] Bargaining weight This represents the bargaining power coefficients for photovoltaic power generators, energy storage service providers, and users, reflecting the relative negotiating positions of each participant in the game. The weighting coefficients in the weighted product... satisfy:

[0117] ;

[0118] Bargaining weight can be determined based on factors such as each entity's installed capacity, market share, and historical transaction reputation. A larger weighting... This means that the entity has stronger bargaining power in the distribution of benefits, and its utility increment has a greater weight in the objective function.

[0119] To facilitate the solution, taking the logarithm of the objective function transforms it into:

[0120] ;

[0121] This transformation does not change the optimal solution and converts the product form into a summation form, facilitating subsequent distributed solution. By introducing retained utility and bargaining weights, this invention overcomes the limitation of the assumption of complete equality among all parties in traditional symmetric Nash bargaining, enabling a more realistic reflection of the actual negotiating power differences among different entities in a distributed energy cooperation network, and achieving a fairer and more reasonable distribution of benefits.

[0122] S300: Set constraints for the global optimization problem, including power demand balance constraints, energy storage state of charge constraints, and charge / discharge mutual exclusion constraints;

[0123] Further, S300 includes:

[0124] The power demand balance constraint is the power balance equation between photovoltaic power generation, energy storage charging and discharging and user power demand in each time period.

[0125] The energy storage state of charge constraints include the energy storage state of charge dynamic equation, the energy storage state of charge upper limit constraint, and the energy storage state of charge lower limit constraint.

[0126] The charging and discharging mutual exclusion constraint is achieved by introducing charging behavior variables and discharging behavior variables, which constrain that the charging behavior variables and discharging behavior variables cannot be 1 at the same time during the same period.

[0127] The price reasonableness constraint limits the photovoltaic power sales price, energy storage charging and discharging price, and power usage right leasing unit price to a reasonable range;

[0128] The capacity occupancy constraint limits the chain-shared energy storage capacity leased by each user to no more than the allocation limit, and the total capacity leased by all users to no more than the upper limit of the energy storage system's dispatchable capacity.

[0129] Specifically, the electricity demand balance constraint ensures that the total power generation within the distributed energy cooperation network matches user demand in each time period. In each scheduling period... Within the network, the power generation of photovoltaic power, the charging and discharging of energy storage, and the electricity demand of users must meet a power balance relationship. This constraint ensures the principle of energy conservation, enabling the power supply and demand within the network to maintain a real-time balance and avoiding situations of insufficient or excessive power supply.

[0130] Energy storage state of charge constraints include three aspects:

[0131] First, the energy storage's state of charge (SOC) must satisfy dynamic equation constraints. The energy storage system during the specified time period... State of charge State of charge compared to the previous period Current charging amount and discharge quantity There exists a dynamic relationship between them, which is described by the SOC dynamic equation, taking into account charge and discharge efficiency. and the rated capacity of the energy storage system .

[0132] Secondly, the state of charge (SOC) of the energy storage system must meet the upper limit constraint, meaning that the SOC at any given time period must not exceed the maximum permissible SOC of the energy storage system. This is to protect the safe operation of energy storage equipment.

[0133] Secondly, the state of charge (SOC) of the energy storage system must meet the lower limit constraint, meaning that the SOC at any given time period must not be lower than the minimum permissible SOC of the energy storage system. This avoids energy storage life loss caused by excessive discharge.

[0134] The charge-discharge mutual exclusion constraint ensures that an energy storage system cannot perform charging and discharging operations simultaneously. This constraint is achieved by introducing charging behavior variables. and discharge behavior variables Achieve. At any given time. The charging behavior variable and the discharging behavior variable cannot both be 1 at the same time; that is, when the energy storage system is charging... There must be When the energy storage system discharges There must be This constraint conforms to the physical operating characteristics of energy storage devices and avoids unreasonable energy flow.

[0135] Price rationality constraints ensure that transaction prices remain within a reasonable market range. Specifically, transaction prices for various types of transactions, such as photovoltaic power sales prices, energy storage charging and discharging prices, and power usage right leasing unit prices, must be limited to reasonable upper and lower limits. This ensures that prices reflect market supply and demand and cost structure while avoiding unreasonably high or low prices, thus maintaining the fairness and sustainability of market transactions.

[0136] Capacity occupancy constraints limit the amount of chain-shared energy storage capacity leased by each user to no more than the upper limit. Specifically, at any given time, a single user... The sum of the leased energy storage charging and discharging capacity shall not exceed the maximum available capacity allocated to the user, and the total leased capacity of all users shall not exceed the dispatchable capacity limit of the ChainShare energy storage system. This constraint ensures the reasonable allocation of energy storage resources among multiple users, preventing a single user from excessively occupying shared resources.

[0137] The above constraints together constitute the feasible region of the global optimization problem, providing clear boundary conditions for subsequent distributed solutions and ensuring the technical feasibility and economic rationality of the scheduling scheme.

[0138] S400: The global optimization problem is decomposed into energy storage service provider subproblems, photovoltaic power generator subproblems and user subproblems by using the alternating direction multiplier method. Each of the photovoltaic power generator, energy storage service provider and user solves the local optimal decision variables independently.

[0139] Furthermore, in S400:

[0140] The energy storage service provider subproblem is further decomposed into a charge / discharge decision subproblem and a state of charge subproblem. The local optimal decision variables of the charge / discharge decision subproblem are the charging amount and discharging amount in each time period, and the local optimal decision variables of the state of charge subproblem are the energy storage state of charge in each time period. The energy storage state of charge dynamic equation is coupled with the charge / discharge decision subproblem.

[0141] The locally optimal decision variable for the photovoltaic power generation electronic problem is the power generation in each time period;

[0142] The locally optimal decision variable for the user subproblem is the amount of electricity purchased in each time period.

[0143] Specifically, the alternating direction multiplier method (ADMM) is used to decompose the global optimization problem into a subproblem of energy storage service providers, a subproblem of photovoltaic power generators, and a subproblem of users. Each of the photovoltaic power generators, energy storage service providers, and users independently solves for the local optimal decision variables.

[0144] ADMM is a distributed optimization algorithm that decomposes a coupled global optimization problem into parallel solvable local subproblems by introducing globally consistent variables and dual variables (Lagrange multipliers). Each agent optimizes independently based on local information, and information exchange and global coordination are achieved through a blockchain platform.

[0145] The energy storage service provider subproblem is further decomposed into a charge / discharge decision subproblem and a state of charge (SCC) subproblem. The local optimal decision variables for the charge / discharge decision subproblem are the amount of charge and discharge in each time period, and the local optimal decision variables for the SCC subproblem are the energy storage SCC in each time period. The SCC subproblem is coupled with the charge / discharge decision subproblem through the energy storage SCC dynamic equation.

[0146] The Lagrangian function of the charge / discharge decision subproblem is:

[0147] ;

[0148] in, The Lagrange multiplier represents the energy storage service provider. For globally consistent variables, The penalty parameter controls the degree of punishment for deviations between local decisions and global consistency variables. The energy storage operating cost coefficient (yuan / kWh) To balance service revenue within the network, For ChainShare's energy storage leasing revenue.

[0149] The Lagrangian function for the charged state problem is:

[0150] ;

[0151] The dynamic equation representing the SOC of energy storage is:

[0152] ;

[0153] in, This represents the opportunity cost of SOC deviation. The penalty parameter for SOC constraints, These are the weighting coefficients for the SOC constraint. For fixed dispatch power (kWh), The rated capacity (kWh) of the energy storage system. For charging efficiency, This refers to the discharge efficiency.

[0154] Energy storage service providers obtain the charging volume for each time period by solving the above two sub-problems. Discharge quantity and energy storage state of charge .

[0155] The locally optimal decision variable for the photovoltaic power generation sub-problem is the power generation in each time period. The photovoltaic power generation sub-problem, considering the balancing of service cost allocation, aims to maximize both power generation allocation and revenue.

[0156] The Lagrangian function of the photovoltaic power generation sub-problem is:

[0157] ;

[0158] in, For photovoltaic (PV) power generators, represents the marginal adjustment cost of PV power generation deviating from the system dispatch target. For the global consistency variable of photovoltaic power generation, The penalty parameter controls the degree of penalty applied to the squared deviation term. For time period The electricity sales price of photovoltaic power generators (yuan / kWh), For photovoltaic power generators During the period Balanced service costs borne (in yuan).

[0159] Photovoltaic power generators obtain the power generation for each time period by solving the above sub-problems. .

[0160] The locally optimal decision variable for the user subproblem is the amount of electricity purchased in each time period. The user subproblem aims to minimize the cost of electricity purchase and adjusts the amount of electricity purchased in conjunction with the electricity pricing strategy.

[0161] The Lagrangian function of the user subproblem is:

[0162] ;

[0163] in, This represents the marginal price adjustment for electricity demand. For user time period Electricity purchase volume, For users' electricity purchases, a globally consistent variable. For penalty parameters, For time period The electricity price (yuan / kWh).

[0164] Users obtain the electricity purchase amount for each time period by solving the above sub-problems. .

[0165] Through the above decomposition, each entity can independently solve sub-problems based on its own utility function and local information, without exposing private data, thus protecting the privacy of the participants. The locally optimal decision variables obtained from the solution will be uploaded to the blockchain platform for global coordination.

[0166] S500: The photovoltaic power generator, energy storage service provider and user will upload their respective locally optimal decision variables to the blockchain platform, and the blockchain platform will update the globally consistent variables and dual variables and iterate until convergence.

[0167] Furthermore, the process of iterating to convergence includes:

[0168] The blockchain platform aggregates locally optimal decision variables uploaded by photovoltaic power generators, energy storage service providers, and users through smart contracts, calculates and updates globally consistent variables and dual variables, and then distributes them to each entity.

[0169] Each entity performs the next round of solving based on the updated global consistency variables and dual variables;

[0170] The blockchain platform repeats the steps of updating the global consistency variable and dual variable and distributing them to each subject, as well as each subject solving for the next round of local optimal decision variables, until the change in the global consistency variable is less than the preset convergence threshold.

[0171] Specifically, the photovoltaic power generators, energy storage service providers, and users upload their respective locally optimal decision variables to the blockchain platform, which then updates the globally consistent variables and dual variables and iterates until convergence.

[0172] The consortium blockchain platform performs global coordination and automatic smart contract execution, converging to a joint scheduling scheme through multiple rounds of iteration. Throughout the optimization process, the blockchain platform acts as a trusted third party, ensuring data transparency and immutability, and automatically executing the game-theoretic solution and constraint checks through smart contracts.

[0173] Blockchain coordination includes the following:

[0174] By storing the decision-making and utility information of each entity through a consortium blockchain, data transparency and immutability are achieved. In each iteration, each entity will solve for the locally optimal decision variables (including photovoltaic power generation). Energy storage charging and discharging capacity , Energy storage state of charge Purchase electricity from users The data is uploaded to the blockchain, where it is verified and recorded on the chain by blockchain nodes.

[0175] The game-theoretic process and constraint checks are automatically executed through smart contracts. Based on pre-defined ADMM algorithm rules, the smart contract aggregates the decision variables uploaded by each participant, checks whether constraints such as power balance, energy storage status, and charging / discharging mutual exclusion are met, and automatically triggers the next iteration.

[0176] During the iteration process, the globally consistent variables and Lagrange multipliers are updated until convergence to an asymmetric Nash equilibrium. The process of iterating to convergence includes:

[0177] The first step involves the blockchain platform using smart contracts to aggregate locally optimal decision variables uploaded by photovoltaic power generators, energy storage service providers, and users. After calculating and updating globally consistent variables and dual variables, the data is distributed to each entity.

[0178] The update formula for globally consistent variables is:

[0179] ;

[0180] in, Indicates the iteration round, Indicates the first Fang in the first The decision variables obtained in the iteration are, for photovoltaic power generators, For energy storage service providers For users , This indicates the total number of participants.

[0181] The update formula for the dual variable (Lagrange multiplier) is:

[0182] ;

[0183] in, For the first The dual variable of the round iteration, For the first The decision variables of the corresponding subjects in each iteration. The penalty parameters for the corresponding entities (for photovoltaic power generators) are... For energy storage service providers For users ).

[0184] In the second step, each entity performs the next round of solving based on the updated global consistency variables and dual variables. Each entity receives the data distributed by the blockchain platform. and Substitute these values ​​into the Lagrangian functions of their respective subproblems to resolve the local optimal decision variables. .

[0185] The third step is to repeat the above process until the change in the globally consistent variable is less than the preset convergence threshold. The convergence criterion is:

[0186] ;

[0187] in, To preset the convergence threshold, it is usually taken as... to .

[0188] The algorithm terminates when the convergence condition is met, outputting the final joint scheduling scheme. The entire iterative process is executed in the trusted environment of the blockchain platform, ensuring the fairness and traceability of the optimization results.

[0189] S600: The output includes a scheduling scheme that includes power allocation, market price, and revenue distribution, and is recorded on the blockchain.

[0190] Specifically, the power allocation results include the power generation of photovoltaic power generators in each time period. The charging and discharging capacity of energy storage service providers , and the amount of electricity purchased by users This result achieves power supply and demand balance within the distributed energy cooperative network, ensuring effective absorption of photovoltaic power generation, allowing energy storage systems to play a peak-shaving and valley-filling role, and meeting user needs. (The text also mentions the charging and discharging volume of the network's internal balancing service.) , Helian Shares Energy Storage Leasing's Charging and Discharging Capacity , These respectively demonstrate the contributions of energy storage in mitigating photovoltaic fluctuations and meeting users' personalized needs.

[0191] Market pricing mechanisms include photovoltaic trading prices. The electricity purchase and sale price between energy storage service providers and photovoltaic power generators , And the electricity purchase and sale price from energy storage service providers to users , Unit price for power usage rights leasing The aforementioned prices are automatically generated through equilibrium solving using an asymmetric Nash bargaining model, reflecting the supply and demand relationship and bargaining power of each participant, thus realizing a market-based price discovery mechanism. Price information is publicly and transparently displayed on the blockchain, and all participants can query it in real time, ensuring the fairness of the price formation process.

[0192] The revenue distribution strategy clarifies the ultimate utility of each participant. Photovoltaic power generators receive transaction revenue and bear their share of the network balancing service costs; energy storage service providers receive revenue from network balancing services and chain-shared energy storage leasing and bear operating costs; users pay the electricity purchase cost. The ultimate utility of each party is... , and All of them are higher than their retention utility. , and This satisfies individual rationality constraints, ensuring that all participants benefit from collaborative scheduling. Specifically, it addresses service compensation fees. The premium for fulfilling contracts will be shared by photovoltaic power generators according to their installed capacity share. The cost is shared by the photovoltaic power generation group according to the proportion of the standard deviation of the day-ahead forecast error, thus achieving a fair distribution of costs.

[0193] All information regarding the scheduling scheme, including the decision variables of each participant, transaction prices, utility values, and globally consistent and dual variables during the iteration process, is recorded on the consortium blockchain via smart contracts, forming immutable transaction certificates and scheduling records. Blockchain-based notarization ensures the traceability and auditability of the scheduling results, providing a credible basis for subsequent settlement, dispute resolution, and regulatory review. All participants can query historical scheduling data at any time to verify the reasonableness of revenue distribution.

[0194] Through the above-mentioned output mechanism, this invention realizes the coordinated and optimized scheduling of distributed photovoltaic and chain-shared energy storage, which improves the economic benefits and market operation capabilities of the distributed energy cooperation network while ensuring the safe and stable operation of the power system.

[0195] Example 2: Figure 2 As shown, this invention proposes a photovoltaic and energy storage joint scheduling optimization system based on blockchain trusted game theory. The system adopts the method described in Embodiment 1 and realizes the collaborative scheduling of distributed photovoltaic and blockchain-shared energy storage through modular design.

[0196] This system includes a utility function modeling module, a game theory modeling module, a constraint setting module, a distributed solution module, a blockchain coordination module, and a scheduling scheme output module.

[0197] like Figure 3 As shown, this system adopts a three-layer architecture design, from bottom to top: the application layer (market entities), the blockchain platform layer, and the network and data layer.

[0198] The application layer includes four types of market participants: photovoltaic power generators, energy storage service providers, power users, and grid dispatching agencies. Photovoltaic power generators submit power generation forecasts and transaction prices through the utility function modeling module; energy storage service providers submit SOC status and charging / discharging capacity quotations; power users submit electricity demand and electricity purchase quotations; and grid dispatching agencies publish ancillary service demands.

[0199] The blockchain platform layer forms the core of the system, comprising three components: smart contracts, a consensus mechanism, and a distributed ledger. Smart contracts integrate the Nash bargaining algorithm from the game modeling module, the ADMM coordinator from the distributed solution module, and an automatic settlement program, enabling automated execution of game solving and constraint checks. The consensus mechanism uses the PBFT / DPoS algorithm to ensure nodes reach consensus on the scheduling results. The distributed ledger stores the decision variables, transaction prices, and profit distribution information of each participant, ensuring data transparency and immutability.

[0200] The network and data layers provide communication and data support. The distributed communication network uses a P2P topology to achieve decentralized communication, the data encryption module uses asymmetric encryption to protect transmission security, and the IoT access module collects real-time data on photovoltaic output, energy storage status, and user load through smart meters and sensors.

[0201] like Figure 4 As shown, the system's workflow follows the iterative mechanism of ADMM distributed optimization, specifically including:

[0202] In the first phase, each market participant establishes its own optimization objective and retention utility based on the utility function modeling module and the game theory modeling module. Photovoltaic power generators determine their trading strategies based on predicted output and marginal costs, energy storage service providers formulate charging and discharging plans based on SOC status and operating costs, and users determine their electricity purchase strategies based on electricity demand and price sensitivity.

[0203] In the second stage, the constraint setting module sets constraints such as power balance, energy storage status, charging and discharging mutual exclusion, price rationality, and capacity occupancy for the global optimization problem, ensuring the technical feasibility of the scheduling scheme.

[0204] In the third stage, the distributed solution module decomposes the global problem into local sub-problems for each entity. Photovoltaic power generators solve for the amount of electricity generated. Energy storage service providers are seeking solutions for charging and discharging volumes. , and state of charge Users calculate the amount of electricity purchased. Each entity will send the solution results to the global variable node via the blockchain network.

[0205] In the fourth stage, the blockchain coordination module aggregates the decision variables uploaded by all parties through smart contracts and updates the globally consistent variables. and dual variables The smart contract then broadcasts the updated results to all stakeholders. It automatically checks whether constraints are met and adjusts penalty parameters if violations are found. And trigger the next iteration.

[0206] In the fifth stage, all nodes repeat the above solution and coordination process until the change in the globally consistent variable is reached. The value is less than the preset convergence threshold. When the termination condition is met, the system enters the scheduling scheme output stage.

[0207] In the sixth stage, the scheduling scheme output module records the converged decision variables, transaction prices, and revenue distribution results on the blockchain, forming an immutable transaction certificate. Photovoltaic power generators then receive transaction revenue. They also share the cost of balancing services, while energy storage service providers receive revenue from balancing services within the network. Helian Shares Energy Storage Leasing Revenue Users pay for electricity purchase costs The final utility for all parties is higher than the retention utility, thus satisfying the individual rationality constraint.

[0208] This system addresses the issues of high trust costs and poor transparency inherent in traditional centralized scheduling by utilizing a blockchain-based trusted execution environment. The asymmetric Nash bargaining mechanism ensures fair revenue distribution among entities with varying bargaining power. The ADMM distributed solution guarantees decision-making autonomy and privacy protection. The differentiated cost-sharing mechanism rationally allocates and balances service costs based on each photovoltaic power generator's actual contribution to volatility, reflecting the principle of fairness: "whoever benefits pays, whoever causes the problem bears the consequences."

[0209] Through the above architecture and process, this system realizes the collaborative optimization scheduling of distributed photovoltaic and chain-shared energy storage in a reliable, efficient and fair market environment.

[0210] Example 3: In the third embodiment of the present invention, based on the same inventive concept, the present invention proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the optical-storage joint scheduling optimization method based on blockchain trusted game theory in the above embodiment.

[0211] Example 4: The fourth embodiment of the present invention, based on the same inventive concept, proposes a computer device comprising: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the optical-storage joint scheduling optimization method based on blockchain trusted game theory in the above embodiment.

[0212] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0213] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A photovoltaic-storage joint scheduling optimization method based on blockchain trusted game theory, characterized in that, include: S100: Establish utility functions for photovoltaic power generators, energy storage service providers, and users; among which, the utility function of energy storage service providers includes revenue from network internal balancing services and revenue from chain-shared energy storage leasing. S200: Based on the utility functions of the photovoltaic power generators, energy storage service providers, and users, an asymmetric Nash bargaining model is constructed. The objective function is to maximize the weighted product of the difference between the utility and the retention utility of the photovoltaic power generators, energy storage service providers, and users, and to establish a global optimization problem. S300: Set constraints for the global optimization problem, including power demand balance constraints, energy storage state of charge constraints, charge and discharge mutual exclusion constraints, price rationality constraints, and capacity occupancy constraints; S400: The global optimization problem is decomposed into energy storage service provider subproblems, photovoltaic power generator subproblems and user subproblems by using the alternating direction multiplier method. Each of the photovoltaic power generator, energy storage service provider and user solves the local optimal decision variables independently. S500: The photovoltaic power generator, energy storage service provider and user will upload their respective locally optimal decision variables to the blockchain platform, and the blockchain platform will update the globally consistent variables and dual variables and iterate until convergence. S600: The output includes a scheduling scheme that includes power allocation, market price, and revenue distribution, and is recorded on the blockchain.

2. The optical-storage joint scheduling optimization method based on blockchain trusted game theory according to claim 1, characterized in that, The revenue from the ChainShare energy storage leasing includes revenue from the leasing of power usage rights, which consists of the discharge electricity fees charged by the energy storage service provider to the user, the charging electricity fees paid to the user, and the power usage rights leasing fees.

3. The optical-storage joint scheduling optimization method based on blockchain trusted game theory according to claim 1, characterized in that, The revenue from the network internal balancing service includes charging response revenue and discharging response revenue. The charging response revenue includes the electricity revenue that the energy storage service provider receives from purchasing electricity from the photovoltaic power generator and the consumption service compensation fees paid by the photovoltaic power generator. The discharge response revenue includes the electricity revenue from the energy storage service provider's sale of electricity to the photovoltaic power generator and the performance guarantee premium paid by the photovoltaic power generator; The compensation fee for grid connection services shall be shared by the photovoltaic power generation group according to their respective installed capacity proportions, and the performance guarantee premium shall be shared by the photovoltaic power generation group according to their respective day-ahead forecast error standard deviation proportions.

4. The optical-storage joint scheduling optimization method based on blockchain trusted game theory according to claim 1, characterized in that, The reserved utility is the lowest utility value that each of the photovoltaic power generator, energy storage service provider, and user can obtain when they do not participate in collaborative dispatch; the weighting coefficient in the weighted product is the bargaining weight of each of the photovoltaic power generator, energy storage service provider, and user.

5. The optical-storage joint scheduling optimization method based on blockchain trusted game theory according to claim 1, characterized in that, The S300 includes: The power demand balance constraint is the power balance equation between photovoltaic power generation, energy storage charging and discharging and user power demand in each time period. The energy storage state of charge constraints include the energy storage state of charge dynamic equation, the energy storage state of charge upper limit constraint, and the energy storage state of charge lower limit constraint. The charging and discharging mutual exclusion constraint is achieved by introducing charging behavior variables and discharging behavior variables, which constrain that the charging behavior variables and discharging behavior variables cannot be 1 at the same time during the same period. The price reasonableness constraint limits the photovoltaic power sales price, energy storage charging and discharging price, and power usage right leasing unit price to a reasonable range; The capacity occupancy constraint limits the chain-shared energy storage capacity leased by each user to no more than the allocation limit, and the total capacity leased by all users to no more than the upper limit of the energy storage system's dispatchable capacity.

6. The optical-storage joint scheduling optimization method based on blockchain trusted game theory according to claim 1, characterized in that, In S400: The energy storage service provider subproblem is further decomposed into a charge / discharge decision subproblem and a state of charge subproblem. The local optimal decision variables of the charge / discharge decision subproblem are the charging amount and discharging amount in each time period, and the local optimal decision variables of the state of charge subproblem are the energy storage state of charge in each time period. The energy storage state of charge dynamic equation is coupled with the charge / discharge decision subproblem. The locally optimal decision variable for the photovoltaic power generation electronic problem is the power generation in each time period; The locally optimal decision variable for the user subproblem is the amount of electricity purchased in each time period.

7. The optical-storage joint scheduling optimization method based on blockchain trusted game theory according to claim 1, characterized in that, The process of iterating to convergence includes: The blockchain platform aggregates locally optimal decision variables uploaded by photovoltaic power generators, energy storage service providers, and users through smart contracts, calculates and updates globally consistent variables and dual variables, and then distributes them to each entity. Each entity performs the next round of solving based on the updated global consistency variables and dual variables; The blockchain platform repeats the steps of updating the global consistency variable and dual variable and distributing them to each subject, as well as each subject solving for the next round of local optimal decision variables, until the change in the global consistency variable is less than the preset convergence threshold.

8. A photovoltaic-storage joint scheduling optimization system based on blockchain trusted game theory, characterized in that, include: The utility function modeling module is used to establish the utility functions of photovoltaic power generators, energy storage service providers, and users; among which, the utility function of energy storage service providers includes the revenue from network internal balancing services and the revenue from chain-shared energy storage leasing. The game modeling module is used to construct an asymmetric Nash bargaining model based on the utility functions of the photovoltaic power generator, energy storage service provider, and user. The objective function is to maximize the weighted product of the difference between the utility and the retention utility of the photovoltaic power generator, energy storage service provider, and user, and to establish a global optimization problem. The constraint setting module is used to set constraints for the global optimization problem, including power demand balance constraints, energy storage state of charge constraints, charge and discharge mutual exclusion constraints, price rationality constraints, and capacity occupancy constraints. The distributed solution module is used to decompose the global optimization problem into subproblems for energy storage service providers, photovoltaic power generators, and users using the alternating direction multiplier method. Each of the photovoltaic power generators, energy storage service providers, and users independently solves for the local optimal decision variables. The blockchain coordination module is used by the photovoltaic power generator, energy storage service provider and user to upload their respective locally optimal decision variables to the blockchain platform, and the blockchain platform updates the globally consistent variables and dual variables and iterates until convergence. The scheduling scheme output module is used to output a scheduling scheme that includes power allocation, market price and revenue allocation, and record it on the blockchain.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the optical-storage joint scheduling optimization method based on blockchain trusted game theory as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, The readable storage medium stores a computer program, which, when executed by a processor, implements the optical-storage joint scheduling optimization method based on blockchain trusted game theory as described in any one of claims 1 to 7.

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