Optical storage joint scheduling optimization method and system based on block chain trusted game

Abstract

The invention relates to the technical field of distributed energy scheduling, in particular to an optical storage joint scheduling optimization method and system based on a block chain trusted game. The method comprises the following steps: firstly, establishing utility functions of a photovoltaic power generator, an energy storage service provider and a user, wherein the utility of the energy storage service provider comprises network internal balance service earnings and chain shared energy storage lease earnings; then constructing an asymmetric Nash bargaining model, and taking maximization of a weighted product of a difference between utility and reserved utility as a target; setting constraint conditions such as electric quantity balance, energy storage state, charge and discharge mutual exclusion, price rationality and capacity occupation; further decomposing the global problem into sub-problems by adopting an alternating direction multiplier method, and independently solving a local optimal decision by each main body; and finally, the global variable is updated through the block chain platform and iterated to convergence, and a scheduling scheme including electric quantity distribution, market price and income distribution is output. According to the method, the trust and efficiency problems of distributed energy collaboration are effectively solved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of distributed energy scheduling, in particular to a photovoltaic and energy storage joint scheduling optimization method and system based on a blockchain trusted game. BACKGROUND

[0002] With the rapid development of distributed photovoltaics, the problems of volatility and uncertainty are increasingly prominent, and energy storage systems have become a key means to realize the stable access of distributed renewable energy due to their good power regulation capability. The distributed energy collaboration network mechanism has gradually become an important path for integrating photovoltaics, energy storage and load resources.

[0003] However, the traditional energy scheduling mode has the following significant deficiencies: firstly, the data collection and settlement process of the centralized platform lacks transparency and has tampering risks, and the trust cost between participants is high; secondly, the centralized scheduling algorithm is difficult to simultaneously consider efficiency, fairness and privacy protection; thirdly, the existing shared energy storage mode is open to the market and lacks special protection for members within a specific network, making it difficult to achieve precise and fair allocation of energy storage resources in dynamic scenarios and unable to establish a compensation mechanism directly linked to photovoltaic volatility absorption. Existing researches are mostly limited to single resources or single markets, and there is no technical solution to organically combine distributed photovoltaics with energy storage resources with network specificity, transaction credibility and allocation gamification in a unified framework. SUMMARY

[0004] The application provides a photovoltaic and energy storage joint scheduling optimization method and system based on a blockchain trusted game, which realizes multi-agent collaborative optimization and fair income distribution.

[0005] To achieve the above-mentioned purposes, the application provides the following technical solutions: The photovoltaic and energy storage joint scheduling optimization method based on a blockchain trusted game comprises the following steps: S100: establishing utility functions of photovoltaic power generators, energy storage service providers and users, wherein the utility function of the energy storage service provider includes network internal balanced service income and chain-entertainment energy storage rental income; S200: constructing an asymmetric Nash bargaining model based on the utility functions of the photovoltaic power generators, the energy storage service providers and the users, taking the weighted product of the difference between the utility and the reservation utility of the photovoltaic power generators, the energy storage service providers and the users as an objective function, and establishing a global optimization problem; S300: setting constraint conditions for the global optimization problem, including power demand balance constraints, energy storage state of charge constraints, charge-discharge mutual exclusion constraints, price rationality constraints and capacity occupation constraints; S400: decompose the global optimization problem into energy storage service provider sub-problems, photovoltaic power generation company sub-problems and user sub-problems by using the alternating direction multiplier method, and independently solve local optimal decision variables of the photovoltaic power generation company, the energy storage service provider and the user; S500: the photovoltaic power generation company, the energy storage service provider and the user upload the local optimal decision variables obtained by solving respectively to the blockchain platform, and the blockchain platform updates global consistency variables and dual variables and iterates to convergence; S600: output a scheduling scheme including power distribution, market price and income distribution, and record on the blockchain.

[0006] As a preferred technical solution of the present application, the chain energy storage leasing income includes power usage right leasing income, which is composed of discharge electricity charges collected by the energy storage service provider from the user, charging electricity charges paid by the energy storage service provider to the user and power usage right leasing fees.

[0007] As a preferred technical solution of the present application, the network internal balance service income includes charging response income and discharging response income; The charging response income includes electricity charge income of the energy storage service provider purchasing electricity from the photovoltaic power generation company and consumption service compensation fees paid by the photovoltaic power generation company; The discharging response income includes electricity charge income of the energy storage service provider selling electricity to the photovoltaic power generation company and performance guarantee premium paid by the photovoltaic power generation company; The consumption service compensation fees are apportioned by the photovoltaic power generation company group according to the proportion of their respective installed capacities, and the performance guarantee premium is apportioned by the photovoltaic power generation company group according to the proportion of their respective day-ahead prediction error standard deviations.

[0008] As a preferred technical solution of the present application, the reserved utility is the lowest utility value that the photovoltaic power generation company, the energy storage service provider and the user can obtain when they do not participate in cooperative scheduling; and the weighting coefficient in the weighted product is the bargaining weight of the photovoltaic power generation company, the energy storage service provider and the user respectively.

[0009] As a preferred technical solution of the present application, the S300 includes: The power demand balance constraint is a power balance equation between photovoltaic power generation capacity, energy storage charging and discharging capacity and user electricity demand in each period; The energy storage state of charge constraint includes an energy storage state of charge dynamic equation, an upper limit constraint of energy storage state of charge and a lower limit constraint of energy storage state of charge; The charging and discharging mutual exclusion constraint is realized by introducing charging behavior variables and discharging behavior variables, and the constraint is that the charging behavior variable and the discharging behavior variable cannot be 1 at the same time in the same period; The price rationality constraint limits photovoltaic power selling price, energy storage charging and discharging price and power usage right leasing unit price within a reasonable range. The capacity occupation constraint limits the capacity of the chain sharing energy rented by each user to be less than the upper limit of the allocation, and the total capacity of all user rentals is less than the upper limit of the schedulable capacity of the energy storage system.

[0010] As a preferred technical solution of the present application, in the S400: The energy storage service sub-problem is further decomposed into a charging and discharging decision sub-problem and a state of charge sub-problem, the local optimal decision variable of the charging and discharging decision sub-problem is the charging capacity and the discharging capacity of each period, and the local optimal decision variable of the state of charge sub-problem is the state of charge of each period, and the state of charge of each period is coupled with the charging and discharging decision sub-problem through a state of charge dynamic equation; The local optimal decision variable of the photovoltaic power plant sub-problem is the power generation capacity of each period; The local optimal decision variable of the user sub-problem is the power purchase capacity of each period.

[0011] As a preferred technical solution of the present application, the process of iterating to convergence includes: The blockchain platform aggregates the local optimal decision variables uploaded by the photovoltaic power plant, the energy storage service provider and the user through the smart contract, calculates and updates the global consistency variable and the dual variable, and then distributes them to each subject; Each subject solves the next round based on the updated global consistency variable and the dual variable; The steps of updating the global consistency variable and the dual variable by the blockchain platform and distributing them to each subject, and solving the next round of local optimal decision variables by each subject are repeated until the change of the global consistency variable is less than the preset convergence threshold.

[0012] The present application also proposes a photovoltaic and energy storage joint scheduling optimization system based on a blockchain trusted game, which includes: A utility function modeling module is used to establish the utility functions of the photovoltaic power plant, the energy storage service provider and the user; wherein the utility function of the energy storage service provider includes the network internal balance service income and the chain sharing energy rental income; A game modeling module is used to construct an asymmetric Nash bargaining model based on the utility functions of the photovoltaic power plant, the energy storage service provider and the user, to maximize the weighted product of the difference between the utility and the reservation utility of the photovoltaic power plant, the energy storage service provider and the user as the objective function, and to establish a global optimization problem; A constraint setting module is used to set constraint conditions for the global optimization problem, including power demand balance constraints, state of charge constraints, charging and discharging exclusion constraints, price rationality constraints and capacity occupation constraints; a distributed solving module configured to decompose the global optimization problem into a storage service provider sub-problem, a photovoltaic power generation company sub-problem and a user sub-problem by using an alternating direction method of multipliers, and configured to independently solve local optimal decision variables of the photovoltaic power generation company, the storage service provider and the user; a blockchain coordination module configured to upload the local optimal decision variables obtained by the photovoltaic power generation company, the storage service provider and the user to a blockchain platform, and configured to update global consistency variables and dual variables by the blockchain platform and iterate to convergence; a scheduling scheme output module configured to output a scheduling scheme including power distribution, market price and income distribution, and record on the blockchain.

[0013] The application further provides a computer device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the photovoltaic storage joint scheduling optimization method based on a blockchain trusted game when executing the computer program.

[0014] The application further provides a readable storage medium, and the readable storage medium stores a computer program, and the computer program is executable on a processor to implement the photovoltaic storage joint scheduling optimization method based on a blockchain trusted game.

[0015] The application has the following beneficial effects: 1. The application proposes a chain storage energy concept and establishes a differentiated cost allocation mechanism based on risk contribution: the consumption service compensation fee is allocated according to the installed capacity proportion, and the performance guarantee premium is allocated according to the prediction error standard deviation proportion. The mechanism accurately links the storage balancing service cost with the volatility contribution of each photovoltaic power generation company, realizes the fair principle of "who contributes risk who bears cost", and effectively solves the problem of mismatch between cost allocation and risk responsibility in the traditional mode.

[0016] 2. The application embeds an asymmetric Nash bargaining model in a blockchain smart contract, and describes the negotiation ability difference of different subjects through bargaining weights, breaking through the limitation of symmetric game assumption. The blockchain platform automatically executes game solving and income distribution, realizes the trustworthiness of the game process and the non-tamperability of the distribution result, reflects the real power contrast of the market, and eliminates the trust cost and manipulation risk of the centralized platform.

[0017] 3. The application uses an alternating direction method of multipliers to decompose the storage service provider sub-problem into a charging and discharging decision sub-problem and a state of charge sub-problem, and realizes coupled solving through a SOC dynamic equation. The decomposition strategy not only ensures the accurate satisfaction of the storage physical constraints, but also realizes independent decision and privacy protection of each subject, and significantly improves the calculation efficiency and practicability of distributed collaborative optimization. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings are used to provide further understanding of the present application, and constitute a part of the specification, together with embodiments of the present application, to explain the present application, and do not constitute a limitation of the present application. In the drawings: Figure 1 is a flowchart of the light storage joint scheduling optimization method based on the blockchain trusted game of the present application; Figure 2 is a structural diagram of the light storage joint scheduling optimization system based on the blockchain trusted game of the present application; Figure 3 is a system architecture diagram of the light storage joint scheduling optimization system based on the blockchain trusted game of the present application; Figure 4 is an interactive flowchart of the multi-agent distributed optimization based on the alternating direction multiplier method in the present application. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and do not limit the present application.

[0020] Embodiment one: as shown in the present application, a light storage joint scheduling optimization method based on a blockchain trusted game is proposed, the core of the present application is to build a trusted game execution environment through a blockchain technology, and embed an asymmetric Nash bargaining mechanism into a blockchain smart contract, to realize fair bargaining, income distribution and joint scheduling among photovoltaic power suppliers, energy storage service providers and users. Figure 1

[0021] The distributed energy collaboration network involved in the present application includes: a chain-sharing energy storage resource pool, a distributed photovoltaic unit, a user group, an energy collaboration network platform operator and a power grid dispatching institution. The photovoltaic unit provides renewable electric energy, the energy storage resource pool participates in market transactions through capacity or power usage right leasing, the user group acts as both a power purchaser and an energy storage lessor, the operator is responsible for market matching and scheduling, and the power grid institution provides auxiliary service demand. Each subject realizes transparent recording of transaction information and automatic execution of smart contracts through an alliance chain.

[0022] The light storage joint scheduling optimization method based on the blockchain trusted game of the present application comprises: S100: establishing utility functions of photovoltaic power suppliers, energy storage service providers and users; wherein the utility function of the energy storage service provider includes network internal balance service income and chain-sharing energy leasing income; Further, the chain-sharing energy leasing income includes power usage right leasing income, which is composed of discharge electricity charges collected by the energy storage service provider from the user, charge electricity charges paid by the energy storage service provider to the user and power usage right leasing fees.

[0023] ​Further, the network internal balance service benefit includes a charging response benefit and a discharging response benefit. The charging response benefit includes an electricity fee income of the energy storage service provider purchasing electricity from the photovoltaic power generator and a consumption service compensation fee paid by the photovoltaic power generator; The discharging response benefit includes an electricity fee income of the energy storage service provider selling electricity to the photovoltaic power generator and a performance guarantee premium paid by the photovoltaic power generator; The consumption service compensation fee is apportioned by the photovoltaic power generator group according to the proportion of the respective installed capacity, and the performance guarantee premium is apportioned by the photovoltaic power generator group according to the proportion of the respective day-ahead prediction error standard deviation.

[0024] Specifically, the utility functions of the energy storage service provider, the photovoltaic power generator and the user are established respectively, which provides a basis for subsequent game model construction.

[0025] The utility function of the energy storage service provider includes three parts of network internal balance service benefit, chain storage energy leasing benefit and operation cost, and the mathematical expression is: ; Among them, is the network internal balance service benefit, is the chain storage energy leasing benefit, and the operation cost of the distributed energy cooperation network.

[0026] The network internal balance service benefit includes two parts of charging response benefit and discharging response benefit. The benefit is borne by the photovoltaic power generator group and is used to compensate the two-way service cost paid by the energy storage for smoothing photovoltaic fluctuations. The calculation formula is: ; Among them, is the total number of dispatching periods.

[0027] The charging response benefit part includes: the electricity fee paid by the energy storage service provider to the photovoltaic power generator for purchasing electricity (expenditure item, so it is negative), and the consumption service compensation fee paid by the photovoltaic power generator (income item). The compensation fee is used to compensate the services of providing immediate consumption and avoiding light loss.

[0028] The discharging response benefit part includes: the electricity fee income of the energy storage service provider selling electricity to the photovoltaic power generator , and the performance guarantee premium paid by the photovoltaic power generator . The premium is used to hedge the error between day-ahead prediction and actual output to ensure the reliability of performance.

[0029] Among them, , respectively Charging and discharging request electricity (kWh) of energy storage service providers in response to the collaborative network; 、 respectively Purchasing and selling price of energy storage service providers to photovoltaic power generation groups (yuan / kWh); 、 Charging and discharging behavior of energy storage service providers in time period If charging, then 、 If discharging, then 、 .

[0030] Consumption service compensation fee and contract performance guarantee premium are calculated as follows: ; ;

[0031] wherein, is the compensation fee / guarantee premium unit price (yuan / kWh) paid by the photovoltaic power generation company to the energy storage system, is the balancing demand electricity of the collaborative network in time period .

[0032] The innovation of the present application is that the consumption service compensation fee is apportioned by the photovoltaic power generation company group according to the proportion of the installed capacity, and the contract performance guarantee premium is apportioned by the photovoltaic power generation company group according to the proportion of the standard deviation of the day-ahead prediction error. Specifically, the photovoltaic power generation company assumes the charging response cost apportioning coefficient based on its installed capacity : ; The coefficient measures the contribution of the photovoltaic power generation company to the potential over-generation risk.

[0033] The photovoltaic power generation company assumes the discharging response cost apportioning coefficient based on its standard deviation of day-ahead prediction error: ; wherein, is the standard deviation of the day-ahead prediction error (kWh) of the photovoltaic power generation company , and the coefficient measures the contribution of the photovoltaic power generation company to the prediction deviation risk. This differentiated apportioning mechanism can reasonably allocate the cost according to the actual contribution of each photovoltaic power generation company to the volatility, embodying the principle of fairness.

[0034] The chain energy storage leasing revenue includes power usage right leasing revenue. The power usage right leasing revenue is composed of three parts, i.e., discharge electricity fee collected by the energy storage service provider from the user, charging electricity fee paid by the energy storage service provider to the user, and power usage right leasing fee, and the calculation formula is: ; ; wherein, is the total number of users; is the time period is the revenue (yuan) obtained by the energy storage service provider from the user through participating in power usage right transaction; , is the time period is the electricity selling price (yuan / kWh) of the energy storage service provider to the user; , is the electricity quantity (kWh) sold by the energy storage service provider to the user ; , is the behavior variable of the user in the time period to lease the energy storage for charging and discharging, wherein, if the user leases the energy storage for charging, then , and if the user leases the energy storage for discharging, then ; ; is the power usage right leasing unit price (yuan / kWh).

[0035] The operation cost of the distributed energy cooperative network is borne by the energy storage service provider, including fixed investment cost and variable compensation cost: ; The fixed investment cost is: ; wherein, is the fixed leasing cost coefficient (yuan / kWh) of unit energy capacity of the chain energy storage system per unit time, is the upper limit of the dispatchable electricity quantity of the energy storage system (kWh).

[0036] The variable compensation cost is: ; wherein, , is the total charging and discharging power (kW) of the energy storage system in the time period , is the first term cost coefficient of the energy storage calling, These are quadratic coefficients, reflecting the linear and nonlinear effects of energy storage call frequency and depth on lifespan loss, respectively.

[0037] Total charging and discharging power , It consists of two parts: network internal balancing service and chain-shared energy storage leasing. ; ; 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. ; 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.

[0038] The user's utility function aims to minimize the cost of purchasing electricity. ; 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).

[0039] 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.

[0040] 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. 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.

[0041] 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.

[0042] 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. ; 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.

[0043] 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: Retention utility of photovoltaic power generators The revenue it can obtain by selling electricity directly to the grid without participating in the distributed energy cooperation network: ; in, For time period Grid purchase price (RMB / kWh).

[0044] 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.

[0045] User retention utility The cost it would have to pay if it purchased electricity entirely from the grid: ; 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.

[0046] Bargaining weight The bargaining power coefficient of the photovoltaic power generator, the energy storage service provider and the user respectively reflects the relative negotiation position of each participant in the game. The weighted coefficient in the weighted product satisfies: ; The bargaining weight can be determined according to factors such as installed capacity, market share, historical transaction credit and the like of each subject. A larger means that the subject has stronger bargaining power in income distribution, and the weight of the utility increment in the objective function is larger.

[0047] For easy solving, the objective function is converted into: ; The conversion does not change the optimal solution, and converts the product form into the summation form, which is convenient for subsequent distributed solving. The present application solves the limitation of the assumption that all parties are completely equal in the traditional symmetric Nash bargaining by introducing the reserved utility and the bargaining weight, can more truly reflect the actual negotiation ability difference of different subjects in the distributed energy cooperation network, and realizes more fair and reasonable income distribution.

[0048] S300: setting constraint conditions for the global optimization problem, including power demand balance constraint, energy storage state of charge constraint and charge-discharge mutual exclusion constraint; Further, the S300 includes: The power demand balance constraint is a power balance equation between photovoltaic power generation, energy storage charge-discharge capacity and user power demand in each period; The energy storage state of charge constraint includes an energy storage state of charge dynamic equation, an upper limit constraint of energy storage state of charge and a lower limit constraint of energy storage state of charge; The charge-discharge mutual exclusion constraint is realized by introducing a charging behavior variable and a discharging behavior variable, and the constraint is that the charging behavior variable and the discharging behavior variable cannot be 1 at the same time in the same period; The price rationality constraint limits the photovoltaic power selling price, the energy storage charge-discharge price and the single price of power usage right leasing within a reasonable range; The capacity occupation constraint limits the leased chain energy storage capacity of each user to be less than the upper limit of distribution, and the total capacity of all users to be less than the upper limit of the adjustable capacity of the energy storage system.

[0049] Specifically, the power demand balance constraint ensures that the total power generation in the distributed energy cooperation network matches the user demand in each period. In each dispatching period , the photovoltaic power generation, the energy storage charge-discharge capacity and the user power demand need to satisfy the power balance relationship. This constraint guarantees the principle of energy conservation, so that the supply and demand of electrical energy in the network is kept in real-time balance, avoiding the situation of power supply shortage or excess.

[0050] Energy storage state of charge constraints include three aspects: 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 .

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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. The constraint ensures reasonable allocation of energy storage resources among multiple users, avoiding overuse of shared resources by a single user.

[0056] The above constraints together constitute the feasible region of the global optimization problem, providing clear boundary conditions for subsequent distributed solving, ensuring the technical feasibility and economic rationality of the scheduling scheme.

[0057] S400: using the alternating direction multiplier method to decompose the global optimization problem into energy storage service provider sub-problems, photovoltaic power generation service provider sub-problems and user sub-problems, each of which is independently solved for local optimal decision variables; Further, in the S400: The energy storage service provider sub-problem is further decomposed into a charging and discharging decision sub-problem and a state of charge sub-problem. The local optimal decision variable of the charging and discharging decision sub-problem is the charging and discharging amount of each time period, and the local optimal decision variable of the state of charge sub-problem is the state of charge of each time period, which is coupled with the charging and discharging decision sub-problem through the state of charge dynamic equation. The local optimal decision variable of the photovoltaic power generation service provider sub-problem is the power generation amount of each time period. The local optimal decision variable of the user sub-problem is the power purchase amount of each time period.

[0058] Specifically, the global optimization problem is decomposed into energy storage service provider sub-problems, photovoltaic power generation service provider sub-problems and user sub-problems using the alternating direction multiplier method (ADMM), each of which is independently solved for local optimal decision variables.

[0059] ADMM is a distributed optimization algorithm that decomposes coupled global optimization problems into parallel solvable local sub-problems by introducing global consistency variables and dual variables (Lagrange multipliers). Each subject optimizes independently based on local information, and information exchange and global coordination are achieved through a blockchain platform.

[0060] The energy storage service provider sub-problem is further decomposed into a charging and discharging decision sub-problem and a state of charge sub-problem. The local optimal decision variable of the charging and discharging decision sub-problem is the charging and discharging amount of each time period, and the local optimal decision variable of the state of charge sub-problem is the state of charge of each time period, which is coupled with the charging and discharging decision sub-problem through the state of charge dynamic equation.

[0061] The Lagrangian function of the charging and discharging decision sub-problem is: ; Where, represents the Lagrange multiplier of the energy storage service provider, is a global consistency variable, 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.

[0062] The Lagrangian function for the charged state problem is: ; The dynamic equation representing the SOC of energy storage is: ; 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.

[0063] 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 .

[0064] 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.

[0065] The Lagrangian function of the photovoltaic power generation sub-problem is: ; 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).

[0066] Photovoltaic power generators obtain the power generation for each time period by solving the above sub-problems. .

[0067] 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.

[0068] The Lagrangian function of the user subproblem is: ; 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).

[0069] Users obtain the electricity purchase amount for each time period by solving the above sub-problems. .

[0070] 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.

[0071] 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. Furthermore, 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.

[0072] 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.

[0073] The global coordination and automatic execution of the smart contract are performed based on a consortium chain platform, and the joint scheduling scheme is converged through multiple rounds of iteration. The blockchain platform plays a role of a trusted third party in the entire optimization process, guarantees data transparency and non-tamperability, and automatically executes the game solving process and constraint checking through the smart contract.

[0074] The blockchain coordination includes the following: The decision and utility information of each subject are stored through the consortium chain to realize data transparency and non-tamperability. In each round of iteration, each subject uploads the locally optimal decision variable (including the photovoltaic power generation amount , the energy storage charge and discharge amount , , the state of charge of the energy storage , and the user power purchase amount ) to the blockchain, which is verified and recorded by the blockchain node.

[0075] The game solving process and constraint checking are automatically executed through the smart contract. The smart contract collects the decision variables uploaded by each subject according to the preset ADMM algorithm rule, checks whether the constraints such as power balance, energy storage state, and charge and discharge exclusion are met, and automatically triggers the next round of iteration.

[0076] The global consistency variable and Lagrange multiplier are updated in the iteration process until the asymmetric Nash equilibrium is converged. The process of the iteration to convergence includes: First, the blockchain platform collects the locally optimal decision variables uploaded by the photovoltaic power generator, the energy storage service provider and the user through the smart contract, calculates and updates the global consistency variable and the dual variable, and distributes them to each subject.

[0077] The update formula of the global consistency variable is: ; Wherein, denotes the iteration round, denotes the decision variable solved by the th part in the th iteration, for the photovoltaic power generator , for the energy storage service provider , and for the user , denotes the total number of participants.

[0078] The update formula of the dual variable (Lagrange multiplier) is: ; Wherein, is the dual variable of the th iteration, is the dual variable of the ththe decision variable of the corresponding subject in the current iteration, the penalty parameter of the corresponding subject (for the photovoltaic power generator , for the energy storage service provider , and for the user ).

[0079] Secondly, each subject solves the next round based on the updated global consistency variable and dual variable. Each subject receives the and distributed by the blockchain platform, substitutes them into the Lagrangian function of the corresponding sub-problem, and re-solves the local optimal decision variable .

[0080] Thirdly, the above process is repeated until the change of the global consistency variable is less than the preset convergence threshold. The convergence judgment condition is: ; wherein is the preset convergence threshold, usually taking to .

[0081] When the convergence condition is met, the algorithm terminates, and the final joint scheduling scheme is output. The entire iteration process is executed in the trusted environment of the blockchain platform, ensuring the fairness and traceability of the optimization result.

[0082] S600: Output the scheduling scheme including power allocation, market price and income allocation, and record it on the blockchain.

[0083] Specifically, the power allocation result includes the power generation of the photovoltaic power generator , the charging and discharging capacity of the energy storage service provider , and the power purchase of the user . This result realizes the balance of power supply and demand within the distributed energy cooperation network, ensures the effective consumption of photovoltaic power generation, and ensures the peak load shifting effect of the energy storage system and the satisfaction of user demand. The charging and discharging capacity of the network internal balance service , and the charging and discharging capacity of the chain storage energy leasing , respectively reflect the contribution of energy storage in stabilizing photovoltaic fluctuations and meeting individualized user demand.

[0084] The market pricing mechanism includes the photovoltaic transaction price , the purchase and sale price of the energy storage service provider to the photovoltaic power generator , , and the purchase and sale price of the energy storage service provider to the user , and power usage right lease unit price The above price is automatically formed by equilibrium solving of the asymmetric Nash bargaining model, reflects the supply and demand relationship and bargaining power of each participant, and realizes the market-oriented price discovery mechanism. The price information is open and transparent on the blockchain, and all participants can query in real time, ensuring the fairness of the price formation process.

[0085] The income distribution strategy clarifies the final utility of each participant. The photovoltaic power generator obtains transaction income and bears its share of network internal balancing service cost, the energy storage service provider obtains network internal balancing service income and chain enjoyment energy storage lease income and bears operation cost, and the user pays for electricity purchase cost. The final utility of each party is , and , all of which are higher than the reservation utility , and , meet the individual rationality constraint, and ensure that all participants can benefit from cooperative scheduling. In particular, the consumption service compensation cost is shared by the photovoltaic power generator group according to the proportion of installed capacity, and the performance guarantee premium is shared by the photovoltaic power generator group according to the proportion of standard deviation of day-ahead prediction error, realizing the fair distribution of cost.

[0086] All information of the scheduling scheme, including decision variables of each subject, transaction price, utility value, and global consistency variables and dual variables in the iteration process, are recorded on the consortium chain through the smart contract, forming an unalterable transaction certificate and scheduling record. The blockchain evidence ensures the traceability and auditability of the scheduling result, providing a credible basis for subsequent settlement, dispute resolution and regulatory review. Each participant can query historical scheduling data at any time to verify the rationality of income distribution.

[0087] Through the above output mechanism, the present application realizes the collaborative optimization scheduling of distributed photovoltaic and chain enjoyment energy storage, improves the economic benefit and market operation ability of the distributed energy cooperation network under the premise of ensuring the safe and stable operation of the power system.

[0088] Embodiment two: as shown in Figure 2 , the present application proposes a photovoltaic and storage joint scheduling optimization system based on blockchain trusted game, which adopts the method described in embodiment one and realizes the collaborative scheduling of distributed photovoltaic and chain enjoyment energy storage through modular design.

[0089] The system includes an utility function modeling module, a game modeling module, a constraint setting module, a distributed solving module, a blockchain coordination module and a scheduling scheme output module.

[0090] As shown in Figure 3As 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] like Figure 4 As shown, the system's workflow follows the iterative mechanism of ADMM distributed optimization, specifically including: 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.

[0095] 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.

[0096] In the third stage, the distributed solution module decomposes the global problem into local sub-problems for each entity. The photovoltaic power generator solves 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.

[0097] In the fourth stage, the blockchain coordination module aggregates the decision variables uploaded by each subject through the smart contract, updates the global consistency variable, and broadcasts the update result to each subject. The smart contract automatically checks whether the constraint condition is met, adjusts the penalty parameter if the constraint is violated, and triggers the next round of iteration. and the dual variable .

[0098] In the fifth stage, all nodes repeat the above solving and coordination process until the change of the global consistency variable is less than the preset convergence threshold. When the termination condition is met, the system enters the scheduling scheme output stage.

[0099] In the sixth stage, the scheduling scheme output module records the converged decision variables, transaction price and income allocation results on the blockchain to form an unalterable transaction certificate. The photovoltaic power supplier obtains transaction income and bears the balance service cost allocation, the energy storage service provider obtains the network internal balance service income and the chain-entertainment energy storage rental income , and the user pays the electricity purchase cost . The final utility of each party is higher than the reservation utility, satisfying the individual rationality constraint.

[0100] The system solves the problems of high trust cost and poor transparency of traditional centralized scheduling through the blockchain trusted execution environment. The asymmetric Nash bargaining mechanism realizes the fair income allocation of subjects with different bargaining abilities. The ADMM distributed solving ensures decision autonomy and privacy protection. The differentiated cost allocation mechanism reasonably allocates the balance service cost according to the actual contribution of each photovoltaic power supplier to volatility, embodying the fair principle of “who benefits who pays, who causes who bears”.

[0101] Through the above architecture and process, the system realizes the collaborative optimization scheduling of distributed photovoltaic and chain-entertainment energy storage in a trusted, efficient and fair market environment.

[0102] Embodiment three: the third embodiment of the present application, based on the same inventive concept, the present application proposes a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the above-mentioned embodiment of the photovoltaic and storage joint scheduling optimization method based on blockchain trusted game.

[0103] Embodiment four: the fourth embodiment of the present application, based on the same inventive concept, the present application proposes a computer device, which includes: a processor, 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, and execute the photovoltaic and storage joint scheduling optimization method based on blockchain trusted game of the above-mentioned embodiment. ​​

[0104] It should be understood that portions of the present application can be implemented with hardware, software, firmware, or a combination thereof. In the above embodiments, several 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, implementation can be with any or a combination of the following technologies, which are all well-known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0105] Finally, it should be noted that the above-described embodiments are merely possible implementations of the present application, but not to be taken in a limiting sense. Although the present application has been described in some detail with reference to the above embodiments, one of ordinary skill in the art will appreciate that various modifications, substitutions and changes can be made to the embodiments disclosed herein without departing from the spirit and scope of the present application. Any modification, equivalent substitution, improvement, and the like which do not depart from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for photovoltaic storage combined dispatching optimization based on blockchain trusted game, characterized in that, The method comprises the following steps: S100: establishing utility functions of photovoltaic power suppliers, energy storage service providers and users; wherein the utility function of the energy storage service provider comprises network internal balance service income and chain sharing energy leasing income; S200: constructing an asymmetric Nash bargaining model based on the utility functions of the photovoltaic power suppliers, energy storage service providers and users, taking the weighted product of the utility and the difference between the utility and the reservation utility of the photovoltaic power suppliers, energy storage service providers and users as the objective function, and establishing a global optimization problem; S300: setting constraint conditions for the global optimization problem, including power demand balance constraints, energy storage state of charge constraints, charging and discharging mutual exclusion constraints, price rationality constraints and capacity occupation constraints; S400: decomposing the global optimization problem into energy storage service provider sub-problems, photovoltaic power supplier sub-problems and user sub-problems by using an alternating direction multiplier method, and independently solving local optimal decision variables of the photovoltaic power suppliers, energy storage service providers and users; S500: uploading the local optimal decision variables obtained by the photovoltaic power suppliers, energy storage service providers and users to a blockchain platform, updating global consistency variables and dual variables by the blockchain platform, and iterating to convergence; S600: outputting a dispatching scheme including power allocation, market price and income allocation, and recording on the blockchain.

2. The blockchain-based trusted game for optical storage joint scheduling optimization method according to claim 1, characterized in that, The chain sharing energy leasing income comprises power usage right leasing income, which is composed of discharge electricity charges collected by the energy storage service provider from the user, charging electricity charges paid by the energy storage service provider to the user and power usage right leasing fees.

3. The blockchain-based trustable game for optical storage joint scheduling optimization method according to claim 1, characterized in that, The network internal balance service income comprises charging response income and discharging response income; The charging response income comprises electricity charge income of the energy storage service provider purchasing electricity from the photovoltaic power supplier and compensation fees of the photovoltaic power supplier for consumption service; The discharging response income comprises electricity charge income of the energy storage service provider selling electricity to the photovoltaic power supplier and overcharge premium of the photovoltaic power supplier for performance guarantee; The compensation fees of the consumption service are shared by the photovoltaic power supplier group according to the proportion of the respective installed capacity, and the overcharge premium is shared by the photovoltaic power supplier group according to the proportion of the respective standard deviation of day-ahead prediction error.

4. The blockchain-based trustable game for optical storage joint scheduling optimization method according to claim 1, characterized in that, The reservation utility is the lowest utility value that the photovoltaic power supplier, energy storage service provider and user can obtain without participating in cooperative dispatching; and the weighting coefficient in the weighted product is the bargaining weight of the photovoltaic power supplier, energy storage service provider and user.

5. The blockchain-based trustable game for optical storage joint scheduling optimization method according to claim 1, characterized in that, The S300 comprises: The power demand balance constraint is a power balance equation among photovoltaic power generation, energy storage charging and discharging and user electricity demand in each period; The energy storage state of charge constraint comprises an energy storage state of charge dynamic equation, an upper limit constraint of the energy storage state of charge and a lower limit constraint of the energy storage state of charge; The charging and discharging mutual exclusion constraint is realized by introducing charging behavior variables and discharging behavior variables, and the charging behavior variables and the discharging behavior variables in the same period cannot be 1 at the same time; The price rationality constraint limits the photovoltaic power selling price, energy storage charging and discharging price and power usage right leasing unit price within a reasonable range; The capacity occupation constraint limits the chain sharing energy capacity leased by each user to be less than an upper limit, and the total capacity leased by all users to be less than an upper limit of the energy storage system dispatchable capacity.

6. The blockchain-based trustable game for optical storage joint scheduling optimization method according to claim 1, characterized in that, In the S400, The energy storage service sub-problem is further decomposed into a charging and discharging decision sub-problem and a state of charge sub-problem, the local optimal decision variable of the charging and discharging decision sub-problem is the charging and discharging amount of each time period, and the local optimal decision variable of the state of charge sub-problem is the state of charge of each time period, and the state of charge is coupled with the charging and discharging decision sub-problem through a state of charge dynamic equation; The local optimal decision variable of the photovoltaic power plant sub-problem is the power generation amount of each time period; The local optimal decision variable of the user sub-problem is the power purchase amount of each time period.

7. The blockchain-based trustable game for optical storage joint scheduling optimization method according to claim 1, characterized in that, The process of iteration to convergence includes: The blockchain platform aggregates the local optimal decision variables uploaded by the photovoltaic power plant, the energy storage service provider and the user, calculates and updates the global consistency variable and the dual variable, and then distributes them to each subject; Each subject solves the next round based on the updated global consistency variable and the dual variable; The steps of updating the global consistency variable and the dual variable by the blockchain platform and distributing them to each subject, and solving the local optimal decision variable of the next round by each subject are repeated until the change of the global consistency variable is less than the preset convergence threshold.

8. A photovoltaic storage combined dispatching optimization system based on a blockchain trusted game, characterized in that, It includes: An utility function modeling module is configured to establish utility functions of the photovoltaic power plant, the energy storage service provider and the user, wherein the utility function of the energy storage service provider includes a network internal balance service revenue and a chain-entertaining energy rental revenue; A game modeling module is configured to construct an asymmetric Nash bargaining model based on the utility functions of the photovoltaic power plant, the energy storage service provider and the user, to maximize the weighted product of the difference between the utility and the reservation utility of the photovoltaic power plant, the energy storage service provider and the user as an objective function, and to establish a global optimization problem; A constraint setting module is configured to set constraint conditions for the global optimization problem, including power demand balance constraints, state of charge constraints, charging and discharging exclusion constraints, price rationality constraints and capacity occupation constraints; A distributed solving module is configured to decompose the global optimization problem into an energy storage service provider sub-problem, a photovoltaic power plant sub-problem and a user sub-problem by using an alternating direction multiplier method, and to independently solve local optimal decision variables by the photovoltaic power plant, the energy storage service provider and the user; A blockchain coordination module is configured to upload the local optimal decision variables obtained by the photovoltaic power plant, the energy storage service provider and the user to a blockchain platform, and to update the global consistency variable and the dual variable by the blockchain platform and iterate to convergence; A dispatching scheme output module is configured to output a dispatching scheme including power allocation, market price and revenue allocation, and to record on the blockchain.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the blockchain-based trusted game photovoltaic and energy storage joint dispatching optimization method of any one of claims 1 to 7.

10. A readable storage medium, characterized by, The readable storage medium stores a computer program, and the computer program is executed by the processor to realize the blockchain-based trusted game photovoltaic and energy storage joint dispatching optimization method of any one of claims 1 to 7.

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