Green electricity direct connection project shared energy storage configuration method considering power distribution node electricity price incentive

Abstract

The present application belongs to the technical field of power distribution network planning and optimal configuration of shared energy storage, and relates to a method for configuring shared energy storage of a green power direct connection project considering price incentives of power distribution nodes, comprising: 1, input of typical load distributed wind and light data collection; 2, obtaining parameter data, establishing a transfer factor matrix and a voltage sensitivity factor matrix; 3, establishing a shared energy storage planning double-layer model framework considering the influence of power distribution node prices; 4, establishing a shared energy storage planning model based on Nash bargaining at the upper layer; 5, establishing a power distribution network node price calculation model considering line loss at the lower layer; 6, solving the shared energy storage configuration problem by a nested iteration method based on a genetic algorithm; and 7, outputting a green power direct connection project shared energy storage rated power and rated capacity configuration scheme; the present application improves the economic benefits of industrial and commercial users and new energy manufacturers, and realizes optimal configuration of shared energy storage under accurate price signal incentives, thereby assisting green power direct connection projects in making energy storage investment decisions.

Description

Technical Field

[0001] This invention belongs to the field of distribution network planning and shared energy storage optimization configuration technology, specifically involving a shared energy storage configuration method for green electricity direct connection projects that takes into account electricity price incentives at distribution nodes. Background Technology

[0002] With the continuous expansion of distributed wind and solar power installations, the pressure on power grids to absorb renewable energy is becoming increasingly prominent. Coupled with carbon border constraints, industrial and commercial users have a strong demand for carbon reduction. Green electricity direct connection projects established between renewable energy manufacturers and industrial and commercial users can achieve local consumption of green electricity and meet the carbon reduction needs of enterprises. Current policies impose mandatory self-consumption ratio requirements on green electricity direct connection projects, making energy storage a necessary condition for compliant operation. Since self-built energy storage by a single entity is costly, multi-party co-construction and sharing of energy storage has become the preferred solution.

[0003] Under the background of power market reform, distribution node tariffs adapted to distribution network losses and voltage constraints can output refined time-of-use pricing signals. Green electricity direct connection projects are located within a single node of the distribution network, with renewable energy manufacturers and industrial and commercial users belonging to different entities. They follow the "self-generation and self-consumption, surplus power to the grid" model and sign long-term power purchase agreements internally. Shared energy storage can smooth out the difference between output and load, reduce curtailment, lower power purchase costs, and improve the benefits for both parties.

[0004] However, most existing shared energy storage configuration schemes for green electricity direct connection projects only consider basic constraints such as new energy output, load timing characteristics, and energy storage investment and operation and maintenance costs for optimization calculations. They generally ignore the economic incentive effect brought by the market-based pricing mechanism of the distribution network and do not introduce the differentiated electricity price signals of distribution nodes into the energy storage configuration decision.

[0005] On the one hand, the electricity price at distribution nodes fluctuates in real time with the distribution network power flow congestion, line losses, and node voltage status. The revenue from purchasing and selling electricity and the cost of congestion penalties vary significantly between different nodes, directly changing the arbitrage space for shared energy storage charging and discharging and the overall investment return of the project. Traditional configuration methods do not distinguish the differences in electricity prices at distribution nodes and uniformly use fixed time-of-use prices for modeling. The results of energy storage site selection and capacity configuration are out of touch with the actual market operation scenario of the distribution network. This can easily lead to problems such as over-configuration of energy storage capacity, resulting in resource waste, or under-configuration capacity, which fails to fully utilize the arbitrage space of node electricity prices and reduces the overall economic benefits of the project.

[0006] On the other hand, existing technologies do not couple and coordinate the incentive of distribution node electricity prices, the assessment indicators of green electricity self-generation and self-consumption, and the constraints of balancing the interests of multiple stakeholders. They only meet the single goal of consumption or cost, and cannot take into account the constraints of distribution network safety operation, carbon emission reduction needs, and the profit demands of multiple operators. The resulting shared energy storage solutions are difficult to adapt to the current market-oriented operation environment of distribution networks, and have practical shortcomings such as poor economic efficiency, high difficulty in achieving consumption targets, and increased distribution network power flow congestion.

[0007] Therefore, how to fully consider the market-based revenue differences brought about by the electricity price incentives at distribution nodes, and how to construct a shared energy storage optimization configuration method that is suitable for the multi-entity operation model of green electricity direct connection projects and takes into account both the policy constraints of self-generation and self-consumption and the constraints of distribution network operation, is a technical problem that urgently needs to be solved in the field of distributed new energy planning for distribution networks. Summary of the Invention

[0008] This invention aims to overcome the shortcomings of existing green electricity direct connection projects that do not consider the impact of the local electricity market on the distribution network when configuring shared energy storage. It provides a method for configuring shared energy storage in green electricity direct connection projects that takes into account the electricity price incentives at distribution nodes, so as to achieve the optimal configuration of shared energy storage and enable industrial and commercial users and new energy manufacturers to achieve the best economic benefits.

[0009] This invention provides the following technical solution: a method for configuring shared energy storage in green electricity direct-connection projects that takes into account electricity price incentives at distribution nodes, comprising the following steps: Step 1: Determine the power distribution system operation optimization cycle and collect input parameters. Input parameters include typical distributed wind and solar power output and load data within the cycle, distributed new energy installed capacity and maximum load, green electricity self-consumption ratio parameters, green electricity price under long-term power purchase agreement, unit carbon emission cost, carbon emission factor, electrochemical energy storage charge and discharge efficiency, unit power cost, unit capacity cost, and energy storage life cycle. Step 2: Based on the physical model of the distribution network, construct the AC power flow transfer factor matrix and voltage sensitivity factor matrix of the distribution network. Step 3: Construct a two-layer model framework for shared energy storage planning that takes into account the impact of distribution node electricity prices; the upper layer is a shared energy storage planning model based on Nash bargaining theory, and the lower layer is a distribution network node electricity price calculation model that takes into account line losses; the upper layer transmits the shared energy storage configuration scheme to the lower layer, and the lower layer transmits the distribution node electricity prices back to the upper layer, and the equilibrium solution is obtained through nested iteration between the upper and lower layers. Step 4: Construct an upper-level shared energy storage planning model based on Nash bargaining, decomposed into sub-problems SP1 and SP2. Sub-problem SP1 aims to minimize the total cost for industrial and commercial users and new energy manufacturers, with constraints including shared energy storage investment planning constraints, shared energy storage operation constraints, and green electricity direct connection policy constraints. Sub-problem SP2, based on the energy storage configuration, costs and benefits of all parties obtained from solving sub-problem SP1, aims to maximize payment benefits, adds cooperative game participation constraints, and completes the fair allocation of shared energy storage costs among industrial and commercial users and new energy manufacturers.

[0010] Step 5: Construct a lower-level distribution network node electricity price calculation model that takes line losses into account; with the goal of minimizing the total operating cost of the distribution network operator system, introduce system power balance constraints, node injected power constraints, distributed wind and solar power output constraints, distribution network power flow constraints, and node voltage constraints, and derive the active power price of the distribution nodes through the model's Lagrangian function dual multipliers. Step 6: Use a nested iterative solution based on a genetic algorithm to solve the two-layer model: The upper-layer genetic algorithm generates multiple energy storage capacity schemes and sends them to the lower layer. The lower layer iterates and converges to calculate line loss and distribution node electricity price and sends the distribution node electricity price back to the upper layer as the basis for fitness calculation. After the iteration is completed, the optimal shared energy storage power and capacity configuration results are output. Then, the energy storage investment cost is allocated through the Nash bargaining analytical formula.

[0011] Step 7: Output the rated power and rated capacity configuration scheme of the shared energy storage for the green electricity direct connection project, and compare the economic benefits and comprehensive benefits of all parties involved in the project before and after the energy storage is put into construction.

[0012] Preferably, in step 4, the objective function of subproblem SP1 is: (1) (2) (3) (4) (5) In the formula, For industrial and commercial users' costs, For the benefit of new energy manufacturers, To share the investment costs of energy storage, and They are respectively a collection of typical scenarios and a scenario The corresponding probability, T For time period, Typical scenario Next user i Place t Electricity price at distribution nodes at any given time. Typical scenario Next user i exist t Real-time electricity purchase and sale and These represent the purchased electricity volume and the sold electricity volume, respectively; and The green electricity price and typical scenarios are respectively signed under long-term power purchase agreements. Next user i exist t Green energy is constantly being consumed through direct green electricity connections; and These are unit carbon emission cost and carbon emission factor, respectively; These represent the unit power and unit capacity costs of shared energy storage, respectively. Energy storage life Discount rate r, andNumber of days in a year Joint decision; and These refer to the rated power and rated capacity of the shared energy storage project, respectively. For daily energy storage unit power cost, Cost per unit capacity of energy storage daily chemical products.

[0013] More preferably, the green electricity direct connection constraints include node power balance constraints, a lower limit constraint on the annual self-consumption ratio, and an upper limit constraint on the annual surplus electricity grid connection ratio; the shared energy storage investment planning constraints limit the maximum rated power of energy storage, and the rated capacity and rated power of energy storage meet a fixed duration ratio relationship; the shared energy storage operation constraints include upper limit of charging and discharging power, upper and lower limits of state of charge, and constraints that the energy storage capacity is equal at the beginning and end of the cycle.

[0014] Preferably, in step 4, the objective function of subproblem SP2 is: (20) In the formula, and These represent the costs for industrial and commercial users and the revenue for new energy manufacturers, respectively, before the construction of shared energy storage facilities. and These represent the costs for industrial and commercial users and the revenues for new energy manufacturers, respectively, obtained after solving subproblem SP1. and The shared energy storage costs are allocated to industrial and commercial users and new energy manufacturers, respectively.

[0015] More preferably, the cost allocation solution obtained from the Nash bargaining analysis in step 6 is as follows: (36) (37) (38) In the formula: For the remaining profits of the cooperation between the two parties, Solve for the optimal total investment cost of energy storage for SP1.

[0016] Preferably, in step 5, the active power price calculation for the distribution node is derived based on each constraint dual multiplier as follows: (35) In the formula, , For the active and reactive power balance dual multipliers of the system, , These are the system loss factors; For distribution network lines l Active power about nodes i The transfer factor for injected active power; For distribution network lines l Reactive power about nodes i The transfer factor for injected active power. For the dual multiplier of power flow constraints in distribution networks, , The coefficients of the approximate linearized equations within the polyhedron are . C A set of linear equations, Let L be the set of all nodes in the distribution network, and let L be the set of all lines in the distribution network. , These are the dual multipliers of the nodal voltage constraints. For nodes i Voltage amplitude with respect to nodes j Sensitivity factor for injected active power.

[0017] Preferably, the nested iterative solution step in step 6 includes: Step 6-1: Initialize the distribution network topology, load, and new energy parameters; set the genetic algorithm population size, crossover probability, mutation probability, maximum number of iterations, and line loss convergence accuracy. Step 6-2: Randomly generate multiple sets of energy storage power and capacity combinations as the initial population, and distribute each set of energy storage schemes to the lower-level model; Step 6-3: Iteratively update the line power flow, node voltage, and system line loss of the lower-level model until the difference between two adjacent line loss values ​​is less than the convergence accuracy, and solve for the distribution node electricity price. Step 6-4: Feed the nodal electricity price back to the upper-level model, substitute it into the SP1 objective function to calculate the fitness of individual populations, and record the current global optimal energy storage configuration scheme; Step 6-5: Perform selection, crossover, and mutation operations on the population to generate a new generation of energy storage configuration schemes; Step 6-6: Determine whether the current genetic evolution generation has reached the maximum evolution generation. If not, return to step 6-3 for iterative iteration; if it has, output the optimal shared energy storage rated power and rated capacity.

[0018] Preferably, in step 2, the power flow transfer factor matrix quantifies the sensitivity of node power changes to power flow on each line, and the voltage sensitivity factor matrix quantifies the impact of node power changes on node voltage amplitude, which is used to solve the linearization of power flow and node electricity price in the lower-level distribution network.

[0019] The beneficial effects of this invention are: 1. Under the policy constraints of green electricity self-generation and self-consumption ratio, this invention introduces distribution network node electricity price incentives to participate in the optimized allocation of shared energy storage, effectively solving the problems of insufficient new energy consumption and project revenue loss caused by policy restrictions, and simultaneously maximizing the economic benefits for both new energy manufacturers and industrial and commercial users.

[0020] 2. This invention takes into account the bidirectional coupling effect of distribution node electricity price and shared energy storage: node electricity price can guide user energy consumption and change the internal power balance of the project, while the construction of energy storage will have a reverse effect on node electricity price; by relying on the refined distribution node price signal to complete collaborative optimization, the optimal energy storage configuration scheme that adapts to the market operation rules can be obtained.

[0021] 3. This invention integrates multiple real-world conditions, including energy storage investment costs, distribution network power market conditions, and green electricity direct connection management policies. It can provide multiple operating entities within a project with objective and economically feasible shared energy storage capacity and investment decision-making basis, making it highly practical. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method for configuring shared energy storage in green electricity direct connection projects that takes into account the electricity price incentives of distribution nodes according to the present invention. Figure 2 This is a two-layer model framework diagram for the shared energy storage planning of the present invention; Figure 3 This is a diagram showing the convergence of the outer loop in the nested iterative solution method of this invention. Figure 4 A comparison chart showing the economic benefits of configuring shared energy storage for industrial and commercial users and new energy manufacturers before and after the present invention. Figure 5 This is a comparison chart showing the overall benefits of green electricity direct connection projects before and after configuring shared energy storage according to the present invention. Detailed Implementation

[0023] The relevant technologies of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0024] like Figures 1-5 As shown, this embodiment of the method for configuring shared energy storage in green electricity direct-connection projects that takes into account distribution node electricity price incentives includes the following steps: Step 1: Determine the power distribution system operation optimization cycle, obtain typical distributed wind and solar power output and load data within the cycle, distributed new energy installed capacity and maximum load in green electricity direct connection projects, green electricity self-consumption ratio parameters, green electricity price under long-term power purchase agreements, unit carbon emission cost and carbon emission factor, and related technical and economic parameters of electrochemical energy storage, including electrochemical energy storage charge and discharge efficiency coefficient, unit power cost, unit capacity cost, life cycle, etc., as input to the program.

[0025] Step 2: Based on the physical model of the distribution network, establish the AC power flow transfer factor matrix and voltage sensitivity factor matrix to prepare for subsequent calculations of line power flow and node voltage magnitude. The AC power flow transfer factor matrix includes all information about the network topology, and its elements quantify the sensitivity of node power changes to line power flow. The voltage sensitivity factor matrix elements quantify the sensitivity of node power changes to node voltage magnitude.

[0026] Step 3: Establish a two-layer model framework for shared energy storage planning that takes into account the impact of distribution node electricity prices. The upper-layer model is a shared energy storage planning model based on Nash bargaining theory, including sub-problem 1, which aims to minimize the total cost for industrial and commercial users and renewable energy manufacturers, and sub-problem 2, which aims to achieve cost sharing of shared energy storage. The lower-layer model is a distribution node electricity price calculation model that takes into account line losses, and the distribution node electricity price is calculated based on duality theory. In the interaction between the upper and lower-layer models, the upper-layer model transmits the shared energy storage configuration scheme to the lower layer, while the lower-layer electricity price calculation model transmits the distribution node electricity price to the upper layer to calculate the total cost for industrial and commercial users and renewable energy manufacturers. The equilibrium solution is finally obtained by iteratively solving the upper and lower-layer models.

[0027] Step 4: Establish a shared energy storage planning model based on Nash bargaining, which can be decomposed into subproblems SP1 and SP2. Subproblem SP1 aims to minimize the total cost for industrial and commercial users and new energy manufacturers participating in the construction of shared energy storage, considering constraints on shared energy storage investment planning, shared energy storage operation, and direct green electricity connection. Subproblem SP2, based on the optimal costs for industrial and commercial users, revenues for new energy manufacturers, and shared electrochemical energy storage costs obtained from solving subproblem SP1, aims to maximize payment benefits, considering the constraints that industrial and commercial users and new energy manufacturers must meet in a cooperative game to achieve a fair distribution of energy storage costs.

[0028] The subproblem SP1 of the shared energy storage planning model based on Nash bargaining is: Objective function: (1) (2) (3) (4) (5) The constraints are: (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) Among them, Equation (2) is the cost for industrial and commercial users; Equation (3) is the revenue of new energy manufacturers; Equation (4) is the investment cost of shared energy storage; Equation (5) is the investment cost coefficient of daily unit power and daily unit capacity of energy storage; Equations (6)-(12) are the green electricity direct connection constraints; Equations (13)-(14) are the investment planning constraints of shared energy storage; Equations (15)-(19) are the operation constraints of shared energy storage.

[0029] and They are respectively a collection of typical scenarios and a scenario The corresponding probability; T For time period; Typical scenario Next user i Place t Electricity price at distribution nodes at any given time; Typical scenario Next user i exist t Real-time electricity purchase and sale and These represent the purchased electricity volume and the sold electricity volume, respectively; and The green electricity price and typical scenarios are respectively signed under long-term power purchase agreements. Next user i exist t Green energy is constantly being consumed through direct green electricity connections; and These are unit carbon emission cost and carbon emission factor, respectively; These represent the unit power and unit capacity costs of shared energy storage, respectively. Energy storage life Discount rate r， and number of days per year Joint decision; and These refer to the rated power and rated capacity of the shared energy storage project, respectively. Typical scenario Next user i exist t The workload of the moment; Typical scenario Next user i The connected new energy sources t Predicting output at any given moment; and These are the annual limits on the proportion of self-generated and self-consumed electricity from new energy sources to total available power generation and total electricity consumption, respectively. The annual on-grid electricity volume is limited to the total available power generation. Maximum limit of rated power for shared energy storage; b The number of hours that an energy storage device can continuously charge and discharge is the ratio of its rated capacity to its rated power. and Shared energy storage in typical scenarios Down t The charging power and discharging power at any given time; For shared energy storage in typical scenarios Down t Actual capacity at any given moment; and These are the energy storage charge and discharge efficiency coefficients, respectively. and These are the minimum and maximum state of charge limits for shared energy storage, respectively.

[0030] The subproblem SP2 of the shared energy storage planning model based on Nash bargaining is: Objective function: (20) The constraints are: (twenty one) (twenty two) (twenty three) Among them, Equations (21) to (23) are the prerequisite constraints for industrial and commercial users and new energy manufacturers to participate in cooperative game.

[0031] and These represent the costs for industrial and commercial users and the revenue for new energy manufacturers before the construction of shared energy storage, respectively, which is the point at which negotiations break down. and These represent the costs for industrial and commercial users and the revenues for new energy manufacturers, respectively, obtained after solving subproblem SP1. and The shared energy storage costs are allocated to industrial and commercial users and new energy manufacturers, respectively. To calculate the shared energy storage cost obtained after solving subproblem SP1.

[0032] Step 5: Establish a calculation model for the electricity price of the distribution network nodes that takes line losses into account. The distribution network operator takes the minimum total operating cost of the system as the optimization objective, considering system power balance constraints, node injected power constraints, distributed wind and solar power output constraints, distribution network power flow constraints, and node voltage constraints, and derives the active power price of the distribution network nodes based on the Lagrangian function of the model.

[0033] The calculation model for distribution network node electricity prices that take line losses into account is as follows: Objective function: (twenty four) The constraints are: (25) (26) (27) (28) (29) (30) (31) (32) (33) Equations (25)-(26) are system power balance constraints; Equation (27) is the power injection constraint of green electricity direct-connected nodes; Equations (28)-(29) are the power injection constraints of non-green electricity direct-connected nodes; Equations (30)-(31) are distributed wind and solar power output constraints; Equation (32) is the power flow constraint of the distribution network after linearization based on the polyhedron internal approximation method; and Equation (33) is the node voltage constraint.

[0034] PV and W These are respectively distributed photovoltaic and distributed wind power collections; The price of electricity in the wholesale market on the transmission side; and Typical scenarios Distributed photovoltaic pv and distributed wind power w exist t Actual output at any given moment; and These are the unit power generation costs for distributed photovoltaic and distributed wind power, respectively. The unit electricity sales cost for grid-connected green energy direct connection projects; and Typical scenarios Next node i exist t The active and reactive power injected at all times; and Typical scenarios Given the known active power line loss and reactive power line loss of the system; and In typical scenarios Down t Active and reactive loads at any given time; and Typical scenarios Distributed photovoltaic pv and distributed wind power w exist t Predicting output at any given moment; , and The coefficients of the approximate linearized equations within the polyhedron are . C It is a set of linear equations; and Typical scenarios Next node i In t The active and reactive components of the virtual node's demand are known at all times. For the line l The greatest current limitation; and Distribution network lines l Active power about nodes i Transfer factors for injected active and reactive power; and Distribution network lines l Reactive power about nodes i Transfer factors for injected active and reactive power; The voltage amplitude at the equilibrium node is set to 1. and These are the minimum and maximum allowable voltage values ​​for distribution network nodes, respectively. and They are nodes i Voltage amplitude with respect to nodes j Sensitivity factors for injected active and reactive power; , , , , , , , , , and These are the dual multipliers of the constraints corresponding to equations (25) to (33), listed after the corresponding constraints, separated by colons.

[0035] The Lagrangian function of the above model is: (34) Taking the partial derivative of equation (34) with respect to the active power load at the nodes, we can obtain the typical scenario. The active power price at the downstream distribution network node is: (35) Step 6: Due to the strong nonlinearity of the constructed two-layer model for shared energy storage configuration in the green electricity direct connection project, and the fact that the calculation of distribution network node electricity prices involves line loss updates, a nested iterative solution method based on a genetic algorithm is proposed. The upper-layer model is solved using a genetic algorithm to generate a shared energy storage configuration scheme, which is then passed to the lower layer. The lower layer uses a solver to iteratively solve for the active power price of the distribution network nodes, which is then passed to the upper layer for fitness calculation. The specific solution steps are as follows: Step 6-1: Initialize distribution network parameters, such as line impedance, node load, and renewable energy output curves; set genetic algorithm parameters, such as population size. Maximum Algebra Limit Crossover probability Probability of mutation and iterative convergence accuracy Set the current generation. .

[0036] Step 6-2: Randomly generate using a genetic algorithm The initial population is formed by combining the rated power and rated capacity of shared energy storage, and each combination scheme is passed to the lower-level model.

[0037] Step 6-3: For each energy storage scheme in the population, perform the following iterative process to calculate the distribution network node price: (1) Set k=1 and use a commercial solver to solve the distribution network node price calculation model to obtain the initial line power flow, node voltage and line loss. .

[0038] (2) Calculate the line loss factor and the demand of the fictitious node based on the power flow results, and let k=k+1.

[0039] (3) Taking into account the line loss factor and the demand of the fictitious node, the distribution network node electricity price calculation model is re-solved to obtain the updated line power flow, node voltage and line loss. .

[0040] (4) If satisfied If the convergence condition is met, the active power price of the distribution network node is calculated; otherwise, (2)-(3) are repeated until convergence.

[0041] Step 6-4: Transfer the active power price of the distribution network nodes to the upper-level model, calculate the total cost of the cooperative alliance corresponding to each energy storage scheme based on the upper-level objective function, use it as the individual fitness, and record the optimal total cost of the current population. .

[0042] Step 6-5: Perform selection, crossover, and mutation operations on the current population to update and generate new populations. A shared energy storage configuration scheme constitutes a new generation of population.

[0043] Step 6-6: Determine if the current evolutionary generation has reached the maximum evolutionary generation limit. If it has, output the shared energy storage configuration scheme; otherwise, return to step 3 and calculate the optimal total cost of the new population. ,like Then the global optimal solution is updated to The corresponding individual is selected; otherwise, the original optimal solution is retained.

[0044] Steps 6-7: Using Nash bargaining theory, the cost of shared energy storage can be fairly distributed between new energy manufacturers and industrial and commercial users. Since the upper-level model is a standard Nash bargaining model, subproblem SP2 can be solved analytically. The specific calculation process is as follows: (36) (37) (38) Step 7: Based on the proposed method, solve the problem of shared energy storage configuration for green electricity direct connection projects that takes into account the electricity price incentives of distribution nodes, obtain the shared energy storage configuration capacity of green electricity direct connection projects, analyze the solution performance through iterative convergence, and compare the comprehensive benefits of green electricity direct connection projects before and after configuring shared energy storage.

[0045] In summary, this invention proposes a shared energy storage configuration method for green electricity direct connection projects that takes into account distribution node electricity price incentives. At the upper planning layer, the optimal configuration scheme for shared energy storage is determined with the goal of minimizing the total cost of the cooperative alliance, and Nash bargaining is used to achieve fair cost sharing of energy storage. At the lower operation layer, the distribution network node electricity price, taking into account the impact of line losses, is calculated with the goal of minimizing the total operating cost of the distribution network system. The price signal from the node electricity price guides the optimized operation of shared energy storage. The proposed method can effectively balance the interests of industrial and commercial users and new energy manufacturers, reduce the overall energy cost of green electricity direct connection projects, improve the green electricity consumption level and the comprehensive economic benefits of the projects, and provide feasible technical support for the planning and configuration of shared energy storage in green electricity direct connection scenarios.

[0046] It should be emphasized that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for configuring shared energy storage in green electricity direct-connection projects that takes into account distribution node electricity price incentives, characterized in that, Includes the following steps: Step 1: Determine the power distribution system operation optimization cycle and collect input parameters; the input parameters include typical distributed wind and solar power output and load data within the cycle, distributed new energy installed capacity and maximum load, green electricity self-consumption ratio parameters, green electricity price under long-term power purchase agreement, unit carbon emission cost, carbon emission factor, electrochemical energy storage charge and discharge efficiency, unit power cost, unit capacity cost, and energy storage life cycle; Step 2: Based on the physical model of the distribution network, construct the AC power flow transfer factor matrix and voltage sensitivity factor matrix of the distribution network; Step 3: Construct a two-layer model framework for shared energy storage planning that takes into account the impact of distribution node electricity prices. The upper layer of the two-layer model framework is a shared energy storage planning model based on Nash bargaining theory, and the lower layer is a distribution network node electricity price calculation model that takes into account line losses. The upper layer transmits the shared energy storage configuration scheme to the lower layer, and the lower layer transmits the distribution node electricity prices back to the upper layer. The equilibrium solution is obtained through nested iteration between the upper and lower layers. Step 4: Construct an upper-level shared energy storage planning model based on Nash bargaining, decomposing it into sub-problems SP1 and SP2. Sub-problem SP1 aims to minimize the total cost for industrial and commercial users and new energy manufacturers, with constraints including shared energy storage investment planning constraints, shared energy storage operation constraints, and green electricity direct connection constraints. Sub-problem SP2, based on the energy storage configuration, costs, and benefits obtained from solving sub-problem SP1, aims to maximize payment benefits and adds cooperative game participation constraints to achieve fair cost sharing of energy storage. Step 5: Construct a lower-level distribution network node electricity price calculation model that takes line losses into account; with the goal of minimizing the total operating cost of the distribution network operator system, introduce system power balance constraints, node injected power constraints, distributed wind and solar power output constraints, distribution network power flow constraints, and node voltage constraints, and derive the active power price of the distribution nodes through the model's Lagrangian function; Step 6: Use a nested iterative solution based on a genetic algorithm to solve the two-layer model: the upper-layer genetic algorithm generates an energy storage capacity scheme and sends it to the lower layer. The lower layer iterates and converges to calculate line loss and distribution node electricity price and sends the distribution node electricity price back to the upper layer as the basis for fitness calculation. After the iteration is completed, the optimal shared energy storage power and capacity configuration results are output. Then, the energy storage investment cost is allocated through the Nash bargaining analytical formula. Step 7: Output the rated power and rated capacity configuration scheme of the shared energy storage for the green electricity direct connection project, and compare the economic benefits and comprehensive benefits of all parties involved in the project before and after the energy storage is put into construction.

2. The method for configuring shared energy storage in green electricity direct-connection projects considering distribution node electricity price incentives as described in claim 1, characterized in that, In step 4, the objective function of subproblem SP1 is: (1) (2) (3) (4) (5) In the formula, For industrial and commercial users' costs, For the benefit of new energy manufacturers, To share the investment costs of energy storage, and They are respectively a collection of typical scenarios and a scenario The corresponding probability, T For time period, Typical scenario Next user i Place t Electricity price at distribution nodes at any given time. Typical scenario Next user i exist t Real-time electricity purchase and sale and These represent the purchased electricity volume and the sold electricity volume, respectively; and The green electricity price and typical scenarios are respectively signed under long-term power purchase agreements. Next user i exist t Green energy is constantly being consumed through direct green electricity connections; and These are unit carbon emission cost and carbon emission factor, respectively; These represent the unit power and unit capacity costs of shared energy storage, respectively. Energy storage life Discount rate r， and number of days per year Joint decision; and These refer to the rated power and rated capacity of the shared energy storage project, respectively. For daily energy storage unit power cost, Cost per unit capacity of energy storage daily chemical products.

3. The method for configuring shared energy storage in green electricity direct-connection projects considering distribution node electricity price incentives as described in claim 2, characterized in that, The constraints on direct green electricity connection include node power balance constraints, a lower limit constraint on the annual self-consumption ratio, and an upper limit constraint on the annual surplus electricity grid connection ratio; the constraints on shared energy storage investment planning limit the maximum rated power of energy storage, and the rated capacity and rated power of energy storage meet a fixed duration ratio relationship; the constraints on shared energy storage operation include upper limit of charging and discharging power, upper and lower limits of state of charge, and constraints on equal energy storage capacity at the beginning and end of the cycle.

4. The method for configuring shared energy storage in green electricity direct-connection projects considering distribution node electricity price incentives as described in claim 1, characterized in that, In step 4, the objective function of subproblem SP2 is: (20) In the formula, and These represent the costs for industrial and commercial users and the revenue for new energy manufacturers, respectively, before the construction of shared energy storage facilities. and These represent the costs for industrial and commercial users and the revenues for new energy manufacturers, respectively, obtained after solving subproblem SP1. and The shared energy storage costs are allocated to industrial and commercial users and new energy manufacturers, respectively.

5. The method for configuring shared energy storage in green electricity direct-connection projects considering distribution node electricity price incentives as described in claim 4, characterized in that, In step 6, the Nash bargaining analytical solution for cost allocation is: (36) (37) (38) In the formula: For the remaining profits of the cooperation between the two parties, Solve for the optimal total investment cost of energy storage for SP1.

6. The method for configuring shared energy storage in green electricity direct-connection projects considering distribution node electricity price incentives as described in claim 1, characterized in that, In step 5, the active power price calculation for the distribution node is derived based on each constraint dual multiplier: (35) In the formula, , For the active and reactive power balance dual multipliers of the system, , These are the system loss factors; For distribution network lines l Active power about nodes i The transfer factor for injected active power; For distribution network lines l Reactive power about nodes i The transfer factor for injected active power. For the dual multiplier of power flow constraints in distribution networks, , The coefficients of the approximate linearized equations within the polyhedron are . C A set of linear equations, Let L be the set of all nodes in the distribution network, and let L be the set of all lines in the distribution network. , These are the dual multipliers of the nodal voltage constraints. For nodes i Voltage amplitude with respect to nodes j Sensitivity factor for injected active power.

7. The method for configuring shared energy storage in green electricity direct-connection projects considering distribution node electricity price incentives as described in claim 1, characterized in that, The nested iterative solution steps in step 6 include: Step 6-1: Initialize the distribution network topology, load, and new energy parameters; set the genetic algorithm population size, crossover probability, mutation probability, maximum number of iterations, and line loss convergence accuracy. Step 6-2: Randomly generate multiple sets of energy storage power and capacity combinations as the initial population, and distribute each set of energy storage schemes to the lower-level model; Step 6-3: Iteratively update the line power flow, node voltage, and system line loss of the lower-level model until the difference between two adjacent line loss values ​​is less than the convergence accuracy, and solve for the distribution node electricity price. Step 6-4: Feed the nodal electricity price back to the upper-level model, substitute it into the SP1 objective function to calculate the fitness of individual populations, and record the current global optimal energy storage configuration scheme; Step 6-5: Perform selection, crossover, and mutation operations on the population to generate a new generation of energy storage configuration schemes; Step 6-6: Determine whether the current genetic evolution generation has reached the maximum evolution generation. If not, return to step 6-3 for iterative iteration; if it has, output the optimal shared energy storage rated power and rated capacity.

8. The method for configuring shared energy storage in green electricity direct-connection projects considering distribution node electricity price incentives as described in claim 1, characterized in that, In step 2, the power flow transfer factor matrix quantifies the sensitivity of node power changes to line power flow, and the voltage sensitivity factor matrix quantifies the impact of node power changes on node voltage amplitude, which is used to solve the linearization of power flow and node electricity price in the lower-level distribution network.

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