A capacity optimization configuration method for multi-stakeholders of a micro-grid system

Abstract

This invention presents a capacity optimization method for a microgrid system involving multiple stakeholders, including virtual power plants and independent energy storage power stations. First, an objective function for the annual cost of the virtual power plant is constructed, considering factors such as minimizing annual cost and reducing wind and solar curtailment. Similarly, an objective function for the independent energy storage power station is constructed, considering factors such as minimizing annual cost and maximizing the lifespan of the energy storage batteries. Then, constraints for the stable operation of each stakeholder are obtained. Finally, a mutually beneficial capacity configuration model for the microgrid system is constructed based on Pareto optimality. The Pareto front is found using a multi-objective particle swarm optimization algorithm, and the point closest to the center point is selected as the win-win solution for the two stakeholders. Finally, the installed capacity of distributed energy resources and the rated capacity and power of energy storage batteries are calculated based on the operating costs and constraints of each stakeholder. This method improves the utilization rate of distributed energy resources while achieving mutual benefit for multiple stakeholders, providing a reference for capacity configuration of multiple stakeholders under a shared energy storage model.

Description

Technical Field

[0001] This invention belongs to the field of capacity optimization configuration technology for microgrid systems. Specifically, it is a capacity optimization configuration method for microgrid systems with multiple stakeholders, achieving mutual benefit and win-win results for virtual power plants (VPPs) and independent energy storage power stations. Background Technology

[0002] With the vigorous development of wind and solar power generation, relying solely on energy management methods on the generation or grid side is insufficient to achieve real-time load and power supply balance and efficient utilization of renewable energy. To overcome the volatility, intermittency, and randomness of wind and solar energy and fully realize energy efficiency, microgrid systems are constructed by combining wind and solar power, along with energy storage devices. Energy storage, as a crucial component of microgrid systems, effectively mitigates the randomness and volatility of renewable energy generation, improves power quality, maintains system stability, and enables seamless switching between grid-connected and islanded systems. Currently, microgrid systems face several constraints: firstly, current energy storage devices are expensive and have short lifespans, significantly limiting their large-scale promotion and application; secondly, managing large numbers of energy storage devices on the generation side is difficult. Therefore, rationally allocating the capacity of each component while meeting system operational requirements is of significant practical importance.

[0003] Commonly used microgrid system capacity configuration methods mainly fall into three categories: energy balance method, fluctuation smoothing method, and economic characteristic optimization method. The energy balance method generally starts from the perspective of ensuring continuous power supply, and uses energy storage to balance the imbalance between the power generation of generator sets and the power consumption of the load for capacity configuration. The fluctuation smoothing method mainly optimizes the capacity configuration based on the microgrid system's ability to smooth fluctuating power. The economic characteristic optimization method mainly establishes an optimization objective function to minimize the operating cost of the entire wind, solar and energy storage project, uses the capacity of each entity as a decision variable, and uses intelligent optimization algorithms to obtain the optimal energy storage capacity configuration.

[0004] Most existing literature focuses on the overall wind, solar, and energy storage projects, optimizing the capacity configuration of the entire system from the perspectives of maximizing overall benefits, power quality, or environmental pollution. With the promotion of independent shared energy storage models for large-scale wind, solar, and energy storage projects, multiple stakeholders are cooperating and achieving mutual benefits. Therefore, providing win-win capacity configuration solutions for multiple stakeholders is a pressing technical problem that needs to be solved in capacity configuration optimization research. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the technical problem this invention aims to solve is to provide a capacity optimization configuration method for multiple stakeholders in a microgrid system.

[0006] The technical solution adopted by the present invention to solve the aforementioned technical problem is as follows:

[0007] A method for optimizing capacity allocation for multiple stakeholders in a microgrid system, wherein the stakeholders of the microgrid system include virtual power plants and independent energy storage power stations, characterized in that the method includes the following:

[0008] First, each stakeholder should consider their own influencing factors while taking into account annual costs, and construct their own objective function.

[0009] 1.1) The virtual power plant aims to minimize annual costs and reduce wind and solar curtailment. The objective function for the stakeholders in the virtual power plant is constructed as follows:

[0010] minC VPP =C grid +C ess.b -C ess.s +C serve +C cut +C f (7)

[0011] In the formula, C grid C represents the cost of purchasing electricity from the main grid for the virtual power plant. ess.b C represents the cost of purchasing electricity from independent energy storage power stations for the virtual power plant. ess.s For the revenue generated by the virtual power plant selling electricity to independent energy storage power stations, C serve C is the service fee paid by the virtual power plant to the independent energy storage power station. cut C f The investment cost of distributed generation energy in a virtual power plant;

[0012] Among them, the cost C of the virtual power plant purchasing electricity from the main grid grid The representation is:

[0013]

[0014] In the formula, Let be the cost of purchasing electricity from the main grid on the i-th typical day, I be the number of typical days in each quarter, d be the number of quarters in a year, δ0(t) be the unit price of electricity purchased from the main grid during the t-th dispatch period, t0 be the number of dispatch periods in a typical day, and Δt be the number of hours in each dispatch period. Let be the electrical power purchased by the virtual power plant from the main power grid during the t-th dispatch period on the i-th typical day;

[0015] The cost C of a virtual power plant purchasing electricity from an independent energy storage power station ess.b Represented as:

[0016]

[0017] In the formula, δ represents the cost of purchasing electricity from an independent energy storage power station on the i-th typical day. b (t) represents the unit electricity price for electricity purchased from the independent energy storage power station during the t-th scheduling time period. The power purchased by the virtual power plant from the independent energy storage power station during the t-th dispatch period on the i-th typical day;

[0018] Revenue C from virtual power plants selling electricity to independent energy storage power stations ess.s Represented as:

[0019]

[0020] In the formula, For the revenue of the virtual power plant from selling electricity to the independent energy storage power station on the i-th typical day, δ s (t) represents the unit electricity price sold to the independent energy storage power station during the t-th scheduling period. The power sold by the virtual power plant to the independent energy storage power station during the t-th dispatch period on the i-th typical day;

[0021] The service fee C paid by the virtual power plant to the independent energy storage power station serve Represented as:

[0022]

[0023] In the formula, δ is the service fee paid by the virtual power plant to the independent energy storage power station on the i-th typical day. serve (t) represents the unit service fee paid by the virtual power plant to the independent energy storage power station during the t-th scheduling period, where T represents matrix transpose;

[0024] The penalty cost C for wind and solar power curtailment in virtual power plants cut Represented as:

[0025]

[0026] In the formula, Let λ be the penalty cost for wind and solar curtailment in the virtual power plant on the i-th typical day. cut The penalty cost coefficient for abandoning wind and solar power. For the power of wind and solar curtailment, ξ wp (t) represents the on-grid electricity price for wind-solar hybrid power generation, and t1 represents the number of points in the sampling interval of the i-th typical midday test point;

[0027] Investment cost C of distributed generation energy in virtual power plants f Represented as:

[0028]

[0029] In the formula, C WT C PVThe annual investment costs for wind turbines and photovoltaic generators are N, respectively. WT N PV These represent the installed capacity of wind turbines and photovoltaic generators, respectively. WT1 C PV1 These represent the investment costs of a single wind turbine and a photovoltaic generator set, respectively, where τ is the depreciation rate, β is the annual interest rate, and L... WT and L PV These refer to the service life of wind turbines and photovoltaic generators, respectively.

[0030] 1.2) The objective function for an independent energy storage power station is constructed with the goal of minimizing annual cost and maximizing battery lifespan. The expression is as follows:

[0031]

[0032] in, and These represent the rated power and rated capacity of the energy storage battery, respectively. p and c E These are the rated power factor and rated capacity factor of the energy storage battery, respectively. Y represents the total replacement cost of the energy storage battery. a The total lifespan designed for energy storage batteries. For the annual maintenance cost of energy storage batteries, Y t λ represents the actual lifespan of the energy storage battery, and λ is the lifespan weighting factor for the energy storage battery. The cost of purchasing and selling electricity to the i-th typical solar-facing virtual power plant of an independent energy storage power station;

[0033] Second, obtain the constraints for the stable operation of various stakeholders;

[0034] 2.1) The constraints of the virtual power plant include the power generation and consumption balance constraints of the virtual power plant, the constraints of the virtual power plant on the absorption of distributed energy, and the power constraints on purchasing electricity from the large power grid.

[0035] The power generation and consumption balance constraints for the virtual power plant are:

[0036]

[0037] In the formula, Let be the output power of the wind turbine unit during the t-th scheduling period on the i-th typical day. Let be the output power of the photovoltaic generator unit during the t-th scheduling period on the i-th typical day. Let be the power consumption at the load end during the t-th scheduling period of the i-th typical day;

[0038] Virtual power plants' constraints on the absorption of distributed generation energy

[0039]

[0040] In the formula: α represents the maximum output power of the photovoltaic generator and the wind turbine during the t-th scheduling period on the i-th typical day, respectively, and α is the annual comprehensive absorption rate of distributed energy by the virtual power plant.

[0041] Power constraints on purchasing electricity from the main power grid

[0042]

[0043] In the formula: Let P be the electrical power purchased by the virtual power plant from the main grid during the t-th dispatch period on the i-th typical day. grid.max This represents the maximum electrical power that the virtual power plant purchases from the main power grid;

[0044] 2.2) The constraints of independent energy storage power stations include the energy rate constraint of the energy storage battery, the rated power and rated capacity constraint of the energy storage battery, the charge and discharge power constraint of the independent energy storage power station, and the state of charge constraint.

[0045] The energy rate constraint of energy storage batteries is expressed as:

[0046]

[0047] In the formula, δ represents the energy rate of the energy storage battery. These are the upper limits of the rated capacity and rated power of the energy storage battery, respectively.

[0048] The rated power and rated capacity constraints of energy storage batteries are expressed as follows:

[0049]

[0050] In the formula: These are the lower limits of the rated power and rated capacity of the energy storage battery, respectively.

[0051] The charging and discharging power constraints of an independent energy storage power station are expressed as follows:

[0052]

[0053] In the formula, and Let be the electrical power purchased and sold during the t-th scheduling period on the i-th typical day. and These are the charging and discharging flags for the independent energy storage power station during the t-th scheduling period on the i-th typical day.

[0054] The state-of-charge constraint of an independent energy storage power station is expressed as follows:

[0055]

[0056] In the formula: These represent the state of charge (SOC) of an independent energy storage power station during dispatch periods t and t-1 on the i-th typical day, respectively, α abs and α relea These represent the charging and discharging efficiencies of an independent energy storage power station, E ESS.min and E ESS.max These are the lower and upper limits of the state of charge for independent energy storage power stations, respectively.

[0057] III. Based on Pareto optimality, a capacity allocation model for microgrid systems is constructed to achieve mutual benefit and win-win outcomes for multiple stakeholders, resulting in optimal capacity allocation while achieving mutually beneficial operating costs. The expression for the capacity allocation model is as follows:

[0058] minZ = [F,C VPP ] T (20)

[0059] The Pareto front is found using the multi-objective particle swarm optimization algorithm. The point closest to the center point is selected from the Pareto front as the win-win solution for the two stakeholders, i.e., the operating cost for the two stakeholders to achieve mutual benefit. Then, based on the operating costs and constraints of each stakeholder, the installed capacity of wind turbines, photovoltaic generators, and the rated capacity and rated power of energy storage batteries are solved to complete the capacity optimization configuration of the microgrid system for multiple stakeholders.

[0060] Furthermore, the total replacement cost of energy storage batteries is related to their actual service life, and the calculation formula is as follows:

[0061]

[0062] Among them, Y t This indicates the actual lifespan of the energy storage battery;

[0063] The cost of purchasing and selling electricity to the i-th typical daytime virtual power plant of an independent energy storage power station Represented as:

[0064]

[0065] In the formula, c buy (t) and c sell (t) represents the electricity purchase and sales costs of an independent energy storage power station during the t-th dispatch period on the i-th typical day.

[0066] Compared with the prior art, the beneficial effects of the present invention are:

[0067] This invention treats existing distributed photovoltaic (PV) and wind turbine units, along with the load side, as virtual power plants (VPPs). Shared energy storage systems (such as lithium titanate or lead-acid batteries) are introduced as another independent energy storage market entity. Based on this, independent energy storage power stations are introduced, with energy storage service leasing as the profit driver. Independent energy storage power stations and VPPs form distinct stakeholders, each pursuing the lowest possible operating costs, thus achieving a win-win situation. This invention addresses the collaborative model of multiple stakeholders in integrated wind-solar-storage projects. Each stakeholder considers factors beneficial to themselves while pursuing their own goals, achieving mutual benefit and providing a mutually beneficial capacity configuration scheme. The objective function of the virtual power plant considers its own operating costs while incorporating the penalty cost of wind and solar curtailment. The objective function of the independent energy storage power station considers its own operating costs while also taking into account the lifespan of the energy storage batteries. A capacity configuration model for the microgrid system is established based on the objective functions of the two stakeholders. Optimizing the model yields the optimal capacity configuration result, further improving the utilization rate of renewable energy while achieving a win-win situation for multiple stakeholders. Compared to the traditional capacity configuration method that maximizes the overall benefits of integrated wind, solar and energy storage projects, the method of this invention is more in line with the capacity configuration model of multi-stakeholder cooperation in completing integrated wind, solar and energy storage projects. It provides a reference for the capacity configuration of the units to be built by various manufacturers in the collaborative completion of integrated wind, solar and energy storage projects by multiple manufacturers such as virtual power plants and independent energy storage power stations. Attached Figure Description

[0068] Figure 1 This is a diagram showing the connection relationship between the microgrid system and the main power grid;

[0069] Figure 2 This is a component configuration diagram of the capacity configuration of various stakeholders in the microgrid system of this invention;

[0070] Figure 3 This is the overall flowchart of the present invention. Detailed Implementation

[0071] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments, but this does not limit the scope of protection of this application.

[0072] Figure 1This diagram illustrates the connection between a microgrid system and the mains power grid. The microgrid system consists of two parts: a virtual power plant (VPP) and an independent energy storage station, both connected to the mains power grid. The VPP comprises distributed generation energy sources (wind turbines and photovoltaic generators) and load terminals. The photovoltaic and wind turbines convert solar and wind energy into electricity, respectively, and supply it to the load terminals. The independent energy storage station increases the absorption rate of renewable energy sources such as wind and solar power, and mitigates the intermittency and volatility of renewable energy, thus smoothing the output power of renewable energy. The busbar of the independent energy storage station is connected to the VPP, and the two exchange power through the busbar, realizing the spatial transfer of power between the VPP and the independent energy storage station. As two stakeholders in the system, the independent energy storage station and the VPP achieve mutual benefit and win-win results while pursuing their own goals.

[0073] Figure 2 This flowchart outlines the capacity allocation for various stakeholders in a microgrid system, including cost calculation modules, planning objective modules, constraint modules, and solution modules for each stakeholder. The cost calculation module for the Virtual Power Plant (VPP) includes: the cost of purchasing electricity from the main grid, the cost of purchasing and selling electricity to independent energy storage stations, the service fee cost paid to independent energy storage stations, and the cost of wind and solar curtailment penalties. The independent energy storage station calculation module includes: the purchase cost of energy storage batteries, operation and maintenance costs, revenue from selling electricity to the VPP, the cost of purchasing electricity, and the lifespan of the energy storage batteries. The constraint module includes the distributed energy consumption balance in the VPP, power constraints for purchasing electricity from the main grid, distributed energy consumption constraints in the VPP, energy rate constraints for energy storage batteries, energy storage power and capacity constraints, energy storage charge and discharge constraints, and energy storage operation charge constraints. It also determines the objective function value for a win-win situation for all stakeholders in the microgrid system and the capacity configuration size of components from various vendors.

[0074] Figure 3 To organize the flowchart, this invention provides a method for capacity optimization allocation among multiple stakeholders in a microgrid system (hereinafter referred to as the method, see [link]). Figures 1-3 ), including the following:

[0075] I. Design the objective function for the annual cost of the virtual power plant and the independent energy storage power station;

[0076] 1.1 A virtual power plant consists of wind turbines, photovoltaic generators, and loads. Loads include residential electricity consumption and industrial electricity consumption (e.g., hydrogen production units). Virtual power plants aim to minimize their operating costs while keeping wind and solar curtailment as low as possible. Decision variables affecting the operating costs of virtual power plants include the cost of purchasing electricity from the main grid, the cost of purchasing electricity from independent energy storage stations, the revenue from selling electricity to independent energy storage stations, the service fees paid to independent energy storage stations, and the cost of penalties for wind and solar curtailment.

[0077] 1) Cost of purchasing electricity from the main power grid for the virtual power plant

[0078]

[0079] In the formula, C grid The cost of purchasing electricity from the main power grid for the virtual power plant. Let be the cost of purchasing electricity from the main grid on the i-th typical day, I be the number of typical days in each quarter, d be the number of quarters in a year, δ0(t) be the unit price of electricity purchased from the main grid during the t-th dispatch period, t0 be the number of dispatch periods in a typical day, and Δt be the number of hours in each dispatch period. Let be the electrical power purchased by the virtual power plant from the main power grid during the t-th dispatch period on the i-th typical day;

[0080] 2) Cost of purchasing electricity from independent energy storage power stations for virtual power plants

[0081]

[0082] In the formula, C ess.b The cost for a virtual power plant to purchase electricity from an independent energy storage power station. δ represents the cost of purchasing electricity from an independent energy storage power station on the i-th typical day. b (t) represents the unit electricity price for electricity purchased from the independent energy storage power station during the t-th scheduling time period. The power purchased by the virtual power plant from the independent energy storage power station during the t-th dispatch period on the i-th typical day;

[0083] 3) Revenue from virtual power plants selling electricity to independent energy storage power stations

[0084]

[0085] In the formula, C ess.s Revenue from the sale of electricity by the virtual power plant to independent energy storage power stations. For the revenue of the virtual power plant from selling electricity to the independent energy storage power station on the i-th typical day, δ s (t) represents the unit electricity price sold to the independent energy storage power station during the t-th scheduling period. The power sold by the virtual power plant to the independent energy storage power station during the t-th dispatch period on the i-th typical day;

[0086] 4) Service fees paid by the virtual power plant to the independent energy storage power station

[0087]

[0088] In the formula, C serve The service fee paid by the virtual power plant to the independent energy storage power station. δ is the service fee paid by the virtual power plant to the independent energy storage power station on the i-th typical day.serve (t) represents the unit service fee paid by the virtual power plant to the independent energy storage power station during the t-th scheduling period, where T represents matrix transpose;

[0089] 5) Penalty costs for wind and solar power curtailment at virtual power plants

[0090]

[0091] In the formula, C cut The penalty cost for virtual power plants curtailing wind and solar power. Let λ be the penalty cost for wind and solar curtailment in the virtual power plant on the i-th typical day. cut The penalty cost coefficient for abandoning wind and solar power. For the power of wind and solar curtailment, ξ wp (t) represents the on-grid electricity price for wind-solar hybrid power generation, and t1 represents the number of points in the sampling interval of the i-th typical midday test point;

[0092] 6) Investment costs of distributed generation energy in virtual power plants

[0093]

[0094] In the formula, C f C represents the annual investment cost of distributed generation energy in a virtual power plant. WT C PV The annual investment costs for wind turbines and photovoltaic generators are N, respectively. WT N PV These represent the installed capacity of wind turbines and photovoltaic generators, respectively. WT1 C PV1 These represent the investment costs of a single wind turbine and a photovoltaic generator set, respectively, where τ is the depreciation rate, β is the annual interest rate, and L... WT and L PV These refer to the service life of wind turbines and photovoltaic generators, respectively.

[0095] In summary, with the goal of minimizing annual costs, the objective function for constructing the stakeholder group in the virtual power plant is as follows:

[0096] minC VPP =C grid +C ess.b -C ess.s +C serve +C cut +C f (7)

[0097] 1.2 In a microgrid system, independent energy storage power stations primarily provide shared energy storage services. These stations aim to minimize their annual costs while maximizing the lifespan of their energy storage batteries to reduce battery replacement costs. Therefore, with the optimization objectives of minimizing annual costs and maximizing battery lifespan, the objective function for the stakeholders of independent energy storage power stations is constructed as follows:

[0098]

[0099] In the formula, and These represent the rated power and rated capacity of the energy storage battery, respectively. p and c E These are the rated power factor and rated capacity factor of the energy storage battery, respectively. Y represents the total replacement cost of the energy storage battery. a The total lifespan designed for energy storage batteries. For the annual maintenance cost of energy storage batteries, Y t λ represents the actual service life of the energy storage battery; λ is the energy storage battery life weighting factor, which is related to the service life of the energy storage battery and the economic efficiency of co-operation of the microgrid system. The cost of purchasing and selling electricity to the i-th typical solar-facing virtual power plant of an independent energy storage power station; This indicates the purchase cost of the energy storage battery;

[0100] Total replacement cost of energy storage batteries Compared with actual service life Y t The relevant calculation formula is expressed as follows:

[0101]

[0102] Analysis of experimental data from the U.S. Renewable Energy Laboratory shows that the lifespan of energy storage batteries is mainly related to factors such as the depth of charge and discharge, the discharge rate, and the number of charge and discharge cycles. Each discharge causes irreversible damage to the battery's lifespan until it reaches the end of its life. Therefore, if an energy storage battery contains N discharge events within its lifespan T0, then the actual lifespan of the energy storage battery can be expressed as:

[0103]

[0104] In the formula, Γ R This represents the total effective throughput during the discharge process, expressed in amperes per hour (d). eff (n) represents the nth discharge event, L R D represents the number of charge-discharge cycles of an energy storage battery at its rated depth of discharge and rated discharge current. R Indicates the rated depth of discharge, C R Indicates the rated capacity under the rated discharge current;

[0105] Discharge event d eff Represented as:

[0106]

[0107] In the formula: D A For the actual depth of discharge, C A For the actual discharge capacity, d act U0 and U1 are the ampere-hours under the actual discharge current, and u0 and u1 are fitting parameters that can be obtained by fitting the curve of the relationship between the depth of discharge and the number of failure cycles of the energy storage battery.

[0108] Therefore, by reasonably and effectively controlling the depth of charge and discharge and the discharge power of energy storage batteries, the service life can be extended.

[0109] The cost of purchasing and selling electricity to the i-th typical daytime virtual power plant of an independent energy storage power station The formula for calculation is:

[0110]

[0111] In the formula, c buy (t) and c sell (t) represents the electricity purchase and sales costs of an independent energy storage power station during the t-th dispatch period on the i-th typical day. and These represent the electrical power purchased and sold during the t-th scheduling period on the i-th typical day.

[0112] II. Obtain the constraints for the stable operation of virtual power plants and independent energy storage power stations;

[0113] 2.1 The constraints of the virtual power plant include the power generation and consumption balance constraints of the virtual power plant, the constraints of the virtual power plant on the absorption of distributed energy, and the power constraints on purchasing electricity from the large power grid.

[0114] (1) The power output of the virtual power plant needs to meet the power generation and consumption balance during each dispatch period. Therefore, the constraints are as follows:

[0115]

[0116] In the formula, Let be the output power of the wind turbine unit during the t-th scheduling period on the i-th typical day. Let be the output power of the photovoltaic generator unit during the t-th scheduling period on the i-th typical day. Let be the power consumption at the load end during the t-th scheduling period on the i-th typical day. P u.min P u.max These represent the minimum and maximum power consumption at the load end, respectively.

[0117] (2) Constraints of virtual power plants on the absorption of distributed generation energy

[0118]

[0119] In the formula: α represents the maximum output power of the photovoltaic generator and the wind turbine during the t-th scheduling period on the i-th typical day, respectively, and α is the annual comprehensive absorption rate of distributed energy by the virtual power plant.

[0120] (3) Power constraints for purchasing electricity from the main power grid

[0121]

[0122] In the formula: Let P be the electrical power purchased by the virtual power plant from the main grid during the t-th dispatch period on the i-th typical day. grid.max This represents the maximum electrical power that the virtual power plant purchases from the main power grid;

[0123] 2.2 The constraints of independent energy storage power stations include the energy rate constraint of the energy storage battery, the rated power and rated capacity constraint of the energy storage battery, the charge and discharge power constraint of the independent energy storage power station, and the state of charge constraint.

[0124] (1) Energy rate constraint of energy storage battery

[0125] Maximum rated capacity of energy storage batteries With the upper limit of rated power There is an energy multiplier constraint, specifically expressed as:

[0126]

[0127] In the formula, δ represents the energy rate of the energy storage battery;

[0128] (2) Rated power and rated capacity constraints of energy storage batteries

[0129]

[0130] In the formula: This refers to the minimum investment power of the energy storage battery, i.e., the lower limit of the rated power. This refers to the minimum investment capacity of the energy storage battery, i.e., the lower limit of the rated capacity.

[0131] (3) Charging and discharging power constraints of independent energy storage power stations

[0132] Within the same dispatch period, the charging and discharging status of an independent energy storage power station is determined by the energy demand at the power station bus in the virtual power plant. Therefore, it is stipulated that an independent energy storage power station cannot charge and discharge simultaneously within the same dispatch period, which imposes the following constraints:

[0133]

[0134] In the formula, and These are the charging and discharging flags for the independent energy storage power station during the t-th scheduling period on the i-th typical day.

[0135] (4) State of charge constraints of independent energy storage power stations

[0136]

[0137] In the formula: These represent the state of charge (SOC) of an independent energy storage power station during dispatch periods t and t-1 on the i-th typical day, respectively, α abs and α relea These represent the charging and discharging efficiencies of an independent energy storage power station, E ESS.min and E ESS.max These represent the lower and upper limits of the state of charge for independent energy storage power stations, respectively.

[0138] III. Based on Pareto optimality, a capacity allocation model for a microgrid system with mutual benefit among multiple stakeholders is constructed. This model obtains the objective function values ​​for each stakeholder while simultaneously achieving optimal capacity allocation for that stakeholder. The expression for mutual benefit is as follows:

[0139] minZ = [F,C VPP ] T (20)

[0140] The multi-objective particle swarm optimization algorithm is used to find a series of non-dominated solutions on the Pareto front, i.e., the Pareto front. The elements in the Pareto front are called Pareto optimal or non-dominated optimal. From the Pareto front, the point closest to the center point is selected as the win-win solution for both stakeholders, i.e., the operating cost for both stakeholders to achieve mutual benefit. Then, based on the operating costs of each stakeholder and the constraints, the installed capacity N of the wind turbine is calculated. WT The installed capacity N of photovoltaic power generation units PV and the rated capacity of the energy storage battery and rated power Complete the capacity configuration for multiple stakeholders in the microgrid system.

[0141] Any aspects not covered in this invention are applicable to existing technologies.

Claims

1. A capacity optimization configuration method for a microgrid system with multiple stakeholders, wherein the stakeholders of the microgrid system include virtual power plants and independent energy storage power stations; the method includes the following: First, each stakeholder should consider their own influencing factors while taking into account the annual cost, and construct an objective function for the annual cost. 1.1) The virtual power plant aims to minimize annual cost and reduce wind and solar curtailment. The objective function for the annual cost of the virtual power plant is constructed as follows: minC VPP =C grid +C ess.b -C ess.s +C serve +C cut +C f (7) In the formula, C grid C represents the cost of purchasing electricity from the main grid for the virtual power plant. ess.b C represents the cost of purchasing electricity from independent energy storage power stations for the virtual power plant. ess.s For the revenue generated by the virtual power plant selling electricity to independent energy storage power stations, C serve C is the service fee paid by the virtual power plant to the independent energy storage power station. cut C f The investment cost of distributed generation energy in a virtual power plant; in, The cost C of the virtual power plant purchasing electricity from the main grid grid The representation is: In the formula, Let be the cost of purchasing electricity from the main grid on the i-th typical day, I be the number of typical days in each quarter, d be the number of quarters in a year, δ0(t) be the unit price of electricity purchased from the main grid during the t-th dispatch period, t0 be the number of dispatch periods in a typical day, and Δt be the number of hours in each dispatch period. Let be the electrical power purchased by the virtual power plant from the main power grid during the t-th dispatch period on the i-th typical day; The cost C of a virtual power plant purchasing electricity from an independent energy storage power station ess.b Represented as: In the formula, δ represents the cost of purchasing electricity from an independent energy storage power station on the i-th typical day. b (t) represents the unit electricity price for electricity purchased from the independent energy storage power station during the t-th scheduling time period. The power purchased by the virtual power plant from the independent energy storage power station during the t-th dispatch period on the i-th typical day; Revenue C from virtual power plants selling electricity to independent energy storage power stations ess.s Represented as: In the formula, For the revenue of the virtual power plant from selling electricity to the independent energy storage power station on the i-th typical day, δ s (t) represents the unit electricity price sold to the independent energy storage power station during the t-th scheduling period. The power sold by the virtual power plant to the independent energy storage power station during the t-th dispatch period on the i-th typical day; The service fee C paid by the virtual power plant to the independent energy storage power station serve Represented as: In the formula, δ is the service fee paid by the virtual power plant to the independent energy storage power station on the i-th typical day. serve (t) represents the unit service fee paid by the virtual power plant to the independent energy storage power station during the t-th scheduling period, where T represents matrix transpose; The penalty cost C for wind and solar power curtailment in virtual power plants cut Represented as: In the formula, Let λ be the penalty cost for wind and solar curtailment in the virtual power plant on the i-th typical day. cut The penalty cost coefficient for abandoning wind and solar power. For the power of wind and solar curtailment, ξ wp (t) represents the on-grid electricity price for wind-solar hybrid power generation, and t1 represents the number of points in the sampling interval of the i-th typical midday test point; Investment cost C of distributed generation energy in virtual power plants f Represented as: In the formula, C WT C PV The annual investment costs for wind turbines and photovoltaic generators are N, respectively. WT N PV These represent the installed capacity of wind turbines and photovoltaic generators, respectively. WT1 C PV1 These represent the investment costs of a single wind turbine and a photovoltaic generator set, respectively, where τ is the depreciation rate, β is the annual interest rate, and L... WT and L PV These refer to the service life of wind turbines and photovoltaic generators, respectively. 1.2) To achieve the lowest possible annual cost and the longest possible battery life for independent energy storage power stations, an objective function for the annual cost of independent energy storage batteries is constructed, expressed as follows: in, and These represent the rated power and rated capacity of the energy storage battery, respectively. p and c E These are the rated power factor and rated capacity factor of the energy storage battery, respectively. Y represents the total replacement cost of the energy storage battery. a The total lifespan designed for energy storage batteries. For the annual maintenance cost of energy storage batteries, Y t λ represents the actual lifespan of the energy storage battery, and λ is the lifespan weighting factor for the energy storage battery. The cost of purchasing and selling electricity to the i-th typical solar-facing virtual power plant of an independent energy storage power station; Second, obtain the constraints for the stable operation of various stakeholders; 2.1) The constraints of the virtual power plant include the power generation and consumption balance constraints of the virtual power plant, the constraints of the virtual power plant on the absorption of distributed energy, and the power constraints on purchasing electricity from the large power grid. The power generation and consumption balance constraints for the virtual power plant are: In the formula, Let be the output power of the wind turbine unit during the t-th scheduling period on the i-th typical day. Let be the output power of the photovoltaic generator unit during the t-th scheduling period on the i-th typical day. Let be the power consumption at the load end during the t-th scheduling period of the i-th typical day; Virtual power plants' constraints on the absorption of distributed generation energy In the formula: α represents the maximum output power of the photovoltaic generator and the wind turbine during the t-th scheduling period on the i-th typical day, respectively, and α is the annual comprehensive absorption rate of distributed energy by the virtual power plant. Power constraints on purchasing electricity from the main power grid In the formula: Let P be the electrical power purchased by the virtual power plant from the main grid during the t-th dispatch period on the i-th typical day. grid.max This represents the maximum electrical power that the virtual power plant purchases from the main power grid; 2.2) The constraints of independent energy storage power stations include the energy rate constraint of the energy storage battery, the rated power and rated capacity constraint of the energy storage battery, the charge and discharge power constraint of the independent energy storage power station, and the state of charge constraint. The energy rate constraint of energy storage batteries is expressed as: In the formula, δ represents the energy rate of the energy storage battery. These are the upper limits of the rated capacity and rated power of the energy storage battery, respectively. The rated power and rated capacity constraints of energy storage batteries are expressed as follows: In the formula: These are the lower limits of the rated power and rated capacity of the energy storage battery, respectively. The charging and discharging power constraints of an independent energy storage power station are expressed as follows: In the formula, and Let be the electrical power purchased and sold during the t-th scheduling period on the i-th typical day. and These are the charging and discharging flags for the independent energy storage power station during the t-th scheduling period on the i-th typical day. The state-of-charge constraint of an independent energy storage power station is expressed as follows: In the formula: These represent the state of charge (SOC) of an independent energy storage power station during dispatch periods t and t-1 on the i-th typical day, respectively, α abs and α relea These represent the charging and discharging efficiencies of an independent energy storage power station, E ESS.min and E ESS.max These are the lower and upper limits of the state of charge for independent energy storage power stations, respectively. III. Based on Pareto optimality, a multi-stakeholder mutually beneficial capacity allocation model for microgrid systems is constructed, achieving the optimal capacity allocation scheme while obtaining mutually beneficial annual cost values. The expression for the capacity allocation model is: minZ=[F,C VPP ] T (20) The Pareto front is found using a multi-objective particle swarm optimization algorithm. The point closest to the center point in the Pareto front is selected as the win-win solution for the two stakeholders, i.e., the annual cost value for the two stakeholders to achieve mutual benefit. Then, based on the annual cost function and constraints of each stakeholder, the installed capacity of wind turbines, the installed capacity of photovoltaic generators, and the rated capacity and rated power of energy storage batteries are solved to complete the capacity optimization configuration of the microgrid system for multiple stakeholders.

2. The capacity optimization configuration method for a microgrid system with multiple stakeholders according to claim 1, characterized in that, The total replacement cost of the energy storage battery is related to its actual service life, and the calculation formula is as follows: Among them, Y t This indicates the actual lifespan of the energy storage battery; The cost of purchasing and selling electricity to the i-th typical daytime virtual power plant of an independent energy storage power station Represented as: In the formula, c buy (t) and c sell (t) represents the electricity purchase and sales costs of an independent energy storage power station during the t-th dispatch period on the i-th typical day.

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