Cross-regional energy storage type virtual power plant multi-scene capacity planning method and system

Abstract

The invention relates to a cross-regional energy storage type virtual power plant multi-scene capacity planning method and system. The method comprises the steps of obtaining thermal power generating unit data, power grid load data and wind power photovoltaic installation data in a conventional operation scene and an unconventional operation scene of each region as historical data; sampling the historical data to generate a capacity planning scene, and reducing the capacity planning scene through clustering to obtain the occurrence probability of each scene and the output parameter of each scene after clustering; according to the energy storage investment cost, the system operation cost, the flexibility mutual aid service cost and the occurrence probability of each scene, constructing a target function with the lowest system comprehensive cost and the maximum new energy consumption as targets; constructing constraint conditions of the target function according to the output parameters of each scene; solving energy storage configuration capacities under different capacity planning scenes according to the constructed objective function and constraint conditions; and outputting the optimized energy storage configuration capacity planning strategy of each region and each time period.

Description

Technical Field

[0001] The present application relates to the field of power dispatching technology, and in particular to a multi-scenario capacity planning method and system for a cross-regional energy storage virtual power plant. Background Art

[0002] With the rapid transformation of the global energy structure and the intelligent upgrade of the power system, the power system has an increasing demand for flexible and efficient regulation resources. As an innovative power resource management platform, virtual power plants have become an important solution to meet this demand. It uses advanced information and communication technologies and intelligent algorithms to integrate and optimize decentralized power generation units, energy storage systems, and demand-side management resources to achieve efficient configuration and dynamic balance of power resources.

[0003] However, most existing virtual power plant capacity planning methods focus on single scenarios or small-scale configurations, lacking comprehensive consideration of cross-regional areas. This limitation results in virtual power plants being unable to fully utilize resources in a wider area, resulting in low power utilization and low investment returns, which severely limits their regulation capabilities and economic benefits, affecting the sustainable development of virtual power plants.

[0004] In addition, the power system faces greater uncertainty and volatility when faced with complex scenarios such as seasonal changes and extreme weather. For example, there are obvious seasonal differences in power demand and renewable energy output, and extreme weather can also lead to large fluctuations in renewable energy output and abnormal changes in load demand. Existing planning methods often have difficulty in dealing with these complex situations, and the capacity planning and adjustment capabilities are limited. Summary of the invention

[0005] In order to at least to some extent overcome the problem that the capacity planning methods of virtual power plants in related technologies lack comprehensive consideration of cross-regional and unconventional operating scenarios, resulting in limited capacity planning adjustment capabilities, the present application provides a multi-scenario capacity planning method and system for cross-regional energy storage virtual power plants.

[0006] The scheme of this application is as follows:

[0007] According to a first aspect of an embodiment of the present application, a multi-scenario capacity planning method for a cross-regional energy storage virtual power plant is provided, comprising:

[0008] Obtain data on thermal power units, grid loads, and wind and photovoltaic installed capacity in conventional and unconventional operating scenarios in each region as historical data;

[0009] Sampling the historical data to generate capacity planning scenarios, reducing the capacity planning scenarios by clustering, and obtaining the occurrence probability of each scenario after clustering and the output parameter of each scenario;

[0010] Based on the energy storage investment cost, system operation cost, flexibility mutual assistance service cost and the probability of occurrence of each scenario, an objective function is constructed with the goal of minimizing the overall system cost and maximizing the consumption of new energy;

[0011] Constructing the constraint conditions of the objective function according to the output parameters of each scenario;

[0012] Based on the constructed objective function and constraints, the energy storage configuration capacity under different capacity planning scenarios is solved;

[0013] Output the optimized energy storage configuration capacity planning strategy for each region and time period.

[0014] Preferably, the method further comprises:

[0015] The energy storage configuration capacity planning strategy is analyzed and verified.

[0016] Preferably, analyzing and verifying the energy storage configuration capacity planning strategy includes:

[0017] Compare the thermal power unit data, grid load data and renewable energy curtailment data before and after the implementation of energy storage configuration capacity planning strategies in conventional operation scenarios in various regions;

[0018] The feasibility of the energy storage configuration capacity planning strategy is output based on the comparison results.

[0019] Preferably, sampling the historical data to generate a capacity planning scenario includes:

[0020] The historical data is sampled based on Latin hypercube sampling to generate a capacity planning scenario.

[0021] Preferably, reducing the capacity planning scenario by clustering includes:

[0022] The capacity planning scenario is reduced based on K-means clustering.

[0023] Preferably, based on the energy storage investment cost, system operation cost, flexibility mutual assistance service cost and the probability of occurrence of each scenario, an objective function with the goal of minimizing the system comprehensive cost and maximizing the consumption of new energy is constructed, including:

[0024]

[0025] in, They respectively represent the system comprehensive cost, energy storage investment cost, system operation cost and flexibility mutual assistance service cost, and the unit is yuan / MWh; α(s) represents the probability of occurrence of each scenario; N represents the number of scenarios.

[0026] Preferably, the method further comprises:

[0027] Calculate energy storage investment cost:

[0028]

[0029] Where, b represents the bth electrical energy storage device; Ω ES Represents a collection of energy storage power stations; To represent the annualized investment cost of electric energy storage equipment, the unit is RMB / kW; Indicates the planned capacity of the electric energy storage equipment, in MWh;

[0030] Calculate system operating costs:

[0031]

[0032] in, They represent the system operation cost, the operation cost of the electric energy storage equipment, the unit cost of wind power curtailment and the unit cost of photovoltaic power curtailment respectively, in RMB / MWh; t represents the tth period; and They represent the discharge and charge outputs of the electric energy storage device b, in MW; and Respectively represent the abandoned power of wind power and photovoltaic power, in MW.

[0033] Preferably, the constraint conditions of the objective function are constructed according to the output parameters of each scenario, including:

[0034] Constructing power balance constraints:

[0035]

[0036] Among them, P g,t , P w,t , P p,t , P b,t Respectively represent the output of conventional thermal power units, wind farm output, photovoltaic power station output and electric energy storage equipment output, the unit is MW; Ω G represents the set of conventional thermal power units; Ω W represents the set of wind turbines; Ω P represents a collection of photovoltaic units; and They represent the total output and total input of the inter-regional tie lines at time t respectively; It represents the regional load in MW;

[0037] Construct cross-region flexibility constraints:

[0038]

[0039] in, represents the flexibility power provided by region m to region n; represents the flexibility level of region m at time t;

[0040] Constructing inter-regional interconnection line transmission constraints:

[0041]

[0042] in, represents the maximum transmission capacity of the tie line between area m and area n;

[0043] Construct the maximum buildable capacity constraint of energy storage:

[0044]

[0045] Among them, Cap B,Lim Indicates the installed capacity limit of the electric energy storage equipment, in MW; Indicates the upper limit of the planned capacity of the energy storage system, in MW;

[0046] Constructing energy storage capacity constraints:

[0047]

[0048] in, Indicates the energy storage level of the electric energy storage device, in MWh;

[0049] Constructing the coupling of output and power:

[0050]

[0051] in, and They represent the discharge and charge outputs of the electric energy storage device b, in MW;

[0052] Construct energy balance constraints for energy storage power stations:

[0053]

[0054] in, Indicates the energy storage level of the electric energy storage device in the previous period, in MWh; Indicates the charging and discharging efficiency of electrical energy storage equipment; They represent the planned charging / discharging output of the energy storage device, in MW;

[0055] Constructing charging and discharging power constraints for energy storage power stations:

[0056]

[0057] Among them, α b,kRepresents the charging operation state variable of the electric energy storage device; δ b,k Indicates the discharge operation state variable of the electric energy storage device;

[0058] Construct the equal power constraints at the beginning and end of the energy storage period:

[0059] in, Respectively represent the energy storage level of the electric energy storage device at the beginning and end, in MWh; Indicates the initial energy storage level percentage of the electric energy storage device;

[0060] Constructing the maximum energy storage output constraint:

[0061]

[0062] in, Indicates the maximum planned output of the electric energy storage device;

[0063] Construct output constraints for thermal power units:

[0064]

[0065] in, They represent the minimum output limit of thermal power units and the maximum output limit of thermal power units respectively;

[0066] Construct output constraints for new energy units:

[0067]

[0068] in, and They represent the planned output of wind turbines and photovoltaic units respectively. and They represent the abandoned power of wind turbines and photovoltaic units respectively;

[0069] Construct energy storage charging and discharging duration constraints:

[0070]

[0071] Among them, T ch and T dis Respectively represent the charging and discharging duration of the energy storage device, in MIN; α b,k Represents the charging operation state variable of the electric energy storage device; δ b,k Represents the discharge operation state variable of the electric energy storage device; T range Indicates the shortest charging and discharging time of electrical energy storage; and They represent the state transition variables of the electric energy storage device starting to charge and starting to discharge respectively;

[0072] Construct energy storage charging and discharging time interval constraints:

[0073]

[0074] Construct coupling constraints between energy storage operation status and start / stop status:

[0075]

[0076] in, and Respectively represent the state transition variables of energy storage end charging and end discharging; α b,t-1 and δ b,t-1 They respectively represent the charging and discharging operating state variables of the energy storage device at the previous moment.

[0077] Preferably, solving the energy storage configuration capacity under different capacity planning scenarios includes:

[0078] The energy storage configuration capacity under different capacity planning scenarios is solved based on the CPLEX mathematical programming solver.

[0079] According to a second aspect of an embodiment of the present application, a multi-scenario capacity planning system for a cross-regional energy storage virtual power plant is provided, including:

[0080] Processor and memory;

[0081] The processor and the memory are connected via a communication bus:

[0082] Wherein, the processor is used to call and execute the program stored in the memory;

[0083] The memory is used to store a program, and the program is at least used to execute a multi-scenario capacity planning method for a cross-regional energy storage virtual power plant as described in any one of the above items.

[0084] The technical solution provided by this application may have the following beneficial effects:

[0085] The multi-scenario capacity planning method for a cross-regional energy storage virtual power plant in the present application includes: obtaining thermal power unit data, grid load data and wind power and photovoltaic installed capacity data under conventional operation scenarios and unconventional operation scenarios in each region as historical data; sampling historical data to generate capacity planning scenarios, reducing the capacity planning scenarios by clustering, and obtaining the probability of occurrence of each scenario after clustering and the output parameters of each scenario; constructing an objective function with the goal of minimizing the overall system cost and maximizing the new energy consumption according to the energy storage investment cost, system operation cost, flexibility mutual assistance service cost and the probability of occurrence of each scenario; constructing the constraint conditions of the objective function according to the output parameters of each scenario; solving the energy storage configuration capacity under different capacity planning scenarios according to the constructed objective function and constraint conditions; and outputting the optimized energy storage configuration capacity planning strategy for each region and each time period.

[0086] In this application, a multi-scenario capacity planning model is constructed to comprehensively consider the power demand and new energy output characteristics in different scenarios, optimize the energy storage configuration, and ensure the efficient operation of the virtual power plant. Through cross-regional resource aggregation, capacity planning linkage is carried out to achieve energy mutual assistance between regions, optimize resource allocation, and improve the flexibility and anti-interference ability of the system. Focus on the capacity configuration of energy storage virtual power plants, optimize the charging and discharging strategy of energy storage systems, give full play to their role in peak shaving and valley filling, and further improve the flexibility and adaptability of the power system.

[0087] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0089] Figure 1 It is a flowchart of a multi-scenario capacity planning method for a cross-regional energy storage virtual power plant provided by an embodiment of the present application;

[0090] Figure 2 It is a structural diagram of a multi-scenario capacity planning system for a cross-regional energy storage virtual power plant provided by an embodiment of the present application.

[0091] Reference numerals: processor-21; memory-22. DETAILED DESCRIPTION

[0092] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0093] Embodiment 1

[0094] Figure 1 is a flowchart of a multi-scenario capacity planning method for a cross-regional energy storage virtual power plant provided by an embodiment of the present application, with reference to Figure 1 , a multi-scenario capacity planning method for a cross-regional energy storage virtual power plant, comprising:

[0095] S11: Obtain thermal power unit data, grid load data, and wind and photovoltaic installed capacity data under conventional and unconventional operation scenarios in various regions as historical data;

[0096] It should be noted that the conventional operation scenarios in this embodiment refer to the operation scenarios on typical days in spring, summer and autumn, and the unconventional operation scenarios refer to extreme weather scenarios (cold waves, strong winds, heavy rains, etc.).

[0097] The data on thermal power units mainly include unit serial number, power plant, unit name, minimum output and rated capacity; the grid load data mainly include the power load and corresponding interconnection line load on historical typical days in spring, summer and autumn and historical typical days of extreme weather (96 time periods); the wind power and photovoltaic installed capacity data mainly include unit serial number, unit type and total installed capacity.

[0098] S12: Sampling historical data to generate capacity planning scenarios, reducing the capacity planning scenarios through clustering, and obtaining the occurrence probability of each scenario after clustering and the output parameters of each scenario;

[0099] In practice, historical data is sampled based on Latin hypercube sampling to generate capacity planning scenarios, and capacity planning scenarios are reduced based on K-means clustering. Finally, the probability of occurrence of each scenario and the output parameters of each scenario are obtained after clustering.

[0100] S13: Based on the energy storage investment cost, system operation cost, flexibility mutual assistance service cost and the probability of occurrence of each scenario, an objective function is constructed with the goal of minimizing the system comprehensive cost and maximizing the consumption of new energy;

[0101] It should be noted that the energy storage investment cost and system operating cost need to be calculated, while the flexibility mutual aid service cost can be obtained directly.

[0102] Based on the capacity planning scenario, the goal of the energy storage planning strategy is set to determine the system energy storage configuration by optimizing the capacity of energy storage investment under the premise of minimum planning cost and meeting grid security constraints.

[0103] Among them, the thermal power units calculate the unit operating status on a fixed day based on the pre-plan, and have the flexibility to adjust up and down; the new energy units use short-term forecast data; the energy storage unit only considers the 2-hour energy storage type, and after optimization, the charging and discharging power of the energy storage is equivalent to the rated charging and discharging power.

[0104] Based on this, according to the energy storage investment cost, system operation cost, flexibility mutual assistance service cost and the probability of occurrence of each scenario, an objective function with the goal of minimizing the system comprehensive cost and maximizing the consumption of new energy is constructed, including:

[0105]

[0106] in, They represent the system comprehensive cost, energy storage investment cost, system operation cost and flexibility mutual service cost respectively, and the unit is k$ / MWh; α(s) represents the probability of occurrence of each scenario; N represents the number of scenarios.

[0107] Among them, calculate the energy storage investment cost:

[0108]

[0109] Where, b represents the bth electrical energy storage device; Ω ES Represents a collection of energy storage power stations; To represent the annualized investment cost of electric energy storage equipment, the unit is $ / kW-yr; Indicates the planned capacity of the electric energy storage equipment, in MWh;

[0110] System operation costs include: energy storage operation costs, renewable energy curtailment costs, and intermittent renewable energy, and penalties for renewable energy curtailment.

[0111] Calculate system operating costs:

[0112]

[0113] in, They represent the system operation cost, the operation cost of the electric energy storage equipment, the unit power abandonment cost of wind power and the unit power abandonment cost of photovoltaic power, respectively, in units of k$ / MWh; t represents the tth time period; and They represent the discharge and charge outputs of the electric energy storage device b, in MW; and Respectively represent the abandoned power of wind power and photovoltaic power, in MW.

[0114] S14: constructing constraint conditions of the objective function according to the output parameters of each scenario;

[0115] The constraints of the objective function are constructed according to the output parameters of each scenario, as follows:

[0116] Cross-regional power balance constraints are mainly used to ensure that the system's power generation, load demand, energy storage charging and discharging and other activities can be balanced in each time period. Specifically, the relationship between the power generation power of the power system, the charging / discharging power of the energy storage equipment and the load demand needs to be consistent at each time point.

[0117] Constructing power balance constraints:

[0118]

[0119] Among them, P g,t , P w,t , P p,t , P b,t Respectively represent the output of conventional thermal power units, wind farm output, photovoltaic power station output and electric energy storage equipment output, the unit is MW; Ω G represents the set of conventional thermal power units; Ω W represents the set of wind turbines; Ω P represents a collection of photovoltaic units; and They represent the total output and total input of the inter-regional tie lines at time t respectively; It represents the regional load in MW;

[0120] Cross-region flexibility constraints are used to ensure that the maximum flexibility delivery capacity of a region does not exceed its own flexibility level.

[0121] Construct cross-region flexibility constraints:

[0122]

[0123] in, represents the flexibility power provided by region m to region n; represents the flexibility level of region m at time t;

[0124] The inter-regional flexibility constraint takes into account the transmission capacity limitations of inter-regional tie lines.

[0125] Constructing inter-regional interconnection line transmission constraints:

[0126]

[0127] in, represents the maximum transmission capacity of the tie line between area m and area n;

[0128] The maximum buildable capacity constraint of the energy storage system means that the installed capacity of the energy storage equipment cannot exceed a certain upper limit. This constraint is usually limited by technical, economic or policy factors. At the same time, the total installed capacity of all energy storage is also limited. Based on this, the maximum buildable capacity constraint of energy storage is constructed:

[0129]

[0130] Among them, Cap B,Lim Indicates the installed capacity limit of the electric energy storage equipment, in MW; Indicates the upper limit of the planned capacity of the energy storage system, in MW;

[0131] The energy storage capacity constraint defines the upper and lower limits of the energy storage capacity during the charging and discharging process of the energy storage device. Specifically, the energy storage capacity of the energy storage system must be within its planned capacity range at any time. Based on this, the energy storage capacity constraint is constructed:

[0132]

[0133] in, Indicates the energy storage level of the electric energy storage device, in MWh;

[0134] There is a coupling relationship between the output of the energy storage system and its power. Specifically, the output of the energy storage is equal to the difference between the discharge power and the charging power of the energy storage. Based on this, the coupling of output and power is constructed:

[0135]

[0136] in, and They represent the discharge and charge outputs of the electric energy storage device b, in MW;

[0137] The energy balance constraint of the energy storage power station represents the relationship between the charging and discharging power of the energy storage and the energy of the energy storage. Assume that the power of the energy storage power station at the time is Then the energy balance constraint of the energy storage power station is constructed:

[0138]

[0139] in, Indicates the energy storage level of the electric energy storage device in the previous period, in MWh; Indicates the charging and discharging efficiency of electrical energy storage equipment; They represent the planned charging / discharging output of the energy storage device, in MW;

[0140] The charging and discharging power of the energy storage station is related to the capacity of the energy storage and the charging and discharging time. and discharge power They are all capacity divided by the charging and discharging time. Based on this, the charging and discharging power constraints of the energy storage power station are constructed:

[0141]

[0142] Among them, α b,k Represents the charging operation state variable of the electric energy storage device; δ b,k Indicates the discharge operation state variable of the electric energy storage device;

[0143] The power of the energy storage power station at the beginning and end of the period needs to be consistent, that is, the power of the power station at the beginning and the end are equal. This is usually used for periodic optimization problems to ensure that the energy storage equipment is in the same state as at the beginning after the optimization cycle ends. Based on this, the energy storage initial and final period equal power constraints are constructed:

[0144] in, Respectively represent the energy storage level of the electric energy storage device at the beginning and end, in MWh; Indicates the initial energy storage level percentage of the electric energy storage device;

[0145] The output power of the energy storage system is limited, usually less than the product of the current power and the discharge efficiency. Based on this, the maximum output constraint of the energy storage system is constructed:

[0146]

[0147] in, Indicates the maximum planned output of the electric energy storage device;

[0148] The output of the thermal power unit needs to meet its minimum load and maximum load limits. Based on this, the output constraints of the thermal power unit are constructed:

[0149]

[0150] in, They represent the minimum output limit of thermal power units and the maximum output limit of thermal power units respectively;

[0151] For new energy wind power and photovoltaic units, since their output is affected by natural conditions, there are usually variable power output constraints, and the excess energy produced will be abandoned or stored in energy storage. Assume that the actual output power of wind power and photovoltaic units at time t is P w,t and P p,t Based on this, the output constraints of new energy units are constructed:

[0152]

[0153] in, and They represent the planned output of wind turbines and photovoltaic units respectively. and They represent the abandoned power of wind turbines and photovoltaic units respectively;

[0154] The charging or discharging duration of the energy storage device is limited by a time range and shall not exceed the specified 2h. b,k When it is 1, it means that the energy storage is being charged. At the same time, when the charging state δ b,k When it is 1, it means that the energy storage is discharging. Based on this, the energy storage charging and discharging duration constraint is constructed:

[0155]

[0156] Among them, T ch and T dis Respectively represent the charging and discharging duration of the energy storage device, in MIN; α b,k Represents the charging operation state variable of the electric energy storage device; δ b,k Represents the discharge operation state variable of the electric energy storage device; T range Indicates the shortest charging and discharging time of electrical energy storage; and They represent the state transition variables of the electric energy storage device starting to charge and starting to discharge respectively;

[0157] The charge and discharge time interval constraint is used to limit the coupling between the charge and discharge state and the charge start state of the energy storage device during continuous charging or discharging, and to construct the energy storage charge and discharge time interval constraint:

[0158]

[0159] There is a coupling relationship between the start and stop state of the energy storage system and the charge and discharge power. When the energy storage starts charging and discharging, the charge and discharge state is enabled; when the energy storage stops charging and discharging, the charge and discharge state stops. Based on this, the coupling constraints between the energy storage operation state and the start and stop state are constructed:

[0160]

[0161] in, and Respectively represent the state transition variables of energy storage end charging and end discharging; α b,t-1 and δ b,t-1 They respectively represent the charging and discharging operating state variables of the energy storage device at the previous moment.

[0162] S15: Solve the energy storage configuration capacity under different capacity planning scenarios based on the constructed objective function and constraints;

[0163] In specific practice, the energy storage configuration capacity under different capacity planning scenarios is solved based on the CPLEX mathematical programming solver.

[0164] The CPLEX mathematical programming solver has efficient algorithms and optimization capabilities and can quickly solve large-scale linear programming problems.

[0165] S16: Output the optimized energy storage configuration capacity planning strategy for each region and each time period.

[0166] It should be noted that the method also includes:

[0167] Analyze and verify the energy storage configuration capacity planning strategy.

[0168] Analyze and verify the energy storage configuration capacity planning strategy, including:

[0169] Compare the thermal power unit data, grid load data and renewable energy curtailment data before and after the implementation of energy storage configuration capacity planning strategies in conventional operation scenarios in various regions;

[0170] Based on the comparison results, the feasibility of the energy storage configuration capacity planning strategy is output.

[0171] After the solution is completed, the results need to be analyzed and verified to ensure that the solution is reasonable and meets all constraints. The specific analysis includes the load of thermal power units before and after energy storage configuration, the operation of the power grid, and the abandonment of renewable energy in different typical scenarios. The rationality and feasibility of the capacity planning solution are verified through comparative analysis.

[0172] A reasonable virtual power plant capacity planning scheme can ensure the balance between power supply and demand. In an emergency, it can identify and plan potential power abandonment or supply shortages in advance, and use the planned capacity to respond quickly to improve energy utilization efficiency and ensure the reliability and stability of power system operation. In addition, the implementation of the virtual power plant capacity planning scheme helps to reduce load pressure. By predicting and planning power generation capacity, it can reduce dependence on expensive peak power generation resources, thereby reducing the overall cost of power supply. At the same time, capacity planning helps to allocate resources reasonably. By optimizing the configuration of power generation and energy storage resources, it can reduce energy waste and improve energy utilization efficiency. Finally, the capacity planning scheme provides a solid foundation for the development of the power market and promotes fair competition and healthy development of the market by providing transparent power supply information.

[0173] This technical solution builds a multi-scenario capacity planning model, comprehensively considers the power demand and new energy output characteristics in different scenarios, optimizes energy storage configuration, and ensures the efficient operation of virtual power plants. Through cross-regional resource aggregation, capacity planning linkage is carried out to achieve energy mutual assistance between regions, optimize resource allocation, and improve the flexibility and anti-interference ability of the system. Focus on the capacity configuration of energy storage virtual power plants, optimize the charging and discharging strategy of energy storage systems, give full play to their role in peak shaving and valley filling, and further improve the flexibility and adaptability of the power system.

[0174] Embodiment 2

[0175] A multi-scenario capacity planning system for cross-regional energy storage virtual power plants, referring to Figure 2 ,include:

[0176] Processor 21 and memory 22;

[0177] The processor 21 and the memory 22 are connected via a communication bus:

[0178] The processor 21 is used to call and execute the program stored in the memory;

[0179] The memory 22 is used to store programs, and the programs are used to execute at least a multi-scenario capacity planning method for a cross-regional energy storage virtual power plant as in any of the above embodiments.

[0180] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0181] It should be noted that, in the description of this application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0182] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0183] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0184] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0185] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0186] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0187] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0188] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A multi-scenario capacity planning method for a cross-regional energy storage virtual power plant, characterized in that: include: Obtain data on thermal power units, grid loads, and wind and photovoltaic installed capacity in conventional and unconventional operating scenarios in each region as historical data; Sampling the historical data to generate capacity planning scenarios, reducing the capacity planning scenarios by clustering, and obtaining the occurrence probability of each scenario after clustering and the output parameter of each scenario; Based on the energy storage investment cost, system operation cost, flexibility mutual assistance service cost and the probability of occurrence of each scenario, an objective function is constructed with the goal of minimizing the overall system cost and maximizing the consumption of new energy; Constructing the constraint conditions of the objective function according to the output parameters of each scenario; Based on the constructed objective function and constraints, the energy storage configuration capacity under different capacity planning scenarios is solved; Output the optimized energy storage configuration capacity planning strategy for each region and time period.

2. The method according to claim 1, characterized in that The method further comprises: The energy storage configuration capacity planning strategy is analyzed and verified.

3. The method according to claim 1, characterized in that Analyze and verify the energy storage configuration capacity planning strategy, including: Compare the thermal power unit data, grid load data and renewable energy curtailment data before and after the implementation of energy storage configuration capacity planning strategies in conventional operation scenarios in various regions; The feasibility of the energy storage configuration capacity planning strategy is output based on the comparison results.

4. The method according to claim 1, characterized in that The historical data is sampled to generate a capacity planning scenario, including: The historical data is sampled based on Latin hypercube sampling to generate a capacity planning scenario.

5. The method according to claim 1, characterized in that The capacity planning scenario is reduced by clustering, including: The capacity planning scenario is reduced based on K-means clustering.

6. The method according to claim 1, characterized in that Based on the energy storage investment cost, system operation cost, flexibility mutual assistance service cost and the probability of occurrence of each scenario, an objective function is constructed with the goal of minimizing the system comprehensive cost and maximizing the consumption of new energy, including: in, They respectively represent the system comprehensive cost, energy storage investment cost, system operation cost and flexibility mutual assistance service cost, and the unit is yuan / MWh; α(s) represents the probability of occurrence of each scenario; N represents the number of scenarios.

7. The method according to claim 6, characterized in that The method further comprises: Calculate energy storage investment cost: Where, b represents the bth electrical energy storage device; Ω ES Represents a collection of energy storage power stations; To represent the annualized investment cost of electric energy storage equipment, the unit is RMB / kW; Indicates the planned capacity of the electric energy storage equipment, in MWh; Calculate system operating costs: in, They represent the system operation cost, the operation cost of the electric energy storage equipment, the unit cost of wind power curtailment and the unit cost of photovoltaic power curtailment respectively, in RMB / MWh; t represents the tth period; and They represent the discharge and charge outputs of the electric energy storage device b, in MW; and Respectively represent the abandoned power of wind power and photovoltaic power, in MW.

8. The method according to claim 1, characterized in that The constraint conditions of the objective function are constructed according to the output parameters of each scenario, including: Constructing power balance constraints: Among them, P g,t , P w,t , P p,t , P b,t Respectively represent the output of conventional thermal power units, wind farm output, photovoltaic power station output and electric energy storage equipment output, the unit is MW; Ω G represents the set of conventional thermal power units; Ω W represents the set of wind turbines; Ω P represents a collection of photovoltaic units; and They represent the total output and total input of the inter-regional tie lines at time t respectively; It represents the regional load in MW; Construct cross-region flexibility constraints: in, represents the flexibility power provided by region m to region n; represents the flexibility level of region m at time t; Constructing inter-regional interconnection line transmission constraints: in, represents the maximum transmission capacity of the tie line between area m and area n; Construct the maximum buildable capacity constraint of energy storage: Among them, Cap B,Lim Indicates the installed capacity limit of the electric energy storage equipment, in MW; Indicates the upper limit of the planned capacity of the energy storage system, in MW; Constructing energy storage capacity constraints: in, Indicates the energy storage level of the electric energy storage device, in MWh; Constructing the coupling of output and power: in, and They represent the discharge and charge outputs of the electric energy storage device b, in MW; Construct energy balance constraints for energy storage power stations: in, Indicates the energy storage level of the electric energy storage device in the previous period, in MWh; Indicates the charging and discharging efficiency of electrical energy storage equipment; They represent the planned charging / discharging output of the energy storage device, in MW; Constructing charging and discharging power constraints for energy storage power stations: Among them, α b,k Represents the charging operation state variable of the electric energy storage device; δ b,k Indicates the discharge operation state variable of the electric energy storage device; Construct the equal power constraints at the beginning and end of the energy storage period: in, Respectively represent the energy storage level of the electric energy storage device at the beginning and end, in MWh; Indicates the initial energy storage level percentage of the electric energy storage device; Constructing the maximum energy storage output constraint: in, Indicates the maximum planned output of the electric energy storage device; Construct output constraints for thermal power units: in, They represent the minimum output limit of thermal power units and the maximum output limit of thermal power units respectively; Construct output constraints for new energy units: in, and They represent the planned output of wind turbines and photovoltaic units respectively. and They represent the abandoned power of wind turbines and photovoltaic units respectively; Construct energy storage charging and discharging duration constraints: Among them, T ch and T dis Respectively represent the charging and discharging duration of the energy storage device, in min; α b,k Represents the charging operation state variable of the electric energy storage device; δ b,k Represents the discharge operation state variable of the electric energy storage device; T range y represents the shortest charging and discharging time of the electric energy storage; and They represent the state transition variables of the electric energy storage device starting to charge and starting to discharge respectively; Construct energy storage charging and discharging time interval constraints: Construct coupling constraints between energy storage operation status and start / stop status: in, and Respectively represent the state transition variables of energy storage end charging and end discharging; α b,t-1 and δ b,t-1 They respectively represent the charging and discharging operating state variables of the energy storage device at the previous moment.

9. The method according to claim 1, characterized in that: Solve the energy storage configuration capacity under different capacity planning scenarios, including: The energy storage configuration capacity under different capacity planning scenarios is solved based on the CPLEX mathematical programming solver.

10. A multi-scenario capacity planning system for a cross-regional energy storage virtual power plant, characterized in that: include: Processor and memory; The processor and the memory are connected via a communication bus: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store a program, and the program is at least used to execute a multi-scenario capacity planning method for a cross-regional energy storage virtual power plant as described in any one of claims 1-9.

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