An energy storage configuration method, device, apparatus and storage medium

Abstract

The application discloses an energy storage configuration method and device, equipment and a storage medium, relates to the technical field of power system planning and operation, and comprises the following steps: determining the peak regulation power and the frequency modulation power of the mth round of the receiving end regional power grid in the tth time period, and then determining the peak regulation and frequency modulation sub-cost of the mth round in the tth time period; adding the peak regulation and frequency modulation sub-cost of the mth round in the tth time period to other sub-costs to obtain the target cost of the mth round in the tth time period; if the peak regulation power and the frequency modulation power meet the stop iteration condition, then the target peak regulation power and the target frequency modulation power of the tth time period are determined according to the peak regulation power and the frequency modulation power, and the energy storage of the receiving end regional power grid is configured according to the target peak regulation power and the target frequency modulation power. The method realizes the collaborative optimization configuration of the peak regulation and frequency modulation energy storage under the premise of clearly distinguishing the function boundary of the energy storage, and improves the actual utilization efficiency of the energy storage resources.

Description

Technical Field

[0001] This application relates to the field of power system planning and operation technology, and in particular to an energy storage configuration method, device, equipment and storage medium. Background Technology

[0002] In receiving-end regional power grids, in order to cope with the frequency regulation and peak shaving pressure brought about by the high proportion of new energy access, battery energy storage systems are widely regarded as a key technical means to improve grid flexibility due to their bidirectional regulation capabilities and fast response characteristics.

[0003] However, existing methods often treat energy storage systems as a general regulatory resource capable of simultaneously undertaking frequency regulation and peak shaving tasks during modeling, neglecting the fundamental differences in their functional roles during actual operation. Frequency regulation requires energy storage to have rapid response capabilities on the order of seconds to minutes, while peak shaving corresponds to continuous energy support on the order of hours. These two functions differ fundamentally in power command intervals, operating modes, and scheduling logic. Existing planning models fail to effectively distinguish between these two functional attributes, resulting in configurations that cannot simultaneously meet the requirements of both rapid frequency regulation and continuous peak shaving in actual scheduling, thus affecting the actual utilization efficiency of energy storage resources. Summary of the Invention

[0004] This application provides an energy storage configuration method, device, equipment, and storage medium, which can achieve coordinated configuration of energy storage under the premise of clear functional boundaries, so that the planning results can simultaneously meet the rapid response requirements of frequency regulation and the continuous energy support requirements of peak shaving, thereby improving the actual utilization efficiency of energy storage resources.

[0005] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application provides an energy storage configuration method, the method comprising: Determine the variables to be optimized in the receiving-end regional power grid, including the peak-shaving power and frequency regulation power in the m-th round of the t-th time period; Based on the peak-shaving power and frequency-modulation power of the m-th round in the t-th time period, determine the peak-shaving and frequency-modulation sub-cost of the m-th round in the t-th time period; The target cost for the m-th round of the t-th time period is obtained by summing the costs of peak shaving and frequency regulation, thermal power units, wind turbines, photovoltaic units, load shedding penalties, and water curtailment. Specifically, the cost of thermal power units is determined by their output in the m-th round of the t-th time period; the cost of wind turbines is determined by their output in the m-th round of the t-th time period; the cost of photovoltaic units is determined by their output in the m-th round of the t-th time period; the cost of load shedding penalties is determined by the amount of load shedding in the m-th round of the t-th time period; and the cost of water curtailment penalties is determined by the amount of water curtailed in the m-th round of the t-th time period. The peak shaving power, frequency regulation power, output of thermal power units, output of wind turbines, output of photovoltaic units, load shedding amount, and water curtailment amount in the m-th round of the t-th time period satisfy the power balance relationship. Determine whether the peak-shaving power and frequency-modulated power in the m-th round of time period t meet the stopping iteration condition; If the peak-shaving power and frequency regulation power of the m-th round in the t-th time period meet the stopping iteration condition, then the target peak-shaving power and target frequency regulation power of the t-th time period are determined based on the peak-shaving power and frequency regulation power of the m-th round in the t-th time period, and energy storage is configured for the receiving-end area power grid according to the target peak-shaving power and target frequency regulation power of the t-th time period. If the peak power and frequency modulation power of the m-th round in the t-th time period do not meet the stopping iteration condition, then the peak power and frequency modulation power of the (m+1)-th round in the t-th time period are determined based on the peak power and frequency modulation power of the m-th round in the t-th time period.

[0006] In some possible implementations, determining whether the peak-shaving power and frequency-modulated power in the m-th round of time period t meet the stopping iteration condition includes: Obtain the convergence degree of the target cost, peak power, and frequency modulation power, the proximity of the constraint boundary, and the iteration penalty for the m-th round in the t-th time period; determine the iteration score based on the convergence degree of the target cost, peak power, and frequency modulation power, the proximity of the constraint boundary, and the iteration penalty for the m-th round in the t-th time period; if the iteration score is greater than or equal to the score threshold, then determine that the peak power and frequency modulation power in the m-th round of the t-th time period meet the stopping iteration condition.

[0007] In some possible implementations, the method further includes: If the iterative score is less than the score threshold, determine whether m is greater than or equal to the iteration number threshold; if m is greater than or equal to the iteration number threshold, then determine that the peak power and frequency modulation power of the m-th round in the t-th time period meet the stopping iteration condition.

[0008] In some possible implementations, the method further includes: If m is less than the iteration number threshold, then determine whether the improvement of the iteration score over N consecutive rounds is less than the amplitude threshold; if the improvement of the iteration score over N consecutive rounds is less than the amplitude threshold, then determine that the peak-shaving power and frequency-modulating power of the m-th round in the t-th time period meet the iteration stop condition.

[0009] In some possible implementations, the method further includes: If the improvement in the score over N consecutive iterations is greater than or equal to the magnitude threshold, then the peak-shaving power and frequency-modulating power in the m-th round of time period t do not meet the stop-iteration condition.

[0010] In some possible implementations, the peak-shaving power in the (m+1)th round of time period t is determined in the following way: Obtain the target frequency modulation power from the tn-th time period to the (t-1)-th time period; based on the target frequency modulation power from the tn-th time period to the (t-1)-th time period and the peak modulation power of the m-th round in the t-th time period, determine the peak modulation power of the (m+1)-th round in the t-th time period.

[0011] In some possible implementations, the frequency modulation power in the (m+1)th round of the t-th time period is determined in the following way: Obtain the target peak-shaving power from the nth time period to the (t-1th time period); based on the target peak-shaving power from the nth time period to the (t-1th time period) and the frequency modulation power of the mth round in the tth time period, determine the frequency modulation power of the (m+1th)th round in the tth time period.

[0012] Secondly, this application provides an energy storage configuration device, the device comprising: The determination module is used to determine the variables to be optimized in the receiving-end regional power grid, including the peak-shaving power and frequency regulation power in the m-th round of the t-th time period; and to determine the peak-shaving and frequency regulation sub-costs in the m-th round of the t-th time period based on the peak-shaving power and frequency regulation power in the m-th round of the t-th time period. The calculation module is used to add up the sub-costs of peak shaving and frequency regulation, thermal power units, wind power units, photovoltaic units, load shedding penalties, and water curtailment penalties in the m-th round of the t-th time period to obtain the target cost in the m-th round of the t-th time period. Specifically, the sub-cost of thermal power units is determined by the output of thermal power units in the m-th round of the t-th time period; the sub-cost of wind power units is determined by the output of wind power units in the m-th round of the t-th time period; the sub-cost of photovoltaic units is determined by the output of photovoltaic units in the m-th round of the t-th time period; the sub-cost of load shedding penalties is determined by the amount of load shedding in the m-th round of the t-th time period; and the sub-cost of water curtailment penalties is determined by the amount of water curtailed in the m-th round of the t-th time period. The peak shaving power, frequency regulation power, output of thermal power units, output of wind power units, output of photovoltaic units, load shedding amount, and water curtailment amount in the m-th round of the t-th time period satisfy the power balance relationship. The judgment module is used to determine whether the peak-shaving power and frequency-modulated power of the m-th round in time period t meet the stopping iteration condition; if the peak-shaving power and frequency-modulated power of the m-th round in time period t meet the stopping iteration condition, then based on the peak-shaving power and frequency-modulated power of the m-th round in time period t, the value of the m-th round is determined. The target peak-shaving power and target frequency modulation power for each time period are determined according to the following: The target peak-shaving power and target frequency regulation power for each time period are used to configure energy storage in the receiving-end regional power grid; if the peak-shaving power and frequency regulation power of the m-th round in the t-th time period do not meet the stopping iteration condition, then the peak-shaving power and frequency regulation power of the (m+1)-th round in the t-th time period are determined based on the peak-shaving power and frequency regulation power of the m-th round in the t-th time period.

[0013] Thirdly, this application provides a computing device, including a memory and a processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of the first aspects.

[0014] Fourthly, this application provides a computer-readable storage medium for storing a computer program for performing the method as described in any one of the first aspects.

[0015] Fifthly, this application provides a computer program product comprising one or more computer instructions, wherein when the computer instructions are executed by a computer, the computer performs the method as described in any one of the first aspects.

[0016] As can be seen from the above technical solution, this application has at least the following beneficial effects: In this application, the variables to be optimized in the receiving-end regional power grid are determined, including the peak-shaving power and frequency regulation power in the m-th round of time period t; based on the peak-shaving power and frequency regulation power in the m-th round of time period t, the peak-shaving and frequency regulation sub-costs in the m-th round of time period t are determined; the peak-shaving and frequency regulation sub-costs, thermal power unit sub-costs, wind power unit sub-costs, photovoltaic unit sub-costs, load shedding penalty sub-costs, and hydropower abandonment penalty sub-costs in the m-th round of time period t are added together to obtain the target cost in the m-th round of time period t; wherein, the thermal power unit sub-cost is determined by the output of the thermal power unit in the m-th round of time period t, and the wind power unit cost is determined by the output of the thermal power unit in the m-th round of time period t. The output of the m-th wind turbine is determined, the cost of the photovoltaic unit is determined by the output of the m-th photovoltaic unit in time period t, the cost of the load shedding penalty is determined by the load shedding amount in the m-th time period, and the cost of the water curtailment penalty is determined by the amount of water curtailed in the m-th time period; the peak-shaving power, frequency regulation power, output of thermal power units, output of wind turbines, output of photovoltaic units, load shedding amount, and amount of water curtailed in the m-th time period satisfy the power balance relationship; determine whether the peak-shaving power and frequency regulation power of the m-th time period meet the stopping iteration condition; if the peak-shaving power and frequency regulation power of the m-th time period meet the stopping iteration condition, then according to the... The peak-shaving power and frequency regulation power of the m-th round of time period are determined, the target peak-shaving power and target frequency regulation power of the t-th time period are determined, and the energy storage configuration of the receiving-end regional power grid is carried out according to the target peak-shaving power and target frequency regulation power of the t-th time period.

[0017] In existing technologies, energy storage systems are typically treated as a general-purpose regulatory resource capable of simultaneously undertaking frequency regulation and peak shaving tasks. This neglects the fundamental differences between the two in terms of power command time intervals and operating modes, resulting in configurations that fail to simultaneously meet the requirements of rapid frequency regulation and continuous peak shaving in actual scheduling. Therefore, this application, by clearly distinguishing the functional boundaries of energy storage, achieves coordinated and optimized configuration of peak shaving and frequency regulation energy storage, thereby improving the actual utilization efficiency of energy storage resources.

[0018] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0019] Figure 1 A schematic diagram illustrating an application scenario provided in an embodiment of this application; Figure 2 A flowchart illustrating an energy storage configuration method provided in this application embodiment; Figure 3 A schematic diagram of an energy storage configuration device provided in an embodiment of this application; Figure 4 This is a schematic diagram of a computing device provided in an embodiment of this application. Detailed Implementation

[0020] The terms "first," "second," and "third," etc., used in this application specification and accompanying drawings are used to distinguish different objects, not to limit a specific order.

[0021] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0022] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first: Currently, in energy storage planning for receiving-end regional power grids, energy storage systems are typically viewed as a general-purpose regulating resource capable of simultaneously undertaking frequency regulation and peak shaving tasks. The planning model aims to minimize the total system operating cost, comprehensively considering constraints such as power balance, thermal power unit output, and renewable energy consumption, and obtains the power and capacity configuration scheme for energy storage through optimization solutions.

[0023] Current technologies fail to differentiate the different operational requirements of frequency regulation and peak shaving during the planning phase. Frequency regulation services require energy storage with rapid response capabilities ranging from seconds to minutes, while peak shaving services require continuous energy capacity on an hourly basis. These two services differ fundamentally in terms of power command intervals, depth of charge and discharge, and number of daily operations. Existing planning models conflate these differences, resulting in configurations that cannot simultaneously meet the demands of both rapid frequency regulation and continuous peak shaving in actual scheduling, thus impacting the actual utilization efficiency of energy storage resources.

[0024] In view of this, embodiments of this application provide an energy storage configuration method. This method can be applied to a processing device, which can be a terminal or a server. Terminals include, but are not limited to, smartphones, tablets, laptops, personal digital assistants, or smart wearable devices. Servers can be cloud servers, such as central servers in a central cloud computing cluster or edge servers in an edge cloud computing cluster. Alternatively, servers can be located in a local data center. A local data center refers to a data center directly controlled by the user. This method enables the energy storage configuration results to simultaneously meet the rapid response requirements of frequency regulation services and the continuous energy requirements of peak shaving services, thereby improving the actual utilization efficiency of energy storage resources.

[0025] To make the technical solution of this application clearer and easier to understand, the application scenarios of the technical solution of this application are described below with reference to the accompanying drawings. Figure 1 As shown in the figure, this figure is a schematic diagram of an application scenario provided by an embodiment of this application.

[0026] In this application scenario, this application is applied to the receiving-end regional power grid 101. This power grid includes renewable energy sources such as wind farms 102 and photovoltaic power plants 103, as well as thermal power units 104. The dispatch center 105 issues different types of power commands to the peak-shaving energy storage station 106 and the frequency-regulating energy storage station 107 based on system load demand and renewable energy output. The load center 108 represents industrial and residential users, constituting the system's electricity demand.

[0027] In this scenario, as the grid connection ratio of wind farm 102 and photovoltaic power station 103 continues to increase, the receiving-end regional power grid 101 faces the following problems: the volatility and uncertainty of renewable energy output leads to increased frequency regulation pressure on the system, requiring energy storage to have a rapid response capability of seconds to minutes; at the same time, the intraday anti-peak-shaving characteristics of renewable energy result in insufficient regulation capacity during peak load periods, requiring energy storage to have continuous energy capacity of hours. Existing technologies typically treat energy storage systems as a general-purpose regulation resource that can simultaneously undertake frequency regulation and peak-shaving tasks, failing to distinguish the different requirements of the two functions for power command time intervals and operating modes during the planning stage. This results in the configured energy storage failing to simultaneously meet the requirements of rapid frequency regulation and continuous peak-shaving in actual dispatching.

[0028] To address the aforementioned issues, this application provides an energy storage configuration method. This method first determines the variables to be optimized in the receiving-end regional power grid 101, including the peak-shaving power and frequency regulation power in the m-th round of time period t. Based on the peak-shaving and frequency regulation power of the current round, the peak-shaving and frequency regulation sub-costs are calculated. These are then superimposed with the sub-costs of thermal power units, wind power units, photovoltaic units, load shedding penalties, and water wastage penalties to obtain the target cost for the m-th round of time period t. Simultaneously, the peak-shaving power, frequency regulation power, and the output, load shedding, and water wastage of various units are ensured to meet the power balance relationship. Subsequently, it is determined whether the peak-shaving power and frequency regulation power of the current round meet the iteration stop condition. If they do, the target peak-shaving power and target frequency regulation power for time period t are determined, and energy storage is configured in the receiving-end regional power grid according to the target power for each time period. If they do not meet the condition, the historical target peak-shaving power and target frequency regulation power from time period tn to time period t-1 are combined to generate the peak-shaving power and frequency regulation power for the (m+1)-th round of time period t, and the iterative calculation continues. Using the above method, this application adopts an iterative optimization mechanism that divides time periods and rounds, and achieves the coordinated configuration of the two types of energy storage under the premise of clearly distinguishing the functional boundaries of peak shaving and frequency regulation. This enables the planning results to simultaneously meet the power command requirements of different time scales issued by the dispatch center 105, avoids the local optimum problem caused by the one-time solution of traditional methods, and improves the actual utilization efficiency of energy storage resources.

[0029] To make the technical solution of this application clearer and easier to understand, the following describes an energy storage configuration method provided by an embodiment of this application, in conjunction with the above application scenarios. For example... Figure 2 As shown in the figure, this figure is a flowchart of an energy storage configuration method provided in an embodiment of this application.

[0030] The method includes: S201. The processing equipment acquires basic data of the power grid in the receiving area.

[0031] The receiving-end regional power grid refers to a regional power grid that mainly relies on external power input and has a high proportion of local renewable energy generation. Processing equipment can acquire basic data of the receiving-end regional power grid through methods such as data collection by dispatch automation systems, uploading from power station terminals, cross-regional data exchange, and manual import. The basic data acquired by the processing equipment includes: load time-series data, i.e., the power load values ​​of each node in the power grid at different times; renewable energy output time-series data, i.e., the actual power generation values ​​of wind farms and photovoltaic power plants at different times; regional tie-line power data, i.e., the power exchange values ​​between the receiving-end regional power grid and the external power grid; thermal power unit parameters, including technical parameters such as the upper and lower limits of output, ramp rate, and minimum start-up and shutdown time of thermal power units and gas turbine units; and system reserve capacity, i.e., the regulation capacity reserved by the power grid to cope with emergencies. The temporal resolution of the basic data must simultaneously cover the time scale requirements of peak shaving and frequency regulation, with a minimum sampling interval of no more than 5 minutes.

[0032] S202. The processing equipment processes the basic data to obtain several typical scenario data.

[0033] The processing equipment cleans the acquired basic data, for example, by correcting outliers and filling in missing values ​​caused by equipment malfunctions or communication interruptions. After cleaning, the equipment aligns the cleaned data. Since load time-series data and renewable energy output time-series data may come from different acquisition systems with inconsistent sampling frequencies, resampling and timestamp matching are performed according to peak-shaving command intervals (15 minutes) and frequency regulation command intervals (5 minutes) respectively, generating two sets of standardized time-series data corresponding to different time scales to eliminate calculation errors caused by time-series misalignment. After alignment, the equipment normalizes the aligned data, converting data with significant differences in units and numerical ranges to a unified numerical range, resulting in preprocessed data. Cluster analysis is used to extract several typical operating scenarios from the preprocessed time-series data, including typical weekdays, typical weekends, extreme load days, and extreme renewable energy output days. Each typical scenario is divided into T consecutive t-periods, where t ranges from 1 to T, and the length of each period matches the command interval of the corresponding function. This application effectively reduces the data size required for optimization calculations by introducing cluster analysis to extract typical scenarios during the data preprocessing stage.

[0034] S203. The processing equipment determines the variables to be optimized in the power grid of the receiving end area.

[0035] The variables to be optimized include peak-shaving power and frequency-regulating power in the m-th iteration of time period t, where t is the time period number in a typical scenario and m is the current iteration number. Peak-shaving power represents the charging and discharging power that the peak-shaving energy storage station needs to provide in the m-th iteration of time period t; frequency-regulating power represents the charging and discharging power that the frequency-regulating energy storage station needs to provide in the m-th iteration of time period t. This application uniformly stipulates that charging power is a positive value and discharging power is a negative value.

[0036] The processing equipment initializes the iteration round counter m=1 and sets the initial peak-shaving power and initial frequency regulation power for each time period t. The initial values ​​can be set based on historical operating data of the receiving-end regional power grid, experience with energy storage configurations of similar power grids, or system regulation requirements, or they can be initialized to 0 as the starting point for iterative calculations.

[0037] S204. The processing equipment calculates the target cost of the m-th round in the t-th time period based on the peak-shaving power and frequency-modulating power of the m-th round in the t-th time period.

[0038] The processing equipment determines the peak-shaving and frequency-regulating sub-cost of the m-th round in the t-th time period based on the peak-shaving power and frequency-regulating power. It then adds up the peak-shaving and frequency-regulating sub-cost, thermal power unit sub-cost, wind power unit cost, photovoltaic unit cost, load shedding penalty sub-cost, and water wastage penalty sub-cost to obtain the target cost of the m-th round in the t-th time period.

[0039] The sub-cost of thermal power units is determined by the output of thermal power units in the m-th round of time period t; the sub-cost of wind power units is determined by the output of wind power units in the m-th round of time period t; the sub-cost of photovoltaic units is determined by the output of photovoltaic units in the m-th round of time period t; the sub-cost of load shedding penalty is determined by the load shedding amount in the m-th round of time period t; and the sub-cost of water curtailment penalty is determined by the amount of water curtailed in the m-th round of time period t. The mathematical expression for the target cost in the m-th round of time period t is as follows: (1) in, Let $t$ be the cost of the thermal power unit in the $m$ round during time period $t$. Let $t$ be the cost of the wind turbine unit in the $m$-th round during time period $t$. Let be the cost of the photovoltaic unit in the m-th round of the t-th time period. Let $t$ be the peak-shaving and frequency-modulation sub-cost for the $m$ round in time period $t$. Let $t$ be the load shedding penalty cost for the $m$ round in time period $t$. Let $t$ be the cost of the water abandonment penalty in the $m$ round during time period $t$. The unit fuel cost of thermal power units, For thermal power units The output in the m-th round during time period t, For thermal power units Startup costs, For thermal power units Downtime costs, For thermal power units The startup state variables of the m-th round in time period t. For thermal power units The shutdown status variable in the m-th round of time period t. For wind farm The unit cost of wind curtailment penalty For wind farm The predicted maximum output in the m-th round of time period t. For wind farm The actual output in the m-th round during time period t. For photovoltaic units The unit cost of waste light penalty For photovoltaic units The predicted maximum output in the m-th round of time period t. For photovoltaic units The actual output in the m-th round during time period t To reduce the cost per kilowatt-hour of charging and discharging for peak-shaving energy storage power stations, To reduce the cost per kilowatt-hour of charging and discharging for frequency regulation energy storage power stations, Let m be the peak-shaving power of the peak-shaving energy storage power station in the m-th round of time period t. Let m be the frequency regulation power of the frequency regulation energy storage power station in the m-th round of the t-th time period. This represents the unit load shedding penalty cost for node b. Load shedding refers to the forced power outage / limitation operation performed on some user loads to maintain grid stability when the system's power supply capacity is insufficient. Let the load shedding of node b in the m-th round of time period t be... The unit water discharge penalty cost of the hydropower station is h. Let h be the amount of water discharged by the hydropower station in the m-th cycle during time period t. For the total time, This represents the total number of thermal power units. Let j be the total number of wind farms, and j be the wind farm number. Let k be the total number of photovoltaic (PV) units, and k be the PV unit number. This represents the total number of energy storage power stations. The total number of system nodes. This represents the total number of hydropower stations.

[0040] When calculating the target cost of the m-th round in time period t, the peak-shaving power, frequency regulation power, output of thermal power units, output of wind power units, output of photovoltaic units, load shedding, and water wastage in the m-th round of time period t need to satisfy the power balance relationship. That is, the algebraic sum of the output of all power sources, the charging and discharging power of energy storage, and the switching power of tie lines in this round is equal to the difference between the load demand and the load shedding in this period. The specific expression is as follows: (2) In the formula, This is the node correlation matrix of thermal power units. Let be the output vector of the thermal power unit in the m-th cycle of time period t. The node-branch correlation matrix, For branch power flow vectors, This is the node correlation matrix of the wind farm. Let be the power output vector of the wind farm in the m-th cycle of time period t. This is the node association matrix of the photovoltaic unit. Let be the output vector of the photovoltaic unit in the m-th cycle of time period t. This is the node correlation matrix of the peak-shaving energy storage power station. Let m be the peak-shaving power of the peak-shaving energy storage power station in the m-th round of time period t. This is the node correlation matrix of a frequency regulation energy storage power station. Let m be the frequency regulation power of the frequency regulation energy storage power station in the m-th round of the t-th time period. This is the node association matrix of the hydropower station. Let be the power output vector of the hydropower station in the m-th cycle of time period t. This is the node correlation matrix of a pumped storage power station. Let be the pumping power vector of the pumped storage power station in the m-th cycle of time period t. Let be the power generation vector of the pumped storage power station in the m-th cycle of time period t. The node correlation matrix for power input to the tie line. Let m be the input power vector of the connection line in the m-th round of time period t. The node correlation matrix for power output from the tie line. Let be the power vector transmitted by the connector in the m-th round of time period t. For node load vectors, Let be the load shedding vector of the node in the m-th round of the t-th time period.

[0041] In this node association matrix, the values ​​represent a mathematical abstraction of the power grid topology connections. Each number directly corresponds to the power injection / outflow association attribute between components and nodes. The matrix row numbers represent power grid node numbers, the column numbers represent system component numbers, and the matrix elements... The value of strictly follows the following physical rules: When an element has a value of 1, it means that the j1th element is directly connected to the i1th node, and all the power of that element will be fully injected into the i1th node to participate in power balancing. For example, if the first row of the node association matrix of a thermal power unit has a value of 1, it means that the 100MW output of the thermal power unit is fully injected into node 1, and it only makes a direct contribution to the power balance of node 1.

[0042] When an element has a value of 0, it means that the j1th element is not connected to the i1th node, and the power change of this element has no direct impact on the power balance of the i1th node. For example, if the first row of the wind farm node correlation matrix has 0 elements, it means that the 50MW output of the wind farm will not flow into node 1, but will only act on node 2 to which it is connected.

[0043] This numerical definition method transforms the complex physical topology of the power grid into a standardized matrix operation form, ensuring the rigor of power balance calculations while supporting the linear expansion of power grids of arbitrary size. In practical engineering, for components such as transmission lines and transformers connected across nodes, +1 and -1 can be used to represent power outflow and inflow nodes, respectively. In the simplified example of this application, all components are connected to a single node, so only the values ​​0 and 1 are needed to fully characterize the topological relationships.

[0044] The vector described above is a single-dimensional ordered sequence of values ​​used to store all power values ​​of the same type of component in the system, forming a strict one-to-one correspondence with the node association matrix. The length of the vector is determined by the total number of components of that type. The position of each element in the vector corresponds to the component's number, and the value of the element represents the actual operating power of that component in the current cycle. The sign follows a uniform power direction rule.

[0045] By setting this power balance constraint, real-time power matching between power output, energy storage charging and discharging power, tie-line switching power and node load and load shedding can be forced during the energy storage configuration optimization process. This effectively avoids the physical infeasibility of supply and demand imbalance in the optimization scheme, ensures that the planning scheme can maintain grid frequency stability in actual dispatch scenarios, and improves the engineering feasibility and operational reliability of the energy storage configuration scheme.

[0046] In addition to satisfying power balance constraint 1, the above variables must also satisfy the following constraints related to the operating state in the m-th round of time period t. All constraints only apply to the operating variables in the current round of the current time period: Constraint 2: Power grid operation constraints.

[0047] Power grid operation constraints are a series of constraints that ensure the safe operation of the power grid, including line DC power flow constraints, line capacity constraints, node voltage phase angle constraints, regional tie line power flow constraints, and regional tie line capacity constraints.

[0048] Based on the power grid topology and physical transmission characteristics, each constraint is mathematically described as follows: A2. Line DC power flow constraints: The DC power flow constraint on a power line is a constraint relationship where the active power flow on the line is proportional to the phase angle difference of the voltages at the two ends of the line. Based on the fundamental principles of DC power flow in a power grid, the corresponding mathematical expression is: (3) In the formula, For the line In the Trends of the time For the line The susceptance value, Representative node voltage phase angle, Representative node The voltage phase angle.

[0049] B2. Line capacity constraints: Line capacity constraint is the constraint that the active power transmitted by a line must not exceed the line's design capacity. To ensure that the line is not overloaded, the power is limited to a safe range, as shown in the following formula: (4) In the formula, For the line The maximum value of the current. For the line The minimum value of the power flow.

[0050] C2. Node voltage phase angle constraint: Node voltage phase angle constraints are constraints that require the voltage phase angles at each node of the power grid to be kept within an allowable range. To maintain grid voltage stability, the phase angle range is limited as follows: (5) In the formula, For nodes The upper limit of the voltage phase angle, For nodes The lower limit of the voltage phase angle, For nodes The voltage phase angle.

[0051] D2. DC power flow constraints of zone tie lines (considering zone tie line capacity): Zone tie-line power flow constraints are constraints where the active power flow on the tie-line between zones is proportional to the phase angle difference of the voltages at the nodes at both ends of the tie-line. Consistent with the principle of line power flow constraints, tie-line power flow can be expressed as: (6) In the formula, For regional connection lines In the Trends of the time For connecting lines The susceptance value.

[0052] E2. Zone tie line capacity constraints (considering zone tie line capacity): The capacity constraint of inter-regional tie lines is a constraint that the active power transmitted by tie lines between regions must not exceed the design capacity of the tie lines. To ensure the safety of inter-regional power transmission, the following restrictions are imposed on the power of tie lines: (7) In the formula, For regional connection lines The maximum value of the current. For regional connection lines The minimum value of the power flow.

[0053] By introducing line power flow constraints, the impact of energy storage power station charging and discharging behavior on grid power distribution can be directly limited. This avoids line power flow exceeding limits or reverse overload due to excessive energy storage charging and discharging power, ensuring that the energy storage configuration scheme meets the line thermal stability and safety requirements while pursuing the lowest system operating cost. It also prevents the optimization results from having the most economically optimal but physically infeasible energy storage configuration scheme, thereby improving the engineering feasibility and operational safety of energy storage planning results.

[0054] Constraint 3: Operating constraints of thermal power units.

[0055] Operating constraints of thermal power units are the physical and logical limitations that thermal power units must meet during operation, including output constraints, ramp-up constraints, spinning reserve constraints, state variable constraints, and minimum start-up and shutdown time constraints. Based on the actual operating characteristics of thermal power units, the mathematical expressions for each constraint are as follows: A3. Output constraints of thermal power units: Output constraints are the constraints that the power generation output of a thermal power unit must be between its minimum technical output and its maximum technical output.

[0056] Based on the unit's operating conditions, the output should be limited to a reasonable range, as expressed by: (8) In the formula, Represents thermal power units In the Operating status during a given time period Represents thermal power units The upper limit of output, Represents thermal power units The lower limit of output, For thermal power units In the Efforts during specific time periods.

[0057] B3. Climbing constraints for thermal power units: The ramp constraint is a restriction that prevents a thermal power unit from increasing or decreasing its output by more than a certain amount within two adjacent time intervals. To avoid equipment damage caused by rapid changes in unit output, the ramp limit formula is as follows: (9) In the formula, Represents thermal power units In the Operating status during a given time period Represents thermal power units The upper limit of the upward climbing rate, Represents thermal power units The upper limit of the downward climbing rate, For thermal power units In the Efforts during specific time periods.

[0058] C3. Rotational standby constraint for thermal power units: Spinning reserve constraints are requirements for thermal power units operating in a system to maintain a certain percentage of reserve capacity to cope with unforeseen circumstances such as load fluctuations or unit failures. To ensure sufficient system regulation capacity, the spinning reserve constraints are as follows: (10) (11) In the formula, For emergency hot standby, For emergency standby, Reserved for load. This is the emergency hot standby factor. This is the emergency cold standby coefficient. This is the load reserve factor. For node b in the th The original load demand for the time period.

[0059] D3. State variable constraints: State variable constraints are constraints that describe the logical relationships between 0-1 variables representing the start-up, shutdown, and maintenance states of a thermal power unit. To ensure the self-consistency of the unit's state logic, the constraint relationships are as follows: (12) In the formula, This variable represents the starting status of the thermal power unit; 1 indicates the unit is running, and 0 indicates the unit is not running. This variable represents the shutdown status of the thermal power unit; 1 indicates shutdown, and 0 indicates it is not shut down. This represents the maintenance status variable of the thermal power unit, where 0 indicates maintenance is in progress and 1 indicates no maintenance is in progress.

[0060] E3. Minimum start-up and shutdown time constraint: The minimum start-up and shutdown time constraint is the minimum operating time that a thermal power unit must run after startup, and the minimum shutdown time that it must be shut down after shutdown. To protect the unit equipment and avoid frequent start-ups and shutdowns, the time constraint formula is as follows: (13) In the formula, This is the minimum startup time for thermal power units. This is the minimum downtime for thermal power units.

[0061] The output of thermal power units is limited by physical components such as boilers and turbines, and cannot change instantaneously. Furthermore, spinning reserve is required to cope with load fluctuations or unit failures. This constraint encompasses upper and lower output limits, ramp-up rates, spinning reserve, start-up and shutdown states, and minimum start-up and shutdown times, ensuring the optimization model closely approximates the actual operating boundaries of the unit. Its beneficial effects include preventing planning results from requiring units to execute power commands that are impossible to fulfill, while the spinning reserve constraint enhances the system's resilience to disturbances and improves power supply reliability.

[0062] Constraint 4: Wind turbine operating constraints: Wind turbine operating constraints are the physical limitations that a wind farm must meet during operation, including predicted output constraints and actual output constraints. Considering the randomness of wind power output and equipment limitations, the specific constraints are as follows: A4. Wind turbine predicted output constraints: The predicted output constraint is the constraint that the actual output of renewable energy units must not exceed their predicted maximum output. Wind power predicted output consists of a baseline value and a deviation, expressed as follows: (14) In the formula, For wind farm In the Maximum output during the period This is the original predicted maximum output of the wind turbine. This represents the maximum output deviation of the wind turbine.

[0063] B4. Actual output constraints of wind turbine units: Actual output constraints mean that the actual output of new energy units must take into account the constraints of the unit's maintenance status. Under maintenance and normal conditions, the actual output of wind power should meet the following limitations: (15) In the formula, For wind farm In the middle of Actual output during the time period This represents the maintenance status of the wind turbine unit; 0 indicates it is under maintenance, and 1 indicates it is not under maintenance.

[0064] Actual wind power output is limited by weather conditions and cannot exceed the predicted maximum value; output is zero during maintenance periods. Ignoring this constraint would lead to optimization models over-reliance on unavailable wind power. The beneficial effect of this constraint is that it accurately characterizes the intermittency and uncertainty of wind power, enabling energy storage solutions to effectively address wind curtailment and promote the integration of new energy sources.

[0065] Constraint 5: Operating constraints of photovoltaic units.

[0066] Photovoltaic (PV) unit operating constraints are the physical limitations that a PV power plant must meet during operation, including predicted output constraints and actual output constraints. Consistent with wind power constraints, PV output constraints are as follows: A5. Predicted output constraints of photovoltaic units: The predicted output of photovoltaic power also consists of a baseline value and a deviation, which can be expressed as: (16) In the formula, For photovoltaic units In the Maximum output during the period This is the maximum output originally predicted for the photovoltaic unit. This represents the maximum output deviation of the photovoltaic unit.

[0067] B5. Actual output constraints of photovoltaic units: Considering the impact of maintenance on photovoltaic power output, the actual power output constraints are as follows: (17) In the formula, For photovoltaic units In the Actual output during the time period This represents the maintenance status of the photovoltaic unit; 0 indicates maintenance in progress, and 1 indicates no maintenance.

[0068] Similar to wind power, photovoltaic (PV) output is limited by irradiance and maintenance status; actual output cannot exceed the predicted maximum. This constraint prevents the optimization results from incorrectly allocating PV power at night or during periods of no sunlight. Its beneficial effects include ensuring the optimization model accurately simulates PV output, providing reasonable boundary conditions for energy storage to participate in midday PV peak shaving, and improving energy storage utilization efficiency.

[0069] Constraint 6: Operational constraints of peak-shaving energy storage power stations.

[0070] The peak-shaving energy storage power station in this application is specifically designed for participating in system peak-shaving services, and its characteristic is providing continuous energy on an hourly basis. To ensure the stable, safe, and independent operation of the peak-shaving energy storage, the following operational constraints are set: A6. Charging and discharging power constraints of peak-shaving energy storage systems: The charge / discharge power constraint is a constraint that the absolute value of the charge / discharge power of an energy storage power station at any given time must not exceed its rated power. The charging power of an energy storage power station is defined as positive, and the discharging power as negative. According to the rated power limit, the charge / discharge power should satisfy: (18) In the formula, This refers to the rated power of the peak-shaving energy storage power station.

[0071] B6. State of charge constraints for peak-shaving energy storage power stations: State of charge (SOC) constraints are requirements that ensure the SOC of an energy storage power station remains within a set upper and lower limit range, and they also define the recursive relationship between SOC and charge / discharge power and rated capacity. To prevent overcharging and over-discharging, the SOC variation and range limits are as follows: (19) In the formula, This is the minimum state-of-charge value for peak-shaving energy storage power stations. This is the upper limit of the state of charge for peak-shaving energy storage power stations. This represents the state of charge of the peak-shaving energy storage power station during time period t.

[0072]

[0073] In the formula, This represents the state of charge of the peak-shaving energy storage power station during the (t-1)th time period. To improve the charging efficiency of peak-shaving energy storage power stations. To improve the discharge efficiency of peak-shaving energy storage power stations. For the rated capacity of the peak-shaving energy storage power station, To calculate the time interval, This refers to the initial state of charge of a peak-shaving energy storage power station. This refers to the state of charge of a peak-shaving energy storage power station at the end of its lifecycle. Indicates charging. This indicates a discharge.

[0074] By introducing state-of-charge (POC) constraints for peak-shaving energy storage power stations, the POC of the energy storage can be forcibly controlled within a safe operating range. This avoids pseudo-optimal configuration schemes that repeatedly switch between overcharging, over-discharging, or extreme operating conditions in an effort to reduce system operating costs during the optimization process. This constraint ensures the cycle life and operational safety of the energy storage power station without affecting the overall system cost optimization objective, guaranteeing that the planned energy storage capacity can stably participate in system peak shaving over the long term, and improving the engineering reliability and actual operational economy of the energy storage configuration scheme.

[0075] C6. Constraints on simultaneous charging and discharging of peak-shaving energy storage power stations: The simultaneous charging and discharging constraint of this peak-shaving energy storage power station is a logical constraint that prevents the energy storage power station from performing charging and discharging operations simultaneously. To ensure correct operation, this constraint can be expressed as: (twenty one) in, For the charging power of peak-shaving energy storage power stations, This refers to the discharge power of the peak-shaving energy storage power station.

[0076] Peak-shaving energy storage power stations cannot be overcharged or over-discharged; charging and discharging power is limited by converter capacity, and simultaneous charging and discharging are prohibited. This constraint includes sub-items such as upper limits for charging and discharging power, upper and lower limits for state of charge and their recursive relationships, and prohibition of simultaneous charging and discharging. Setting independent constraints for peak-shaving energy storage is to match its functional positioning as "hourly continuous energy." Its beneficial effect is to enforce a clear distinction of the functional boundaries of peak-shaving energy storage, avoiding configuration requirements that necessitate rapid charging and discharging tasks, allowing planning schemes to directly correspond to energy-type energy storage in engineering projects, and improving the actual utilization efficiency of energy storage resources.

[0077] Constraint 7: Operational constraints of frequency regulation energy storage power stations.

[0078] The frequency regulation energy storage power station in this application is specifically designed for participating in the system's secondary frequency regulation service, characterized by providing rapid response regulation at the second to minute level. To adapt to the rapid frequency regulation characteristics and ensure safe operation, the frequency regulation energy storage constraints are as follows: A7. Charging and discharging power constraints of frequency regulation energy storage systems: The charging and discharging power of frequency-modulated energy storage is also limited by the rated power, and the constraint formula is as follows: (twenty two) In the formula, This is the rated power of the frequency regulation energy storage power station.

[0079] B7. State of charge constraints for frequency regulation energy storage power stations: The SOC of frequency-regulated energy storage needs to be maintained within a safe range and satisfy a recursive relationship, as follows: (twenty three) In the formula, This is the upper limit of the state of charge for a frequency regulation energy storage power station. This is the minimum state-of-charge value for frequency regulation energy storage power stations. This represents the state of charge of the frequency-regulated energy storage power station during time period t.

[0080]

[0081] In the formula, To improve the charging efficiency of frequency regulation energy storage power stations To improve the discharge efficiency of frequency regulation energy storage power stations, The rated capacity of the frequency regulation energy storage power station, To calculate the time interval, This represents the initial state of charge of a frequency-modulated energy storage power station. This represents the state of charge of a frequency-regulated energy storage power station at the end of its cycle.

[0082] C7. Constraints on simultaneous charging and discharging of frequency regulation energy storage power stations: The simultaneous charging and discharging constraints of this frequency regulation energy storage power station are the same as those of peak-shaving energy storage. Frequency regulation energy storage also cannot be charged and discharged simultaneously; the constraints are as follows: (25) in, For the charging power of the frequency regulation energy storage power station, This refers to the discharge power of the frequency regulation energy storage power station.

[0083] Frequency regulation energy storage power stations also have charging and discharging power constraints, state of charge constraints, and simultaneous charging and discharging constraints, but their parameters (such as shorter power command intervals and wider SOC upper and lower limits) differ from those of peak-shaving energy storage. Frequency regulation energy storage needs to provide rapid responses at the second to minute level, and its charging and discharging power changes frequently. Separate constraints help ensure response speed. The beneficial effect of this constraint is that the configuration of frequency regulation energy storage can meet the rapid response requirements of secondary frequency regulation, while avoiding the impact of overcharging and discharging on service life, thus achieving precise planning of power-type energy storage.

[0084] Constraint 8: Operational constraints of pumped storage power stations.

[0085] Operational constraints of pumped storage power stations are the physical limitations that must be met during operation. These include reservoir capacity constraints, upper and lower capacity limits, initial and final reservoir capacity constraints, output constraints, and state constraints. Considering the dual operation modes of pumping and power generation, the constraints are as follows: A8. Reservoir capacity constraints for pumped storage power stations: The reservoir capacity reflects the adjustable capacity of hydropower. The reservoir capacity changes with pumping and power generation, as expressed by: (26) In the formula, Pumped storage power station In the Storage capacity during different time periods Pumped storage power station In the Storage capacity during a given time period Pumped storage power station The conversion coefficient between electricity and water volume, Pumped storage power station In the Pumping power during the period Pumped storage power station In the Power generation during a given time period.

[0086] B8. Upper and lower limits of reservoir capacity constraints for pumped storage power stations: To prevent storage capacity from exceeding limits, upper and lower limits are set: (27) In the formula, Pumped storage power station The upper limit of the storage capacity, Pumped storage power station The lower limit of the storage capacity.

[0087] C8. State constraints of pumped storage power station reservoir capacity at initial and final times: To ensure a closed-loop daily cycle, the initial and final storage capacities should be equal: (28) In the formula, Pumped storage power station The initial storage capacity, Pumped storage power station Storage capacity at the end of the time.

[0088] D8. Output constraints of pumped storage power stations: Pumping and power generation capacity are limited by equipment capacity, as follows: (29) In the formula, Represents pumped storage power station The upper limit of pumping power, Represents pumped storage power station The lower limit of pumping power. Represents pumped storage power station The upper limit of power generation capacity, Represents pumped storage power station The lower limit of power generation capacity, Pumped storage power station In the Pumping power during the period Pumped storage power station In the Power generation during the period Represents pumped storage power station The pumping status is a 0-1 variable. Represents pumped storage power station The power generation state is a 0-1 variable.

[0089] E8. State constraints for pumped storage power stations: Pumped storage hydroelectric power plants must be able to pump water and generate electricity simultaneously, while also meeting maintenance restrictions. (30) The first term in equation (30) This indicates that pumped-storage power stations cannot simultaneously pump water and generate electricity; the other two items... and This represents the maintenance status constraint, among which, Represents pumped storage power station In the The maintenance status for a given time period, where 0 indicates maintenance in progress and 1 indicates maintenance not in progress.

[0090] F8. Initial reservoir capacity constraints for pumped storage power stations: The initial storage capacity must match the initial operating status, as shown in the following formula: (31) In the formula, Pumped storage power station The initial storage capacity.

[0091] Pumped-storage power stations have upper and lower reservoirs, with reservoir capacity limiting the duration of pumping or power generation; pumping and power generation cannot occur simultaneously; daily cyclic operation requires ensuring the reservoir capacity returns to its initial value to maintain the next day's regulation capacity. These constraints encompass reservoir status and upper / lower limits, initial and final reservoir capacity equality, upper and lower limits for pumping and power generation, mutually exclusive states, and maintenance states. The beneficial effects include accurately simulating the dual-condition regulation characteristics of pumped-storage, rationally utilizing existing pumped-storage resources in energy storage optimization configurations, avoiding overlap or conflict with newly constructed energy storage functions, and reducing the overall system cost.

[0092] Constraint 9: Operational constraints of hydropower stations.

[0093] Hydropower station operational constraints are the physical limitations that a hydropower station must meet during operation. These include reservoir capacity constraints, upper and lower capacity limits, initial and final reservoir capacity constraints, power output constraints, power generation flow constraints, water discharge flow constraints, and maintenance constraints. Based on water resource balance and unit characteristics, the constraints are as follows: A9. Reservoir capacity constraints for hydropower units: The reservoir capacity is determined by the inflow, power generation, and wastewater discharge. (32) In the formula, Represents hydroelectric power station In the Storage capacity during a given time period Represents hydroelectric power station In the Storage capacity during a given time period For hydroelectric power station In the Inbound flow during a specific time period For hydroelectric power station In the Power generation flow during the period For hydroelectric power station In the The amount of water discharged during a given time period.

[0094] B9. Upper and lower limits of reservoir capacity constraints for hydropower units: The warehouse capacity must operate within the design limits: (33) In the formula, For hydroelectric generator units The upper limit of the storage capacity, For hydroelectric generator units The lower limit of the storage capacity.

[0095] C9. State constraints of the hydropower unit reservoir capacity at the beginning and end of the time: The initial and final storage cap volumes remain consistent throughout the daily cycle. (34) In the formula, For hydroelectric generator units The initial storage capacity, Hydropower units Storage capacity at the end of the time.

[0096] D9. Output constraints of hydropower units: Hydropower output is related to water head and power generation flow rate, and the expression is: (35) In the formula, For hydroelectric generator units In the Efforts during a specific time period For hydroelectric generator units The output coefficient, For hydroelectric generator units In the Water head during the period, For hydroelectric generator units In the Power generation flow during a given period.

[0097] E9. Upper and lower limits of hydropower unit power generation flow rate constraints: The power generation flow rate must be within the equipment's allowable range: (36) In the formula, For hydroelectric generator units The upper limit of power generation flow, For hydroelectric generator units The lower limit of power generation flow.

[0098] F9. Upper and lower limits of water discharge flow from hydropower units: The discharge rate is also limited by engineering conditions: (37) In the formula, For hydroelectric generator units The upper limit of the water discharge flow rate, For hydroelectric generator units The lower limit of the water discharge flow rate, For hydroelectric power station In the The amount of water discharged during a given time period.

[0099] G9. Upper and lower limits of hydropower unit output constraints: Considering the maintenance status, the unit output is limited as follows: (38) In the formula, For hydroelectric generator units In the Efforts during a specific time period For hydroelectric generator units The upper limit of output, For hydroelectric generator units The lower limit of output, For hydroelectric generator units In the The maintenance status for a given time period, where 0 indicates maintenance in progress and 1 indicates maintenance not in progress.

[0100] H9. Initial reservoir capacity constraints for hydropower units: Initial storage capacity matches initial conditions: (39) In the formula, For hydroelectric generator units The initial storage capacity, For hydroelectric power station The initial storage capacity, Let h be the inflow rate of the hydropower station at the initial time. For hydroelectric generator units The power generation flow rate at the initial moment, For hydroelectric power station The initial water discharge rate.

[0101] The regulation capacity of a hydropower station is affected by inflow, reservoir capacity limitations, and head. Water wastage is a waste of water resources and should be included as a penalty to encourage full utilization. This constraint includes sub-items such as reservoir status and upper / lower limits, initial and final reservoir capacity equality, the relationship between output and head / flow, upper / lower limits of power generation and water wastage, and maintenance status. Its beneficial effect is that it comprehensively considers the hydraulic-electric coupling characteristics of hydropower stations, enabling the optimization model to rationally allocate peak-shaving tasks of hydropower and energy storage during both wet and dry seasons, avoiding unrealistic over-utilization and improving water resource utilization efficiency.

[0102] Constraint 10: Minimum level of new energy consumption constraint.

[0103] The minimum renewable energy consumption level constraint is a constraint that the local renewable energy consumption ratio of the system must not be lower than a set value, used to ensure the utilization rate of renewable energy. To improve the renewable energy consumption level, the constraint formula is shown in (40): (40) In the formula, This refers to the proportion of renewable energy in the electricity transmitted from regional power transmission lines. This represents the minimum local consumption ratio for new energy sources. The actual power absorbed by new energy sources (wind power and photovoltaics) during time period t. The total number of regional connection lines, The predicted maximum output of new energy sources during time period t is the theoretically available power generation. For the connection line in time period t Power sent from node b Let be the set of connecting lines that are connected to node b. This is the connecting line to node b. When the denominator expression is zero, this constraint is automatically satisfied. At this time, the local consumption level of new energy is considered to be infinite, that is, there is no local consumption pressure and no need to impose a minimum consumption ratio limit.

[0104] This constraint mandates that the ratio of actual renewable energy consumption (excluding the portion transmitted to other regions) to theoretically available power generation (excluding the portion transmitted to other regions) must meet a minimum consumption ratio. Its beneficial effect is that it directly incorporates renewable energy utilization into energy storage planning targets, prompting optimization results to favor the allocation of sufficient energy storage capacity to reduce wind and solar curtailment, thus achieving a balance between system economics and environmental friendliness.

[0105] Constraint 11: Load shedding constraint.

[0106] Load shedding constraints are restrictions that limit the amount of load that can be shelved during system operation to a set upper limit, ensuring power supply reliability. To control load loss, the load shedding limits are as follows: (41) In the formula, Let the load shedding at node b be the load shedding in time period t. Let be the original load demand of node b in time period t.

[0107] System load shedding is a last resort and must be kept within acceptable limits, typically set at an upper limit of a certain percentage or absolute value of load demand. This constraint restricts the load shedding amount at each node at any given time from exceeding the node's original load demand. Its beneficial effect is to prevent planning schemes from reducing costs at the expense of user electricity consumption, while allowing for a small amount of load shedding as a feasible relaxation in extreme scenarios, ensuring the model has a solution under severe operating conditions.

[0108] Constraint 12: To further distinguish the time scale characteristics of peak shaving and frequency modulation, and to supplement the peak shaving power command interval constraint and frequency modulation power command interval constraint, in this application, the peak shaving power command interval is set to 15 minutes, and the frequency modulation power command interval is set to 5 minutes.

[0109] In the formula, This refers to the charging and discharging power of the peak-shaving energy storage power station during the t-14 period. This refers to the charging and discharging power of the peak-shaving energy storage power station during the t-13 period. This refers to the charging and discharging power of the peak-shaving energy storage power station during the t-12 period. This represents the charging and discharging power of the peak-shaving energy storage power station during the t-1 time period. This refers to the charging and discharging power of the peak-shaving energy storage power station during time period t. This represents the charging and discharging power of the frequency regulation energy storage power station during the t-4 period. This represents the charging and discharging power of the frequency regulation energy storage power station during the t-3 time period. This represents the charging and discharging power of the frequency regulation energy storage power station during the t-2 time period. This represents the charging and discharging power of the frequency regulation energy storage power station during the t-1 time period. This represents the charging and discharging power of the frequency-regulated energy storage power station during time period t.

[0110] Peak shaving services typically have a 15-minute command response cycle (with power remaining constant), while secondary frequency regulation requires rapid changes on the order of seconds to minutes, usually at intervals of 5 minutes or even less. Without this distinction, energy storage might be required to perform peak shaving tasks at frequency regulation speeds, leading to wasted capacity or response failures. The beneficial effect of this constraint is to clearly differentiate the functional characteristics of the two types of energy storage on a time scale, allowing peak shaving energy storage to be planned for continuous charging and discharging on an hourly basis, and frequency regulation energy storage for rapid response, avoiding configuration deviations caused by mixing them and improving the utilization efficiency of energy storage resources.

[0111] Constraint 13: Constraints on peak shaving duration and daily peak shaving frequency. The daily peak shaving frequency shall not exceed 2 times, and the duration of each peak shaving operation shall be 2-4 hours. Define that there can be at most two intervals within a day, denoted as Where k1 is the peak-shaving interval number, k1=1,2, ,and Interval length ,satisfy: (43) The intervals do not overlap, that is, for k1=1,2: (44) Power command constraints, for each interval ,all have: (45) And for all : (46) In the formula, Let this be the starting time period number for the k1th peak-shaving interval. This is the segment number at the end of the k1th peak-shaving interval. The length of the peak-shaving interval for the k1th time is... This is the starting time period number for the (k1+1)th peak shaving interval. This represents the charging and discharging power of the peak-shaving energy storage power station during time period t.

[0112] Peak-shaving energy storage should have a continuous support capacity of 2 to 4 hours, and the number of starts per day should not exceed 2, as excessive starts will accelerate battery life degradation and are inconsistent with the economical operating mode of peak shaving. This constraint limits the duration of each continuous operation of peak-shaving energy storage to between 120 and 240 minutes, with a maximum of two non-overlapping peak-shaving intervals per day, and the energy storage power within each interval must be non-zero. Its beneficial effects include ensuring the engineering applicability of peak-shaving energy storage, avoiding optimization results that lead to peak-shaving schemes with frequent short-term charge and discharge, making energy storage selection more consistent with the actual operating mode of energy-type energy storage, extending the service life of energy storage equipment, and reducing the total life cycle cost.

[0113] This step decomposes the large-scale receiving-end regional power grid energy storage configuration problem into time-period and round-period solution units, significantly reducing computational complexity. Furthermore, in each iteration, boundary constraints are applied for power balance, unit physical characteristics, grid security, and peak-shaving and frequency regulation functions. This ensures that each set of candidate peak-shaving and frequency-regulating power is a physically feasible solution, avoiding infeasible intermediate results during iteration and improving the stability and reliability of iterative convergence.

[0114] S205. The processing device determines whether the peak-shaving power and frequency-modulating power of the m-th round in the t-th time period meet the stop iteration condition.

[0115] After the processing equipment completes the construction of the target cost function and the setting of all constraints for the m-th round of time period t, it calls the mixed-integer linear programming solver to solve the single-time optimization problem for the m-th round of time period t. Within the feasible region jointly defined by power balance constraints, thermal power unit operation constraints, renewable energy unit operation constraints, grid security operation constraints, peak shaving and frequency regulation function boundary constraints, and power supply reliability and resource utilization constraints, the solver traverses all combinations of operating variables that satisfy the constraints to find the solution that minimizes the target cost for the m-th round of time period t. This solution includes the output of thermal power units, wind power units, photovoltaic units, load shedding, and water wastage for the m-th round of time period t, as well as the corresponding peak shaving power and frequency regulation power for the m-th round of time period t.

[0116] Obtain the convergence degree of the target cost, peak-shaving power, and frequency-modulated power in the m-th round of time period t, the proximity of the constraint boundary, and the iteration penalty; based on the convergence degree of the target cost, peak-shaving power, and frequency-modulated power, the proximity of the constraint boundary, and the iteration penalty in the m-th round of time period t, determine the iteration score, which is calculated using the following formula: (47) (48) (49) (50) (51) in, is the convergence degree of the target cost in the m-th round of the t-th time period, a positive index, with a value range of [0,1]. The closer the value is to 1, the better the convergence of the total operating cost of the system. Let m be the total system operating cost in time period t and round m. Let be the total system operating cost in the (m-1)th round of the t-th time period. To optimize the overall convergence of variables, a positive index is used, with a value range of [0,1]. The closer the value is to 1, the smaller the fluctuation range of peak-shaving / frequency-modulation power. Let m be the peak-shaving power in the m-th round of the t-th time period. Let be the peak-shaving power in the (m-1)th round of the t-th time period. Let be the frequency modulation power of the m-th round in the t-th time period. Let be the frequency modulation power in the (m-1)th round of the t-th time period. To constrain boundary proximity, a positive index is used, with a value range of [0,1]. The smaller the value, the closer the variable to be optimized is to the boundary of the feasible interval, and the higher the risk of constraint violation. A value of 0 indicates that the variable has touched the boundary, and a value of 1 indicates that the variable is located at the center of the interval. This is the lower limit of peak-shaving power. This is the upper limit of peak-shaving power. This is the upper limit of the frequency modulation power. This is the lower limit of the frequency modulation power. For the iterative penalty term, The threshold for the number of iterations. For iterative scoring.

[0117] Among them, the convergence of the target cost and the proximity of the constraint boundary have the highest weights, followed by the comprehensive convergence of the optimization variables, and the iteration penalty term has the lowest weight. This ensures the priority of core indicators while avoiding meaningless over-iteration.

[0118] If the iterative score is greater than or equal to the score threshold, then the peak-shaving power and frequency-modulated power in the m-th round of time period t meet the stopping iteration condition. In this embodiment, the score threshold can be set to 0.85.

[0119] If the iteration score is less than the score threshold, determine whether m is greater than or equal to the iteration number threshold. If m is greater than or equal to the iteration number threshold, then the peak power and frequency modulation power of the m-th round in the t-th time period meet the stopping iteration condition.

[0120] If m is less than the iteration count threshold, then determine whether the improvement in the iteration score over N consecutive rounds is less than the magnitude threshold. If the improvement in the iteration score over N consecutive rounds is less than the magnitude threshold, then determine that the peak-shaving power and frequency-modulated power of the m-th round in time period t meet the iteration stop condition. If the improvement in the iteration score over N consecutive rounds is greater than or equal to the magnitude threshold, then determine that the peak-shaving power and frequency-modulated power of the m-th round in time period t do not meet the iteration stop condition.

[0121] Specifically, if the score improvement is less than 10 for five consecutive iterations... -4 Then it is determined that the peak-shaving power and frequency-modulated power of the m-th round in the t-th time period meet the stopping iteration condition.

[0122] S206. If the peak-shaving power and frequency-modulating power of the m-th round in the t-th time period meet the stopping iteration condition, then according to the... Time period The peak-shaving power and frequency-modulated power of the first round are used to determine the peak-shaving power and frequency-modulated The target peak-shaving power and target frequency modulation power for each time period are determined according to the following: The target peak-shaving power and target frequency regulation power for each time period are used to configure energy storage in the receiving-end regional power grid.

[0123] The processing equipment determines the peak-shaving power of the m-th round in time period t as the target peak-shaving power for time period t. The processing equipment determines the frequency modulation power of the m-th round in time period t as the target frequency modulation power for time period t.

[0124] The processing equipment calculates the maximum target peak-shaving power for all time periods under all typical scenarios to obtain the rated power of the peak-shaving energy storage power station. The processing equipment also calculates the cumulative target peak-shaving power for all time periods under all typical scenarios, and combines this with the charging and discharging efficiency of the peak-shaving energy storage power station to obtain its rated capacity. Finally, the processing equipment calculates the maximum target frequency regulation power for all time periods under all typical scenarios to obtain the rated power of the frequency regulation energy storage power station. Finally, the processing equipment calculates the cumulative target frequency regulation power for all time periods under all typical scenarios, and combines this with the charging and discharging efficiency of the frequency regulation energy storage power station to obtain its rated capacity.

[0125] The processing equipment is configured with peak-shaving energy storage power stations based on their rated power and rated capacity. Similarly, the processing equipment is configured with frequency-regulating energy storage power stations based on their rated power and rated capacity.

[0126] The processing equipment uses the rated power and rated capacity of the peak-shaving energy storage power station as the selection criteria. It selects candidate equipment models from the product catalogs provided by energy storage equipment manufacturers, ensuring that the deviation between the rated power and the rated power of the peak-shaving energy storage power station is within the allowable range and that the rated capacity is not lower than the rated capacity of the peak-shaving energy storage power station. The processing equipment further compares the candidate equipment models according to the principle of lowest total life cycle cost, calculating the purchase cost, installation cost, operation and maintenance cost, and replacement cost of each model, and selects the model with the lowest total life cycle cost as the final configuration scheme.

[0127] For frequency regulation energy storage power stations, the processing equipment uses the rated power and rated capacity of the frequency regulation energy storage power station as the selection criteria. Because frequency regulation energy storage has higher requirements for response speed, the processing equipment adds an additional response time constraint when screening equipment models, requiring that the response time of candidate equipment does not exceed a set threshold. The processing equipment also compares and selects based on the principle of lowest total life cycle cost, prioritizing battery types with high power density and long cycle life.

[0128] After outputting the configuration scheme, the processing equipment further generates the operation strategy for the energy storage system. Specifically, based on the energy storage charge and discharge power time-series curves obtained during the optimization process under various typical scenarios, the processing equipment extracts the typical operation modes of peak-shaving energy storage power stations and frequency-regulating energy storage power stations. The processing equipment uses an association rule mining algorithm to analyze the correlation between energy storage charge and discharge power and system load levels, new energy output, and the start-up and shutdown status of thermal power units, generating a series of operation rules. The processing equipment compiles these operation rules into energy management strategies for the energy storage power station and distributes them to the local controller of the energy storage power station.

[0129] The processing equipment uses the rated power and capacity of peak-shaving energy storage stations, as well as the rated power and capacity of frequency-regulating energy storage stations, as constraints to further construct detailed grid connection schemes for the energy storage stations. The processing equipment obtains electrical parameters such as short-circuit capacity, voltage level, and protection configuration of the grid nodes to which the energy storage stations are to be connected. It verifies whether the short-circuit current after the energy storage station's connection exceeds the circuit breaker's breaking capacity, whether the voltage deviation is within the allowable range, and whether the protection devices meet the selectivity requirements. If the verification passes, the processing equipment outputs technical documents such as the connection point location, grid-connected switch selection, and protection setting recommendations. If the verification fails, the processing equipment adjusts the connection nodes of the energy storage stations and re-verifies until a grid connection scheme that meets safety requirements is found.

[0130] S207. If the peak-shaving power and frequency-modulated power of the m-th round in time period t do not meet the stopping iteration condition, then determine the peak-shaving power and frequency-modulated power of the m-th round in time period t. Time period Peak-shaving power and frequency-modulated power of the wheel.

[0131] The processing equipment first establishes a standardized rule for the correspondence between power values ​​and energy storage states. A power value greater than 0 indicates that the energy storage is in a charging state. A power value less than 0 indicates that the energy storage is in a discharging state. When the power value increases, the charging power increases in the charging state and the discharging power decreases in the discharging state. When the power value decreases, the charging power decreases in the charging state and the discharging power increases in the discharging state.

[0132] The t-th time period The peak-shaving power of the wheel is determined in the following way: Obtain the target frequency modulation power from time period tn to time period (t-1); based on the target frequency modulation power from time period tn to time period (t-1) and the target frequency modulation power in time period t... The peak-shaving power of the round is used to determine the peak power of the t-th time period. Peak-shaving power of the wheel.

[0133] Specifically, setting n=3, the processing device obtains the target frequency modulation power that has converged from the (t-3)th time period to the (t-1)th time period, and based on the target peak modulation power from the (t-3)th time period to the (t-1)th time period and the... Time period The peak-shaving power of the round is obtained. Time period The peak-shaving power of the wheel is calculated using the following formula: (52) (53) in, For the first Time period Peak-shaving power of the wheel Let be the target frequency modulation power for the (t-1)th time period. Let be the target frequency modulation power for the (t-2)th time period. The target frequency modulation power for the (t-3)th time period is... The coupling coefficient is... This represents the peak power correction amount from round m to round m+1.

[0134] When the average value of the target frequency regulation power from time period t-3 to time period t-1 is less than 0, the peak regulation correction term is positive. The peak regulation power in the (m+1)th round of time period t increases. Peak regulation energy storage reduces discharge or increases charging to share the continuous discharge task of frequency regulation energy storage. When the average value of the target frequency regulation power from time period t-3 to time period t-1 is greater than 0, the peak regulation correction term is negative. The peak regulation power in the (m+1)th round of time period t decreases. Peak regulation energy storage increases discharge or reduces charging to share the continuous charging task of frequency regulation energy storage. When the average value of the target frequency regulation power from time period t-3 to time period t-1 is close to 0, the peak regulation correction term is close to 0. The peak regulation power in the (m+1)th round of time period t remains basically unchanged. The frequency regulation energy storage function is operating normally, and there is no need to adjust the power of peak regulation energy storage.

[0135] No. Time period The frequency modulation power of the wheel is determined in the following way: Obtain the target peak-shaving power from time period tn to time period (t-1); based on the target peak-shaving power from time period tn to time period (t-1) and the... Time period The frequency modulation power of the wheel determines the first Time period The formula for calculating the frequency modulation power of the wheel is as follows: (54) (55) in, For the first Time period The frequency modulation power of the wheel, The target peak-shaving power for the (t-1)th time period is... The target peak-shaving power for the (t-2)th time period is... The target peak-shaving power for the (t-3)th time period is... The coupling coefficient is... For the first It's the turn of the first The frequency modulation power correction amount of the wheel.

[0136] When the average value of the target peak-shaving power from time period t-3 to time period t-1 is less than the frequency regulation power of the m-th round in time period t, the frequency regulation correction term is negative. The frequency regulation power of the (m+1)-th round in time period t decreases. The frequency regulation energy storage increases discharge or decreases charging, adjusting its own power according to the continuous energy trend of the peak-shaving energy storage. When the average value of the target peak-shaving power from time period t-3 to time period t-1 is greater than the frequency regulation power of the m-th round in time period t, the frequency regulation correction term is positive. The frequency regulation power of the (m+1)-th round in time period t increases. The frequency regulation energy storage decreases discharge or increases charging, adjusting its own power according to the continuous energy trend of the peak-shaving energy storage. When the average value of the target peak-shaving power from time period t-3 to time period t-1 is close to the frequency regulation power of the m-th round in time period t, the frequency regulation correction term is close to 0. The frequency regulation power of the (m+1)-th round in time period t remains basically unchanged. The peak-shaving energy storage function is operating normally, and there is no need to adjust the power of the frequency regulation energy storage.

[0137] This step employs an iterative update method based on historical converged power, enabling peak-shaving and frequency-regulating energy storage to dynamically adjust their power according to each other's historical operating status. During the iteration process, the functional roles of the two types of energy storage are automatically distinguished, preventing peak-shaving storage from undertaking rapid fluctuation regulation tasks and frequency-regulating storage from undertaking continuous energy support tasks. The iterative formula has a simple structure and low computational load, improving the convergence speed of the overall optimization process. A fixed coupling coefficient controls the power adjustment amplitude, preventing large power fluctuations during iteration. Each iteration is based on verified historical convergence data, ensuring the stability and reliability of the iteration process. The final energy storage configuration better matches the different operating characteristics of peak-shaving and frequency regulation, improving the actual utilization efficiency of energy storage resources.

[0138] The above text combined Figures 1 to 2 The energy storage configuration method provided in the embodiments of this application has been described in detail. The apparatus and equipment provided in the embodiments of this application will be described below with reference to the accompanying drawings.

[0139] This application also provides an energy storage configuration device, such as... Figure 3 As shown in the figure, this is a schematic diagram of an energy storage configuration device provided in an embodiment of this application. The device includes: The determination module 301 is used to determine the variables to be optimized in the receiving-end regional power grid, the variables to be optimized including the peak-shaving power and frequency regulation power in the m-th round of the t-th time period; and to determine the peak-shaving and frequency regulation sub-costs in the m-th round of the t-th time period based on the peak-shaving power and frequency regulation power in the m-th round of the t-th time period. Calculation module 302 is used to add up the peak-shaving and frequency-regulation sub-costs, thermal power unit sub-costs, wind power unit sub-costs, photovoltaic unit sub-costs, load shedding penalty sub-costs, and water curtailment penalty sub-costs for the m-th round of the t-th time period to obtain the target cost for the m-th round of the t-th time period. Specifically, the thermal power unit sub-cost is determined by the output of the thermal power units in the m-th round of the t-th time period; the wind power unit sub-cost is determined by the output of the wind power units in the m-th round of the t-th time period; the photovoltaic unit sub-cost is determined by the output of the photovoltaic units in the m-th round of the t-th time period; the load shedding penalty sub-cost is determined by the load shedding amount in the m-th round of the t-th time period; and the water curtailment penalty sub-cost is determined by the water curtailment amount in the m-th round of the t-th time period. The peak-shaving power, frequency-regulation power, thermal power unit output, wind power unit output, photovoltaic unit output, load shedding amount, and water curtailment amount for the m-th round of the t-th time period satisfy the power balance relationship. The judgment module 303 is used to determine whether the peak-shaving power and frequency regulation power of the m-th round in time period t meet the stopping iteration condition. If the peak-shaving power and frequency regulation power of the m-th round in time period t meet the stopping iteration condition, then the target peak-shaving power and target frequency regulation power of time period t are determined based on the peak-shaving power and frequency regulation power of the m-th round in time period t, and energy storage configuration is carried out on the receiving-end area power grid according to the target peak-shaving power and target frequency regulation power of time period t. If the peak-shaving power and frequency regulation power of the m-th round in time period t do not meet the stopping iteration condition, then the target peak-shaving power and target frequency regulation power of the m-th round in time period t are determined based on the peak-shaving power and target frequency regulation power of the m-th round in time period t. +1 round of peak shaving power and frequency modulation power.

[0140] In some possible implementations, the judgment module 303 is specifically used to obtain the convergence degree of the target cost, the convergence degree of the peak power and the frequency modulation power, the constraint boundary proximity, and the iteration penalty of the target cost in the m-th round of the t-th time period; determine the iteration score based on the convergence degree of the target cost, the convergence degree of the peak power and the frequency modulation power, the constraint boundary proximity, and the iteration penalty of the m-th round of the t-th time period; if the iteration score is greater than or equal to the score threshold, then it is determined that the peak power and the frequency modulation power in the m-th round of the t-th time period meet the stopping iteration condition.

[0141] In some possible implementations, the judgment module 303 is further configured to determine whether m is greater than or equal to the iteration number threshold if the iterative score is less than the score threshold; if m is greater than or equal to the iteration number threshold, then determine that the peak power and frequency modulation power of the m-th round in the t-th time period meet the stop iteration condition.

[0142] In some possible implementations, the judgment module 303 is further configured to determine whether the improvement of the iteration score over N consecutive rounds is less than the amplitude threshold if m is less than the iteration number threshold; and if the improvement of the iteration score over N consecutive rounds is less than the amplitude threshold, determine that the peak power and frequency modulation power of the m-th round in the t-th time period meet the stop iteration condition.

[0143] In some possible implementations, the judgment module 303 is further configured to determine that the peak power and frequency modulation power of the m-th round in the t-th time period do not meet the stop iteration condition if the improvement of the iteration score of N consecutive rounds is greater than or equal to the magnitude threshold.

[0144] In some possible implementations, the t-th time period... The peak-shaving power for the +1 round is determined in the following way: Obtain the target frequency modulation power from the tn-th time period to the (t-1)-th time period; based on the target frequency modulation power from the tn-th time period to the (t-1)-th time period and the peak modulation power of the m-th round in the t-th time period, determine the peak modulation power of the (m+1)-th round in the t-th time period.

[0145] In some possible implementations, the frequency modulation power in the (m+1)th round of the t-th time period is determined in the following way: Obtain the target peak-shaving power from the nth time period to the (t-1th time period); based on the target peak-shaving power from the nth time period to the (t-1th time period) and the frequency modulation power of the mth round in the tth time period, determine the frequency modulation power of the (m+1th)th round in the tth time period.

[0146] The energy storage configuration device according to the embodiments of this application can correspond to the execution of the method described in the embodiments of this application, and the other operations and / or functions of each module / unit of the energy storage configuration device are respectively for implementing Figure 1 For the sake of brevity, the corresponding processes of each method in the illustrated embodiments will not be described in detail here.

[0147] This application also provides a computing device. For example... Figure 4 As shown in the figure, this is a schematic diagram of a computing device provided in an embodiment of this application. The computing device 400 includes a bus 401, a processor 402, a communication interface 403, and a memory 404. The processor 402, the memory 404, and the communication interface 403 communicate with each other via the bus 401.

[0148] Bus 401 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0149] Processor 402 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0150] Communication interface 403 is used for communication with external devices.

[0151] Memory 404 may include volatile memory, such as random access memory (RAM). Memory 404 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0152] The memory 404 stores executable code, and the processor 402 executes the executable code to perform the aforementioned energy storage configuration method.

[0153] Specifically, in achieving Figure 3 In the case of the illustrated embodiment, and Figure 3 When the modules or units of the energy storage configuration device described in the embodiment are implemented by software, the execution... Figure 3 The software or program code required for the functions of each module / unit can be partially or wholly stored in memory 404. Processor 402 executes the program code corresponding to each unit stored in memory 404 to execute the aforementioned energy storage configuration method.

[0154] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the above-described energy storage configuration method.

[0155] This application also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application are generated.

[0156] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0157] When the computer program product is executed by a computer, the computer performs any of the aforementioned energy storage configuration methods. The computer program product can be a software installation package; when any of the aforementioned energy storage configuration methods needs to be used, the computer program product can be downloaded and executed on the computer.

[0158] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0159] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. An energy storage configuration method, characterized in that, The method includes: Determine the variables to be optimized in the receiving-end regional power grid, including the peak-shaving power and frequency regulation power in the m-th round of the t-th time period; Based on the peak-shaving power and frequency-modulation power of the m-th round in the t-th time period, determine the peak-shaving and frequency-modulation sub-cost of the m-th round in the t-th time period; The target cost for the m-th round of the t-th time period is obtained by summing the costs of peak shaving and frequency regulation, thermal power units, wind turbines, photovoltaic units, load shedding penalties, and water curtailment. Specifically, the cost of thermal power units is determined by their output in the m-th round of the t-th time period; the cost of wind turbines is determined by their output in the m-th round of the t-th time period; the cost of photovoltaic units is determined by their output in the m-th round of the t-th time period; the cost of load shedding penalties is determined by the amount of load shedding in the m-th round of the t-th time period; and the cost of water curtailment penalties is determined by the amount of water curtailed in the m-th round of the t-th time period. The peak shaving power, frequency regulation power, output of thermal power units, output of wind turbines, output of photovoltaic units, load shedding amount, and water curtailment amount in the m-th round of the t-th time period satisfy the power balance relationship. Determine whether the peak-shaving power and frequency-modulated power in the m-th round of time period t meet the stopping iteration condition; If the peak-shaving power and frequency regulation power of the m-th round in the t-th time period meet the stopping iteration condition, then the target peak-shaving power and target frequency regulation power of the t-th time period are determined based on the peak-shaving power and frequency regulation power of the m-th round in the t-th time period, and energy storage is configured for the receiving-end area power grid according to the target peak-shaving power and target frequency regulation power of the t-th time period. If the peak power and frequency modulation power of the m-th round in the t-th time period do not meet the stopping iteration condition, then the peak power and frequency modulation power of the (m+1)-th round in the t-th time period are determined based on the peak power and frequency modulation power of the m-th round in the t-th time period. Determine whether the peak-shaving power and frequency-modulated power in the m-th round of time period t meet the stopping iteration condition, including: Obtain the convergence of the target cost, peak power and frequency modulation power, constraint boundary proximity and iteration penalty for the m-th round in the t-th time period; The iteration score is determined based on the convergence of the target cost in the m-th round of the t-th time period, the convergence of the peak power and the frequency modulation power, the proximity of the constraint boundary, and the iteration penalty. If the iterative score is greater than or equal to the score threshold, then the peak-shaving power and frequency-modulating power of the m-th round in the t-th time period meet the stopping iteration condition. The convergence of the peak-shaving power and frequency-modulated power is defined as the comprehensive convergence of the optimization variables. The iterative score is the weighted sum of the convergence of the target cost in the m-th round of the t-th time period, the comprehensive convergence of the optimization variables, the proximity of the constraint boundary, and the iterative penalty. The convergence of the target cost and the proximity of the constraint boundary have the highest weights, followed by the comprehensive convergence of the optimization variables, and the iterative penalty term has the lowest weight.

2. The method according to claim 1, characterized in that, The method further includes: If the iterative score is less than the score threshold, determine whether m is greater than or equal to the iteration number threshold; If m is greater than or equal to the iteration number threshold, then the peak power and frequency modulation power of the m-th round in the t-th time period meet the stopping iteration condition.

3. The method according to claim 2, characterized in that, The method further includes: If m is less than the iteration number threshold, then determine whether the improvement of the score over N consecutive iterations is less than the magnitude threshold. If the improvement in the score over N consecutive iterations is less than the magnitude threshold, then the peak-shaving power and frequency-modulating power in the m-th round of time period t meet the stopping iteration condition.

4. The method according to claim 3, characterized in that, The method further includes: If the improvement in the score over N consecutive iterations is greater than or equal to the magnitude threshold, then the peak-shaving power and frequency-modulating power in the m-th round of time period t do not meet the stop-iteration condition.

5. The method according to claim 1, characterized in that, The peak-shaving power in the (m+1)th round of time period t is determined in the following way: Obtain the target frequency modulation power from the nth time period to the (t-1)th time period; Based on the target frequency modulation power from time period tn to time period t-1 and the peak modulation power of the m-th round in time period t, determine the peak modulation power of the m+1-th round in time period t.

6. The method according to claim 1, characterized in that, The frequency modulation power in the (m+1)th round of time period t is determined in the following way: Obtain the target peak-shaving power from the nth time period to the (t-1th time period); Based on the target peak-shaving power from time period tn to time period t-1 and the frequency modulation power of the m-th round in time period t, determine the frequency modulation power of the m+1-th round in time period t.

7. An energy storage configuration device, characterized in that, The device includes: The determination module is used to determine the variables to be optimized in the receiving-end regional power grid, including the peak-shaving power and frequency regulation power in the m-th round of the t-th time period; and to determine the peak-shaving and frequency regulation sub-costs in the m-th round of the t-th time period based on the peak-shaving power and frequency regulation power in the m-th round of the t-th time period. The calculation module is used to add up the sub-costs of peak shaving and frequency regulation, thermal power units, wind power units, photovoltaic units, load shedding penalties, and water curtailment penalties in the m-th round of the t-th time period to obtain the target cost in the m-th round of the t-th time period. Specifically, the sub-cost of thermal power units is determined by the output of thermal power units in the m-th round of the t-th time period; the sub-cost of wind power units is determined by the output of wind power units in the m-th round of the t-th time period; the sub-cost of photovoltaic units is determined by the output of photovoltaic units in the m-th round of the t-th time period; the sub-cost of load shedding penalties is determined by the amount of load shedding in the m-th round of the t-th time period; and the sub-cost of water curtailment penalties is determined by the amount of water curtailed in the m-th round of the t-th time period. The peak shaving power, frequency regulation power, output of thermal power units, output of wind power units, output of photovoltaic units, load shedding amount, and water curtailment amount in the m-th round of the t-th time period satisfy the power balance relationship. The judgment module is used to determine whether the peak-shaving power and frequency-modulated power of the m-th round in time period t meet the stopping iteration condition; if the peak-shaving power and frequency-modulated power of the m-th round in time period t meet the stopping iteration condition, then based on the peak-shaving power and frequency-modulated power of the m-th round in time period t, the target peak-shaving power and target frequency-modulated power of time period t are determined, and then the process is carried out according to the... The target peak-shaving power and target frequency regulation power for each time period are used to configure energy storage in the receiving-end regional power grid. If the peak-shaving power and frequency regulation power in the m-th round of time period t do not meet the stopping iteration condition, then the peak-shaving power and frequency regulation power in the (m+1)-th round of time period t are determined based on the peak-shaving power and frequency regulation power in the m-th round of time period t. Determining whether the peak-shaving power and frequency regulation power in the m-th round of time period t meets the stopping iteration condition includes: obtaining the convergence of the target cost, the convergence of the peak-shaving power and frequency regulation power, the constraint boundary proximity, and the iteration penalty for the m-th round of time period t. The convergence degree of frequency modulation power, the proximity of constraint boundaries, and the iteration penalty are used to determine the iteration score. If the iteration score is greater than or equal to the score threshold, the peak-shaving power and frequency modulation power in the m-th round of the t-th time period meet the stopping iteration condition. The convergence degree of the peak-shaving power and frequency modulation power is determined as the comprehensive convergence degree of the optimization variables. The iteration score is the weighted sum of the convergence degree of the target cost, the comprehensive convergence degree of the optimization variables, the proximity of constraint boundaries, and the iteration penalty in the m-th round of the t-th time period. The convergence degree of the target cost and the proximity of constraint boundaries have the highest weights, followed by the comprehensive convergence degree of the optimization variables, and the iteration penalty term has the lowest weight.

8. A computing device, characterized in that, Including memory and processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program for performing the method as described in any one of claims 1 to 6.

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