A multi-stage planning method for energy storage power stations, computer equipment, and storage media

Abstract

This invention discloses a multi-stage planning method for energy storage power stations, computer equipment, and storage media. The multi-stage planning method includes the following steps: Step 1: Establishing a power supply and demand growth model; Step 2: Establishing an upper-level energy storage power station capacity-determining objective function model; Step 3: Establishing an upper-level energy storage power station capacity-determining constraint model; Step 4: Establishing a lower-level energy storage power station location-determining objective function model; Step 5: Establishing a lower-level energy storage power station location-determining constraint model; Step 6: Establishing a solution algorithm for the two-level planning model, where the upper-level model uses a genetic algorithm to optimize and update the energy storage power station capacity, which is then passed to the lower-level model. This invention's multi-stage planning method for energy storage power stations, considering both renewable energy consumption and grid loss reduction, can adapt to future renewable energy and user growth across multiple stages.

Description

Technical Field

[0001] This invention relates to the field of power engineering technology, and in particular to multi-stage planning technology for energy storage power stations. Background Technology

[0002] Under the dual-carbon background, the scale of new energy grid connection capacity will continue to expand. As an important carrier for receiving new energy into the power system, the distribution network has the inherent attributes of high penetration rate of new energy output, such as strong volatility, randomness, and anti-peak characteristics, which increase the difficulty of stable operation of the distribution network and also cause problems such as increased active power loss and insufficient new energy absorption capacity. The unique power throughput and power control capabilities of energy storage technology play an important role in regulating the output fluctuation of new energy, reducing the active power loss of the distribution network, and improving the absorption rate of new energy. However, its effectiveness is greatly affected by the energy storage capacity setting and site selection planning. The main manifestations are: (1) Capacity setting: If the capacity setting is too small, it is difficult to regulate the fluctuation of new energy and improve the local absorption of new energy. If the capacity setting is too large, it will lead to the ineffective and wasteful allocation of some energy storage resources; (2) Site selection: The site selection scheme affects the transmission of new energy through factors such as line power flow constraints and voltage stability constraints, which limits the effectiveness of the energy storage configuration capacity. At the same time, unreasonable site selection will also increase the active power loss of the distribution network.

[0003] Existing studies on the capacity and site selection planning of energy storage power stations often set new energy output and load demand as constants, ignoring future growth potential caused by policy, market, and other factors. This leads to planning results that are difficult to adapt to future new energy development. At the same time, the planning schemes mainly focus on meeting the short-term stable operation requirements of the distribution network system, such as smoothing different power fluctuations and voltage stability, and achieving the economic efficiency of the comprehensive costs in the planning of energy storage power stations, such as minimizing energy storage investment and construction costs and network loss costs. However, they ignore the respective goals and mutual influences achieved by the long-term planning of energy storage power stations and the short-term operation of the distribution network, and lack research on the extent to which the future scale of new energy development can be absorbed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the technical problem this invention aims to solve is to provide a multi-stage planning method for energy storage power stations that considers both renewable energy consumption and network loss reduction. This method adapts to the growth of renewable energy and users in future stages. Its capacity design can improve the consumption rate after renewable energy growth and reduce the overall cost of building and maintaining energy storage power stations in the distribution network. Its site selection scheme can reduce the active power loss after distributed energy is connected to the distribution network and improve the economic efficiency of distribution network operation.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A multi-stage planning method for energy storage power stations that considers renewable energy integration and grid loss reduction includes the following steps:

[0007] Step 1: Establish a power supply and demand growth model, which includes a new energy output growth model and a load growth model;

[0008] Step 2: Establish a fixed-capacity objective function model for the upper-level energy storage power station, with the objective function being the minimization of the total comprehensive cost of each planning stage. The comprehensive cost includes: the investment and construction cost of the energy storage power station, the operation and maintenance cost, and the cost of wind and solar curtailment in the distribution network.

[0009] Step 3: Establish a fixed-capacity constraint model for the upper-level energy storage power station. The constraints include: power balance constraints, new energy output constraints, charging and discharging power of the energy storage power station, and state of charge constraints.

[0010] Step 4: Establish the objective function model for the location and capacity determination of the lower-level energy storage, with the objective function being the minimization of active power loss in the distribution network during the overall planning stage;

[0011] Step 5: Establish a lower-level energy storage location constraint model. The constraints include: the allowed energy storage capacity constraint for each node, the AC power flow constraint of the distribution network, the node voltage constraint, and the branch power flow constraint.

[0012] Step 6: Establish a solution algorithm for the two-level programming model. The upper-level model uses a genetic algorithm to optimize and update the capacity of the energy storage power station, which is then passed to the lower-level model. The lower-level model uses the MOSEK solver to solve the network loss value, the location nodes and capacity of the energy storage power station, and then passes the network loss value to the upper-level model. Finally, in the process of information interaction, an equilibrium solution is obtained.

[0013] Preferably, the new energy output growth model and the load growth model are respectively:

[0014] (1.1) New Energy Output Growth Model

[0015] The output of new energy sources exhibits a clear seasonality; therefore, output under various scenarios should be considered:

[0016]

[0017] In the formula, For scenario s in the nth planning phase, the total output of new energy, wind power, and photovoltaic power during time period t; These represent the projected growth rates of wind power and solar power output in the nth planning phase compared to the (n-1)th planning phase, respectively.

[0018] (1.2) Load Growth Model

[0019]

[0020] In the formula, D n,s,tLet be the electricity load value during time period t in scenario s during the nth planning phase; The projected growth rate of load output in the nth planning phase compared to the (n-1)th planning phase.

[0021] Preferably, the target function for locating the energy storage power station in step 2 is:

[0022] To minimize the overall cost of the power distribution network during the overall planning phase, the configuration capacity of energy storage power stations is optimized to reduce investment and construction costs, operation and maintenance costs, and wind and solar curtailment costs.

[0023]

[0024] In the formula, F total The overall cost of the distribution network during the master planning phase. These represent the investment and construction costs, operation and maintenance costs, and wind and solar curtailment costs of energy storage power stations in the nth planning stage of the power distribution network, respectively, where N is the total number of planning stages.

[0025]

[0026] In the formula, C IE C IP These represent the investment and construction costs per unit capacity and per unit power of the energy storage power station, respectively; C OC For the operation and maintenance costs of energy storage power stations; μ n,s,t To indicate the charging and discharging status of the energy storage power station during time period t in scenario s of the nth planning phase, μ is the charging status. n,s,t =1, μ in discharge state n,s,t =-1, μ in float charge state n,s,t =0; These are the grid connection prices for photovoltaic and wind power respectively during time period t; These are the proportions of unused photovoltaic and wind power in time period t during scenario s in the nth planning stage, representing the total amount of wind and solar power curtailed. Let t represent the amount of wind and solar power curtailment in the distribution network during time period t in scenario s during the nth planning phase.

[0027] Preferably, the constraint condition in step 3 is:

[0028] (3.1) Power balance constraints

[0029]

[0030] In the formula, L S This represents the total number of branches connecting the distribution network to the upper-level main network. Let L be the active power transmitted from the upper main grid to the distribution network through branch l during time period t in scenario s during the nth planning phase. The active power loss of the distribution network in time period t in scenario s during the nth planning stage is obtained by solving the lower-level model.

[0031] (3.2) Power constraints of backfeed from main grid to distribution network branches

[0032]

[0033] In the formula, The maximum reverse power allowed to be transmitted by the main grid-connected distribution network branch;

[0034] (3.3) Constraints on charging and discharging power and state of charge of energy storage stations in the distribution network

[0035]

[0036] In the formula, η d η c These are discharge efficiency and charging efficiency, respectively; η n,s,t S n,s,t These represent the charging / discharging efficiency and charge capacity of the energy storage power station during time period t in scenario s during the nth planning phase; S min S max S0 and S0 represent the lower limit, upper limit, and initial value of the energy storage power station's charge capacity, respectively; T represents the number of time periods within a short-term operation optimization cycle of the energy storage power station.

[0037] (3.4) Constraints on Investment and Construction of Energy Storage Power Stations

[0038]

[0039] Preferably, the objective function for addressing the energy storage power station in step 4 is:

[0040] (4.1) With the goal of minimizing the network loss cost of the distribution network during the overall planning stage, the grid connection location of the energy storage power station in the distribution network is optimized to reduce the operating cost of the distribution network. The objective function is expressed as follows:

[0041]

[0042] In the formula, F loss For the network loss cost of the distribution network during the overall planning stage, The network loss cost in the nth planning stage is calculated using the following formula:

[0043]

[0044] In the formula, p loss P is the unit price for network loss. ij,n,s,t Q ij,n,s,tLet V represent the active power, reactive power, and resistance value (V) on the branch from node i to node j during time period t in scenario s during the nth planning phase. i,n,s,t Let R be the voltage value of node i in time period t in scenario s during the nth planning phase. ij Let be the resistance value on the branch from node i to node j.

[0045] Preferably, the constraint condition in step 5 is:

[0046] (5.1) Allowable energy storage capacity constraints for each node

[0047]

[0048] In the formula, These represent the capacity and upper limit of the energy storage power station connected to node i in the nth planning phase, respectively.

[0049] (5.2) AC power flow constraints in distribution networks

[0050] The distribution network architecture is generally radial, and its AC power flow constraints are described using the Distflow model.

[0051]

[0052]

[0053]

[0054] In the formula, P j,n,s,t Q j,n,s,t D j,n,s,t Q j,n,s,t , These represent the net load active power, net load reactive power, load active power, load reactive power, renewable energy active power output, renewable energy reactive power output, wind and solar curtailment, reactive power of reactive power compensation equipment, active power output of energy storage power station, and reactive power output of energy storage power station at node j in time period t during scenario s in the nth planning phase. ij,n,s,t X represents the squared current value of the branch from node i to node j in time period t during scenario s in the nth planning stage. ij P is the reactance value of the branch from node i to node j. jl,n,s,t Q jl,n,s,t These represent the active power and reactive power from node j to branch l during time period t in scenario s during the nth planning phase;

[0055] (5.3) Node voltage constraints

[0056] U N (1-ε1)≤U k ≤U N(1+ε2) (15)

[0057] In the formula, U k U is the voltage at node k; N ε1 represents the voltage amplitude at the substation outlet; ε2 and ε1 represent the upper limit of the negative voltage deviation and the upper limit of the positive voltage deviation, respectively.

[0058] (5.4) Branch flow constraints

[0059]

[0060] In the formula, S l , These represent the transmission power, lower limit of transmission power, and upper limit of transmission power of branch l in the distribution network, respectively.

[0061] Preferably, the algorithm for solving the bi-level programming model in step 6 includes:

[0062] (6.1) Basic data preparation: Initialize the parameters of the distribution network, new energy and load, and determine the parameters in the genetic algorithm, including mutation probability, crossover probability, total number of generations G, population number m, genetic generation count value k, and convergence error η, where the initial value of k is set to 1;

[0063] (6.2) Population initialization: m groups are randomly generated using a genetic algorithm. The initial values ​​of the energy storage power station's capacity and power are then transferred to the lower-level energy storage site selection model.

[0064] (6.2) Solving the lower-level energy storage site selection model, based on the capacity of the energy storage power station. and Determine the grid connection nodes and capacity of the energy storage power station to minimize grid loss costs F. loss Using the objective function, the capacity of the energy storage power station at the grid-connected node is obtained using the MOSEK solver. And retain the network loss cost in the k-th iteration.

[0065] (6.3) Calculate the comprehensive cost of the upper-level energy storage power station at its fixed capacity. The lower-level model transmits the network loss value to the upper-level model to calculate the comprehensive cost of the energy storage power station at its fixed capacity in the k-th iteration.

[0066] (6.4) Update the genetic generation count, k = k + 1;

[0067] (6.5) Update the genetic generation population data and the fixed-size data. Use the genetic algorithm for selection, crossover, and mutation to generate new population data, with m groups. and And repeat steps (6.2) and (6.3) to obtain

[0068] (6.6) Determine whether the bilevel programming model has obtained an equilibrium solution. and This indicates that an equilibrium solution has been obtained; otherwise, repeat steps (6.4) and (6.5).

[0069] (6.7) Output the equilibrium results, which are the optimization variables of the upper and lower models under the equilibrium state. and their respective target values As the final output.

[0070] The present invention also provides a computer device, including at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the multi-stage planning method for energy storage power stations that considers renewable energy consumption and grid loss reduction.

[0071] The present invention also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the aforementioned multi-stage planning method for energy storage power stations that considers renewable energy consumption and grid loss reduction.

[0072] This invention comprehensively considers the optimization granularity and objectives of long-term planning for energy storage power stations and short-term operation of distribution networks at different time scales. It proposes a hierarchical coordinated planning approach, taking into account the growth of renewable energy output and load demand, and establishes a two-layer coordinated planning model for multi-stage energy storage power station capacity determination and site selection. The upper-layer model takes the entire distribution network as the object, deciding the capacity of energy storage power stations at each stage from the distribution network level, so as to achieve the economic efficiency of investment and construction costs and renewable energy absorption of energy storage power stations at multiple stages. The lower-layer model takes each node of the distribution network as the object, deciding the access node of energy storage power stations at each stage from the grid connection level, so as to minimize the active power loss of the distribution network in the short-term operation.

[0073] Therefore, its significant advantages compared to existing technologies are:

[0074] 1) Taking into account the growth of new energy sources and loads within the planning timeframe of multiple phases, the planning results for the capacity and site selection of energy storage power stations can adapt to future growth in new energy sources and loads;

[0075] 2) The capacity of energy storage power stations is optimized in multiple stages. The objective function considers multiple long-term comprehensive costs such as investment and construction costs, operation and maintenance costs, and wind and solar curtailment costs, which can realize the economic efficiency of investment and construction of energy storage power stations.

[0076] 3) With the goal of minimizing active power loss during the overall planning phase, the grid connection nodes of the energy storage power station were determined, effectively ensuring the economic efficiency of the distribution network in the short term.

[0077] The specific technical solutions adopted in this invention and their beneficial effects will be disclosed in detail in the following specific embodiments in conjunction with the accompanying drawings. Attached Figure Description

[0078] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0079] Figure 1 This is a flowchart of a multi-stage planning method for an energy storage power station according to the present invention. Detailed Implementation

[0080] The technical solutions of the embodiments of the present invention will be explained and described below with reference to the accompanying drawings. However, the following embodiments are only preferred embodiments of the present invention and not all of them. Other embodiments obtained by those skilled in the art based on the embodiments in the implementation methods without creative effort are all within the protection scope of the present invention.

[0081] Example 1

[0082] like Figure 1 As shown, this embodiment provides a multi-stage planning method for energy storage power stations that considers both renewable energy consumption and network loss reduction. It comprehensively considers the optimization granularity and objectives of long-term energy storage power station planning and short-term distribution network operation at different time scales, proposing a hierarchical coordinated planning approach. Taking into account the growth of renewable energy output and load demand, a two-layer coordinated planning model for multi-stage energy storage power station capacity determination and site selection is established. The upper-layer model focuses on the entire distribution network, determining the capacity of energy storage power stations at each stage from the distribution network perspective, achieving cost-effectiveness in investment and construction and renewable energy consumption across multiple stages. The lower-layer model focuses on each node of the distribution network, determining the access nodes for energy storage power stations at each stage from the grid connection perspective, minimizing active power losses during short-term distribution network operation.

[0083] Specifically, the following steps are included:

[0084] (1) Establish a power supply and demand growth model, including a new energy output growth model and a load growth model;

[0085] (2) Establish the target function model for the upper-level energy storage power station with the objective function of minimizing the total comprehensive cost of each planning stage, including: the investment and construction cost of the energy storage power station, the operation and maintenance cost, and the cost of wind and solar curtailment in the distribution network;

[0086] (3) Establish a fixed-capacity constraint model for the upper-level energy storage power station. The constraints include: power balance constraints, new energy output constraints, charging and discharging power and state of charge constraints of the energy storage power station.

[0087] (4) The objective function model for the location and capacity determination of the lower-level energy storage is to minimize the active power loss of the distribution network during the overall planning stage.

[0088] (5) Establish the target function model for the location and capacity determination of the lower-level energy storage, with constraints including: the allowed energy storage capacity of each node, the AC power flow constraints of the distribution network, the node voltage constraints, and the branch power flow constraints.

[0089] (6) The upper-level model is solved using a genetic algorithm, while the lower-level model is solved using a solver.

[0090] The new energy output growth model and load growth model mentioned in step (1) are specifically as follows:

[0091] (1.1) New Energy Output Growth Model

[0092] The output of new energy sources exhibits a clear seasonality; therefore, it is necessary to consider the output under various scenarios and reflect this in the model.

[0093]

[0094] In the formula, For scenario s in the nth planning phase, the total output of new energy, wind power, and photovoltaic power during time period t; These represent the projected growth rates of wind power and solar power output in the nth planning phase compared to the (n-1)th planning phase, respectively.

[0095] (1.2) Load Growth Model

[0096]

[0097] In the formula, D n,s,t Let be the electricity load value during time period t in scenario s during the nth planning phase; The projected growth rate of load output in the nth planning phase compared to the (n-1)th planning phase.

[0098] The objective function for locating the energy storage power station in step (2) is:

[0099] (2.1) To minimize the overall cost of the distribution network during the overall planning stage, optimize the configuration capacity of energy storage power stations to reduce investment and construction costs, operation and maintenance costs, and wind and solar curtailment costs.

[0100]

[0101] In the formula, F totalThe overall cost of the distribution network during the master planning phase. These represent the investment and construction costs, operation and maintenance costs, and wind and solar curtailment costs of the energy storage power station in the nth planning stage of the distribution network, respectively, where N is the total number of planning stages.

[0102]

[0103] In the formula, C IE C IP These represent the investment and construction costs per unit capacity and per unit power of the energy storage power station, respectively; C OC For the operation and maintenance costs of energy storage power stations; μ n,s,t To indicate the charging and discharging status of the energy storage power station during time period t in scenario s of the nth planning phase (μ during charging state). n,s,t =1, μ during discharge state n,s,t =-1, μ in float charge state n,s,t =0); These are the grid connection prices for photovoltaic and wind power respectively during time period t; These are the proportions of unused photovoltaic and wind power in time period t during scenario s in the nth planning stage, representing the total amount of wind and solar power curtailed. Let t represent the amount of wind and solar power curtailment in the distribution network during time period t in scenario s during the nth planning phase.

[0104] The detailed constraint model described in step (3) includes:

[0105] (3.1) Power balance constraints

[0106]

[0107] In the formula, L S This represents the total number of branches connecting the distribution network to the upper-level main network. Let L be the active power transmitted from the upper main grid to the distribution network through branch l during time period t in scenario s during the nth planning phase. The active power loss of the distribution network in time period t in scenario s during the nth planning stage is obtained by solving the lower-level model.

[0108] (3.2) Power constraints of backfeed from main grid to distribution network branches

[0109]

[0110] In the formula, The maximum reverse power that can be transmitted by the branch of the main grid connected to the distribution network.

[0111] (3.3) Constraints on charging and discharging power and state of charge of energy storage stations in the distribution network

[0112]

[0113] In the formula, η d η c These are discharge efficiency and charging efficiency, respectively; η n,s,t S n,s,t These represent the charging / discharging efficiency and charge capacity of the energy storage power station during time period t in scenario s during the nth planning phase; S min S max S0 and S0 represent the lower limit, upper limit, and initial value of the energy storage power station's charge capacity, respectively; T represents the number of time periods within a short-term operation optimization cycle of the energy storage power station.

[0114] (3.4) Constraints on Investment and Construction of Energy Storage Power Stations

[0115]

[0116] The objective function for the addressing of the energy storage power station mentioned in step (4) is:

[0117] (4.1) With the goal of minimizing the network loss cost of the distribution network during the overall planning stage, the grid connection location of the energy storage power station in the distribution network is optimized to reduce the operating cost of the distribution network. The objective function is expressed as follows:

[0118]

[0119] In the formula, F loss For the network loss cost of the distribution network during the overall planning stage, The network loss cost in the nth planning stage is calculated using the following formula:

[0120]

[0121] In the formula, p loss P is the unit price for network loss. ij,n,s,t Q ij,n,s,t Let V represent the active power, reactive power, and resistance value (V) on the branch from node i to node j during time period t in scenario s during the nth planning phase. i,n,s,t Let R be the voltage value of node i in time period t in scenario s during the nth planning phase. ij Let be the resistance value on the branch from node i to node j.

[0122] The detailed constraint model described in step (5) includes:

[0123] (5.1) Allowable energy storage capacity constraints for each node

[0124]

[0125] In the formula, These represent the capacity and upper limit of the energy storage power station connected to node i in the nth planning phase, respectively.

[0126] (5.2) AC power flow constraints in distribution networks

[0127] The distribution network architecture is a radial network, and its AC power flow constraints are described using the Distflow model.

[0128]

[0129]

[0130]

[0131] In the formula, P j,n,s,t Q j,n,s,t D j,n,s,t Q j,n,s,t , These represent the net load active power, net load reactive power, load active power, load reactive power, renewable energy active power output, renewable energy reactive power output, wind and solar curtailment, reactive power of reactive power compensation equipment, active power output of energy storage power station, and reactive power output of energy storage power station at node j in time period t during scenario s in the nth planning phase. ij,n,s,t X represents the squared current value of the branch from node i to node j in time period t during scenario s in the nth planning stage. ij P is the reactance value of the branch from node i to node j. jl,n,s,t Q jl,n,s,t These represent the active power and reactive power from node j to branch l during time period t in scenario s during the nth planning stage.

[0132] (5.3) Node voltage constraints

[0133] U N (1-ε1)≤U k ≤U N (1+ε2) (31)

[0134] In the formula, U k U is the voltage at node k; N ε1 represents the voltage amplitude at the substation outlet; ε2 represents the upper limit of the negative voltage deviation and the upper limit of the positive voltage deviation, respectively, as specified in GB / T 12325—2008 "Power Quality—Power Supply Deviation".

[0135] (5.4) Branch flow constraints

[0136]

[0137] In the formula, S l , These represent the transmission power, lower limit of transmission power, and upper limit of transmission power of branch l in the distribution network, respectively.

[0138] The solution algorithm described in step (6) includes:

[0139] (6.1) Basic data preparation. Initialize the parameters of the distribution network, new energy sources, and loads, and determine the parameters in the genetic algorithm, such as mutation probability, crossover probability, total number of generations G, population size m, genetic generation count value k, and convergence error η, where the initial value of k is set to 1;

[0140] (6.2) Population Initialization. Use a genetic algorithm to randomly generate m groups. The initial values ​​of the energy storage power station's capacity and power are then transferred to the lower-level energy storage site selection model.

[0141] (6.2) Solution of the lower-level energy storage site selection model. Based on the capacity requirements of the energy storage power station. and Determine the grid connection nodes and capacity of the energy storage power station to minimize grid loss costs F. loss Using the objective function, the capacity of the energy storage power station at the grid-connected node is obtained using the MOSEK solver. And retain the network loss cost in the k-th iteration.

[0142] (6.3) Calculation of the comprehensive cost of the upper-level energy storage power station at its fixed capacity. The lower-level model transmits the network loss value to the upper-level model to calculate the comprehensive cost of the energy storage power station at its fixed capacity in the k-th iteration.

[0143] (6.4) Update the generation count. k = k + 1;

[0144] (6.5) Update the genetic generation population data and the fixed-size data. Use a genetic algorithm for selection, crossover, and mutation to generate a new population dataset, with m groups. and And repeat steps (6.2) and (6.3) to obtain (6.6) Determine whether the bilevel programming model has obtained an equilibrium solution. If and This indicates that an equilibrium solution has been obtained; otherwise, repeat steps (6.4) and (6.5).

[0145] (6.7) Output the equilibrium results. Output the optimization variables of the upper and lower models under equilibrium conditions. and their respective target values As the final output.

[0146] In summary, the multi-stage planning method for energy storage power stations that considers both renewable energy consumption and network loss reduction in this invention can adapt to the growth of renewable energy and users in multiple future stages. Its capacity setting scheme can improve the consumption rate after renewable energy growth and reduce the overall cost of building and maintaining energy storage power stations in the distribution network. Its site selection scheme can reduce the active power loss after distributed energy is connected to the distribution network and improve the economic efficiency of distribution network operation.

[0147] Example 2

[0148] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a multi-stage planning method for energy storage power stations that considers renewable energy consumption and grid loss reduction, as described in Embodiment 1.

[0149] The computer devices in the embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptops, PDAs (personal digital assistants), and PADs (tablet computers), as well as fixed terminals such as desktop computers.

[0150] Computer devices may include processing units (such as a central processing unit), which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) or loaded from storage devices into random access memory (RAM). RAM also stores various programs and data required for the operation of the computer device. The processing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0151] A computer program carried on a computer-readable medium includes program code for performing an algorithm. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, it performs the functions defined in the methods of embodiments of this disclosure.

[0152] It should be noted that the computer-readable medium disclosed in this invention may be a computer-readable signal medium, a computer-readable medium, or any combination of the two.

[0153] The aforementioned computer-readable medium may be included in the aforementioned computer device; or it may exist independently and not assembled into the computer device.

[0154] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Those skilled in the art should understand that the present invention includes, but is not limited to, the contents described in the accompanying drawings and the specific embodiments above. Any modifications that do not depart from the functional and structural principles of the present invention will be included within the scope of the claims.

Claims

1. A multi-stage planning method for energy storage power stations that considers both renewable energy consumption and grid loss reduction, characterized in that, Includes the following steps: Step 1: Establish a power supply and demand growth model, which includes a new energy output growth model and a load growth model; Step 2: Establish a fixed-capacity objective function model for the upper-level energy storage power station, with the objective function being the minimization of the total comprehensive cost of each planning stage. The comprehensive cost includes: the investment and construction cost of the energy storage power station, the operation and maintenance cost, and the cost of wind and solar curtailment in the distribution network. Step 3: Establish a fixed-capacity constraint model for the upper-level energy storage power station. The constraints include: power balance constraints, new energy output constraints, charging and discharging power of the energy storage power station, and state of charge constraints. Step 4: Establish the objective function model for the location and capacity determination of the lower-level energy storage, with the objective function being the minimization of active power loss in the distribution network during the overall planning stage; Step 5: Establish a lower-level energy storage location constraint model. The constraints include: the allowed energy storage capacity constraint for each node, the AC power flow constraint of the distribution network, the node voltage constraint, and the branch power flow constraint. Step 6: Establish a solution algorithm for the two-level programming model. The upper-level model uses a genetic algorithm to optimize and update the capacity of the energy storage power station, which is then passed to the lower-level model. The lower-level model uses the MOSEK solver to solve the network loss value, the location nodes and capacity of the energy storage power station, and then passes the network loss value to the upper-level model. Finally, in the process of information interaction, an equilibrium solution is obtained. The new energy output growth model and load growth model are as follows: (1.1) New Energy Output Growth Model The output of new energy sources exhibits a clear seasonality; therefore, output under various scenarios should be considered: In the formula, , , For scenario s in the nth planning phase, the total output of new energy, wind power, and photovoltaic power during time period t; , These represent the projected growth rates of wind power and solar power output in the nth planning phase compared to the (n-1)th planning phase, respectively. (1.2) Load Growth Model In the formula, Let be the electricity load value during time period t in scenario s during the nth planning phase; The projected growth rate of load output in the nth planning phase compared to the (n-1)th planning phase; The algorithm for solving the bi-level programming model in step 6 includes: (6.1) Basic data preparation: Initialize various parameters of the distribution network, new energy sources, and loads; determine various parameters in the genetic algorithm, including mutation probability, crossover probability, total number of generations G, population size m, generation count k, and convergence error. , where the initial value of k is set to 1; (6.2) Population initialization: m groups are randomly generated using a genetic algorithm. , The initial values ​​of the energy storage power station's capacity and power are then passed to the lower-level energy storage site selection model. (6.2) Solving the lower-level energy storage site selection model, based on the capacity of the energy storage power station. and Determine the grid connection nodes and capacity of the energy storage power station to minimize grid loss costs. Using the objective function, the capacity of the energy storage power station at the grid-connected node is obtained using the MOSEK solver. And retain the network loss cost in the k-th iteration. ; (6.3) Calculate the comprehensive cost of the upper-level energy storage power station at its fixed capacity. The lower-level model transmits the network loss value to the upper-level model to calculate the comprehensive cost of the energy storage power station at its fixed capacity in the k-th iteration. ; (6.4) Update the genetic generation count, k = k + 1; (6.5) Update the genetic generation population data and the fixed-size data. Use the genetic algorithm for selection, crossover, and mutation to generate new population data, with m groups. and And repeat steps (6.2) and (6.3) to obtain , ; (6.6) Determine whether the bilevel programming model has obtained an equilibrium solution. and This indicates that an equilibrium solution has been obtained; Otherwise, repeat steps (6.4) and (6.5); (6.7) Output the equilibrium results, which are the optimization variables of the upper and lower models under the equilibrium state. , and their respective target values , As the final output.

2. The multi-stage planning method for energy storage power stations considering renewable energy consumption and grid loss reduction as described in claim 1, characterized in that, The objective function for calibrating the energy storage power station in step 2 is: To minimize the overall cost of the power distribution network during the overall planning phase, the configuration capacity of energy storage power stations is optimized to reduce investment and construction costs, operation and maintenance costs, and wind and solar curtailment costs. In the formula, The overall cost of the distribution network during the master planning phase. , , These represent the investment and construction costs, operation and maintenance costs, and wind and solar curtailment costs of energy storage power stations in the nth planning stage of the power distribution network, respectively, where N is the total number of planning stages. In the formula, , These are the unit capacity investment and construction cost and the unit power investment and construction cost of the energy storage power station, respectively. The operating and maintenance costs of the energy storage power station; To indicate the charging and discharging status of the energy storage power station during time period t in scenario s of the nth planning phase, the charging status is... During discharge state In float charge state ; , These are the grid connection prices for photovoltaic and wind power respectively during time period t; , These are the proportions of unused photovoltaic and wind power in time period t during scenario s in the nth planning stage, representing the total amount of wind and solar power curtailed. Let t represent the amount of wind and solar power curtailment in the distribution network during time period t in scenario s during the nth planning phase.

3. The multi-stage planning method for energy storage power stations considering renewable energy consumption and grid loss reduction according to claim 2, characterized in that, The constraint conditions in step 3 are as follows: (3.1) Power balance constraints In the formula, This represents the total number of branches connecting the distribution network to the upper-level main network. Let L be the active power transmitted from the upper main grid to the distribution network through branch l during time period t in scenario s during the nth planning phase. The active power loss of the distribution network in time period t in scenario s during the nth planning stage is obtained by solving the lower-level model. (3.2) Power constraints of backfeed from main grid to distribution network branches In the formula, The maximum reverse power allowed to be transmitted by the main grid-connected distribution network branch; (3.3) Constraints on charging and discharging power and state of charge of energy storage stations in the distribution network In the formula, , These are discharge efficiency and charging efficiency, respectively. , These represent the charging and discharging efficiency and the charge capacity of the energy storage power station during time period t in scenario s during the nth planning phase. , , These are the lower limit, upper limit, and initial value of the energy storage power station's charge capacity, respectively. This refers to the number of time periods within a short-term operational optimization cycle for an energy storage power station. (3.4) Constraints on Investment and Construction of Energy Storage Power Stations 。 4. The multi-stage planning method for energy storage power stations considering renewable energy consumption and grid loss reduction according to claim 3, characterized in that, The objective function for addressing the energy storage power station in step 4 is: (4.1) With the goal of minimizing the network loss cost of the distribution network during the overall planning stage, the grid connection location of the energy storage power station in the distribution network is optimized to reduce the operating cost of the distribution network. The objective function is expressed as follows: In the formula, For the network loss cost of the distribution network during the overall planning stage, The network loss cost in the nth planning stage is calculated using the following formula: In the formula, This is the unit price for network loss. , These represent the active power, reactive power, and resistance value on the branch from node i to node j during time period t in scenario s during the nth planning phase. Let the voltage value of node i be the voltage value at time t in scenario s during the nth planning phase. Let be the resistance value on the branch from node i to node j.

5. The multi-stage planning method for energy storage power stations considering renewable energy consumption and grid loss reduction according to claim 4, characterized in that, The constraint condition in step 5 is: (5.1) Allowable energy storage capacity constraints for each node In the formula, , These represent the capacity and upper limit of the energy storage power station connected to node i in the nth planning phase, respectively. (5.2) AC power flow constraints in distribution networks The distribution network architecture is generally radial, and its AC power flow constraints are described using the Distflow model. In the formula, , , , , , , , , , These represent the net load active power, net load reactive power, load active power, load reactive power, active power output of renewable energy, reactive power output of renewable energy, wind and solar curtailment, reactive power of reactive power compensation equipment, active power output of energy storage power station, and reactive power output of energy storage power station at node j in time period t during scenario s in the nth planning phase. This represents the squared current value of the branch from node i to node j in time period t during scenario s in the nth planning stage. Let be the reactance value of the branch from node i to node j. , These represent the active power and reactive power from node j to branch l during time period t in scenario s during the nth planning phase; (5.3) Node voltage constraints In the formula, Let k be the voltage at node k. This refers to the voltage amplitude at the substation's outlet. , These are the upper limits for negative voltage deviation and positive voltage deviation, respectively. (5.4) Branch flow constraints In the formula, , , These represent the transmission power, lower limit of transmission power, and upper limit of transmission power of branch l in the distribution network, respectively.

6. A computer device, characterized in that, It includes at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute a multi-stage planning method for energy storage power stations that considers renewable energy consumption and grid loss reduction as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions. When the processor executes the computer-executable instructions, it implements a multi-stage planning method for energy storage power stations that considers renewable energy consumption and grid loss reduction as described in any one of claims 1 to 5.

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