An active power distribution network mobile energy storage planning and configuration method

Abstract

The application provides a mobile energy storage planning and configuration method for an active power distribution network, comprising: establishing a charging and discharging model and a displacement model of the mobile energy storage; taking the maximum annual income of the mobile energy storage as an objective function, taking the maximum annual income of the mobile energy storage as an objective function, taking the distributed power output constraint, the mobile energy storage access power constraint, the power distribution network power flow constraint, the safety constraint, and the established charging and discharging model and displacement model of the mobile energy storage as constraint conditions, and establishing a mobile energy storage planning model for the active power distribution network; converting the mobile energy storage planning model for the active power distribution network into a mixed integer second-order cone model; and solving the mixed integer second-order cone model to obtain an optimal configuration scheme of the mobile energy storage. The application can improve the power fluctuation and the abandoned wind and light problems after the distributed power is connected to the power distribution network, and can maximize the investment income of the mobile energy storage under the premise of ensuring the safe operation of the power distribution network, thereby improving the economy of the power distribution network operation.

Description

Technical Field

[0001] This invention relates to the field of power system optimization configuration, specifically a method for planning and configuring mobile energy storage in active distribution networks. Background Technology

[0002] With the advancement of distributed generation technology, various distributed resources, such as distributed photovoltaic and wind power, are increasingly being connected to the power grid, bringing more complex challenges to the operation of the distribution network. Due to their inherent characteristics, distributed resources exhibit significant output uncertainty, which may lead to risks such as power fluctuations and voltage exceeding limits in the distribution network, affecting the safe and stable operation of the grid. Energy storage devices can transfer energy over time, solving the problem of intermittent output from distributed new energy sources. However, most energy storage systems in my country are currently stationary, only covering areas near the connection point. Cost constraints also limit the deployment of multiple stationary energy storage stations, thus posing significant limitations to stationary energy storage.

[0003] Mobile energy storage, as an emerging means of distribution network regulation, can regulate power and energy in time and space, overcoming the limitations of fixed energy storage's limited coverage. Furthermore, it improves the utilization rate of distributed power sources while also improving power flow distribution, reducing network losses and the risk of power exceeding limits, demonstrating significant development potential and application prospects. However, despite the rapid development of energy storage technology, the cost of mobile energy storage remains high. In active distribution networks containing renewable energy, inappropriate mobile energy storage configuration not only affects the operation strategy of the active distribution network and the absorption of renewable energy but also incurs substantial costs. Therefore, this paper proposes a planning and configuration method for mobile energy storage in active distribution networks, which is beneficial for maximizing the economic benefits of mobile energy storage, absorbing renewable energy, and improving the safe, economical, and stable operation of the distribution network. Summary of the Invention

[0004] The purpose of this invention is to provide a planning and configuration method for mobile energy storage in active distribution networks. This method optimizes the configuration of mobile energy storage connected to active distribution networks. By rationally configuring mobile energy storage in the distribution network, it improves the power fluctuation and wind / solar curtailment problems that occur after the distribution network is connected to distributed power sources. Furthermore, it maximizes the return on investment in mobile energy storage while ensuring the safe operation of the distribution network, thereby improving the economic efficiency of distribution network operation.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for planning and configuring mobile energy storage in an active power distribution network includes the following steps:

[0007] Based on the energy characteristics of mobile energy storage, a charging and discharging model for mobile energy storage is established.

[0008] Based on the spatial displacement characteristics of mobile energy storage, a displacement model for mobile energy storage is established.

[0009] With the objective function of maximizing the annual revenue of mobile energy storage, and with constraints such as the output of distributed power sources, the access power of mobile energy storage, the power flow of the distribution network, security constraints, as well as the mobile energy storage charging and discharging model and the mobile energy storage displacement model established above, an active distribution network mobile energy storage planning model is established.

[0010] The active distribution network mobile energy storage planning model is transformed into a mixed integer second-order cone model based on the second-order cone transformation and the big M method.

[0011] The YALMIP toolbox was used to call the GUROBI solver to solve the mixed integer second-order cone model, and the optimal configuration scheme for mobile energy storage was obtained.

[0012] Furthermore, a charging and discharging model for mobile energy storage is established, specifically including:

[0013] Based on the charging and discharging laws of mobile energy storage, the functional relationship between the energy storage state of charge and the charging and discharging power and efficiency is described as follows:

[0014]

[0015] In the formula: m represents the mobile energy storage sequence number, and t represents the time period. This represents the energy storage capacity during time period t. The charging and discharging power of the stored energy during time period t, in μ c μ d Indicates the charging and discharging efficiency of mobile energy storage;

[0016] Setting up mobile energy storage charging and discharging indicators to constrain a single mobile energy storage device from charging and discharging simultaneously is described by the following formula:

[0017]

[0018] In the formula: This indicates the charging and discharging status of the mobile energy storage device. A value of 1 indicates that it is charging or discharging, while a value of 0 indicates that it is not charging or discharging.

[0019] Add a constraint that the mobile energy storage has the same charge at the beginning and end of the day, described by the following formula:

[0020]

[0021] Furthermore, establishing a mobile energy storage displacement model specifically includes:

[0022] Establish a three-dimensional mobile energy storage node access matrix, using 0 and 1 variables to represent whether a certain mobile energy storage is connected to a certain candidate node at a certain moment. When the matrix element value is 1, it indicates connection, and when it is 0, it indicates non-connection.

[0023] Establish energy storage access constraints to limit a single mobile energy storage device to only one candidate node at a time.

[0024] Furthermore, a three-dimensional mobile energy storage node access matrix is ​​established, as shown in the following equation:

[0025] x = [x m，j，t ] s×n×T

[0026] In the formula: x m,j,t When 1 represents the energy storage m being connected to the distribution network node j at time t, j represents the candidate access node;

[0027] Establish energy storage access constraints, as shown in the following formula:

[0028]

[0029] Where: Ω j This is the set of candidate access nodes.

[0030] Furthermore, the annual revenue of mobile energy storage includes the average annual investment cost and annual operating cost of mobile energy storage, wind and solar curtailment costs, grid loss costs, and energy storage peak-valley arbitrage revenue. The average annual investment cost of mobile energy storage includes battery capacity costs and power converter power costs.

[0031] Furthermore, the average annual investment cost, annual operating cost, wind and solar curtailment cost, grid loss cost, and energy storage peak-valley arbitrage revenue are shown in the following formula:

[0032]

[0033] In the formula: f1 represents the average annual cost of mobile energy storage, including investment cost and operating cost; C e C p These are the unit capacity cost of batteries and the unit power cost of power converters; C m This represents the annual operating cost per unit charge / discharge power of the battery, where r is the annual interest rate, Y is the investment period for mobile energy storage, and f2 is the cost of wind and solar power curtailment. Predict the output power of distributed generation. c represents the actual output power of the distributed power source. g For the cost of distributed power generation, Ω DG For distributed power source access nodes, f3 is the network loss cost, Ω L For the set of distribution network branches, c tf4 represents the electricity price during time period t, and f4 represents the peak-valley arbitrage profit from energy storage.

[0034] Furthermore, a. The output constraint of the distributed power source is shown in the following formula:

[0035]

[0036] b. The power constraint for mobile energy storage access is shown in the following formula:

[0037]

[0038] In the formula: Let represent the energy storage power connected to node j during time period t.

[0039] c. The power flow constraints of the distribution network are shown in the following formula:

[0040]

[0041] In the formula: P ij,t Q ij,t I ij,t r ij x ij These are the active power, reactive power, current, resistance, and reactance of branch ij, respectively. i,t For node voltage, These represent the injected active and reactive power at node j, φ. j ψ j Let these represent the sets of parent and child nodes of node j, respectively. For the reactive power output of the distributed power source at node j, These represent the active and reactive loads of node j, respectively.

[0042] d. The safety constraints are shown in the following formula:

[0043]

[0044] In the formula: These are the upper and lower limits of the node voltage, respectively. This is the upper limit of the branch current.

[0045] Furthermore, the transformation of the active distribution network mobile energy storage planning model into a mixed-integer second-order cone model based on the second-order cone transformation and the Big M method specifically includes:

[0046] The power flow constraints in the established active distribution network mobile energy storage planning model are nonlinear terms, defined by the following variables:

[0047]

[0048] The power flow constraints of the distribution network are transformed into a solvable second-order cone model using a second-order cone transformation, as shown in the following equation:

[0049]

[0050] The mobile energy storage access power constraint in the established active distribution network mobile energy storage planning model is a bilinear term. Considering the introduction of auxiliary variables and the use of the Big M method, it is transformed into a linear constraint form, as shown in the following equation:

[0051]

[0052] In the formula: M is a relatively large positive integer.

[0053] The objective function of the active distribution network mobile energy storage planning model established in this invention considers the cost of wind and solar curtailment. Therefore, when the output of new energy sources such as wind and solar power exceeds the load demand, mobile energy storage will be preferentially connected to wind and solar power generation nodes to store the excess output and reduce curtailment. When the grid load is heavy, the large power flowing through the lines leads to large network losses. Since the objective function of the active distribution network mobile energy storage planning model considers network loss costs, mobile energy storage will be preferentially connected to load nodes at the end of the grid to supply power to the load, thereby reducing network losses caused by line transmission. Furthermore, since the objective function of the active distribution network mobile energy storage planning model considers peak-valley arbitrage benefits, when the grid power can balance with the output of new energy sources, mobile energy storage will charge during periods of low electricity prices and discharge during periods of high electricity prices to obtain peak-valley arbitrage benefits. This invention utilizes the time- and temperature-controlled energy management capabilities of mobile energy storage, which can significantly improve the economic efficiency of distribution network operation and the level of new energy consumption. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the active distribution network mobile energy storage planning and configuration method of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] like Figure 1 As shown, this invention provides a method for planning and configuring mobile energy storage in an active power distribution network, comprising the following steps:

[0057] Step (1): Based on the energy characteristics of mobile energy storage, establish a charging and discharging model for mobile energy storage; Step (1) establishing a charging and discharging model for mobile energy storage, the specific content of which is as follows:

[0058] a. Based on the charging and discharging laws of mobile energy storage, the functional relationship between the energy storage state of charge and the charging and discharging power and efficiency is described as follows:

[0059]

[0060] In the formula: m represents the mobile energy storage sequence number, and t represents the time period. This represents the energy storage capacity during time period t. The charging and discharging power of the stored energy during time period t, μ c μ d Indicates the charging and discharging efficiency of mobile energy storage;

[0061] b. Set mobile energy storage charging and discharging indicators to constrain mobile energy storage from charging and discharging simultaneously, which can be described by the following formula:

[0062]

[0063] In the formula: This indicates the charging and discharging status of the mobile energy storage device. A value of 1 indicates that it is charging or discharging, while a value of 0 indicates that it is not charging or discharging.

[0064] c. Adding the constraint that the mobile energy storage has the same amount of electricity at the beginning and end of the day can be described as follows:

[0065]

[0066] Step (2): Based on the spatial displacement characteristics of mobile energy storage, establish a mobile energy storage displacement model; the specific content of establishing the mobile energy storage displacement model in step (2) is as follows:

[0067] a. Establish a three-dimensional mobile energy storage node access matrix, as shown in the following formula:

[0068] x = [x m，j，t ] s×n×T

[0069] In the formula: x m,j,t When 1 represents the energy storage m being connected to the distribution network node j at time t, j represents the candidate access node;

[0070] b. Establish energy storage access constraints to limit a single mobile energy storage device to only one candidate node at a time, as shown in the following formula:

[0071]

[0072] Where: Ω jThis is the set of candidate access nodes.

[0073] Step (3): With the goal of maximizing the annual revenue of mobile energy storage, and with the constraints of distributed power output, mobile energy storage access power, power flow of the distribution network, security, as well as the mobile energy storage charging and discharging model established in step (1) and the mobile energy storage displacement model established in step (2), an active distribution network mobile energy storage planning model is established.

[0074] The annual revenue of mobile energy storage mentioned in step (3) includes the average annual investment cost and annual operating cost of mobile energy storage, wind and solar curtailment costs, grid loss costs, and energy storage peak-valley arbitrage revenue, as shown in the following formula:

[0075]

[0076] In the formula: f1 represents the average annual cost of mobile energy storage, including investment cost and operating cost; C e C p These are the unit capacity cost of batteries and the unit power cost of power converters; C m This represents the annual operating cost per unit charge / discharge power of the battery, where r is the annual interest rate, Y is the investment period for mobile energy storage, and f2 is the cost of wind and solar power curtailment. Predict the output power of distributed generation. c represents the actual output power of the distributed power source. g For the cost of distributed power generation, Ω DG For distributed power source access nodes; f3 is the network loss cost, Ω L For the set of distribution network branches, c t f4 represents the electricity price during time period t; f4 represents the energy storage peak-valley arbitrage profit.

[0077] The specific contents of the distributed power output constraints, mobile energy storage access power constraints, distribution network power flow constraints, and security constraints mentioned in step (3) are as follows:

[0078] a. The output constraint of distributed power sources is shown in the following formula:

[0079]

[0080] b. The power constraint for mobile energy storage access is shown in the following formula:

[0081]

[0082] In the formula: Let represent the energy storage power connected to node j during time period t.

[0083] c. The power flow constraints of the distribution network are shown in the following formula:

[0084]

[0085] In the formula: P ij,t Q ij,t I ij,t r ij x ij These represent the active power, reactive power, current, resistance, and reactance of branch ij, respectively. i,t For node voltage, φ represents the injected active and reactive power at node j, respectively. j ψ j Let represent the sets of parent and child nodes of node j, respectively. For the reactive power output of the distributed power source at node j, These represent the active and reactive loads of node j, respectively.

[0086] d. The safety constraints are shown in the following formula:

[0087]

[0088] In the formula: These are the upper and lower limits of the node voltage, respectively. This is the upper limit of the branch current.

[0089] Step (4): Based on the second-order cone transformation and the Big M method, the active distribution network mobile energy storage planning model is transformed into a mixed integer second-order cone model; specifically,

[0090] a. The power flow constraints of the distribution network are transformed into a solvable second-order cone model using the second-order cone transformation, and the following variables are defined:

[0091]

[0092] b. Transform the power flow constraints of the distribution network into a second-order cone model, as shown in the following equation:

[0093]

[0094] c. The Big M method is used to transform the mobile energy storage access power constraint into a linear constraint, as shown in the following equation:

[0095]

[0096] In the formula: M is a relatively large positive integer.

[0097] Step (5): Use the YALMIP toolbox to call the solver GUROBI to solve the mixed integer second-order cone model and obtain the optimal configuration scheme for mobile energy storage.

[0098] To address the power fluctuations and curtailment issues caused by the integration of numerous distributed generation sources into active distribution networks, this invention proposes a mobile energy storage planning and configuration method for active distribution networks. This method optimizes the configuration of mobile energy storage integrated into active distribution networks. By rationally configuring mobile energy storage within the distribution network, it mitigates power fluctuations and curtailment issues arising from the integration of distributed generation sources. Furthermore, while ensuring the safe operation of the distribution network, it maximizes the return on investment in mobile energy storage, thereby improving the economic efficiency of distribution network operation.

[0099] The objective function of the active distribution network mobile energy storage planning model established in this invention considers the cost of wind and solar curtailment. Therefore, when wind and solar power output exceeds load demand, mobile energy storage will be preferentially connected to wind and solar power generation nodes to store the excess output and reduce curtailment. When the grid load is heavy, the large power flowing through the lines leads to significant network losses. Since the objective function of the active distribution network mobile energy storage planning model considers network loss costs, mobile energy storage will be preferentially connected to load nodes at the end of the grid to supply power to the load, thereby reducing network losses caused by line transmission. Furthermore, since the objective function of the active distribution network mobile energy storage planning model considers peak-valley arbitrage benefits, when grid power and renewable energy output are balanced, mobile energy storage will charge during periods of low electricity prices and discharge during periods of high electricity prices to obtain peak-valley arbitrage benefits. This invention utilizes the time- and temperature-controlled energy management capabilities of mobile energy storage, which can significantly improve the economic efficiency of distribution network operation and the level of renewable energy absorption.

[0100] 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. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for planning and configuring mobile energy storage in an active power distribution network, characterized in that, Includes the following steps: Based on the energy characteristics of mobile energy storage, a charging and discharging model for mobile energy storage is established. Based on the spatial displacement characteristics of mobile energy storage, a displacement model for mobile energy storage is established. With the objective function of maximizing the annual revenue of mobile energy storage, and with constraints such as distributed power output, mobile energy storage access power, distribution network power flow, security, as well as the established mobile energy storage charging and discharging model and mobile energy storage displacement model, an active distribution network mobile energy storage planning model is established. The active distribution network mobile energy storage planning model is transformed into a mixed integer second-order cone model based on the second-order cone transformation and the big M method. The YALMIP toolbox was used to call the GUROBI solver to solve the mixed integer second-order cone model, and the optimal configuration scheme of mobile energy storage was obtained. Establishing a mobile energy storage displacement model specifically includes: Establish a three-dimensional mobile energy storage node access matrix, using 0 and 1 variables to represent whether a certain mobile energy storage is connected to a certain candidate node at a certain moment. When the matrix element value is 1, it indicates connection, and when it is 0, it indicates non-connection. Establish energy storage access constraints to limit a single mobile energy storage device to only one candidate node at a time. The three-dimensional mobile energy storage node access matrix is ​​established as shown in the following formula: ； In the formula: x m,j,t When 1 represents the energy storage m being connected to the distribution network node j at time t, j represents the candidate access node; Establish energy storage access constraints, as shown in the following formula: ； Where: Ω j For the set of candidate access nodes; a. The output constraint of distributed power sources is shown in the following formula: ； b. The power constraint for mobile energy storage access is shown in the following formula: ； In the formula: Let be the energy storage power connected to node j in time period t; c. The power flow constraints of the distribution network are shown in the following formula: ； In the formula: P ij,t Q ij,t I ij,t r ij x ij These are the active power, reactive power, current, resistance, and reactance of branch ij, respectively. i,t For node voltage, , ϕ represents the injected active and reactive power at node j, respectively. j ψ j Let these represent the sets of parent and child nodes of node j, respectively. For the reactive power output of the distributed power source at node j, , These represent the active and reactive loads of node j, respectively. d. The safety constraints are shown in the following formula: ； In the formula: , These are the upper and lower limits of the node voltage, respectively. This is the upper limit of the branch current.

2. The active distribution network mobile energy storage planning and configuration method according to claim 1, characterized in that: Establish a charging and discharging model for mobile energy storage, specifically including: Based on the charging and discharging laws of mobile energy storage, the functional relationship between the energy storage state of charge and the charging and discharging power and efficiency is described as follows: ； In the formula: m represents the mobile energy storage sequence number, and t represents the time period. This represents the energy storage capacity during time period t. , The charging and discharging power of the stored energy during time period t, μ c μ d Indicates the charging and discharging efficiency of mobile energy storage; Setting up mobile energy storage charging and discharging indicators to constrain a single mobile energy storage device from charging and discharging simultaneously is described by the following formula: ； In the formula: , This indicates the charging and discharging status of the mobile energy storage device. A value of 1 indicates that it is charging or discharging, while a value of 0 indicates that it is not charging or discharging. Add a constraint that the mobile energy storage has the same charge at the beginning and end of the day, described by the following formula: = 。 3. The active distribution network mobile energy storage planning and configuration method according to claim 1, characterized in that: The annual revenue of mobile energy storage includes the average annual investment cost and annual operating cost of mobile energy storage, wind and solar curtailment costs, grid loss costs, and energy storage peak-valley arbitrage revenue. The average annual investment cost of mobile energy storage includes battery capacity costs and power converter power costs.

4. The active distribution network mobile energy storage planning and configuration method according to claim 3, characterized in that: The average annual investment cost, annual operating cost, wind and solar curtailment cost, grid loss cost, and energy storage peak-valley arbitrage revenue are shown in the following formula: ； In the formula: f1 represents the average annual cost of mobile energy storage, including investment cost and operating cost; C e C p These are the unit capacity cost of batteries and the unit power cost of power converters; C m This represents the annual operating cost per unit charge / discharge power of the battery, where r is the annual interest rate, Y is the investment period for mobile energy storage, and f2 is the cost of wind and solar power curtailment. Predict the output power of distributed generation. c represents the actual output power of the distributed power source. g For the cost of distributed power generation, Ω DG For distributed power source access nodes, f3 is the network loss cost, Ω L For the set of distribution network branches, c t f4 represents the electricity price during time period t, and f4 represents the peak-valley arbitrage profit from energy storage.

5. The active distribution network mobile energy storage planning and configuration method according to claim 1, characterized in that: The transformation of the active distribution network mobile energy storage planning model into a mixed integer second-order cone model based on the second-order cone transformation and the Big M method specifically includes: The power flow constraints in the established active distribution network mobile energy storage planning model are nonlinear terms, defined by the following variables: ； The power flow constraints of the distribution network are transformed into a solvable second-order cone model using a second-order cone transformation, as shown in the following equation: ； The mobile energy storage access power constraint in the established active distribution network mobile energy storage planning model is a bilinear term. Considering the introduction of auxiliary variables and the use of the Big M method, it is transformed into a linear constraint form, as shown in the following equation: ； In the formula: M is a relatively large positive integer.

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