Coordinated planning method and system for distributed resources of power system

Abstract

This invention discloses a collaborative planning method for distributed resources in a power system, comprising: acquiring data information of the target power system; constructing a planning model for the energy storage system, network topology, and photovoltaic inverters in the target power system; constructing a multi-objective collaborative planning model for the distributed resources of the target power system with the objectives of minimizing system peak load, minimizing overall network voltage deviation, and minimizing reactive power dependence of photovoltaic inverters; and solving the constructed model based on the Pareto optimality criterion to complete the collaborative planning of the distributed resources of the target power system. This invention also discloses a system for implementing the collaborative planning method for distributed resources in a power system. This invention not only achieves collaborative planning of distributed resources in a power system but also offers higher reliability and better accuracy.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, and specifically relates to a collaborative planning method and system for distributed resources in a power system. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Currently, with the large-scale integration of distributed photovoltaic power, electric vehicles, and flexible loads, the power distribution network is gradually evolving from traditional passive power supply to active power distribution, and its operation is exhibiting significant spatiotemporal fluctuations and uncertainties. At the power distribution network planning level, the mismatch between renewable energy output and peak load times leads to voltage exceeding limits and insufficient power supply capacity during peak load periods.

[0004] Currently, traditional planning approaches for network expansion or single reactive power compensation are not only costly but also inadequate for responding to short-term operational constraints. Furthermore, distribution network operation planning often focuses on single regulation methods, such as network topology reconfiguration, inverter reactive power support, or electrochemical energy storage configuration. While these methods can improve voltage or reduce losses in specific scenarios, their independent application often has significant limitations: frequent network reconfiguration accelerates the aging of switching equipment, over-reliance on photovoltaic inverter reactive power support may lead to increased thermal stress and shortened lifespan of devices, and traditional energy storage faces cost and safety constraints in high-capacity configurations. In addition, existing research is largely based on single-phase or simplified models, failing to fully reflect the actual operating characteristics of three-phase unbalanced distribution networks, thus limiting the applicability of planning conclusions in engineering applications. Summary of the Invention

[0005] One of the objectives of this invention is to provide a highly reliable and accurate collaborative planning method for distributed resources in power systems.

[0006] The second objective of this invention is to provide a system for implementing the collaborative planning method for distributed resources in the power system.

[0007] The collaborative planning method for distributed resources in a power system provided by this invention includes the following steps:

[0008] S1. Obtain data information about the target power system;

[0009] S2. Based on the data obtained in step S1, construct a planning model for the energy storage system, network topology, and photovoltaic inverter in the target power system;

[0010] S3. Combining the model constructed in step S2, with the objectives of minimizing system peak load, minimizing grid voltage deviation, and minimizing reactive power dependence of photovoltaic inverters, construct a multi-objective collaborative planning model for distributed resources of the target power system;

[0011] S4. Based on the Pareto optimality criterion, solve the model constructed in step S3 to complete the collaborative planning of distributed resources of the target power system.

[0012] Step S1, which involves acquiring data information about the target power system, specifically includes the following steps:

[0013] Acquire data information from the target power system;

[0014] The data information includes the charging and discharging efficiency of the energy storage system, the energy state of the energy storage system, the network structure of the target power system, the network parameters of the target power system, the efficiency of the photovoltaic inverter, and the power of the photovoltaic inverter.

[0015] Obtain all candidate schemes for distributed resource planning of the target power system.

[0016] The constructed energy storage system planning model specifically includes the following:

[0017] The following formula is used as the planning model for the energy storage system:

[0018] In the formula Let be the energy stored in the energy storage system at time t; For charging efficiency; This refers to the charging power. For time step; For discharge efficiency; This refers to the discharge efficiency.

[0019] The constructed network topology planning model includes the following:

[0020] The following formula is used as the network topology planning model:

[0021] In the formula The objective function value of the network topology planning model; This represents the total number of time segments within the planning period. Number the time sections; The set of nodes in the target power system; Let be the voltage amplitude at node i in the t-th time segment; The node reference voltage; The weighting coefficients for the network loss term; For the set of branches of the target power system; For the branch connecting node i and node j; branch road The equivalent resistance; For the branch at time section t The current amplitude; The weighting coefficient for the cost term of the switching action; For the branch at time section t The switch state variables, the branch at the t-th time section When put into operation The branch at the t-th time section Disconnect ;

[0022] Used to characterize the degree of deviation of each node voltage from the reference voltage; Used to characterize network branch loss; Used to characterize the number of switching state changes between adjacent time sections.

[0023] The constructed photovoltaic inverter planning model includes the following:

[0024] The following formula is used as the planning model for photovoltaic inverters:

[0025] In the formula The objective function value for the photovoltaic inverter planning model; A collection of photovoltaic inverters; This is the weighting coefficient for the abandoned light power term; Let be the curtailed power of the k-th photovoltaic inverter at the t-th time segment; This represents the weighting coefficient for the voltage deviation term at the photovoltaic access node. Let be the voltage amplitude of the k-th photovoltaic inverter at the t-th time segment;

[0026] Used to represent the total amount of solar power wasted by each photovoltaic inverter at the t-th time segment; This is used to indicate the degree of deviation of the voltage at each photovoltaic inverter access node relative to the reference voltage at the t-th time segment.

[0027] Step S3, combined with the model constructed in step S2, aims to minimize the system peak load, the overall grid voltage deviation, and the reactive power dependence of photovoltaic inverters. It constructs a multi-objective collaborative planning model for distributed resources of the target power system, specifically including the following steps:

[0028] The following formula is used as the objective function to minimize the system's peak load:

[0029] In the formula This represents the objective function value that minimizes the system's peak load. This represents the total power drawn from the power grid during each time period; The current phase of the power grid; The current phase extracted from the power grid The active power; A set of candidate network topology schemes; To characterize whether topology was selected at time t The indicator variable, time t, selected the topology. but At time t, no topology was selected. but ; For topology Next current phase Power requirements; This refers to the collection of all nodes where TESS units are installed; a TESS unit is a portable energy storage unit. To represent the TESS cell at node n, current phase The power injected or absorbed over time t;

[0030] The following formula is used as the objective function to minimize the voltage deviation of the entire network:

[0031] In the formula The objective function value is the minimum voltage deviation across the entire network. Total time; The total number of nodes in the target power system; For node n in the current phase and the actual voltage amplitude at time t; This is the rated phase voltage;

[0032] The following formula is used as the objective function to minimize the reactive power dependence of the photovoltaic inverter:

[0033] In the formula The objective function value for minimizing the reactive power dependence of the photovoltaic inverter; This represents the total number of photovoltaic inverters. For the photovoltaic inverter at node n, current phase The reactive power output at time t;

[0034] Construct constraints:

[0035] The following formula is used as the peak demand constraint:

[0036] The following formula is used as the upper and lower voltage limits:

[0037] In the formula This is the set lower limit of the allowable voltage. The set upper limit for voltage;

[0038] The following formula is used as the power constraint for the photovoltaic inverter:

[0039] In the formula For the inverter at node n in phase The power capacity utilization value at time t, and , For the inverter at node n in phase and active power at time t, For the inverter at node n in phase and reactive power at time t; This represents the upper limit of the available capacity of the inverter in the planning model; This refers to the rated capacity of the inverter.

[0040] The following formula is used as the network topology constraint:

[0041] The following formula is used as the topology change constraint:

[0042] In the formula This is the maximum limit value for topology changes set;

[0043] The following formula is used as the energy storage charging and discharging power constraint:

[0044] In the formula This is the set lower limit of the operating power of the energy storage system; The charging and discharging power of the energy storage system at time t. A positive value indicates discharge. A negative value indicates charging; The upper limit of the operating power of the energy storage system is set.

[0045] The following formula is used as the energy constraint for the energy storage system:

[0046] In the formula This is the set minimum energy value for the energy storage system; This is the maximum energy value set for the energy storage system.

[0047] Step S4, based on the Pareto optimality criterion, solves the model constructed in step S3 to complete the collaborative planning of distributed resources of the target power system. Specifically, it includes the following steps:

[0048] A multi-objective optimization algorithm is used to solve the model constructed in step S3 to obtain the Pareto non-dominated solution set;

[0049] In the obtained Pareto non-dominated solution set, the optimal and worst values ​​of each candidate solution on the objective function of the model constructed in step S3 are statistically analyzed, and the objective function values ​​corresponding to each candidate solution are normalized.

[0050] Based on the normalized objective function, the comprehensive distance index from each candidate solution to the ideal point is calculated using the following formula:

[0051] In the formula Let be the comprehensive distance index value of the i-th candidate solution; Let be the normalized objective function value for minimizing the peak system load of the i-th candidate scheme; Let be the normalized objective function value of the i-th candidate scheme that minimizes the overall network voltage deviation; Let be the normalized objective function value of the photovoltaic inverter to minimize reactive power dependence for the i-th candidate scheme;

[0052] The candidate scheme with the smallest comprehensive distance index is selected as the final planning scheme. If there are NN candidate schemes, and the difference between the comprehensive distance index of any two candidate schemes is less than a set threshold, then the final planning scheme is selected in the order of smaller total energy storage capacity and fewer network topology switching. .

[0053] This invention also provides a system for implementing the collaborative planning method for distributed resources of the power system, comprising a data acquisition module, a planning modeling module, a collaborative modeling module, and a collaborative planning module; the data acquisition module, planning modeling module, collaborative modeling module, and collaborative planning module are connected in series; the data acquisition module is used to acquire data information of the target power system and upload the data information to the planning modeling module; the planning modeling module is used to construct a planning model of the energy storage system, network topology, and photovoltaic inverter in the target power system based on the received data information and the acquired data information, and upload the data information to the collaborative modeling module; the collaborative modeling module is used to construct a multi-objective collaborative planning model of the distributed resources of the target power system based on the received data information and the constructed model, with the objectives of minimizing system peak load, minimizing network voltage deviation, and minimizing reactive power dependence of photovoltaic inverters, and upload the data information to the collaborative planning module; the collaborative planning module is used to solve the constructed model based on the Pareto optimality criterion based on the received data information to complete the collaborative planning of the distributed resources of the target power system.

[0054] The collaborative planning method and system for distributed resources in a power system provided by this invention not only achieves collaborative planning of distributed resources in a power system by constructing a planning model for the energy storage system, network topology, and photovoltaic inverter in the target power system, and by constructing and solving a model based on the operating objectives of the target power system, but also achieves higher reliability and better accuracy. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0056] Figure 2 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0057] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The collaborative planning method for distributed resources in a power system disclosed in this invention includes the following steps:

[0058] S1. Obtain data information of the target power system; specifically including the following steps:

[0059] Acquire data information from the target power system;

[0060] The data information includes the charging and discharging efficiency of the energy storage system, the energy state of the energy storage system, the network structure of the target power system, the network parameters of the target power system, the efficiency of the photovoltaic inverter, and the power of the photovoltaic inverter.

[0061] Obtain all candidate schemes for distributed resource planning of the target power system;

[0062] S2. Based on the data obtained in step S1, construct a planning model for the energy storage system, network topology, and photovoltaic inverter in the target power system;

[0063] In practical implementation, the energy storage system planning model includes the following:

[0064] The following formula is used as the planning model for the energy storage system:

[0065] In the formula Let be the energy stored in the energy storage system at time t; For charging efficiency; This refers to the charging power. For time step; For discharge efficiency; This refers to the discharge efficiency.

[0066] The constructed network topology planning model includes the following:

[0067] The following formula is used as the network topology planning model:

[0068] In the formula The objective function value of the network topology planning model; This represents the total number of time segments within the planning period. Number the time sections; The set of nodes in the target power system; Let be the voltage amplitude at node i in the t-th time segment; The node reference voltage; The weighting coefficients for the network loss term; For the set of branches of the target power system; For the branch connecting node i and node j; branch road The equivalent resistance; For the branch at time section t The current amplitude; The weighting coefficient for the cost term of the switching action; For the branch at time section t The switch state variables, the branch at the t-th time section When put into operation The branch at the t-th time section Disconnect ;

[0069] in, Used to characterize the degree of deviation of each node voltage from the reference voltage; Used to characterize network branch loss; Used to characterize the number of switching state changes between adjacent time sections;

[0070] The constructed photovoltaic inverter planning model includes the following:

[0071] The following formula is used as the planning model for photovoltaic inverters:

[0072] In the formula The objective function value for the photovoltaic inverter planning model; A collection of photovoltaic inverters; This is the weighting coefficient for the abandoned light power term; Let be the curtailed power of the k-th photovoltaic inverter at the t-th time segment; This represents the weighting coefficient for the voltage deviation term at the photovoltaic access node. Let be the voltage amplitude of the k-th photovoltaic inverter at the t-th time segment;

[0073] in, Used to represent the total amount of solar power wasted by each photovoltaic inverter at the t-th time segment; Used to indicate the degree of deviation of the voltage at each photovoltaic inverter access node relative to the reference voltage at the t-th time segment;

[0074] S3. Based on the model constructed in step S2, and with the objectives of minimizing system peak load, minimizing grid voltage deviation, and minimizing reactive power dependence of photovoltaic inverters, construct a multi-objective collaborative planning model for distributed resources of the target power system; specifically including the following steps:

[0075] The following formula is used as the objective function to minimize the system's peak load:

[0076] In the formula The objective function value for minimizing the system's peak load is used to minimize the maximum demand by optimizing battery storage scheduling and effective topology identification. This represents the total power drawn from the power grid during each time period; The current phase of the power grid; The current phase extracted from the power grid The active power; A set of candidate network topology schemes; To characterize whether topology was selected at time t The indicator variable, time t, selected the topology. but At time t, no topology was selected. but ; For topology Next current phase Power requirements; This refers to the collection of all nodes where TESS units are installed; a TESS unit is a portable energy storage unit. To represent the TESS cell at node n, current phase The power injected or absorbed over time t;

[0077] The following formula is used as the objective function to minimize the voltage deviation of the entire network:

[0078] In the formula The objective function value is the minimum voltage deviation across the entire network, which is used to improve voltage distribution. Total time; The total number of nodes in the target power system; For node n in the current phase and the actual voltage amplitude at time t; This is the rated phase voltage;

[0079] The following formula is used as the objective function to minimize the reactive power dependence of the photovoltaic inverter:

[0080] In the formula The objective function value for minimizing the reactive power dependence of the photovoltaic inverter; This represents the total number of photovoltaic inverters. For the photovoltaic inverter at node n, current phase The reactive power output at time t; this objective function value effectively reduces the operating burden of a single device by balancing the reactive power output of each inverter at different times.

[0081] Construct constraints:

[0082] The following formula is used as the peak demand constraint:

[0083] This constraint ensures It is always not less than the total grid power at any given time, thus accurately characterizing the peak demand of the system;

[0084] The following formula is used as the upper and lower voltage limits:

[0085] In the formula This is the set lower limit of the allowable voltage. This is the set upper limit for voltage; this constraint is used to prevent voltage exceedances anywhere in the active distribution network.

[0086] The following formula is used as the power constraint for the photovoltaic inverter:

[0087] In the formula For the inverter at node n in phase The power capacity utilization value at time t, and , For the inverter at node n in phase and active power at time t, For the inverter at node n in phase and reactive power at time t; This represents the upper limit of the available capacity of the inverter in the planning model; This refers to the rated capacity of the inverter.

[0088] The following formula is used as the network topology constraint:

[0089] This constraint is used to ensure the effective selection of network topology and prevent frequent switching operations.

[0090] The following formula is used as the topology change constraint:

[0091] In the formula This is the maximum limit for topology changes set; this constraint is used to prevent excessive changes to the selected topology and extend the lifespan of the switch.

[0092] The following formula is used as the energy storage charging and discharging power constraint:

[0093] In the formula This is the set lower limit of the operating power of the energy storage system; The charging and discharging power of the energy storage system at time t. A positive value indicates discharge. A negative value indicates charging; The upper limit of the operating power of the energy storage system is set.

[0094] The following formula is used as the energy constraint for the energy storage system:

[0095] In the formula This is the set minimum energy value for the energy storage system; The maximum energy value set for the energy storage system;

[0096] S4. Based on the Pareto optimality criterion, solve the model constructed in step S3 to complete the collaborative planning of distributed resources of the target power system; specifically including the following steps:

[0097] In practical implementation, this invention employs a Pareto optimality-based multi-objective optimization criterion, constructing a Pareto non-dominated solution set by comparing the dominance relationships of different candidate solutions; specifically, for any two feasible solutions... and If their corresponding three objective function values The following conditions must be met:

[0098] And if there exists at least one k such that the strict inequality holds, then the solution is called a solution. Dominant Solution All solutions not dominated by any other solution constitute the Pareto non-dominated solution set (Pareto front), and each solution in this set represents an effective trade-off among the three objectives. Based on the above criteria, this invention uses a multi-objective optimization algorithm to solve the mixed-integer nonlinear programming model, obtains the Pareto non-dominated solution set, and selects a compromise solution from it as the final collaborative programming scheme.

[0099] Therefore, a multi-objective optimization algorithm is used to solve the model constructed in step S3 to obtain the Pareto non-dominated solution set;

[0100] In the obtained Pareto non-dominated solution set, the optimal and worst values ​​of each candidate solution on the objective function of the model constructed in step S3 are statistically analyzed, and the objective function values ​​corresponding to each candidate solution are normalized.

[0101] Based on the normalized objective function, the comprehensive distance index from each candidate solution to the ideal point is calculated using the following formula:

[0102] In the formula Let be the comprehensive distance index value of the i-th candidate solution; Let be the normalized objective function value for minimizing the peak system load of the i-th candidate scheme; Let be the normalized objective function value of the i-th candidate scheme that minimizes the overall network voltage deviation; Let be the normalized objective function value of the photovoltaic inverter to minimize reactive power dependence for the i-th candidate scheme;

[0103] The candidate scheme with the smallest comprehensive distance index is selected as the final planning scheme. If there are NN candidate schemes, and the difference between the comprehensive distance index of any two candidate schemes is less than a set threshold, then the final planning scheme is selected in the order of smaller total energy storage capacity and fewer network topology switching. .

[0104] To verify the effectiveness of the planning method proposed in this invention, simulation verification was conducted on a test system based on an improved three-phase unbalanced distribution network. This model integrates five photovoltaic power generation systems with a rated capacity of 90 kVA each, and sets up five different optimization cases based on a standard distribution network. For energy storage planning, the system sets up several candidate energy storage units, with a maximum unit capacity of 800 kWh and a maximum power of 90 kW. The overall configuration can support approximately 10% of peak electricity demand. The energy storage system stores energy during off-peak hours at night and releases it during peak hours during the day, thus achieving effective peak shaving and valley filling. To optimize the grid structure, 12 sets of flexibly operable segmented and tie switches are also configured in the network. The simulation covers a complete 24-hour operating cycle, with detailed analysis at 15-minute intervals. Simultaneously, for each time period, an optimal topology is selected from 15 feasible topologies, and the number of topology switching operations is limited to no more than once per day to reduce wear and tear on switching equipment. To comprehensively evaluate the effectiveness of different control strategies, a comparison scheme, namely the basic operation scheme, was set up.

[0105] The comparison results are shown in Table 1 and Table 2:

[0106] As can be seen from Tables 1 and 2, the proposed solution has significant advantages. Compared to the baseline solution, the proposed solution reduces the system peak load from 4443kW to 4168kW, a reduction of 6.19%; the total voltage deviation of the entire network decreases from 0.022 to 0.0191, a reduction of approximately 13.18%. This indicates that the proposed solution is effective in reducing operational pressure.

[0107] This invention integrates the coordinated optimization of energy storage configuration and scheduling, network topology selection, and reactive power support from photovoltaic inverters. Compared to a single control strategy or a simple combination, it better addresses the inherent conflicts between objectives and achieves effective trade-offs through the Pareto optimal mechanism. The resulting comprehensive planning scheme not only exhibits superior performance at the operational level but also clearly defines planning decisions regarding energy storage resource allocation, network structure selection, and reactive power support capacity arrangement.

[0108] like Figure 2 The diagram shows the functional modules of the system of the present invention: The system for implementing the collaborative planning method of distributed resources in a power system disclosed in this invention includes a data acquisition module, a planning modeling module, a collaborative modeling module, and a collaborative planning module; the data acquisition module, planning modeling module, collaborative modeling module, and collaborative planning module are connected in series; the data acquisition module is used to acquire data information of the target power system and upload the data information to the planning modeling module; the planning modeling module is used to construct a planning model of the energy storage system, network topology, and photovoltaic inverter in the target power system based on the received data information and the acquired data information, and upload the data information to the collaborative modeling module; the collaborative modeling module is used to construct a multi-objective collaborative planning model of the distributed resources of the target power system based on the received data information and the constructed model, with the objectives of minimizing the system peak load, minimizing the overall network voltage deviation, and minimizing the reactive power dependence of the photovoltaic inverter, and upload the data information to the collaborative planning module; the collaborative planning module is used to solve the constructed model based on the Pareto optimality criterion based on the received data information to complete the collaborative planning of the distributed resources of the target power system.

Claims

1. A collaborative planning method for distributed resources in a power system, comprising the following steps: S1. Obtain data information about the target power system; S2. Based on the data obtained in step S1, construct a planning model for the energy storage system, network topology, and photovoltaic inverter in the target power system; S3. Combining the model constructed in step S2, with the objectives of minimizing system peak load, minimizing grid voltage deviation, and minimizing reactive power dependence of photovoltaic inverters, construct a multi-objective collaborative planning model for distributed resources of the target power system; S4. Based on the Pareto optimality criterion, solve the model constructed in step S3 to complete the collaborative planning of distributed resources of the target power system.

2. The collaborative planning method for distributed resources in a power system according to claim 1, characterized in that... Step S1, which involves acquiring data information about the target power system, specifically includes the following steps: Acquire data information from the target power system; The data information includes the charging and discharging efficiency of the energy storage system, the energy state of the energy storage system, the network structure of the target power system, the network parameters of the target power system, the efficiency of the photovoltaic inverter, and the power of the photovoltaic inverter. Obtain all candidate schemes for distributed resource planning of the target power system.

3. The collaborative planning method for distributed resources in a power system according to claim 2, characterized in that... The constructed energy storage system planning model specifically includes the following: The following formula is used as the planning model for the energy storage system: In the formula Let be the energy stored in the energy storage system at time t; For charging efficiency; This refers to the charging power. For time step; For discharge efficiency; This refers to the discharge efficiency.

4. The collaborative planning method for distributed resources in a power system according to claim 3, characterized in that... The constructed network topology planning model includes the following: The following formula is used as the network topology planning model: In the formula The objective function value of the network topology planning model; This represents the total number of time segments within the planning period. Number the time sections; The set of nodes in the target power system; Let be the voltage amplitude at node i in the t-th time segment; The node reference voltage; The weighting coefficients for the network loss term; For the set of branches of the target power system; For the branch connecting node i and node j; branch road The equivalent resistance; For the branch at time section t The current amplitude; The weighting coefficient for the cost term of the switching action; For the branch at time section t The switch state variables, the branch at the t-th time section When put into operation The branch at the t-th time section Disconnect .

5. The collaborative planning method for distributed resources in a power system according to claim 4, characterized in that... The constructed photovoltaic inverter planning model includes the following: The following formula is used as the planning model for photovoltaic inverters: In the formula The objective function value for the photovoltaic inverter planning model; A collection of photovoltaic inverters; This is the weighting coefficient for the abandoned light power term; Let be the curtailed power of the k-th photovoltaic inverter at the t-th time segment; This represents the weighting coefficient for the voltage deviation term at the photovoltaic access node. Let be the voltage amplitude of the k-th photovoltaic inverter at the t-th time segment.

6. The collaborative planning method for distributed resources in a power system according to claim 5, characterized in that... Step S3, combined with the model constructed in step S2, aims to minimize the system peak load, the overall grid voltage deviation, and the reactive power dependence of photovoltaic inverters. It constructs a multi-objective collaborative planning model for distributed resources of the target power system, specifically including the following steps: The following formula is used as the objective function to minimize the system's peak load: In the formula This represents the objective function value that minimizes the system's peak load. This represents the total power drawn from the power grid during each time period; The current phase of the power grid; The current phase extracted from the power grid The active power; A set of candidate network topology schemes; To characterize whether topology was selected at time t The indicator variable, time t, selected the topology. but At time t, no topology was selected. but ; For topology Next current phase Power requirements; The set of all nodes for which TESS units are installed; TESS units are portable energy storage units; To represent the TESS cell at node n, current phase The power injected or absorbed over time t; The following formula is used as the objective function to minimize the voltage deviation of the entire network: In the formula The objective function value is the minimum voltage deviation across the entire network. Total time; The total number of nodes in the target power system; For node n in the current phase and the actual voltage amplitude at time t; This is the rated phase voltage; The following formula is used as the objective function to minimize the reactive power dependence of the photovoltaic inverter: In the formula The objective function value for minimizing the reactive power dependence of the photovoltaic inverter; This represents the total number of photovoltaic inverters. For the photovoltaic inverter at node n, current phase The reactive power output at time t; Construct constraints: The following formula is used as the peak demand constraint: The following formula is used as the upper and lower voltage limits: In the formula This is the set lower limit of the allowable voltage. The set upper limit for voltage; The following formula is used as the power constraint for the photovoltaic inverter: In the formula For the inverter at node n in phase The power capacity utilization value at time t, and , For the inverter at node n in phase and active power at time t, For the inverter at node n in phase and reactive power at time t; This represents the upper limit of the available capacity of the inverter in the planning model; This refers to the rated capacity of the inverter. The following formula is used as the network topology constraint: The following formula is used as the topology change constraint: In the formula This is the maximum limit value for topology changes set; The following formula is used as the energy storage charging and discharging power constraint: In the formula This is the set lower limit of the operating power of the energy storage system; The charging and discharging power of the energy storage system at time t; The upper limit of the operating power of the energy storage system is set. The following formula is used as the energy constraint for the energy storage system: In the formula This is the set minimum energy value for the energy storage system; This is the maximum energy value set for the energy storage system.

7. The collaborative planning method for distributed resources in a power system according to claim 6, characterized in that... Step S4, based on the Pareto optimality criterion, solves the model constructed in step S3 to complete the collaborative planning of distributed resources of the target power system. Specifically, it includes the following steps: A multi-objective optimization algorithm is used to solve the model constructed in step S3 to obtain the Pareto non-dominated solution set; In the obtained Pareto non-dominated solution set, the optimal and worst values ​​of each candidate solution on the objective function of the model constructed in step S3 are statistically analyzed, and the objective function values ​​corresponding to each candidate solution are normalized. Based on the normalized objective function, the comprehensive distance index from each candidate solution to the ideal point is calculated using the following formula: In the formula Let be the comprehensive distance index value of the i-th candidate solution; Let be the normalized objective function value for minimizing the peak system load of the i-th candidate scheme; Let be the normalized objective function value of the i-th candidate scheme that minimizes the overall network voltage deviation; Let be the normalized objective function value of the photovoltaic inverter to minimize reactive power dependence for the i-th candidate scheme; The candidate scheme with the smallest comprehensive distance index is selected as the final planning scheme. If there are NN candidate schemes, and the difference between the comprehensive distance index of any two candidate schemes is less than a set threshold, then the final planning scheme is selected in the order of smaller total energy storage capacity and fewer network topology switching. .

8. A system for implementing the collaborative planning method for distributed resources in a power system as described in any one of claims 1 to 7, characterized in that... It includes a data acquisition module, a planning and modeling module, a collaborative modeling module, and a collaborative planning module; the data acquisition module, planning and modeling module, collaborative modeling module, and collaborative planning module are connected in series; the data acquisition module is used to acquire data information of the target power system and upload the data information to the planning and modeling module; The planning and modeling module is used to construct planning models of energy storage systems, network topologies, and photovoltaic inverters in the target power system based on the received and acquired data, and upload the data to the collaborative modeling module. The collaborative modeling module is used to construct a multi-objective collaborative planning model for the distributed resources of the target power system based on the received data information and the constructed model, with the objectives of minimizing the system peak load, minimizing the overall grid voltage deviation, and minimizing the reactive power dependence of the photovoltaic inverter, and upload the data information to the collaborative planning module. The collaborative planning module is used to solve the constructed model based on the received data and the Pareto optimality criterion, thereby completing the collaborative planning of distributed resources of the target power system.

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