Multi-objective programming optimization method and system for distribution networks considering distribution micro-coordination

Abstract

This invention proposes a multi-objective planning optimization method and system for distribution networks considering distribution-microgrid collaboration. First, it analyzes the regional characteristics, planning period, and current status of the distribution network to be planned. Second, it forecasts load growth and new photovoltaic installations, constructs typical load and photovoltaic curves, and diagnoses existing problems in the current power grid. Then, with system investment economy and power supply reliability as optimization objectives, it establishes a multi-objective, two-layer planning optimization model that considers both distribution network infrastructure upgrades and microgrid equipment upgrades. Finally, iterative calculations using a multi-objective solving algorithm yield an optimized distribution network planning scheme incorporating distribution-microgrid collaboration characteristics. This invention enables coordinated optimization of the distribution network and microgrids, balancing economy and reliability, and provides a scientific decision-making basis for medium- and long-term power grid planning.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and optimization technology, specifically involving a multi-objective planning and optimization method and system for distribution networks that considers distribution-micro-network collaboration. Background Technology

[0002] With the continuous advancement of the construction of new power systems, the operational characteristics of distribution networks are undergoing profound changes. On the one hand, distribution networks are gradually transforming from passive to active networks. The large-scale integration of distributed power sources, energy storage devices, and flexible loads has enabled distribution networks to not only perform the function of power transmission but also gradually acquire the capabilities of local power generation, regulation, and consumption. On the other hand, the flow of power in distribution networks is also evolving from the traditional unidirectional flow to bidirectional interaction, with power exhibiting more flexible and diverse transmission modes between the main grid and distribution networks, and between distribution networks and microgrids. Furthermore, due to the uncertainty of distributed power output and the volatility of load demand, the operating state of distribution networks has evolved from a relatively deterministic model in the past to a complex system with high randomness and uncertainty.

[0003] Against this backdrop, the planning objectives of distribution networks have also shifted. The core objective of traditional distribution network planning primarily focuses on enhancing power supply security, i.e., meeting the ever-increasing load demand through grid reinforcement and equipment expansion. However, in the context of new power systems, simply pursuing power supply security is insufficient to meet the needs of system development. The new planning objectives should be further expanded to promote multi-energy integration and complementarity, and to drive the aggregation and interaction of multiple stakeholders. This means achieving complementary supply of comprehensive energy sources such as electricity, heat, and gas through the coordinated utilization of various energy forms, and optimizing the overall system operation by mobilizing the enthusiasm of users, distributed power sources, and microgrids. Simultaneously, with the rapid development of diverse loads and massive distributed power sources, they often connect to the distribution network in the form of microgrids. As autonomous units integrating distributed power sources, energy storage, and adjustable loads, microgrids possess characteristics of localization, flexibility, and controllability, which can alleviate the operational pressure on the distribution network to a certain extent and improve the flexibility and reliability of power supply. However, existing traditional distribution network planning methods mostly rely on the distribution network's own load growth and equipment capacity, failing to fully consider the collaborative relationship between microgrids and the distribution network. This deficiency is mainly reflected in two aspects: 1. Insufficient resource utilization: Traditional planning has failed to fully explore the value of the flexibility resources on the microgrid side. For example, the regulation capabilities of distributed energy storage and the peak-shaving and valley-filling functions of adjustable loads in microgrids have not been fully utilized in the overall distribution network planning.

[0004] 2. Underestimation of Investment Value: Traditional planning fails to reflect the role of microgrids in delaying distribution network investment. In fact, through reasonable distribution-microgrid coordinated planning, the local power supply and regulation capabilities of microgrids can be fully utilized, thereby effectively reducing the need for expansion of distribution network equipment and lowering system investment costs.

[0005] Research on distribution network planning oriented towards distribution-microgrid collaboration has significant theoretical and practical implications. It helps to promote the development of smart distribution networks, improve energy utilization efficiency, ensure power supply reliability, promote the consumption of new energy sources, and achieve comprehensive optimization of economy, reliability, and environmental protection.

[0006] Current research on distribution network-microgrid system planning, such as the patent application with publication number CN119742759A, uses historical operational data of the distribution network and microgrids to obtain typical scenario characteristics. Based on these typical scenario characteristics and preset grid collaborative operation requirements, constraints and objective functions are established to obtain a collaborative planning and operation model for the distribution network and microgrids. This collaborative planning and operation model includes a grid planning layer and a grid operation layer. Solving the linearized collaborative planning and operation model yields configuration and scheduling strategies. Another example is the patent application with publication number CN108197766A, which proposes a two-layer optimal scheduling model suitable for active distribution networks containing microgrid clusters. The upper-layer model focuses on the distribution network, with the optimization objective being to improve power quality and reduce line losses. The lower-layer model focuses on the microgrid, with the optimization objective being to minimize costs.

[0007] For distribution network planning that considers distribution-microgrid collaboration, most existing studies only focus on single distribution network power source planning or single microgrid power source planning, without fully considering distribution network upgrades and the collaboration between distribution network and microgrid power source planning, making it difficult to meet the actual power grid planning needs. In terms of multi-objective planning optimization of distribution networks, existing methods usually only consider the economic efficiency of system investment, while ignoring the impact of power supply reliability on planning decisions. For solving the multi-objective bi-level planning model of distribution networks that takes into account both economic efficiency and power supply reliability, there is an urgent need to design innovative multi-objective optimization methods and establish a dynamic adjustment mechanism for multi-objective weights to achieve the optimization and improvement of the overall system performance.

[0008] Therefore, in order to solve the above problems, it is urgent to study a multi-objective programming optimization method and system for distribution networks that considers distribution-micro-cooperation. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides a multi-objective planning optimization method and system for distribution networks that considers distribution-microgrid collaboration. By analyzing the current status of the planning area and predicting load and photovoltaic growth, a two-layer optimization model is constructed that takes into account both distribution network structure transformation and microgrid equipment upgrades. With the objectives of system investment economy and power supply reliability, a multi-objective algorithm is used to obtain the Pareto optimal solution set, achieving a holistic optimization of economy and reliability, and providing a scientific decision-making basis for power grid planning.

[0010] This invention discloses a multi-objective programming optimization method for distribution networks that considers distribution-micro-network collaboration, comprising: S1. For the planned distribution network, a multi-objective bi-level planning optimization model for the distribution network is constructed and represented by a multi-objective function. The multi-objective bi-level planning optimization model for the distribution network includes an upper-level model with the system investment cost of the distribution network as the objective and a lower-level model with the power supply reliability as the objective. S2, Solve the upper-level model to obtain the system investment cost and different planning schemes, and pass them to the lower-level model; S3. Based on each of the planning schemes, solve the lower-level model to calculate the power distribution flow; based on the power distribution flow, calculate the power supply reliability index. S4, based on the system investment cost and the power supply reliability index, calculate the multi-objective function value of the planning scheme by weighted summation; based on the multi-objective function value of each planning scheme, form a non-dominated solution set; S5. When the preset iteration stopping condition is not met, adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function, and then repeat steps S3-S5. When the iteration stopping condition is met, generate a Pareto solution set based on all the non-dominated solution sets generated by the iteration. Obtain the optimal planning scheme based on the Pareto solution set.

[0011] More preferably, in S2, the specific calculation process of the upper-level model is as follows: At each node, the product of the unit construction cost of the line in the distribution network and the length of the power supply point outgoing line is weighted and summed with the unit construction cost of the switch. The result of the weighted sum is multiplied with the switch quantity indicating whether to construct the power supply point outgoing line to obtain the first multiplication result. The product of the unit construction cost of the line in the distribution network and the length of the adjacent transfer line is summed with the unit construction cost of the switch. The summation result is then multiplied by the switch quantity indicating whether to construct the adjacent transfer line to obtain the second multiplication result. The third multiplication result is obtained by multiplying the unit construction cost of energy storage, the capacity of energy storage equipment, and the switching quantity indicating whether or not to build energy storage equipment. Add the first, second, and third multiplication results together to obtain a summation result; add the summation results of all nodes together to obtain the upper-level model; Setting constraints for the upper-level model includes: The sum of the switch quantity indicating whether to build a power supply point outgoing line, the switch quantity indicating whether to build a nearby transfer line, and the switch quantity indicating whether to build an energy storage device is 1; Maximum installation capacity constraints for energy storage devices.

[0012] More preferably, in S3, the specific calculation process of the lower-level model is as follows: At each node, calculate the expected outage time function based on the switching quantity representing whether to build a power supply point outgoing line, the switching quantity representing whether to build a nearby transfer line, the switching quantity representing whether to build an energy storage device, and the capacity of the energy storage device; The expected power outage time functions corresponding to all nodes are summed, and the summation result is divided by the number of nodes to obtain the lower-level model. Constraints are set for the lower-level model, including distribution network security constraints, renewable energy maximization consumption constraints, energy storage operation constraints, root node power constraints, and power flow constraints.

[0013] More preferably, in S3, the lower-level model is solved based on each planning scheme to calculate the power distribution flow; based on the power distribution flow, the power supply reliability index is calculated, specifically by: For each of the planning schemes, the power flow model is made convex using the second-order cone relaxation method and then solved to obtain the power flow of the distribution network. Based on the power flow of the distribution network, the affine minimum path method is used to solve the problem and obtain the system power supply reliability index under the planning scheme, including user power outage time and power supply reliability rate.

[0014] More preferably, a dynamic weight adjustment mechanism is used to adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function. The specific steps are as follows: The system investment cost and the power supply reliability index The weights are set as follows: and initialize it; In each iteration, calculate and Change and ; Based on the change, calculate the weight for the next iteration: for Multiply by the adjustment coefficient and The difference is used to obtain the fourth product result; the fourth product result is then compared with the result obtained in the current iteration. The corresponding weights are added together to obtain the weight. ; for Multiply by the adjustment coefficient and The difference is used to obtain the fifth product result; the fifth product result is then compared with the result obtained in the current iteration. The corresponding weights are added together to obtain the weight. ; Weight and Normalize the weights and use the normalized weights in the next iteration.

[0015] This invention also proposes a multi-objective planning optimization system for distribution networks that considers distribution-micro-network collaboration, including a multi-objective two-level planning optimization model construction module, an iterative solution module, an optimal planning scheme acquisition module, and a planning judgment module: The system includes a planning and judgment module and a distribution network multi-objective bi-level planning optimization model construction module. The distribution network multi-objective bi-level planning optimization model is characterized by a multi-objective function. The distribution network multi-objective bi-level planning optimization model includes an upper-level model with the system investment cost of the distribution network as the objective and a lower-level model with the power supply reliability as the objective. The iterative solution module, in which each iteration of the multi-objective bi-level programming optimization model for the distribution network is solved includes: The system investment cost and different planning schemes are obtained by solving the upper-level model and then passed to the lower-level model. Based on each planning scheme, the lower-level model is solved to calculate the power flow of the distribution network. Based on the power distribution network flow, the power supply reliability index is calculated; based on the system investment cost and the power supply reliability index, the multi-objective function value of the planning scheme is calculated by weighted summation; wherein, in each iteration, a dynamic weight adjustment mechanism is used to adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function; Based on the multi-objective function values ​​of each of the planning schemes, a non-dominated solution set is formed; The optimal planning scheme acquisition module generates a Pareto solution set based on all the non-dominated solution sets generated by the iteration when the iteration stopping condition is met; and obtains the optimal planning scheme based on the Pareto solution set.

[0016] In the iterative solution module, a dynamic weight adjustment mechanism is used in each iteration to adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function.

[0017] The weights of the system investment cost and the power supply reliability index in the multi-objective function are adjusted using a dynamic weight adjustment mechanism. The specific steps are as follows: The system investment cost and the power supply reliability index The weights are set as follows: and initialize it; In each iteration, calculate and Change and ; Based on the change, calculate the weight for the next iteration: for Multiply by the adjustment coefficient and The difference is used to obtain the fourth product result; the fourth product result is then compared with the result obtained in the current iteration. The corresponding weights are added together to obtain the weight. ; for Multiply by the adjustment coefficient and The difference is used to obtain the fifth product result; the fifth product result is then compared with the result obtained in the current iteration. The corresponding weights are added together to obtain the weight. ; Weight and Normalize the weights and use the normalized weights in the next iteration.

[0018] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps according to the method described above.

[0019] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0020] The beneficial effects of this invention are that the distribution network planning method considering micro-distribution coordination has the following significant advantages over traditional distribution network power supply reliability: 1) Traditional distribution network planning typically neglects the role of microgrids, focusing only on the expansion and upgrading of the main distribution network structure, making it difficult to fully utilize the regulation potential of distributed power sources and energy storage systems. This invention, however, introduces a distribution network-microgrid collaborative mechanism, considering multi-level collaborative planning during the planning stage: rational upgrading and expansion of the distribution network structure, and rational layout and operational optimization of microgrid energy storage systems. This not only improves the overall reliability and economy of the power grid but also significantly enhances the grid's adaptability to high-proportion renewable energy integration, achieving coordinated development of the distribution network and microgrids. 2) Traditional power distribution network planning is primarily driven by a single objective: investment cost or power supply reliability. This invention proposes a multi-objective, two-layer optimization model that considers both economic efficiency and power supply reliability simultaneously. It introduces a dynamic weight adjustment mechanism, allowing the weights to be automatically adjusted at different optimization stages based on the trade-offs between objectives. This achieves optimal overall investment efficiency while ensuring the safety and stability of the power grid. 3) Traditional single optimization methods often struggle to efficiently obtain satisfactory solutions, frequently exhibiting slow convergence, local optima, or difficulty in handling nonlinear constraints. This invention proposes a novel metaheuristic algorithm, the Kangaroo Escape Optimizer (KEO), which improves the global optimality and practicality of the results while maintaining solution efficiency. 4) Through case studies, the present invention can effectively alleviate line overload, improve the capacity for renewable energy absorption, and enhance power supply reliability. At the same time, it improves economic efficiency by more than 10%, which is better than the traditional approach of relying on new lines or simply expanding capacity. Attached Figure Description

[0021] Figure 1 This is a flowchart of a multi-objective planning optimization method for distribution networks that considers distribution micro-cooperation according to the present invention; Figure 2 This is a schematic diagram of the multi-objective planning and optimization method for distribution networks that considers distribution-micro-cooperation in this invention; Figure 3 This is a system diagram of the power distribution network example in this invention; Figure 4 This is a system diagram of the distribution network after the addition of photovoltaic power in this invention; Figure 5 This is a typical source-load output curve diagram in this invention; Figure 6 This is a solution diagram of the multi-objective bi-level programming model for power distribution networks in this invention; Figure 7 This is a diagram of the power distribution network planning scheme of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0023] As attached Figure 1 As shown, this invention discloses a multi-objective programming optimization method for distribution networks that considers distribution-micro-network collaboration, comprising the following steps: S1. For the planned distribution network, a multi-objective bi-level planning optimization model for the distribution network is constructed and represented by a multi-objective function. The multi-objective bi-level planning optimization model for the distribution network includes an upper-level model with the system investment cost of the distribution network as the objective and a lower-level model with the power supply reliability as the objective. S2, Solve the upper-level model to obtain the system investment cost and different planning schemes, and pass them to the lower-level model; The specific calculation process of the upper-level model is as follows: At each node, the product of the unit construction cost of the line in the distribution network and the length of the power supply point outgoing line is weighted and summed with the unit construction cost of the switch. The result of the weighted sum is multiplied with the switch quantity indicating whether to construct the power supply point outgoing line to obtain the first multiplication result. The product of the unit construction cost of the line in the distribution network and the length of the adjacent transfer line is summed with the unit construction cost of the switch. The summation result is then multiplied by the switch quantity indicating whether to construct the adjacent transfer line to obtain the second multiplication result. The third multiplication result is obtained by multiplying the unit construction cost of energy storage, the capacity of energy storage equipment, and the switching quantity indicating whether or not to build energy storage equipment. Add the first, second, and third multiplication results together to obtain a summation result; add the summation results of all nodes together to obtain the upper-level model; Setting constraints for the upper-level model includes: The sum of the switch quantity indicating whether to build a power supply point outgoing line, the switch quantity indicating whether to build a nearby transfer line, and the switch quantity indicating whether to build an energy storage device is 1; Maximum installation capacity constraints for energy storage devices.

[0024] S3. Based on each of the planning schemes, solve the lower-level model to calculate the power distribution flow; based on the power distribution flow, calculate the power supply reliability index. The specific calculation process of the lower-level model is as follows: At each node, calculate the expected outage time function based on the switching quantity representing whether to build a power supply point outgoing line, the switching quantity representing whether to build a nearby transfer line, the switching quantity representing whether to build an energy storage device, and the capacity of the energy storage device; The expected power outage time functions corresponding to all nodes are summed, and the summation result is divided by the number of nodes to obtain the lower-level model. Constraints are set for the lower-level model, including distribution network security constraints, renewable energy maximization consumption constraints, energy storage operation constraints, root node power constraints, and power flow constraints.

[0025] Based on each of the planning schemes, the lower-level model is solved to calculate the power distribution network flow; based on the power distribution network flow, the power supply reliability index is calculated, specifically using the following method: For each of the planning schemes, the power flow model is made convex using the second-order cone relaxation method and then solved to obtain the power flow of the distribution network. Based on the power flow of the distribution network, the affine minimum path method is used to solve the problem and obtain the system power supply reliability index under the planning scheme, including user power outage time and power supply reliability rate.

[0026] S4, based on the system investment cost and the power supply reliability index, calculate the multi-objective function value of the planning scheme by weighted summation; based on the multi-objective function value of each planning scheme, form a non-dominated solution set; S5. When the preset iteration stopping condition is not met, adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function, and then repeat steps S3-S5. When the iteration stopping condition is met, generate a Pareto solution set based on all the non-dominated solution sets generated by the iteration. Obtain the optimal planning scheme based on the Pareto solution set.

[0027] The weights of the system investment cost and the power supply reliability index in the multi-objective function are adjusted using a dynamic weight adjustment mechanism. The specific steps are as follows: The system investment cost and the power supply reliability index The weights are set as follows: and initialize it; In each iteration, calculate and Change and ; Based on the change, calculate the weight for the next iteration: for Multiply by the adjustment coefficient and The difference is used to obtain the fourth product result; the fourth product result is then compared with the result obtained in the current iteration. The corresponding weights are added together to obtain the weight. ; for Multiply by the adjustment coefficient and The difference is used to obtain the fifth product result; the fifth product result is then compared with the result obtained in the current iteration. The corresponding weights are added together to obtain the weight. ; Weight and Normalize the weights and use the normalized weights in the next iteration.

[0028] This invention also proposes a multi-objective planning optimization system for distribution networks that considers distribution-micro-network collaboration, including a multi-objective two-level planning optimization model construction module, an iterative solution module, an optimal planning scheme acquisition module, and a planning judgment module: The system includes a planning and judgment module and a distribution network multi-objective bi-level planning optimization model construction module. The distribution network multi-objective bi-level planning optimization model is characterized by a multi-objective function. The distribution network multi-objective bi-level planning optimization model includes an upper-level model with the system investment cost of the distribution network as the objective and a lower-level model with the power supply reliability as the objective. The iterative solution module, in which each iteration of the multi-objective bi-level programming optimization model for the distribution network is solved includes: The system investment cost and different planning schemes are obtained by solving the upper-level model and then passed to the lower-level model. Based on each planning scheme, the lower-level model is solved to calculate the power flow of the distribution network. Based on the power distribution network flow, the power supply reliability index is calculated; based on the system investment cost and the power supply reliability index, the multi-objective function value of the planning scheme is calculated by weighted summation; wherein, in each iteration, a dynamic weight adjustment mechanism is used to adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function; Based on the multi-objective function values ​​of each of the planning schemes, a non-dominated solution set is formed; The optimal planning scheme acquisition module generates a Pareto solution set based on all the non-dominated solution sets generated by the iteration when the iteration stopping condition is met; and obtains the optimal planning scheme based on the Pareto solution set.

[0029] In the iterative solution module, a dynamic weight adjustment mechanism is used in each iteration to adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function.

[0030] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps according to the method described above.

[0031] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0032] Example 1 The first embodiment of this example provides a multi-objective programming optimization method and system for distribution networks that considers distribution-micro-cooperation, such as... Figure 2 As shown, it includes the following steps: Step 1: Taking system investment cost and power supply reliability as objectives, a multi-objective two-level planning optimization model for distribution networks that considers distribution network and microgrid collaboration is proposed, taking into account both distribution network structure transformation and microgrid equipment upgrades; Specifically, building upon the previous step's analysis of issues such as line overload and voltage exceeding limits in the distribution network, a multi-objective, two-layer programming optimization model is constructed. The upper layer prioritizes system investment cost, focusing on the rational modification and expansion of the distribution network structure, the configuration of distributed power sources in microgrids, and the optimization of energy storage systems. The lower layer, constrained by local supply-demand balance and optimized energy scheduling, focuses on system power supply reliability and the distribution network's overload operation under distribution-microgrid coordination. By introducing the collaborative coupling relationship between the distribution network and microgrids, coordinated optimization design of the macro-grid and microgrids is achieved, ensuring that power supply reliability requirements are met while also considering the economic efficiency and flexibility of the planning.

[0033] Before planning a distribution network, analyze the planning area, planning period, and general situation of the distribution network to be planned. In a preferred but non-limiting embodiment of the present invention: Specifically, by analyzing the geographical location, load density, user structure, and energy resource endowment of the planning area, and combining the electricity demand growth trend within the planning period, the overall development needs and constraints of the distribution network are obtained. Furthermore, a comprehensive review of the existing grid structure, power supply capacity, operating characteristics, and equipment status is conducted to provide basic data support for subsequent planning optimization.

[0034] Furthermore, load growth and photovoltaic new installation prospects are predicted for the planned distribution network. These source-load data are input into the distribution network power flow calculation model for simulation calculation, and the operation problems of the planned distribution network are analyzed and evaluated. Specifically, the calculation method for predicting load growth during the planning period is as follows: the load growth during the planning period is the sum of natural load growth and the load of newly added users in the region.

[0035] In the formula: Indicates the target year Natural growth load forecast (unit: MW); Indicates the base year Natural growth load (unit: MW); Annual growth rate (expressed as a decimal); Indicates the number of years from the base year to the target year (unit: years); Indicates the target year The new user load value (unit: MW).

[0036] Furthermore, we forecast the future prospects of new photovoltaic installations, namely the planned photovoltaic capacity. The calculation formula is: ; In the formula, The total building footprint; The ratio of the building's roof area to its total floor area; Roof utilization factor; This refers to the photovoltaic installation density per unit area.

[0037] The power flow calculation model for the distribution network is as follows:

[0038] In the formula: , All are distribution network node numbers; For nodes i It is the set of branch end nodes of the first node; For nodes The set of the starting nodes of the branches of the terminal nodes; and They are nodes Time period The net active power and net reactive power injection; , and Time periods via branch road The active power, reactive power, and squared current amplitude; and Branch roads Resistance and reactance; For time period node The square of the voltage amplitude; This is a collection of distribution network branches. and They are nodes Upper and lower limits of the square of voltage amplitude; branch road The upper limit of the square of the current amplitude; branch road The capacity.

[0039] Input these source and load data into the power flow calculation model of the distribution network to analyze whether there are problems such as line overload or voltage exceeding limits in the distribution network. If these problems occur, then distribution network planning is carried out.

[0040] The objective function F of the multi-objective bi-level programming optimization model for distribution networks considering micro-distribution coordination is to maximize investment economics. Power supply reliability :

[0041] In the formula: It is a 0-1 variable, indicating whether to build a power supply point outgoing line; The length of the power supply cable; It is a 0-1 variable, indicating whether to build a nearby power transfer line; The length of the adjacent transfer line; It is a 0-1 variable, indicating whether or not to build energy storage equipment; This refers to the capacity of the energy storage device.

[0042] Note: Weighting coefficients are added to the objective functions for economic efficiency and reliability. Different microgrids have different objectives, so a dynamic weight adjustment mechanism is introduced to automatically adjust the weights at different optimization stages based on the trade-offs between objectives. The dynamic weight adjustment mechanism is as follows: Step 1.1, Initialize weights ; Step 1.2: Calculate the current improvement in the objective, and calculate the change in the objective function value at each iteration (or time step): ; Step 1.3, Dynamic Adjustment: If a certain objective improves slowly, increase its weight.

[0043] in It is an adjustment coefficient (determined based on actual experimental performance, such as...). ), control the adjustment range.

[0044] Step 1.4. Normalized weights:

[0045] Step 1.5, Update the objective function:

[0046] Step 1.6: Proceed to the next iteration and repeat steps 1.2-1.5.

[0047] Upper-level planning model: 1) Objective function: System investment economy; the lower the investment cost, the higher the system economy.

[0048]

[0049] In the formula: The number of nodes; , , These represent the unit construction costs for lines, switches, and energy storage, respectively.

[0050] 2) Constraints Investment plan: Line site selection or energy storage site selection and capacity determination:

[0051] In the formula: This represents the maximum installed capacity of the energy storage device.

[0052] Lower-level operating model: 1) Objective function: System power supply reliability. The shorter the average power outage time per household, the higher the system power supply reliability.

[0053]

[0054] In the formula: For load point j Regarding decision variables The expected power outage time function.

[0055] 2) Constraints ① Distribution network safety constraints: line load rate, voltage / current over-limit constraints:

[0056] In the formula: for t Timetable ij Load rate; for t Timetable ij Maximum load rate; for t Time Node j The square of the voltage value; for t Timetable ij The square of the current value; This represents the maximum carrying capacity of the line.

[0057] ② Constraints on maximizing the absorption of renewable energy:

[0058] In the formula: for t Time Node j The actual output of photovoltaic power; for t Time Node j The maximum output of photovoltaic power is predicted.

[0059] ③ Energy storage operation constraints:

[0060]

[0061] In the formula: , , , , , , Let be the stored energy, charging power, discharging power, charging efficiency, discharging efficiency, charging state, and discharging state of node j at time t, respectively. , , , These represent the maximum charging power, maximum discharging power, upper limit, and lower limit of the stored energy at node j at time t, respectively.

[0062] ④ Root node power constraint:

[0063] In the formula: , , , These are the active power, active power limit, reactive power, and reactive power limit of the root node, respectively.

[0064] ⑤ Current constraints:

[0065]

[0066]

[0067] In the formula: A 0-1 variable, representing the line ij The on / off state; , They are nodes j Active and reactive loads; , The lines are respectively ij Active and reactive power ； , The lines are respectively ij Resistance and reactance; A very large value is set by an individual, which is set to 10 to the power of 5 in this invention; and These represent the active and reactive loads transmitted from node j to node j in line jk, respectively.

[0068] Step 2: Iteratively solve the two-level planning model of the distribution network to obtain a distribution network planning scheme that considers distribution-micro-network coordination; During the solution process, factors such as distribution network infrastructure upgrade investment, microgrid equipment upgrade costs, system operational reliability, and renewable energy absorption capacity are comprehensively considered, gradually converging to a set of Pareto optimal solutions. Finally, based on the decision-maker's preferences, a distribution network optimization planning scheme that balances economy and reliability is selected from the Pareto solution set to achieve the goal of coordinated development of distribution networks and microgrids.

[0069] Specifically, for the established upper-level model, an intelligent optimization algorithm suitable for multi-objective optimization problems, namely the Multi-Objective Kangaroo Escape Optimization Algorithm (MOKEO), is selected to solve the planning scheme through iterative search.

[0070] The Kangaroo Espe Optimization Technique (KET) is a novel metaheuristic algorithm inspired by the survival-driven escape strategies of kangaroos in unpredictable environments. This algorithm simulates several key kangaroo behaviors, such as zigzag movement under threat, long-distance jumps to avoid predators, and the use of decoys for distraction, thereby effectively balancing exploration and exploitation in the search space. The KET algorithm integrates a chaotic logic energy adaptation strategy, a two-stage exploration process (zigzag movement and long-jump escape), an adaptive exploitation phase, and a unique decoy deployment mechanism to prevent premature convergence and ensure population diversity. The algorithm steps are as follows: Step 1. Initialization: At the start of the algorithm, a kangaroo population is initialized, and the position of each kangaroo represents a potential solution in a D-dimensional decision space. The position of the j-th kangaroo... Randomly generate the values ​​within the given upper and lower bounds Up and Low using the following formula:

[0071] in, Generate a D-dimensional random vector, where each element is between [0,1].

[0072] Step 2. Kangaroo's Adaptive Energy The KET algorithm introduces a chaotic logic graph to simulate the fluctuations in the kangaroo's energy level during iterations, reflecting biological rhythms and environmental stress. The kangaroo's energy (time) is calculated at each iteration time using the following formula:

[0073] in, It is a chaotic variable that is updated through logical mapping; These are uniformly distributed random numbers generated for each kangaroo; This represents the maximum number of iterations. In the early iterations, the high energy level prompts the kangaroo to conduct extensive exploration.

[0074] Step 3. Strategies for the Exploration Phase Depending on their energy levels, kangaroos will choose two different exploration strategies.

[0075] Strategy 1: Escape by zigzagging movement When the energy is [0, 0.5], the kangaroo will perform a zigzag movement to explore the surrounding area. This is done by randomly rotating its direction vector. To achieve this, first, calculate a random angle. :

[0076] in, It is the maximum permissible angle of motion; It is a random number. Next, a rotated direction vector is generated. :

[0077] in, It refers to the current global optimal solution. The direction vector; U is a vector with respect to... orthogonal unit vectors This is used to generate non-linear paths. Ultimately, the kangaroo's new location... The update is as follows:

[0078] in, It is the step length of the zigzag movement; It is a D-dimensional Gaussian distributed random vector; This represents element-wise product.

[0079] Strategy 2: Escape by long-distance jump When the energy is (0.5 1], the kangaroo will make long jumps to explore the distant search space. Its position update formula is:

[0080] Among them, Decoy It is a binary mask that simulates the behavior of a kangaroo dropping bait. This mechanism promotes global exploration and helps escape local optima. The constant 2 is used to increase the exploration step size.

[0081] Step 4. Utilization Phase Strategy: Safe Zone Search and Bait Deployment When there is no direct threat, the kangaroo will search for resources within a safe area, corresponding to the exploitation phase of the algorithm. This phase is guided by one of three modes: exploration mode (randomly selecting kangaroos), exploitation mode (selecting a local optimum), or deep exploitation mode (using the global optimum). The location update rule integrates the decoy deployment mask:

[0082] in, This represents a kangaroo location that is considered safe. It is through random variables The value of the binary mask is used to select different generation methods. This mechanism enhances population diversity and helps prevent premature convergence by introducing controlled randomness.

[0083] Step 5. Boundary handling and memory update To ensure that all newly generated solutions are within the feasible region, the algorithm performs boundary handling:

[0084] Finally, a greedy selection mechanism is adopted, whereby the kangaroo's position is only updated when the fitness of the new position is better than that of the current position, thus ensuring that the population evolves toward a better solution region.

[0085]

[0086] The solution process, such as Figure 6 As shown. The specific process is as follows: 1) For the proposed multi-objective model of economic cost and power supply reliability, since the model involves nonlinear multi-objective problems, a heuristic algorithm, namely the MOKEO optimization algorithm, is considered to generate the planning scheme. 2) After the planning scheme is formulated, power flow calculation and reliability assessment of the distribution network are required. Therefore, a two-layer optimization method is considered. The upper-layer planning model solves the problem by generating different planning schemes using a heuristic algorithm and calculating the distribution network investment cost. The lower-layer operational model solves the problem by calculating power flow and power supply reliability for each planning scheme, using these as inputs to a multi-objective function. A dynamic weight adjustment mechanism is introduced, allowing the weights to be automatically adjusted at different optimization stages based on the trade-offs between objectives.

[0087] 3) To address the non-convexity in the traditional power flow model of the distribution network, we propose to use the second-order cone relaxation method to make the power flow model convex and solve it to obtain the power flow of the distribution network, thereby verifying whether the lines are overloaded. 4) In terms of distribution network reliability assessment, based on the distribution network power flow, the affine minimum path method is used to solve the problem, thereby evaluating the system power supply reliability indicators under each planning scheme, including user power outage time and power supply reliability rate.

[0088] 5) Finally, the numerous non-dominated solutions obtained from the heuristic algorithm are evaluated using power flow and reliability assessments to form the final Pareto solution set. A fuzzy decision-making method is then used to select the optimal solution based on the multi-objective results of the proposed solutions.

[0089] Example 2 To verify the impact of distribution network coordination on distribution network planning results, the second embodiment of this invention adopts the following... Figure 3 The following simulation analysis of distribution network planning is performed using a distribution network containing an industrial microgrid as an example. Among them, the distribution network to be planned... The feeder is line 1, and the microgrid to be planned is industrial microgrid D. Based on the proposed distribution network optimization steps, the scope, lifespan, and grid overview of the planning area are analyzed first; then, load / PV growth and load characteristics are predicted; furthermore, the current challenges of the grid are analyzed; finally, based on the proposed model and solution method, a planning scheme is formulated and the optimal scheme is selected.

[0090] I. Planning Scope and Term The planned area is located in a certain district of City B, Province A. The proposed access line for the D microgrid is Line 1, and the proposed site is for User 8. The line is 3.5 kilometers long, and the users connected by this line are mainly industrial users.

[0091] Planning period: The base year is 2025; the planning year is 2027.

[0092] II. Overview of the Power Grid in the Planning Area like Figure 2 As shown, the planned area is powered by three 110kV substations, namely Main Transformer 1, Main Transformer 2, and Main Transformer 3. Lines 1, 2, and 3 are connected by two interconnecting connection groups.

[0093] The area is currently under development and construction. With the acceleration of urbanization, the regional load density is expected to continue to rise in the future. At the same time, the proportion of renewable energy integration will also increase significantly, the power grid structure will become increasingly complex, and the requirements for power supply reliability and flexibility will continue to rise.

[0094] III. Load Growth Forecast Near-term load forecasting is performed for areas containing microgrids.

[0095] 1) Natural growth load The maximum annual load of Line 1 in 2025 is 4.085MW. It is predicted to continue to grow at an average annual growth rate of 5%, which calculates to a natural increase load of 4.5MW in 2027.

[0096] 2) Regional new user load: There were no new users in this region in 2027.

[0097] IV. Forecast of New Photovoltaic Installations Based on the calculation formula for user transformer capacity and photovoltaic installed capacity: Land area Industrial rooftop area accounts for 40%. Roof utilization factor (0.78) Based on the photovoltaic panel installed capacity per unit area (180W / m2), the predicted maximum planned new installed capacity (MW) for each user on Line 1 in 2027 is [0,0,0,0.57,0,4.44,3.31,0], with a total new installed capacity of 8.32MW. Figure 4 As shown.

[0098] V. Load Characteristic Analysis Line 1 serves a total of 8 users, Line 2 serves a total of 13 users, and Line 3 has no users, with a total capacity of 30,535 kVA. The planned Line 1 primarily serves industrial users, all of whom will be supplied with 10kV power. Typical daily load characteristics are as follows: Figure 5 As shown.

[0099] VI. Current Problems of the Distribution Network After the photovoltaic system was connected to Line 1, simulations showed that the maximum reverse load rate of the line on a typical day (holidays) was 90.69%. Regarding the heavy load situation, if the load is transferred through connecting lines 2 or 3, the heavy load situation still occurs, meaning it cannot be resolved using traditional operator adjustment methods. The nearby Line 4 has a load rate of 71.52% and also has photovoltaic reverse load issues, which cannot be resolved by transferring power through nearby lines.

[0100] Furthermore, as shown in Table 1, the 110kV main transformers 1 and 2 near the power supply points have no outgoing line bays, with only the more distant main transformer 3 having remaining bays. The roads near factories such as users D and 9 already have overhead lines (including double-circuit lines on the same pole), and multiple lines already occupy overhead passageways, making it impossible to add new overhead lines. If a new line from main transformer 3 were to supply power to line 1, only laying a cable passage could be considered.

[0101] Table 1. Relevant information for 110kV main transformers 1, 2, and 3 near the power source.

[0102] VII. Scheme Formulation and Optimization Based on a comprehensive analysis of the current status and development needs of the power grid in the planning area, this invention derives four planning schemes, such as... Figure 7 As shown, four different distribution network planning schemes are obtained using the model and solution method proposed in Example 1: Option 1: Power supply point outgoing cable; Option 2: Dedicated supply to nearby lines; Option 3: Construct centralized energy storage on the grid side; Option 4: Build new distributed energy storage on the user side.

[0103] Table 2 shows the system economy, power supply reliability, and line load rate results under different planning schemes: Table 2. Results of system economy, power supply reliability, and line load rate under different planning schemes.

[0104] Schemes 2 and 3 were deemed unfeasible due to continued heavy loads after planning. Schemes 1 and 4 were compared, with Scheme 4 being the recommended option: constructing a new 1300kW / 2568MWh energy storage system at a factory. This improves system economics by 12.65%, fully integrates renewable energy, enhances power supply reliability to 99.999%, and reduces line load to 66.35%.

[0105] Example 3 This invention also proposes a multi-objective planning optimization system for distribution networks that considers distribution-micro-network collaboration, including a multi-objective two-level planning optimization model construction module, an iterative solution module, and an optimal planning scheme acquisition module: A module for constructing a multi-objective bi-level programming optimization model for a distribution network, wherein the multi-objective bi-level programming optimization model for a distribution network is characterized by a multi-objective function; the multi-objective bi-level programming optimization model for a distribution network includes an upper-level model with the system investment cost of the distribution network as the objective, and a lower-level model with the power supply reliability as the objective. The iterative solution module, in which each iteration of the multi-objective bi-level programming optimization model for the distribution network is solved includes: The system investment cost and different planning schemes are obtained by solving the upper-level model and then passed to the lower-level model. Based on each planning scheme, the lower-level model is solved to calculate the power flow of the distribution network. Based on the power distribution network flow, the power supply reliability index is calculated; based on the system investment cost and the power supply reliability index, the multi-objective function value of the planning scheme is calculated by weighted summation; wherein, in each iteration, a dynamic weight adjustment mechanism is used to adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function; Based on the multi-objective function values ​​of each of the planning schemes, a non-dominated solution set is formed; The optimal planning scheme acquisition module generates a Pareto solution set based on all the non-dominated solution sets generated by the iteration when the iteration stopping condition is met; and obtains the optimal planning scheme based on the Pareto solution set.

[0106] In the iterative solution module, a dynamic weight adjustment mechanism is used in each iteration to adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function.

[0107] Example 4 The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to Embodiment 1.

[0108] Example 5 The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0109] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0110] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0111] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0112] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A multi-objective programming optimization method for distribution networks considering distribution-micro-cooperation, characterized in that, The method includes: S1. For the planned distribution network, a multi-objective bi-level planning optimization model for the distribution network is constructed and represented by a multi-objective function. The multi-objective bi-level planning optimization model for the distribution network includes an upper-level model with the system investment cost of the distribution network as the objective and a lower-level model with the power supply reliability as the objective. S2, Solve the upper-level model to obtain the system investment cost and different planning schemes, and pass them to the lower-level model; S3. Based on each of the planning schemes, solve the lower-level model to calculate the power distribution flow; based on the power distribution flow, calculate the power supply reliability index. S4, based on the system investment cost and the power supply reliability index, calculate the multi-objective function value of the planning scheme by weighted summation; based on the multi-objective function value of each planning scheme, form a non-dominated solution set; The weights of the system investment cost and the power supply reliability index in the multi-objective function are adjusted using a dynamic weight adjustment mechanism. The specific steps are as follows: The system investment cost and the power supply reliability index The weights are set as follows: and initialize it; In each iteration, calculate and Change and ; Based on the change, calculate the weight for the next iteration: for Multiply by the adjustment coefficient and The difference is used to obtain the fourth product result; the fourth product result is then compared with the result obtained in the current iteration. The corresponding weights are added together to obtain the weight. ; for Multiply by the adjustment coefficient and The difference is used to obtain the fifth product result; the fifth product result is then compared with the result obtained in the current iteration. The corresponding weights are added together to obtain the weight. ; Weight and Perform normalization, and use the normalized weights in the next iteration; S5. When the preset iteration stopping condition is not met, adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function, and then repeat steps S3-S5. When the iteration stopping condition is met, generate a Pareto solution set based on all the non-dominated solution sets generated by the iteration. Obtain the optimal planning scheme based on the Pareto solution set.

2. The multi-objective programming optimization method for distribution networks considering distribution-micro-coordination as described in claim 1, characterized in that: In S2, the specific calculation process of the upper-level model is as follows: At each node, the product of the unit construction cost of the line in the distribution network and the length of the power supply point outgoing line is weighted and summed with the unit construction cost of the switch. The result of the weighted sum is multiplied with the switch quantity indicating whether to construct the power supply point outgoing line to obtain the first multiplication result. The product of the unit construction cost of the line in the distribution network and the length of the adjacent transfer line is summed with the unit construction cost of the switch. The summation result is then multiplied by the switch quantity indicating whether to construct the adjacent transfer line to obtain the second multiplication result. The third multiplication result is obtained by multiplying the unit construction cost of energy storage, the capacity of energy storage equipment, and the switching quantity indicating whether or not to build energy storage equipment. Add the first, second, and third multiplication results together to obtain the sum. The summation results of all nodes are added together to obtain the upper-level model; Setting constraints for the upper-level model includes: The sum of the switch quantity indicating whether to build a power supply point outgoing line, the switch quantity indicating whether to build a nearby transfer line, and the switch quantity indicating whether to build an energy storage device is 1; Maximum installation capacity constraints for energy storage devices.

3. The multi-objective programming optimization method for distribution networks considering distribution-micro-cooperation as described in claim 1, characterized in that: In S3, the specific calculation process of the lower-level model is as follows: At each node, calculate the expected outage time function based on the switching quantity representing whether to build a power supply point outgoing line, the switching quantity representing whether to build a nearby transfer line, the switching quantity representing whether to build an energy storage device, and the capacity of the energy storage device; The expected power outage time functions corresponding to all nodes are summed, and the summation result is divided by the number of nodes to obtain the lower-level model. Constraints are set for the lower-level model, including distribution network security constraints, renewable energy maximization consumption constraints, energy storage operation constraints, root node power constraints, and power flow constraints.

4. The multi-objective programming optimization method for distribution networks considering distribution-micro-coordination as described in claim 1, characterized in that: In S3, based on each planning scheme, the lower-level model is solved to calculate the power distribution network flow; based on the power distribution network flow, the power supply reliability index is calculated, specifically using the following method: For each of the planning schemes, the power flow model is made convex using the second-order cone relaxation method and then solved to obtain the power flow of the distribution network. Based on the power flow of the distribution network, the affine minimum path method is used to solve the problem and obtain the system power supply reliability index under the planning scheme, including user power outage time and power supply reliability rate.

5. A multi-objective planning optimization system for a distribution network considering distribution-micro-coordination, utilizing the method described in any one of claims 1-4, comprising a multi-objective two-level planning optimization model construction module for the distribution network, an iterative solution module, an optimal planning scheme acquisition module, and a planning judgment module, characterized in that: The system includes a planning and judgment module and a distribution network multi-objective bi-level planning optimization model construction module. The distribution network multi-objective bi-level planning optimization model is characterized by a multi-objective function. The distribution network multi-objective bi-level planning optimization model includes an upper-level model with the system investment cost of the distribution network as the objective and a lower-level model with the power supply reliability as the objective. The iterative solution module, in which each iteration of the multi-objective bi-level programming optimization model for the distribution network is solved includes: The system investment cost and different planning schemes are obtained by solving the upper-level model and then passed to the lower-level model. Based on each planning scheme, the lower-level model is solved to calculate the power flow of the distribution network. Based on the power distribution network flow, the power supply reliability index is calculated; based on the system investment cost and the power supply reliability index, the multi-objective function value of the planning scheme is calculated by weighted summation; wherein, in each iteration, a dynamic weight adjustment mechanism is used to adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function; Based on the multi-objective function values ​​of each of the planning schemes, a non-dominated solution set is formed; The optimal planning scheme acquisition module generates a Pareto solution set based on all the non-dominated solution sets generated by the iteration when the iteration stopping condition is met; and obtains the optimal planning scheme based on the Pareto solution set.

6. A multi-objective programming optimization system for distribution networks considering distribution-micro-cooperation as described in claim 5, characterized in that: In the iterative solution module, a dynamic weight adjustment mechanism is used in each iteration to adjust the weights of the system investment cost and the power supply reliability index in the multi-objective function.

7. A multi-objective programming optimization system for distribution networks considering distribution-micro-cooperation as described in claim 6, characterized in that: The weights of the system investment cost and the power supply reliability index in the multi-objective function are adjusted using a dynamic weight adjustment mechanism. The specific steps are as follows: The system investment cost and the power supply reliability index The weights are set as follows: and initialize it; In each iteration, calculate and Change and ; Based on the change, calculate the weight for the next iteration: for Multiply by the adjustment coefficient and The difference is used to obtain the fourth product result; Compare the fourth product result with the current iteration. The corresponding weights are added together to obtain the weight. ; for Multiply by the adjustment coefficient and The difference is used to obtain the fifth product result; Compare the fifth product result with the current iteration. The corresponding weights are added together to obtain the weight. ; Weight and Normalize the weights and use the normalized weights in the next iteration.

8. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-4.

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