Distribution network asset planning method and terminal

Abstract

The invention discloses a power distribution network asset planning method and terminal, and the method comprises the steps: building a two-stage asset planning model of a power distribution network based on a carbon emission permit transaction mechanism and a demand response strategy, inputting collected operation data and geographic space data into the two-stage asset planning model, and solving the two-stage asset planning model, the optimal site, capacity and type of the capacitor bank, the distributed power generation device and the energy storage system are obtained, a carbon emission permit transaction mechanism and a demand response strategy are innovatively incorporated into an asset planning process, a power distribution company can participate in a carbon market through the carbon emission permit transaction mechanism, carbon emission is reduced, and the purpose of carbon emission reduction is achieved. The consumption behavior can be adjusted through the demand response, the power consumption is more efficient and environment-friendly, the carbon emission permit transaction and the demand response are incorporated into the asset planning process, the carbon dioxide emission is reduced to the maximum extent, and the development and utilization of clean energy are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and dispatching, and in particular to a distribution network asset planning method and terminal. Background Art

[0002] In today's society, the world is facing dual challenges: increasing carbon emissions and urgent demand for renewable energy. With the rapid development of industrialization and urbanization, traditional energy consumption patterns can no longer meet people's demand for energy, and have led to serious environmental problems, especially increased carbon emissions. Therefore, in order to meet these challenges, people have begun to explore more environmentally friendly and sustainable ways of energy production and use. As the main supplier of energy, the power system has a particularly prominent carbon emission problem. Traditional power systems mainly rely on fossil fuels such as coal and oil, which release a large amount of greenhouse gases such as carbon dioxide during combustion, exacerbating global climate change. In addition, traditional power systems usually adopt a centralized power generation mode, which has problems such as energy waste and transmission loss. In order to promote the development of clean energy and reduce carbon emissions, it is necessary to comprehensively optimize and transform the power system.

[0003] In this context, distribution network asset planning has become a critical task. Traditional distribution network asset planning mainly focuses on the stability and economy of power supply, and often ignores the impact on carbon emissions and the integration of renewable energy. Therefore, there is an urgent need for an innovative distribution network asset planning method that can simultaneously consider carbon emission trading and demand response to achieve effective integration of carbon emission reduction and renewable energy. At present, although some studies have focused on the impact of carbon emission trading and demand response on the power system, few studies have incorporated these two aspects into the process of distribution network asset planning. Therefore, a new method is needed to fill this research gap and provide distribution companies with a comprehensive set of asset planning tools to adapt to the new requirements of carbon emission reduction and renewable energy integration. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a distribution network asset planning method and terminal, which can reduce carbon dioxide emissions to the greatest extent and promote the development and utilization of clean energy.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for power distribution network asset planning, comprising the steps of:

[0007] A two-stage asset planning model for distribution networks is established based on carbon emission trading mechanism and demand response strategy;

[0008] Collect operational data and geospatial data of the power system;

[0009] The operating data and the geospatial data are input into the two-stage asset planning model, and the two-stage asset planning model is solved to obtain the optimal site, capacity and type of capacitor banks, distributed generation devices and energy storage systems.

[0010] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0011] A distribution network asset planning terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0012] A two-stage asset planning model for distribution networks is established based on carbon emission trading mechanism and demand response strategy;

[0013] Collect operational data and geospatial data of the power system;

[0014] The operating data and the geospatial data are input into the two-stage asset planning model, and the two-stage asset planning model is solved to obtain the optimal site, capacity and type of capacitor banks, distributed generation devices and energy storage systems.

[0015] The beneficial effects of the present invention are as follows: a two-stage asset planning model for a distribution network is established based on a carbon emission trading mechanism and a demand response strategy, the collected operating data and geospatial data are input into the two-stage asset planning model, and the two-stage asset planning model is solved to obtain the optimal sites, capacities and types of capacitor banks, distributed generation devices and energy storage systems, and the carbon emission trading mechanism and demand response strategy are innovatively incorporated into the asset planning process. Through the carbon emission trading mechanism, distribution companies can participate in the carbon market, reduce carbon emissions and achieve the goal of carbon emission reduction. At the same time, through demand response, consumer behavior can be adjusted to make electricity consumption more efficient and environmentally friendly. Carbon emission trading and demand response are incorporated into the asset planning process, thereby minimizing carbon dioxide emissions and promoting the development and utilization of clean energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of the steps of a distribution network asset planning method according to an embodiment of the present invention;

[0017] Figure 2 A schematic diagram of the structure of a power distribution network asset planning terminal according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0019] Please refer to Figure 1 , a distribution network asset planning method, comprising the steps of:

[0020] A two-stage asset planning model for distribution networks is established based on carbon emission trading mechanism and demand response strategy;

[0021] Collect operational data and geospatial data of the power system;

[0022] The operating data and the geospatial data are input into the two-stage asset planning model, and the two-stage asset planning model is solved to obtain the optimal site, capacity and type of capacitor banks, distributed generation devices and energy storage systems.

[0023] From the above description, it can be seen that the beneficial effects of the present invention are: a two-stage asset planning model for the distribution network is established based on the carbon emission trading mechanism and the demand response strategy, the collected operation data and geospatial data are input into the two-stage asset planning model, and the two-stage asset planning model is solved to obtain the optimal site, capacity and type of capacitor banks, distributed power generation devices and energy storage systems, and the carbon emission trading mechanism and demand response strategy are innovatively incorporated into the asset planning process. Through the carbon emission trading mechanism, distribution companies can participate in the carbon market, reduce carbon emissions, and achieve the goal of carbon emission reduction. At the same time, through demand response, consumer behavior can be adjusted to make electricity consumption more efficient and environmentally friendly, and carbon emission trading and demand response are incorporated into the asset planning process, thereby minimizing carbon dioxide emissions and promoting the development and utilization of clean energy.

[0024] Furthermore, the two-stage asset planning model for establishing a distribution network based on the carbon emission trading mechanism and the demand response strategy includes:

[0025] Determine the first cost of transferring emission allowances from other regions, the second cost of purchasing emission allowances from the carbon trading market, the third cost of selling emission allowance surpluses to the carbon trading market, and the compensation costs and parameters paid to consumers participating in the demand response strategy;

[0026] Establishing a first objective function according to the first cost, the second cost, the third cost, the compensation cost and the parameter;

[0027] Establishing an investment budget constraint, a capacitor bank constraint, a photovoltaic module and a wind unit constraint, an energy storage system and a power conversion unit constraint, a charge and discharge constraint of the energy storage system, and a carbon emission quota constraint, and obtaining a first constraint condition according to the investment budget constraint, the capacitor bank constraint, the photovoltaic module and the wind unit constraint, the energy storage system and the power conversion unit constraint, the charge and discharge constraint of the energy storage system, and the carbon emission quota constraint;

[0028] Establishing a first-stage planning model according to the first objective function and the first constraint condition;

[0029] establishing a second objective function based on energy costs and changes in active power provided by the distribution network;

[0030] Establishing uncertainty intervals and robustness interval constraints for demand response and renewable power production, active and reactive power balance constraints, and voltage amplitude constraints, and obtaining a second constraint condition based on the uncertainty intervals and robustness interval constraints for demand response and renewable power production, the active and reactive power balance constraints, and the voltage amplitude constraints;

[0031] Establishing a second-stage two-level linear programming model according to the second objective function and the second constraint condition;

[0032] A two-stage asset planning model for the power distribution network is generated according to the first-stage planning model and the second-stage two-level linear programming model.

[0033] From the above description, it can be seen that the first objective function of the first-stage planning model takes into account the first cost of transferring emission quotas from other regions, the second cost of purchasing emission quotas from the carbon trading market, the third cost of selling emission quota surpluses to the carbon trading market, the compensation cost paid to consumers participating in the demand response strategy, and the subsequent parameters obtained by solving the second-stage two-level linear programming model. Therefore, the first-stage planning model defines investment and operating decisions, and the second-stage two-level linear programming model takes into account the uncertainty and robustness of renewable energy and completes asset planning. The two-stage asset planning model of the distribution network generated by the first-stage planning model and the second-stage two-level linear programming model takes into account the uncertainty of power demand and renewable energy. Distribution companies can effectively configure a variety of assets in the asset planning stage to meet the challenges of carbon emission reduction, and the model can comprehensively consider various uncertainties to improve the robustness and reliability of planning.

[0034] Further, establishing a first objective function according to the first cost, the second cost, the third cost, the compensation cost and the parameter comprises:

[0035]

[0036] In the formula, S represents the distribution network area set, T represents the level set, and Y represents the planning year set. represents the emission quota transfer price between regions in year y under level t, represents the CO2 emission limit transferred between regions in year y under level t, represents the purchase price of emission allowances in year y between carbon markets and regions at level t, represents the CO2 quota sold from the carbon market in year y under level t, represents the selling price of emission allowances in year y at level t between carbon markets and regions, represents the CO2 quota purchased from the carbon market in year y under level t, r represents the currency update in year y, and N dr represents the set of demand response nodes, represents the compensation price of demand response strategy in year y at level t, represents the change in management demand at node k, and ψ represents a parameter.

[0037] From the above description, it can be seen that establishing the first objective function according to the first cost, the second cost, the third cost and the compensation cost and parameters can minimize carbon dioxide emissions, promote the development and utilization of clean energy, further optimize energy utilization, and improve the overall efficiency of the system.

[0038] Furthermore, the investment budget constraint is:

[0039]

[0040] Where N represents the set of nodes in the distribution network, Ω represents the set of capacitor bank capacities, Indicates the first investment price of capacity b type circuit breaker, represents the first binary variable, Indicates the second investment price of capacity b type circuit breaker, represents the second binary variable, ζ wt represents the investment price of wind-based distributed generation installations, represents the operation and maintenance costs of wind-based renewable energy installations, represents the number of wind-based distributed generation devices installed at node i, ζ pv represents the investment price of distributed generation devices based on photovoltaics, represents the operation and maintenance costs of renewable energy installations based on solar energy, represents the number of photovoltaic-based distributed generation devices installed at node i, represents the investment price of the power unit of the energy storage system, represents the operation and maintenance cost of the energy storage system, represents the unit capacity of the power conversion unit of the energy storage system at node i, represents the investment price of the energy storage unit of the energy storage system, represents the price of CO2 generated during the energy storage system recovery process, ζ wr represents the disposal and recovery costs of the energy storage system, represents the unit capacity of the energy storage system at node i, and Π represents the total investment budget of the planning scheme;

[0041] The capacitor bank constraints are:

[0042]

[0043] In the formula, represents the maximum number of capacitor bank modules at node i, represents the number of capacitor bank operating modules at node i;

[0044] The photovoltaic modules and wind units are constrained as follows:

[0045]

[0046] In the formula, represents the maximum capacity of the photovoltaic-based distributed generation unit installed at node i, represents the maximum capacity of wind-based distributed generation units installed at node i.

[0047] From the above description, it can be seen that the investment budget constraint defines the investment budget allocated by the planning scheme. The budget takes into account the investment cost corresponding to the allocation of fixed or switchable capacitor banks, distributed renewable energy generation devices such as wind or photovoltaic power generation and their maintenance and operation costs, and the investment cost and maintenance cost of the energy storage system allocation. The capacitor bank constraint defines the optimal size and type of candidate capacitor banks. The photovoltaic module and wind unit constraints limit the number of photovoltaic modules and wind units to be installed at node i, thereby ensuring the economy and reliability of the final asset planning scheme.

[0048] Furthermore, the energy storage system and the power conversion unit are constrained as follows:

[0049]

[0050] In the formula, represents the maximum capacity of the energy storage system, Indicates the maximum capacity of the power conversion unit, A binary variable indicating whether an energy storage system is installed at node i;

[0051] The charge and discharge constraints of the energy storage system are:

[0052]

[0053] In the formula, represents the discharge state of the energy storage system at level t at node i, represents the charging state of the energy storage system at level t at node i;

[0054] The carbon emission quota constraints are:

[0055]

[0056] In the formula, Indicates that region s sends emission quotas in year y under level t, Indicates that region s receives emission allowances in year y under level t, represents the CO2 quota purchased from the carbon market for region s in year y under level t, represents the CO2 quota sold from the carbon market to region s in year y under level t.

[0057] From the above description, it can be seen that the reliability of the planning results of the first-stage planning model is ensured and the carbon dioxide emissions are minimized through the constraints of the energy storage system and the power conversion unit, the charging and discharging constraints of the energy storage system and the carbon emission quota constraints.

[0058] Furthermore, establishing the second objective function according to the energy cost and the change in active power provided by the power distribution network includes:

[0059]

[0060] In the formula, represents the energy cost in year y at level t, It represents the change of active power provided by the distribution network in area s in year y at level t.

[0061] From the above description, it can be seen that the second objective function is a two-level linear programming problem, including the maximization optimization problem and the minimization optimization problem. The minimization optimization problem is the bottom-level problem, which minimizes the cost of the energy provided by the distribution network in the first two layers respectively under the planning decision and the worst uncertainty realization, so as to improve the economy of the planning results.

[0062] Furthermore, the uncertainty interval and robustness interval constraints of the demand response and renewable power production are:

[0063]

[0064]

[0065] In the formula, f t d represents the maximum value of the uncertainty interval of demand response consumption at level t, f t d represents the demand response consumption at level t, represents the minimum value of the uncertainty interval of demand response consumption at level t, Γ drepresents the maximum value of the robustness interval of demand response consumption at level t, represents the expected value of demand response consumption at level t, represents the minimum value of the robustness interval of demand response consumption at level t, represents the maximum value of the uncertainty interval of photovoltaic renewable energy power generation at level t, represents the photovoltaic renewable energy power generation at level t, represents the minimum value of the uncertainty interval of photovoltaic renewable energy power generation at level t, Γ pv represents the maximum value of the robustness interval of photovoltaic renewable energy power generation at level t, represents the expected value of photovoltaic renewable energy power generation at level t, represents the minimum value of the robustness interval of photovoltaic renewable energy power generation at level t, represents the maximum value of the uncertainty interval of wind renewable energy power generation at level t, represents the amount of wind renewable energy power generation at level t, represents the minimum value of the uncertainty interval of wind renewable energy power generation at level t, Γ wt represents the maximum value of the robustness interval of wind renewable energy power generation at level t, represents the expected value of wind renewable energy power generation at level t, represents the minimum value of the robustness interval of wind renewable energy power generation at level t;

[0066] The active and reactive power balance constraints are:

[0067]

[0068]

[0069] Where L represents the circuit set, P ji,t,y Represents the active power of circuit ji, P ij,t,y represents the active power of circuit ij, R ij represents the resistance of circuit ij, The variable representing the square of the current in circuit ij of level y in year t, represents the active power provided by the distribution network in region s in year y at level t, Represents the active power of photovoltaic renewable power generation device, Represents the active power of wind energy renewable power generation device, Represents the discharge power of the energy storage system, represents the active power demand at node i, Indicates the charging power of the energy storage system, represents the power demand of node k at level y in year t, represents the power demand of node k at level y in year t, Q ji,t,y Represents the reactive power of circuit ji, Q ij,t,y represents the reactive power of circuit ij, X ij represents the reactance of circuit ij, represents the reactive power provided by the distribution network in region s in year y at level t, Represents the reactive power of photovoltaic renewable power generation devices, Represents the reactive power of wind energy renewable power generation device, represents the reactive power provided by the circuit breaker at node i at level t in year y, represents the reactive power demand of node i in year y;

[0070] The voltage amplitude constraint is:

[0071]

[0072] Where U i,t,y represents the square of the voltage amplitude at node i, U j,t,y represents the square of the voltage amplitude at node j, Z ij Represents the impedance of circuit ij.

[0073] From the above description, it can be seen that the uncertainty interval and robustness interval constraints of demand response and renewable power production, active and reactive power balance constraints, and voltage amplitude constraints ensure the stable operation of the distribution network.

[0074] Furthermore, the two-stage asset planning model is solved to obtain the optimal sites, capacities and types of capacitor banks, distributed generation devices and energy storage systems, including:

[0075] Solving the second-stage two-level linear programming model to obtain a solution result;

[0076] After determining the parameters according to the solution results, the first stage planning model is solved to obtain the optimal sites, capacities and types of capacitor banks, distributed generation devices and energy storage systems.

[0077] From the above description, it can be seen that the second-stage two-level linear programming model is first solved, and the parameters are determined according to the solution results. The parameters are substituted into the first-stage planning model to solve it, and the optimal sites, capacities and types of capacitor banks, distributed generation devices and energy storage systems are obtained. By combining carbon emission trading with demand response to optimize asset allocation, various uncertainties are considered, and multiple assets such as capacitor banks, distributed generation devices and energy storage systems are effectively configured in the asset planning stage to meet the challenges of carbon emission reduction and renewable energy integration, effectively reducing carbon emissions and promoting the development of the power system in a cleaner, more environmentally friendly and sustainable direction.

[0078] Furthermore, the collection of operation data and geographic space data of the power system includes:

[0079] Collect initial operation data and initial geospatial data of the power system;

[0080] The initial operation data and the initial geospatial data are cleaned and integrated to obtain operation data and geospatial data.

[0081] From the above description, it can be seen that the collected initial operation data and initial geospatial data are cleaned and integrated to ensure that the data input into the model for solution is valid and reliable, ensuring the final asset planning effect.

[0082] Please refer to Figure 2 Another embodiment of the present invention provides a distribution network asset planning terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements each step of the above-mentioned distribution network asset planning method when executing the computer program.

[0083] The above-mentioned distribution network asset planning method and terminal of the present invention can be applied to distribution network asset planning scenarios, which are described below through specific implementation methods:

[0084] Please refer to Figure 1 , Embodiment 1 of the present invention is:

[0085] A method for power distribution network asset planning, comprising the steps of:

[0086] S1. A two-stage asset planning model for the distribution network is established based on the carbon emission trading mechanism and demand response strategy, including S11-S18:

[0087] Among them, carbon emission trading is a market-based mechanism that encourages the application of energy efficiency technologies and ensures the achievement of emission reduction targets. Compared with emission taxes and emission cap policies, carbon emission trading programs provide carbon emitters with the flexibility to comply with CO2 regulations set by governments or regulatory entities. When a carbon emission trading program is adopted, emitters obtain some initial emission quotas, namely emission caps, which can be based on historical emission data of electricity production or energy generation. However, each emitter can buy / sell emission quotas to other emitters, government entities or companies in the external market, thereby generating emission quota prices. To establish this mechanism, a management entity and a group of emitters should be arranged to actively participate. The regulatory entity identified as the carbon emission market in the present invention is responsible for determining the initial emission cap and the price of selling and purchasing quotas. In addition, emitters should comply with the prescribed emission caps. If they exceed the caps, they can purchase additional carbon emission quotas. From an incentive perspective, if emitters have remaining carbon emission quotas, they can sell them to other emitters or carbon emission markets. On this basis, in order to develop a carbon emission trading scheme at the distribution level, a multi-regional distribution network is considered, in which each region is subject to its own emission cap. If the distribution network of a region reaches or exceeds a predetermined emission cap, quotas can be transferred from other regions and / or purchased from the carbon market. Otherwise, if it is below the cap, the remaining quotas can be transferred to other parties and / or sold to the carbon market, thus establishing a dynamic carbon emission rights trading mechanism between the distribution network region and the carbon emission market.

[0088] In addition, consumers are involved in the planning through demand response strategies, which are considered as predefined contracts between the distribution company and different groups of consumers. A set of nodes in the distribution network is defined to represent a group of consumers gathered at a specific node. These consumer groups provide enough flexibility for the operator of the distribution network. Therefore, it is required that the demand must be optimally managed and an appropriate consumption pattern is proposed. Individual consumption behavior should be managed by an entity that encourages each consumer to adopt a new consumption pattern.

[0089] S11. Determine the first cost of transferring emission allowances from other regions, the second cost of purchasing emission allowances from the carbon trading market, the third cost of selling emission allowance surpluses to the carbon trading market, and the compensation costs and parameters paid to consumers participating in the demand response strategy.

[0090] S12, establishing a first objective function according to the first cost, the second cost, the third cost, the compensation cost and the parameters, specifically:

[0091]

[0092] In the formula, S represents the distribution network area set, T represents the level set, and Y represents the planning year set. represents the emission quota transfer price between regions in year y under level t, represents the CO2 emission limit transferred between regions in year y under level t, represents the purchase price of emission allowances in year y between carbon markets and regions at level t, represents the CO2 quota sold from the carbon market in year y under level t, represents the selling price of emission allowances in year y at level t between carbon markets and regions, represents the CO2 quota purchased from the carbon market in year y under level t, r represents the currency update in year y, and N dr represents the set of demand response nodes, represents the compensation price of demand response strategy in year y at level t, represents the change in management demand at node k, and ψ represents a parameter.

[0093] Among them, the costs of transferring, purchasing and selling emission quotas and compensation costs corresponding to the demand response strategy are present values.

[0094] S13. Establish an investment budget constraint, a capacitor bank constraint, a photovoltaic module and a wind unit constraint, an energy storage system and a power conversion unit constraint, a charge and discharge constraint of the energy storage system, and a carbon emission quota constraint, and obtain a first constraint condition based on the investment budget constraint, the capacitor bank constraint, the photovoltaic module and the wind unit constraint, the energy storage system and the power conversion unit constraint, the charge and discharge constraint of the energy storage system, and the carbon emission quota constraint.

[0095] Wherein, the investment budget constraint is:

[0096]

[0097] Where N represents the set of nodes in the distribution network, Ω represents the set of capacitor bank capacities, Indicates the first investment price of capacity b type circuit breaker, represents the first binary variable, Indicates the second investment price of capacity b type circuit breaker, represents the second binary variable, ζ wt represents the investment price of wind-based distributed generation installations, represents the operation and maintenance costs of wind-based renewable energy installations, represents the number of wind-based distributed generation devices installed at node i, ζ pv represents the investment price of distributed generation devices based on photovoltaics, represents the operation and maintenance costs of renewable energy installations based on solar energy, represents the number of photovoltaic-based distributed generation devices installed at node i, represents the investment price of the power unit of the energy storage system, represents the operation and maintenance cost of the energy storage system, represents the unit capacity of the power conversion unit of the energy storage system at node i, represents the investment price of the energy storage unit of the energy storage system, represents the price of CO2 generated during the energy storage system recovery process, ζ wr represents the disposal and recovery costs of the energy storage system, represents the unit capacity of the energy storage system at node i, and Π represents the total investment budget of the planning scheme.

[0098] The capacitor bank constraints are:

[0099]

[0100]

[0101] In the formula, represents the maximum number of capacitor bank modules at node i, Represents the number of capacitor bank operating modules at node i.

[0102] The photovoltaic modules and wind units are constrained as follows:

[0103]

[0104] In the formula, represents the maximum capacity of the photovoltaic-based distributed generation unit installed at node i, represents the maximum capacity of wind-based distributed generation units installed at node i.

[0105] The energy storage system and power conversion unit constraints are:

[0106]

[0107] In the formula, represents the maximum capacity of the energy storage system, Indicates the maximum capacity of the power conversion unit, A binary variable indicating whether an energy storage system is installed at node i, with a value of 0 or 1.

[0108] The charge and discharge constraints of the energy storage system are:

[0109]

[0110] In the formula, represents the discharge state of the energy storage system at level t at node i, Represents the charging state of the energy storage system at level t at node i.

[0111] The carbon emission quota constraints are:

[0112]

[0113] In the formula, Indicates that region s sends emission quotas in year y under level t, Indicates that region s receives emission allowances in year y under level t, represents the CO2 quota purchased from the carbon market for region s in year y under level t, represents the CO2 quota sold from the carbon market to region s in year y under level t.

[0114] S14. Establish a first-stage planning model according to the first objective function and the first constraint condition.

[0115] S15. Establish a second objective function according to the energy cost and the change in active power provided by the distribution network, specifically:

[0116]

[0117] In the formula, represents the energy cost in year y at level t, It represents the change of active power provided by the distribution network in area s in year y at level t.

[0118] The second objective function of the second-stage two-level linear programming model includes an intermediate-level problem (i.e., maximization optimization problem) and a bottom-level problem (minimization optimization problem), in which the cost of the energy provided by the distribution network in the first two levels respectively under the worst uncertainty realization is minimized.

[0119] S16. Establish uncertainty intervals and robustness interval constraints for demand response and renewable power production, active and reactive power balance constraints, and voltage amplitude constraints, and obtain a second constraint condition based on the uncertainty intervals and robustness interval constraints for demand response and renewable power production, the active and reactive power balance constraints, and the voltage amplitude constraints.

[0120] The uncertainty interval and robustness interval constraints of demand response and renewable power production are:

[0121]

[0122] In the formula, f t d represents the maximum value of the uncertainty interval of demand response consumption at level t, ft d represents the demand response consumption at level t, represents the minimum value of the uncertainty interval of demand response consumption at level t, Γ d represents the maximum value of the robustness interval of demand response consumption at level t, represents the expected value of demand response consumption at level t, represents the minimum value of the robustness interval of demand response consumption at level t, represents the maximum value of the uncertainty interval of photovoltaic renewable energy power generation at level t, represents the photovoltaic renewable energy power generation at level t, represents the minimum value of the uncertainty interval of photovoltaic renewable energy power generation at level t, Γ pv represents the maximum value of the robustness interval of photovoltaic renewable energy power generation at level t, represents the expected value of photovoltaic renewable energy power generation at level t, represents the minimum value of the robustness interval of photovoltaic renewable energy power generation at level t, represents the maximum value of the uncertainty interval of wind renewable energy power generation at level t, represents the amount of wind renewable energy power generation at level t, represents the minimum value of the uncertainty interval of wind renewable energy power generation at level t, Γ wt represents the maximum value of the robustness interval of wind renewable energy power generation at level t, represents the expected value of wind renewable energy power generation at level t, It represents the minimum value of the robustness interval of wind renewable energy power generation at level t.

[0123] The active and reactive power balance constraints are:

[0124]

[0125] Where L represents the circuit set, P ji,t,y Represents the active power of circuit ji, P ij,t,y represents the active power of circuit ij, R ij represents the resistance of circuit ij, The variable representing the square of the current in circuit ij of level y in year t, represents the active power provided by the distribution network in region s in year y at level t, Represents the active power of photovoltaic renewable power generation device, Represents the active power of wind energy renewable power generation device, Represents the discharge power of the energy storage system, represents the active power demand at node i, Indicates the charging power of the energy storage system, represents the power demand of node k at level y in year t, represents the power demand of node k at level y in year t, Q ji,t,y Represents the reactive power of circuit ji, Q ij,t,y represents the reactive power of circuit ij, X ij represents the reactance of circuit ij, represents the reactive power provided by the distribution network in region s in year y at level t, Represents the reactive power of photovoltaic renewable power generation devices, Represents the reactive power of wind energy renewable power generation device, represents the reactive power provided by the circuit breaker at node i at level t in year y, represents the reactive power demand of node i in year y.

[0126] The voltage amplitude constraint is:

[0127]

[0128] Where U i,t,y represents the square of the voltage amplitude at node i, U j,t,y represents the square of the voltage amplitude at node j, Z ij Represents the impedance of circuit ij.

[0129] S17. Establish a second-stage two-level linear programming model according to the second objective function and the second constraint condition.

[0130] S18. Generate a two-stage asset planning model for the distribution network according to the first-stage planning model and the second-stage two-layer linear programming model.

[0131] S2. Collecting the operation data and geographic space data of the power system, including S21-S22:

[0132] S21. Collect initial operation data and initial geospatial data of the power system.

[0133] Specifically, smart meters and sensor networks are used to collect initial operating data of the power system, such as parameters such as voltage, current and power. At the same time, the initial geospatial data of the power system, including information such as terrain, land use and climate, is collected through the Geographic Information System (GIS).

[0134] S22. Clean and integrate the initial operation data and the initial geographic spatial data to obtain operation data and geographic spatial data.

[0135] S3, inputting the operation data and the geospatial data into the two-stage asset planning model, and solving the two-stage asset planning model to obtain the optimal site, capacity and type of capacitor bank, distributed generation device and energy storage system, specifically including S31-S33:

[0136] S31, inputting the operation data and the geospatial data into the two-stage asset planning model;

[0137] S32, solving the second-stage two-level linear programming model to obtain a solution result;

[0138] S33. After determining the parameters according to the solution results, the first stage planning model is solved to obtain the optimal site, capacity and type of capacitor banks, distributed generation devices and energy storage systems to minimize the energy loss and carbon emissions of the system.

[0139] Among them, photovoltaic and wind power plants are assumed to be renewable distributed generation (DG) sources to be installed in the distribution network. Some DG sources based on renewable energy sources contain reactive power control devices that can be applied to provide voltage, and DG units based on renewable energy sources are modeled as AC sources, providing reactive support through fixed and switchable capacitor banks, each type of capacitor bank has a different reactive power capacity, where the installation cost is associated with each type of capacitor bank and its respective capacity, and on the other hand, the optimal capacity of the storage unit and the power conversion unit is determined for the energy storage system by considering the optimization objectives defined by the decision maker.

[0140] First, traditional distribution network asset planning methods often ignore the impact of carbon emissions, resulting in increasing carbon emissions from the power system. The present invention optimizes the distribution network structure through a carbon emission rights trading mechanism, which can minimize carbon emissions and achieve the goal of carbon emission reduction. It can make the distribution network more environmentally friendly and sustainable while meeting electricity demand.

[0141] Secondly, with the rapid development of renewable energy, how to effectively integrate renewable energy has become a key issue. Traditional distribution network asset planning methods often lack consideration of renewable energy integration, resulting in low utilization efficiency of renewable energy. However, the present invention can better realize the integration of renewable energy, improve energy utilization efficiency, and reduce dependence on traditional energy by considering renewable energy resources, optimizing distribution network structure, and formulating demand response strategies.

[0142] In addition, traditional distribution network asset planning methods often assume that the distribution of power demand and renewable energy is certain, ignoring the impact of uncertainty factors on asset planning. However, the present invention, by establishing a robust mixed integer programming model, takes into account the uncertainty of power demand and renewable energy, and can improve the robustness and adaptability of asset planning, which enables the distribution network to better cope with various external changes and challenges and improve the stability and reliability of the system.

[0143] Finally, through the carbon emission trading mechanism and demand response strategy, the present invention can optimize the operation strategy of the distribution network and improve the overall efficiency of the power system. This can not only reduce carbon emissions, but also reduce energy costs and improve the company's economic benefits. In contrast, traditional distribution network asset planning methods often only focus on the stability and economy of power supply, ignoring the optimization of carbon emissions and renewable energy integration.

[0144] Please refer to Figure 2 , Embodiment 2 of the present invention is:

[0145] A distribution network asset planning terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements each step of the distribution network asset planning method in the first embodiment when executing the computer program.

[0146] In summary, the present invention provides a distribution network asset planning method and terminal, which establishes a two-stage asset planning model for the distribution network based on the carbon emission trading mechanism and demand response strategy, inputs the collected operation data and geographic spatial data into the two-stage asset planning model, and solves the two-stage asset planning model to obtain the optimal site, capacity and type of capacitor banks, distributed generation devices and energy storage systems, and innovatively incorporates the carbon emission trading mechanism and demand response strategy into the asset planning process. Through the carbon emission trading mechanism, distribution companies can participate in the carbon market, reduce carbon emissions, and achieve the goal of carbon emission reduction. At the same time, through demand response, consumer behavior can be adjusted to make electricity consumption more efficient and environmentally friendly, integrating carbon emission trading with demand. The response is incorporated into the asset planning process, thereby minimizing carbon dioxide emissions and promoting the development and utilization of clean energy. In addition, the second-stage two-level linear programming model is first solved, and the parameters are determined based on the solution results. The parameters are substituted into the first-stage planning model to solve it, and the optimal sites, capacities, and types of capacitor banks, distributed generation devices, and energy storage systems are obtained. By combining carbon emission trading with demand response to optimize asset allocation, various uncertainties are considered, and multiple assets such as capacitor banks, distributed generation devices, and energy storage systems are effectively configured in the asset planning stage to meet the challenges of carbon emission reduction and renewable energy integration, effectively reducing carbon emissions and promoting the development of the power system in a cleaner, more environmentally friendly and sustainable direction.

[0147] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A distribution network asset planning method, characterized in that: Includes steps: A two-stage asset planning model for distribution networks is established based on carbon emission trading mechanism and demand response strategy; Collect operational data and geospatial data of the power system; The operating data and the geospatial data are input into the two-stage asset planning model, and the two-stage asset planning model is solved to obtain the optimal site, capacity and type of capacitor banks, distributed generation devices and energy storage systems.

2. A distribution network asset planning method according to claim 1, characterized in that: The two-stage asset planning model for establishing a distribution network based on the carbon emission trading mechanism and demand response strategy includes: Determine the first cost of transferring emission allowances from other regions, the second cost of purchasing emission allowances from the carbon trading market, the third cost of selling emission allowance surpluses to the carbon trading market, and the compensation costs and parameters paid to consumers participating in the demand response strategy; Establishing a first objective function according to the first cost, the second cost, the third cost, the compensation cost and the parameter; Establishing an investment budget constraint, a capacitor bank constraint, a photovoltaic module and a wind unit constraint, an energy storage system and a power conversion unit constraint, a charge and discharge constraint of the energy storage system, and a carbon emission quota constraint, and obtaining a first constraint condition according to the investment budget constraint, the capacitor bank constraint, the photovoltaic module and the wind unit constraint, the energy storage system and the power conversion unit constraint, the charge and discharge constraint of the energy storage system, and the carbon emission quota constraint; Establishing a first-stage planning model according to the first objective function and the first constraint condition; establishing a second objective function based on energy costs and changes in active power provided by the distribution network; Establishing uncertainty intervals and robustness interval constraints for demand response and renewable power production, active and reactive power balance constraints, and voltage amplitude constraints, and obtaining a second constraint condition based on the uncertainty intervals and robustness interval constraints for demand response and renewable power production, the active and reactive power balance constraints, and the voltage amplitude constraints; Establishing a second-stage two-level linear programming model according to the second objective function and the second constraint condition; A two-stage asset planning model for the power distribution network is generated according to the first-stage planning model and the second-stage two-level linear programming model.

3. A distribution network asset planning method according to claim 2, characterized in that: The establishing of the first objective function according to the first cost, the second cost, the third cost, the compensation cost and the parameter comprises: In the formula, S represents the distribution network area set, T represents the level set, and Y represents the planning year set. represents the emission quota transfer price between regions in year y under level t, represents the CO2 emission limit transferred between regions in year y under level t, represents the purchase price of emission allowances in year y between carbon markets and regions at level t, represents the CO2 quota sold from the carbon market in year y under level t, represents the selling price of emission allowances in year y at level t between carbon markets and regions, represents the CO2 quota purchased from the carbon market in year y under level t, r represents the currency update in year y, and N dr represents the set of demand response nodes, represents the compensation price of demand response strategy in year y at level t, represents the change in management demand at node k, and ψ represents a parameter.

4. A distribution network asset planning method according to claim 3, characterized in that: The investment budget constraint is: Where N represents the set of nodes in the distribution network, Ω represents the set of capacitor bank capacities, Indicates the first investment price of capacity b type circuit breaker, represents the first binary variable, Indicates the second investment price of capacity b type circuit breaker, represents the second binary variable, ζ wt represents the investment price of wind-based distributed generation installations, represents the operation and maintenance costs of wind-based renewable energy installations, represents the number of wind-based distributed generation devices installed at node i, ζ pv represents the investment price of distributed generation devices based on photovoltaics, represents the operation and maintenance costs of renewable energy installations based on solar energy, represents the number of photovoltaic-based distributed generation devices installed at node i, represents the investment price of the power unit of the energy storage system, represents the operation and maintenance cost of the energy storage system, represents the unit capacity of the power conversion unit of the energy storage system at node i, represents the investment price of the energy storage unit of the energy storage system, represents the price of CO2 generated during the energy storage system recovery process, ζ wr represents the disposal and recovery costs of the energy storage system, represents the unit capacity of the energy storage system at node i, and Π represents the total investment budget of the planning scheme; The capacitor bank constraints are: In the formula, represents the maximum number of capacitor bank modules at node i, represents the number of capacitor bank operating modules at node i; The photovoltaic modules and wind units are constrained as follows: In the formula, represents the maximum capacity of the photovoltaic-based distributed generation unit installed at node i, represents the maximum capacity of wind-based distributed generation units installed at node i.

5. A distribution network asset planning method according to claim 4, characterized in that: The energy storage system and power conversion unit constraints are: In the formula, represents the maximum capacity of the energy storage system, Indicates the maximum capacity of the power conversion unit, A binary variable indicating whether an energy storage system is installed at node i; The charge and discharge constraints of the energy storage system are: In the formula, represents the discharge state of the energy storage system at level t at node i, represents the charging state of the energy storage system at level t at node i; The carbon emission quota constraints are: In the formula, Indicates that region s sends emission quotas in year y under level t, Indicates that region s receives emission allowances in year y under level t, represents the CO2 quota purchased from the carbon market for region s in year y under level t, represents the CO2 quota sold from the carbon market to region s in year y under level t.

6. A distribution network asset planning method according to claim 3, characterized in that: The second objective function is established according to the energy cost and the change in active power provided by the power distribution network, comprising: In the formula, represents the energy cost in year y at level t, It represents the change of active power provided by the distribution network in area s in year y at level t.

7. A distribution network asset planning method according to claim 3, characterized in that: The uncertainty interval and robustness interval constraints for demand response and renewable electricity production are: In the formula, f t d represents the maximum value of the uncertainty interval of demand response consumption at level t, f t d represents the demand response consumption at level t, represents the minimum value of the uncertainty interval of demand response consumption at level t, Γ d represents the maximum value of the robustness interval of demand response consumption at level t, represents the expected value of demand response consumption at level t, represents the minimum value of the robustness interval of demand response consumption at level t, represents the maximum value of the uncertainty interval of photovoltaic renewable energy power generation at level t, represents the photovoltaic renewable energy power generation at level t, represents the minimum value of the uncertainty interval of photovoltaic renewable energy power generation at level t, Γ pv represents the maximum value of the robustness interval of photovoltaic renewable energy power generation at level t, represents the expected value of photovoltaic renewable energy power generation at level t, represents the minimum value of the robustness interval of photovoltaic renewable energy power generation at level t, represents the maximum value of the uncertainty interval of wind renewable energy power generation at level t, represents the amount of wind renewable energy power generation at level t, represents the minimum value of the uncertainty interval of wind renewable energy power generation at level t, Γ wt represents the maximum value of the robustness interval of wind renewable energy power generation at level t, represents the expected value of wind renewable energy power generation at level t, represents the minimum value of the robustness interval of wind renewable energy power generation at level t; The active and reactive power balance constraints are: Where L represents the circuit set, P ji,t,y Represents the active power of circuit ji, P ij,t,y Represents the active power of circuit ij, R ij represents the resistance of circuit ij, The variable representing the square of the current in circuit ij of level y in year t, represents the active power provided by the distribution network in region s in year y at level t, Represents the active power of photovoltaic renewable power generation device, Represents the active power of wind energy renewable power generation device, Represents the discharge power of the energy storage system, represents the active power demand at node i, Indicates the charging power of the energy storage system, represents the power demand of node k at level y in year t, represents the power demand of node k at level y in year t, Q ji,t,y Represents the reactive power of circuit ji, Q ij,t,y represents the reactive power of circuit ij, X ij represents the reactance of circuit ij, represents the reactive power provided by the distribution network in region s in year y at level t, Represents the reactive power of photovoltaic renewable power generation devices, Represents the reactive power of wind energy renewable power generation device, represents the reactive power provided by the circuit breaker at node i at level t in year y, represents the reactive power demand of node i in year y; The voltage amplitude constraint is: Where U i,t,y represents the square of the voltage amplitude at node i, U j,t,y represents the square of the voltage amplitude at node j, Z ij Represents the impedance of circuit ij.

8. A distribution network asset planning method according to claim 2, characterized in that: The two-stage asset planning model is solved to obtain the optimal sites, capacities and types of capacitor banks, distributed generation devices and energy storage systems, including: Solving the second-stage two-level linear programming model to obtain a solution result; After determining the parameters according to the solution results, the first stage planning model is solved to obtain the optimal sites, capacities and types of capacitor banks, distributed generation devices and energy storage systems.

9. A distribution network asset planning method according to claim 1, characterized in that: The collection of power system operation data and geospatial data includes: Collect initial operation data and initial geospatial data of the power system; The initial operation data and the initial geospatial data are cleaned and integrated to obtain operation data and geospatial data.

10. A distribution network asset planning terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step in the distribution network asset planning method described in any one of claims 1 to 9 is implemented.

Images