Power distribution network asset planning method and terminal

Abstract

The application discloses a power distribution network asset planning method and a terminal, a two-stage asset planning model of a power distribution network is established based on a carbon emission right transaction mechanism and a demand response strategy, collected operation data and geographic space 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 a capacitor group, a distributed power generation device and an energy storage system, the carbon emission right transaction mechanism and the demand response strategy are innovatively integrated into an asset planning process, a power distribution company can participate in a carbon market, reduce carbon emission and realize the goal of carbon emission reduction through the carbon emission right transaction mechanism, meanwhile, consumption behavior can be adjusted through the demand response, power consumption is more efficient and environment-friendly, the carbon emission right transaction and the demand response are integrated into the asset planning process, so that 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 application relates to the technical field of power system optimization scheduling, in particular to a power distribution network asset planning method and terminal. BACKGROUND

[0002] In today's society, the world is facing double challenges: the increase of carbon emissions and the urgent demand for renewable energy. With the rapid development of industrialization and urbanization, the traditional energy consumption mode has been unable to meet people's demand for energy, and has caused serious environmental problems, especially the increase of carbon emissions. Therefore, in order to cope with these challenges, people began to explore more environmentally friendly and sustainable ways of energy production and use. As the main supplier of energy, the carbon emission problem of power system is particularly prominent. Traditional power system mainly relies on fossil fuels such as coal and oil, which releases a large amount of carbon dioxide and other greenhouse gases during combustion, exacerbating global climate change. In addition, the traditional power system usually adopts 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 optimize and transform the power system comprehensively.

[0003] Under this background, power distribution network asset planning has become a key task. The traditional power distribution network asset planning mainly focuses on the stability and economy of power supply, often ignoring the influence of carbon emissions and the integration of renewable energy. Therefore, an innovative power distribution network asset planning method is needed, which can consider carbon emission trading and demand response at the same time to realize carbon emission reduction and effective integration of renewable energy. At present, although there have been some researches on the influence of carbon emission trading and demand response on power system, few researches have included these two aspects in the process of power distribution network asset planning. Therefore, a new method is needed to fill this research gap and provide a comprehensive asset planning tool for power companies to adapt to the new requirements of carbon emission reduction and renewable energy integration. SUMMARY

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

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

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

[0007] Establishing a two-stage asset planning model of power distribution network based on carbon emission right trading mechanism and demand response strategy;

[0008] Collecting operation data and geographic spatial data of power system;

[0009] 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 optimal sites, capacities and types of the capacitor banks, the distributed power generation devices and the energy storage systems.

[0010] To solve the above technical problems, another technical solution adopted by the present application is:

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

[0012] establishing a two-stage asset planning model of the power distribution network based on a carbon emission right trading mechanism and a demand response strategy;

[0013] collecting operation data and geospatial data of a power system;

[0014] 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 optimal sites, capacities and types of the capacitor banks, the distributed power generation devices and the energy storage systems.

[0015] The present application has the beneficial effects that: a two-stage asset planning model of the power distribution network is established based on a carbon emission right trading mechanism and a 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 optimal sites, capacities and types of the capacitor banks, the distributed power generation devices and the energy storage systems, the carbon emission right trading mechanism and the demand response strategy are innovatively incorporated into the asset planning process, the power distribution company can participate in the carbon market, reduce carbon emissions and achieve the goal of carbon emission reduction through the carbon emission right trading mechanism, meanwhile, the consumption behavior can be adjusted through the demand response to make the power consumption more efficient and environmentally friendly, the carbon emission right trading and the demand response are incorporated into the asset planning process, thereby reducing the carbon dioxide emission to the greatest extent and promoting the development and utilization of clean energy. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A step flowchart of a power distribution network asset planning method according to an embodiment of the present application;

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

[0018] To describe the technical content, achieved purposes and effects of the present application in detail, the following will be described in combination with the embodiments and the accompanying drawings.

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

[0020] A two-stage asset planning model of the power distribution network is established based on a carbon emission right trading mechanism and a demand response strategy;

[0021] Operation data and geographic spatial data of the power system are collected;

[0022] The operation data and the geographic spatial data are input into the two-stage asset planning model, and the two-stage asset planning model is solved to obtain optimal sites, capacities and types of capacitor banks, distributed power generation devices and energy storage systems.

[0023] From the above description, the beneficial effects of the present application are that: based on the carbon emission right trading mechanism and the demand response strategy, a two-stage asset planning model of the power distribution network is established, the collected operation data and geographic spatial data are input into the two-stage asset planning model, and the two-stage asset planning model is solved to obtain optimal sites, capacities and types of capacitor banks, distributed power generation devices and energy storage systems, the carbon emission right trading mechanism and the demand response strategy are innovatively integrated into the asset planning process, through the carbon emission right trading mechanism, the power distribution company can participate in the carbon market, reduce carbon emissions and achieve the goal of carbon emission reduction, at the same time, through the demand response, the consumption behavior can be adjusted, the power consumption is more efficient and environmentally friendly, the carbon emission right trading and the demand response are integrated into the asset planning process, thereby the carbon dioxide emission is reduced to the greatest extent, and the development and utilization of clean energy are promoted.

[0024] Further, the two-stage asset planning model of the power distribution network based on the carbon emission right trading mechanism and the demand response strategy comprises:

[0025] A first cost of transferring emission quotas from other regions, a second cost of purchasing emission quotas from a carbon trading market, a third cost of selling emission quota surpluses to the carbon trading market, a compensation cost paid to consumers participating in the demand response strategy, and parameters are determined;

[0026] A first objective function is established according to the first cost, the second cost, the third cost, the compensation cost and the parameters;

[0027] Investment budget constraints, capacitor bank constraints, photovoltaic module and wind unit constraints, energy storage system and power conversion unit constraints, energy storage system charging and discharging constraints, and carbon emission quota constraints are established, and a first constraint condition is obtained according to the investment budget constraints, the capacitor bank constraints, the photovoltaic module and wind unit constraints, the energy storage system and power conversion unit constraints, the energy storage system charging and discharging constraints, and the carbon emission quota constraints;

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

[0029] establish a second objective function according to energy cost and a variation of active power provided by a power distribution network;

[0030] establish uncertainty interval and robustness interval constraints of demand response and renewable power production, active and reactive power balance constraints and voltage amplitude constraints, and obtain a second constraint condition according to the uncertainty interval and robustness interval constraints of the demand response and renewable power production, the active and reactive power balance constraints and the voltage amplitude constraints;

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

[0032] generate a two-stage asset planning model of the power distribution network according to the first-stage planning model and the second-stage bi-level linear programming model.

[0033] As can be seen from the above description, the first objective function of the first-stage planning model considers a first cost of transferring emission quota from other regions, a second cost of purchasing emission quota from a carbon trading market, a third cost of selling emission quota surplus to the carbon trading market, a compensation cost paid to consumers participating in a demand response strategy and a parameter obtained by subsequently solving the second-stage bi-level linear programming model, and thus the first-stage planning model defines investment and operation decisions, the second-stage bi-level linear programming model considers uncertainty and robustness of renewable energy and completes asset planning, and the two-stage asset planning model of the power distribution network generated by the first-stage planning model and the second-stage bi-level linear programming model considers uncertainty of power demand and renewable energy, and a power distribution company can effectively configure various assets in an asset planning stage to cope with carbon emission reduction challenges, and various uncertain factors can be comprehensively considered through the model to improve robustness and reliability of planning.

[0034] Further, the first objective function is established according to the first cost, the second cost, the third cost, the compensation cost and the parameter, and the first objective function includes:

[0035]

[0036] wherein S represents a set of power distribution network regions, T represents a set of levels, Y represents a set of planning years, represents a price of emission quota transfer between regions at level t in year y, represents CO2 emission limit transferred between regions at level t in year y, represents a purchase price of emission quota between a carbon market and a region at level t in year y, represents CO2 quota sold from carbon market at level t in year y, represents the selling price of emission quota between carbon market and region at level t in year y, represents CO2 quota purchased from carbon market at level t in year y, r represents currency update in year y, N dr represents demand response node set, represents demand response strategy compensation price at level t in year y, represents the amount of demand change managed at node k, ψ represents parameter.

[0037] As can be seen from the above description, the first objective function is established according to the first cost, the second cost, the third cost and the compensation cost and the parameter, which can maximize the reduction of carbon dioxide emissions, promote the development and utilization of clean energy, and further optimize the energy utilization and improve the overall efficiency of the system.

[0038] Further, the investment budget constraint is:

[0039]

[0040] In the formula, N represents a power distribution network node set, Ω represents a capacitor group capacity set, represents the first investment price of capacity b type circuit breaker, represents the first binary variable, represents the second investment price of capacity b type circuit breaker, represents the second binary variable, ζ wt represents the investment price of wind energy based distributed power generation device, represents the operation and maintenance cost of wind energy based renewable energy device, represents the number of wind energy based distributed power generation devices installed at node i, ζ pv represents the investment price of photovoltaic based distributed power generation device, represents the operation and maintenance cost of photovoltaic based renewable energy device, represents the number of photovoltaic based distributed power generation devices installed at node i, represents the power unit investment price of energy storage system, represents the operation and maintenance cost of energy storage system, represents the unit capacity of power conversion unit of energy storage system at node i, represents the energy storage unit investment price of energy storage system, represents the CO2 price generated by energy storage system recycling process, ζ wr represents the disposal and recycling cost of energy storage system, Pi represents the total investment budget of the planning scheme;

[0041] The capacitor group constraint is:

[0042]

[0043] In the formula, Pi represents the maximum number of capacitor group modules at node i, Pi represents the number of capacitor group operation modules at node i;

[0044] The photovoltaic module and wind unit constraint is:

[0045]

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

[0047] As can be seen from the above description, the investment budget constraint defines the investment budget allocated by the planning scheme, which takes into account the investment cost corresponding to the allocation of fixed or switchable capacitor groups, the distributed renewable energy power generation device such as wind energy or photovoltaic, and the maintenance and operation cost thereof, the investment cost and maintenance cost of the energy storage system allocation, the capacitor group constraint defines the optimal size and type of the candidate capacitor group, and the photovoltaic module and wind unit constraint limits 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] Further, the energy storage system and power conversion unit constraint is:

[0049]

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

[0051] The charge and discharge constraint of the energy storage system is:

[0052]

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

[0054] The carbon emission quota constraint is:

[0055]

[0056] In the formula, represents the emission quota sent by region s in year y at level t, represents the emission quota received by region s in year y at level t, represents the CO2 quota purchased from the carbon market for region s in year y at level t, represents the CO2 quota sold to the carbon market for region s in year y at level t.

[0057] As can be seen from the above description, the reliability of the planning result of the first-stage planning model is ensured by the energy storage system and power conversion unit constraint, the charge and discharge constraint of the energy storage system, and the carbon emission quota constraint, and the amount of carbon dioxide emission is maximally reduced.

[0058] Further, the second objective function is established according to the energy cost and the active power variation amount provided by the power distribution network, and the second objective function includes:

[0059]

[0060] In the formula, represents the energy cost in year y at level t, represents the active power variation amount provided by the s region power distribution network in year y at level t.

[0061] As can be seen from the above description, the second objective function is a two-level linear programming problem, including a maximization optimization problem and a minimization optimization problem, the minimization optimization problem is a bottom problem, and the cost of the energy provided in the previous two layers respectively by the power distribution network considering the planning decision and the worst uncertainty is maximally reduced to improve the economy of the planning result.

[0062] Further, the uncertainty interval and robustness interval constraint of the demand response and renewable power production is:

[0063]

[0064]

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

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

[0067]

[0068]

[0069] where L denotes the set of circuits, P ji,t,y denotes the active power of the circuit ji, ij,t,y denotes the active power of the circuit ij, ij denotes the resistance of the circuit ij, denotes the variable of the square of the current magnitude of the circuit ij at the level t year y, denotes the active power provided by the regional distribution network s at the level t year y, denotes the active power of the photovoltaic renewable energy production device, denotes the active power of the wind energy renewable energy production device, denotes the discharging power of the energy storage system, denotes the active power demand at the node i, represents the charging power of the energy storage system, represents the provided power demand at the node k in the year y at the level t, represents the required power demand at the node k in the year y at the level t, Q ji,t,y represents the reactive power of the circuit ji, Q ij,t,y represents the reactive power of the circuit ij, X ij represents the reactance of the circuit ij, represents the reactive power provided by the regional distribution network s in the year y at the level t, represents the reactive power of the photovoltaic renewable power generation device, represents the reactive power of the wind energy renewable power generation device, represents the reactive power provided by the circuit breaker at the node i in the year y at the level t, represents the reactive power demand at the node i in the year y;

[0070] The voltage amplitude constraint is:

[0071]

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

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

[0074] Further, the two-stage asset planning model is solved to obtain the optimal site, capacity, and type of the capacitor bank, the distributed power generation device, and the energy storage system, which includes:

[0075] The second-stage bi-level linear programming model is solved to obtain a solution result;

[0076] After the parameters are determined according to the solution result, the first-stage planning model is solved to obtain the optimal site, capacity, and type of the capacitor bank, the distributed power generation device, and the energy storage system.

[0077] From the above description, the second stage double-layer linear programming model is solved first, the parameters are determined according to the solving result, the parameters are substituted into the first stage programming model, and the optimal site, capacity and type of the capacitor bank, the distributed power generation device and the energy storage system are obtained. Through the combination of carbon emission trading and demand response optimization asset allocation, various uncertain factors are considered, and various assets such as capacitor banks, distributed power generation devices and energy storage systems are effectively allocated in the asset planning stage to cope with carbon emission reduction and renewable energy integration challenges, effectively reduce carbon emissions, and promote the development of the power system to a more clean, environmentally friendly and sustainable direction.

[0078] Further, the operation data and geospatial data of the power system are collected, including:

[0079] Collecting 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, the collected initial operation data and initial geospatial data are cleaned and integrated to ensure that the data input into the model for solving is effective and reliable, and the final asset planning effect is ensured.

[0082] Please refer to Figure 2 The embodiment of the present application provides a power distribution network asset planning terminal, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor realizes each step of the power distribution network asset planning method when executing the computer program.

[0083] The power distribution network asset planning method and terminal described above can be applied to the power distribution network asset planning scene, and the following specific embodiments are described:

[0084] Please refer to Figure 1 The embodiment one of the present application is:

[0085] A power distribution network asset planning method comprises the following steps:

[0086] S1, based on the carbon emission trading mechanism and the demand response strategy, a two-stage asset planning model of the power distribution network is established, specifically comprising S11-S18:

[0087] The 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 the emission tax and emission cap policy, the carbon emission trading plan provides the carbon emitters with flexibility to comply with the CO2 regulations established by the government or regulatory entity. When the carbon emission trading plan is adopted, the emitters are given some initial emission allowances, i.e. emission caps, which can be based on the historical emission data of power production or through energy generation. However, each emitter can buy / sell the emission allowances on the external market from other emitters, government entities or companies, thereby generating the emission allowance price. To establish this mechanism, a regulatory entity and a group of emitters should be arranged to actively participate. In the present invention, the regulatory entity determined as the carbon emission market is responsible for determining the initial emission cap and the price of selling and buying the allowances, in addition, the emitters should comply with the specified emission cap, and if the cap is exceeded, additional carbon emission allowances can be purchased. From the perspective of incentives, if the emitters have surplus carbon emission allowances, they can sell to other emitters or the carbon emission market. On this basis, in order to develop the carbon emission trading scheme at the distribution level, a multi-regional power distribution network is considered, in which each region is subject to a respective emission cap. If the power distribution network of a region reaches or exceeds the predetermined emission cap, the allowances can be transferred from other regions and / or purchased from the carbon market, otherwise, if the cap is lower, the remaining allowances can be transferred to other parties and / or sold to the carbon market, thus establishing a dynamic carbon emission trading mechanism between the power distribution network regions and the carbon emission market.

[0088] In addition, consumers participate in the planning through demand response strategies, which are considered as distribution companies and different groups of consumers between predefined contracts. A set of nodes in the power distribution network is defined to represent a group of consumers gathered at a particular node, and these consumer groups provide sufficient flexibility for the operator of the power distribution network, therefore, it is required to optimize the management of demand and propose an appropriate consumption pattern, and the individual consumption behavior should be managed by an entity that encourages each consumer to adopt a new consumption pattern.

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

[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 a set of power distribution network regions, T represents a set of levels, Y represents a set of planning years, represents the price of emission allowance transfer between regions in year y at level t, represents the CO2 emission limit transferred between regions in year y at level t, represents the purchase price of emission allowance between carbon market and regions in year y at level t, represents the CO2 allowance sold from carbon market in year y at level t, represents the sale price of emission allowance between carbon market and regions in year y at level t, represents the CO2 allowance purchased from carbon market in year y at level t, r represents the currency update of year y, N dr represents the demand response node set, represents the compensation price of demand response strategy in year y at level t, represents the amount of demand change managed at node k, ψ represents a parameter.

[0093] wherein the transfer, purchase and sale emission allowance cost and compensation cost corresponding to the demand response strategy are present values.

[0094] S13, establishing investment budget constraints, capacitor bank constraints, photovoltaic module and wind unit constraints, energy storage system and power conversion unit constraints, charge and discharge constraints of energy storage system, and carbon emission quota constraints, and obtaining first constraint conditions according to the investment budget constraints, the capacitor bank constraints, the photovoltaic module and wind unit constraints, the energy storage system and power conversion unit constraints, the charge and discharge constraints of the energy storage system, and the carbon emission quota constraints.

[0095] wherein the investment budget constraint is:

[0096]

[0097] wherein, N represents a set of power distribution network nodes, Ω represents a set of capacitor bank capacities, represents the first investment price of capacity b type circuit breaker, represents the first binary variable, represents the second investment price of capacity b type circuit breaker, represents the second binary variable, ζ wt represents the investment price of wind energy based distributed power generation device, represents the operation and maintenance cost of wind energy based renewable energy device, represents the number of wind energy based distributed power generation devices installed at node i, ζ pv represents the investment price of photovoltaic based distributed power generation device, represents the operation and maintenance cost of photovoltaic energy based renewable energy device, denotes the number of photovoltaic based distributed generation units installed at node i, denotes the investment price of the power unit of the energy storage system, denotes the operation and maintenance cost of the energy storage system, denotes the unit capacity of the power conversion unit of the energy storage system at node i, denotes the investment price of the energy storage unit of the energy storage system, denotes the CO2 price generated by the recycling process of the energy storage system, ζ wr denotes the disposal and recycling cost of the energy storage system, denotes the unit capacity of the energy storage system at node i, Π denotes the total budget of the investment of the planning scheme.

[0098] The capacitor bank constraint is:

[0099]

[0100]

[0101] wherein, denotes the maximum number of capacitor bank modules at node i, denotes the number of capacitor bank operation modules at node i.

[0102] The photovoltaic module and wind unit constraint is:

[0103]

[0104] wherein, denotes the maximum capacity of the photovoltaic based distributed generator installed at node i, denotes the maximum capacity of the wind based distributed generator installed at node i.

[0105] The energy storage system and power conversion unit constraint is:

[0106]

[0107] wherein, denotes the maximum capacity of the energy storage system, denotes the maximum capacity of the power conversion unit, denotes the binary variable of whether the energy storage system is installed at node i, taking the value of 0 or 1.

[0108] The charge and discharge constraint of the energy storage system is:

[0109]

[0110] wherein, denotes the discharge state of the energy storage system at node i at time t, denotes the state of charge of the energy storage system at node i at time period t.

[0111] The carbon emission quota constraint is:

[0112]

[0113] wherein, denotes the emission quota sent by region s in year y at time period t, denotes the emission quota received by region s in year y at time period t, denotes the CO2 quota purchased from the carbon market by region s in year y at time period t, denotes the CO2 quota sold to the carbon market by region s in year y at time period t.

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

[0115] S15, a second objective function is established according to the energy cost and the active power variation provided by the power distribution network, specifically:

[0116]

[0117] wherein, denotes the energy cost in year y at time period t, denotes the active power variation provided by the power distribution network of region s in year y at time period t.

[0118] The second objective function of the second-stage bi-level linear programming model includes an intermediate problem (i.e. a maximization optimization problem) and a bottom problem (a minimization optimization problem), in which the cost of the energy provided in the previous two layers is maximally reduced by the power distribution network considering the planning decision and the worst uncertainty implementation.

[0119] S16, uncertainty interval and robustness interval constraints of demand response and renewable power production, active and reactive power balance constraints, and voltage amplitude constraints are established, and a second constraint condition is obtained according to the uncertainty interval and robustness interval constraints of demand response and renewable power production, the active and reactive power balance constraints, and the voltage amplitude constraints.

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

[0121]

[0122] wherein, f t d denotes the maximum value of the uncertainty interval of demand response consumption at time period t, ft d denotes the demand response consumption at level t, denotes the minimum of the uncertainty interval of the demand response consumption at level t, Γ d denotes the maximum of the robustness interval of the demand response consumption at level t, denotes the expected value of the demand response consumption at level t, denotes the minimum of the robustness interval of the demand response consumption at level t, denotes the maximum of the uncertainty interval of the photovoltaic renewable energy production at level t, denotes the photovoltaic renewable energy production at level t, denotes the minimum of the uncertainty interval of the photovoltaic renewable energy production at level t, Γ pv denotes the maximum of the robustness interval of the photovoltaic renewable energy production at level t, denotes the expected value of the photovoltaic renewable energy production at level t, denotes the minimum of the robustness interval of the photovoltaic renewable energy production at level t, denotes the maximum of the uncertainty interval of the wind energy renewable energy production at level t, denotes the wind energy renewable energy production at level t, denotes the minimum of the uncertainty interval of the wind energy renewable energy production at level t, Γ wt denotes the maximum of the robustness interval of the wind energy renewable energy production at level t, denotes the expected value of the wind energy renewable energy production at level t, denotes the minimum of the robustness interval of the wind energy renewable energy production at level t.

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

[0124]

[0125] where L denotes the set of circuits, P ji,t,y denotes the active power of circuit ji, P ij,t,y denotes the active power of circuit ij, R ij denotes the resistance of circuit ij, denotes the variable of the square of the current magnitude of circuit ij at level t year y, denotes the active power provided by the regional distribution network at level t from year y in area s, denotes the active power of the photovoltaic renewable energy production device, active power of a wind energy renewable power generation device, discharge power of an energy storage system, active power demand at node i, charge power of an energy storage system, active power demand of a supply node k in year y at level t, active power demand of a demand node k in year y at level t, Q ji,t,y reactive power of a circuit ji, Q ij,t,y reactive power of a circuit ij, X ij reactive power of a circuit ij, reactive power provided by a regional distribution network in year y at level t, reactive power of a photovoltaic renewable power generation device, reactive power of a wind energy renewable power generation device, reactive power provided by a circuit breaker at node i in year y at level t, reactive power demand of a node i in year y.

[0126] The voltage amplitude constraint is:

[0127]

[0128] wherein, U i,t,y voltage amplitude square at node i, U j,t,y voltage amplitude square at node j, Z ij impedance of a circuit ij.

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

[0130] S18, generating a two-stage asset planning model of the distribution network according to the first-stage planning model and the second-stage bi-level linear programming model.

[0131] S2, collecting operation data and geospatial data of the power system, specifically including S21-S22:

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

[0133] Specifically, the initial operation data of the power system, such as voltage, current and power, are collected by using smart meters and sensor networks, and at the same time, the initial geospatial data of the power system, including information such as terrain, land use and climate, are collected by using a geographic information system (GIS).

[0134] S22, cleaning and integrating the initial operation data and the initial geospatial data to obtain operation data and geospatial 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 the capacitor bank, the distributed power generation device and the energy storage system, specifically comprising 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 bi-level linear programming model to obtain a solution result;

[0138] S33, after determining the parameters according to the solution result, solving the first-stage planning model to obtain the optimal site, capacity and type of the capacitor bank, the distributed power generation device and the energy storage system, so as to maximize the energy loss and carbon emission of the system.

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

[0140] Firstly, the traditional distribution network asset planning method often ignores the impact of carbon emissions, leading to the continuous increase of carbon emissions of the power system, while the present application optimizes the distribution network structure through the carbon emission rights trading mechanism, which can maximize the reduction of carbon emissions and achieve the goal of carbon emission reduction, and can make the distribution network more environmentally friendly and sustainable while meeting the demand for electricity.

[0141] Secondly, with the rapid development of renewable energy, how to effectively integrate renewable energy has become a key issue. The traditional distribution network asset planning method often lacks consideration of renewable energy integration, resulting in low utilization efficiency of renewable energy. While the present application can better integrate renewable energy, improve energy utilization efficiency and reduce dependence on traditional energy by considering renewable energy resources, optimizing distribution network structure and developing demand response strategies.

[0142] And the traditional power distribution network asset planning method often assumes that the distribution of power demand and renewable energy is certain, ignoring the impact of uncertainty factors on asset planning. The present application considers the uncertainty of power demand and renewable energy by establishing a robust mixed integer programming model, which can improve the robustness and adaptability of asset planning, making the power distribution network better cope with various external changes and challenges, and improving the stability and reliability of the system.

[0143] Finally, through the carbon emission trading mechanism and demand response strategy, the present application can optimize the operation strategy of the power distribution network and improve the overall efficiency of the power system. This not only reduces carbon emissions, but also reduces energy costs and improves the economic benefits of the company. In contrast, the traditional power distribution network asset planning method often only focuses 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 two of the present application is:

[0145] A power distribution network asset planning terminal, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to realize each step of the power distribution network asset planning method in embodiment one.

[0146] In summary, the present application provides a power distribution network asset planning method and terminal, which establishes a two-stage asset planning model for the power 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 the capacitor bank, distributed power generation device and energy storage system. The carbon emission trading mechanism and demand response strategy are innovatively integrated into the asset planning process, and the power distribution company can participate in the carbon market through the carbon emission trading mechanism, reduce carbon emissions, achieve the goal of carbon emission reduction, and adjust consumer behavior through demand response to make power consumption more efficient and environmentally friendly. The carbon emission trading and demand response are integrated into the asset planning process to maximize the reduction of carbon dioxide emissions and promote the development and utilization of clean energy. In addition, the second-stage bi-level linear programming model is solved first, the parameters are determined according to the solving result, and the parameters are substituted into the first-stage planning model to obtain the optimal site, capacity and type of the capacitor bank, distributed power generation device and energy storage system. By combining carbon emission trading and demand response to optimize asset allocation, various uncertain factors are considered, and various assets such as capacitor banks, distributed power generation devices and energy storage systems are effectively allocated in the asset planning stage to cope with carbon emission reduction and renewable energy integration challenges, effectively reduce carbon emissions, and promote the development of the power system towards a cleaner, more environmentally friendly and sustainable direction.

[0147] The above merely illustrates the embodiments of the present application, and is not intended to limit the patent scope of the present application. Any equivalent variation or direct or indirect application in the related technical field made according to the content of the present application specification and drawings shall be included in the patent protection scope of the present application.

Claims

1. A power distribution network asset planning method characterized by, The method comprises the steps of: establishing a two-stage asset planning model of a distribution network based on a carbon emission right trading mechanism and a demand response strategy; collecting operation data and geographic spatial data of a power system; inputting the operation data and the geographic spatial data into the two-stage asset planning model and solving the two-stage asset planning model to obtain optimal sites, capacities and types of capacitor banks, distributed power generation devices and energy storage systems; the two-stage asset planning model of the distribution network based on the carbon emission right trading mechanism and the demand response strategy comprises: determining a first cost of transferring emission quotas from other regions, a second cost of purchasing emission quotas from a carbon trading market, a third cost of selling emission quota surpluses to the carbon trading market, a compensation cost of paying to consumers participating in the demand response strategy and parameters; establishing a first objective function according to the first cost, the second cost, the third cost, the compensation cost and the parameters; establishing investment budget constraints, capacitor bank constraints, photovoltaic module and wind unit constraints, energy storage system and power conversion unit constraints, energy storage system charging and discharging constraints and carbon emission quota constraints, and obtaining first constraint conditions according to the investment budget constraints, the capacitor bank constraints, the photovoltaic module and wind unit constraints, the energy storage system and power conversion unit constraints, the energy storage system charging and discharging constraints and the carbon emission quota constraints; establishing a first-stage planning model according to the first objective function and the first constraint conditions; establishing a second objective function according to energy costs and a change amount of active power provided by the distribution network; establishing uncertainty interval and robustness interval constraints of demand response and renewable power production, active and reactive power balance constraints and voltage amplitude constraints, and obtaining second constraint conditions according to the uncertainty interval and robustness interval constraints of demand response and renewable power production, the active and reactive power balance constraints and the voltage amplitude constraints; establishing a second-stage bi-level linear programming model according to the second objective function and the second constraint conditions; generating a two-stage asset planning model of a distribution network according to the first-stage planning model and the second-stage bi-level linear programming model; the solving of the two-stage asset planning model to obtain optimal sites, capacities and types of capacitor banks, distributed power generation devices and energy storage systems comprises: solving the second-stage bi-level linear programming model to obtain a solution result; determining the parameters according to the solution result, and solving the first-stage planning model to obtain optimal sites, capacities and types of capacitor banks, distributed power generation devices and energy storage systems; the collection of operation data and geographic spatial data of a power system comprises: collecting initial operation data and initial geographic spatial data of a power system; cleaning and integrating the initial operation data and the initial geographic spatial data to obtain operation data and geographic spatial data.

2. A power distribution network asset planning method according to claim 1, characterised in that, the establishment of a first objective function according to the first cost, the second cost, the third cost, the compensation cost and the parameters comprises: ； In the formula, S represents a set of power distribution network regions, T represents a set of levels, Y represents a set of planning years, represents the emission quota transfer price between regions in year y at level t, represents the CO2 emission limit transferred between regions in year y at level t, represents the purchase price of emission quota between carbon market and regions in year y at level t, represents the CO2 quota sold from carbon market in year y at level t, represents the sale price of emission quota between carbon market and regions in year y at level t, represents the CO2 quota purchased from carbon market in year y at level t, and r represents the currency update in year y, N dr represents a set of demand response nodes, represents the demand response strategy compensation price in year y at level t, represents the amount of demand change managed at node k, represents a parameter.

3. A method of electricity distribution network asset planning according to claim 2, wherein, the investment budget constraints are: ； where N denotes a set of distribution network nodes, denotes a set of capacitor bank capacities, denotes a first investment price of capacity b type circuit breaker, denotes a first binary variable, denotes a second investment price of capacity b type circuit breaker, denotes a second binary variable, denotes an investment price of wind based distributed generation, denotes an operation and maintenance cost of wind based renewable energy device, denotes a number of wind based distributed generation installed at node i, denotes an investment price of photovoltaic based distributed generation, denotes an operation and maintenance cost of photovoltaic based renewable energy device, denotes a number of photovoltaic based distributed generation installed at node i, denotes an investment price of power unit of energy storage system, denotes an operation and maintenance cost of energy storage system, denotes a unit capacity of power conversion unit of energy storage system at node i, denotes an investment price of energy storage unit of energy storage system, denotes a CO2 price generated by recycling process of energy storage system, denotes a disposal and recycling cost of energy storage system, denotes a unit capacity of energy storage system at node i, denotes a total investment budget of planning scheme; The capacitor group constraint is: ； ； ； ； ； wherein represents the maximum number of capacitor bank modules at node i, represents the number of capacitor bank operating modules at node i; The photovoltaic module and wind unit constraint is: ； ； wherein, denotes the maximum capacity of a photovoltaic based distributed generator set installed at node i, denotes the maximum capacity of a wind energy based distributed generator set installed at node i.

4. A power distribution network asset planning method according to claim 3, wherein, The energy storage system and power conversion unit constraint is: ； ； wherein represents the maximum capacity of the energy storage system, represents the maximum capacity of the power conversion unit, represents a binary variable whether an energy storage system is installed at node i; The charge and discharge constraint of the energy storage system is: ； wherein denotes the discharging state of the energy storage system at node i at level t, denotes the charging state of the energy storage system at node i at level t; The carbon emission quota constraint is: ； ； wherein represents the emission quota sent by region s in year y at level t, represents the emission quota received by region s in year y at level t, represents the CO2 quota bought from the carbon market for region s in year y at level t, represents the CO2 quota sold from the carbon market for region s in year y at level t.

5. A power distribution network asset planning method according to claim 2, characterised in that, The second objective function is established according to the energy cost and the active power variation provided by the power distribution network, and includes: ； wherein represents the energy cost at year y at level t, represents the change in active power provided by the s regional distribution network at year y at level t.

6. A power distribution network asset planning method according to claim 2, characterised by, The uncertainty interval and robustness interval constraint of the demand response and renewable power production is: ； ； ； ； ； ； wherein represents the maximum value of the uncertainty interval of the demand response consumption at the level t, represents the demand response consumption at the level t, represents the minimum value of the uncertainty interval of the demand response consumption at the level t, represents the maximum value of the robustness interval of the demand response consumption at the level t, represents the expected value of the demand response consumption at the level t, represents the minimum value of the robustness interval of the demand response consumption at the level t, represents the maximum value of the uncertainty interval of the photovoltaic renewable energy production at the level t, represents the photovoltaic renewable energy production at the level t, represents the minimum value of the uncertainty interval of the photovoltaic renewable energy production at the level t, represents the maximum value of the robustness interval of the photovoltaic renewable energy production at the level t, represents the expected value of the photovoltaic renewable energy production at the level t, represents the minimum value of the robustness interval of the photovoltaic renewable energy production at the level t, represents the maximum value of the uncertainty interval of the wind energy renewable energy production at the level t, represents the wind energy renewable energy production at the level t, represents the minimum value of the uncertainty interval of the wind energy renewable energy production at the level t, represents the maximum value of the robustness interval of the wind energy renewable energy production at the level t, represents the expected value of the wind energy renewable energy production at the level t, represents the minimum value of the robustness interval of the wind energy renewable energy production at the level t; The active and reactive power balance constraint is: ； ； where L represents a set of circuits, represents the active power of circuit ji, represents the active power of circuit ij, represents the resistance of circuit ij, represents the variable of the square of the current magnitude of circuit ij at year y of horizon t, represents the active power provided by the distribution network of area s at year y of horizon t, represents the active power of the photovoltaic renewable power plant, represents the active power of the wind energy renewable power plant, represents the discharging power of the energy storage system, represents the active power demand at node i, represents the charging power of the energy storage system, represents the power supply demand at node k at year y of horizon t, represents the power demand at node k at year y of horizon t, represents the reactive power of circuit ji, represents the reactive power of circuit ij, represents the reactance of circuit ij, represents the reactive power provided by the distribution network of area s at year y of horizon t, represents the reactive power of the photovoltaic renewable power plant, represents the reactive power of the wind energy renewable power plant, represents the reactive power provided by the circuit breaker at node i at year y of horizon t, represents the reactive power demand at node i at year y. The voltage amplitude constraint is: ； wherein represents the voltage amplitude squared at node i, represents the voltage amplitude squared at node j, represents the impedance of the circuit ij.

7. A distribution network asset planning terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements each step in the power distribution network asset planning method of any one of claims 1 to 6 when executing the computer program.

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