WO2023010760A1 - Procédé d'évaluation de capacité d'alimentation électrique pour réseau de distribution régional dans une situation concurrentielle de distribution d'énergie et de vente - Google Patents

Procédé d'évaluation de capacité d'alimentation électrique pour réseau de distribution régional dans une situation concurrentielle de distribution d'énergie et de vente Download PDF

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WO2023010760A1
WO2023010760A1 PCT/CN2021/138597 CN2021138597W WO2023010760A1 WO 2023010760 A1 WO2023010760 A1 WO 2023010760A1 CN 2021138597 W CN2021138597 W CN 2021138597W WO 2023010760 A1 WO2023010760 A1 WO 2023010760A1
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load
power supply
distribution network
supply capacity
power
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PCT/CN2021/138597
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Chinese (zh)
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钱仲豪
茅雷
张骏
袁松
吴茜
李伟伦
毛艳芳
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国网江苏省电力有限公司南通供电分公司
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Publication of WO2023010760A1 publication Critical patent/WO2023010760A1/fr

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Definitions

  • the invention belongs to the field of distribution network optimization dispatching, and specifically relates to a method for evaluating the power supply capacity of a regional distribution network under the competition situation of distribution and sale of electricity.
  • the linear programming method and the mixed integer second-order cone programming method are commonly used.
  • the linear programming algorithm takes the distribution network main transformer constraints and feeder capacity constraints as constraints, and The maximum load of each main transformer in the distribution network is the optimization target, and linear programming software (such as lingo) is used to solve the problem;
  • the mixed integer second-order cone programming method considers the power flow calculation, and establishes a more accurate model for the distribution network.
  • the convex and nonlinear constraints are relaxed, and the original problem is converted into a mixed integer second-order cone programming problem for solution. The result is more accurate and the calculation efficiency is higher.
  • the present invention chooses to use the mixed integer second-order cone programming method to establish the solution model of the maximum power supply capacity in the electricity market environment.
  • the purpose of the present invention is to provide a method for evaluating the power supply capacity of the regional distribution network under the competition situation of power distribution and sales to realize the maximum power supply capacity of the distribution network and the operation economy of the distribution network.
  • the present invention is a method for evaluating the power supply capacity of a regional distribution network under the situation of distribution and sales competition, including the following steps:
  • Step 1) Construct a multi-objective optimization model that comprehensively considers the maximum power supply capacity of the distribution network and the operating economy of the distribution network;
  • Step 2) Construct the model constraints under the comprehensive consideration of distributed power sources and flexible loads participating in power market transactions;
  • Step 3) The Pareto frontier is introduced to convert the multi-objective function into a series of single-objective functions, and solve the Pareto optimal solution for the multi-objective nonlinear optimization model.
  • the present invention considers the safe operation of the system and the transformer N-1 scenario and realizes the reconfiguration of the distribution network through the system topology under the application of various resources of the active distribution network oriented by the power market transaction.
  • the present invention also provides a Pareto frontier-based solution algorithm that can effectively solve the model.
  • Fig. 1 is the algorithm flow chart of model solving
  • Figure 2 is an improved 94-node topology diagram for model testing
  • Figure 3 is a diagram of the maximum power supply capacity of the power distribution system in different scenarios
  • Figure 4 Figure 5, Figure 6, and Figure 7 are the node diagrams of power distribution system reconstruction in different scenarios
  • the present invention provides a method for evaluating the power supply capacity of a regional distribution network under the situation of power distribution and sales competition. Based on the system shown in Figure 2, the test includes the following steps:
  • a method for evaluating the power supply capacity of a regional distribution network under the situation of power distribution competition comprising the following steps:
  • Step 1 Construct a multi-objective optimization model that comprehensively considers the maximum power supply capacity of the distribution network and the operation economy of the distribution network;
  • Step 2 Construct model constraints under the comprehensive consideration of distributed power sources and flexible loads participating in power market transactions
  • Step 3 Introduce the Pareto front to convert the multi-objective function into a series of single-objective functions, and solve the Pareto optimal solution of the multi-objective nonlinear optimization model.
  • step 1 can be implemented using the following process:
  • Step 101 Establish a multi-objective optimization model as follows:
  • Equation (0.39) is the objective function considering the maximum power supply capacity of the distribution network, and G t is the maximum load magnification of the distribution network at time t; Equation (0.40) is the objective function considering the minimum operating cost of the distribution network, specifically including substation power supply Cost C Sub , distributed power generation operating cost C DG , energy storage operating cost C ES , flexible load dispatching cost C FL , and network loss cost C Loss are as follows:
  • N t , N Sub , NPV , N WT , N ES , N LA , N CL , N TL , and N branch represent the scheduling period, substation, photovoltaic power station (PV), wind farm (WT), energy storage Collection of equipment (ES), dispatchable resident load (LA), interruptible load (CL), transferable load (TL), and lines;
  • the active power of the transfer load; I ij,t represents the current flowing through the line ij at time t, and r ij represents the resistance of the branch ij.
  • Step 102 In the objective function of simply calculating the maximum power supply capacity, it can be found that the network loss in the obtained result is too large, which is unreasonable in actual operation. For this reason, this paper adds network loss to the objective function, and uses the second-order cone relaxation technique to convexize the model, so that the model can be solved efficiently.
  • the modified objective function looks like this:
  • N node represents the collection of nodes in the distribution network, Indicates the active load of node i at time t, and ⁇ is the weight coefficient.
  • step 2 can be implemented by the following process:
  • Step 201 Give power flow constraints, including node power balance constraints, line voltage drop constraints, and line power balance constraints, as shown in formulas (0.42)-(0.44) respectively.
  • u(i), v(i) respectively represent the set of node i as the head end node and end node;
  • P ji,t and Q ji,t represent the active and reactive power flowing in the line ji, respectively, respectively represent the active and reactive load power of node i; respectively represent the reactive power of photovoltaic distributed power and wind power distributed power of node i;
  • Step 202 Line second-order cone constraints
  • Step 203 System operation security constraints
  • Formulas (0.48) ⁇ (0.50) represent the upper and lower limit constraints of node voltage and branch current respectively.
  • V max and V min represent the maximum value and minimum value of the node voltage respectively, and I max represents the maximum value of the line current.
  • Step 204 Run variable upper and lower bound constraints
  • the active and reactive power of the operating line should not exceed the rated power of the line.
  • the active and reactive power of the substation should not exceed the rated power of the substation; as shown in the formula (0.52), in addition, the active and reactive power
  • the power should satisfy the formula (0.53); the charging and discharging power of the energy storage should not exceed its rated power, as shown in the formula (0.54); the active power, abandoned wind and light, and reactive power of photovoltaic distributed
  • the range is shown in formula (0.55) ⁇ (0.56).
  • Step 205 Radiation constraints
  • Equation (0.57) indicates that the load node has and only one power inflow path, and the substation node has no power inflow path; since the active distribution network contains active devices such as distributed power generation and energy storage, it is necessary to further introduce virtual power constraints to ensure
  • formula (0.58) represents the virtual power balance constraint
  • formula (0.59) ⁇ (0.60) represent the upper and lower limit ranges of line virtual power, load virtual power and substation virtual power.
  • N LD represents the set of load nodes; Respectively represent the virtual power of the line, load node and substation node.
  • Step 206 Adjustable and controllable resource constraints
  • Formula (0.62) indicates that the access power and abandoned light power of photovoltaic distributed power are equal to the output power of photovoltaic distributed power; formula (0.63) indicates that the access power and abandoned wind power of wind power distributed power are equal to the output power of wind power distributed power; formula ( 0.64) means that the battery state of charge of the energy storage at the initial moment and the end moment of the dispatch cycle is both 0.5, and the conversion relationship between the battery state of charge at time t and the battery state of charge and charging and discharging behavior at the previous moment.
  • Step 207 Flexible Load Constraints
  • Formula (0.65) represents the load power of dispatchable resident load i at time t considering the demand-side response; formula (0.66) represents the upper and lower limits of the dispatchable resident load up-regulation power and down-regulation power; formula (0.67) represents the demand-side response The load power of the interruptible load i on the side response at time t; the formula (0.68) represents the upper and lower limits of the interruption power of the interruptible load; the formula (0.69) represents the load power of the transferable load i considering the demand side response at time t; the formula (0.70) represents the upper and lower limits of the up-regulated power and down-regulated power of transferable loads, and the total energy up-regulated in the scheduling period is equal to the total energy down-regulated.
  • ⁇ LA , ⁇ CL , ⁇ TL represent the upper limit of adjustment percentages of dispatchable resident load, interruptible load and transferable load; It is a 0-1 variable, respectively indicating whether the dispatchable residential load can adjust the power upward or downward; It is a 0-1 variable, respectively indicating whether the transferable load adjusts the power upwards or downwards.
  • Step 208 N-1 constraints
  • N fault represents the set of lines connected to the fault transformer.
  • step 3 can be realized by the following process:
  • the maximum power supply capacity model of distribution network is a multi-objective nonlinear optimization model.
  • the two objective functions of maximum power supply capacity and economy conflict with each other.
  • the maximum power supply capacity is large, the economy is relatively poor, and when the maximum power supply capacity is small, the economy is relatively good. It is difficult to find two objective functions that are optimal at the same time Excellent solution.
  • Step 301 Transform the multi-objective function into a series of single-objective functions, and write the above model in matrix form as follows:
  • Step 302 Separately find the minimum and maximum values under the two objective functions and E min 2 , E max 2 ;
  • Step 303 Standardize the two objective functions as follows:
  • Step 304 Segment the utopia line, and the specific node constraints are generated as follows:
  • s is the number of split nodes on the utopia line; ⁇ 1,p and ⁇ 2,p are values between 0-1 respectively.
  • Step 305 Take the maximum power supply capacity as the main objective function, and generate a modified model as shown below.
  • the fault operation of the lower G, H, and I feeders and the scenario 5 is the fault operation of the J and K feeders of the transformer T4.
  • the maximum power supply capacity of the distribution network is relatively high, and the power supply capacity at any time is greater than 1.5.
  • the distribution network has a large load margin and high security. And the maximum power supply capacity of the distribution network decreases with the increase of the load.
  • the power load is small and the maximum power supply capacity is high. From 18:00 to 20:00 in the evening, the power load is heavy and the maximum power supply capacity is low. .
  • the transformer N-1 fault occurs, the maximum power supply capacity of the distribution network is significantly reduced compared with the normal situation, but it also remains above 1.2.
  • Figure 4 shows the reconstructed network topology when substation T1 exits due to a fault.
  • the feeder line H On the feeder line H, it is supplied by the main transformer T3; the load carried by the feeder line B is transferred to the feeder F through the tie line 86, and is supplied by the main transformer T2; the load carried by the feeder line C is transferred to the feeder D through the tie line 89 , supplied by the main transformer T2.
  • Fig. 8 to Fig. 10 respectively represent the load scheduling situation of node 3 (schedulable load), node 6 (interruptible load), and node 4 (transferable load) in the scheduling cycle.
  • the maximum dispatching ratio in each time period is 20%, and the total amount of upward dispatching load in the dispatching period must be greater than 50% of the total downward dispatching load.
  • the load dispatching amount in the dispatching period Consistent with expectations; for interruptible loads, the amount of interrupted loads within the dispatching period is not greater than 45% of the total load, and it can be seen from Figure 9 that the interruption of interruptible loads is in line with expectations, in order to increase the maximum power supply capacity of the distribution network , the total amount of interrupted load in the time period with high load is larger; for transferable load, the maximum load transfer amount in each time period is 20%, and the total amount of load increase is equal to the total amount of load decrease in the dispatching cycle, as shown in Figure 10 As expected, the transferable load takes into account the requirements of economy and power supply capacity, and adjusts downward during periods with high load and high electricity prices, and increases during periods with low load and low electricity prices.
  • Table 1 The range of maximum power supply capacity and economy
  • the sequence of substations that have a greater impact on the maximum power supply capacity is substation T1, substation T2, substation T3, and substation T4.
  • the theory of power supply capacity can identify the weak link of power supply in the distribution network, so as to strengthen it in a targeted manner. It can be further seen that the enhancement of the maximum power supply capacity of the distribution network increases the operating cost in the dispatch cycle, that is, the maximum power supply capacity and economic efficiency cannot be optimized at the same time.
  • N LD represents the set of load nodes; Respectively represent the virtual power of the line, load node and substation node.
  • Formula (0.24) indicates that the access power and abandoned light power of photovoltaic distributed power are equal to the output power of photovoltaic distributed power; formula (0.25) indicates that the access power and abandoned wind power of wind power distributed power are equal to the output power of wind power distributed power; formula ( 0.26) indicates that the battery state of charge of the energy storage at the initial moment and the end moment of the dispatch cycle is both 0.5, and the conversion relationship between the battery state of charge at time t and the battery state of charge and charging and discharging behavior at the previous moment.
  • Formula (0.27) represents the load power of dispatchable resident load i at time t considering the demand-side response; formula (0.28) represents the upper and lower limits of the dispatchable residential load up-regulation power and down-regulation power; formula (0.29) represents the demand-side response
  • the formula (0.31) represents the load power of the transferable load i considering the demand side response at time t;
  • the formula (0.32) represents the upper and lower limits of the up-regulated power and down-regulated power of transferable loads, and the total energy up-regulated in the scheduling period is equal to the total energy down-regulated.
  • ⁇ LA , ⁇ CL , ⁇ TL represent the upper limit of adjustment percentages of dispatchable resident load, interruptible load and transferable load; It is a 0-1 variable, respectively indicating whether the dispatchable residential load can adjust the power upward or downward; It is a 0-1 variable, respectively indicating whether the transferable load adjusts the power upwards or downwards.
  • N fault represents the set of lines connected to the fault transformer.
  • the maximum power supply capacity model of the distribution network in the power market environment described above is a multi-objective nonlinear optimization model.
  • the maximum power supply capacity and economy of the two objective functions are in conflict with each other.
  • the maximum power supply capacity is large, the economy is relatively poor, and when the maximum power supply capacity is small, the economy is relatively good. It is difficult to find two objective functions that are optimal at the same time. Excellent solution.
  • s is the number of split nodes on the utopia line; ⁇ 1,p and ⁇ 2,p are values between 0-1 respectively.
  • the maximum power supply capacity is taken as the main objective function, and the modified model is generated as follows.

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Abstract

Procédé d'évaluation de la capacité d'alimentation électrique, destiné à un réseau de distribution régional dans une situation concurrentielle de distribution d'énergie et de vente. Le procédé comprend les étapes suivantes consistant 1), à construire un modèle d'optimisation à objectifs multiples, lequel prend en compte globalement la capacité d'alimentation électrique maximale d'un réseau de distribution et l'économie d'exploitation du réseau de distribution ; 2), à construire une condition de contrainte de modèle lorsque la participation des ressources distribuées et des charges flexibles sur le marché de l'électricité est prise en compte globalement ; et 3), à introduire un front de Pareto afin de convertir une fonction à objectifs multiples en une série de fonctions à objectif unique, de manière à résoudre un modèle d'optimisation non linéaire à objectifs multiples pour obtenir la solution de Pareto optimale. La capacité d'alimentation électrique maximale d'un réseau de distribution, et l'économie d'exploitation du réseau de distribution, sont obtenues. Un modèle d'optimisation à objectifs multiples est proposé, et un algorithme de solution fondé sur un front de Pareto, pouvant résoudre efficacement le modèle, est également décrit.
PCT/CN2021/138597 2021-08-04 2021-12-16 Procédé d'évaluation de capacité d'alimentation électrique pour réseau de distribution régional dans une situation concurrentielle de distribution d'énergie et de vente WO2023010760A1 (fr)

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CN116090782A (zh) * 2023-02-16 2023-05-09 国网湖南省电力有限公司 区域电网500kV变电站供电方案选择方法及系统
CN116109216A (zh) * 2023-04-13 2023-05-12 国网湖北省电力有限公司营销服务中心(计量中心) 一种源网储荷系统的可调性评估方法
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