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CN-121984077-A - Power distribution network and optical storage and charging system collaborative planning method considering N-1 safety criterion

CN121984077ACN 121984077 ACN121984077 ACN 121984077ACN-121984077-A

Abstract

The invention discloses a collaborative planning method for a power distribution network and an optical storage and charging system in consideration of N-1 safety criteria, and belongs to the field of novel power systems. The method comprises the steps of constructing a power distribution network computable power flow model based on second order cone relaxation and linearization, constructing an optical storage and charge energy hub model based on EH multi-energy coupling, constructing a double-stage stochastic programming framework, conducting joint optimization on investment and operation, conducting N-1 fault recovery simulation, maximizing an uncertainty interval of photovoltaic output and charging load fluctuation at fault moment by adopting an information gap decision theory, decomposing a fault problem into a main problem and a corresponding sub problem based on the double-stage stochastic programming framework, conducting key element identification, and completing fault verification. The invention realizes the minimization of the total cost of the whole life cycle of the system on the premise of meeting the N-1 safety criterion, ensures the power supply safety in extreme scenes, and ensures the calculation efficiency of practical engineering application.

Inventors

  • WU YINGXIN
  • Lin tuo
  • ZHOU DAN

Assignees

  • 浙江工业大学

Dates

Publication Date
20260505
Application Date
20260408

Claims (10)

  1. 1. A power distribution network and optical storage and inflation system collaborative planning method considering N-1 safety criteria is characterized by comprising the following steps: Performing phase angle relaxation on a standard branch power flow model to obtain a real equation model suitable for optimization calculation, performing second order cone relaxation and linearization on nonlinear constraint in the real equation model, and constructing a power distribution network computable power flow model; abstracting each photovoltaic power generation equipment, energy storage equipment and charging station into an energy hub, taking electric energy from a power distribution network and photovoltaic power generation electric energy as input, and constructing a light storage and charge energy hub model; Aiming at two states of normal operation or N-1 fault occurrence of the optical storage and filling system, constructing a double-stage random planning framework, optimizing investment and operation in a combined way with the aim of minimizing the annual total cost of the optical storage and filling system, and carrying out N-1 fault recovery simulation; an information gap decision theory is adopted, and an uncertainty interval of photovoltaic output and charging load fluctuation at the moment of failure is maximized; Based on the double-stage stochastic programming framework, the fault problem is decomposed into a main problem and a corresponding sub problem, the main problem is used for solving an economic scheme without considering N-1 constraint, the sub problem is used for carrying out N-1 fault verification on the economic scheme after the main problem is solved, a fault element is identified, if the fault exists, the fault is used as the main problem constraint, the solution is carried out again, and the solution process is iterated until the verification is correct.
  2. 2. The method of claim 1, wherein the linearization is achieved by an circumscribed polygon linearization technique.
  3. 3. The method of claim 1, wherein the optical storage and retrieval energy hub model comprises: the photovoltaic model is used for representing the output characteristic of the photovoltaic system based on a typical sunlight curve; The electrochemical energy storage model is used for tracking the energy state change of the energy storage equipment and is constrained by the charge and discharge efficiency, the power, the energy state and the upper and lower limits of the capacity; the electric automobile charging load model is used for aggregating the distributed electric automobile charging load into a controllable overall load and is constrained by the number of charging piles, the power level and a user charging demand curve.
  4. 4. The method of claim 1, wherein the joint optimization of investment and operation with the goal of minimizing the annual total cost of the optical storage and inflation system comprises: constructing an investment operation model by taking the annual comprehensive cost of the minimum optical storage and filling system as an objective function, wherein the annual comprehensive cost comprises the investment cost and the expected operation cost; the constraint conditions are power distribution network convex tide constraint, optical storage and charging system operation constraint and optical storage and charging system power balance constraint.
  5. 5. The method of claim 1, wherein the N-1 fault recovery simulation comprises: simulating the response of the optical storage and filling system when encountering any preset N-1 fault; the network reconfiguration switch state after the fault, the emergency discharge power of the energy storage device, the photovoltaic active and reactive support power and the adjustment quantity of the interruptible or transferable load are taken as decision variables; Taking the total load reduction amount under the minimum specific N-1 fault scene as an objective function, carrying out optimization solution, introducing power flow constraint of the power distribution network based on a new topological structure after the fault and operation constraint of the optical storage and charging system, and allowing non-critical load to be reduced; And checking the result of the optimization solution, and if the total load reduction exceeds a preset value, judging that the current planning framework does not meet the N-1 safety criterion and generating feedback information.
  6. 6. The method of claim 1, wherein maximizing an uncertainty interval for photovoltaic output and charge load fluctuations at a fault time using information gap decision theory comprises: Based on an information gap decision theory, an uncertainty model is built aiming at uncertainty of fluctuation of photovoltaic output and charging load at the moment of failure, and the uncertainty of fluctuation of the photovoltaic output and the charging load is represented by a fluctuation deviation coefficient; the minimization of the annual total cost of the optical storage and filling system in the objective function is replaced by maximizing the fluctuation deviation coefficient, and meanwhile, the cost constraint is added.
  7. 7. The method of claim 1, wherein decomposing the fault problem into a main problem and a corresponding sub-problem comprises: The main problem comprises a key N-1 fault scene constraint set, the key N-1 fault scene constraint set is an empty set in an initial state, and whenever a sub-problem is solved to obtain a certain N-1 fault scene, the N-1 fault scene is added into the N-1 fault scene constraint set.
  8. 8. The utility model provides a distribution network and optical storage system collaborative planning device of taking into account N-1 safety rule which characterized in that includes: the power distribution network computable power flow model construction module is used for performing phase angle relaxation on the standard branch power flow model to obtain a real equation model suitable for optimization calculation, performing second order cone relaxation and linearization on nonlinear constraint in the real equation model, and constructing the power distribution network computable power flow model; the photovoltaic energy storage and charging hub model building module is used for abstracting each photovoltaic power generation equipment-energy storage equipment-charging station into an energy hub, taking electric energy from a power distribution network and photovoltaic power generation electric energy as inputs, and building a photovoltaic energy storage and charging hub model; The double-stage random planning framework construction module is used for constructing a double-stage random planning framework aiming at two states of normal operation or N-1 fault occurrence of the optical storage and filling system, optimizing investment and operation in a combined way with the aim of minimizing annual total cost of the optical storage and filling system, and carrying out N-1 fault recovery simulation; The risk avoidance module is used for maximizing an uncertainty interval of photovoltaic output and charging load fluctuation at the moment of failure according to the information gap decision theory; The problem decomposition and iteration verification module is used for decomposing the fault problem into a main problem and a corresponding sub problem based on the double-stage stochastic programming framework, wherein the main problem is used for solving an economic scheme without considering N-1 constraint, the sub problem is used for carrying out N-1 fault verification on the economic scheme after the main problem is solved, and identifying a fault element, if the fault exists, the fault is used as the main problem constraint, the solution is carried out again, and the iteration solution process is continued until the verification is correct.
  9. 9. An electronic device comprising a memory coupled to the processor and a processor, wherein the memory is configured to store program data and the processor is configured to execute the program data to implement the method of any of claims 1-7.
  10. 10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-7.

Description

Power distribution network and optical storage and charging system collaborative planning method considering N-1 safety criterion Technical Field The invention relates to the field of novel power systems, in particular to a collaborative planning method for a power distribution network and an optical storage and charging system by considering N-1 safety rules. Background The traditional power distribution network planning method generally carries out planning and splitting of power grid expansion (circuits and substations) and distributed power supplies, energy storage and charging facilities, or only carries out superposition under a simple assumption, so that global optimal coordination of 'network-source-storage-load' resources cannot be realized. Meanwhile, the full-scene N-1 security check is directly carried out on the large-scale complex power distribution network, the calculated amount is huge, and the method becomes a main bottleneck of engineering application. Lin Zhe, hu Zechun and Song Yonghua, which are published in "Power distribution network and distributed energy storage Joint planning taking N-1 criteria into consideration", are constructed based on the characteristics of actual Power distribution networks, such as "closed-loop design and open-loop operation". The physical structure of the system comprises a power distribution net rack to be planned and a plurality of distributed energy storage systems to be addressed and fixed in volume. Photovoltaic and charging piles are typically incorporated into the model as known or predicted boundary conditions, but their configuration is not itself a core optimization decision variable. The core contribution of the nearest scheme is to bring energy storage into a planning framework as flexible resources, and design an efficient iterative verification strategy to solve the calculation difficulty. However, it still has limitations in dealing with the "photovoltaic-energy storage-charging" deep fusion system for which the present invention is directed: Photovoltaic and charging piles are regarded as passive, fixed boundary conditions, rather than active decision variables that are co-optimized with the grid, energy storage. The charging pile layout and the weak link of the power grid cannot be complemented, and the economical efficiency and the reliability cannot be improved by maximally utilizing the cooperative charging and discharging characteristics of the photovoltaic and the energy storage. The photovoltaic output and charging demand uncertainty (especially fluctuation of fault moment) is simplified, a typical scene or a simple conservation interval can be adopted, investment cost and power supply risk in extreme cases are difficult to accurately balance in planning, and the robustness of the obtained scheme is to be improved. The method mainly relies on the 'discharging' of energy storage to support fault recovery, and the controllability of photovoltaic during faults and the 'vehicle network interaction' potential of charging loads of electric vehicles are not fully considered, so that an 'light-storage-charging' integrated comprehensive response and fault support strategy cannot be constructed. In the prior art, when the power distribution network is cooperatively planned for high-proportion distributed photovoltaic, energy storage and electric vehicle charging load access, the following problems exist: (1) The method has the problems of 'net-source-load' planning disconnection and high safety redundancy cost. The conventional power distribution network planning method generally regards network frame expansion and configuration of user side distributed resources (such as photovoltaic, energy storage and charging piles) as two independent or sequential links. This planning mode of fracturing results in an inability to comprehensively consider the potential of the distributed resource as a supporting power source in the event of failure during the planning phase. To meet stringent N-1 safety guidelines, planning schemes tend to over-invest in building physical lines and substation capacity to provide sufficient redundancy, resulting in low grid asset utilization and poor overall economics. (2) In the aspect of coping with uncertainty, the problem of insufficient consideration of dynamic risks at the moment of failure exists. The existing planning method is mostly based on a determined typical scene, and cannot fully consider the superposition influence of various uncertainty factors such as the occurrence time of N-1 faults, the randomness of photovoltaic output, the time-space fluctuation of charging load of an electric automobile, the conventional load prediction deviation and the like. The problem that the planning scheme is insufficient in robustness under a real fault scene, a preset fault recovery strategy is invalid, the light rejection rate is increased sharply or the load loss exceeds the expected value and the like possibly occurs, an