A substation openable capacity access evaluation method considering flexibility resources

Abstract

The application discloses a substation openable capacity access evaluation method considering flexible resources, and belongs to the field of substation openable capacity evaluation. First, a substation openable capacity calculation model is established, then a substation optimization scheduling model considering flexible resources is established by considering energy storage systems and orderly charging of electric vehicles, the maximum value of the equivalent load of the substation is obtained, and finally, the substation openable capacity calculation model is solved. On the basis of the existing openable capacity research, the energy storage system is considered to participate in peak load shifting and valley filling, and the electric vehicles are considered to be orderly charged. The minimum peak-valley difference of the substation load and the lowest charging cost of the electric vehicle users are taken as the objective functions, the substation optimization scheduling model considering flexible resources is established, the effects of suppressing new energy power fluctuation, improving new energy consumption level and relieving peak shaving pressure are achieved, the power supply potential of the substation is fully tapped, the user side load peak is reduced, and thus the openable capacity of the substation is improved.

Description

Technical Field

[0001] This invention relates to the field of assessment of available capacity in a distribution area, and particularly to a method for assessing available capacity access in a distribution area that takes into account flexible resources. Background Technology

[0002] Open capacity assessment is one of the main tools for transformer substation planning and operation, and it has important reference value for improving substation reliability and formulating upgrade and transformation strategies. Due to the scarcity of land in transformer substations and the difficulty in constructing new feeders, it is particularly important to fully explore the adjustable potential of flexible resources within the substation, effectively reduce peak loads through peak shaving and valley filling, and accommodate more users into the system. Therefore, conducting open capacity access assessments for substations that consider flexible resources is crucial. Current research on open capacity is limited, failing to consider the participation of energy storage systems in peak shaving and valley filling, as well as the orderly charging of electric vehicles, thus not fully exploring the power supply potential of substations. Therefore, this invention proposes a method for open capacity access assessment of substations that considers flexible resources.

[0003] With the large-scale integration of new energy sources and the rapid growth of electricity demand, the pressure on power system peak shaving is increasing. Energy storage systems, characterized by high energy density, flexible installation, and fast charging and discharging speeds, can smooth out power fluctuations from new energy sources, improve the absorption of new energy, and alleviate the peak shaving pressure on conventional generating units without altering the existing power grid structure. Furthermore, the application of energy storage systems on the grid side can alleviate grid congestion, delay the expansion and upgrading of transmission and distribution equipment, assist in peak shaving on the generation side, and participate in ancillary services in the electricity market, including system frequency regulation and reserve capacity. Therefore, this invention will consider the peak shaving and valley filling capabilities of energy storage systems.

[0004] Electric vehicles, as an emerging industry, offer advantages such as low operating costs, low greenhouse gas emissions, low noise, high energy conversion efficiency, and diversified energy sources. However, the large-scale integration of electric vehicles into distribution areas will lead to a sharp increase in charging demand. Furthermore, the integration of these vehicles without time or space constraints will result in increased peak-valley load differences, distribution line overload, and exacerbated grid instability, placing significant pressure on the existing power system. Therefore, research on the charging behavior of electric vehicle users is essential. Currently, time-of-use pricing effectively reflects cost differences during different power supply periods, including common forms such as peak-valley pricing and seasonal pricing, and is widely adopted as the most common pricing and incentive model. Therefore, this invention will consider an orderly charging strategy for electric vehicles based on time-of-use pricing.

[0005] Patent CN112332433A discloses a method for analyzing the transferable load capacity of electric vehicles participating in valley-filling ancillary services. The method includes the following steps: Step 1, establishing a probability model based on the logistic function to determine the acceptable scheduling for electric vehicle users and calculating the probability of load transfer scheduling; Step 2, performing maximum response capacity analysis on a single electric vehicle and calculating the physical transferable load capacity of the electric vehicle at each moment during the ancillary service period; Step 3, calculating the expected transferable load capacity of the electric vehicle based on the probability of user acceptance of load transfer scheduling in Step 1 and the physical transferable load capacity of the electric vehicle in Step 2. This method can be applied to assessing the adjustable potential of electric vehicle clusters and setting up control and compensation mechanisms in practical engineering, providing theoretical support for capacity assessment of the model of load aggregators aggregating and acting as agents for electric vehicle resources to participate in the ancillary service market. However, it does not consider the participation of energy storage systems in peak shaving and valley filling, or the orderly charging of electric vehicles. Summary of the Invention

[0006] In view of this, the technical problem to be solved by the present invention is to propose a method for evaluating the open capacity access of a transformer substation that takes into account the deficiencies of the prior art.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] A method for assessing the openable access capacity of a distribution station that considers flexible resources includes the following steps:

[0009] Step 1: Establish a calculation model for the available capacity of the transformer substation.

[0010] Based on the overall open capacity F of the transformer area k And the maximum value F of the equivalent load of the transformer area after considering the orderly charging of energy storage systems and electric vehicles. max Establish an open capacity calculation model: F open,k =F k -F max ;

[0011] Step 2: Establish a transformer area optimization scheduling model that considers flexible resources

[0012] Step 201: Establish an energy storage system model;

[0013] Electrochemical energy storage is an important form of energy storage widely used in power systems to regulate power balance and ensure the safe and economical operation of the power system. The state of charge (SOC) of the electrochemical energy storage system at the end of time period t is... t State of charge (SOC) at the end of time period t-1 t-1 and the charging and discharging power P during time period t Esschr,t P Essdis,t related;

[0014] Step 202: Establish an orderly charging model for electric vehicles;

[0015] The primary consideration is home-use electric vehicles that utilize conventional charging methods; firstly, a user's driving habits model is established, including the probability density function f for the return time of the private car. s (x) The probability density function f at the departure time e (x) and the probability density function of daily mileage f D (d) Then, a Monte Carlo simulation of disordered electric vehicle charging was used. Without any guidance on user charging behavior, electric vehicle users charged their vehicles immediately upon returning home, based on their travel patterns, until the battery level reached their desired level or they needed to leave. Finally, an orderly electric vehicle charging model was established, based on the grid base load P for each time period. load,t Calculate the electricity price s for that period based on the given conditions. t Electricity prices guide the orderly charging of electric vehicles;

[0016] Step 203: Establish a transformer area optimization scheduling model that takes into account flexible resources;

[0017] For distribution transformer systems that take into account flexible resources, a dual-objective optimization scheduling model for distribution transformer systems is established with the objective functions of minimizing the peak-valley difference of the distribution transformer load and minimizing the charging cost for electric vehicle users. The constraints include power balance constraints, energy storage system-related constraints, electric vehicle orderly charging-related constraints, and power limit constraints for each device.

[0018] Step 3: Solve the calculation model for the available capacity of the transformer area.

[0019] First, the NSGA-II algorithm is used to solve the bi-objective optimal scheduling model for transformer substations considering flexible resources proposed in step two, obtaining the Pareto optimal front. Then, the Analytic Hierarchy Process (AHP) is used for multi-objective decision-making, obtaining the normalized eigenvector ω as the weight vector for the objectives. Finally, the maximum value F of the equivalent load considering the energy storage system and the orderly charging of electric vehicles is obtained. max Then, based on the formula for calculating the available capacity, the available capacity value F of the transformer area is obtained. open,k .

[0020] The beneficial effects of this invention are:

[0021] This invention first establishes a calculation model for the available capacity of a distribution transformer area. Then, considering energy storage systems and orderly charging of electric vehicles, it establishes an optimized scheduling model for the distribution transformer area that considers flexible resources, obtaining the maximum value of the equivalent load of the area. Finally, it solves the calculation model for the available capacity of the distribution transformer area. The distribution transformer system considering flexible resources is as follows: Figure 1As shown, it mainly includes photovoltaic power generation systems, energy storage systems, electric vehicles, and base loads.

[0022] This invention takes into account the participation of energy storage systems in peak shaving and valley filling and the orderly charging of electric vehicles. It uses the minimum peak-valley difference of the distribution area load and the minimum charging cost for electric vehicle users as the objective function to establish a distribution area optimization scheduling model that takes into account flexible resources. This model aims to smooth out the power fluctuations of new energy sources, improve the absorption level of new energy sources, alleviate the pressure of peak shaving, fully tap the power supply potential of the distribution area, reduce the peak load on the user side, and thus increase the available capacity of the distribution area. Attached Figure Description

[0023] The present invention will now be described in further detail with reference to the accompanying drawings.

[0024] Figure 1 For transformer substation systems that take into account flexibility resources;

[0025] Figure 2 Here is the flowchart for the NSGA-II algorithm;

[0026] Figure 3 For AHP scaling tables;

[0027] Figure 4 Let A be the pairwise comparison matrix for the target. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will be described in conjunction with the accompanying drawings of the embodiments of the present invention. Figure 1-4 The technical solutions of the embodiments of the present invention will be clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0029] Example

[0030] A method for assessing the available capacity of a transformer substation considering flexible resources is proposed. First, a calculation model for the available capacity of the substation is established. Then, considering energy storage systems and orderly charging of electric vehicles, an optimized scheduling model for the substation considering flexible resources is established to obtain the maximum equivalent load of the substation. Finally, the calculation model for the available capacity of the substation is solved. The substation system considering flexible resources is as follows: Figure 1 As shown, it mainly includes photovoltaic power generation systems, energy storage systems, electric vehicles, and base loads.

[0031] The specific steps are as follows:

[0032] Step 1: Establish a calculation model for the available capacity of the transformer substation.

[0033] Considering the actual load distribution characteristics, and aiming to maximize the overall available capacity of the transformer area, the available capacity calculation model is established as follows:

[0034] F open,k =F k -F max (1)

[0035] In the formula: F k For the overall capacity of the transformer area; F max This is the maximum value of the equivalent load of the transformer area after the orderly charging of energy storage systems and electric vehicles.

[0036] Step 2: Establish a transformer area optimization scheduling model that considers flexible resources

[0037] Step 201, Energy Storage System Model

[0038] Electrochemical energy storage is an important form of energy storage widely used in power systems to regulate power balance and ensure the safe and economical operation of the power system. The state of charge (SOC) of an electrochemical energy storage system at the end of time period t is related to the SOC at the end of time period t-1 and the charging and discharging power during time period t.

[0039]

[0040] In the formula: U Esschr,t U Essdis,t These represent the state of charge / discharge variables of the energy storage battery; SOC (State of Charge). t η is the SOC value of the energy storage battery during time period t; σ is the self-discharge rate of the energy storage battery; η is the SOC value of the energy storage battery during time period t. chr η dis These represent the charge and discharge efficiencies of the energy storage battery; P Esschr,t P Essdis,t These represent the charging and discharging power of the energy storage system during time period t.

[0041] Step 202, Electric Vehicle Ordered Charging Model

[0042] This invention studies household electric vehicles, which use conventional charging methods.

[0043] (1) User's car usage habits

[0044] The load model for electric vehicles depends on the user's return time, departure time, and daily mileage. The probability density function for the return time of a private car is...

[0045]

[0046] Where: μ s σ is the expected value; s The standard deviation is denoted as .

[0047] The probability density function of the private car at the moment of departure is:

[0048]

[0049] Where: μ e σ is the expected value; e The standard deviation is denoted as .

[0050] The probability density function of daily mileage for private cars is:

[0051]

[0052] Where: σ D The expected value; μ D d represents the standard deviation; d represents the mileage traveled.

[0053] (2) Monte Carlo simulation of disordered charging of electric vehicles

[0054] Without any guidance on user charging behavior, electric vehicle users, based on their travel patterns, immediately charge their vehicles upon returning home until the battery level reaches their desired level or charging stops when they need to leave. The simulation time interval is set to 1 hour, dividing the day into 24 time periods. The Monte Carlo method is used to simulate the disordered charging load of electric vehicles within this day.

[0055] Based on the electric vehicle's driving range and battery parameters, the state of charge of the electric vehicle at the return time can be expressed as:

[0056]

[0057] In the formula: S start,i The state of charge at the start of charging; S end,i B represents the state of charge at the end of charging. i E represents the battery capacity; E represents the power consumption per kilometer.

[0058] The charging time for an electric vehicle can be expressed as

[0059]

[0060] In the formula: P i The charging power for electric vehicles.

[0061] The total load of electric vehicles after they are connected to the grid can be expressed as:

[0062]

[0063] In the formula: U ev,i,t Let N represent the charging state of the i-th electric vehicle at time t; N is the number of electric vehicles.

[0064] (3) Electric vehicle orderly charging model

[0065] Traditional time-of-use pricing divides a day into three time periods based on daily electricity consumption. To reduce the peak-valley load difference in distribution transformer areas and user charging costs, a new time-of-use pricing policy is proposed, which divides a day into T time periods at equal intervals Δt, and calculates the electricity price for each time period based on the base load of the distribution transformer area.

[0066] The relationship between electric vehicle charging prices and transformer load can be expressed as follows:

[0067]

[0068] In the formula: P load,t The base load of the transformer area during time period t; s is the average base load of the day's transformer area; s0 is the electricity price before optimization. The charging load of electric vehicles is transferable. In terms of electricity price guidance, in order to reduce their charging costs, users will charge during periods of low electricity prices.

[0069] Step 203: Optimized scheduling model for transformer substations considering flexible resources.

[0070] For distribution transformer systems that take into account flexible resources, this invention establishes an optimal scheduling model for distribution transformers that takes into account flexible resources, with the objective function of minimizing the peak-valley difference of the distribution transformer load and minimizing the charging costs for electric vehicle users.

[0071] (1) Objective function

[0072] Objective function one, with the goal of minimizing the peak-to-valley load difference in the transformer area, can be expressed as follows:

[0073] f1=min(max{P sum,t}-min{P sum,t})

[0074]

[0075] In the formula: P sum,t The total load of the transformer area during time period t is denoted as t.

[0076] Objective function two is established with the goal of minimizing user-side charging costs, which can be expressed as follows:

[0077]

[0078] (2) Constraints

[0079] ① Power balance constraints

[0080]

[0081] In the formula: P PV,t For the photovoltaic power generation unit in the distribution area during time period t; Pgrid,t This refers to the power exchanged with the power grid.

[0082] ② Constraints related to energy storage systems

[0083] Energy storage system charging and discharging power constraints are

[0084]

[0085] The depth of charge and discharge of energy storage systems is constrained.

[0086]

[0087] Energy storage battery SOC cycle balance constraint is

[0088] SOC(0) = SOC(24)

[0089] Where: SOC min SOC max These are the upper and lower limits of the state of charge of the energy storage system.

[0090] ③ Constraints related to the orderly charging of electric vehicles

[0091] The number of electric vehicle charging stations is constrained.

[0092] n≤N

[0093] State of charge constraints of the electric vehicle at the end time

[0094]

[0095] Charging time constraint is

[0096]

[0097] The state of charge is constrained as follows

[0098] S start,i ≤S soc,i ≤S end,i ≤S max

[0099] Number of charging piles in the area

[0100] N≤Q

[0101] In the formula: n is the total number of electric vehicles in the transformer area; T leave,i S is the moment the electric vehicle leaves. start,i The state of charge (S) at the moment the electric vehicle begins charging; soc,i Electricity charge for electric vehicles; S end,i It is the state of charge of an electric vehicle at the moment it finishes charging; S max =1 represents the maximum state of charge; Q represents the number of charging piles in the area.

[0102] ④ Power limitations of each device

[0103]

[0104] In the formula: P PV,min P grid,min These are the lower limits of photovoltaic power generation and grid interconnection power, respectively; P PV,max P grid,max These are the upper limits for photovoltaic power generation and grid interaction power, respectively.

[0105] Step 3: Solve the calculation model for the available capacity of the transformer area.

[0106] Heuristic methods, as one of the solutions to multi-objective optimization problems, possess good convergence and searchability, and have gradually become the mainstream method for solving multi-objective problems. Among them, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with elitist strategy is one of the best methods, mainly employing the following three key strategies: a hierarchical fast non-dominated sorting strategy, a crowding distance-based comparison strategy for individuals with the same ordinal value, and an elitist individual retention strategy. Furthermore, the NSGA-II algorithm also addresses the individual dominance problem considering constraints.

[0107] (1) When individual x1 is a feasible solution and individual x2 is an infeasible solution, individual x1 is said to be dominant over individual x2.

[0108] (2) When neither individual x1 nor individual x2 is a feasible solution, compare their constraint violation degrees. The individual with the smaller constraint violation degree is superior.

[0109] (3) When both individuals x1 and x2 are feasible solutions, compare their ordinal values ​​and crowding distances. Individuals with smaller ordinal values ​​are preferred. If the ordinal values ​​are the same, individuals with larger crowding distances are preferred.

[0110] The NSGA-II algorithm flow is as follows: Figure 2 As shown, first, an initial population is randomly generated as the parent population of the first generation. Then, selection, crossover, and mutation processes are performed to generate a temporary population of the same size. Next, the two populations are merged into one, and the individuals in this new population are sorted based on their ordinal value and crowding distance. Finally, individuals from the merged population are selected according to their ordinal value to enter the next generation population, until the preset population size N is reached. pop And determine whether the algorithm has reached the maximum number of iterations g. max If the optimal solution is not reached, the algorithm continues to select alternatives; otherwise, it outputs the Pareto optimal solution set and the frontier, and the algorithm ends.

[0111] After obtaining the Pareto optimal frontier, the final solution needs to be determined based on preferences, i.e., multi-objective decision making (MODM) is performed. This invention utilizes the Analytic Hierarchy Process (AHP) for multi-objective decision making, the steps of which are: firstly, based on… Figure 3 The basic scaling table shown determines the relative importance of each objective, thereby constructing pairwise comparison matrices A, as follows. Figure 4 As shown; then calculate the largest eigenvalue λ of the pairwise comparison matrix A. max The method is validated by generating the corresponding normalized eigenvector ω and performing a consistency check on the pairwise comparison matrices to ensure the effectiveness of the method. Finally, ω is used as the weight vector of the target.

[0112] Solving the optimal scheduling strategy for transformer substations that considers flexible resources yields the maximum value F of the equivalent load after considering energy storage systems and orderly charging of electric vehicles. max Then, based on the formula for calculating the available capacity, the available capacity value of the transformer area is obtained.

[0113] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing the openable access capacity of a distribution area considering flexible resources, characterized in that, Includes the following steps: Step 1: Establish a calculation model for the available capacity of the transformer substation; Based on the overall open capacity of the area And the maximum value of the equivalent load of the transformer area after considering the orderly charging of energy storage systems and electric vehicles. Establish an open capacity calculation model: ; Step 2: Establish a transformer area optimization scheduling model that considers flexible resources, including: Step 201: Establish an energy storage system model; In step 201, the energy storage system is in State of charge at the end of the period and State of charge at the end of the period as well as Charge and discharge power during the period , related; Step 202: Establish an orderly charging model for electric vehicles; Step 202 involves using a conventional charging method for a home-use electric vehicle. First, a user's driving habit model is established, including the probability density function of the private car's return time. The probability density function at the departure time and the probability density function of daily driving mileage Then, a Monte Carlo simulation of disordered electric vehicle charging was used. Without any guidance on user charging behavior, electric vehicle users charged their vehicles immediately upon returning home based on their travel patterns, stopping charging when the battery level reached their desired level or when they needed to leave. Finally, an orderly electric vehicle charging model was established, based on the grid load at each time period. Calculate the electricity price for that period based on the given conditions. Electricity prices guide the orderly charging of electric vehicles; Step 203: Establish a transformer area optimization scheduling model that takes into account flexible resources; In step 203, for the distribution area system that considers flexible resources, a dual-objective optimization scheduling model for the distribution area that considers flexible resources is established with the objective functions of minimizing the peak-valley difference of the distribution area load and minimizing the charging cost for electric vehicle users. The constraints include power balance constraints, energy storage system-related constraints, electric vehicle orderly charging-related constraints, and power limit constraints for each device. Step 3: Solve the calculation model for the available capacity of the transformer area.

2. The method for evaluating the openable capacity access of a distribution area considering flexible resources according to claim 1, characterized in that, In step 3, firstly, the NSGA-II algorithm is used to solve the dual-objective optimization scheduling model for the transformer area considering flexibility resources proposed in step 2 to obtain the Pareto optimal front; then, the Analytic Hierarchy Process (AHP) is used for multi-objective decision-making to obtain the normalized eigenvector. The weight vector serves as the objective; finally, the maximum value of the equivalent load considering the energy storage system and the orderly charging of electric vehicles is obtained. Then, based on the formula for calculating the available capacity, the available capacity value of the transformer area is obtained. .

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