ADN double-layer optimization configuration method considering electric vehicle access and V2G technology

Abstract

The invention relates to an ADN double-layer optimization configuration method considering electric vehicle access and a V2G technology, and the method comprises the steps: obtaining an orderly charging and discharging scheduling scheme of an EV cluster from the two levels of time and space based on the parking behavior of an EV user under the electric vehicle access; constructing an ADN source-load-storage model based on a power supply side, a load side and an energy storage side by using the ordered charging and discharging scheduling scheme; solving variables in a planning layer and an operation layer in the ADN source-load-storage model by using an improved mixed seagull-grey wolf optimization algorithm; and performing optimal configuration of the ADN source-load-storage model on the basis of a solving result. The method aims at obtaining the optimal locating and sizing scheme of the electric vehicle charging pile and other equipment in the active power distribution network at the same time, and deeply discusses the positive influence of the application of the V2G technology and the space-time scheduling characteristic of the electric vehicle charging and discharging load on the economic benefit of the active power distribution network, the utilization rate of the distributed power supply and the stability of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field related to active distribution network optimization, and in particular to an ADN double-layer optimization configuration method considering electric vehicle access and V2G technology. Background Art

[0002] At present, the world is facing a series of severe challenges such as increasingly severe energy resource constraints, prominent ecological and environmental problems, and climate change. Large-scale access to distributed power sources is the only way forward. However, the output of wind and photovoltaic power generation has strong randomness and timing characteristics. When these power sources are connected to the distribution network in large quantities, they may cause problems such as grid voltage fluctuations and resource waste. In order to meet these challenges, the power grid needs to have higher scheduling flexibility, management capabilities, and advanced intelligent and automated technologies. Therefore, the concept of active distribution network (ADN) came into being. Its core concept is to achieve two-way interaction and coordinated optimization between the power grid and various energy resources through information and automation technology. ADN can respond to changes in the power grid and energy market in real time, optimize resource allocation, and thus improve the operating efficiency, reliability and economy of the power grid, and better adapt to the volatility and uncertainty of renewable energy.

[0003] As the number of electric vehicles continues to grow, the construction of infrastructure such as electric vehicle charging stations has become an important consideration in the planning of active distribution networks. Advances in V2G technology have made electric vehicles not only dispatchable loads, but also flexible energy storage units, which not only helps the consumption of distributed power sources, but also brings new opportunities for distribution network optimization. However, the large-scale access of electric vehicles may also pose a threat to the stability of the power grid and increase the difficulty of load balancing.

[0004] Therefore, active distribution networks are facing a new challenge: how to reasonably plan electric vehicle charging stations and achieve coordinated configuration with distributed power sources and other equipment. It is urgent to propose an ADN two-layer optimization configuration method that considers electric vehicle access and V2G technology. Summary of the invention

[0005] The purpose of the present invention is to propose an ADN two-layer optimization configuration method considering electric vehicle access and V2G technology to solve the problems existing in the above-mentioned prior art. The method aims to simultaneously obtain the optimal location and capacity scheme of electric vehicle charging piles and other equipment in the active distribution network, and deeply explore the positive impact of the application of V2G technology and the spatiotemporal scheduling characteristics of electric vehicle charging and discharging loads on the economic benefits of the active distribution network, the utilization rate of distributed power sources and the stability of the distribution network.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A two-layer ADN optimization configuration method considering electric vehicle access and V2G technology includes:

[0008] Based on the parking behavior of EV users under electric vehicle access, the orderly charging and discharging scheduling scheme of the EV cluster is obtained from the two levels of time and space;

[0009] Using the orderly charging and discharging scheduling scheme, an ADN source-load-storage model based on the power supply side, the load side and the energy storage side is constructed, wherein the ADN source-load-storage model includes a planning layer and an operation layer;

[0010] The improved hybrid Seagull-Grey Wolf optimization algorithm is used to solve the planning layer and the operation layer in the ADN source-load-storage model;

[0011] Based on the solution results, the ADN source-load-storage model is optimized.

[0012] Optionally, constructing an ADN source-load-storage model based on the power supply side, the load side and the energy storage side includes:

[0013] Model the power supply side based on the output characteristics of wind power generation, photovoltaic power generation and gas turbine power generation;

[0014] Based on the flexible load response output characteristics and the charging demand characteristics of electric vehicles as dispatchable loads, the load side is modeled;

[0015] Model the energy storage side based on the state of charge of the battery constraints and the charging and discharging characteristics of electric vehicles as mobile energy storage devices;

[0016] Based on the constructed power side model, load side model and energy storage side model, the construction of the ADN source-load-storage model considering V2G is completed.

[0017] The beneficial effects of the present invention are:

[0018] The present invention establishes a two-layer planning model and realizes the coordination of "planning" and "operation". This method can reduce the investment and operation costs of the active distribution network while improving the reliability, safety and sustainability of the distribution network during operation.

[0019] The improved hybrid seagull-grey wolf optimization algorithm proposed in the present invention not only has a strong global optimization capability in solving the bi-level programming problem, thereby avoiding the solution from falling into the local optimal solution; it also has higher computational efficiency and saves running time.

[0020] The present invention introduces V2G technology, which enables electric vehicles to participate in the source-load-storage coordinated planning and optimization process of the active distribution network as a flexible energy storage unit. While reducing the investment in other energy storage equipment, it realizes the consumption of distributed power sources and reduces the total optimization operation cost of the active distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0022] Figure 1 A topological diagram of a power distribution system according to an embodiment of the present invention;

[0023] Figure 2 A coupling system diagram of a power distribution system and an actual geographical area according to an embodiment of the present invention;

[0024] Figure 3 A wind speed and wind power output curve diagram of an embodiment of the present invention;

[0025] Figure 4 1 is a graph showing the time series characteristic of light intensity and temperature in a typical day of an embodiment of the present invention, wherein (a) is a graph showing light intensity, and (b) is a graph showing temperature intensity;

[0026] Figure 5 1 is a graph showing the output characteristic curves of wind power and photovoltaic equipment when DG output is strong or weak according to an embodiment of the present invention, wherein (a) is a graph showing the output characteristic curves of wind power and photovoltaic equipment when DG output is strong, and (b) is a graph showing the output characteristic curves of wind power and photovoltaic equipment when DG output is weak;

[0027] Figure 6 The load output characteristic curves of the working day and rest day scenarios are obtained by taking the annual maximum power of the load in each scenario as the reference value, where (a) is the weekend load power characteristic curve and (b) is the working day load power characteristic curve;

[0028] Figure 7 is a parking behavior characteristic curve of an EV user according to an embodiment of the present invention;

[0029] Figure 8 A flow chart of generating load distribution of electric vehicles in a V2G environment within a typical day according to an embodiment of the present invention;

[0030] Fig. 9 A two-layer planning framework diagram of an embodiment of the present invention;

[0031] Fig.10 A flowchart of solving a two-level programming model according to an embodiment of the present invention;

[0032] Fig.11 It is a convergence curve diagram corresponding to each algorithm in the embodiment of the present invention;

[0033] Fig.12 This is a comparison chart of operating indicators under different schemes of an embodiment of the present invention;

[0034] Fig.13 A spatiotemporal distribution diagram of charging behavior of an electric vehicle according to an embodiment of the present invention;

[0035] Fig.14 This is a spatiotemporal distribution diagram of the discharge behavior of an electric vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] This embodiment proposes an ADN dual-layer optimization configuration method considering electric vehicle access and V2G technology, including:

[0039] Based on the parking behavior of EV users under electric vehicle access, the orderly charging and discharging scheduling scheme of the EV cluster is obtained from the two levels of time and space;

[0040] Using the orderly charging and discharging scheduling scheme, an ADN source-load-storage model based on the power supply side, the load side and the energy storage side is constructed, wherein the ADN source-load-storage model includes a planning layer and an operation layer;

[0041] Using an improved hybrid Seagull-Grey Wolf optimization algorithm, the variables in the planning layer and the operation layer of the ADN source-load-storage model are solved;

[0042] Based on the solution results, the ADN source-load-storage model is optimized.

[0043] Specifically, in this embodiment, the "source-load-storage" system is first modeled and analyzed, and the load response analysis of the time-of-use electricity price mechanism in the price-driven demand response is combined. The upper layer of the model is the planning layer and the lower layer is the operation layer. The two layers have corresponding objective functions and constraints. The interaction between the two-layer model considers the V2G ADN source-load-storage collaborative two-layer optimization configuration method, and uses the improved hybrid seagull gray wolf optimization algorithm to solve the two-layer model.

[0044] As a specific embodiment, this embodiment adopts the IEEE-33 node power distribution system for case analysis, and its topology is as follows: Figure 1 In this system, the total active load can be 3.715MW, the rated base capacity is 10MVA, the rated voltage is 12.66kV, the node voltage is generally limited to between 0.95pu and 1.05pu, and the branch current is limited to 400A. Other parameters of the system on the power supply side, load side, and energy storage side are shown in Tables 1 to 3, and the relevant parameters of the electric vehicle charging pile are shown in Table 4.

[0045] Table 1

[0046]

[0047] Table 2

[0048]

[0049] Table 3

[0050]

[0051] Table 4

[0052]

[0053] Furthermore, constructing an ADN source-load-storage model based on the power side, load side and energy storage side includes:

[0054] Model the power supply side based on the output characteristics of wind power generation, photovoltaic power generation and gas turbine power generation;

[0055] Based on the flexible load response output characteristics and the charging demand characteristics of electric vehicles as dispatchable loads, the load side is modeled;

[0056] Model the energy storage side based on the state of charge of the battery constraints and the charging and discharging characteristics of electric vehicles as mobile energy storage devices;

[0057] Based on the constructed power side model, load side model and energy storage side model, the construction of the ADN source-load-storage model considering V2G is completed.

[0058] Specifically, in this embodiment, in the modeling and analysis of the "source-load-storage" system, the power supply side considers the wind power output characteristics, photovoltaic power output characteristics and gas turbine power output characteristics; the load side distinguishes between rigid loads and flexible loads, and determines the transferable loads, shiftable loads and reducible loads in the flexible loads to regulate the loads in the optimal way; the energy storage side considers the charge state of the battery constraints to increase the service life of the energy storage equipment.

[0059] Power supply side modeling and analysis process:

[0060] The inherent variability and intermittent characteristics of wind energy make wind power output show significant randomness and volatility, which undoubtedly increases the uncertainty and difficulty of wind turbine power output. In order to more accurately simulate wind power output and predict its changing trend, this embodiment introduces the Weibull probability density function, which can accurately describe the statistical characteristics of wind speed and comprehensively evaluate and predict the wind speed changes in the target area during the wind farm planning stage, as shown in the following formula:

[0061]

[0062] In the formula, v represents the local wind speed; k and c are the shape parameter and scale parameter, respectively. The shape parameter describes the shape of the distribution and reflects the degree of variation of the wind speed. The scale parameter determines the degree of spread of the distribution, that is, the average level of wind speed. The two can be calculated by the following formulas (2) and (3), respectively:

[0063]

[0064] In the formula, σ w , μ w All can be obtained through historical data.

[0065] Wind power output can be calculated by wind speed and characteristic parameters of wind turbine generator set. According to the internationally accepted power characteristic curve of wind turbine generator set, this embodiment can convert wind speed into power P W The output is shown in formula (4):

[0066]

[0067] Where P Wn is the rated active output of the fan; V i is the cut-in wind speed of the fan; V o is the cut-out wind speed of the fan; V n is the rated wind speed. Specifically, when the wind speed is lower than the cut-in wind speed or higher than the cut-out wind speed, the wind power output is zero. When the wind speed is between the cut-in wind speed and the rated wind speed, the wind power output increases linearly. When the wind speed reaches the rated wind speed but does not reach the cut-out wind speed, the wind power output remains at the rated power.

[0068] Figure 2 The figure shows that the wind speed curve of a typical day is obtained by clustering the wind speed conditions in a certain North China region using the k-means time series clustering algorithm, and the data in the curve is substituted into formula (4) to obtain the wind power output curve of a single wind turbine.

[0069] The output of photovoltaic systems is affected by many factors, including solar radiation intensity, battery module temperature, weather and cloud cover. Changes in these factors can cause large fluctuations in photovoltaic power generation and have a certain degree of randomness. Among them, sunlight is one of the key factors affecting photovoltaic power generation. Its variability and uncertainty bring challenges to the prediction of photovoltaic power generation output. Therefore, the output research of photovoltaic power stations requires the analysis of solar intensity.

[0070] This embodiment is based on the light intensity distribution obtained by the Beta distribution model. The Beta distribution is a distribution suitable for describing variables between 0 and 1, and can fit different types of distributions well. In this embodiment, the sunlight intensity is normalized to between 0 and 1, and the Beta distribution is used to model its probability distribution as shown in formula (5):

[0071]

[0072] In the formula, r PH is the solar intensity at a certain time section; r PHmax is the maximum historical solar intensity of the time section; f(r PH ) represents r PH The corresponding probability density function; α PH and β PH is the shape parameter of the Beta distribution. By choosing a suitable α PH and β PH Parameters, Beta distribution can well simulate the probability distribution of sunshine intensity in different time periods, and then be used to calculate the expected photovoltaic power output in different time periods, as shown in equations (6) and (7):

[0073]

[0074] In the formula, μ PH and σ PH Respectively represent the mean and standard deviation of historical data of solar intensity.

[0075] The output of photovoltaic power generation can be characterized as a function with temperature and radiation intensity as independent variables. The corresponding output characteristic function is shown in formula (8):

[0076]

[0077] Where P pv Indicates that the photovoltaic power station is under the condition of sunlight intensity G c Active power output at time G r and P r is the radiation intensity and maximum output power under standard test conditions (the temperature is 25°C), k is the power temperature coefficient, T c and T r are the photovoltaic cell temperature and the reference temperature respectively.

[0078] Figure 3 Shown are the time series characteristics of light intensity and temperature in a typical day.

[0079] Substituting the temperature curve and light intensity curve in a typical day into equation (8), we can obtain the photovoltaic output characteristics in a typical day as follows: Figure 4 As shown, Figure 4 (a) is the light intensity curve. Figure 4 (b) is the temperature intensity curve.

[0080] According to the strength of distributed generation (DG) output, DG strong output and DG weak output are generated respectively. Under this characteristic, two sets of wind power output and photovoltaic output characteristic per unit value curves are generated, with the rated capacity of the equipment as the reference value, such as Figure 5 (a) and Figure 5 (b) as shown.

[0081] The historical load data of a certain area in North China were selected and clustered to obtain the load output characteristic curves under the two scenarios of working days and rest days. The annual maximum power of the load in each scenario was taken as the benchmark value. Figure 6 (a) and Figure 6 (b) as shown.

[0082] It is assumed that the DG high output and DG low output time each account for 1 / 2 of the total time, weekdays and weekends account for 5 / 7 and 2 / 7 respectively, and it is assumed that the DG output is irrelevant to whether it is a weekday. In this way, four scenarios are divided according to the DG output and the user's work and rest time. The scenario settings and their corresponding probabilities are shown in Table 5:

[0083] Table 5

[0084]

[0085]

[0086] Combined with local climate conditions and load characteristics, for the equipment to be planned, photovoltaic equipment is preferentially built near nodes with good lighting conditions, so {7,13,18,28,32} are determined as potential installation nodes. Considering the characteristics of wind resources, nodes close to the edge of the system with small loads but abundant wind resources are selected as candidate installation nodes, thus {6,15,19,28,32} are selected as candidate installation locations for wind power generation equipment. As a peak-shaving resource, gas turbines can be installed at load centers or nodes with large system capacity, and {8,10,23,24,30} are selected as candidate nodes. At the same time, considering the key role of energy storage equipment in improving system stability and reliability, it can be installed near nodes with large load or new energy generation fluctuations, and {4,11,15,21,23,31} are selected as candidate nodes. In addition, {3,9,16,22,25,29} can be selected as installation nodes for flexible loads. The unit capacity of photovoltaic generator sets, wind generator sets and gas turbine generator sets is uniformly set to 20kW, and the upper limit of installation of each node is 1000kW. The 24-hour time-of-use electricity price data of the selected areas are shown in Table 6:

[0087] Table 6

[0088]

[0089] Micro gas turbines can generate high-temperature and high-pressure gas through natural gas combustion to drive the turbine to rotate and generate electricity, thereby realizing the conversion of chemical energy-thermal energy-mechanical energy-electrical energy. The gas turbine output model is shown in formula (9):

[0090] P gas (t) = V gas (t)η gas θ gas (9)

[0091] Where P gas (t) is the power of the gas turbine generator set; V gas (t) is the natural gas consumption at time t, η gas is the power generation efficiency of the gas turbine, θ gas is the power angle size.

[0092] In this embodiment, the micro gas turbine is used as a type of fully dispatchable distributed power source, and its output range is shown in formula (10):

[0093] 0≤P gas ≤S gasmax (10)

[0094] Where P gas is the active power output of the gas turbine; S gasmax is the installed capacity of the gas turbine.

[0095] Load side modeling and analysis process:

[0096] In terms of participating in grid regulation, user loads can be divided into two categories: rigid loads and flexible loads. Compared with rigid loads, flexible loads can be actively adjusted and responded through dynamic electricity price adjustments and policy-driven methods, and adjust power consumption characteristics within a specified time, so as to better adapt to the operation needs of the grid.

[0097] Flexible loads can be divided into transferable loads, shiftable loads and curtailable loads according to the characteristics of their interaction with the power grid. Transferable loads can flexibly adjust the characteristics and time of electricity consumption while keeping the total electricity consumption unchanged. Shiftable loads are limited by the production process and can only shift electricity loads in different time periods. Interruptible loads can reduce electricity consumption within a specific time.

[0098] This embodiment does not involve industrial production issues, so only the application of transferable loads and interruptible loads is considered. According to the output plan of each user, the total response output of the flexible load per unit time can be obtained:

[0099]

[0100] In the formula, S n,t represents the response state of user n at time t. If it is 1, it means the load increases, -1 means the load decreases, and 0 means the load remains unchanged. N represents the number of users that can participate in the scheduling. P n,t represents the power of user n at time t.

[0101] Energy storage side modeling and analysis process:

[0102] This embodiment uses a battery to represent the energy storage system. During the charging and discharging process of the battery energy storage system, the charge state of the energy storage system in adjacent time periods is shown in formula (12):

[0103]

[0104] In the formula, SOC E，t is the state of charge of the initial energy storage at time t; E ESS is the rated capacity of the energy storage device; D ESS is the self-loss coefficient of the energy storage system; P char,t and P dis,t are the charging and discharging power of the energy storage device in period t respectively; η char and η dis are the charging and discharging efficiency of the energy storage device; U Ech,t and U Edis,t Respectively represent the charging and discharging states of the energy storage device, which are 0 and 1 variables. The charging and discharging states of the energy storage system cannot exist at the same time, and the following constraints need to be met:

[0105] U Ech,t +U Edis,t ≤1 (13)

[0106] In addition, in order to improve the life of energy storage equipment, the charging and discharging state of the battery needs to meet the following constraints:

[0107] SOC Emin ≤SOC E，t ≤SOC Emax (14)

[0108] P Emin ≤P char,t ≤P Emax (15)

[0109] P Emin ≤P dis,t ≤P Emax (16)

[0110] In the formula, SOC Emax With SOC Emin Respectively represent the maximum and minimum values ​​of the energy storage system’s state of charge; P Emin With P Emax Respectively represent the maximum value of charging and discharging power of the energy storage system.

[0111] Analysis of temporal and spatial distribution characteristics of electric vehicle load:

[0112] Divide the target area into N evt For land block d, the collection of charging loads of each electric vehicle with d as the destination can be regarded as the total charging demand of electric vehicles in this land block. By analyzing each land block, the spatial distribution of electric vehicle charging load can be obtained.

[0113] This embodiment assumes that the state of charge of electric vehicles follows a uniform distribution in the interval [0,1], and randomly generates the state of charge of the battery for each electric vehicle entering the destination, so that the corresponding capacity to be charged can be calculated. d,k represents the electric car numbered k among the electric cars with d as the destination. The charging capacity E of this electric car at time t is d,k,t As shown in formula (17):

[0114] E d,k,t =Cap d,k ·(1-SOC d,k,t ) (17)

[0115] In the formula, Cap d,k For EV d,k Battery capacity, SOCd,k,t For EV d,k The state of charge at time t.

[0116] The total daily load demand E corresponding to land block d total,d As shown in formula (18):

[0117]

[0118] Where N dt represents the number of electric vehicles with land block d as their destination throughout a typical day.

[0119] The traffic volume of electric vehicles and the proportion of EVs that can accept V2G discharge are shown in Table 7:

[0120] Table 7

[0121]

[0122] Combination Figure 7 Relevant data, considering the charge state of each electric vehicle, can reasonably generate the load situation of electric vehicles in each block in the target area on any typical day, such as Figure 8 shown.

[0123] This embodiment is based on the characteristics of the active distribution network, applies the time-of-use electricity price mechanism in the price-driven demand response, and considers resources such as interruptible loads and transferable loads in the incentive-driven demand response, so as to make full use of the initiative of flexible loads.

[0124] Furthermore, price-driven demand response (PDR) mainly uses the difference in electricity prices in different time periods to encourage users to adjust their electricity consumption behavior according to electricity price signals. This model covers mechanisms such as time-of-use electricity prices and real-time electricity prices. In time-of-use electricity prices, the electricity price level is set according to the level of grid load, aiming to encourage users to increase electricity consumption during low electricity price periods and reduce usage during high electricity price periods. Real-time electricity prices are more flexible and adjust electricity prices according to the real-time supply and demand conditions of the market, thereby providing users with more accurate electricity price period information to achieve more detailed load adjustments.

[0125] Incentive-Driven Demand Response (IDR) mainly encourages users to actively reduce electricity consumption when the grid load is high through economic incentives. Due to its design and implementation mechanism, IDR can usually provide a faster load regulation response.

[0126] Policy regulation and electricity price incentives are important means to regulate the orderly charging and discharging of EVs. This embodiment uses peak and valley electricity prices to guide the charging and discharging behavior of EV users. By guiding EV users to charge during the valley period of electricity prices and discharge during the peak period, not only can the charging cost of EV users be reduced, but also the peak can be shaving and valley filling can be achieved, alleviating the pressure on the power grid during the peak period of electricity consumption.

[0127] Furthermore, the planning layer is used to obtain the total cost of the planning layer by summing the investment and construction costs of wind power stations, photovoltaic power stations, gas turbine generator sets, energy storage equipment, and electric vehicle charging piles and the results returned by the operation layer planning; then, the power balance constraint, the capacity constraint of the newly built generator set, the proportion of new energy access constraints, the number of charging piles installed constraints, and the charging demand constraint are used as constraint conditions, and the minimization of the annual total economic operation cost is used as the objective function to obtain the optimal planning scheme for the site selection and capacity determination of each device;

[0128] The optimal planning scheme is a solution set consisting of the optimal solutions for the number of wind turbines, photovoltaic generators, gas turbines, energy storage equipment and charging stations to be built per unit capacity;

[0129] Furthermore, the operation layer is used to obtain the total cost of the operation layer by weighted summing the economic cost of the operation layer, the voltage offset of the distribution network node, the wind power abandonment rate, the photovoltaic abandonment rate and the standard deviation of the equivalent load curve; then, the unit output constraint, the interruptible load constraint, the transferable load constraint, the energy storage device output constraint, the upper power purchase constraint, the power flow constraint, the node voltage constraint and the EV load time-space constraint are used as constraint conditions, and the total cost of the operation layer is used as the objective function to obtain the optimal scheduling strategy;

[0130] The economic cost of the operation layer includes the operation and maintenance cost of power supply equipment, the management cost of load demand response, the depreciation cost of energy storage equipment, the operation and maintenance cost of charging piles and the dispatch cost of electric vehicles, the annual cost of purchasing electricity from the superior power grid, and the network loss cost.

[0131] The optimal dispatching strategy is a solution set consisting of the active output of each wind power station, the active output of each photovoltaic power station, the active output of each gas turbine power station and the optimal solution for electric vehicle charging and discharging dispatching.

[0132] Specifically,

[0133] Fig. 9 What is shown is a two-level planning framework diagram of the source-load-storage collaborative two-level optimization configuration model considering V2G, which reflects the relationship between the decision-making models and decision variables of the upper planning layer and the lower operation layer under different situations.

[0134] The upper layer is the planning layer, which aims to obtain the optimal planning scheme by planning the site selection and capacity of the equipment, with the goal of minimizing the annual comprehensive planning and operation cost. The objective function is mainly divided into the investment and construction cost of each equipment and the results returned by the operation layer planning. The upper layer constraints include power balance constraints, new generator capacity constraints, new energy access ratio constraints, charging pile installation quantity constraints, and charging demand constraints.

[0135] Specifically, the objective function of the upper planning layer is to minimize the total cost, as shown in the following formula:

[0136] C Etotal =C Eght +f Eeco (19)

[0137] In the formula, C Eght is the annualized total investment and construction cost; f Eeco The annualized total economic operating cost returned for the lower tier.

[0138]

[0139] In the formula, They are the investment and construction costs of wind power stations, photovoltaic power stations, gas turbine generator sets, and energy storage equipment. The corresponding calculation formulas for the planning and construction cost of electric vehicle charging piles are shown in equations (21) to (25):

[0140]

[0141] In the above formula, a W ,a PH ,a gas ,a E are the investment discount rates of the wind power station, photovoltaic power station, gas turbine power station and energy storage equipment to be built, respectively. EC is the annual value coefficient of the electric vehicle charging pile; d W ,d PH ,d gas ,d E , d EC N are the service life of wind power station, photovoltaic power station, gas turbine power station, energy storage equipment and electric vehicle charging piles respectively; W ,N PH ,N gas ,N E are the number of candidate node locations for new wind power stations, photovoltaic power stations, gas turbine generator sets, and energy storage equipment, respectively. represents the number of charging piles at the i-th node; C IW , C IPH , CIgas E is the unit capacity cost of the i-th newly built wind power station, photovoltaic power station, and gas turbine generator set respectively; W0 、E PH0 、E gas0 C is the new unit capacity of wind power station, photovoltaic power station and new gas turbine generator set respectively; IEP,i , C IES,i are the unit power cost and unit capacity cost of the i-th energy storage device respectively; P E0 , S E0 are the unit energy storage power and unit capacity of the i-th energy storage device to be built; v W,i ,v PH,i ,v gas,i are the unit capacities of the newly built wind power station, photovoltaic power station, and gas turbine generator set in the i-th place; v PE,i ,v SE,i are the unit energy storage power and unit energy storage capacity of the i-th energy storage device respectively.

[0142] The upper planning layer constraints include power balance constraints, new generator capacity constraints, new energy access ratio constraints, charging pile installation quantity constraints, and charging demand constraints. The power balance constraint is shown in the following formula:

[0143]

[0144] Where P W,i , P PH,i , P gas,i , P EC,i are the output of the i-th wind power generation, photovoltaic power generation, gas turbine power station, and electric vehicle charging station; P E,i is the charging or discharging power of the i-th energy storage device, when P E,i When the value is positive, the energy storage device discharges outward; when P E,i When it takes a negative value, the energy storage device is in a charging state; P load,i is the total load on the electricity consumption side of the i-th node; N node is the total number of active distribution network nodes.

[0145] The capacity constraints of the newly built generator sets are shown in the following formula, which are the capacity constraints of the newly built gas turbine generator sets, the capacity constraints of the newly built wind power stations and the capacity constraints of the newly built photovoltaic power stations:

[0146]

[0147] In the formula, E gas0 、E W0 、E PH0 They are the new unit capacities of new gas turbine power stations, wind power stations, and photovoltaic power stations; They are the maximum new capacities of gas turbine power stations, wind power stations and photovoltaic power stations respectively.

[0148] Constraints on the proportion of new energy access:

[0149]

[0150] In the formula, ζ min The minimum proportion of new energy installed capacity in the total installed capacity; max The maximum proportion of renewable energy installed capacity in the total installed capacity; W,n ,y PH,n ,y G,n They are the construction decision variables of wind power station, photovoltaic power station and gas turbine power station, with values ​​of 0 or 1, indicating whether to build the nth power station;

[0151] This embodiment takes into account policy requirements and the availability of new energy resources, the technical capabilities of the distribution network, and the overall demand, stability and flexibility of the power system. Taking into account the use of electric vehicle V2G technology and time scheduling characteristics, it can have the effect of peak shaving and valley filling for DG, and the upper and lower limits of the proportion of new energy power generation are 30% to 60% respectively.

[0152] Restrictions on the number of charging piles installed:

[0153] At any time t, the charging station should be equipped with enough charging piles to meet the usage needs of EVs. As shown in formula (31):

[0154]

[0155] In the formula, represents the number of charging piles at the i-th node;

[0156] Charging demand constraints:

[0157] After the construction of the charging station is completed, the total charging power provided by all charging piles must be greater than the total charging demand of all electric vehicles, as shown in formula (32):

[0158]

[0159] In the formula, p EV It is the rated charging and discharging power of electric vehicles through charging piles;

[0160] As the operation layer, the lower layer, based on the upper-level planning scheme, takes the operating economic cost of each operation scenario, the wind and solar power consumption situation, and the stability of voltage and load as the objective function. By coordinating and optimizing the output distribution of distributed power sources and conventional thermal power units, the demand response control of flexible loads, the coordinated control of multiple energy storage devices, and the power purchasing behavior based on time-of-use electricity prices, the optimal scheduling results of various active resources are obtained, and the optimal solutions of the decision variables are obtained. The solution set is fed back to the upper-level planning model to update the corresponding objective function value.

[0161] The lower objective function includes the operation and maintenance cost of power supply equipment, the management cost of load demand response, the depreciation cost of energy storage equipment, the annual power purchase cost from the upper power grid, network loss cost, distribution network node voltage offset, wind power abandonment rate and photovoltaic abandonment rate, standard deviation of equivalent load curve, operation and maintenance cost of EV charging piles and EV dispatch cost. The lower constraint conditions include unit output constraint, interruptible load constraint, transferable load constraint, energy storage equipment output constraint, upper power purchase constraint, power flow constraint, node voltage constraint and EV load time-space constraint; the upper planning layer provides the basis for equipment configuration and parameter setting for the lower operation layer, and the lower operation layer feeds back the actual operation results to the upper layer for the upper layer to make planning adjustments.

[0162] Furthermore, its objective function includes the following parts:

[0163] Operation and maintenance costs of power supply equipment:

[0164] During the operation of ADN, the operation and maintenance cost of power supply equipment is shown in formula (33):

[0165]

[0166] In the formula, C YWP is the total operation and maintenance cost of all power equipment; and are the operation and maintenance costs of wind power generation equipment, photovoltaic power generation equipment, and gas turbine power generation equipment respectively; S is the number of days of typical day scenarios; is the probability of occurrence of each typical day; C OP,W , C OP,PH , C OP,gas are the operation and maintenance costs of wind power generation equipment, photovoltaic power generation equipment, and gas turbine power generation equipment corresponding to unit power generation; P W,s,i,t , P PH,s,i,t , P gas,s,i,t They are respectively the active output of each wind power station, photovoltaic power station and gas turbine power station of the ith wind power generation equipment, photovoltaic power generation equipment and gas turbine power generation equipment at time t under the typical day scenario s; Δt is the duration, which is 1h in this model.

[0167] Management costs of load demand response:

[0168] In this embodiment, while using interruptible loads and transferable loads to maintain the stable operation of the distribution network, it is necessary to provide certain subsidies to the users corresponding to the loads, as shown in formula (37):

[0169]

[0170] In the formula, C loadT The total load demand response management cost; and are load transfer compensation cost and load dispatch compensation cost respectively; C OLT,i and C OLI,i are the unit load transfer compensation cost and the unit load interruption compensation cost respectively; P LT,s,i,t and P LI,s,i,t They are respectively the transfer load and interruption load of the demand response node at the i-th node at time t under the typical day scenario s.

[0171] Depreciation cost of energy storage equipment:

[0172] The depreciation cost of energy storage equipment is shown in formula (40):

[0173]

[0174] Where P E,s,i,t represents the charging and discharging power of the i-th energy storage device at time t in scenario s; U Edis,s,i,t and U Ech,s,i,t are state variables, representing the discharge and charge states of the i-th energy storage device at time t under scenario s, and both are 0 and 1 variables.

[0175] Annual electricity purchase cost from the upper grid:

[0176]

[0177] In the formula, C buy The cost of purchasing electricity from the active distribution network to the upper grid; C price,s,t is the electricity price at time t in scenario s; P buy,s,t It is the power obtained by the active distribution network from the upper distribution network at time t.

[0178] Network loss costs:

[0179]

[0180] In the formula, is the network loss of the active distribution network during operation; P line,s,i,t is the active power of the line flow, Cnloss is the network loss cost per unit power of the transmission network.

[0181] Operation and maintenance costs of EV charging stations:

[0182]

[0183] In the formula, C OP,EC Annual operation and maintenance costs for each charging station.

[0184] Dispatch costs of electric vehicles It consists of three parts, namely the additional driving distance cost required for EV space scheduling Charging and discharging costs of electric vehicle users considering the time-of-use electricity price mechanism and the annual wear and tear cost of EV batteries

[0185]

[0186] In the formula, p EV It is the rated charging and discharging power of electric vehicles through charging piles; is the charging and discharging electricity price of electric vehicles at time t, and are the charging and discharging states of the kth electric vehicle charging and discharging at node i in scenario s, with node j as the destination. Both are 0-1 variables, with 1 indicating that charging / discharging is in progress and 0 indicating that it is not in the charging / discharging state. ev d is the scheduling cost per unit distance that EV users travel extra due to spatial scheduling; is the set of EV numbers in scenario s, with node i as the destination and whose charging and discharging period includes time t; d lj represents the geographical distance between node i and node j; and They represent the arrival time and estimated stay time of the kth electric vehicle with node i as its destination. Since the side reactions inside the battery during the charging and discharging process of the EV will lead to a decrease in its battery capacity and a decrease in its lifespan, it is approximately assumed here that the degree of EV battery degradation is linearly related to its charge and discharge amount.

[0187] The above costs can be summarized as the operating layer economic cost f Eeco , as shown in formula (48):

[0188]

[0189] Distribution network node voltage offset:

[0190] This embodiment takes the minimum total offset of the node voltage as part of the objective function:

[0191]

[0192] In the formula, f dU is the total offset of the voltage of each node in the active distribution network; is the node voltage per unit value of the i-th node at time t in each scenario.

[0193] Wind power abandonment rate and photovoltaic abandonment rate:

[0194] In order to ensure the consumption of distributed power sources, this embodiment sets the wind power abandonment rate f LW and the PV abandonment rate f LPH Incorporate into the overall objective function, as shown in equations (50) and (51):

[0195]

[0196] Where, T W and T PH are the theoretical available hours of wind power and photovoltaic power, respectively; P WF,s,i,t and P W,s,t,i are the wind power forecast value and actual output power value of the ith node at time t under scenario s; P PHF,s,t,i and P PH,s,t,i They are respectively the predicted photovoltaic power value and actual output power value of the i-th node at time t under scenario s.

[0197] Standard deviation of the load curve:

[0198] In order to measure the effect of demand-side response on reducing load fluctuations and achieving peak shaving and valley filling, this embodiment calculates the standard deviation of the net load curve, as shown in formula (52):

[0199]

[0200] In the formula, f sd is the standard deviation of the equivalent load curve, P JLoad,s,i,t In scenario s, the net load of the i-th node at time t, P Load,s,i,t It represents the total power load of the i-th node at time t in scenario s.

[0201] In summary, the calculation formula of the total objective function of the operation layer is shown in formula (53):

[0202] f Edown =ω1f deco +ω2f dU +ω3f LW +ω4f LPH +ω5f SD (32)

[0203] Where ω1, ω2, ω3, ω4, and ω5 are the weight coefficients of each sub-objective function respectively.

[0204] The lower level constraints also consist of the following parts:

[0205] Unit output constraints:

[0206] The upper and lower limits of wind turbine output are shown in formula (54):

[0207] 0≤P W,s,i,t ≤E W0 v W,i i=1,2,…N W (33)

[0208] The upper and lower limits of the photovoltaic generator set output are shown in formula (55):

[0209] 0≤P PH,s,i,t ≤E PH0 v PH,i i=1,2,…N PH (34)

[0210] The upper and lower limits of the gas turbine generator set output are shown in formula (56):

[0211] 0.5E gas0 v gas,i ≤P gas,s,i,t ≤E gas0 v gas,i i=1,2,…N gas (35)

[0212] Constraints for interruptible loads:

[0213] 0≤P LI,s,i,t ≤P LI,s,i,MAX (36)

[0214]

[0215] Where P LI,s,i,MAX is the maximum active power of the interruptible load at node i under scenario s; It is the power percentage of interruptible load in the total load.

[0216] Constraints on transferable loads:

[0217] Transferable load power constraints:

[0218] P LT,s,i,MIN ≤P LT,s,i,t ≤P LT,s,i,MAX (38)

[0219] Where P LT,s,i,MAX and PLT,s,i,MIN They represent the maximum power and minimum power limits of the transferable load at node i at time t under scenario s, respectively;

[0220] In addition, the transferable load satisfies the load transfer conservation constraint within a scheduling cycle:

[0221]

[0222] Energy storage equipment output constraints:

[0223] The energy storage device charging / discharging power constraint and device SOC constraint are shown in equations (61) and (62), respectively;

[0224] P E,s,j,t ≤v PE,i P E0 (40)

[0225] SOC min ≤SOC E,t ≤SOC max (41)

[0226] Constraints on power purchase from higher authorities:

[0227] P buy,s,t ≥0 (42)

[0228] Power flow constraints:

[0229]

[0230] Where P i t and Respectively represent the active and reactive power values ​​of node i; U i,t and U j,t are the voltage amplitudes of nodes i and j respectively; G ij and B ij is the admittance between node i and node j; θ ij is the phase angle difference between node i and node j.

[0231] Node voltage constraints:

[0232]

[0233] In the formula, and Represent the voltage amplitude of nodes i and j respectively; r ij and x ij is the impedance between node i and node j.

[0234] EV load time-sharing constraints:

[0235]

[0236] In the formula, the parameter ρ is used to limit the variable When EV does not participate in V2G scheduling, EV cannot discharge, and parameter ρ = 0; when EV participates in V2G scheduling, parameter ρ = 1. max The above constraints indicate that: from a temporal perspective, for the same vehicle, its battery can only be in one state, that is, the EV cannot be charged and discharged at the same time, and a charging station can only charge one EV at any time; from a spatial perspective, when the EV's destination exceeds a certain set range d max After that, EV users will not go to this charging station to charge.

[0237] In the two-layer model constructed in this embodiment, the planning layer and the operation layer do not exist in isolation, but achieve global optimization by interacting with each other and transmitting decision information to each other.

[0238] The upper layer is the planning layer, which optimizes the joint site selection and capacity determination scheme of power source, energy storage equipment and electric vehicle charging station to obtain v W,i 、v PH，i 、v gas，i 、v PE，i , The optimal solution of variables such as , constitutes the solution set X as the optimal planning result and is passed to the lower operation layer, which affects the objective function and constraints of the lower problem. As the operation layer, based on the upper planning scheme, the lower layer takes the operation economic cost of each operation scenario, the wind and solar power consumption situation, and the stability of voltage and load as the objective function, and coordinates and optimizes the output distribution of distributed power sources and conventional thermal power units, the demand response control of flexible loads, the coordinated control of multiple energy storage devices, and the purchase of electricity based on time-of-use electricity prices, etc., to obtain the optimal scheduling results of each active resource, and obtain P W,s,i,t , P PH,s,i,t , P line,s,i,t , P gas,s,i,t , P LT,s,i,t , P LI,s,i,t , P E,s,j,t , P buy,s,t , The optimal solutions of the variables constitute the solution set Y and are fed back to the upper-level planning model to update the corresponding objective function value.

[0239] Furthermore, the improved hybrid Seagull-Grey Wolf Optimization Algorithm includes: on the basis of Grey Wolf Optimization (GWO), integrating the Metropolis standard in the SA algorithm, and introducing the spiral flight and jumping search mechanism of the Seagull Optimization Algorithm (SOA).

[0240] This embodiment uses the Improved Hybrid Seagull-Grey Wolf Optimization (IHSGWO):

[0241] GWO is a natural heuristic algorithm based on group behavior, inspired by the behavior of gray wolves hunting, besieging and chasing prey. The algorithm is widely used in many optimization problems due to its simple mechanism, low parameter dependence and strong global optimization ability. The distance between the gray wolf and the prey is calculated as follows:

[0242]

[0243] in, is the activity space dimension of the gray wolf group, t is the current iteration number, is the direction position vector of the prey, is the direction position vector of the gray wolf, is the penalty coefficient, is a random number between [0, 1].

[0244] Using the Tent mapping expression in the search space, a uniformly distributed Tent chaotic sequence is generated to obtain a balanced and random initial population of gray wolves. The gray wolf position update formula and fitness value calculation formula are as follows:

[0245]

[0246] in, is the location of the gray wolf after t+1 generations, is the direction position vector of the prey, is the location parameter vector, is the convergence factor, which gradually decreases from 2 to 0. is a random number between [0, 1].

[0247] In the hybrid seagull-grey wolf optimization algorithm, the characteristics of SOA are introduced into the following steps of the grey wolf algorithm to enhance the global search capability and local development capability of the algorithm:

[0248] When the gray wolf population initializes its position, it is no longer completely randomly generated. Instead, the dynamic initialization strategy of SOA is used to generate the initial position according to the following formula:

[0249] X i =X lb +(X ub -X lb )SOA_Init(r) (53)

[0250] Among them, X ub and X lb are the upper and lower bounds of the variable, SOA_Init(r) represents the initial position generation function based on SOA, and r is a random factor.

[0251] The spiral flight model in SOA has efficient search capabilities. Introducing it into the hunting stage of gray wolves can enhance the population's ability to explore unknown areas. When |A|>1, that is, in the exploration stage, the gray wolf's position is updated as follows:

[0252]

[0253] Where β is the spiral contraction coefficient.

[0254] When |A|<1, that is, in the development stage, combined with the random jump strategy of SOA, the search diversity of the gray wolf can be increased, so that it has a certain random disturbance during local development to avoid premature convergence. The position update formula is as follows:

[0255]

[0256] Among them, λ is the jump factor, X rand is a randomly selected solution.

[0257] The global and local search balance characteristics of SOA are introduced into the Gray Wolf Algorithm by adjusting the probability factors of spiral search and jump search. The range of |A| is dynamically adjusted according to the number of iterations, so that the search gradually transitions from the initial global exploration to the later local development. At the same time, a balance factor is set according to the empirical formula of SOA to dynamically adjust the weights of jump and spiral search:

[0258] δ=e -σt (56)

[0259] Where σ is the convergence rate parameter and t is the current iteration number.

[0260] Simulated Annealing (SA) is a probabilistic global optimization algorithm that simulates the annealing process of matter to find the global optimal solution of a given function in a huge search space.

[0261] The specific process of the SA algorithm is as follows:

[0262] (1) Initialization parameters: Select a higher initial temperature T high , initialize the solution state X0, and set the termination temperature T end .

[0263] (2) Add perturbation, that is, starting from the current solution X, generate a new candidate solution X′ through some perturbation mechanism.

[0264] (3) Calculate the energy difference, that is, calculate the energy difference ΔE=E(X′)-E(X) between the new solution X′ and the current solution X.

[0265] (4) Judge according to the Metropolis criterion, that is: if ΔE>0, the new solution is worse than the current solution, and the new solution X′ is directly accepted; if ΔE<0, it can be judged that the new solution is not as good as the current solution or is equal to it. At this time, there is still a certain probability to accept the new solution, and this probability P ME =e -ΔE / T(t) , where T(t) is the temperature at time t. This step allows the algorithm to accept poor solutions to a certain extent, thus making it possible to escape the trap of local optimal solutions.

[0266] (5) Cool down, and the temperature is updated as shown in formula (78):

[0267] T(t+1)=χT(t) (57)

[0268] Where χ is a cooling coefficient less than 1, which is used to gradually reduce the temperature.

[0269] (6) Termination condition: When the temperature drops to T end The algorithm stops when the optimal solution is not found after multiple consecutive iterations or other termination conditions are met.

[0270] The IHSGWO algorithm used in this embodiment integrates the Metropolis standard in the SA algorithm on the basis of the GWO algorithm, allowing the acceptance of poor solutions with a certain probability during the iterative exploration process. This strategy effectively avoids the algorithm from falling into the dilemma of local optimality in the early stages and significantly enhances the ability to find the best solution globally. In the improved hybrid seagull-grey wolf optimization algorithm, the spiral flight and jump search mechanism of the SOA algorithm are introduced to enhance the global exploration ability and local development accuracy of the grey wolf algorithm. The spiral search feature of SOA is used to enhance the exploration direction of the population in unknown areas, while the jump search strategy helps the grey wolf get rid of the constraints of the local optimality, making the search more diversified and stable. In addition, by dynamically adjusting the balance weight between global and local searches, the overall optimization of the algorithm search efficiency is achieved.

[0271] As an implementable method, the process of solving the two-layer model using the improved hybrid seagull-grey wolf optimization algorithm adopted in this embodiment includes:

[0272] IHSGWO comprehensively improves the search performance of GWO by integrating the characteristics of SOA. First, IHSGWO combines the dynamic initialization strategy of SOA in the population initialization stage, and uses the spiral flight and jumping search behavior of seagulls to generate a more evenly distributed initial population position, thus laying a good foundation for global exploration. Secondly, the algorithm introduces the spiral search mechanism of SOA in the hunting process of gray wolves, which strengthens the directionality and coverage of the algorithm in the global search stage; at the same time, the random jumping characteristics of SOA are incorporated into the development stage to help the algorithm effectively escape the dilemma of local optimal solutions. Finally, IHSGWO dynamically adjusts the balance mechanism of global and local searches, and flexibly switches between macro exploration and local development by combining the balance factor of SOA with the gray wolf hunting model, thereby greatly improving the convergence speed and solution accuracy of the algorithm.

[0273] In order to verify the superiority of IHSGWO algorithm in the optimization process, the proposed model was solved by IHSGWO algorithm, standard SA algorithm, standard CS algorithm, QPSO algorithm and PSO algorithm respectively. The traditional PSO algorithm has a faster solution speed, but the comprehensive cost of the corresponding solution is higher because it quickly falls into the local optimal solution; the solution obtained by QPSO algorithm is more ideal, which shows that it may have an advantage over PSO in finding the global optimal solution, but there is still a certain possibility of falling into the local optimal solution; the curve of SA algorithm shows a rapid initial decline and a slow decline in the later stage, reflecting the characteristic of "accepting poor solutions" in the simulated annealing process, which helps to jump out of the local optimal solution, and the comprehensive cost corresponding to the result is also lower; the final planning solution corresponding to the curve of standard CS algorithm has a more ideal result, which means that cuckoo search algorithm can continuously find better solutions instead of falling into the local optimal solution in the early stage, but at the same time, the algorithm needs more iterations to find the optimal solution, and it takes a long time to solve complex planning problems. The IHSGWO algorithm basically does not fall into the situation where it has an optimal solution. The optimal solution obtained has the lowest comprehensive total cost, that is, the optimal operation plan is not missed because it stays at the local optimal solution; and the algorithm is used to solve the model at a fast speed, and it can converge in only 8 iterations. In summary, the hybrid optimization method proposed in this article has better optimization performance. The convergence curves corresponding to the algorithms in the embodiments of the present invention are shown in Figure 2. Fig.11 shown.

[0274] As an implementable method, the specific process of using the IHSGWO algorithm to solve the model of this embodiment is as follows: Fig.10 The operation steps are as follows:

[0275] Step 1: Input the active distribution network system parameters and the typical daily curves of DG and load under each scenario, and input the relevant data of electric vehicles;

[0276] Step 2: Based on the electric vehicle data, classify each EV according to whether it can support V2G technology, and model the parking behavior and load of each EV;

[0277] Step 3: Assign values ​​to the key parameters in IHSGWO, including population size, maximum number of iterations, dynamic adjustment factor, global and local search balance weight, etc. At the same time, initialize the number of iterations t i =0;

[0278] Step 4: Initialization of upper-level models and parameters. Initialize the location and capacity of the power source, energy storage device, and EV charging station in the upper-level model (including v W,i 、v PH,i 、v gas,i 、v PE,i 、v SE,i , etc.), as the initial decision variable X(t1) to the planning layer, substitute it into the IHSGWO algorithm, and set the spiral search parameters, jump search parameters and related parameters of the global and local switching mechanisms.

[0279] Step 5: Initialization of algorithm parameters in the lower model. Define the size of the population and initialize individual positions, including the generator sets, flexible loads, energy storage equipment, purchased power, and electric vehicle charging and discharging conditions of each node, namely P W,s,i,t , P PH,s,i,t , P line,s,i,t , P gas,s,i,t , P LT,s,i,t , P LI,s,i,t , P E,s,j,t , P buy,s,t , According to the mechanism of IHSGWO, the dynamic adjustment factor and jump / spiral search weight parameters are set.

[0280] Step 6: Substitute the planning layer decision variable X(t1) into the lower layer planning model, call the IHSGWO algorithm to solve the lower layer model, and obtain the optimal solution Y(t1) of the lower layer model as the decision variable of the operation layer;

[0281] Step 7: Bring the optimal solution Y(t1) of the lower model into the upper model for solution, and update the solution X(t1) of the upper model by calling the IHSGWO algorithm;

[0282] Step 8: Substitute X(t1) and Y(t1) into the upper-level planning model, calculate the objective function and use it as the fitness;

[0283] Step 9: Compare the newly obtained fitness value with the old fitness value, and use the Metropolis criterion to determine whether to accept the new solution. If accepted, update the optimal solution and record the upper and lower layer decision variables at this time; otherwise, keep the original solution;

[0284] Step 10: Call the standard SA algorithm and use formula (78) to complete the annealing process. If t1 ≥ M th And T(t1)≤T f (When the maximum number of iterations is reached and the SA algorithm cools down to the termination temperature), go to step 11, otherwise go to step 6 and set t1=t1+1;

[0285] Step 11: Output the group optimal solution. At this time, the solution of the upper model is used as the optimal planning solution, and the solution of the lower model is used as the optimal scheduling strategy. The calculation is completed.

[0286] The IHSGWO algorithm parameter settings for solving this example are shown in Table 8:

[0287] Table 8

[0288]

[0289] In order to study the impact of wind and solar grid connection on system stability, and the impact of implementing the demand response mechanism on the comprehensive cost of the configuration scheme, the following configuration schemes are given for comparison and solution:

[0290] (1) Solution 1: adopt the source-load-storage collaborative planning method in the V2G mode established in this embodiment.

[0291] (2) Scheme 2: Compared with Scheme 1, electric vehicles are charged using a time-space scheduling method, but at this time the electric vehicles do not discharge to the active distribution network through V2G technology.

[0292] (3) Scheme 3: Compared with Scheme 1, electric vehicles do not discharge to the active distribution network through V2G technology, nor do they use scheduling methods. Instead, they go directly to the charging station closest to the demand node to complete charging, and charge immediately when returning until the battery is fully charged.

[0293] Through Matlab R2020a programming and simulation calculation verification, the optimal solution for the source-load-storage coordinated planning of the active distribution network considering electric vehicle charging facilities is obtained. Among them, the site selection and capacity planning results of the power supply equipment, charging piles and energy storage equipment obtained by solution 1 are shown in Table 9:

[0294] Table 9

[0295]

[0296] The total number of electric vehicle charging piles installed at this time is 112. Under this plan, the economic costs and various operating indicators generated during all planning and operation processes are shown in Table 10:

[0297] Table 10

[0298]

[0299] The above planning and operation results show that EV charging piles are also deployed on all candidate nodes in a decentralized and fully covered manner. This configuration allows charging stations to fully cover the charging needs of electric vehicles in the target area, reduce the distance of electric vehicle load space scheduling, and provide convenience for electric vehicle owners' charging behavior.

[0300] In order to verify the use of V2G technology and the effectiveness of the proposed electric vehicle charging and discharging scheduling method, this paper simulates the methods proposed in Scheme 2 and Scheme 3 for comparative effect. The site selection and sizing scheme of source-load-storage equipment and electric vehicle charging stations corresponding to Scheme 2 is shown in Table 11:

[0301] Table 11

[0302]

[0303]

[0304] The total number of electric vehicle charging stations installed at this time is 109.

[0305] The site selection and sizing scheme for source-load-storage equipment and electric vehicle charging stations corresponding to Scheme 3 is shown in Table 12:

[0306] Table 12

[0307]

[0308] The total number of electric vehicle charging stations installed at this time is 159.

[0309] In the operation results, the economic costs and operation indicators of each scheme are compared as shown in Table 13 and Fig.12 shown.

[0310] Table 13

[0311]

[0312] As shown in Table 13: In terms of annual comprehensive planning operation cost, Scheme 3 is the highest, followed by Scheme 2, and Scheme 1 is the lowest. This comparison can be analyzed from the following perspectives:

[0313] From the perspective of power supply planning and operation, the capacity of distributed power generation equipment in Scheme 1 and Scheme 2 is significantly higher than that in Scheme 3, and the wind and solar abandonment rate and the comprehensive investment and operation cost on the power supply side are lower. This is because in the V2G mode, the time-space adjustment of EV load can help absorb distributed power sources, adjust the randomness and intermittency of DG output, and help DG access a high proportion in ADN. At the same time, the operation and maintenance costs of gas turbine equipment are significantly higher than those of distributed power sources, and the increase in the proportion of DG capacity can also reduce the use of gas turbine power generation and the purchase of electricity from the upper power grid, thereby reducing the operation and maintenance costs on the power supply side.

[0314] From the perspective of EV charging pile construction and active distribution network investment and operation, the construction and operation costs of charging piles in Scheme 1 and Scheme 2 are lower than those in Scheme 3. This is because the spatiotemporal scheduling of EV can improve the utilization rate of charging piles, so that the scheme under orderly charging and discharging can meet the EV load demand with fewer charging piles. In contrast, the number of charging piles required to be installed in Scheme 1 is more than that in Scheme 2. This is because through the use of V2G technology, the EV in Scheme 1 plays the role of dispatchable and movable energy storage equipment and flexible load to a certain extent through charging and discharging at reversible charging piles, thereby replacing energy storage units and DR loads while helping DG consumption, thereby further increasing the proportion of DG power generation. At this time, although the planning cost related to charging piles increases, the number of gas turbine equipment and energy storage equipment required to be built decreases, so that Scheme 1 can significantly reduce the operating cost while improving the operation quality of the distribution network with only a slight increase in investment cost.

[0315] From the perspective of distribution network operation indicators, many operation indicators of Scheme 1 are better than those of Schemes 2 and 3. At this time, EV users can use V2G technology and orderly charging and discharging time-sharing to charge at nodes and times with low total load and discharge at nodes and times with high total load, thereby reducing network losses, balancing node voltages, improving load curves, and reducing the abandonment rate of wind and solar power.

[0316] In summary, Scheme 1 can reduce the planning and operation costs in the active distribution network source-load-storage collaborative planning process to a certain extent and improve the operation performance of the distribution network through the use of V2G technology and the spatiotemporal scheduling of EV charging and discharging loads.

[0317] Fig.12 The following is a comparison chart of operating indicators under different schemes of an embodiment of the present invention.

[0318] Analysis of EV charging and discharging scheduling results:

[0319] In order to study the impact of orderly EV charging and discharging scheduling on planning and operation results in V2G mode, this chapter takes the working day scenario (i.e., scenario 1) under strong DG output as an example to analyze the spatiotemporal scheduling of EV charging and discharging load corresponding to scenario 1, as shown in the following figure: Fig.13 and Fig.14 As shown. Among them, Fig.13 is the number of electric vehicles being charged at each charging node at different times in a typical day; Fig.14 is the number of electric vehicles being discharged at each charging node at different times in a typical day.

[0320] from Fig.13 and Fig.14 It can be seen from the figure that, through the time-space scheduling of EV, the peak period of EV users' charging behavior is concentrated from 23:00 to 5:00 the next day, and the second peak period is from 11:00 to 13:00. The discharge behavior of electric vehicles is distributed between 16:00 and 21:00, with the peak period concentrated between 19:00 and 21:00. Comparing this result with the total load output and DG output in scenario 1, it can be found that through time scheduling, electric vehicles generally choose to charge when the DG output is large and the load demand is small, and discharge when the DG output is small and the load demand is large. Specifically, some electric vehicles that arrive at the charging station in the morning choose to postpone charging until noon to consume the surplus electricity at the peak time of photovoltaic power generation; while electric vehicles that arrive at the charging station in the evening not only postpone their charging plans, but also discharge in a concentrated manner at 19:00-21:00 when the net load of the distribution system is large. In addition, the orderly spatial scheduling of charging and discharging demands also enables electric vehicles to choose to charge at nodes with smaller total load demands at the same time among charging piles near the destination, and discharge at nodes with larger total load demands, thereby making the power flow distribution of the distribution system more reasonable, reducing system network losses while improving voltage distribution, and thus obtaining a comprehensive planning and operation plan with a lower annual comprehensive cost.

[0321] The ADN source-load-storage coordinated dual-layer optimization configuration method for electric vehicle access and V2G technology proposed in this embodiment realizes the coordination of "planning" and "operation". This method enables electric vehicles to be scheduled in time and space, making electric vehicles a movable flexible load to achieve demand response in time and space. Specifically, through the time-space scheduling of EV charging and discharging loads, the charging and discharging loads can be connected to the power grid at a more appropriate location, making the power flow distribution of the distribution system more reasonable, and improving the voltage distribution while reducing system network losses; it also allows electric vehicles to charge when the total load is small and the output of distributed power sources is large, and discharge when the total load is large and the output of distributed power sources is small, so as to achieve the effect of peak shaving and valley filling, and improve the economy and reliability of the distribution network operation. In the process of solving the two-layer planning problem, the proposed ICSQPSO algorithm not only has a strong global optimization capability, so as to avoid the solution from falling into the local optimal solution; it also has higher computing efficiency and saves running time.

[0322] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A dual-layer ADN optimization configuration method considering electric vehicle access and V2G technology, characterized in that: include: Based on the parking behavior of EV users under electric vehicle access, the orderly charging and discharging scheduling scheme of the EV cluster is obtained from the two levels of time and space; Using the orderly charging and discharging scheduling scheme, an ADN source-load-storage model based on the power supply side, the load side and the energy storage side is constructed, wherein the ADN source-load-storage model includes a planning layer and an operation layer; The improved hybrid Seagull-Grey Wolf optimization algorithm is used to solve the planning layer and the operation layer in the ADN source-load-storage model; Based on the solution results, the ADN source-load-storage model is optimized.

2. The ADN dual-layer optimization configuration method considering electric vehicle access and V2G technology according to claim 1 is characterized in that: The construction of the ADN source-load-storage model based on the power side, load side and energy storage side includes: Model the power supply side based on the output characteristics of wind power generation, photovoltaic power generation and gas turbine power generation; Based on the flexible load response output characteristics and the charging demand characteristics of electric vehicles as dispatchable loads, the load side is modeled; Model the energy storage side based on the state of charge of the battery constraints and the charging and discharging characteristics of electric vehicles as mobile energy storage devices; Based on the constructed power side model, load side model and energy storage side model, the construction of the ADN source-load-storage model considering V2G is completed.

3. The ADN dual-layer optimization configuration method considering electric vehicle access and V2G technology according to claim 2 is characterized in that: Modeling the power supply side includes: building wind power output models, photovoltaic output models and gas turbine output models; The wind power output model is: Among them, P Wn is the rated active output of the fan; V i is the cut-in wind speed of the fan; V o is the cut-out wind speed of the fan; V n is the rated wind speed, P W is the power of wind power output, v is the local wind speed; The photovoltaic output model is: Among them, P pv The photovoltaic power station is under the condition of solar radiation intensity G c Active power output at time G r and P r is the radiation intensity and maximum output power under standard test conditions, k is the power temperature coefficient, T c and T r are the photovoltaic cell temperature and the reference temperature respectively; The gas turbine output model is: P gas (t)=V gas (t)h gas i gas Among them, P gas (t) is the power of the gas turbine generator set; V gas (t) is the natural gas consumption at time t, ηg as is the power generation efficiency of the gas turbine, θ gas is the power angle size.

4. The ADN dual-layer optimization configuration method considering electric vehicle access and V2G technology according to claim 2 is characterized in that: Load side modeling includes: flexible load modeling and electric vehicle load modeling; The flexible load is modeled as: Among them, S n,t represents the response state of user n at time t. If S is 1, it means an increase in load, -1 means a decrease in load, and 0 means that the load remains unchanged. N represents the number of users that can participate in the scheduling. n,t represents the power of user n at time t, P LA (t) represents the total response output of the flexible load per unit time; The electric vehicle load model is: Among them, N dt represents the number of electric vehicles with land block d as their destination in a typical day, E total,d represents the daily total load demand corresponding to land block d, E d,k,t It represents the charging capacity of the electric car numbered k with destination d at time t.

5. The ADN dual-layer optimization configuration method considering electric vehicle access and V2G technology according to claim 2 is characterized in that: Modeling the energy storage side includes: Among them, SOC E，t and SOC V，t are the charge states of the energy storage device and the electric vehicle battery at time t, E ESS and E V are the rated capacities of the energy storage device and the electric vehicle battery, respectively. ESS and D V are the self-consumption coefficients of the energy storage system and the electric vehicle battery, P Echar,t , P Edis,t and P Vchar,t , P Vdis,t are the charging and discharging power of the energy storage device and the electric vehicle battery in period t, respectively, char and η dis are the charge and discharge efficiency, U Ech,t , U Edis,t and U Vch,t , U Vdis,t They represent the charging and discharging states of the energy storage device and the electric vehicle battery, respectively, and are 0 and 1 variables; The charging and discharging states of the energy storage side cannot exist at the same time and must meet the following constraints: IN Ech,t +U Edis,t ≤1 IN Vch,t +U Vdis,t ≤1 The charging and discharging states of the energy storage side need to meet the following constraints: SOC Emin ≤SOC E，t ≤SOC Emax SOC Vmin ≤SOC V，t ≤SOC Vmax P Emin ≤P Echar,t ≤P Emax P Vmin ≤P Vchar,t ≤P Vmax P Emin ≤P Edis,t ≤P Emax P Vmin ≤P Vdis,t ≤P Vmax Among them, SOC Emax , SOC Emin With SOC Vmax , SOC Vmin Respectively represent the maximum and minimum state of charge of the energy storage system and the electric vehicle battery; P Emin , P Emax With P Vmin , P Vmax Represent the maximum value of charging and discharging power of energy storage system and electric vehicle battery respectively.

6. The ADN dual-layer optimization configuration method considering electric vehicle access and V2G technology according to claim 1 is characterized in that: The planning layer is used to obtain the total cost of the planning layer by summing the investment and construction costs of wind power stations, photovoltaic power stations, gas turbine generator sets, energy storage equipment, and electric vehicle charging piles and the results returned by the operation layer planning; then, the power balance constraint, the capacity constraint of the newly built generator set, the proportion of new energy access constraints, the number of charging piles installed constraints, and the charging demand constraint are used as constraints, and the minimization of the annual total economic operation cost is used as the objective function to obtain the optimal planning scheme for site selection and capacity determination of each device; The optimal planning scheme is a solution set consisting of the optimal solutions for the number of unit capacity wind turbines, photovoltaic generators, gas turbines, energy storage equipment and charging stations to be built.

7. The ADN dual-layer optimization configuration method considering electric vehicle access and V2G technology according to claim 1 is characterized in that: The operation layer is used to obtain the total cost of the operation layer by weighted summing the economic cost of the operation layer, the voltage offset of the distribution network node, the wind power abandonment rate, the photovoltaic abandonment rate and the standard deviation of the equivalent load curve; then, the unit output constraint, the interruptible load constraint, the transferable load constraint, the energy storage device output constraint, the upper power purchase constraint, the power flow constraint, the node voltage constraint and the EV load time-space constraint are used as constraint conditions, and the total cost of the operation layer is used as the objective function to obtain the optimal scheduling strategy; The economic cost of the operation layer includes the operation and maintenance cost of power supply equipment, the management cost of load demand response, the depreciation cost of energy storage equipment, the operation and maintenance cost of charging piles and the dispatch cost of electric vehicles, the annual cost of purchasing electricity from the superior power grid, and the network loss cost. The optimal dispatching strategy is a solution set consisting of the active output of each wind power station, the active output of each photovoltaic power station, the active output of each gas turbine power station and the optimal solution for electric vehicle charging and discharging dispatching.

8. The ADN dual-layer optimization configuration method considering electric vehicle access and V2G technology according to claim 1 is characterized in that: The improved hybrid seagull-grey wolf optimization algorithm includes: on the basis of the grey wolf optimization algorithm, the Metropolis standard in the simulated annealing algorithm is integrated, and the spiral flight and jumping search mechanism of the seagull optimization algorithm is introduced.

9. The ADN dual-layer optimization configuration method considering electric vehicle access and V2G technology according to claim 1 is characterized in that: Using the improved hybrid seagull-grey wolf optimization algorithm, solving the variables in the planning layer and the operation layer in the ADN source-load-storage model includes: Step 1: Input the active distribution network system parameters and the typical daily curves of DG and load under each scenario, and input the relevant data of electric vehicles; Step 2: Based on the electric vehicle data, classify each EV according to whether it can support V2G technology, and model the parking behavior and load of each EV; Step 3: Assign values ​​to the key parameters in the improved hybrid seagull-grey wolf optimization algorithm, including: population size, maximum number of iterations, dynamic adjustment factor, global and local search balance weight; and initialize the number of iterations t i =0; Step 4: Initialization of algorithm parameters in the operation layer: Initialize the location and capacity of the power source, energy storage device, and EV charging station in the operation layer as the initial decision variable X(t1) to the planning layer, substitute it into the improved hybrid seagull-grey wolf optimization algorithm, and set the spiral search parameters, jump search parameters, and related parameters of the global and local switching mechanisms; Step 5: Initialization of algorithm parameters in the planning layer; define the size of the population, initialize individual positions, including the generator sets, flexible loads, energy storage equipment, purchased power, and electric vehicle charging and discharging conditions of each node, and set dynamic adjustment factors and jump / spiral search weight parameters according to the mechanism of the improved hybrid seagull-grey wolf optimization algorithm; Step 6: Substitute the initial decision variable X(t1) into the lower-level planning model, call the improved hybrid seagull-grey wolf optimization algorithm to solve the lower-level model, and obtain the optimal solution Y(t1) of the lower-level model as the decision variable of the operation layer; Step 7: Bring the optimal solution Y(t1) of the lower model into the upper model for solution, and update the solution X(t1) of the upper model by calling the IHSGWO algorithm; Step 8: Substitute X(t1) and Y(t1) into the upper-level planning model, calculate the objective function and use it as the fitness; Step 9: Compare the newly obtained fitness value with the old fitness value, and use the Metropolis criterion to determine whether to accept the new solution. If accepted, update the optimal solution and record the upper and lower layer decision variables at this time; otherwise, keep the original solution; Step 10: Call the standard SA algorithm to complete the annealing process. If the maximum number of iterations is reached and the SA algorithm cools down to the termination temperature, go to step 11. Otherwise, go to step 6 and set t1 = t 1+1 ; Step 11: Output the group optimal solution. At this time, the solution of the upper model is used as the optimal planning solution, and the solution of the lower model is used as the optimal scheduling strategy. The calculation is completed.

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