Optimal dispatching method of regional distribution network based on non-dominated genetic algorithm based on reference point

Abstract

The regional distribution network optimization scheduling method based on the non-dominated genetic algorithm of the reference point of the present invention involves a multi-objective optimization scheduling method for photovoltaic storage and charging in the regional distribution network. The present invention includes the following steps: S1. Establishing a demand-side response time-of-use electricity price model; S2. Building a photovoltaic power generation output model, an energy storage charging and discharging model, and an electric vehicle charging load model respectively; S3. Establishing a variety of energy flexibility supply and demand models and a complementary demand model; S4. Establishing a photovoltaic storage and charging multi-objective optimization scheduling model; S5. Using the NSGA-III algorithm to solve the optimization scheduling model, and using IGTDM to obtain the best compromise solution. The present invention fully realizes the economic, stable and flexible operation of the distribution network by coordinating the supply and demand relationship on the power side and the load side, and innovatively applies the NSGA-III algorithm and IGTDM together in the optimization scheduling of the distribution network.

Description

Technical Field

[0001] The invention discloses a regional distribution network optimization dispatching method based on a non-dominated genetic algorithm of a reference point, which relates to a multi-objective optimization dispatching method for a regional distribution network photovoltaic storage and charging. Background Art

[0002] With the intensification of energy crisis and environmental problems, building a clean, economical and efficient multi-energy coordinated distribution network system is one of the future development trends; however, the optimal scheduling of multi-energy complementary distribution networks needs to face the uncertainties of intermittent renewable energy output and randomly fluctuating loads, which brings great challenges to system scheduling.

[0003] The optimal dispatching of a multi-energy complementary distribution network system considering the uncertainty of source and load is essentially a random sequential decision-making multi-objective problem. Traditional single-objective optimization dispatching methods often find it difficult to take into account the economy, stability, flexibility and other objectives of the distribution network at the same time, resulting in the dispatching results being difficult to achieve comprehensive optimization. For example: single-objective optimization dispatching often takes the network loss of the distribution network as the optimization objective, considering its economy, but not considering the stability and flexibility of the distribution network. Whether it is fully optimized remains to be discussed. At the same time, although there have been relevant studies on the multi-objective optimization dispatching methods of regional distribution networks, because the research focuses on the establishment and solution of the multi-objective optimization model, and the traditional PSO algorithm is used to solve the multi-objective optimization model, it is easy to produce problems such as convergence difficulties and slow speed, which is difficult to meet the real-time dispatching needs of the system. Therefore, there are still deficiencies, so it is necessary to design a new technical solution to meet the actual needs. Summary of the invention

[0004] The present invention aims to overcome the limitations of the prior art and proposes a regional distribution network optimization scheduling method based on a non-dominated genetic algorithm of a reference point. A multi-objective coordinated optimization scheduling model of light, storage and charging is constructed with the minimum total system operation cost, system network loss, voltage fluctuation and multi-energy complementary demand index as the optimization target. The multi-objective optimization problem is solved by a non-dominated genetic algorithm based on a reference point, and the optimal compromise solution is selected from the Pareto solution set using an improved grey target decision based on the entropy weight method. The above method overcomes the one-sidedness of traditional single-objective optimization algorithms and the difficulties in convergence of traditional multi-objective optimization, improves the globality and usability of optimization scheduling, effectively coordinates the source, network, load and storage in the region, realizes the economic, stable and flexible operation of the distribution network, and can smooth out load fluctuations.

[0005] The regional distribution network optimization dispatching method based on the non-dominated genetic algorithm of the reference point of the present invention is:

[0006] An optimization scheduling model is constructed, wherein the model takes minimization of total system operating cost, minimization of system network loss, minimization of voltage fluctuation, and minimization of complementary demand index of power supply and load as multi-objective functions; the optimization scheduling model is constructed including the following steps S1-S4 respectively:

[0007] S1. Establish a demand-side response time-of-use electricity price model;

[0008] S2. Build photovoltaic power generation output model, energy storage charging and discharging model and electric vehicle charging load model respectively;

[0009] S3. Establish a variety of energy flexibility supply and demand models and complementary demand models;

[0010] S4. Establish a multi-objective optimization scheduling model for photovoltaic storage and charging;

[0011] S5. The NSGA-III algorithm is used to solve the optimization scheduling model, and the IGTDM is used to obtain the best compromise solution.

[0012] According to step S1., a distribution network demand side response optimization model is established, including establishing a time-of-use electricity price model and an electricity quantity and price elasticity matrix model respectively. The specific method is as follows:

[0013] S1.1 Time-of-use electricity price model

[0014] The distribution network needs to absorb the photovoltaic power output. When the photovoltaic power output cannot meet the user's electricity demand, the user needs to purchase electricity from the main grid. At this time, the manager can smooth the net load fluctuation and reduce the load peak-valley difference by formulating a reasonable time-of-use electricity price. After the implementation of the time-of-use electricity price, the electricity price of each period will fluctuate on the original basis, that is,

[0015] e p =e0(1+α),t∈T p (1)

[0016] e f =e0(1+β),t∈T f (2)

[0017] e v =e0(1+γ),t∈T v (3)

[0018] Where, e0 is the electricity price before the implementation of time-of-use electricity price; e p 、e f 、e v are the electricity prices in peak, flat and valley periods after the implementation of time-of-use electricity prices; α, β, γ are the fluctuations of electricity prices in each period; t represents the time; T p 、T f With T v Indicates peak, flat and valley time periods.

[0019] S1.2 Electricity quantity and price elasticity matrix model

[0020] The electricity price elasticity matrix reflects the relationship between electricity price changes and user electricity consumption changes, and is a multi-period response. User electricity consumption is not only related to the electricity price of the current period, but also to the electricity prices of other periods. The relationship between the change of user electricity consumption and the change of electricity price is shown in formula (4):

[0021]

[0022] Where P 0,s With ΔP s Indicates the power consumption and its change in time period s; e 0,s With Δe s is the electricity price and its change in period s; k sz is the price elasticity coefficient of demand; the subscripts s and z represent peak, flat and valley periods, s, z∈{p,f,v}.

[0023] In the time-of-use electricity price model, the load in a day is divided into three periods: peak, flat and valley. After the implementation of time-of-use electricity prices, the load changes in peak, flat and valley periods are as follows:

[0024]

[0025] P0(t)=P load (t)-P PV (t) (7)

[0026] Where P0 represents the amount of electricity purchased by the user from the main network; P 0,p , P 0,f , P 0,v They represent the amount of electricity purchased from the main grid by users during the pre-peak, flat and valley periods of the time-of-use electricity price (in this article, the amount of electricity purchased from the main grid by users is represented by net load); e 0,p 、e 0,f 、e 0,v is the electricity price before the implementation of time-of-use electricity price, which is equal to e0; Δe p , Δe f , Δe v is the change in electricity price in each period; P load , P PV are load demand and photovoltaic output respectively; the diagonal elements in the electricity quantity and electricity price elasticity matrix K are self-elasticity coefficients, and the non-diagonal elements are mutual elasticity coefficients. This paper uses the data of the electricity quantity and electricity price elasticity matrix in the literature to reflect the response of the load to changes in electricity prices.

[0027] According to step S2, a photovoltaic power generation output model, an energy storage charging and discharging model, and an electric vehicle charging load model are respectively constructed, as follows:

[0028] S2.1 Photovoltaic power generation output model, including active output model and reactive output model, the specific method is as follows:

[0029] S2.1.1 Active output model

[0030] Solar photovoltaic cells use the photoelectric effect of semiconductor materials (such as monocrystalline silicon and polycrystalline silicon) under predetermined light intensity, and form an array by connecting single cells in series and parallel to convert solar energy into direct current and store it in the energy storage system. Then, according to the load demand, the direct current is converted into usable alternating current through the grid-connected inverter. Photovoltaic output power is mainly affected by the intensity and temperature of sunlight. Photovoltaic power generation has the advantages of no fuel consumption, no pollution, no regional restrictions, no influence of grid dispatch, flexibility and reliability, simple maintenance, safety and economy. Based on Kirchhoff's current law, the output current of photovoltaic cells can be described by a simple expression:

[0031]

[0032] In the formula, I ph Represents the current generated by the photovoltaic cell; I D represents the total diffusion current of the diode; V is the no-load potential; q and K represent the electron charge and the Boltzmann constant, respectively; T is the thermodynamic temperature; n represents the diode characteristic fitting coefficient; R s and R sh They represent series resistance and leakage resistance respectively.

[0033] The output power of photovoltaic cells is affected by the local solar radiation intensity and ambient temperature, and can be expressed by the following formula:

[0034]

[0035] Where P PV (t) represents the output of the photovoltaic cell at time t; P PVN represents the installed capacity of photovoltaic cells under standard conditions; t e 、T e and T ref Respectively represent the ambient temperature coefficient, actual temperature coefficient and standard ambient temperature (usually 25°C); G T and G ref Represent the solar irradiance and the standard reference irradiance respectively.

[0036] S2.1.2 Reactive output model

[0037] In addition to converting the DC output of photovoltaic panels into AC active power, photovoltaic inverters still have additional capacity to provide / absorb reactive power to the system (without affecting active power output). Compared with controlling distributed energy storage active power, tap equipment, etc., photovoltaic inverter reactive power control is an effective voltage control method, and this solution has the best control economy.

[0038] The relationship between the inverter's adjustable reactive capacity and the inverter capacity is:

[0039]

[0040] In the formula, Q PV is the inverter output reactive power, S PV is the rated capacity of the inverter, P Pv is the active power of photovoltaic power generation.

[0041] There are two main types of inverter reactive power control strategies: 1) Power factor This method uses the photovoltaic grid-connected active power P as the control input and adjusts the inverter reactive power to achieve the grid-connected power factor of the photovoltaic inverter. Control: 2) = Q(U) control. This method uses the photovoltaic grid-connected point voltage U as the control input to achieve photovoltaic inverter reactive power Q control.

[0042] S2.2 Energy storage charging and discharging model

[0043] The battery energy storage system (BESS) can act as a load or as a power source, depending on whether the BESS absorbs or releases electrical energy from the grid. Its charging and discharging model satisfies equation (11).

[0044]

[0045] In the formula, E BEsS is the capacity of BESS; P ch,BE With P di,BE is the charging and discharging power of BESS; η ch With η di is the charge and discharge efficiency of BESS. The state of charge (SOC) is a measure of the remaining power of BESS during the charge and discharge cycle, and is calculated as follows:

[0046]

[0047] In the formula, SOC BE is the SOC of BESS; E BE,rated is the rated capacity of BESS.

[0048] S2.3 Electric vehicle charging load model (charging station discharge model)

[0049] The daily mileage m of an electric vehicle (EV) follows a log-normal distribution, and its probability density function is:

[0050]

[0051] Where μ1 and σ1 are the mean and standard deviation of lnm respectively.

[0052] Initial state of charge of EV when entering the highway It obeys uniform distribution, and its probability density function is:

[0053]

[0054] In the formula, a and b are upper and lower limits.

[0055] The state of charge of an EV when it arrives at a charging station is:

[0056]

[0057] In the formula, E h is the battery capacity of EV; E b E is the power consumption of EV per 100 kilometers. Compared with traditional fuel vehicles, the mileage of EV is greatly affected by the ambient temperature. b The values ​​are different.

[0058] Arrival EV starts charging time T s It obeys normal distribution, and its probability density function is:

[0059]

[0060] In the formula, μ c , σ c Respectively represent T s The mean and standard deviation of .

[0061] According to S EV The EV charging time can be calculated as:

[0062]

[0063] Where P che is the rated charging power of the charging pile; η EV For EV charging efficiency.

[0064] Charging end time:

[0065] T k =Tc +T s (18)

[0066] From this, the charging power requirement for each EV is:

[0067]

[0068] After multiple calculations, the average value is the EV charging load power on the highway:

[0069]

[0070] Where M c is the number of calculations; N c is the total number of EVs.

[0071] According to step S3, a multiple energy flexibility supply and demand model and a multiple energy complementarity demand model are established. The specific method is as follows:

[0072] S3.1 Multiple energy flexibility supply and demand model

[0073] The sum of the adjustable output of all power generation units participating in the grid connection in each time period is the flexibility that the system can provide in that time period, which is called power flexibility supply, and can be spatially divided into upward flexibility supply and downward flexibility supply.

[0074]

[0075]

[0076] In the formula, and are the upward and downward flexibility supply of the system in period t, respectively; are the upward and downward adjustment flexibility of thermal power unit g, gas power unit r, and hydropower unit h in period t respectively; P th,g,t , P mt,r,t , P hy,h,t are the outputs of thermal power unit g, gas power unit r, and hydropower unit h in period t respectively; P th,g,max , P mt,r,max , P hy,h,max , P th,g,min , P mt,r,min , P hy,h,min They are the upper and lower limits of the output of thermal power unit g, gas power unit r, and hydropower unit h respectively; are the upslope or downslope of thermal power unit g, gas power unit r, and hydropower unit h respectively; N G 、N R 、N H They are the number of thermal power units, gas power units and hydropower units respectively.

[0077] The demand for flexibility in multi-energy power systems mainly comes from the uncertainty of renewable energy and the volatility of loads, which can be divided into the system's upward flexibility demand and downward flexibility demand.

[0078]

[0079] In the formula, is the up and down flexibility demand of the system in period t; q u ,q d are the flexibility coefficients for upward and downward adjustment caused by the uncertainty of photovoltaic power; e u 、e d are the flexible error coefficients for upward and downward adjustment caused by the randomness of EV charging; P pv,t+1 , P BE,t+1 , P ev,t+1 are the power dispatching values ​​of photovoltaic discharge, energy storage charging and discharging, and EV discharge in the time period t+1 respectively; P i,t is the predicted value of load power in time period t; ΔP i It is the difference between the load forecast power in time period t+1 and time period t.

[0080] Finally, the overall flexibility margin is evaluated. The difference between flexibility supply and flexibility demand is the power system power flexibility margin, which is divided into upward flexibility margin and downward flexibility margin.

[0081]

[0082] In the formula, They are the upward flexibility margin and downward flexibility margin of the system in period t respectively.

[0083] S3.2 Multiple energy complementary demand model

[0084] Starting from the relationship between power output and system load, and based on the complementary mechanism of multi-energy power systems, a mathematical model of power supply complementarity demand is introduced from the perspective of improving the adaptability and absorption capacity of renewable energy.

[0085]

[0086] In the formula, r i,t P is the power change rate of the analysis object i during period t, where the analysis object is the power supply or load; i,t is the power of the power supply or load during period t; P i,t-1 It is the power of the power source or load in the period t-1.

[0087] Calculation method of relative change rate of load power and relative change rate of power output power:

[0088]

[0089] In the formula, δ s,t , δ i,t are the relative change rates of the output power of the power supply and the load in period t; r s,t 、r i,t are the power change rates of power supply and load respectively; P SC is the total installed capacity of various power sources; P i,max,T It is the maximum value of the load during the analysis period T; T is the number of periods.

[0090] According to step S4, constructing a multi-objective optimization scheduling model for photovoltaic storage and charging includes setting up objective functions and constraints. The specific method is as follows:

[0091] S4.1 Objective function

[0092] In order to encourage the power supply to give full play to its diversified role and realize the economy, stability of system operation and the smoothness between power supply and load, a multi-objective function is constructed with the minimum total system operating cost, system network loss and voltage fluctuation, as well as the complementary demand indicators of power supply and load.

[0093] (1) Total system operating cost

[0094] minF1=C TH +C MT +C BE +C Pv (28)

[0095]

[0096] Where N G 、N R 、N M Respectively represent the number of thermal power units, gas-fired units, and photovoltaic systems, C TH , C MT , C BE , C PV They represent the coal burning cost of thermal power units, the gas cost of steam turbine units, the operating cost of energy storage systems, and the power abandonment loss of photovoltaic systems respectively; u th,g,t 、u mt,r,t are the operating state variables of thermal power unit g and gas power unit r in period t respectively; a g 、b g 、c g is the coal cost coefficient of thermal power unit g; a r 、b r 、c r is the gas cost coefficient of gas unit r; c BE is the cost coefficient of the energy storage system; P ch,BE,t , P di,BE,tare the charging and discharging power of the energy storage system in period t respectively; is the unit photovoltaic power abandonment loss cost; is the predicted output of photovoltaic power station m in period t; P Pv,m,t is the actual output of photovoltaic power station m during period t.

[0097] (2) System losses and voltage fluctuations

[0098]

[0099] Where L is the number of lines, R d is the resistance of the dth circuit; I d (t) is the current on the dth line at time t.

[0100] Because the connection of solar energy storage and charging to the distribution network will change the power flow distribution of the distribution network, the voltage fluctuation of the distribution network will be significantly enhanced. Therefore, the standard deviation of the voltage within a day is selected to define the voltage fluctuation of the distribution network, as follows:

[0101]

[0102] Where N nodes is the number of grid nodes, V j (t) is the node voltage of node j at time t; is the average node voltage within 24 hours.

[0103] (3) Complementary demand index of power source and load

[0104] The complementary demand index of power supply and load can be obtained by the relative change rate of load and power supply power. The complementary demand index of power supply and load D sl The smaller it is, the closer the changing trends of power supply and load on a unit time scale are, and the better the matching between the two is.

[0105]

[0106] In the formula, δ s,t , δ 1,t are the relative change rates of the output power of the power supply and the load in period t respectively

[0107] S4.2 Constraints

[0108] The constraints include: photovoltaic battery installed capacity constraints, EV charging station capacity constraints, BESS SOC constraints, BESS charging and discharging power constraints, node voltage constraints, node power balance, flexibility constraints and photovoltaic inverter reactive power regulation constraints; the specific constraints are as follows:

[0109] (1) Constraints on installed capacity of photovoltaic cells

[0110]

[0111] (2) EV charging station capacity constraints

[0112]

[0113] (3) SOC constraints of BESS

[0114]

[0115] In the formula and are the upper and lower limits of the state of charge of the BESS respectively.

[0116] (4) BESS charging and discharging power constraints

[0117]

[0118] In the formula, Respectively represent the rated charging and discharging power of BESS.

[0119] (5) Node voltage constraints

[0120]

[0121] In the formula, and They are the upper and lower limits of the node voltage of node k after connecting to the solar storage and charging, which are 1.05pu and 0.93pu respectively.

[0122] (6) Node power balance

[0123]

[0124] Where P k and Q k are the active power and reactive power injected into node k respectively; P loss and Q loss are the total active network loss and reactive network loss of the system respectively; P load,k and Q load,k are the active load and reactive load of node k respectively; P DG,k and Q DG,k are the active power and reactive power output by the solar energy storage connected at node k respectively.

[0125] (7) Flexible constraints

[0126]

[0127] (8) Reactive power regulation constraints of photovoltaic inverters

[0128] In actual projects, the capacity of photovoltaic inverters will leave margin to support the reactive power network. Since photovoltaic inverters have fast response speed, no action frequency limit, and no additional investment is required, reactive power regulation based on photovoltaic inverters is widely used. This paper mainly considers capacity constraints and power factor constraints.

[0129]

[0130] In the formula, S is the maximum active power allowed by the photovoltaic at node j in time period t; PV,t,j is the capacity of the PV inverter at node j in time period t; min It is the minimum power factor (leading or lagging) required for the photovoltaic inverter to be connected to the grid.

[0131] S5. According to S5., the method for NSGA-III to solve the optimization scheduling model is as follows:

[0132] S5.1 sets the hourly time-of-use electricity price, the active and reactive output of photovoltaic cells, and the charging and discharging power of BESS as decision variables;

[0133] S5.2 Set the initialization parameters such as upper and lower limits of decision variables, number of iterations, and population size;

[0134] S5.3 calculates the hourly charging demand through the demand-side time-of-use electricity price model and the electric vehicle charging load model;

[0135] S5.4 inputs the data collected by the cloud master station (PV output forecast, load forecast, power output data) into the model;

[0136] S5.5 initialize the population and calculate the fitness of each objective function according to equations (28)-(35);

[0137] S5.6 Update the best individual according to the NSGA-III algorithm;

[0138] S5.7 performs crossover operation on individuals and calculates the fitness of each objective function according to equations (28)-(35);

[0139] S5.8 Select and save the individuals after the crossover is completed;

[0140] S5.9 performs mutation operation on the individuals and calculates the fitness of each objective function according to equations (28)-(35);

[0141] S5.10 merge the best individuals after initialization, crossover and mutation to form a new population;

[0142] S5.11 sorts and selects through the NSGA-III algorithm, updates the Pareto optimal solution set, and stores the optimal individual;

[0143] S5.12 Repeat steps S5.7-S5.11 until the maximum number of iterations is reached;

[0144] S5.13 selects the optimal solution from the Pareto optimal solution set through IGTDM.

[0145] The present invention has the following advantages:

[0146] (1) Establish a demand-side response time-of-use electricity price model:

[0147] In view of the disorderly charging of electric vehicles, load volatility and uncertainty in the output of new energy, a reasonable time-of-use electricity price is formulated by establishing a time-of-use electricity price model and a quantity and price elasticity matrix model to guide the charging time of electric vehicles and the consumption of photovoltaic output, so as to smooth the net load fluctuations and reduce the peak-to-valley difference of load, thereby reducing the difficulty of optimizing the scheduling of photovoltaic storage and charging in the distribution network and obtaining the optimal net load curve of the distribution network.

[0148] (2) Build a mathematical model of light, storage and charging:

[0149] By establishing a photovoltaic power generation model, an energy storage charging and discharging model, and a charging station discharge model, the photovoltaic active and reactive output, storage and charging active output, and load demand in the distribution network are digitized and concretized. This makes it easier to set the light and storage output as decision variables, and the optimal scheduling plan can be directly obtained by solving the model through the algorithm, which improves the solution speed and the effectiveness of the optimization plan.

[0150] (3) Establish multiple energy flexibility supply and demand models and complementary demand models:

[0151] In view of the various problems brought to the distribution network by the uncertainty of load fluctuations and the increase in the types of power sources, it is necessary to analyze the flexibility and complementarity of the multi-energy power system. By establishing a multi-energy flexibility supply and demand model and combining it with the time-of-use electricity price model, the various available resources in the distribution network can be optimized and allocated to adapt to the random changes in power generation and load at a certain cost, thereby improving the economy and flexibility of the distribution network. In addition, the multi-energy complementary demand model can be combined with the energy storage system. The energy storage system has the dual nature of power supply and load, which can complement the uncertainty of photovoltaic power generation, improve the light absorption capacity, and improve the utilization rate of new energy.

[0152] (4) Establish a multi-objective optimization scheduling model for photovoltaic storage and charging:

[0153] In order to solve the problem of one-sidedness of single-objective optimization, this scheme proposes a multi-objective coordinated optimization scheduling model of light, storage and charging with the minimum total system operation cost, system network loss, voltage fluctuation and complementary demand indicators as the optimization objectives. By adjusting the active and reactive output of photovoltaic cells and the charging and discharging power of the energy storage system at different times and guiding the charging load of electric vehicles, the supply and demand relationship in the distribution network and the complementary relationship between multiple energy sources can be coordinated to achieve the economy, stability, flexibility and complementarity between multiple energy sources of the light-storage-charging distribution network.

[0154] (5) The non-dominated sorting genetic algorithm III (NSGA-III) based on the reference point is used to solve the optimization scheduling model, and the improved grey target decision making (IGTDM) based on the entropy weight method is used to obtain the best compromise solution:

[0155] In order to solve the problem that the traditional multi-objective algorithm has a slow solution speed and poor convergence, making its scheduling plan not timely, the NSGA-III adopted in this scheme has a faster solution speed and better convergence, making its scheduling plan more timely. In addition, by selecting the optimal compromise solution in the Pareto solution set through IGTDM, the scheduling plan can be more comprehensive and effective. The specific steps of IGTDM are as follows: 1) Establish a sample matrix based on the normalized Pareto solution set, the Euclidean distance of each non-dominated solution to the ideal point, and the Mahalanobis distance to the equilibrium point. 2) Standardize the sample matrix to obtain the decision matrix, and determine the bull's eye in the gray decision area formed by the decision matrix. 3) Calculate all distances to the bull's eye, and the one with the smallest distance is the best compromise solution.

[0156] The above describes the technical solution adopted by the present invention for the multi-objective optimization scheduling problem of photovoltaic storage and charging in regional distribution networks, establishes a regional distribution network optimization scheduling model under the background of large-scale access of electric vehicles, photovoltaic power generation, and energy storage systems to the distribution network, and comprehensively realizes the economic, stable and flexible operation of the distribution network by coordinating the supply and demand relationship between the power supply side and the load side. The NSGA-III algorithm and IGTDM are innovatively applied together in the optimization scheduling of the distribution network. The combination and innovation of these technical means are expected to solve the limitations of the existing technologies and provide a more comprehensive and efficient solution for the optimization scheduling of regional distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0157] The present invention will be further described below in conjunction with the accompanying drawings.

[0158] Figure 1 This is a model structure diagram of the photovoltaic storage and charging optimization scheduling method in the distribution network of the present invention.

[0159] Figure 2 This is a flow chart of solving the optimization model of the NSGA-III-IGTDM algorithm of the present invention.

[0160] Figure 3 This is the overall structure diagram of the NSGA-III-IGTDM-based photovoltaic storage and charging optimization scheduling method in the distribution network of the present invention. DETAILED DESCRIPTION

[0161] See attached Figure 1-3 The present invention discloses a multi-objective optimization scheduling method for photovoltaic storage and charging in distribution networks based on NSGA-III, which aims to solve the scheduling problem of photovoltaic storage and charging systems in distribution networks, with the goal of minimizing system operating costs and improving flexibility and power quality among multiple power sources. The core idea of ​​the invention is to comprehensively consider factors such as photovoltaic storage and charging systems and power demand in distribution networks, renewable energy generation, grid load balance, and demand-side response, and establish a multi-objective optimization model. The supply and demand relationship in the distribution network is coordinated by adjusting the active output of photovoltaic cells and the charging and discharging power of the energy storage system at different times and guiding the charging load of electric vehicles, so that the coordinated control terminal coordinates the distributed energy strategies in the region (local consumption, peak shaving and valley filling, disaster recovery and emergency response, etc.) to coordinate the source, network, load and storage in the region to achieve the preset goals. The model structure diagram of the photovoltaic storage and charging optimization scheduling method in the distribution network is shown in the figure. Figure 1 shown.

[0162] The method specifically includes the following steps:

[0163] S1. Establish a demand-side response time-of-use electricity price model;

[0164] S2. Build mathematical models for light, storage and charging respectively;

[0165] S3. Establish a variety of energy flexibility supply and demand models and complementary demand models;

[0166] S4. Establish a multi-objective optimization scheduling model for photovoltaic storage and charging;

[0167] S5. The NSGA-III algorithm is used to solve the optimization scheduling model, and the IGTDM is used to obtain the best compromise solution.

[0168] According to step S1., the method for establishing the distribution network demand side response optimization model is as follows:

[0169] S1.1 Time-of-use electricity price model

[0170] The distribution network needs to absorb the photovoltaic power output. When the photovoltaic power output cannot meet the user's electricity demand, the user needs to purchase electricity from the main grid. At this time, the manager can smooth the net load fluctuation and reduce the load peak-valley difference by formulating a reasonable time-of-use electricity price. After the implementation of the time-of-use electricity price, the electricity price of each period will fluctuate on the original basis, that is,

[0171] e p =e0(1+α),t∈T p (1)

[0172] e f =e0(1+β),t∈T f (2)

[0173] e v =e0(1+γ),t∈T v (3)

[0174] Where, e0 is the electricity price before the implementation of time-of-use electricity price; e p 、e f 、e v are the electricity prices in peak, flat and valley periods after the implementation of time-of-use electricity prices; α, β, γ are the fluctuations of electricity prices in each period; t represents the time; T p 、T f With T v Indicates peak, flat and valley time periods.

[0175] S1.2 Electricity quantity and price elasticity matrix model

[0176] The electricity price elasticity matrix reflects the relationship between electricity price changes and user electricity consumption changes, and is a multi-period response. User electricity consumption is not only related to the electricity price of the current period, but also to the electricity prices of other periods. The relationship between the change of user electricity consumption and the change of electricity price is shown in formula (4):

[0177]

[0178] Where P 0,s With ΔP s Indicates the power consumption and its change in time period s; e 0,s With Δe s is the electricity price and its change in period s; k sz is the price elasticity coefficient of demand; the subscripts s and z represent peak, flat and valley periods, s, z∈{p,f,v}.

[0179] In the time-of-use electricity price model, the load in a day is divided into three periods: peak, flat and valley. After the implementation of time-of-use electricity prices, the load changes in peak, flat and valley periods are as follows:

[0180]

[0181] P0(t)=P load (t)-P Pv (t) (7)

[0182] In the formula, P0 represents the amount of electricity purchased by the user from the main network, P 0,p , P0,f , P 0,v They represent the amount of electricity purchased from the main grid by users during the pre-peak, flat and valley periods of the time-of-use electricity price (in this article, the amount of electricity purchased from the main grid by users is represented by net load); e 0,p 、e 0,f 、e 0,v is the electricity price before the implementation of time-of-use electricity price, which is equal to e0; Δe p , Δe f , Δe v is the change in electricity price in each period; P load , P PV are load demand and photovoltaic output respectively; the diagonal elements in the electricity quantity and electricity price elasticity matrix K are self-elasticity coefficients, and the non-diagonal elements are mutual elasticity coefficients. This paper uses the data of the electricity quantity and electricity price elasticity matrix in the literature to reflect the response of the load to changes in electricity prices.

[0183] According to step S2, mathematical models of light, storage and charging are constructed respectively, as follows:

[0184] S2.1 Photovoltaic power generation output model

[0185] S2.1.1 Active output model

[0186] Solar photovoltaic cells use the photoelectric effect of semiconductor materials (such as monocrystalline silicon and polycrystalline silicon) under predetermined light intensity, and form an array by connecting single cells in series and parallel to convert solar energy into direct current and store it in the energy storage system. Then, according to the load demand, the direct current is converted into usable alternating current through the grid-connected inverter. Photovoltaic output power is mainly affected by the intensity and temperature of sunlight. Photovoltaic power generation has the advantages of no fuel consumption, no pollution, no regional restrictions, no influence of grid dispatch, flexibility and reliability, simple maintenance, safety and economy. Based on Kirchhoff's current law, the output current of photovoltaic cells can be described by a simple expression:

[0187]

[0188] In the formula, I ph Represents the current generated by the photovoltaic cell; I D represents the total diffusion current of the diode; V is the no-load potential; q and K represent the electron charge and the Boltzmann constant, respectively; T is the thermodynamic temperature; n represents the diode characteristic fitting coefficient; R s and R sh They represent series resistance and leakage resistance respectively.

[0189] The output power of photovoltaic cells is affected by the local solar radiation intensity and ambient temperature, and can be expressed by the following formula:

[0190]

[0191] Where P PV (t) represents the output of the photovoltaic cell at time t; P PVN represents the installed capacity of photovoltaic cells under standard conditions; t e 、T e and T ref Respectively represent the ambient temperature coefficient, actual temperature coefficient and standard ambient temperature (usually 25°C); G T and G ref Represent the solar irradiance and the standard reference irradiance respectively.

[0192] S2.1.2 Reactive output model

[0193] In addition to converting the DC output of photovoltaic panels into AC active power, photovoltaic inverters still have additional capacity to provide / absorb reactive power to the system (without affecting active power output). Compared with controlling distributed energy storage active power, tap equipment, etc., photovoltaic inverter reactive power control is an effective voltage control method, and this solution has the best control economy.

[0194] The relationship between the inverter's adjustable reactive capacity and the inverter capacity is:

[0195]

[0196] In the formula, Q PV is the inverter output reactive power, S PV is the rated capacity of the inverter, P Pv is the active power of photovoltaic power generation.

[0197] There are two main types of inverter reactive power control strategies: 1) Power factor This method uses the photovoltaic grid-connected active power P as the control input and adjusts the inverter reactive power to achieve the grid-connected power factor of the photovoltaic inverter. Control: 2) = Q(U) control. This method uses the photovoltaic grid-connected point voltage U as the control input to achieve photovoltaic inverter reactive power Q control.

[0198] S2.2 Energy storage charging and discharging model

[0199] The battery energy storage system (BESS) can act as a load or as a power source, depending on whether the BESS absorbs or releases electrical energy from the grid. Its charging and discharging model satisfies equation (11).

[0200]

[0201] In the formula, E BESS is the capacity of BESS; P ch,BEWith P di,BE is the charging and discharging power of BESS; η ch With η di is the charge and discharge efficiency of BESS. The state of charge (SOC) is a measure of the remaining power of BESS during the charge and discharge cycle, and is calculated as follows:

[0202]

[0203] In the formula, SOC BE is the SOC of BESS; E BE,rated is the rated capacity of BESS.

[0204] S2.3 Electric vehicle charging load model (charging station discharge model)

[0205] The daily mileage m of an electric vehicle (EV) follows a log-normal distribution, and its probability density function is:

[0206]

[0207] Where μ1 and σ1 are the mean and standard deviation of lnm respectively.

[0208] Initial state of charge of EV when entering the highway It obeys uniform distribution, and its probability density function is:

[0209]

[0210] In the formula, a and b are upper and lower limits.

[0211] The state of charge of an EV when it arrives at a charging station is:

[0212]

[0213] In the formula, E h is the battery capacity of EV; E b E is the power consumption of EV per 100 kilometers. Compared with traditional fuel vehicles, the mileage of EV is greatly affected by the ambient temperature. b The values ​​are different.

[0214] Arrival EV starts charging time T s It obeys normal distribution, and its probability density function is:

[0215]

[0216] In the formula, μ c , σ c Respectively represent T sThe mean and standard deviation of .

[0217] According to S EV The EV charging time can be calculated as:

[0218]

[0219] Where P che is the rated charging power of the charging pile; η EV For EV charging efficiency.

[0220] Charging end time:

[0221] T k =T c +T s (18)

[0222] From this, the charging power requirement for each EV is:

[0223]

[0224] After multiple calculations, the average value is the EV charging load power on the highway:

[0225]

[0226] Where M c is the number of calculations; N c is the total number of EVs.

[0227] According to step S3, a multiple energy flexibility supply and demand model and a complementary demand model are established as follows: S3.1 Multiple energy flexibility supply and demand model

[0228] The sum of the adjustable output of all power generation units participating in the grid connection in each time period is the flexibility that the system can provide in that time period, which is called power flexibility supply, and can be spatially divided into upward flexibility supply and downward flexibility supply.

[0229]

[0230]

[0231] In the formula, and are the upward and downward flexibility supply of the system in period t, respectively; are the upward and downward adjustment flexibility of thermal power unit g, gas power unit r, and hydropower unit h in period t respectively; P th,g,t , P mt,r,t , P hy,h,t are the outputs of thermal power unit g, gas power unit r, and hydropower unit h in period t respectively; Pth,g,max , P mt,r,max , P hy,h,max , P th,g,min , P mt,r,min , P hy,h,min They are the upper and lower limits of the output of thermal power unit g, gas power unit r, and hydropower unit h respectively; are the upslope or downslope of thermal power unit g, gas power unit r, and hydropower unit h respectively; N G 、N R 、N H They are the number of thermal power units, gas power units and hydropower units respectively.

[0232] The demand for flexibility in multi-energy power systems mainly comes from the uncertainty of renewable energy and the volatility of loads, which can be divided into the system's upward flexibility demand and downward flexibility demand.

[0233]

[0234] In the formula, is the up and down flexibility demand of the system in period t; q u ,q d are the flexibility coefficients for upward and downward adjustment caused by the uncertainty of photovoltaic power; e u 、e d are the flexible error coefficients for upward and downward adjustment caused by the randomness of EV charging; P pv,t+1 , P BE,t+1 , P ev,t+1 are the power dispatching values ​​of photovoltaic discharge, energy storage charging and discharging, and EV discharge in the time period t+1 respectively; P i,t is the predicted value of load power in time period t; ΔP i It is the difference between the load forecast power in time period t+1 and time period t.

[0235] Finally, the overall flexibility margin is evaluated. The difference between flexibility supply and flexibility demand is the power system power flexibility margin, which is divided into upward flexibility margin and downward flexibility margin.

[0236]

[0237] In the formula, They are the upward flexibility margin and downward flexibility margin of the system in period t respectively.

[0238] S3.2 Multiple energy complementary demand model

[0239] Starting from the relationship between power output and system load, and based on the complementary mechanism of multi-energy power systems, a mathematical model of power supply complementarity demand is introduced from the perspective of improving the adaptability and absorption capacity of renewable energy.

[0240]

[0241] In the formula, r i,t P is the power change rate of the analysis object i during period t, where the analysis object is the power supply or load; i,t is the power of the power supply or load during period t; P i,t-1 It is the power of the power source or load in the period t-1.

[0242] Calculation method of relative change rate of load power and relative change rate of power output power:

[0243]

[0244] In the formula, δ s,t , δ i,t are the relative change rates of the output power of the power supply and the load in period t; r s,t 、r i,t are the power change rates of power supply and load respectively; P SC is the total installed capacity of various power sources; P i,max,T It is the maximum value of the load during the analysis period T; T is the number of periods.

[0245] According to step S4), the method for constructing a multi-objective optimization scheduling model for photovoltaic storage and charging is as follows:

[0246] S4.1 Objective function

[0247] In order to encourage the power supply to give full play to its diversified role and realize the economy, stability of system operation and the smoothness between power supply and load, a multi-objective function is constructed with the minimum total system operating cost, system network loss and voltage fluctuation, as well as the complementary demand indicators of power supply and load.

[0248] (1) Total system operating cost

[0249] minF1=C TH +C MT +C BE +C PV (28)

[0250]

[0251] Where N G 、N R 、N M Respectively represent the number of thermal power units, gas-fired units, and photovoltaic systems, C TH , C MT , C BE , C Pv They represent the coal burning cost of thermal power units, the gas cost of steam turbine units, the operating cost of energy storage systems, and the power abandonment loss of photovoltaic systems respectively; u th,g,t 、umt,r,t are the operating state variables of thermal power unit g and gas power unit r in period t respectively; a g 、b g 、c g is the coal cost coefficient of thermal power unit g; a r 、b r 、c r is the gas cost coefficient of gas unit r; c BE is the cost coefficient of the energy storage system; P ch,BE,t , P di,BE,t are the charging and discharging power of the energy storage system in period t respectively; is the unit photovoltaic power abandonment loss cost; is the predicted output of photovoltaic power station m in period t; P PV,m,t is the actual output of photovoltaic power station m during period t.

[0252] (2) System losses and voltage fluctuations

[0253]

[0254] Where L is the number of lines, R d is the resistance of the dth circuit; I d (t) is the current on the dth line at time t.

[0255] Because the connection of solar energy storage and charging to the distribution network will change the power flow distribution of the distribution network, the voltage fluctuation of the distribution network will be significantly enhanced. Therefore, the standard deviation of the voltage within a day is selected to define the voltage fluctuation of the distribution network, as follows:

[0256]

[0257] Where N nodes is the number of grid nodes, V j (t) is the node voltage of node j at time t; is the average node voltage within 24 hours.

[0258] (3) Complementary demand index of power source and load

[0259] The complementary demand index of power supply and load can be obtained by the relative change rate of load and power supply power. The complementary demand index of power supply and load D sl The smaller it is, the closer the changing trends of power supply and load on a unit time scale are, and the better the matching between the two is.

[0260]

[0261] In the formula, δ s,t , δ 1,t are the relative rates of change of output power of the power supply and load during period t respectively.

[0262] S4.2 Constraints

[0263] The constraints include: photovoltaic battery installed capacity constraints, EV charging station capacity constraints, BESS SOC constraints, BESS charging and discharging power constraints, node voltage constraints, node power balance, flexibility constraints and photovoltaic inverter reactive power regulation constraints; the specific constraints are as follows:

[0264] (1) Constraints on installed capacity of photovoltaic cells

[0265]

[0266] (2) EV charging station capacity constraints

[0267]

[0268] (3) SOC constraints of BESS

[0269]

[0270] In the formula and are the upper and lower limits of the state of charge of the BESS respectively.

[0271] (4) BESS charging and discharging power constraints

[0272]

[0273] In the formula, Respectively represent the rated charging and discharging power of BESS.

[0274] (5) Node voltage constraints

[0275]

[0276] In the formula, and They are the upper and lower limits of the node voltage of node k after connecting to the solar storage and charging, which are 1.05pu and 0.93pu respectively.

[0277] (6) Node power balance

[0278]

[0279] Where P k and Q k are the active power and reactive power injected into node k respectively; P loss and Q loss are the total active network loss and reactive network loss of the system respectively; P load,k and Q load,kare the active load and reactive load of node k respectively; P DG,k and Q DG,k are the active power and reactive power output by the solar energy storage connected at node k respectively.

[0280] (7) Flexible constraints

[0281]

[0282] (8) Reactive power regulation constraints of photovoltaic inverters

[0283] In actual projects, the capacity of photovoltaic inverters will leave margin to support the reactive power network. Since photovoltaic inverters have fast response speed, no action frequency limit, and no additional investment is required, reactive power regulation based on photovoltaic inverters is widely used. This paper mainly considers capacity constraints and power factor constraints.

[0284]

[0285] In the formula, S is the maximum active power allowed by the photovoltaic at node j in time period t; PV,t,j is the capacity of the PV inverter at node j in time period t; min It is the minimum power factor (leading or lagging) required for the photovoltaic inverter to be connected to the grid.

[0286] The method of solving the optimization scheduling model by S5 and NSGA-III is as follows:

[0287] S5.1 sets the hourly time-of-use electricity price, the active and reactive output of photovoltaic cells, and the charging and discharging power of BESS as decision variables;

[0288] S5.2 Set the initialization parameters such as upper and lower limits of decision variables, number of iterations, and population size;

[0289] S5.3 calculates the hourly charging demand through the demand-side time-of-use electricity price model and the electric vehicle charging load model;

[0290] S5.4 inputs the data collected by the cloud master station (PV output forecast, load forecast, power output data) into the model;

[0291] S5.5 initialize the population and calculate the fitness of each objective function according to equations (28)-(35);

[0292] S5.6 Update the best individual according to the NSGA-III algorithm;

[0293] S5.7 performs crossover operation on individuals and calculates the fitness of each objective function according to equations (28)-(35);

[0294] S5.8 Select and save the individuals after the crossover is completed;

[0295] S5.9 performs mutation operation on the individuals and calculates the fitness of each objective function according to equations (28)-(35);

[0296] S5.10 merge the best individuals after initialization, crossover and mutation to form a new population;

[0297] S5.11 sorts and selects through the NSGA-III algorithm, updates the Pareto optimal solution set, and stores the optimal individual;

[0298] S5.12 Repeat steps S5.7-S5.11 until the maximum number of iterations is reached;

[0299] S5.13 selects the optimal solution from the Pareto optimal solution set through IGTDM.

[0300] like Figure 2 As shown, the flow chart of the model solution using NSGA-III-IGTDM;

[0301] like Figure 3 As shown, the overall structure diagram of a photovoltaic storage and charging optimization scheduling method in a distribution network based on NSGA-III-IGTDM.

[0302] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

Claims

1. A regional distribution network optimization dispatching method based on a non-dominated genetic algorithm with reference points, characterized in that: The steps include: Constructing an optimal dispatching model, wherein the model takes minimizing the total system operating cost, minimizing the system network loss, minimizing the voltage fluctuation, and minimizing the complementary demand index of the power supply and the load as multi-objective functions; The construction optimization scheduling model includes the following steps S1-S4: S1. Establish a demand-side response time-of-use electricity price model; S2. Build photovoltaic power generation output model, energy storage charging and discharging model and electric vehicle charging load model respectively; S3. Establish a variety of energy flexibility supply and demand models and complementary demand models; S4. Establish a multi-objective optimization scheduling model for photovoltaic storage and charging; S5. The optimization scheduling model is solved by using the NSGA-III algorithm, and the best compromise solution is obtained by using lGTDM; The optimization dispatch model includes a distribution network demand side response optimization model, including establishing a time-of-use electricity price model and an electricity quantity and price elasticity matrix model respectively; In the TOU electricity price model, the electricity price in each period fluctuates based on the electricity price before TOU electricity price is implemented, that is, by p =e0(1+α),t∈T p (1) by f ?e0(1+β),t∈T f (2) e v =e0(1+γ), t∈T v (3) Where, e0 is the electricity price before the implementation of time-of-use electricity price; e p 、e f 、e v are the electricity prices in peak, flat and valley periods after the implementation of time-of-use electricity prices; α, β, γ are the fluctuations of electricity prices in each period; t represents the time; T p , T f With T v Indicates peak, flat and valley time periods; In the electricity price elasticity matrix, the relationship between user electricity consumption and electricity price is: Where P 0，s With ΔP s Indicates the power consumption and its change in time period s; e 0，s With Δe s is the electricity price and its change in period s; k sz is the demand price elasticity coefficient; the subscripts s and represent any peak, flat or valley period; In the time-of-use electricity price model, the load in a day is divided into three periods: peak, flat and valley. After the implementation of time-of-use electricity prices, the loads in peak, flat and valley periods are: P0(t)=P load (t)-P PV (t) (7) Where P0 represents the amount of electricity purchased by the user from the main network; P 0，p , P 0，f , P 0，v They represent the amount of electricity purchased from the main grid by users during the pre-peak, flat and valley periods of the time-of-use electricity price respectively; e 0，p 、e 0,f 、e 0，v is the electricity price before the implementation of time-of-use electricity price, which is equal to e0; Δe p , Δe f , Δe v is the change in electricity price in each period; P load , P PV are load demand and photovoltaic output respectively; the diagonal elements in the electricity quantity and price elasticity matrix K are self-elasticity coefficients, and the non-diagonal elements are mutual elasticity coefficients; According to step S3, the optimization model includes: establishing a multiple energy flexibility supply and demand model and a multiple energy complementarity demand model; In the energy flexibility supply and demand model, power supply flexibility supply is divided into upward flexibility supply and downward flexibility supply; In the formula, and are the upward and downward flexibility supply of the system in period t, respectively; are the upward and downward adjustment flexibility of thermal power unit g, gas power unit r, and hydropower unit h in period t respectively; P th,g,t , P mt,r,t , P hy,h,t are the outputs of thermal power unit g, gas power unit r, and hydropower unit h in period t respectively; P th,g,max , P mt,r,max , P hy,h,max , P th,g,min , P mt,r,min , P hy,h,min They are the upper and lower limits of the output of thermal power unit g, gas power unit r, and hydropower unit h respectively; are the upslope or downslope of thermal power unit g, gas power unit r, and hydropower unit h respectively; N G 、N R 、N H are the number of thermal power units, gas power units, and hydropower units respectively; The flexibility of multi-energy power systems comes from the uncertainty of renewable energy and the volatility of loads, and is divided into upward flexibility demand and downward flexibility demand of the system; In the formula, is the up and down flexibility demand of the system in period t; q u ,q d are the flexibility coefficients for upward and downward adjustment caused by the uncertainty of photovoltaic power; e u 、e d are the flexible error coefficients for upward and downward adjustment caused by the randomness of EV charging; P pv，t+1 , P BE，t+1 , P ev，t+1 are the power dispatching values ​​of photovoltaic discharge, energy storage charging and discharging, and EV discharge in the time period t+1 respectively; P i，t is the predicted value of load power in time period t; ΔP i It is the difference between the load forecast power in time period t+1 and time period t. The difference between flexibility supply and flexibility demand is the power system power supply flexibility margin, which is divided into upward flexibility margin and downward flexibility margin; In the formula, are the upward flexibility margin and downward flexibility margin of the system in period t respectively; In the complementary demand model of multiple energy sources, starting from the relationship between power output and system load, and based on the complementary mechanism of multi-energy power systems, a mathematical model of complementary demand for power sources is introduced from the perspective of improving the adaptability and absorption capacity of renewable energy. In the formula, r i，t P is the power change rate of the analysis object i during period t, where the analysis object is the power supply or load; i，t is the power of the power supply or load during period t; P i,t-1 is the power of the power source or load during period t-1; Calculation method of relative change rate of load power and relative change rate of power output power: In the formula, δ s，t , δ i，t are the relative change rates of the output power of the power source and the load in period t; r s，t 、r i，t are the power change rates of power supply and load respectively; P SC is the total installed capacity of various power sources; P i,max，T It is the maximum value of the load during the analysis period T; T is the number of periods.

2. The regional distribution network optimization dispatching method based on the reference point non-dominated genetic algorithm according to claim 1 is characterized by: According to step S4, constructing a multi-objective optimization scheduling model for photovoltaic storage and charging includes setting up objective functions and constraints.

3. The method for optimizing and dispatching regional distribution networks based on a non-dominated genetic algorithm based on reference points according to claim 2 is characterized in that: Construct a multi-objective function that minimizes the total system operating cost, system network loss and voltage fluctuation, and the complementary demand indicators of power supply and load; (1) Total system operating cost: minF1=C TH +C MT +C BE +C PV (28) Where N G 、N R 、N M Respectively represent the number of thermal power units, gas turbine units, and photovoltaic systems, C TH , C MT , C BE , C PV They represent the coal burning cost of thermal power units, the gas cost of steam turbine units, the operating cost of energy storage systems, and the power abandonment loss of photovoltaic systems respectively; u th,g,t 、u mt,r,t are the operating state variables of thermal power unit g and gas power unit r in period t respectively; a g 、b g 、c g is the coal cost coefficient of thermal power unit g; a r 、b r 、c r is the gas cost coefficient of gas unit r; c BE is the cost coefficient of the energy storage system; P ch,BE,t , P di,BE,t are the charging and discharging power of the energy storage system in period t respectively; is the unit photovoltaic power abandonment loss cost; is the predicted output of photovoltaic power station m in period t; P PV，m，t is the actual output of photovoltaic power station m during period t; (2) System loss and voltage fluctuation: In the formula, L represents the number of lines, R d is the resistance of the dth circuit; I d (t) is the current on the dth line at time t; Because the connection of solar energy storage and charging to the distribution network will change the power flow distribution of the distribution network, which will significantly increase the voltage fluctuation of the distribution network, the standard deviation of the voltage within a day is selected to define the voltage fluctuation of the distribution network, as follows: Where N nodes Represents the number of grid nodes, V j (t) is the node voltage of node j at time t; is the average node voltage within 24 hours; (3) Complementary demand index of power source and load The complementary demand index of power supply and load can be obtained through the relative change rate of load and power supply power, and the complementary demand index can be minimized: In the formula, δ s，t , δ 1，t are the relative change rates of the output power of the power source and the load in period t respectively; 4. The method for optimizing and dispatching regional distribution networks based on a non-dominated genetic algorithm of reference points according to claim 1 is characterized in that: The method of NSGA-III to solve the optimization scheduling model is as follows: S5.1 sets the hourly time-of-use electricity price, the active and reactive output of photovoltaic cells, and the charging and discharging power of BESS as decision variables; S5.2 Set the initialization parameters such as upper and lower limits of decision variables, number of iterations, and population size; S5.3 calculates the hourly charging demand through the demand-side time-of-use electricity price model and the electric vehicle charging load model; S5.4 inputs the data collected by the cloud master station (PV output forecast, load forecast, power output data) into the model; S5.5 initialize the population and calculate the fitness of each objective function according to equations (28)-(35); S5.6 Update the best individual according to the NSGA-III algorithm; S5.7 performs crossover operation on individuals and calculates the fitness of each objective function according to equations (28)-(35); S5.8 Select and save the individuals after the crossover is completed; S5.9 performs mutation operation on the individuals and calculates the fitness of each objective function according to equations (28)-(35); S5.10 merge the best individuals after initialization, crossover and mutation to form a new population; S5.11 sorts and selects through the NSGA-III algorithm, updates the Pareto optimal solution set, and stores the optimal individual; S5.12 Repeat steps S5.7-S5.11 until the maximum number of iterations is reached; S5.13 selects the optimal solution from the Pareto optimal solution set through IGTDM.

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