Distributed multi-energy complementary system design and operation hybrid optimization method considering energy storage characteristics of electric vehicle

Abstract

The invention belongs to the field of distributed multi-energy complementary system planning and operation optimization, and particularly discloses a distributed multi-energy complementary system design and operation hybrid optimization method considering electric vehicle energy storage. According to the building load clustering method and device, the DBSCAN density clustering algorithm, the fuzzy C-means algorithm, the fast elitist multi-target genetic algorithm (NSGA-II), the mixed integer linear programming algorithm (MILP) and the entropy weight method are combined, building load clustering is achieved based on the DBSCAN density clustering algorithm and the fuzzy C-means algorithm, and the complexity of the optimization calculation process is reduced. By combining NSGA-II and MILP, integrated optimization of capacity planning and an operation scheme of the distributed multi-energy complementary system considering energy storage of the electric vehicle is realized; and based on an entropy weight method, screening an optimal solution in a Pareto frontier solution set of capacity planning, and obtaining an optimal operation scheme of the system under the planning. The method provided by the invention fills the blank of the planning and operation optimization technology of the distributed multi-energy complementary system considering the energy storage of the electric vehicle.

Description

Technical Field

[0001] The present application belongs to the field of distributed multi-energy complementary system planning and operation optimization, and more specifically, to a distributed multi-energy complementary system design and operation hybrid optimization method considering electric vehicle energy storage. Background Art

[0002] Distributed multi-energy complementary systems have become an important low-carbon energy supply path due to their advantages of freely coupling multiple energy sources, improving energy conversion efficiency, reducing transmission and distribution losses, and reducing carbon emissions. At the same time, in order to promote the low-carbon transformation of the transportation industry, the number of electric vehicles continues to grow. Electric vehicles can participate in power dispatch as mobile energy storage devices through charging stations, and transfer energy with the grid (Vehicle to Grid, V2G), buildings (Vehicle to Building, V2B), and users (Vehicle to Home, V2H). Large-scale electric vehicles will be connected to the distributed multi-energy complementary system, bringing huge energy storage resources to the distributed multi-energy complementary system, but the impact of these energy storage resources on the distributed multi-energy complementary system is not clear, resulting in its inability to be fully utilized.

[0003] At present, a large number of studies have proposed a variety of optimization methods based on energy efficiency, economy, environmental protection and other objectives for the separate planning and operation of distributed multi-energy complementary systems and electric vehicles. The fast elite multi-objective genetic algorithm (NSGA-II) is one of the most popular multi-objective optimization algorithms, with the advantages of fast running speed and good solution set convergence. The NSGA-II algorithm continuously updates the decision variables through the elite retention strategy, and judges the quality of the decision variables by the performance of different objective functions under the decision variables. Therefore, this algorithm is suitable for multi-objective optimization problems with fewer decision variables. The mixed integer linear programming (MILP) algorithm can set continuous variables, discrete variables and 0-1 variables, and there is no limit on the number of decision variables; however, this algorithm is suitable for solving single-objective optimization problems and cannot consider multiple objectives at the same time. Therefore, the NSGA-II algorithm cannot complete the coordinated optimization of the design and operation of distributed multi-energy complementary systems alone, and the MILP algorithm cannot perform multi-objective optimization of distributed multi-energy complementary systems. In addition, with the expansion of the scale of electric vehicles, some scholars are committed to exploring the impact of electric vehicle operation on the power system, but most of the research focuses on the impact of electric vehicle charging load on the distributed generation and distribution sides, and a few studies focus on electric vehicles as mobile energy storage devices to participate in power dispatching. Therefore, there is a gap in the design and operation collaborative optimization methods of distributed multi-energy coupling systems considering electric vehicle energy storage. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a hybrid optimization method for the design and operation of a distributed multi-energy complementary system considering the energy storage of electric vehicles, aiming to solve the problem of technical gaps in the planning and operation optimization of a distributed multi-energy complementary system considering the energy storage of electric vehicles.

[0005] To achieve the above objectives, the present application provides a hybrid optimization method for designing and operating a distributed multi-energy complementary system considering electric vehicle energy storage, and the specific steps include:

[0006] Step 1: Cluster building loads based on DBSCAN density clustering algorithm combined with fuzzy C-means algorithm;

[0007] Step 2: Based on the fast elite multi-objective genetic algorithm (NSGA-II) and the mixed integer linear programming algorithm (MILP), an integrated optimization model of capacity planning and operation scheme of the distributed multi-energy complementary system considering electric vehicle energy storage is constructed;

[0008] Step 3: Based on the entropy weight method, the optimal solution in the Pareto front solution set of capacity planning is obtained, and the optimal operation plan of the system under the plan is obtained.

[0009] Each step of the above process is as follows:

[0010] The step 1 comprises the following specific steps:

[0011] Step 1: Divide the acquired building load into 4 parts according to spring, summer, autumn and winter;

[0012] Step 2: Use DBSCAN density clustering algorithm to denoise the building load of each part, in which the k-distance method is used to determine the parameters required by the algorithm (domain radius and minimum number of included points);

[0013] Step 3: Use the fuzzy C-means algorithm to cluster the denoised building load data into 2 categories to represent the typical building load of each part; the data of the 4 parts are clustered into 8 categories to constitute the reduced annual building load.

[0014] The step 2 comprises the following specific steps:

[0015] Step 1: Randomly generate model decision variable values ​​as the initial population;

[0016] Step 2: Calculate the adaptability function of the initial population, that is, the problem to be optimized, which includes the MILP model;

[0017] Step 3: Non-dominated sorting and congestion calculation;

[0018] Step 4: Population selection, crossover, and mutation;

[0019] Step 5: Generate the first generation population;

[0020] Step 6: Repeatedly calculate population fitness, non-dominated sorting and crowding, population selection, crossover, and mutation until the population algebra meets the maximum algebra and obtains the Pareto front solution set.

[0021] In the first step, the model decision variables are the planned capacities of the equipment and electric vehicle charging stations in the distributed multi-energy coupling system.

[0022] In the second step, the problem to be optimized is to minimize the total system cost and system carbon emissions. Therefore, the total system cost and system carbon emissions are the optimization targets. The total system cost includes system investment cost, maintenance cost and operating cost, among which the operating cost is the sum of the operating costs of 8 typical days obtained by clustering in step one.

[0023] TDC=CAPEX+OPEX

[0024] Where: TDC is the total system cost, RMB; CAPEX is the system investment and maintenance cost, RMB / year; OPEX is the total operating cost of 8 typical days, RMB.

[0025]

[0026] Where: k represents the six types of equipment in the distributed multi-energy complementary system, including photovoltaic power generation equipment (PV), gas turbine (GT), absorption heat pump (AHP), supplementary combustion boiler (AB) and electric vehicle charging station (EVCS); CAP is the equipment capacity, kW, where the electric vehicle charging station is the number of charging piles; is the initial investment cost per unit equipment capacity, RMB / unit capacity. is the annual maintenance cost per unit equipment capacity, RMB / year.

[0027]

[0028] Where: h represents the number of operating hours, and the time range is 1 to 192; They are respectively the hourly fuel cost, the cost of purchasing electricity from the public power grid and the income from selling electricity to the power grid during the operation of the distributed multi-energy complementary system, RMB.

[0029]

[0030] Where: Q GT,h , Q AB,h are the natural gas consumption per hour of the gas turbine and supplementary-fired boiler, kWh; P fuel is the unit natural gas cost, RMB / kWh.

[0031]

[0032] Where: The amount of electricity purchased by the system hourly, kWh; It is the hourly electricity price, Yuan / kWh.

[0033]

[0034] Where: is the electricity sold by the system to the grid hourly, kWh; P Sell-GRID The revenue per unit of electricity sold to the grid, RMB / kWh.

[0035] Regarding the environmental friendliness of the system, this study considered the carbon emissions generated during the system operation phase:

[0036]

[0037] Where: CO2 is the carbon emission generated during the operation of the system, kgCO2; ef fuel is the carbon emission factor of natural gas, kgCO2 / kWh; ef power is the carbon emission factor of grid electricity, kgCO2 / kWh.

[0038] In order to solve the above objective function, the operation of the distributed multi-energy complementary system needs to meet the following constraints: 1. Energy balance

[0039] The supply and demand of energy must be consistent. In the distributed multi-energy complementary system, the power supply side includes gas turbine power generation, photovoltaic panel power generation, battery discharge, electric vehicle discharge, and grid power supply; the power demand side includes building electricity, electric vehicle charging, battery charging, and electricity sales to the grid.

[0040]

[0041] Where: The power required by the building at hour h; The power required by the electric vehicle charging station at hour h; is the charge capacity of the battery at hour h; is the amount of electricity sold by the system to the grid at hour h; E solar,h is the power generation of the photovoltaic panel at hour h; E GT,h is the power generation of the gas turbine at hour h; is the discharge amount of the battery at the hth hour; is the discharge amount of the electric vehicle station at the hth hour; is the amount of electricity purchased by the system from the grid in the hth hour.

[0042]

[0043] Where: H is the heat load of the building at hour h; AHP,h is the heat supply of the absorption heat pump in the h hour.

[0044]

[0045] Where: is the cooling load of the building at hour h; R AHP,h is the cooling capacity of the absorption heat pump at the hth hour.

[0046] The unit of the above variables is kWh.

[0047] 2. Power supply equipment

[0048] (1) Photovoltaic panels (PV)

[0049] The capacity of photovoltaic panels is limited by the area where photovoltaic panels can be laid; the photovoltaic power generation is related to the capacity of photovoltaic panels and solar irradiance. The power generation calculation formula is:

[0050] E solar,h =CAP solar ×SRI h / SRI ref

[0051] Where: CAP solar is the photovoltaic panel capacity, kW; SRI h is the actual solar irradiance in hour h, W / m 2 ;SRI ref is the rated solar irradiance, which is 1000W / m 2 .

[0052] (2) Gas turbine (GT)

[0053]

[0054] Where: The efficiency of natural gas in gas turbines for power generation.

[0055]

[0056] Where: H GT,h is the waste heat generated by the gas turbine at hour h, kWh; is the combustion efficiency of natural gas in a gas turbine.

[0057]

[0058] Where: is the waste heat of the gas turbine used for heating at hour h, kWh; It is the waste heat of the gas turbine used for cooling at the hth hour, kWh.

[0059] E GT,h ≤CAP GT

[0060] 3. Cooling / heating equipment

[0061] (1) Supplementary combustion boiler (AB)

[0062]

[0063] Where: H AB,h is the heat output of the supplementary-fired boiler at hour h, kWh; is the thermal efficiency of the supplementary-fired boiler.

[0064] H AB,h ≤CAP AB

[0065]

[0066] Where: is the heat used for heating by the supplementary-fired boiler at hour h, kWh; It is the heat used by the supplementary-fired boiler for cooling in hour h, kWh.

[0067] (2) Absorption heat pump (AHP)

[0068]

[0069]

[0070] H AHP,h ≤CAP AHP

[0071] R AHP,h ≤CAP AHP ×ratioRtoH AHP

[0072] Where: η HE is the heat exchanger efficiency, which is taken as 85%; is the heating efficiency of the absorption heat pump, which is taken as 1.8; is the cooling efficiency of the absorption heat pump, which is taken as 1; ratioRtoH AHP is the conversion ratio between cooling and heating capacity of absorption heat pump, which is taken as 0.9.

[0073] 4. Battery (Batt)

[0074]

[0075]

[0076]

[0077]

[0078] Where: is the storage capacity of the battery at the hth hour, kWh; CAP batt is the capacity of the battery, kWh; η cha_batt is the charging efficiency of the battery, which is taken as 95%; η disc_batt is the discharge efficiency of the battery, which is taken as 95%. 5. Electric Vehicles and Charging Stations (EVCS)

[0079] The operation process of electric vehicles and electric vehicle charging stations is relatively complex due to the involvement of multiple decentralized actors. In order to improve the calculation efficiency of the model and facilitate case simulation, the following assumptions are made:

[0080] (1) The electric vehicle charging station in this distributed multi-energy complementary system is responsible for 100 electric vehicles in the area, and the power battery capacity of each electric vehicle is the same, which is 85kWh.

[0081] (2) Each electric vehicle will not leave the charging station between the time it arrives at and the time it leaves the charging station, and will only enter the charging station once a day.

[0082] (3) The target power level of each electric vehicle when leaving the charging station every day is fully charged; the charging efficiency and discharging efficiency of each electric vehicle are the same.

[0083] (4) Each electric vehicle can only be charged or discharged at the same time, and cannot be charged and discharged at the same time. The maximum charging and discharging power of each electric vehicle is 25% of the power battery capacity.

[0084] The travel behavior of each electric vehicle is different and random. Therefore, multiple random variables are usually used to describe the behavior of electric vehicles, mainly considering the three random variables of the time when the electric vehicle arrives at the charging station every day, the time when the electric vehicle leaves the charging station every day, and the daily mileage of the electric vehicle.

[0085] The time when electric vehicles arrive at the charging station and leave the charging station follow NormalPDF. The time when the pth electric vehicle arrives at the charging station and leaves the charging station can be expressed as:

[0086]

[0087]

[0088] Where: p is the pth electric car; T arr is the time it takes for electric vehicles to arrive at the charging station every day, h; μTarr is the average time for electric vehicles to arrive at the charging station every day, h, here it is 18:00; is the variance of the time it takes for electric vehicles to arrive at the charging station every day; T start is the time that electric vehicles leave the charging station every day, h; μT start is the average time that electric vehicles leave the charging station every day, h, here it is 9 o'clock; is the daily variance of the time when electric vehicles leave the charging station, h.

[0089] The daily mileage of an electric vehicle can be described as Normal PDF.

[0090]

[0091] Where: p is the pth electric car; l mile is the daily mileage of electric vehicles, km; μl mile , σ lmile are the mean and variance of daily electric vehicle mileage, respectively.

[0092] Based on the above random variables, the mathematical models of the operation process of electric vehicles and electric vehicle charging stations are as follows:

[0093]

[0094]

[0095] Where: is the power storage of the pth electric vehicle at the hth hour, kWh; η cha_EV is the charging efficiency of electric vehicles, which is 0.9; is the charging capacity of the pth electric vehicle at the hth hour, kWh; is the discharge of the pth electric vehicle at the hth hour, kWh; η disc_EV is the discharge efficiency of the electric vehicle, which is taken as 0.9; EV_C is the capacity of the electric vehicle, which is taken as 85kWh.

[0096]

[0097] In the formula: d is the number of days, and the time range is 8 days; is the initial power storage of the p-th electric vehicle on the d-th day, that is, the power storage before entering the charging station, kWh; is the mileage of the p-th electric car on the d-th day, km; E consumption_ev It is the power consumed by electric vehicles per unit mileage, kWh / km.

[0098]

[0099] Where: is the time when the pth electric car arrives at the charging station on the dth day; is the time when the pth electric car leaves the charging station on the dth day.

[0100] N_EV_OPER h ≤EVCS_C

[0101] Where: N_EV_OPER h is the number of electric vehicles charged or discharged at the hth hour, vehicles; EVCS_C represents the capacity of the electric vehicle charging station, that is, the number of electric vehicle charging piles.

[0102] All variables involved in the system operation process are solved by the MILP algorithm.

[0103] Compared with the prior art, the above technical solution proposed in this application has the following beneficial effects:

[0104] This method can consider the impact of electric vehicles as mobile energy storage participating in the energy scheduling of distributed multi-energy coupling systems on system design and operation, and at the same time provide a theoretical basis for the coordinated optimization of the design and operation of distributed multi-energy complementary systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 This application provides a flow chart of a hybrid optimization method for designing and operating a distributed multi-energy complementary system taking into account electric vehicle energy storage.

[0106] Figure 2 This is a flow chart of an integrated optimization model for capacity planning and operation scheme of a distributed multi-energy complementary system considering electric vehicle energy storage, as provided in this application.

[0107] Figure 3 It is a distributed multi-energy complementary system provided in the embodiment of the present application.

[0108] Figure 4 This is the case building clustering front load provided in the embodiment of the present application.

[0109] Figure 5 This is the case building clustering post-load provided in the embodiment of the present application.

[0110] Figure 6 It is the Pareto front solution set for system optimization under different scenarios provided by the embodiments of the present application.

[0111] Figure 7 This is the optimal solution for the system operation scheme on typical days in spring and summer provided in the embodiments of the present application.

[0112] Figure 8This is the optimal solution for the system operation plan for typical days in autumn and winter provided in the embodiments of the present application. DETAILED DESCRIPTION

[0113] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0114] Example 1

[0115] like Figure 1 As shown, a hybrid optimization method for designing and operating a distributed multi-energy complementary system considering electric vehicle energy storage is provided, wherein the method comprises the following steps:

[0116] Step 1: Cluster building loads based on DBSCAN density clustering algorithm combined with fuzzy C-means algorithm;

[0117] Step 2: Based on the fast elite multi-objective genetic algorithm (NSGA-II) and the mixed integer linear programming algorithm (MILP), an integrated optimization model of capacity planning and operation scheme of the distributed multi-energy complementary system considering electric vehicle energy storage is constructed;

[0118] Step 3: Based on the entropy weight method, the optimal solution in the Pareto front solution set of capacity planning is obtained, and the optimal operation plan of the system under the plan is obtained.

[0119] The present invention is a hybrid optimization method for designing and operating a distributed multi-energy complementary system considering electric vehicle energy storage. In use, the present invention first establishes a distributed multi-energy coupling system including an electric vehicle charging station, which is composed of a gas turbine (GT), a photovoltaic panel (PV), an auxiliary combustion boiler (AB), an absorption heat pump (AHP), a battery (Batt), and an electric vehicle charging station (EVCS). Figure 3 As shown. The system is connected to the grid, and can purchase electricity from the grid or sell electricity to the grid, realizing free two-way electricity interaction. Photovoltaic panels produce electricity by receiving sunlight; gas turbines produce electricity by burning natural gas, and are important energy supply equipment for distributed multi-energy complementary systems. Absorption heat pumps use heat sources to achieve heating in winter and cooling in summer; the waste heat of gas turbines is the main heat source of absorption heat pumps. When the waste heat of gas turbines cannot meet the demand, supplementary boilers provide supplementary heat sources by burning natural gas. The battery stores excess electricity in the system to achieve "peak shaving and valley filling". Electric vehicle charging stations not only meet the charging needs of electric vehicles, but also dispatch the idle electricity of electric vehicles based on V2G technology to reduce the power supply pressure of distributed multi-energy complementary systems.

[0120] Secondly, residential areas are one of the gathering areas of electric vehicles, and there is a demand for the construction of electric vehicle charging stations. Therefore, the present invention takes a residential area in Chongqing as the energy supply object of the distributed multi-energy complementary system. The load of the building in one year is as follows: Figure 4 shown.

[0121] Example 2

[0122] Based on the distributed multi-energy coupling system including electric vehicle charging stations and system energy supply objects introduced in Example 1, this embodiment introduces a hybrid optimization method for the design and operation of a distributed multi-energy complementary system considering electric vehicle energy storage, wherein the method comprises the following contents: first, based on the DBSCAN density clustering algorithm combined with the fuzzy C-means algorithm, the building load within a year is clustered into 8 typical days; secondly, based on the fast elite multi-objective genetic algorithm (NSGA-II) and the mixed integer linear programming algorithm (MILP), an integrated optimization model of capacity planning and operation scheme of the distributed multi-energy complementary system considering electric vehicle energy storage is constructed; finally, based on the entropy weight method, the optimal solution in the Pareto front solution set of capacity planning is obtained, and at the same time, the optimal operation scheme of the system under the plan is obtained.

[0123] First, the building load in a year is clustered into 8 typical days based on the DBSCAN density clustering algorithm combined with the fuzzy C-means algorithm. The data in a year is divided into 4 parts according to spring, summer, autumn and winter, and the data in each part is clustered separately. The clustering method is: the k-distance method is used to determine the parameters required for the DBSCAN density clustering algorithm: neighborhood radius and minimum number of included points; then, the DBSCAN density clustering algorithm is used to filter out the noise data; finally, the fuzzy C-means algorithm is used to cluster the denoised load data into 2 categories. The building load data after clustering using the above method is as follows Figure 5 shown.

[0124] Secondly, based on the fast elite multi-objective genetic algorithm (NSGA-II) and the mixed integer linear programming algorithm (MILP), an integrated optimization model for capacity planning and operation scheme of distributed multi-energy complementary systems considering electric vehicle energy storage is constructed. The input of the model includes hourly solar irradiance, hourly heating and cooling loads of buildings, equipment investment and operation parameters, and electric vehicle behavior parameters. The output includes the optimal capacity solution set of equipment in the system and the equipment operating status corresponding to different solutions. The objective function of the model is to minimize the total system cost and system carbon emissions. The total system cost includes system investment cost, maintenance cost and operation cost, among which the operation cost is the sum of the operation costs of 8 typical days obtained by clustering in step one.

[0125] TDC=CAPEX+OPEX

[0126] Where: TDC is the total system cost, RMB; CAPEX is the system investment and maintenance cost, RMB / year; OPEX is the total operating cost of 8 typical days, RMB.

[0127]

[0128] Where: k represents the six types of equipment in the distributed multi-energy complementary system, including photovoltaic power generation equipment (PV), gas turbine (GT), absorption heat pump (AHP), supplementary combustion boiler (AB) and electric vehicle charging station (EVCS); CAP is the equipment capacity, kW, where the electric vehicle charging station is the number of charging piles; is the initial investment cost per unit equipment capacity, RMB / unit capacity. is the annual maintenance cost per unit equipment capacity, RMB / year.

[0129]

[0130] Where: h represents the number of operating hours, and the time range is 1 to 192; They are respectively the hourly fuel cost, the cost of purchasing electricity from the public power grid and the income from selling electricity to the power grid during the operation of the distributed multi-energy complementary system, RMB.

[0131]

[0132] Where: Q GT,h , Q AB,h are the natural gas consumption per hour of the gas turbine and supplementary-fired boiler, kWh; P fuel is the unit natural gas cost, RMB / kWh.

[0133]

[0134] Where: The amount of electricity purchased by the system hourly, kWh; It is the hourly electricity price, Yuan / kWh.

[0135]

[0136] Where: is the electricity sold by the system to the grid hourly, kWh; P Sell-GRID The revenue per unit of electricity sold to the grid, RMB / kWh.

[0137] Regarding the environmental friendliness of the system, this study considered the carbon emissions generated during the system operation phase:

[0138]

[0139] Where: CO2 is the carbon emission generated during the operation of the system, kgCO2; ef fuelis the carbon emission factor of natural gas, kgCO2 / kWh; ef power is the carbon emission factor of grid electricity, kgCO2 / kWh.

[0140] In order to solve the above objective function, the operation of the distributed multi-energy complementary system needs to meet the following constraints: 1. Energy balance

[0141] The supply and demand of energy must be consistent. In the distributed multi-energy complementary system, the power supply side includes gas turbine power generation, photovoltaic panel power generation, battery discharge, electric vehicle discharge, and grid power supply; the power demand side includes building electricity, electric vehicle charging, battery charging, and electricity sales to the grid.

[0142]

[0143] Where: The power required by the building at hour h; The power required by the electric vehicle charging station at hour h; is the charge capacity of the battery at hour h; is the amount of electricity sold by the system to the grid at hour h; E solar,h is the power generation of the photovoltaic panel at hour h; E GT,h is the power generation of the gas turbine at hour h; is the discharge amount of the battery at the hth hour; is the discharge amount of the electric vehicle station at the hth hour; is the amount of electricity purchased by the system from the grid in the hth hour.

[0144]

[0145] Where: H is the heat load of the building at hour h; AHP,h is the heat supply of the absorption heat pump in the h hour.

[0146]

[0147] Where: is the cooling load of the building at hour h; R AHP,h is the cooling capacity of the absorption heat pump at the hth hour.

[0148] The unit of the above variables is kWh.

[0149] 2. Power supply equipment

[0150] (1) Photovoltaic panels (PV)

[0151] The capacity of photovoltaic panels is limited by the area where photovoltaic panels can be laid; the photovoltaic power generation is related to the capacity of photovoltaic panels and solar irradiance. The power generation calculation formula is:

[0152] E solar,h =CAP solar ×SRI h / SRI ref

[0153] Where: CAP solar is the photovoltaic panel capacity, kW; SRI h is the actual solar irradiance in hour h, W / m 2 ;SRI ref is the rated solar irradiance, which is 1000W / m 2 .

[0154] (2) Gas turbine (GT)

[0155] During operation, the equipment climbing rate is 30% and the minimum load rate is 20%.

[0156]

[0157] Where: The power generation efficiency of natural gas in gas turbines is taken as 30%.

[0158]

[0159] Where: H GT,h is the waste heat generated by the gas turbine at hour h, kWh; The combustion efficiency of natural gas in the gas turbine is taken as 90%.

[0160]

[0161] Where: is the waste heat of the gas turbine used for heating at hour h, kWh; It is the waste heat of the gas turbine used for cooling at the hth hour, kWh.

[0162] E GT,h ≤CAP GT

[0163] 3. Cooling / heating equipment

[0164] (1) Supplementary combustion boiler (AB)

[0165] During operation, the climbing rate of the supplementary burning boiler is 100% and the minimum load rate is 10%.

[0166]

[0167] Where: H AB,h is the heat output of the supplementary-fired boiler at hour h, kWh; The thermal efficiency of the supplementary-fired boiler is taken as 90%.

[0168] H AB,h ≤CAP AB

[0169]

[0170] Where: is the heat used for heating by the supplementary-fired boiler at hour h, kWh; It is the heat used by the supplementary-fired boiler for cooling in hour h, kWh.

[0171] (2) Absorption heat pump (AHP)

[0172] The ramp rate of the absorption heat pump is 50%.

[0173]

[0174]

[0175] H AHP,h ≤CAP AHP

[0176] R AHP,h ≤CAP AHP ×ratioRtoH AHP

[0177]

[0178] Where: η HE is the heat exchanger efficiency, which is taken as 85%; is the heating efficiency of the absorption heat pump, which is taken as 1.8; is the cooling efficiency of the absorption heat pump, which is taken as 1; ratioRtoH AHP is the conversion ratio between cooling and heating capacity of absorption heat pump, which is taken as 0.9.

[0179] 4. Battery (Batt)

[0180] The maximum charge and discharge power is set to 25% of the battery capacity.

[0181]

[0182]

[0183]

[0184]

[0185] Where: is the storage capacity of the battery at the hth hour, kWh; CAP batt is the capacity of the battery, kWh; η cha_batt is the charging efficiency of the battery, which is taken as 95%; η disc_batt is the discharge efficiency of the battery, which is taken as 95%. 5. Electric Vehicles and Charging Stations (EVCS)

[0186] The operation process of electric vehicles and electric vehicle charging stations is relatively complex due to the involvement of multiple decentralized actors. In order to improve the calculation efficiency of the model and facilitate case simulation, the following assumptions are made:

[0187] (1) The electric vehicle charging station in this distributed multi-energy complementary system is responsible for 100 electric vehicles in the area, and the power battery capacity of each electric vehicle is the same, which is 85kWh.

[0188] (2) Each electric vehicle will not leave the charging station between the time it arrives at and the time it leaves the charging station, and will only enter the charging station once a day.

[0189] (3) The target power level of each electric vehicle when leaving the charging station every day is fully charged; the charging efficiency and discharging efficiency of each electric vehicle are the same.

[0190] (4) Each electric vehicle can only be charged or discharged at the same time, and cannot be charged and discharged at the same time. The maximum charging and discharging power of each electric vehicle is 25% of the power battery capacity.

[0191] The travel behavior of each electric vehicle is different and random. Therefore, multiple random variables are usually used to describe the behavior of electric vehicles, mainly considering the three random variables of the time when the electric vehicle arrives at the charging station every day, the time when the electric vehicle leaves the charging station every day, and the daily mileage of the electric vehicle.

[0192] The time when electric vehicles arrive at the charging station and leave the charging station follow the Normal PDF. The time when the pth electric vehicle arrives at the charging station and leaves the charging station can be expressed as:

[0193]

[0194]

[0195] Where: p is the pth electric car; T arr is the time it takes for electric vehicles to arrive at the charging station every day, h; μT arr is the average time for electric vehicles to arrive at the charging station every day, h, here it is 18:00; is the variance of the time it takes for electric vehicles to arrive at the charging station every day; T start is the time that electric vehicles leave the charging station every day, h; μT startis the average time that electric vehicles leave the charging station every day, h, here it is 9 o'clock; is the daily variance of the time when electric vehicles leave the charging station, h.

[0196] The daily mileage of an electric vehicle can be described as Normal PDF.

[0197]

[0198] Where: p is the pth electric car; l mile is the daily mileage of electric vehicles, km; μl mile , are the mean and variance of daily electric vehicle mileage, which are 51.6 and 6.6 respectively.

[0199] Based on the above random variables, the mathematical models of the operation process of electric vehicles and electric vehicle charging stations are as follows:

[0200]

[0201]

[0202] Where: is the power storage of the pth electric vehicle at the hth hour, kWh; η cha_EV is the charging efficiency of electric vehicles, which is 0.9; is the charging capacity of the pth electric vehicle at the hth hour, kWh; is the discharge of the pth electric vehicle at the hth hour, kWh; η disc_EV is the discharge efficiency of the electric vehicle, which is taken as 0.9; EV_C is the capacity of the electric vehicle, which is taken as 85kWh.

[0203]

[0204] In the formula: d is the number of days, and the time range is 8 days; is the initial power storage of the p-th electric vehicle on the d-th day, that is, the power storage before entering the charging station, kWh; is the mileage of the p-th electric car on the d-th day, km; E consumption_ev It is the power consumed by electric vehicles per unit mileage, kWh / km.

[0205]

[0206] Where: is the time when the pth electric car arrives at the charging station on the dth day; is the time when the pth electric car leaves the charging station on the dth day.

[0207] N_EV_OPER h ≤EVCS_C

[0208] Where: N_EV_OPER h is the number of electric vehicles charged or discharged at the hth hour, vehicles; EVCS_C represents the capacity of the electric vehicle charging station, that is, the number of electric vehicle charging piles.

[0209] Based on the above objective function and constraints, the NSGA-II algorithm of the Python toolbox and the Gurobi optimization analysis software are used to optimize the design and operation of the distributed multi-energy complementary system considering the charging characteristics of electric vehicles. The Pareto frontier solution set obtained by optimization is as follows: Figure 6 shown.

[0210] Finally, based on the entropy weight method, the optimal solution in the Pareto front solution set of system capacity planning is obtained, and the optimal operation plan of the system under the plan is obtained. The steps of the entropy weight method are data standardization, data normalization processing, calculation of information entropy of each indicator, calculation of information utility value, calculation of weight coefficient of indicators, and calculation of comprehensive scores of each sample. The sample with the largest comprehensive score is the optimal solution. By using the entropy weight method, the optimal solution in the above Pareto front solution set is screened out, as shown in Table 1. The system operation plan corresponding to the optimal solution is as follows Figure 7 and Figure 8 shown.

[0211] Table 1 Optimal solution for distributed multi-energy coupling system planning

[0212]

Claims

1. A hybrid optimization method for designing and operating a distributed multi-energy complementary system considering electric vehicle energy storage, characterized in that: include: Step 1: Cluster building loads based on DBSCAN density clustering algorithm combined with fuzzy C-means algorithm; Step 2: Based on the fast elite multi-objective genetic algorithm (NSGA-II) and the mixed integer linear programming algorithm (MILP), an integrated optimization model of capacity planning and operation scheme of the distributed multi-energy complementary system considering electric vehicle energy storage is constructed; Step 3: Based on the entropy weight method, the optimal solution in the Pareto front solution set of capacity planning is obtained, and the optimal operation plan of the system under the plan is obtained.

2. The optimization method according to claim 1, characterized in that: The method for clustering building loads based on the DBSCAN density clustering algorithm combined with the fuzzy C-means algorithm is: Step 1: Divide the acquired building load into 4 parts according to spring, summer, autumn and winter; Step 2: Use DBSCAN density clustering algorithm to denoise the building load of each part, in which the k-distance method is used to determine the parameters required by the algorithm (domain radius and minimum number of included points); Step 3: Use the fuzzy C-means algorithm to cluster the denoised building load data into two categories to represent the typical building load of each part.

3. The optimization method according to claim 1, characterized in that: The construction method of the integrated optimization model of capacity planning and operation scheme of the distributed multi-energy complementary system considering electric vehicle energy storage is as follows: Step 1: Randomly generate model decision variable values ​​as the initial population; Step 2: Calculate the adaptability function of the initial population, that is, the problem to be optimized, in which the optimization of the system operation phase is realized by the MILP model; Step 3: Non-dominated sorting and congestion calculation; Step 4: Population selection, crossover, and mutation; Step 5: Generate the first generation population; Step 6: Repeatedly calculate population fitness, non-dominated sorting and crowding, population selection, crossover, and mutation until the population algebra meets the maximum algebra and obtains the Pareto front solution set.

4. The optimization method according to claim 3, characterized in that: The problem to be optimized in the model is to minimize the total system cost and system carbon emissions. The total system cost includes system investment cost, maintenance cost and operation cost, among which the operation cost is the sum of the operation costs of 8 typical days obtained by clustering in step 1. TDC=CAPEX+OPEX Where: TDC is the total system cost, RMB; CAPEX is the system investment and maintenance cost, RMB / year; OPEX is the total operating cost of 8 typical days, RMB. Where: k represents the six types of equipment in the distributed multi-energy complementary system, including photovoltaic power generation equipment (PV), gas turbine (GT), absorption heat pump (AHP), supplementary combustion boiler (AB) and electric vehicle charging station (EVCS); CAP is the equipment capacity, kW, where the electric vehicle charging station is the number of charging piles; is the initial investment cost per unit equipment capacity, RMB / unit capacity. is the annual maintenance cost per unit equipment capacity, RMB / year. Where: h represents the number of operating hours, and the time range is 1 to 192; They are respectively the hourly fuel cost, the cost of purchasing electricity from the public power grid and the income from selling electricity to the power grid during the operation of the distributed multi-energy complementary system, RMB. Where: Q GT,h , Q AB,h are the natural gas consumption per hour of the gas turbine and supplementary-fired boiler, kWh; P fuel is the unit natural gas cost, RMB / kWh. Where: The amount of electricity purchased by the system hourly, kWh; It is the hourly electricity price, Yuan / kWh. Where: is the electricity sold by the system to the grid hourly, kWh; P Sell-GRID The revenue per unit of electricity sold to the grid, RMB / kWh. Regarding the environmental friendliness of the system, this study considered the carbon emissions generated during the system operation phase: Where: CO2 is the carbon emission generated during the system operation, kgCO2; ef fuel is the carbon emission factor of natural gas, kgCO2 / kWh; ef power is the carbon emission factor of grid electricity, kgCO2 / kWh.

5. The optimization method according to claim 3, characterized in that: The system operation phase optimization is achieved by the MILP model: In order to solve the objective function, the operation of the distributed multi-energy complementary system needs to meet the following constraints: (1) Energy balance The supply and demand of energy must be consistent. In the distributed multi-energy complementary system, the power supply side includes gas turbine power generation, photovoltaic panel power generation, battery discharge, electric vehicle discharge, and grid power supply; the power demand side includes building electricity, electric vehicle charging, battery charging, and electricity sales to the grid. Where: The power required by the building at hour h; The power required by the electric vehicle charging station at hour h; is the charge capacity of the battery at hour h; is the amount of electricity sold by the system to the grid at hour h; E solar,h is the power generation of the photovoltaic panel at hour h; E GT,h is the power generation of the gas turbine at hour h; is the discharge amount of the battery at the hth hour; is the discharge amount of the electric vehicle station at the hth hour; is the amount of electricity purchased by the system from the grid in the hth hour. Where: H is the heat load of the building at hour h; AHP,h is the heat supply of the absorption heat pump in the h hour. Where: is the cooling load of the building at hour h; R AHP,h is the cooling capacity of the absorption heat pump at the hth hour. The unit of the above variables is kWh. (2) Power supply equipment Photovoltaic panels (PV): The capacity of PV panels is limited by the area where the panels can be laid; the amount of PV power generated is related to the capacity of the panels and the solar irradiance. The formula for calculating power generation is: AND solar,h =ZIP solar ×SRI h / SRI ref Where: CAP solar is the photovoltaic panel capacity, kW; SRI h is the actual solar irradiance in hour h, W / m 2 ;SRI ref is the rated solar irradiance, which is 1000W / m 2 . Gas Turbine (GT): Where: The efficiency of natural gas in gas turbines for power generation. Where: H GT,h is the waste heat generated by the gas turbine at hour h, kWh; is the combustion efficiency of natural gas in a gas turbine. Where: is the waste heat of the gas turbine used for heating at hour h, kWh; It is the waste heat of the gas turbine used for cooling at the hth hour, kWh. AND GT,h ≤CAP GT (3) Cooling / heating equipment Supplementary-fired boiler (AB): Where: H AB,h is the heat output of the supplementary-fired boiler at hour h, kWh; is the thermal efficiency of the supplementary-fired boiler. H AB,h ≤CAP AB Where: is the heat used for heating by the supplementary-fired boiler at hour h, kWh; It is the heat used by the supplementary-fired boiler for cooling in hour h, kWh. Absorption Heat Pump (AHP): H AHP,h ≤CAP AHP R AHP,h ≤CAP AHP ×ratioRtoH AHP Where: η HE is the heat exchanger efficiency, which is taken as 85%; is the heating efficiency of the absorption heat pump, which is taken as 1.8; is the cooling efficiency of the absorption heat pump, which is taken as 1; ratioRtoH AHP is the conversion ratio between cooling and heating capacity of absorption heat pump, which is taken as 0.

9. (4) Battery Where: is the storage capacity of the battery at the hth hour, kWh; CAP batt is the capacity of the battery, kWh; η cha_batt is the charging efficiency of the battery, which is taken as 95%; η disc_batt is the discharge efficiency of the battery, which is taken as 95%. (5) Electric Vehicles and Charging Stations (EVCS) The operation process of electric vehicles and electric vehicle charging stations is relatively complex due to the involvement of multiple decentralized actors. In order to improve the calculation efficiency of the model and facilitate case simulation, the following assumptions are made: The electric vehicle charging station in this distributed multi-energy complementary system is responsible for 100 electric vehicles in the area, and the power battery capacity of each electric vehicle is the same, which is 85kWh. Each electric vehicle will not leave the charging station between the time it arrives and the time it leaves the charging station, and will only enter the charging station once a day. The target power level for each electric vehicle when it leaves the charging station every day is fully charged; the charging efficiency and discharging efficiency of each electric vehicle are the same. Each electric vehicle can only be charged or discharged at the same time, and cannot be charged and discharged at the same time. The maximum charging and discharging power of each electric vehicle is 25% of the power battery capacity. The travel behavior of each electric vehicle is different and random. Therefore, multiple random variables are usually used to describe the behavior of electric vehicles, mainly considering the three random variables: the time when the electric vehicle arrives at the charging station every day, the time when the electric vehicle leaves the charging station every day, and the daily mileage of the electric vehicle. The time when electric vehicles arrive at the charging station and leave the charging station follow the Normal PDF. The time when the pth electric vehicle arrives at the charging station and leaves the charging station can be expressed as: Where: p is the pth electric car; T arr is the time it takes for electric vehicles to arrive at the charging station every day, h; μT arr is the average time for electric vehicles to arrive at the charging station every day, h, here it is 18:00; is the variance of the time it takes for electric vehicles to arrive at the charging station every day; T start is the time that electric vehicles leave the charging station every day, h; μT start is the average time that electric vehicles leave the charging station every day, h, here it is 9 o'clock; is the daily variance of the time when electric vehicles leave the charging station, h. The daily mileage of an electric vehicle can be described as Normal PDF. Where: p is the pth electric car; l mile is the daily mileage of electric vehicles, km; μl mile , are the mean and variance of daily electric vehicle mileage, respectively. Based on the above random variables, the mathematical models of the operation process of electric vehicles and electric vehicle charging stations are as follows: Where: is the power storage of the pth electric vehicle at the hth hour, kWh; η cha_EV is the charging efficiency of electric vehicles, which is 0.9; is the charging capacity of the pth electric vehicle at the hth hour, kWh; is the discharge amount of the pth electric vehicle at the hth hour, kWh; η disc_EV is the discharge efficiency of the electric vehicle, which is taken as 0.9; EV_C is the capacity of the electric vehicle, which is taken as 85kWh. In the formula: d is the number of days, and the time range is 8 days; is the initial power storage of the p-th electric vehicle on the d-th day, that is, the power storage before entering the charging station, kWh; is the mileage of the p-th electric car on the d-th day, km; E consumption_ev It is the power consumed by electric vehicles per unit mileage, kWh / km. Where: is the time when the pth electric car arrives at the charging station on the dth day; is the time when the pth electric car leaves the charging station on the dth day. N_EV_OPER h ≤EVCS_C Where: N_EV_OPER h is the number of electric vehicles charged or discharged at the hth hour, vehicles; EVCS_C represents the capacity of the electric vehicle charging station, that is, the number of electric vehicle charging piles.

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