Site selection and optimal configuration method for microgrid comprising electric vehicle charging station
An electric vehicle, optimized configuration technology, applied in the direction of instruments, data processing applications, forecasting, etc., can solve the impact of micro-grid economy, safety and stability, incomplete consideration of charging station site selection, frequent micro-grid load fluctuations, etc. question
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Embodiment 1
[0107] This embodiment provides a method for selecting the location of a microgrid with electric vehicle charging stations, defining the charging service radius R s , as shown in formula (1):
[0108]
[0109]
[0110] In order to ensure the optimal location scheme, in the case of building a certain number of micro-grid systems including electric vehicle charging stations in a certain municipally planned load concentration area, the charging system should be optimized with the maximum sum of service radii of all systems. Station location planning, as shown in formula (3):
[0111]
[0112] In the formula, M is the number of planned micro-grid systems including electric vehicle charging stations.
[0113] The specific calculation example sets up 10 micro-grid systems with electric vehicle charging stations, and the load concentration area of the planned site selection is a square area with a length and a width of 100 units. After performing 1000 times of random sit...
Embodiment 2
[0116] On the basis of Embodiment 1, this embodiment provides the prediction of demand for disorderly charging of electric vehicles:
[0117] First, analyze the charging behavior of electric vehicles, and set the charging start time t 0 is a normal distribution function, Charging start time t 0 The probability density function of :
[0118]
[0119] In formula (25), μ t =17.6,σ t = 3.4;
[0120] The daily mileage s obeys the logarithmic normal distribution, and its probability density function is shown in the following formula (26):
[0121]
[0122] In formula (26), μ s =0.88, σ s = 3.2; Due to the constraints of the battery capacity and state of charge of the electric vehicle, the maximum daily mileage s max is the mileage corresponding to the battery capacity falling from the upper limit to the lower limit.
[0123] Then, predict the disordered charging load of charging electric vehicles; the charging demand prediction of each electric vehicle is an independ...
Embodiment 3
[0129] This embodiment provides a method for optimal configuration of a microgrid system including electric vehicles, taking into account the economic cost objective function f 1 and load fluctuation objective function f 2 ;Economic cost objective function f 1 :
[0130] C=C i +C om +C cs +C ex +C charge +C loss (4);
[0131] In the formula, C is the total planning cost, and C i For the construction cost of four types of distributed power generation, wind power, photovoltaic, diesel engine and energy storage system, C om is the running cost, C cs is the construction cost of the charging station, C ex is the energy exchange cost between the microgrid and the grid, C charge Charging costs for EV users, C loss is the lost load cost, the unit is yuan;
[0132] load fluctuation target f 2 function:
[0133]
[0134] In the formula, P load-fluctuation is the load fluctuation, is the basic load of the microgrid at time t; is the charging load of M electric ve...
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