Optimal Allocation Model of Virtual Power Plant Capacity considering Electric Vehicles

Abstract

To push forward the development of electric vehicles while improving the economy and environment of virtual power plants (VPPs), research on the optimization of VPP capacity considering electric vehicles is carried out. In this paper, based on this, this paper first analyzes the framework of the VPP with electric vehicles and models each unit of the VPP. Secondly, the typical scenarios of wind power, photovoltaic, electric vehicle charging and discharging, and load are formed by the Monte Carlo method to reduce the output deviation of each unit. Then, taking the maximization of the net income and clean energy consumption of the VPP as the objective function, the capacity optimal allocation model of the VPP considering multiobjective is constructed, and the conditional value-at-risk (CVaR) is introduced to represent the investment uncertainty faced by the VPP. Finally, a VPP in a certain area of Shanxi Province is used to analyze a calculation example and solve it with CPLEX. The results of the calculation example show that, on the one hand, reasonable selection of the optimal scale of EV connected to the VPP is able to improve the economy and environment of the VPP. On the other hand, the introduction of CVaR is available for the improvement of the scientific nature of VPP capacity allocation decisions.

Application of the Complex Moments for Selection of an Optimal Sensor

“…The chosen interval in (4) for the set of the moments covers practically all positive values of the real moments and, therefore, can be considered as an optimal one. Usually, the limiting values of r in (4) are taken from the interval (10)(11)(12)(13)(14)(15) and this selected interval is sufficient for evaluation of all possible correlations that are given by the expression (2). We should notice also that (2) being located in the interval [0, 1] has "universal" behavior.…”

“…It is necessary to understand exactly how the received signal is close to the real one, that is, it is always necessary to evaluate the final result containing a certain value of the hidden error. Now, a large number of solutions have been proposed that form the fundamental basis for selection of some efficient methods for processing the measured data [1][2][3][4][5][6][7][8][9][10]. In cases, when the analyzed data source is easily fitted to well-known analytical dependencies that follow from probability theory or from some proposed model, the calibration sensor problems do not arise usually.…”

“…In analysis of various types of data, a potential researcher makes some assumption about the correspondence (even remote and unjustified) of the studied dataset to a certain distribution law. Hypotheses are tested using various known criteria [1][2][3][4][5]. For example, the F-distribution is the ratio of two independent quantities, scaled relative to the original data.…”

“…There is a good trend here-a specific transformation of the original dataset to the new key set. In the case, when it is necessary to set the scatter of some parameters (analysis of the variance), the Chi-square method [2,3] is used. In the case, when initial sampling is small, the Student's t-test can be used to estimate the average of the selected values [1][2][3][4][5][6][7].…”

“…In the case, when it is necessary to set the scatter of some parameters (analysis of the variance), the Chi-square method [2,3] is used. In the case, when initial sampling is small, the Student's t-test can be used to estimate the average of the selected values [1][2][3][4][5][6][7]. These methods work well in the theory of probability.…”