Optimal scheduling for unit commitment with electric vehicles and uncertainty of renewable energy sources

The unit commitment (UC) problem is a typical mixed integer nonlinear programming problem related to power systems. As electric vehicles (EVs) and renewable energy sources (RESs; e.g., wind and solar power) have become integrated into the power system, the UC problem has become increasingly complex. With the continuous growth of the proportion of renewable energy based on wind and solar energy in the power supply structure on the supply side in the future, the random characteristics of the supply and demand sides of the power system will become more obvious, which will inevitably bring about the safety, stability and economic operation of the power system. The study of UC not only has great academic value, but also has important practical significance. The optimal scheduling of UC, including thermal, wind, solar, and EV power units was investigated. The objective function of optimal scheduling was to minimize the operating cost of the unit and optimize the charging and discharging costs of EVs. In view of power generation characteristics of RESs, an interval number was introduced to describe the uncertainties in the outputs of wind and solar power generation. The enhanced-interval linear programming is introduced to deal with the UC problem including EVs and uncertainty of RES. For better accuracy, wind power was considered for each of the four seasons (spring, summer, autumn, and winter) and solar power was divided into sunny, cloudy, and rainy conditions. To reflect the influence of EV charging and discharging on UC, three different charging and discharging pricing schemes were considered. Various scenarios were simulated using the CPLEX solver. The results showed that costs can be affected by season and climate. The interval number solution sets of the total cost and of the charging and unit costs were obtained by using interval number theory to describe the uncertainty of output in RESs; this provides a new approach for solving the UC optimization problem by integrating RESs. The charging and discharging of EVs can also affect costs. By encouraging the orderly charging and discharging of EVs, power load fluctuations could be reduced and peak-load shifting could be achieved. Different pricing schemes had different impacts on cost. The pricing scheme can not only effectively reduce the charging cost borne by EV owners, but could also make EVs profitable. This discovery provides a theoretical basis for guidance on the charging and discharging of EVs through charge and discharge price schemes. The simulation results reflect the relationship between economic cost and seasons/climate and confirm the suitability of using an interval number to describe uncertainty in energy generation.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).