Evaluating the risk of uncertainty in smart grids with electric vehicles using an evolutionary swarm-intelligent algorithm

In the last years, distributed clean energy resources such as solar irradiation, wind generation and electric vehicles as dispatchable units have been targeted as alternatives to fossil fuels in supplying the high energy demand of our society. Managing these distributed energy resources is a complex challenge, due to its uncertain behavior. In order to maintain a stable and profitable functioning of electrical systems, the development of strategies and algorithms to deal with the risk associated with this uncertainty deserves proper attention. This work addresses a risk-based energy resource management (ERM) optimization problem that deals with the day-ahead management, considering the occurrence of extreme events in a risk-averse strategy. In a risk -averse strategy, the worst case scenario cost is evaluated through the use of conditional value-at-risk (CVaR) method. To solve this ERM problem, we proposed the use of an improved version of Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO). This modification relies on the use of an adaptive velocity and on local search operators to improve search capability. The results indicated that, compared to two algorithms based on swarm intelligence, the proposed improved C-DEEPSO is able to provide solutions that not only reduce costs (in terms of thousands of monetary units) but also protects the aggregator against extreme scenarios.