LAUSR

Abstract Uncertainty of different parameters in power systems can put market players at risk. For example, uncertainty of market prices in energy markets with capitalist characteristics can negatively affect decisions of aggregators participating in such markets to make profits. In fact, uncertainty in capitalist energy markets should be controlled since many individuals who participate in such markets have specific expectations (economic goals in most cases) and this may result in some concerns over the future performances of mentioned markets under uncertainty. It should be noted that aggregator can be a person or an authority who is responsible for a group of energy consumers/resources like electric vehicles (EVs) and seeks to participate in energy markets in order to get to some expected goals that can be economical, technical and etc. Moreover, attitude to risk of a decision-maker can change in different working conditions which needs to be considered through various decision-making criteria. In this paper, robust performance (optimal/stable operation against the worst possible condition of uncertainty) of an EV aggregator against the uncertainty of market price is studied by incorporating robust optimization method. The aggregator willing to participate in energy market can benefit from the provided strategies created by this optimization model to satisfy the expected economic goals and control the uncertainty of energy market. In detail, specific plans and strategies are determined toward possible values of uncertain market price that satisfy economic goals of aggregator and keep the uncertainty under control. Mixed integer non-linear programming (MINLP) is used to model the problem which is solved in general algebraic modeling system (GAMS) software. A microgrid system composed of distributed generation units (local generation units that can be either renewable or non-renewable) including dispatchable and non-dispatchable ones, EVs and storage system is studied, and the results are presented. The results show that robust optimization model can minimize the daily market cost while mitigating possible risks for the aggregator of EVs. It should be noted that daily market accounts for energy market in which various energy resources are available to be selected by the related aggregator in order to supply energy demand.