Risk Curtailment Assessment in Smart Deregulated Grid with the Presence of Renewable and Storage Sources

The transition of traditional networks to deregulated and restructured networks have been implemented in some countries. It has resulted in prominent benefits for both demand and supplier sides. The competitive environment procures a circumstance to propel the electricity price toward alleviated and affordable prices. In addition, almost all grid planners are targeting to achieve a network with high penetration of free and clean renewable energy. Even though this matter is attractive, from a technical point of view, there are some barriers to implement such schemes. First of all, the integration of renewable units inserts a considerable amount of risk to the scheduling of operation at various intervals. This risk may be led to undesired incidences such as the outage of units and lines, load shedding, black-outs, the increase of reserve cost, etc. In other words, these risks threaten the security and stability of grids. To guarantee secure operation, sufficient reserve capacity must be procured. In addition, the presence of energy storage units in different scales and flexible response speeds are more preferred due to techno-economic reasons. Hence, in this article, a risk assessment technique is suggested to analyze and mitigate the risk of the presence of renewable sources in order to have a more reliable operation. In this regard, Value at Risk (VaR) and Conditional Value at Risk (CVaR) indices are employed. Moreover, the problem is solved through the discrete non-linear programming (DNLP) classical model as well as the heuristic virus colony search (VCS) algorithm model. The results are compared and the conclusions are discussed. The results imply the effectiveness of the proposed approach.