Robust Energy Resource Management Incorporating Risk Analysis Using Conditional Value-at-Risk

The energy resource management (ERM) problem in today’s systems is complex and challenging due to the increasing penetration of distributed resources with uncertain behavior. Despite improvement forecasting tools, development strategies deal this uncertainty (for instance, considering Monte Carlo simulation generate a set different possible scenarios), risk associated such variable cannot be neglected deserves proper attention guarantee correct functioning entire system. This paper proposes risk-based optimization approach for centralized day-ahead ERM taking into account extreme events. Risk-neutral risk-averse methodologies are implemented, where strategy considers worst scenario costs through conditional value-at-risk ( $CVaR$ ) method. model formulated from perspective an aggregator that manages multiple technologies as generation, demand response, storage systems, among others. case study analysis aggregator’s inserted 13-bus distribution network smart grid context high renewable electric vehicles. Results show increase nearly 4% operational comparing risk-neutral strategy, but reduction up 14% worst-case cost. Thus, proposed can provide safer more robust solutions incorporating CVaR tool management.