Risk-averse operation of microgrids in distribution systems with price-responsive demands

This paper introduces a decentralized, risk-averse operational framework for interconnected, unbalanced microgrids and the distribution system. Distribution system operation is modeled as a risk-averse optimization problem using conditional value-at-risk measures to enhance robustness against uncertainties in price-responsive demand bids. Microgrid operations are structured as scenario-based robust optimization problems, capturing the worst-case scenarios for demand and solar PV generation. Non-convex energy management problems within the distribution system and microgrids are reformulated as convex optimization problems using the moment relaxation technique. A decentralized scheme addresses heterogeneous uncertainties in microgrids and the distribution system. The framework is applied to two case studies: a distribution system connected to two microgrids and a modified IEEE 123-bus distribution system connected to four microgrids. The risk-averse optimization solution is compared to that of a stochastic programming approach. For the IEEE 123-bus system, the results show that introducing risk aversion decreases social welfare by 44.63% compared to the risk-neutral solution but improves the conditional value-at-risk by 44.04%. Additionally, the impacts of dispatchable resources, including energy storage and distributed generation, on operation cost, phase balancing, and uncertainty control at the main feeder are examined. It is shown that incorporating a statistical distance metric to regulate power flow at the main feeder decreases social welfare by 6.48%. • The distribution system operation is modeled as a risk-averse optimization problem. • Using scenario-based robust optimization, microgrid operation problem is structured. • The energy management problems are convexified using moment relaxation technique. • A decentralized operation scheme is developed considering heterogeneous uncertainties.