Abstract
In this paper, we propose a risk-constrained adaptive robust optimization approach to provide proactive resilient scheduling decisions for multiple networked microgrid central controllers under potential extreme events. Our objective is to minimize both risks of false judgement made by microgrid central controllers and damage done to networked microgrids by extreme events through a proactive resilient scheduling process. A risk-constrained adaptive robust optimization approach is developed to handle risks and uncertainties associated with: (i) extreme events that may occur and contingency issues linked to influential buses; (ii) renewable energy sources power generation; (iii) human reactions when faced with an extreme event; and (iv) status of combined cooling, heat and power units. “Budget of uncertainty” and risk-management parameters are utilized together to overcome both overconservative issues of conventional robust optimization and human errors that may occur when making decisions. Extensive simulation results from real-world data sets show that the risk-constrained adaptive robust optimization approach we propose can ensure the resilience of networked microgrids under extreme events.
Highlights
Extreme events, such as earthquakes, tornadoes, hurricanes, floods and ice storms, are happening more and more often than ever before because of climate changes all over the world
SIMULATION RESULTS We first evaluate our two-stage risk-constrained adaptive robust optimization (RARO) approach in three steps: (i) we obtain the first-stage decisions pPt E and qPt E and choose proper budgets of uncertainty, while testing the convergence rate of the proposed solution algorithm; (ii) we solve the second-stage problem repeatedly for each firststage decision with 1,000 randomly generated scenarios to show the adaptiveness of our proposed approach when facing extreme events; and (iii) we compare the capabilities of our approach with those of both the conventional robust approach and a deterministic approach to handling uncertainties
We test the networked microgrids’ resilience under extreme conditions, especially when the extreme event develops to the emergency response stage between t2 and t3
Summary
Extreme events, such as earthquakes, tornadoes, hurricanes, floods and ice storms, are happening more and more often than ever before because of climate changes all over the world Most of these extreme events are a danger to the resilience of power systems, especially distribution systems [1]. The hardening and restoration aspects have been extensively studied [4]–[9]; there are still several challenges that need to be tackled in the proactive scheduling and emergency response areas [10].
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