Distributionally Robust Joint Chance-Constrained Optimization for Networked Microgrids Considering Contingencies and Renewable Uncertainty

TL;DR

This paper introduces a distributionally robust joint chance-constrained optimization framework for networked microgrids, effectively managing renewable uncertainty and contingencies to enhance reliability and reduce operational costs.

Contribution

It develops a novel evolutionary algorithm to solve the NP-hard joint chance-constrained problem with optimized violation rates, improving solution robustness and cost efficiency.

Findings

Reduces operation cost by around 50% compared to baseline methods.

Successfully handles renewable forecast errors with data-driven ambiguity sets.

Ensures robust microgrid operation under various contingencies and uncertainties.

Abstract

In light of a reliable and resilient power system under extreme weather and natural disasters, networked microgrids integrating local renewable resources have been adopted extensively to supply demands when the main utility experiences blackouts. However, the stochastic nature of renewables and unpredictable contingencies are difficult to address with the deterministic energy management framework. The paper proposes a comprehensive distributionally robust joint chance-constrained (DR-JCC) framework that incorporates microgrid island, power flow, distributed batteries and voltage control constraints. All chance constraints are solved jointly and each one is assigned to an optimized violation rate. To highlight, the JCC problem with the optimized violation rates has been recognized to be NP-hard and challenging to be solved. This paper proposes a novel evolutionary algorithm that successfully tackles the problem and reduces the solution conservativeness (i.e. operation cost) by around 50% comparing with the baseline Bonferroni Approximation. Considering the imperfect solar power forecast, we construct three data-driven ambiguity sets to model uncertain forecast error distributions. The solution is thus robust for any distribution in sets with the shared moment and shape assumptions. The proposed method is validated by robustness tests based on those sets and firmly secures the solution robustness.