Risk-Averse Coordinated Operation of Rural Multi-Energy Microgrids Considering Voltage Quality Control

Rural distribution networks increasingly face voltage quality challenges due to high penetration of distributed renewable energy, heterogeneous rural load behavior, and long radial feeder structures with limited voltage regulation capability. Photovoltaic generation variability and agricultural load fluctuations can lead to voltage rise, reverse power flow, and branch congestion, particularly in weak rural grids. Conventional deterministic voltage control approaches relying on tap changers and capacitor banks often struggle to maintain stable voltage profiles under stochastic operating conditions. This paper proposes a risk-aware coordinated operation framework for rural multi-energy microgrids that integrates stochastic scenario modeling, voltage state perception, and adaptive optimization-based control. Renewable generation uncertainty and rural load variability are represented through correlated scenario generation and Wasserstein-distance-based scenario reduction, where 100 raw joint photovoltaic-load trajectories are reduced to 20 representative scenarios after convergence and distributional-fidelity tests. A stochastic optimization model is developed to coordinate photovoltaic inverters, battery energy storage systems, demand-side flexibility, and reactive compensation devices while satisfying network power-flow, voltage-security, storage, and communication-delay-aware implementation constraints. To mitigate extreme voltage deviation events, the framework incorporates a Conditional Value-at-Risk formulation that penalizes tail-risk voltage violations and maintains voltages within a preferred operating band of 0.97–1.03 p.u. Case studies on a modified IEEE 33-bus rural distribution system with 2.00 MW of photovoltaic capacity and 2.50 MWh of battery storage demonstrate consistent performance improvements across deterministic, risk-neutral stochastic, chance-constrained, and robust baselines. The proposed strategy reduces peak branch loading from 0.95 in the deterministic benchmark to 0.72, while the 95th percentile voltage deviation risk decreases from 0.0071 p.u.2 to 0.0020 p.u.2. Sensitivity, scenario-convergence, scalability, and seasonal representative-day analyses further confirm that the CVaR layer suppresses rare but severe voltage excursions without imposing excessive curtailment or computational burden.