Privacy-Preserving Distributed Online Dispatch of Low-Carbon Microgrids with TCN–BiLSTM Probabilistic Photovoltaic Forecasts

Source abstract

Low-carbon microgrids are complex energy systems in which photovoltaic (PV) uncertainty, time-varying operating costs, distributed coordination, and information privacy must be addressed simultaneously. This paper couples TCN–BiLSTM probabilistic PV forecasting with privacy-preserving distributed online economic dispatch. Quantile forecasts are converted into a risk-aware net demand by combining the median PV forecast with a lower-side uncertainty reserve. The resulting dispatch problem also includes a step-type carbon-trading cost. To solve the problem when cost gradients are unavailable, we propose a probabilistic PV forecasting-driven differentially private distributed online one-point bandit optimization algorithm (PPF-DP-DOBO). Each generator uses one function-value query per iteration and perturbs its communicated state with Laplace noise. The analysis establishes a per-release differential privacy guarantee, its sequential composition over the dispatch horizon, and an individual dynamic regret bound that explicitly depends on PV forecasting uncertainty. Under bounded weighted path variation and cumulative forecasting uncertainty, the regret is sublinear with order O(T3/4). Simulations on a modified IEEE 162-bus system show that the method tracks the risk-aware net demand, preserves the expected privacy–performance trade-off, and yields lower average regret and carbon cost in the evaluated probabilistic-PV setting than in the no-PV case.