Data-driven Distributed Robust Optimization-based Coordinated Scheduling Strategy for Multi-energy Systems

Increasing uncertainties from renewable energies make it harder to dispatch multi-energy systems. Conventional robust and stochastic optimization algorithms are risk-averse in all extreme operational scenarios, such that they can take over-conservative strategies. To solve this, a data-driven distributed robust optimization-based coordinated scheduling strategy for multi-energy systems is proposed. The model of the strategy consists of two stages. By taking into account the real-time modification capability in the second stage, the adjustment capability of day-ahead scheduling in the first stage is enhanced. In addition, in order to prevent the scheduling results from being overly conservative, the probability distribution ranges of uncertainties are limited by using integrated norm in the second stage. Besides, transmission section limit are considered to assure safety of power system. Finally, the model is solved by using the modified differential evolution technique. The ZhengDou nearby region of the Sichuan power grid test results show that this method can effectively give consider to the cost, robustness and stability.