Peak-Aware Online Economic Dispatching for Microgrids

By employing local renewable energy sources and power generation units while connected to the central grid, microgrid can usher in great benefits in terms of cost efficiency, power reliability, and environmental awareness. Economic dispatching is a central problem in microgrid operation, which aims at effectively scheduling various energy sources to minimize the operating cost while satisfying the electricity demand. Designing intelligent economic dispatching strategies for microgrids; however, it is drastically different from that for conventional central grids due to two unique challenges. First, the demand and renewable generation uncertainty emphasizes the need for online algorithms. Second, the widely-adopted peak-based pricing scheme brings out the need for new peak-aware strategy design. In this paper, we tackle these critical challenges and devise peak-aware online economic dispatching algorithms. We prove that our deterministic and randomized algorithms achieve the best possible competitive ratios <inline-formula> <tex-math notation="LaTeX">${2-\beta }$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${e/(e-1+\beta )}$ </tex-math></inline-formula> in the fast responding generator scenario, where <inline-formula> <tex-math notation="LaTeX">${\beta \in [{0,1}]}$ </tex-math></inline-formula> is the ratio between the minimum grid spot price and the local-generation price. By extensive empirical evaluations using real-world traces, we show that our online algorithms achieve near offline-optimal performance. In a representative scenario, our algorithm achieves 17.5% and 9.24% cost reduction as compared with the case without local generation units and the case using peak-oblivious algorithms, respectively.