Peak-Aware Online Economic Dispatching for Microgrids

Abstract

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, is drastically different from that for conventional central grids, due to two unique challenges. First, the erratic renewable energy emphasizes the need for online algorithms. Second, the widely-adopted peak-based pricing scheme brings out the need for new peakaware strategy design. In this paper, we tackle these critical challenges and devise peak-aware online economic dispatching algorithms. For microgrids with fast-responding generators, we prove that our deterministic and randomized algorithms achieve the best possible competitive ratios 2 − β and e/(e − 1 + β), respectively, where β ∈ [0, 1] is the ratio between the minimum grid spot price and the localgeneration price. Our results characterize the fundamental price of uncertainty of the problem. For microgrids with slow-responding generators, we first show that a large competitive ratio is inevitable. Then we leverage limited prediction of electricity demand and renewable generation to improve the competitiveness of the algorithms. 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 23% and 11% cost reduction as compared to the case without local generation units and the case using peak-oblivious algorithms, respectively.

Peak-Aware Online Economic Dispatching for Microgrids

“…Our purpose is to investigate (i) the competitiveness of our online algorithms in comparison with the optimal offline one, (ii) the necessity of peak-awareness in economic dispatching of microgrids, and (iii) the performance of online algorithms under various parameter settings. More simulation results can be found in [9].…”

“…That is, R u = R d = C. We note that even though we relax the ramping constraints, the relaxed problem, denoted as FS-PAED, still covers many practical scenarios in microgrids [5]. Moreover, the results in this section serves a building block for designing online algorithm for the original problem PAED with ramping constraints, which is presented in our technical report [9].…”

“…As such, these results characterize the fundamental price of uncertainty for the problem. The results in this part will help to solve the problem in "slow-responding" generator scenario [9].…”

“…Some preliminary results in this paper were presented at ACM e-Energy 2015 [10] and all proofs are in the appendices.…”

Distributed energy management for community microgrids considering phase balancing and peak shaving

“…Note that the peak shaving in this paper is realised by real‐time pricing. Other methods such as demand charge [25] will be investigated in future work. To avoid mal‐operation of zero‐sequence protections, the maximum phase unbalance at the PCC is constrained by (20).…”