Online Energy Generation Scheduling for Microgrids with Intermittent Energy Sources and Co-Generation

TL;DR

This paper introduces CHASE, an online algorithm for microgrid energy scheduling with renewable sources and co-generation, achieving near-optimal cost savings despite unpredictable supply and demand.

Contribution

It proposes a novel online scheduling algorithm, CHASE, that effectively handles renewable variability and co-generation, outperforming existing deterministic methods.

Findings

CHASE achieves a competitive ratio of at most 3.

The algorithm adapts to unpredictable renewable energy supply.

It maximizes cost savings in microgrid operations.

Abstract

Microgrids represent an emerging paradigm of future electric power systems that can utilize both distributed and centralized generations. Two recent trends in microgrids are the integration of local renewable energy sources (such as wind farms) and the use of co-generation (i.e., to supply both electricity and heat). However, these trends also bring unprecedented challenges to the design of intelligent control strategies for microgrids. Traditional generation scheduling paradigms rely on perfect prediction of future electricity supply and demand. They are no longer applicable to microgrids with unpredictable renewable energy supply and with co-generation (that needs to consider both electricity and heat demand). In this paper, we study online algorithms for the microgrid generation scheduling problem with intermittent renewable energy sources and co-generation, with the goal of maximizing the cost-savings with local generation. Based on the insights from the structure of the offline optimal solution, we propose a class of competitive online algorithms, called CHASE (Competitive Heuristic Algorithm for Scheduling Energy-generation), that track the offline optimal in an online fashion. Under typical settings, we show that CHASE achieves the best competitive ratio among all deterministic online algorithms, and the ratio is no larger than a small constant 3.