An Online Scheduling Algorithm for a Community Energy Storage System

TL;DR

This paper introduces an online scheduling algorithm for community energy storage systems that optimizes charging and discharging requests in real-time, maximizing user benefits while handling uncertainties and adversarial request sequences.

Contribution

It presents a novel online primal-dual based algorithm that efficiently manages CES requests, ensuring near-optimal welfare under stochastic and adversarial conditions.

Findings

Algorithm achieves a welfare within a factor of 1/a of the offline optimal.

Handles stochastic and adversarial request sequences effectively.

Promotes charging/discharging cancellations to optimize CES usage.

Abstract

In this paper, we consider a community energy storage (CES) system that is shared by various electricity consumers who want to charge and discharge the CES throughout a given time span. We study the problem facing the manager of such a CES who must schedule the charging, discharging, and capacity reservations for numerous users. Moreover, we consider the case where requests to charge/discharge the CES arrive in an online fashion and the CES manager must immediately allocate charging power and energy capacity to fulfill the request or reject the request altogether. The objective of the CES manager is to maximize the total value gained by all of the users of the CES while accounting for the operational constraints of the CES. We discuss an algorithm titled \textsc{CommunityEnergyScheduling} that acts as a pricing mechanism based on online primal-dual optimization as a solution to the CES manager's problem. The online algorithm estimates the dual variables (prices) in real-time to allow for requests to be allocated or rejected immediately as they arrive. Furthermore, the proposed method promotes charging and discharging cancellations to reduce the CES's usage at popular times and is able to handle the inherent stochastic nature of the requests to charge/discharge stemming from randomness in users' net load patterns and weather uncertainties. Additionally, we are able to show that the algorithm is able to handle any adversarially chosen request sequence and will always yield total welfare within a factor of 1/a of the offline optimal welfare.