Adaptive electricity scheduling in microgrids

Abstract

Abstract-Microgrid (MG) is a promising component for future smart grid (SG) deployment. The balance of supply and demand of electric energy is one of the most important requirements of MG management. In this paper, we present a novel framework for smart energy management based on the concept of quality-of-service in electricity (QoSE). Specifically, the resident electricity demand is classified into basic usage and quality usage. The basic usage is always guaranteed by the MG, while the quality usage is controlled based on the MG state. The microgrid control center (MGCC) aims to minimize the MG operation cost and maintain the outage probability of quality usage, i.e., QoSE, below a target value, by scheduling electricity among renewable energy resources, energy storage systems, and macrogrid. The problem is formulated as a constrained stochastic programming problem. The Lyapunov optimization technique is then applied to derive an adaptive electricity scheduling algorithm by introducing the QoSE virtual queues and energy storage virtual queues. The proposed algorithm is an online algorithm. We derive several "hard" performance bounds for the proposed algorithm, and evaluate its performance with trace-driven simulations. The simulation results demonstrate the efficacy of the proposed electricity scheduling algorithm.

Real-Time Power Balancing in Electric Grids With Distributed Storage

“…Demand side management: In [7], [16], and [17], realtime power balance is achieved by scheduling the demand loads of users, with the objective of minimizing the average system cost. Specifically, [7] proposes to optimally schedule the non-interruptible and deferrable loads of individual users within their deadlines.…”

“…The problem is formulated as a Markov decision process (MDP) problem and is solved distributively. In addition, both [16] and [17] have developed their solutions under the framework of Lyapunov optimization, and considered joint scheduling of flexible load and storage usage. In [16], a centralized real-time algorithm is provided, while in [17], a gradient-based distributed real-time algorithm is suggested.…”

“…The Lyapunov optimization technique used in the development of our real-time sub-problems shares some similarity with [16] and [17]. However, compared with [16] and [17], we focus on the unique characteristics of storage units and employ a more realistic model that takes into account the battery inefficiency and energy gain/loss under charging/discharging.…”

“…However, compared with [16] and [17], we focus on the unique characteristics of storage units and employ a more realistic model that takes into account the battery inefficiency and energy gain/loss under charging/discharging. Moreover, compared with the distributed implementation in [17], our distributed algorithm is enabled by a novel cost cushion parameter design, leading to provably faster convergence.…”

“…We will show later in Section VI that our developed solution in fact also satisfies (4), so it is feasible for P1. This relaxation technique to accommodate the type of time-coupled decision constraints such as (5) and (6) was first introduced in [32] for energy management in a data center equipped with an ideal battery, and later was also applied in [16] and [17]. Compared with [32], besides our problem being different from it, we consider multiple DS units.…”