A dynamic distributed energy storage control strategy for providing primary frequency regulation using multi‐armed bandits method

Abstract

Maintaining frequency stability is a crucial but challenging task for the stable operation of a power system. The distributed energy storage (DES) can charge or discharge for both upward and downward frequency regulation, exploring and effectively using the regulation capabilities will provide a strong backup for frequency regulation. Here, a dynamic DES control strategy for providing primary frequency regulation is proposed. The different behaviours of storage owners are considered when they respond to the regulation requests from the aggregator. This kind of uncertainty would lead to a mismatch between the final aggregation result and the expected target. Hence, the multi-armed bandit approach is applied to learn users' response behaviour and select the optimal set of users to mitigate the mismatch. Case studies on the IEEE RTS 24-bus system demonstrate that the proposed method's mismatch between the actual aggregation result and the regulation target is less than half as much as the conventional method, and it can restore the frequency 20 events earlier than the traditional method.

A Novel Allocation Strategy Based on the Model Predictive Control of Primary Frequency Regulation Power for Multiple Distributed Energy Storage Aggregators

“…However, the proposed PFR power allocation strategy requires predicting the behavior of the system, and the implementation cost of this was relatively high. Although the DIPM was used to transform the model into an unconstrained quadratic programming problem for distributed solutions, its calculation requirements were still larger than those of traditional allocation strategies [5][6][7][8][9]. Moreover, data communication is an essential part of distributed algorithms in real-world scenarios.…”

“…To illustrate the superiority of the proposed PFR power allocation strategy, it was compared with the traditional allocation strategy (i.e., the strategy based on available regulation capacity used in [5][6][7][8][9]) in both case 1 and case 2.…”

“…In [6], a profit-maximizing strategy, including the FR phase and state of charge (SOC) recovery phase, was presented for the DES aggregator to participate in the primary frequency regulation (PFR) market. Considering the uncertain behavior of users, Sun et al [7] proposed a dynamic DES control strategy based on the risk-averse multi-armed bandits method. In [8], a fast hierarchical control framework was designed for DESs to provide frequency and voltage support using the coordinated injection of active and reactive power.…”

“…Considering the uncertainty of system operation, a two-layer MPC-based DES control strategy was developed in [13] to provide efficient control signals that respond to the SFR requirements of the power system. In the above studies, the management of DESs was unified by the power dispatching and control center [5,9,12,13] or an aggregator [6][7][8]. However, with the increase in the number of DESs, multiple DES aggregators may participate in FR, where the realization of their coordinated control is a critical topic of current research.…”

Managing Considerable Distributed Resources for Demand Response: A Resource Selection Strategy Based on Contextual Bandit

“…Several current pieces of research about the application of bandit-based approaches to the energy resource management problem are provided in Table 1. Multi-armed bandit (MAB) approaches were applied to learn the behaviors of residential air conditioning [21]; heating, ventilation, and air conditioning (HVAC) [22]; renewable energy sources [23]; and energy storage [24], selecting the optimal set to participate in primary or secondary frequency regulation. Moreover, the optimal set of electric vehicles [25] and residential demand resources [26,27] were selected based on MAB-based approaches to provide demand response services to the power grid operation.…”