scholarly journals Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8365
Author(s):  
Yushen Miao ◽  
Tianyi Chen ◽  
Shengrong Bu ◽  
Hao Liang ◽  
Zhu Han
Keyword(s):  
Real Time ◽  
Critical Role ◽  
Frequency Regulation ◽  
Power Networks ◽  
Physical Constraints ◽  
Policy Gradient ◽  
Markov Decision ◽  
Battery Energy Storage Systems ◽  
Complex Decision Making ◽  
Battery Energy

Battery energy storage systems (BESSs) play a critical role in eliminating uncertainties associated with renewable energy generation, to maintain stability and improve flexibility of power networks. In this paper, a BESS is used to provide energy arbitrage (EA) and frequency regulation (FR) services simultaneously to maximize its total revenue within the physical constraints. The EA and FR actions are taken at different timescales. The multitimescale problem is formulated as two nested Markov decision process (MDP) submodels. The problem is a complex decision-making problem with enormous high-dimensional data and uncertainty (e.g., the price of the electricity). Therefore, a novel co-optimization scheme is proposed to handle the multitimescale problem, and also coordinate EA and FR services. A triplet deep deterministic policy gradient with exploration noise decay (TDD–ND) approach is used to obtain the optimal policy at each timescale. Simulations are conducted with real-time electricity prices and regulation signals data from the American PJM regulation market. The simulation results show that the proposed approach performs better than other studied policies in literature.

scholarly journals Design of Combined Stationary and Mobile Battery Energy Storage Systems

2021 ◽  
Author(s):  
Hassan Hayajneh ◽  
Xuewei Zhang
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Storage Systems ◽  
Design Parameters ◽  
Frequency Regulation ◽  
Energy Storage Systems ◽  
Battery Energy Storage ◽  
Battery Capacity ◽  
Battery Energy Storage Systems ◽  
Battery Energy

To minimize the curtailment of renewable generation and incentivize grid-scale energy storage deployment, a concept of combining stationary and mobile applications of battery energy storage systems built within renewable energy farms is proposed. A simulation-based optimization model is developed to obtain the optimal design parameters such as battery capacity and power ratings by solving a multi-objective optimization problem that aims to maximize the economic profitability, the energy provided for transportation electrification, the demand peak shaving, and the renewable energy utilized. Two applications considered for the stationary energy storage systems are the end-consumer arbitrage and frequency regulation, while the mobile application envisions a scenario of a grid-independent battery-powered electric vehicle charging station network. The charging stations receive supplies from the energy storage system that absorbs renewable energy, contributing to a sustained DC demand that helps with revenues. Representative results are presented for two operation modes and different sets of weights assigned to the objectives. Substantial improvement in the profitability of combined applications over single stationary applications is shown. Pareto frontier of a reduced dimensional problem is obtained to show the trade-off between design objectives. This work could pave the road for future implementations of the new form of energy storage systems.<br>

scholarly journals A Strategy for Power Generation Optimization in a Hybrid Wind-BESS Power Plant

2019 ◽  
Vol 122 ◽  
pp. 04004
Author(s):  
Daniel Villanueva ◽  
Andrés E. Feijóo ◽  
Neeraj D. Bokde
Keyword(s):  
Optimization Problem ◽  
Storage Systems ◽  
Fitness Function ◽  
Wind Farms ◽  
Power Production ◽  
Power Networks ◽  
Battery Energy Storage ◽  
Electric Power Networks ◽  
Battery Energy Storage Systems ◽  
Battery Energy

The wind is an uncontrollable primary resource, although its energy can be stored. This fact can be used for the design of strategies for a better management of electric power networks. An option for achieving this goal is to install Battery Energy Storage Systems (BESS) in the wind farms (WF). When dealing with WFs combined with BESSs the most important is to manage the power production in order to meet the requirements of the network or those related with the owner of the plant. Both challenges constitute an optimization problem. This paper proposes an Evolutionary Algorithm (EA) to solve it, where a fitness function must be maximized under the consideration of certain constraints. The fitness function depends on the target of the power production, which may be either to help the network become more stable or to maximize the profit, assessing each scenario and accepting the best one. The constraints of the optimization problem are related to the levels of the BESSs: the maximum power transferred to or from it and the output power of the plant.

A Novel Approach of Battery Energy Storage for Improving Value of Wind Power in Deregulated Markets

2013 ◽  
Vol 14 (3) ◽  
pp. 255-264 ◽  
Author(s):  
Y Minh Nguyen ◽  
Yong Tae Yoon
Keyword(s):  
Energy Storage ◽  
Real Time ◽  
Wind Power ◽  
Frequency Control ◽  
Minimum Cost ◽  
Wind Generation ◽  
Frequency Regulation ◽  
Battery Energy Storage ◽  
Novel Approach ◽  
Battery Energy

Abstract Wind power producers face many regulation costs in deregulated environment, which remarkably lowers the value of wind power in comparison with the conventional sources. One of these costs is associated with the real-time variation of power output and being paid in frequency control market according to the variation band. In this regard, this paper presents a new approach to the scheduling and operation of battery energy storage installed in wind generation system. This approach depends on the statistic data of wind generation and the prediction of frequency control market prices to determine the optimal charging and discharging of batteries in real-time, which ultimately gives the minimum cost of frequency regulation for wind power producers. The optimization problem is formulated as the trade-off between the decrease in regulation payment and the increase in the cost of using battery energy storage. The approach is illustrated in the case study and the results of simulation show its effectiveness.

Sign in / Sign up

Export Citation Format

Share Document