Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains

We consider using a battery storage system simultaneously for peak shaving and frequency regulation through a joint optimization framework which captures battery degradation, operational constraints and uncertainties in customer load and regulation signals. Under this framework, using real data we show the electricity bill of users can be reduced by up to 15\%. Furthermore, we demonstrate that the saving from joint optimization is often larger than the sum of the optimal savings when the battery is used for the two individual applications. A simple threshold real-time algorithm is proposed and achieves this super-linear gain. Compared to prior works that focused on using battery storage systems for single applications, our results suggest that batteries can achieve much larger economic benefits than previously thought if they jointly provide multiple services.Comment: To Appear in IEEE Transaction on Power System

Hydro/Battery Hybrid Systems for frequency regulation

An innovative Hydro/Battery Hybrid System (HBHS), composed of a hydropower plant (HPP) and a Battery Energy Storage System (BESS) is proposed to provide frequency regulation services in the Nordic Power System (NPS). The HBHS is envisioned to have a faster and more efficient response compared to HPPs currently providing these services, whilst retaining their high energy capacity and endurance, thus alleviating stand-alone BESS operation constraints. This Thesis aims to explore the operation and optimization of such a hybrid system in order to make it efficient and economically viable. A power plant perspective is employed, evaluating the impact different control algorithms and parameters have on the HBHS performance. Providing Frequency Containment Reserves for Normal Operation (FCR-N), to the national TSO in Sweden, is defined from technology and market analyses as the use case for the HBHS. The characteristics of HPPs suitable for HBHS implementation are found theoretically, by evaluating HPP operational constraints and regulation mechanisms. With the aim of evaluating the dynamic performance of the proposed HBHS, a frequency regulation model of the NPS is built in MATLAB and Simulink. Two different HBHS architectures are introduced, the Hydro Recharge, in which the BESS is regulating the frequency and the HPP is controlling its state of charge (SoC), and the Frequency Split, in which both elements are regulating the frequency with the HPP additionally compensating for the SoC. The dynamic performance of the units is qualitatively evaluated through existing and proposed FCR-N prequalification tests, prescribed by the TSO and ENTSO-E. Quantitative performance comparison to a benchmark HPP is performed with regards to the estimated HPP regulation wear and tear and BESS degradation during 30-day operation with historical frequency data. The two proposed HBHS architectures demonstrate significant reductions of estimated HPP wear and tear compared to the benchmark unit. Simulations consistently report a 90 % reduction in the number of movements HPP regulation mechanism performs and a more than 50 % decrease in the distance it travels. The BESS lifetime is evaluated at acceptable levels and compared for different architectures. Two different applications are identified, the first being installing the HBHS to enable the HPP to pass FCR-N prequalification tests. The second application is increasing the FCR-N capacity of the HPP by installing the HBHS. The Frequency Split HBHS shows more efficient performance when installed in the first application, as opposed to the Hydro Recharge HBHS, which shows better performance in the second application. Finally, it is concluded that a large-scale implementation of HBHSs would improve the frequency quality in the NPS, linearly decreasing the amount of time outside the normal frequency band with increasing the total installed HBHS power capacity

Optimization of Battery Energy Storage to Improve Power System Oscillation Damping

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is setup based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of a faster convergence rate with matched solution qualities.Comment: This paper has been accepted by IEEE Transactions on Sustainable Energy and now still in online-publication phase, IEEE Transactions on Sustainable Energy. 201

Enhanced dynamic control strategy for stacked dynamic regulation frequency response services in battery energy storage systems

Energy storage systems are undergoing a transformative role in the electrical grid, driven by the introduction of innovative frequency response services by system operators to unlock their full potential. However, the limited energy storage capacity of these systems necessitates the development of sophisticated energy management strategies. This paper investigates the newly introduced frequency response service, Dynamic Regulation, within the Great Britain electrical grid. Our study not only establishes control parameters but also demonstrates a novel approach to energy management that pushes the boundaries of the allowable service envelope. We present two distinctive control methods, the first serving as a reference for standard response, and the second as a dynamic control approach, exploiting the extremities of the allowable service envelope. A comprehensive sensitivity analysis that considers availability, the number of equivalent full cycles, and cost–revenue analysis based on grouped dynamic control state of charge setpoints is carried out. Our results underscore that the optimization of average availability takes precedence over merely minimizing the number of cycles, which leads us to define a target state of charge range of between 40% and 45% for a 1-h battery to achieve an availability >95%. Furthermore, our study presents simulated results utilizing real-world frequency data, which reveal the transformative potential of the latter control method. By enhancing the availability of battery energy storage systems, this innovative approach promises not only higher revenues for the asset owner but also assists the system operator in managing frequency

A Convex Cycle-based Degradation Model for Battery Energy Storage Planning and Operation

A vital aspect in energy storage planning and operation is to accurately model its operational cost, which mainly comes from the battery cell degradation. Battery degradation can be viewed as a complex material fatigue process that based on stress cycles. Rainflow algorithm is a popular way for cycle identification in material fatigue process, and has been extensively used in battery degradation assessment. However, the rainflow algorithm does not have a closed form, which makes the major difficulty to include it in optimization. In this paper, we prove the rainflow cycle-based cost is convex. Convexity enables the proposed degradation model to be incorporated in different battery optimization problems and guarantees the solution quality. We provide a subgradient algorithm to solve the problem. A case study on PJM regulation market demonstrates the effectiveness of the proposed degradation model in maximizing the battery operating profits as well as extending its lifetime