The performance of model-based state-of-charge (SOC) estimation method relies on an accurate battery model. Nonlinear models are thus proposed to accurately describe the external characteristics of the lithium-ion battery. The nonlinear estimation algorithms and online parameter identification methods are needed to guarantee the accuracy of the model-based SOC estimation with nonlinear battery models. A new approach forming a dynamic linear battery model is proposed in this paper, which enables the application of the linear Kalman filter for SOC estimation and also avoids the usage of online parameter identification methods. With a moving window technology, partial least squares regression is able to establish a series of piecewise linear battery models automatically. One element state-space equation is then obtained to estimate the SOC from the linear Kalman filter. The experiments on a LiFePO4 battery prove the effectiveness of the proposed method compared with the extended Kalman filter with two resistance and capacitance equivalent circuit model and the adaptive unscented Kalman filter with least squares support vector machines.

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A Simplified Model-Based State-of-Charge Estimation Approach for Lithium-Ion Battery With Dynamic Linear Model

Using electrical energy storage in residential buildings – Sizing of battery and photovoltaic panels based on electricity cost optimization

In this paper a two stage Grasshopper Optimization Algorithm (GOA) based Fuzzy multiobjective approach is proposed for optimum sizing and placement of Distributed Generations (DGs), Shunt Capacitors (SCs) and Electric Vehicle (EV) charging stations for distribution systems. In the first stage Fuzzy GOA approach is used for optimum sizing and allocation of DGs and SCs for improving the substation power factor, real power loss reduction and voltage profile improvement of the distribution system. In the second stage distribution system integrated with DGs and SCs is considered and fuzzy GOA approach is used for identifying optimum locations for EV charging stations and number of vehicles at the charging stations. EV battery charging load models are developed from the Lithium ion battery charging characteristic curves for load flow analysis. Simulation results are shown to show the advantages of fast converging properties of GOA over GA and PSO techniques. Simulation results are demonstrated on 51 bus and 69 bus distribution networks to show the advantages of proposed methodology compared to conventional objective based simultaneous optimization approach. The effect of EV load growth and the effect of uncertainties in DGs and distribution system load are shown on the distribution system performance.

Grasshopper optimization algorithm based two stage fuzzy multiobjective approach for optimum sizing and placement of distributed generations, shunt capacitors and electric vehicle charging stations

Solution to optimal reactive power dispatch in transmission system using meta-heuristic techniques―Status and technological review

Here, the optimal placement and sizing of electric vehicle charging stations (EVCSs) are presented. High penetration of electric vehicles (EVs) and resulted losses in network would consequently impose more complexity to solution of application problem of EVCSs. To overcome this problem, the model would consider the incentive-based demand response programmes (DRPs), which is handled by particle swarm optimisation algorithm. Minimising investment cost, connection cost, total cost of losses, and demand response (DR) cost are the objective functions of this problem here. Finally, the proposed model is applied to a test system and results are discussed. By comparing the results obtained through different scenarios, it is concluded that the application of DRP results in a distinct reduction in grid losses and total costs.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2017.1663

Cost-based optimal siting and sizing of electric vehicle charging stations considering demand response programmes

In the UK power system, there is currently no battery energy storage system (BESS) for providing grid-scale frequency response. However, according to the UK future energy plan, with a high penetration of renewable energy sources, battery technologies have become increasingly attractive to provide firm frequency response (FFR). This paper estimates the current and future balancing requirement of National Grid UK. A methodology for the economic optimization of the parameters of LiFePO4 BESSs is proposed to achieve the lowest tender price in the UK FFR market. The economic analysis is performed based on the estimated battery lifetime and the UK frequency regulation market. Results suggest that it is profitable to deploy BESSs in the UK market. The lowest tender price obtained was £17.4012/MW/h, which is already lower than many successfully tendered prices from FFR providers. By considering the future UK energy scenarios, a BESS with an energy/power ratio around 0.43, energy offset interval of 20 min and state of charge set-point = 50–60% is shown to be the optimal choice.

Optimizing LiFePO4 Battery Energy Storage Systems for Frequency Response in the UK System

https://cyberleninka.org/article/n/1462678.pdf

Artificial Neural Network Based Fault Detection and Fault Location in the DC Microgrid

Active–reactive power approaches for optimal placement of charge stations in power systems

In power systems, load is continuously fluctuating and is difficult for slow generating units, such as coal-fired, nuclear and hydro power plants, to follow. Therefore, fast but expensive generation units like open cycle gas turbine (OCGT) are widely used to provide frequency regulation, maintaining system frequency within its specified limit. Owing to energy storage system (ESS)'s zero-net energy and fast response nature, one of the applications of ESS in power system is to provide frequency regulation. This paper proposes a method of sizing OCGTs and advanced lead-acid batteries to provide frequency regulation under different types of load following patterns. The rain-flow counting algorithm is used for battery lifetime modelling. The cost and savings from displacing OCGTs with ESS are calculated and discussed. It was concluded that the system load following ability is the key factor when considering whether ESS is cost effective when providing frequency regulation in a control area. The method of battery and OCGT rating presented in this paper is applicable to the situation when regulation load requirement is known or can be estimated.

/pdf/investigation-of-energy-storage-and-open-cycle-gas-turbine-1aobo589j0.pdf

Cheng Wang

Papers

Optimizing LiFePO4 Battery Energy Storage Systems for Frequency Response in the UK System

Artificial Neural Network Based Fault Detection and Fault Location in the DC Microgrid

Active–reactive power approaches for optimal placement of charge stations in power systems

Investigation of energy storage and open cycle gas turbine for load frequency regulation

The Active and Reactive Power Dispatch for Charging Station Location Impact Factors Analysis