A model predictive approach for community battery energy storage system optimization

Pezeshki, Houman; Wolfs, Peter; Ledwich, Gerard

doi:10.1109/pesgm.2014.6938788

Cited by 18 publications

(14 citation statements)

References 15 publications

(15 reference statements)

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“…The MPC allowed the system to reduce its power production during the first hours [81]. To obtain the optimal performance of the battery, Pezeshki et al [82] focused on two goals: energy operational cost and smooth charging.…”

Section: Model Predictive Control Of Energy Storage Systems In Stand-mentioning

confidence: 99%

A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems

et al. 2018

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Abstract:Energy storage has become a fundamental component in renewable energy systems, especially those including batteries. However, in charging and discharging processes, some of the parameters are not controlled by the battery's user. That uncontrolled working leads to aging of the batteries and a reduction of their life cycle. Therefore, it causes an early replacement. Development of control methods seeks battery protection and a longer life expectancy, thus the constant-current-constant-voltage method is mostly used. However, several studies show that charging time can be reduced by using fuzzy logic control or model predictive control. Another benefit is temperature control. This paper reviews the existing control methods used to control charging and discharging processes, focusing on their impacts on battery life. Classical and modern methods are studied together in order to find the best approach to real systems.

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Section: Model Predictive Control Of Energy Storage Systems In Stand-mentioning

confidence: 99%

A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems

et al. 2018

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“…The choice of the appropriate mother wavelet depends on the type of application. In power systems, Daubechies (DB) mother wavelets are shown to be more effective than the other wavelets because they are orthogonal and do not cause any information loss [17,18].…”

Section: Wavelet Neural Networkmentioning

confidence: 99%

Load forecasting for diurnal management of community battery systems

Wolfs

Emami

Lin

et al. 2018

J. Mod. Power Syst. Clean Energy

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This paper compares three methods of load forecasting for the optimum management of community battery storages. These are distributed within the low voltage (LV) distribution network for voltage management, energy arbitrage or peak load reduction. The methods compared include: a neural network (NN) based prediction scheme that utilizes the load history and the current metrological conditions; a wavelet neural network (WNN) model which aims to separate the low and high frequency components of the consumer load and an artificial neural network and fuzzy inference system (ANFIS) approach. The batteries have limited capacity and have a significant operational cost. The load forecasts are used within a receding horizon optimization system that determines the state of charge (SOC) profile for a battery that minimizes a cost function based on energy supply and battery wear costs. Within the optimization system, the SOC daily profile is represented by a compact vector of Fourier series coefficients. The study is based upon data recorded within the Perth Solar City high penetration photovoltaic (PV) field trials. The trial studied 77 consumers with 29 rooftop solar systems that were connected in one LV network. Data were available from consumer smart meters and a data logger connected to the LV network supply transformer.

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“…Another research was done by Pezeshki et al [14] about the peak shaving and load smoothing using BESS at the community level in Queensland, Australia. They showed about 18% of the weekly energy cost can be saved.…”

Section: Literature Reviewmentioning

confidence: 99%

Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration

et al. 2016

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High penetration of renewable energy poses a significant challenge in operation of power system. A potential solution for this problem is utilizing Battery Energy Storage System (BESS). The purpose of this paper is to analyze the effectiveness of BESS ability to peak shave and smooth the load curve of an actual circuit on the island of Maui in Hawaii. The distribution circuit has about 850kW of installed rooftop Photo-Voltaic (PV) generation. Higher penetration of PV increases the concern about the potential impacts on the transmission system. At first, we will present two different methods for load forecasting. Reliable forecasting of daily load is required to effectively utilize the BESS system. We have employed two different methods for load forecasting in order to achieve two main purposes including peak shaving and smoothing. For reaching these goals, two approaches are analyzed. The first approach is utilizing a nonlinear programming method in terms of load shifting and smoothing. The second approach includes a real time control strategy to have smoothing and peak shaving at the same time. As a real case study, these proposed methods have been applied within 108-day data collection period and pros and cons of these methods will be discussed.

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A model predictive approach for community battery energy storage system optimization

Cited by 18 publications

References 15 publications

A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems

A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems

Load forecasting for diurnal management of community battery systems

Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration

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