A Review on the Degradation Implementation for the Operation of Battery Energy Storage Systems

A naive battery operation optimization attempts to maximize short-term profits. However, it has been shown that this approach does not optimize long-term profitability, as it neglects battery degradation. Since a battery can perform a limited number of cycles during its lifetime, it may be better to operate the battery only when profits are on the high side. Researchers have dealt with this issue using various strategies to restrain battery usage, reducing short-term benefits in exchange for an increase in long-term profits. Determining this operation restraint is a topic scarcely developed in the literature. It is common to arbitrarily quantify degradation impact into short-term operation, which has proven to have an extensive impact on long-term results. This paper carries out a critical review of different methods of degradation control for short-time operation. A classification of different practices found in the literature is presented. Strengths and weaknesses of each approach are pointed out, and future possible contributions to this topic are remarked upon. The most common methodology is implemented in a simulation for demonstration purposes.This research received no external funding.Publicad

Direct Comparison using Coulomb Counting and Open Circuit Voltage Method for the State of Health Li-Po Battery

Electric cars have undergone many developments in the current digital era. This is to avoid the use of increasingly scarce fuel. Recent studies on electric cars show that battery estimation is an interesting topic to be implemented directly. The battery estimation strategy is carried out by the Battery Management System (BMS). BMS is an indispensable part of electric vehicles or hybrid vehicles to ensure optimal and reliable operation of regulating, monitoring, and protecting batteries. A reliable BMS can extend battery life by setting voltage, temperature, and charging and discharging current limits. The main estimation strategy used by BMS is battery fault, SOH, and battery life. Battery State of Health (SOH) is part of the information provided by the BMS to avoid battery damage and failure. SOC is the proportion of battery capacity SOH is a measure of battery health. This study aims to develop a method for estimating SOH simultaneously using Coulomb Counting and Open Circuit Voltage (OCV) algorithms. The battery is modeled to obtain battery parameters and components of internal resistance, capacitance polarization and OCV voltage source. Several tests were implemented in this research by applying the constant current (CC)-charge CC-discharge test. The state-space system is then formed to apply the Coulomb Counting and OCV algorithms so that SOH can be estimated simultaneously. The OCV-SOC function is obtained in the form of a tenth order polynomial and the battery model parameters say that these parameters change with the health of the battery. The results of the model validation are able to accurately model the battery with an average relative error of 0.027%. Coulomb Counting resulted in an accurate SOH estimation with an error of 3.4%

Caracterización, modelación y comparación de baterías empleadas en electromovilidad en base a su carga y descarga

En el presente artículo de investigación se realiza la caracterización, modelación y comparación de tres baterías eléctricas usadas en electromovilidad: ion-Litio, Plomo Acido e Hidruro metálico de Níquel; con el fin de determinar cuál es la más eficiente, en función de los datos obtenidos en la estimación del estado de carga. Para realizar la estimación del estado de carga se realiza la modelación la batería con el modelo de circuito equivalente de Thévenin y el algoritmo de Conteo de Amperios de Coulomb para la caracterización y modelación de parámetros eléctricos, los cuales nos servirán para estimar el estado de carga de la batería. Al realiza la simulación de los parámetros eléctricos, los datos obtenidos indican que la batería más eficiente es la batería de ion-Litio, ya que es la que presenta un mejor rendimiento en el estado de carga, en comparación a las otras baterías analizadas; mientras que la batería de Plomo acido es la menos eficiente en el escenario de carga y en el escenario de descarga, presentando una descarga profunda.In this article, the characterization, modeling and comparison of three electric batteries used in electromobility are carried out: Lithium-ion, Lead Acid and Nickel Metal Hydride; in order to determine which is the most efficient, based on the data obtained in the estimation of the state of charge. To estimate the state of clyharge, the battery uses the Thevenin equivalent circuit model and the Coulomb Amp Counting algorithm for the characterization and modeling of electrical parameters, which will help estimate the state of charge of the battery. When performing the simulation of the electrical parameters, the data obtained indicate that the most efficient battery is the Lithium-Ion battery, since it has better performance in the state of charge, compared to the other batteries analyzed; while the lead acid battery is the least efficient, in both scenarios, charge and in the discharge, presenting a deep discharge

Ensemble Nonlinear Model Predictive Control for Residential Solar Battery Energy Management

In a dynamic distribution market environment, residential prosumers with solar power generation and battery energy storage devices can flexibly interact with the power grid via power exchange. Providing a schedule of this bidirectional power dispatch can facilitate the operational planning for the grid operator and bring additional benefits to the prosumers with some economic incentives. However, the major obstacle to achieving this win-win situation is the difficulty in 1) predicting the nonlinear behaviors of battery degradation under unknown operating conditions and 2) addressing the highly uncertain generation/load patterns, in a computationally viable way. This paper thus establishes a robust short-term dispatch framework for residential prosumers equipped with rooftop solar photovoltaic panels and household batteries. The objective is to achieve the minimum-cost operation under the dynamic distribution energy market environment with stipulated dispatch rules. A general nonlinear optimization problem is formulated, taking into consideration the operating costs due to electricity trading, battery degradation, and various operating constraints. The optimization problem is solved in real-time using a proposed ensemble nonlinear model predictive control-based economic dispatch strategy, where the uncertainty in the forecast has been addressed adequately albeit with limited local data. The effectiveness of the proposed algorithm has been validated using real-world prosumer datasets

A Review of Lithium-Ion Battery Models in Techno-economic Analyses of Power Systems

The penetration of the lithium-ion battery energy storage system (BESS) into the power system environment occurs at a colossal rate worldwide. This is mainly because it is considered as one of the major tools to decarbonize, digitalize, and democratize the electricity grid. The economic viability and technical reliability of projects with batteries require appropriate assessment because of high capital expenditures, deterioration in charging/discharging performance and uncertainty with regulatory policies. Most of the power system economic studies employ a simple power-energy representation coupled with an empirical description of degradation to model the lithium-ion battery. This approach to modelling may result in violations of the safe operation and misleading estimates of the economic benefits. Recently, the number of publications on techno-economic analysis of BESS with more details on the lithium-ion battery performance has increased. The aim of this review paper is to explore these publications focused on the grid-scale BESS applications and to discuss the impacts of using more sophisticated modelling approaches. First, an overview of the three most popular battery models is given, followed by a review of the applications of such models. The possible directions of future research of employing detailed battery models in power systems' techno-economic studies are then explored

A decision-making approach for the health-aware energy management of ship hybrid power plants

Although autonomous shipping has attracted increasing interest, its further develop-ment requires innovative solutions to operate autonomous ships without the direct in-tervention of human operators. This study aims to develop a health-aware energy management (HAEM) approach for ship hybrid power plants, integrating the health monitoring information from reliability tools with the energy management tools. This approach employs the equivalent consumption minimisation strategy (ECMS) along with a Dynamic Bayesian network (DBN), as well as the utopia decision-making meth-od and a model for the ship hybrid power plant. The HAEM approach is demonstrated for a parallel hybrid power plant of a pilot boat considering realistic operating profiles. The results demonstrate that by employing HAEM approach for the investigated ship power plant operating for 300 hours reduces its failure rate almost fourfold at the cost of fuel consumption increase of around 1.5 %, compared to the respective operation with the ECMS. This study is expected to contribute towards the development of su-pervisory control of autonomous power plants

PLATAFORMA DE GESTÃO DE ENERGIA DE UM EDÍFICIO

Atualmente, cada vez mais equipamentos elétricos são utilizados nas habitações, o que implica um maior gasto de energia, derivado de um maior consumo energético. Para colmatar o consumo energético de forma sustentável, existem tecnologias como os painéis solares e sistemas de armazenamento de energia. O desafio está em tirar o máximo de proveito destas tecnologias para reduzir a energia gasta numa habitação e, consequentemente, a sua pegada ecológica. A solução proposta neste trabalho é uma aplicação de gestão de consumos, que permite ao utilizador inserir a informação personalizada da sua habitação, como a localização, a potência instalada de painéis fotovoltaicos, o sistema de armazenamento de energia e os equipamentos presentes na habitação. Foi implementado um algoritmo de alocação de cargas, cujas variáveis são a produção fotovoltaica, a energia na bateria e o consumo diário, e atua sobre os equipamentos com maior consumo, isto é, a máquina de lavar a roupa, secar a roupa, lavar a loiça e o carregador de veículo elétrico. A aplicação aconselhará o utilizador sobre qual a melhor hora para usar cada um destes equipamentos, fazendo com que a habitação gaste o menos de energia possível num dia. Com os resultados obtidos, concluiu-se que ter um sistema de armazenamento de energia será benéfico para diminuir a quantidade de energia consumida numa habitação, especialmente se o utilizador pretender usar equipamentos em horas onde a produção fotovoltaica é nula. Contudo, o ideal para poupar o máximo de energia numa residência, é alocar os equipamentos para quando a produção fotovoltaica satisfaz o seu consumo, com o auxílio de um algoritmo de alocação de cargas.Currently, more and more electrical equipment is used in homes, which implies greater energy expenditure, resulting from greater energy consumption. To meet energy consumption in a sustainable way, there are technologies such as solar panels and energy storage systems. The challenge is to make the most of these technologies to reduce the energy used in a home and, consequently, its ecological footprint. The solution proposed in this work is a consumption management application, which allows the user to enter personalized information about their home, such as location, the installed power of photovoltaic panels, the energy storage system and the equipment present in the home. A load allocation algorithm was implemented, whose variables are photovoltaic production, battery energy and daily consumption, and acts on the equipment with the highest consumption, that is, the washing machine, drying machine, washing the dishes and the electric vehicle charger. The application will advise the user on the best time to use each of these devices, ensuring that the home uses as little energy as possible in a day. After several simulations, it was concluded that having an energy storage system will be beneficial to reduce the amount of energy consumed in a home, especially if the user intends to use equipment in hours where photovoltaic production is zero. However, the ideal way to save the most energy in a home is to allocate equipment to when photovoltaic production satisfies the consumption, with the help of a load allocation algorithm

Plataforma de apoio à decisão na gestão de cargas no setor residencial

Nos últimos anos existiu um aumento significativo no preço da eletricidade em Portugal. Para além disso, atualmente uma habitação portuguesa consome cada vez mais energia elétrica fruto da maior quantidade de equipamentos instalados. As energias renováveis apre-sentam-se como sendo cada vez a solução para o combate do aumento dos custos de produção e comercialização de eletricidade, e é com esse objetivo que Portugal tem apostado significativamente na produção de energia por meios renováveis. O conceito de Home Energy Management System surge neste contexto por fazer a gestão das cargas residenciais com produção de energia renovável por painéis fotovoltaicos e armazenamento com um sistema de baterias. Sendo assim, o sistema proposto nesta dissertação permite alocar as cargas principais de um consumo residencial (máquinas lavar e secar roupa e lavar loiça) para horários onde a produção fotovoltaica satisfaz na totalidade o consumo numa determinada hora. Caso isto não se verifique, o funcionamento da carga será alocado para o período de menor tarifa em tempo real da eletricidade. Após a alocação das cargas, é executado o algoritmo de otimização que permite uma gestão do sistema onde o objetivo final é minimizar a quantidade energia proveniente da rede elétrica.In the last years there has been a significant increase in the price of electricity in Portu-gal. In addition, currently, a Portuguese home consumes more and more electricity because of the greater amount of equipment installed. Renewable energies present themselves as are the solution to combat the increase in electricity production and commercialization costs, and it is with this objective that Portugal has invested significantly in the production of energy by re-newable means. The concept of Home Energy Management System arises in this context for managing residential loads, with the production of renewable energy by photovoltaic panels and storage with a battery system. Therefore, the system proposed in this dissertation allows allocating the main loads of a residential consumption (washing, drying and dishwashing machines) to a time interval where photovoltaic production fully satisfies consumption in a certain hour. If this is not the case, the load will be allocated to the period with the lowest real-time electricity tariff. After allocating the loads, the optimization algorithm that allows the system management is executed, where the final goal is to minimize the amount of energy provided by the electricity grid