Cooperative energy management for a cluster of households prosumers

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe increment of electrical and electronic appliances for improving the lifestyle of residential consumers had led to a larger demand of energy. In order to supply their energy requirements, the consumers have changed the paradigm by integrating renewable energy sources to their power grid. Therefore, consumers become prosumers in which they internally generate and consume energy looking for an autonomous operation. This paper proposes an energy management system for coordinating the operation of distributed household prosumers. It was found that better performance is achieved when cooperative operation with other prosumers in a neighborhood environment is achieved. Simulation and experimental results validate the proposed strategy by comparing the performance of islanded prosumers with the operation in cooperative modePeer ReviewedPostprint (author's final draft

Powering the future: a comprehensive review of battery energy storage systems

Global society is significantly speeding up the adoption of renewable energy sources and their integration into the current existing grid in order to counteract growing environmental problems, particularly the increased carbon dioxide emission of the last century. Renewable energy sources have a tremendous potential to reduce carbon dioxide emissions because they practically never produce any carbon dioxide or other pollutants. On the other hand, these energy sources are usually influenced by geographical location, weather, and other factors that are of stochastic nature. The battery energy storage system can be applied to store the energy produced by RESs and then utilized regularly and within limits as necessary to lessen the impact of the intermittent nature of renewable energy sources. The main purpose of the review paper is to present the current state of the art of battery energy storage systems and identify their advantages and disadvantages. At the same time, this helps researchers and engineers in the field to find out the most appropriate configuration for a particular application. This study offers a thorough analysis of the battery energy storage system with regard to battery chemistries, power electronics, and management approaches. This paper also offers a detailed analysis of battery energy storage system applications and investigates the shortcomings of the current best battery energy storage system architectures to pinpoint areas that require further study.This publication is part of the project TED2021-132864A-I00, funded by MCIN/ AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR”.Peer ReviewedPostprint (published version

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, etc. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications, multiple battery cells have to be connected in series and/or parallel. While battery technology has advanced significantly in the past decade, existing battery management systems (BMSs) mainly focus on state monitoring and control of battery systems packed in fixed configurations. In fixed configurations, though, the battery system performance is in principle limited by the weakest cells, which can leave large parts severely underutilized. Allowing dynamic reconfiguration of battery cells, on the other hand, allows individual and flexible manipulation of the battery system at cell, module, and pack levels, which may open up a new paradigm for battery management. Following this trend, this paper provides an overview of next-generation BMSs featuring dynamic reconfiguration. Motivated by numerous potential benefits of reconfigurable battery systems (RBSs), the hardware designs, management principles, and optimization algorithms for RBSs are sequentially and systematically discussed. Theoretical and practical challenges during the design and implementation of RBSs are highlighted in the end to stimulate future research and development

Battery Management System for Electric Drive Vehicles – Modeling, State Estimation and Balancing

Electric-drive vehicles (EDVs) have drawn more and more attention worldwide. As one of the most important parts of EDVs, battery management systems (BMSs) manage the huge amount of battery cells in EDVs and assures their safety. To achieve these goals, researches in BMS, such as battery modeling, battery state estimation, and battery balancing have gained a new vigor. This chapter will review the existing researches and introduce several advances in these areas

Performance Optimization of Onboard Lithium Ion Batteries for Electric Vehicles

Next generation of transportation in the form of electric vehicles relies on better operation and control of large battery packs. The individual modules in large battery packs generally do not have identical characteristics and may degrade differently due to manufacturing variability and other factors. Degraded battery modules waste more power, affecting the performance and economy for the whole battery pack. Also, such impact varies with different trip patterns. It will be cost effective if we evaluate the performance of the battery modules prior to replacing the complete battery pack. The knowledge of the driving cycle and battery internal resistance will help to make decision to replace the worst battery modules and directly cut down on user expenditure to replace the battery. Also, optimizing the performance of battery during the driving trip is the challenging task to achieve. The knowledge of energy prices of the grid, internal resistance of the lithium ion battery pack on the electric vehicle, the age of the battery and distance travelled by the electric vehicle are very important factors on which the cost of daily driving cycle is dependent. In near future, the energy consumed by the electric vehicles will create a major consumer market for the smart grids. The smart grid system is complemented by the renewable energy sources that contribute and support the grid. The electric vehicles are not only predicted as energy consumers but also as dynamic sources of energy. These vehicles can now travel more than 100 miles with a single charging cycle whereas average day to day commute is well below the maximum capacity of these vehicles. This leaves the driver with the extra energy on the battery pack which can be used later for supporting energy requirement from the grid. As we know that cells/modules in large battery packs do not have identical properties and these degrade at different rates during the course of their lifespan. It is beneficial for the user to quantify the amount of energy that can be used to support the grid. The improvement of the electric grid to the next generation infrastructure ie ‘Smart Grid’ will enable diverse opportunities to contribute the energy and balance the load on the grid. The information about the grid like price quality, load etc will be available to the people very easily. This information can be useful to make the energy grid more economical and environment friendly. We have used the information for price of energy on the grid to optimize the cost of daily driving cycle. The goal of this research is to accurately predict the battery behavior for the daily driving cycle. The prediction of battery behavior will help the driver to decide the optimum charging patterns, energy consumed during driving and the surplus energy available in the batteries. The prior knowledge of the battery behavior, price of the energy on the grid and the trip travel will help the driver to minimize the cost of travel on daily basis as well as throughout the life of the battery

A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction.

A comprehensive working state monitoring method is proposed to protect the power lithium-ion battery packs, implying accurate estimation effect but using minimal time demand of self-learning treatment. A novel state of charge estimation model is conducted by using the improved unscented Kalman filtering method, in which the state of balance and aging process correction is considered, guaranteeing the powered battery supply reliability effectively. In order to realize the equilibrium state evaluation among the internal battery cells, the numerical description and evaluation is putting forward, in which the improved variation coefficient is introduced into the iterative calculation process. The intermittent measurement and real-time calibration calculation process is applied to characterize the capacity change of the battery pack towards the cycling maintenance number, according to which the aging process impact correction can be investigated. This approach is different to the traditional methods by considering the multi-input parameters with real-time correction, in which every calculation step is investigated to realize the working state estimation by using the synthesis algorithm. The state of charge estimation error is 1.83%, providing the technical support for the reliable power supply application of the lithium-ion battery packs