Design of a cost effective battery-supercapacitor hybrid energy storage system for hourly dispatching solar PV power

This study aims to develop a low cost energy storage system for hourly dispatching solar photovoltaic (PV) power for 1MW grid connected PV array. To fulfill this objective, the optimum (most economical) scaling of a battery and supercapacitor (SC) hybrid storage is developed based on the time constant of a low pass filter (LPF) that is used to allocate the power between a battery and SC. Based on the battery state of charge (SOC), rule based algorithms are developed to estimate the perfect grid reference power for each one-hour dispatching period. Another rule-based algorithm is implemented to keep the battery SOC within a certain limit that helps to increase the battery lifetime. An economic comparison of different kinds of battery and SC combination in hybrid energy storage system (HESS) is presented in this research. This study also considers the relationship between the actual PV cell temperature and the ambient temperature and presents their effects on energy storage price calculations. This study uses actual solar data of four different days recorded at Oak Ridge National Laboratory (ORNL) in MATLAB/Simulink environment simulations to get the better picture about annual energy storage cost for hourly dispatching solar power. According to the results, the HESS combination of li-ion battery and SC, outperforms a battery only or lead acid and SC combination in HESS operation regardless of temperature framework

Hourly Dispatching Wind-Solar Hybrid Power System with Battery-Supercapacitor Hybrid Energy Storage

This dissertation demonstrates a dispatching scheme of wind-solar hybrid power system (WSHPS) for a specific dispatching horizon for an entire day utilizing a hybrid energy storage system (HESS) configured by batteries and supercapacitors. Here, wind speed and solar irradiance are predicted one hour ahead of time using a multilayer perceptron Artificial Neural Network (ANN), which exhibits satisfactory performance with good convergence mapping between input and target output data. Furthermore, multiple state of charge (SOC) controllers as a function of energy storage system (ESS) SOC are developed to accurately estimate the grid reference power (PGrid,ref) for each dispatching period. A low pass filter (LPF) is employed to decouple the power between a battery and a supercapacitor (SC), and the cost optimization of the HESS is computed based on the time constant of the LPF through extensive simulations. Besides, the optimum value of depth of discharge for ESS considering both cycling and calendar expenses has been investigated to optimize the life cycle cost of the ESS, which is vital for minimizing the cost of a dispatchable wind-solar power scheme. Finally, the proposed ESS control algorithm is verified by conducting control hardware-in-the loop (CHIL) experiments in a real-time digital simulator (RTDS) platform

A biased load manager home energy management system for low-cost residential building low-income occupants

© 2018 Elsevier Ltd This research paper presents the development of a biased load manager home energy management system for low-cost residential building occupants. As a smart grid framework, the proposed load manager coordinates the operation of the inverter system of a low cost residential apartment consisting of rooftop solar photovoltaic panels, converter and battery, and provides a platform for discriminating residential loads into on-grid and off-grid supply classes while maximizing solar irradiance for optimum battery charging and improving consumer comfort from base levels. Modelled in a Matlab simulation environment, the system incorporates a converter system for maximum power point tracking using a hopping algorithm, with a dedicated mechanism for smart dispatch of specified loads to meet the users' comfort based on the priority ranking of the loads. Results obtained indicate a 34% reduction in electricity cost, 26% reduction in carbon emissions and a 4% increase in comfort level for the photovoltaic/battery/utility option compared to the utility only option. The results further show that cost is a major factor affecting the users' comfort and not necessarily dispatch of appliances to meet energy needs. The research can be useful for encouraging the adoption of the photovoltaic/battery/utility option by low/middle income energy users in developing countries

Application of a simplified thermal-electric model of a sodium-nickel chloride battery energy storage system to a real case residential prosumer

Recently, power system customers have changed the way they interact with public networks, playing a more and more active role. End-users first installed local small-size generating units, and now they are being equipped with storage devices to increase the self-consumption rate. By suitably managing local resources, the provision of ancillary services and aggregations among several end-users are expected evolutions in the near future. In the upcoming market of household-sized storage devices, sodium-nickel chloride technology seems to be an interesting alternative to lead-acid and lithium-ion batteries. To accurately investigate the operation of the NaNiCl2 battery system at the residential level, a suitable thermoelectric model has been developed by the authors, starting from the results of laboratory tests. The behavior of the battery internal temperature has been characterized. Then, the designed model has been used to evaluate the economic profitability in installing a storage system in the case that end-users are already equipped with a photovoltaic unit. To obtain realistic results, real field measurements of customer consumption and solar radiation have been considered. A concrete interest in adopting the sodium-nickel chloride technology at the residential level is confirmed, taking into account the achievable benefits in terms of economic income, back-up supply, and increased indifference to the evolution of the electricity market

Power Management of Remote Microgrids Considering Battery Lifetime

Currently, 20% (1.3 billion) of the world’s population still lacks access to electricity and many live in remote areas where connection to the grid is not economical or practical. Remote microgrids could be the solution to the problem because they are designed to provide power for small communities within clearly defined electrical boundaries. Reducing the cost of electricity for remote microgrids can help to increase access to electricity for populations in remote areas and developing countries. The integration of renewable energy and batteries in diesel based microgrids has shown to be effective in reducing fuel consumption. However, the operational cost remains high due to the low lifetime of batteries, which are heavily used to improve the system\u27s efficiency. In microgrid operation, a battery can act as a source to augment the generator or a load to ensure full load operation. In addition, a battery increases the utilization of PV by storing extra energy. However, the battery has a limited energy throughput. Therefore, it is required to provide a balance between fuel consumption and battery lifetime throughput in order to lower the cost of operation. This work presents a two-layer power management system for remote microgrids. The first layer is day ahead scheduling, where power set points of dispatchable resources were calculated. The second layer is real-time dispatch, where schedule set points from the first layer are accepted and resources are dispatched accordingly. A novel scheduling algorithm is proposed for a dispatch layer, which considers the battery lifetime in optimization and is expected to reduce the operational cost of the microgrid. This method is based on a goal programming approach which has the fuel and the battery wear cost as two objectives to achieve. The effectiveness of this method was evaluated through a simulation study of a PV-diesel hybrid microgrid using deterministic and stochastic approach of optimization

Optimal Capacity Allocation of Large-Scale Wind-PV-Battery Units

An optimal capacity allocation of large-scale wind-photovoltaic- (PV-) battery units was proposed. First, an output power model was established according to meteorological conditions. Then, a wind-PV-battery unit was connected to the power grid as a power-generation unit with a rated capacity under a fixed coordinated operation strategy. Second, the utilization rate of renewable energy sources and maximum wind-PV complementation was considered and the objective function of full life cycle-net present cost (NPC) was calculated through hybrid iteration/adaptive hybrid genetic algorithm (HIAGA). The optimal capacity ratio among wind generator, PV array, and battery device also was calculated simultaneously. A simulation was conducted based on the wind-PV-battery unit in Zhangbei, China. Results showed that a wind-PV-battery unit could effectively minimize the NPC of power-generation units under a stable grid-connected operation. Finally, the sensitivity analysis of the wind-PV-battery unit demonstrated that the optimization result was closely related to potential wind-solar resources and government support. Regions with rich wind resources and a reasonable government energy policy could improve the economic efficiency of their power-generation units

Implementation of Solar Irradiance Forecasting Using Markov Switching Model and Energy Management System

Photovoltaic (PV) systems integration is increasingly being used to reduce fuel consumption in diesel-based remote microgrids. However, uncertainty and low correlation of PV power availability with load reduce the benefits of PV integration. These challenges can be handled by introducing reserve, which however leads to increased operational cost. Solar irradiance forecasting helps to reduce reserve requirement, thereby improving the utilization of PV energy. In this thesis, a new solar irradiance forecasting method for remote microgrids based on the Markov Switching Model (MSM) is presented. This method uses locally available data to predict one-day-ahead solar irradiance for scheduling energy resources in remote microgrids. The model considers the past solar irradiance data, the Clear Sky Irradiance (CSI), and the Fourier basis functions to create linear models for three regimes or states: high, medium, and low energy regimes for a day corresponding to sunny, mildly cloudy, and extremely cloudy days, respectively. The case study for Brookings, SD, discussed in this thesis, resulted in an average Mean Absolute Percentage Error (MAPE) of 31.8% for five years, 2001 to 2005, with higher errors during summer months than during winter months. The solar irradiance forecasting method was implemented in OPAL-RT real-time digital simulator using PV panels as sensors. For forecasting irradiance, the first four hours of irradiance data in the morning are required. These data were measured using the solar panels rather than pyranometers as the sensors . A case study for real-time irradiance forecasting in Brookings on June 9, 2015 showed RMSE and MAPE of 131.08W=m2 and 45.45%, respectively. The improvement of renewable integration is the future and present prospects for power utilization. Microgrids experience several constraints such as integration of intermittent renewable sources, costlier reliability improvements, restricted expansion of the microgrid system, growth in load, etc. Hence, more research in this field of study is required and a complete laboratory scale microgrid testbed is needed for experimenting different types of microgrid topologies and for studying the coordination of individual components with a well-defined energy management scheme. In this thesis, the development of a laboratory scale single-phase microgrid testbed along with the implementation of microgrid’s Energy Management System (EMS) are discussed. The testbed was developed using central controller and Commercial Off-The-Shelf (COTS) equipment. The EMS comprised of double layers: schedule layer and real-time dispatch layer. A case study conducted for the implementation of the EMS showed that the difference in the scheduled and the dispatched powers were handled by the generator and the energy storage system themselves

Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems:A Review

Renewable energy sources (RESs) are the replacement of fast depleting, environment polluting, costly, and unsustainable fossil fuels. RESs themselves have various issues such as variable supply towards the load during different periods, and mostly they are available at distant locations from load centers. This paper inspects forecasting techniques, employed to predict the RESs availability during different periods and considers the dispatch mechanisms for the supply, extracted from these resources. Firstly, we analyze the application of stochastic distributions especially the Weibull distribution (WD), for forecasting both wind and PV power potential, with and without incorporating neural networks (NN). Secondly, a review of the optimal economic dispatch (OED) of RES using particle swarm optimization (PSO) is presented. The reviewed techniques will be of great significance for system operators that require to gauge and pre-plan flexibility competence for their power systems to ensure practical and economical operation under high penetration of RESs

Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG(Lab)(2)) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications

Optimal energy management of a microgrid system

Mestrado de dupla diplomação com École Superieure en Sciences AppliquéesA smart management strategy for the energy ows circulating in microgrids is necessary to economically manage local production and consumption while maintaining the balance between supply and demand. Finding the optimum set-points of the various generators and the best scheduling of the microgrid generators can lead to moderate and judicious use of the powers available in the microgrid. This thesis aims to apply an energy management system based on optimization algorithms to ensure the optimal control of microgrids by taking as main purpose the minimization of the energy costs and reduction of the gas emissions rate responsible for greenhouse gases. Two approaches have been proposed to nd the optimal operating setpoints. The rst one is based on a uni-objective optimization approach in which several energy management systems are implemented for three case studies. This rst approach treats the optimization problem in a uni-objective way where the two functions price and gas emission are treated separately through optimization algorithms. In this approach the used methods are simplex method, particle swarm optimization, genetic algorithm and a hybrid method (LPPSO). The second situation is based on a multiobjective optimization approach that deals with the optimization of the two functions: cost and gas emission simultaneously, the optimization algorithm used for this purpose is Pareto-search. The resulting Pareto optimal points represent di erent scheduling scenarios of the microgrid system.Uma estrat egia de gest~ao inteligente dos uxos de energia que circulam numa microrrede e necess aria para gerir economicamente a produ c~ao e o consumo local, mantendo o equil brio entre a oferta e a procura. Encontrar a melhor programa c~ao dos geradores de microrrede pode levar a uma utiliza c~ao moderada e criteriosa das pot^encias dispon veis na microrrede. Esta tese visa desenvolver um sistema de gest~ao de energia baseado em algoritmos de otimiza c~ao para assegurar o controlo otimo das microrredes, tendo como objetivo principal a minimiza c~ao dos custos energ eticos e a redu c~ao da taxa de emiss~ao de gases respons aveis pelo com efeito de estufa. Foram propostas duas estrat egias para encontrar o escalonamento otimo para funcionamento. A primeira baseia-se numa abordagem de otimiza c~ao uni-objetivo no qual v arios sistemas de gest~ao de energia s~ao implementados para tr^es casos de estudo. Neste caso o problema de otimiza c~ao e baseado na fun c~ao pre co e na fun c~ao emiss~ao de gases. Os m etodos de otimiza c~ao utilizados foram: algoritmo simplex, algoritmos gen eticos, particle swarm optimization e m etodo h brido (LP-PSO). A segunda situa c~ao baseia-se numa abordagem de otimiza c~ao multi-objetivo que trata a otimiza c~ao das duas fun c~oes: custo e emiss~ao de gases em simult^aneo. O algoritmo de otimiza c~ao utilizado para este m foi a Procura de Pareto. Os pontos otimos de Pareto resultantes representam diferentes cen arios de programa c~ao do sistema de microrrede