Cost Minimization of Battery-Supercapacitor Hybrid Energy Storage for Hourly Dispatching Wind-Solar Hybrid Power System

This study demonstrates a dispatching scheme of wind-solar hybrid power system (WSHPS) for a one-hour dispatching period for an entire day utilizing battery and supercapacitor hybrid energy storage subsystem (HESS). A frequency management approach is deployed to extend the longevity of the batteries through extensively utilizing the high energy density property of batteries and the high power density property of supercapacitors in the HESS framework. A low-pass filter (LPF) is employed to decouple the power between a battery and a supercapacitor (SC). The cost optimization of the HESS is computed based on the time constant of the LPF through extensive simulations in MATLAB/SIMULINK platform. The curve fitting and Particle Swarm Optimization approaches are applied to seek the optimum value of the LPF time constant. Several control algorithms as a function of the battery state of charge are developed to achieve accurate estimation of the grid reference power for each one-hour dispatching period. This estimation helps to minimize the energy storage cost, in addition to ensuring that the HESS has sufficient capacity for next-day operation. The optimum value of depth of discharge for HESS considering both cycling and calendar expenses has also been investigated for the best competitive energy storage cost for hourly dispatching the power of the WSHPS. This research also presents an economic comparison to investigate the significance of using different types of energy storage for hourly dispatching the WSHPS. The simulation results show that the presented HESS is superior to battery or SC-only operation

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

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

Solar farm hourly dispatching using a supercapacitor and battery energy storage system

While most research on solar energy has been concentrated on smoothing intermittent powerbeing pushed into the power grid, this research is focused on improving the complete integrationof solar energy into the power grid by dispatching, or supplying a constant level of power, for 1-hour time periods. A hybrid energy storage system (HESS), consisting of lead-acid batteries andsupercapacitors, will absorb and supply the necessary levels of power to keep the systems outputpower constant. The demand on the overall HESS and the two components in HESS, lead-acid batteries and supercapacitors, will maintain the constant level of power to dispatch. Thepredicted level of output power, for a one hour dispatching period, is determined by an estimationalgorithm that uses actual solar data from Oak Ridge National Laboratory collected every minutethroughout the day. This research shows results from June 9th, 2015, June 10th, 2015, June 12th,2015, and December 25th, 2015 between 5:00 AM and 7:59 PM [3]. The estimation algorithmincorporates the solar irradiance and temperature to estimate the PV arrays average outputpower and its eciency. The demand on the HESS is sent through a low-pass lter with a timeconstant of 1-minute that is then used as the reference for the lead-acid batteries. The remainingdemand on the HESS is used as the reference for the supercapacitors. This utilizes the lead-acidbatteries high energy density property, or slow charge/discharge rates at high energy levels, andthe supercapacitors high power density property, or rapid charge/discharge rates at low energylevels [1, 4]

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

An Optimal State of Charge Feedback Control Strategy for Battery Energy Storage in Hourly Dispatch of PV Sources

AbstractThe effects of intermittent cloud and changes in temperature cause a randomly fluctuated output of a photovoltaic (PV) system. To mitigate the PV impacts particularly on a weak electricity network, battery energy storage (BES) system is an effective means to smooth out the power fluctuations. Consequently, the net power injected to the electricity grid by PV/BES systems can be dispatched smoothly such as on an hourly basis. This paper presents an improved control strategy for a grid-connected BES for mitigating PV farm output power fluctuations. A feedback controller for state of charge is proposed where the control parameters are optimized using genetic algorithm. In this way, the optimal size for the BES is also determined to hourly dispatch a 1.2 MW PV farm. The effectiveness of the proposed control scheme is evaluated using PSCAD/EMTDC-based simulation

Assessment of the potential of solar thermal small power systems in small utilities

The potential economic benefit of small solar thermal electric power systems to small municipal and rural electric utilities is assessed. Five different solar thermal small power system configurations were considered in three different solar thermal technologies. The configurations included: (1) 1 MW, 2 MW, and 10 MW parabolic dish concentrators with a 15 kW heat engine mounted at the focal point of each dish, these systems utilized advanced battery energy storage; (2) a 10 MW system with variable slat concentrators and central steam Rankine energy conversion, this system utilized sensible thermal energy storage; and (3) a 50 MW central receiver system consisting of a field of heliostats concentrating energy on a tower-mounted receiver and a central steam Rankine conversion system, this system also utilized sensible thermal storage. The results are summarized in terms of break-even capital costs. The break-even capital cost was defined as the solar thermal plant capital cost which would have to be achieved in order for the solar thermal plants to penetrate 10 percent of the reference small utility generation mix by the year 2000. The calculated break-even capital costs are presented

Managing power demand from air conditioning benefits solar pv in India scenarios for 2040

An Indian electricity system with very high shares of solar photovoltaics seems to be a plausible future given the ever-falling solar photovoltaic (PV) costs, recent Indian auction prices, and governmental support schemes. However, the variability of solar PV electricity, i.e., the seasonal, daily, and other weather-induced variations, could create an economic barrier. In this paper, we analyzed a strategy to overcome this barrier with demand-side management (DSM) by lending flexibility to the rapidly increasing electricity demand for air conditioning through either precooling or chilled water storage. With an open-source power sector model, we estimated the endogenous investments into and the hourly dispatching of these demand-side options for a broad range of potential PV shares in the Indian power system in 2040. We found that both options reduce the challenges of variability by shifting electricity demand from the evening peak to midday, thereby reducing the temporal mismatch of demand and solar PV supply profiles. This increases the economic value of solar PV, especially at shares above 40%, the level at which the economic value roughly doubles through demand flexibility. Consequently, DSM increases the competitive and cost-optimal solar PV generation share from 33-45% (without DSM) to ∼45-60% (with DSM). These insights are transferable to most countries with high solar irradiation in warm climate zones, which amounts to a major share of future electricity demand. This suggests that technologies, which give flexibility to air conditioning demand, can be an important contribution toward enabling a solar-centered global electricity supply. © 2020 by the authors

Optimal Technology Choice and Investment Timing: A Stochastic Model of Industrial Cogeneration vs. Heat-Only Production

In this paper we develop an economic model that explains the decision-making problem under uncertainty of an industrial firm that wants to invest in a process technology. More specifically, the decision is between making an irreversible investment in a combined heat-and-power production (cogeneration) system, or to invest in a conventional heat-only generation system (steam boiler) and to purchase all electricity from the grid. In our model we include the main economic and technical variables of the investment decision process. We also account for the risk and uncertainty inherent in volatile energy prices that can greatly affect the valuation of the investment project. The dynamic stochastic model presented allows us to simultaneously determine the optimal technology choice and investment timing. We apply the theoretical model and illustrate our main findings with a numerical example that is based on realistic cost values for industrial oil- or gas-fired cogeneration and heat-only generation in Switzerland. We also briefly discuss expected effects of a CO2 tax on the investment decision.Cogeneration, Irreversible investment, Risk, Uncertainty, Real options