Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

To facilitate the development of active distribution networks with high penetration of large‐scale distributed generation (DG) and electric vehicles (EVs), active management strategies should be considered at the planning stage to implement the coordinated optimal allocations of DG and electric vehicle charging stations (EVCSs). In this article, EV charging load curves are obtained by the Monte Carlo simulation method. This article reduces the number of photovoltaic outputs and load scenarios by the K‐means++ clustering algorithm to obtain a typical scenario set. Additionally, we propose a bi‐level programming model for the coordinated DG and EVCSs planning problem. The maximisation of annual overall profit for the power supply company is taken as the objective function for the upper planning level. Then, each scenario is optimised at the lower level by using active management strategies. The improved harmonic particle swarm optimisation algorithm is used to solve the bi‐level model. The validation results for the IEEE‐33 node, PG&E‐69 node test system and an actual regional 30‐node distribution network show that the bi‐level programming model proposed in this article can improve the planning capacity of DG and EVCSs, and effectively increase the annual overall profit of the power supply company, while improving environmental and social welfare, and reducing system power losses and voltage shifts. The study provides a new perspective on the distribution network planning problem.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/1/etep12366.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/2/etep12366_am.pd

Optimal Home Energy Management System for Committed Power Exchange Considering Renewable Generations

This thesis addresses the complexity of SH operation and local renewable resources optimum sizing. The effect of different criteria and components of SH on the size of renewable resources and cost of electricity is investigated. Operation of SH with the optimum size of renewable resources is evaluated to study SH annual cost. The effectiveness of SH with committed exchange power functionality is studied for minimizing cost while responding to DR programs

A Review on Optimization of Microgrid for Rural Electrification

Microgrids are a good technique for applying clean and renewable energy by 2030. Goal 7 of the Sustainable Development Goals calls for the improvement of energy operations. Since the majority of people in rural areas lack access to power, rural electrification is extremely difficult. Currently, more than two billion people lack access to energy, which affects their living conditions. A billion people throughout the globe still do not have access to power, making economic progress impossible. Many individuals live in remote, rural areas that are not connected to the national grid. Optimization techniques by offering an analytical framework may play a crucial part in this advancement. Achieve a wide range of economic, social, and environmental goals that are bound by budget, resources, local population statistics, and other considerations. This paper has been divided into basically two subsections. In section one, we begin by compiling. It gives an overview of the microgrid concept and its properties and presents important reviews of the literature on common microgrids and their remote operation localization of electricity in section second we compile various optimization methods of a microgrid. After that, we compile combining intelligent optimization algorithms with adaptive techniques Each issue type is discussed in detail, with a categorization system based on the problem's goal, suggested solution approach, components, size, and area. Finally, we identify upcoming research issues for microgrid improvement

Recent trends of the most used metaheuristic techniques for distribution network reconfiguration

Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topi

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

Stand-alone solar-pv hydrogen energy systems incorporating reverse osmosis

The world’s increasing energy demand means the rate at which fossil fuels are consumed has increased resulting in greater carbon dioxide emissions. For many small (marginalised) or coastal communities, access to potable water is limited alongside good availability of renewable energy sources (solar or wind). One solution is to utilise small-scale renewably powered stand-alone energy systems to help supply power for everyday utilities and to operate desalination systems serving potable water (drinking) needs reducing diesel generator dependence. In such systems, on-site water production is essential so as to service electrolysis for hydrogen generation for Proton Exchange Membrane (PEM) fuel cells. Whilst small Reverse Osmosis (RO) units may function as a (useful) dump load, it also directly impacts the power management of stand-alone energy systems and affects operational characteristics. However, renewable energy sources are intermittent in nature, thus power generation from renewables may not be adequate to satisfy load demands. Therefore, energy storage and an effective Power Management Strategy (PMS) are vital to ensure system reliability. This thesis utilises a combination of experiments and modelling to analyse the performance of renewably powered stand-alone energy systems consisting of photovoltaic panels, PEM electrolysers, PEM fuel cells, batteries, metal hydrides and Reverse Osmosis (RO) under various scenarios. Laboratory experiments have been done to resolve time-resolved characteristics for these system components and ascertain their impact on system performance. However, the main objective of the study is to ascertain the differences between applying (simplistic) predictive/optimisation techniques compared to intelligent tools in renewable energy systems. This is achieved through applying intelligent tools such as Neural Networks and Particle Swarm Optimisation for different aspects that govern system design and operation as well as solar irradiance prediction. Results indicate the importance of device level transients, temporal resolution of available solar irradiance and type of external load profile (static or time-varying) as system performance is affected differently. In this regard, minute resolved simulations are utilised to account for all component transients including predicting the key input to the system, namely available solar resource which can be affected by various climatic conditions such as rainfall. System behaviour is (generally) more accurately predicted utilising Neural Network solar irradiance prediction compared to the ASHRAE clear sky model when benchmarked against measured irradiance data. Allowing Particle Swarm Optimisation (PSO) to further adjust specific control set-points within the systems PMS results in improvements in system operational characteristics compared to using simplistic rule-based design methods. In such systems, increasing energy storage capacities generally allow for more renewable energy penetration yet only affect the operational characteristics up to a threshold capacity. Additionally, simultaneously optimising system size and PMS to satisfy a multi-objective function, consisting of total Net Present Cost and CO2 emissions, yielded lower costs and carbon emissions compared to HOMER, a widely adopted sizing software tool. Further development of this thesis will allow further improvements in the development of renewably powered energy systems providing clean, reliable, cost-effective energy. All simulations are performed on a desktop PC having an Intel i3 processor using either MATLAB/Simulink or HOMER

Optimisation of stand-alone hybrid energy systems for power and thermal loads

Stand-alone hybrid energy systems are an attractive option for remote communities without a connection to a main power grid. However, the intermittent nature of solar and other renewable sources adversely affects the reliability with which these systems respond to load demands. Hybridisation, achieved by combining renewables with combustion-based supplementary prime movers, improves the ability to meet electric load requirements. In addition, the waste heat generated from backup Internal Combustion Engines or Micro Gas Turbines can be used to satisfy local heating and cooling loads. As a result, there is an expectation that the overall efficiency and Greenhouse Gas Emissions of stand-alone systems can be significantly improved through waste heat recovery. The aims of this PhD project are to identify how incremental increases to the hardware complexity of hybridised stand-alone energy systems affect their cost, efficiency, and CO2 footprint. The research analyses a range of systems, from those designed to meet only power requirements to others satisfying power and heating (Combined Heat and Power), or power plus both heating and cooling (Combined Cooling, Heating, and Power). The majority of methods used focus on MATLAB-based Genetic Algorithms (GAs). The modelling deployed finds the optimal selection of hardware configurations which satisfy single- or multi-objective functions (i.e. Cost of Energy, energy efficiency, and exergy efficiency). This is done in the context of highly dynamic meteorological (e.g. solar irradiation) and load data (i.e. electric, heating, and cooling). Results indicate that the type of supplementary prime movers (ICEs or MGT) and their minimum starting thresholds have insignificant effects on COE but have some effects on Renewable Penetration (RP), Life Cycle Emissions (LCE), CO2 emissions, and waste heat generation when the system is sized meeting electric load only. However, the transient start-up time of supplementary prime movers and temporal resolution have no significant effects on sizing optimisation. The type of Power Management Strategies (Following Electric Load-FEL, and Following Electric and Following Thermal Load- FEL/FTL) affect overall Combined Heating and Power (CHP) efficiency and meeting thermal demand through recovered heat for a system meeting electric and heating load with response to a specific load meeting reliability (Loss of Power Supply Probability-LPSP). However, the PMS has marginal effects on COE. The Electric to Thermal Load Ratio (ETLR) has no effects on COE for PV/Batt/ICE but strongly affects PV/Batt/MGT-based hybridised CHP systems. The higher thermal than the electric loads lead to higher efficiency and better environmental footprint. Results from this study also indicate that for a stand-alone hybridised system operating under FEL/FTL type PMS, the power only system has lower cost compared to the CHP and the Combined Cooling, Heating, and Power (CCHP) systems. This occurs at the expense of overall energy and exergy efficiencies. Additionally, the relative magnitude of heating and cooling loads have insignificant effects on COE for PV/Batt/ICE-based system configurations, however this substantially affects PV/Batt/MGT-based hybridised CCHP systems. Although there are no significant changes in the overall energy efficiency of CCHP systems in relation to variations to heating and cooling loads, systems with higher heating demand than cooling demand lead to better environmental benefits and renewable penetration at the cost of Duty Factor. Results also reveal that the choice of objective functions do not affect the system optimisation significantly

Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency