Optimizing Hybrid Renewable Energy Systems: A Review

With the fast progression of renewable energy markets, the importance of combining different sources of power into a hybrid renewable energy system (HRES) has gained more attraction. These hybrid systems can overcome limitations of the individual generating technologies in terms of their fuel efficiency, economics, reliability and flexibility. One of the main concerns is the stochastic nature of photovoltaic (PV) and wind energy resources. Wind is often not correlated with load patterns and may be discarded sometimes when abundantly available. Also, solar energy is only available during the day time. A hybrid energy system consisting of energy storage, renewable and nonrenewable generation can alleviate the issues associated with renewable uncertainties and fluctuations. Large number of random variables and parameters in a hybrid energy system requires an optimization that most efficiently sizes the hybrid system components to realize the economic, technical and designing objectives. This chapter provides an overview of optimal sizing and optimization algorithms for hybrid renewable energy systems as well as different objective functions considered for designing such systems

Improved backtracking search optimization algorithm for PV/Wind/FC system

This paper uses a novel optimization method based on the improved backtracking search optimization algorithm (IBSA). The study is conducted for a hybrid stand-alone system composed of photovoltaic panel (PV), wind turbine generator and fuel cell electrolyzer (FC). To demonstrate the effectiveness of the IBSA, four benchmark functions are used. The result shows the better exploration and exploitation of the improved backtracking search optimization algorithm in terms of convergence and speed for system comprinsing PV panel wind, turbine generator and fuel cell. The proposed algorithm is used to optimize the annual total cost (ATC) of the energy produced and feed up the load demand. The economic evaluation of the Hybrid PV/Wind/FC system is done throughout hourly demand and daily wind speed and insulation. The simulation results justify the robustness of the IBSA

Stand-alone hybrid renewable energy systems (HRES)

End of Energy Poverty and achieving Sustainable Energy for all by 2030 is a universal challenge. 1.3 billion people without energy access and 2.8 billion people using unsustainable solid fuel for cooking and heating are global challenges for human and societal sustainable development. Nearly 1 trillion of investment is expected in the Sustainable Energy for All (SE4ALL) scenario to achieve universal energy access in 2030. Around 60% of investments will be in isolated off-grid and mini-grid systems with the relevant goal of duplicating the renewable energy sources in the energy mix. Access to innovation trends in renewable energy off-grid would benefit future installations. This work brings to light the recent years research contributions in Hybrid Renewable Energy Systems (HRES) and related aspects that would benefit these required investments in isolated off-grid and mini-grid systems. An overview on the thematic focus of research in Hybrid Renewable Energy Systems (HRES) in the last decade, period 2005 - 2015, is provided. This review covers multiple key aspects of HRES as the main focus of the research (technical, economical, environmental, financial, etc.); the design of the system (type of load, energy sources, storage, availability of meteorology data, etc.); different optimization criteria and objective function; software and modelling tools; and the type of application and country among others. A methodology for searching, identifying and categorizing the innovations related to HRES is proposed. Applying this methodology during this PhD work results in a primary database with a categorized bibliography including nearly 400 entries. Currently system design is mainly technical driven with economic feasibility analysis regarding the energy cost. As for environmental aspects, the beneficial impacts of renewable energy are hardly introduced as an economical value that is so far the most important decision-making criteria. Regarding decision-making tools, the most currently used optimization algorithms and software tools for the design of HRES is HOMER and a case study for understanding is proposed. Following the analysis of most popular and relevant criteria, an easy to use guideline is proposed encouraging decision-making for more sustainable energy access. There are untapped research opportunities for HRES in multi-disciplinary thematic areas. The analysis of innovations regarding the system design for Hybrid Renewable Energy Systems (HRES) have identified potential for research community aligned with the trends to integrate the value chain and foster innovative business models and sustainable energy markets. After the analysis of those different focus that goes from technical and economical, to environmental, regulatory or policy aspects, an integrated value chain for HRES systems is defined. Knowledge, methodologies & tools are provided in this PhD work for more stand-alone hybrid systems creating value for more of the stakeholders involved. After reviewing the latest innovations in HRES per thematic focus, an integrated value chain for those systems has been proposed and multidisciplinary research opportunities have been identified. Identifying the need to include the environmental aspects in early stages of the decision-making has lead to propose an easy to use guideline integrating most relevant criteria for the design of stand-alone renewable power systems. Finally, the research opportunities identified and the untapped potential of transferring latest innovations have result in the creation of the website ElectrifyMe (www.electrifyme.org) to enable valuable international networking contacts among researchers and encouraging multi-disciplinary research. "Knowledge, methodologies & tools" are powerful contributions by research community and innovators to foster more sustainable energy for all.El fi de la pobresa energètica i l'assoliment d'energia sostenible per a tothom l'any 2030 és un repte universal. 1,3 mil milions de persones sense accés a l'energia i 2,8 mil milions de persones que utilitzen combustible sòlid insostenible per cuinar i escalfar són desafiaments globals pel desenvolupament humà sostenible i social. S'espera una inversió aproximada de 1 trilió en l'energia sostenible per a tots (SE4ALL) per aconseguir l'accés universal a l'energia en 2030. Al voltant del 60 % de les inversions seran en sistemes off-grid i mini-grid, amb la corresponent meta de duplicar les fonts d'energia renovables en el mix energétic. En aquesta tesis es facilita una visió general sobre els àmbits temàtics de la recerca en Hybrid Renewable Energy Systems (HRES) en l'última dècada, període 2005-2015. Aquesta revisió es refereix a diversos aspectes clau deis HRES com: el focus principal de la investigació (tècnics, econòmics, ambientals, financers, etc.); el disseny del sistema (tipus de carrega, fonts d'energia, l'emmagatzematge, la disponibilitat de dades de meteorologia, etc.); diferents criteris d'optimització i funció objectiu; programari de modelatge eines; i el tipus d'aplicació i el país, entre d'altres. Es proposa una metodologia per buscar, identificar i categoritzar les innovacions relacionades amb els HRES. L'aplicació d'aquesta metodologia durant aquest treball de doctorat proporciona una base de dades primaria amb una bibliografia classificada incloent prop de 400 entrades. Actualment el disseny dels sistemes incorporen criteris tècnics amb anàlisi de viabilitat econòmica sobre el cost de l'energia. Pel que fa a les eines de presa de decisions, el métode d'optimització més utilitzats en l'actualitat pel disseny de HRES és HOMER, i es proposa un estudi de cas per a la comprensió deis criteris de disseny. Després de l'anàlisi de la majoria deis valors més habituals i rellevants, es proposa una senzilla guia per la presa de decisions per a l'accés a l'energia més sostenible. Després de compartir innovacions i proporcionar metodologies i eines, facilitar la creació de xarxes entre els investigadors ha demostrat ser una poderosa acció per promoure recerca sense explotar amb equips multidisciplinaris i internacionals. La pàgina web ElectrifyMe (www .electrifyme .org) ha estat creada amb la finalitat de facilitar a la comunitat d'investigació descobrir les innovacions i compartir projectes . Coneixements, metodologies i eines es proporcionen en aquest treball de doctorat per afavorir la creació de valor als sistemes aïllats híbrids renovables (stand-alone HRES) pels actors involucrats. Després de revisar les últimes innovacions en la introducció de renovables en sistemes aïllats en diferent enfoc temàtic, s'han estat identificat oportunitats de recerca multidisciplinars i s'ha proposat una cadena de valor integrada per aquests sistemes. La identificació de la necessitat d'incloure els aspectes ambientals en les primeres etapes de la presa de decisions ha portat a proposar una guia fàcil per utilitzar la integració de criteris més rellevants pel disseny de sistemes d'energia renovables independents. Finalment, tes oportunitats de recerca identificades i el potencial sense explotar de transferir les darreres innovacions tenen com a resultat la creació de la pàgina web ElectrifyMe (www.electrifyme.org) per promoure contactes i col·laboracions de xarxes internacionals entre investigadors i el foment de la investigació multidisciplinar. "El coneixement, les metodologies i les eines són poderoses contribucions de la comunitat de recerca per assolir un accés sostenible a l'energia per tots

Optimal sizing of hybrid renewable energy systems: an application for real demand in Qatar remote area

Renewable energy (RE) sources are becoming popular for power generations due to advances in renewable energy technologies and their ability to reduce the problem of global warming. However, their supply varies in availability (as sun and wind) and the required load demand fluctuates. Thus, to overcome the uncertainty issues of RE power sources, they can be combined with storage devices and conventional energy sources in a Hybrid Power Systems (HPS) to satisfy the demand load at any time. Recently, RE systems received high interest to take advantage of their positive benefits such as renewable availability and CO2 emissions reductions. The optimal design of a hybrid renewable energy system is mostly defined by economic criteria, but there are also technical and environmental criteria to be considered to improve decision making. In this study three main renewable sources of the system: photovoltaic arrays (PV), wind turbine generators (WG) and waste boilers (WB) are integrated with diesel generators and batteries to design a hybrid system that supplies the required demand of a remote area in Qatar using heuristic approach. The method utilizes typical year data to calculate hourly output power of PV, WG and WB throughout the year. Then, different combinations of renewable energy sources with battery storage are proposed to match hourly demand during the year. The design which satisfies the desired level of loss of power supply, CO2 emissions and minimum costs is considered as best design

Hybrid hydrogen-battery systems for renewable off-grid telecom power

Off-grid hybrid systems, based on the integration of hydrogen technologies (electrolysers, hydrogen stores and fuel cells) with battery and wind/solar power technologies, are proposed for satisfying the continuous power demands of telecom remote base stations. A model was developed to investigate the preferred role for electrolytic hydrogen within a hybrid system; the analysis focused on powering a 1 kW telecom load in three locations of distinct wind and solar resource availability. When compared with otherwise equivalent off-grid renewable energy systems employing only battery energy storage, the results show that the integration of a 1 kW fuel cell and a 1.6 kW electrolyser at each location is sufficient, in combination with a hydrogen storage capacity of between 13 and 31 kg, to reduce the required battery capacity by 54–77%, to increase the minimum state-of-charge from 37 to 55% to >81.5% year-round despite considerable seasonal variation in supply, and to reduce the amount of wasted renewable power by 55–79%. For the growing telecom sector, the proposed hybrid system provides a ‘green’ solution, which is preferable to shipping hydrogen or diesel to remote base stations

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

Optimal planning and sizing of an autonomous hybrid energy system using multi stage grey wolf optimization

The continuous increase in energy demand and the perpetual dwindling of fossil fuel coupled with its environmental impact have recently attracted research focus in harnessing renewable energy sources (RES) across the globe. Representing the largest RES, solar and wind energy systems are expanding due to the growing evidence of global warming phenomena. However, variability and intermittency are some of the main features that characterize these RES as a result of fluctuation in weather conditions. Hybridization of multiple sources improves the system’s efficiency and reliability of supply due to the varying nature of the RES. Also, the unavailability of solar radiation (SR) and wind speed (WS) measuring equipment in the meteorological stations necessitates the development of prediction algorithms based on Artificial Intelligent (AI) techniques. This thesis presents an autonomous hybrid renewable energy system for a remote community. The hybrid energy system comprises of a photovoltaic module and wind turbine as the main source of energy. Batteries are used as the energy storage devices and diesel generator as a backup energy supply. A new hybrid Wavelet Transform and Adaptive Neuro-Fuzzy Inference System (WT-ANFIS) is developed for the SR prediction, while a hybrid Particle Swarm Optimization (PSO) and ANFIS (PSO-ANFIS) algorithm is developed for the WS prediction. The prediction accuracy of the proposed WT-ANFIS model was validated by comparison with the conventional ANFIS model, Genetic Algorithm (GA) and ANFIS (GA-ANFIS), and PSO-ANFIS models. The proposed PSO-ANFIS for the WS prediction is also compared with ANFIS and GA-ANFIS models. Also, Root Mean Square Error (RMSE), Correlation Coefficient (r) and Coefficient of Determination (R²) are used as statistical indicators to evaluate the performance of the developed prediction models. Additionally, a techno-economic feasibility analysis is carried out using the SR and WS data predicted to assess the viability of the hybrid solar-wind-battery-diesel system for electricity generation in the selected study area. Finally, a new cost-effective Multi Stage – Grey Wolf Optimization (MS-GWO) algorithm is applied to optimally size the different system components. This is aimed at minimizing the net present cost (NPC) while considering reliability and satisfying the load demand. MS-GWO is evaluated by comparison with PSO, GWO and PSO-GWO algorithms. From the results obtained, the statistical evaluators used for model performance assessment of the SR prediction shows that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². Also, from the simulation results, the optimal configuration has an NPC of $1.01 million and cost of energy (COE)$0.110/kWh, with an operating cost of $4,723. The system is environmentally friendly with a renewable fraction of 98.3% and greenhouse gas emission reduction of 65%. Finally, a comparison is done between the proposed MS-GWO algorithm with the PSO, GWO and PSO-GWO algorithms. Based on this comparison, the proposed hybrid MS-GWO algorithm outperforms the individual PSO, GWO and PSO-GWO by 3.17%, 2.53% and 2.11% in terms of NPC and reduces the computational time by 53%, 46% and 36% respectively. Therefore, it can be concluded that the proposed MS-GWO technique can be applied for optimal sizing application globally

Reviewing energy system modelling of decentralized energy autonomy

Research attention on decentralized autonomous energy systems has increased exponentially in the past three decades, as demonstrated by the absolute number of publications and the share of these studies in the corpus of energy system modelling literature. This paper shows the status quo and future modelling needs for research on local autonomous energy systems. A total of 359 studies are roughly investigated, of which a subset of 123 in detail. The studies are assessed with respect to the characteristics of their methodology and applications, in order to derive common trends and insights. Most case studies apply to middle-income countries and only focus on the supply of electricity in the residential sector. Furthermore, many of the studies are comparable regarding objectives and applied methods. Local energy autonomy is associated with high costs, leading to levelized costs of electricity of 0.41 $/kWh on average. By analysing the studies, many improvements for future studies could be identified: the studies lack an analysis of the impact of autonomous energy systems on surrounding energy systems. In addition, the robust design of autonomous energy systems requires higher time resolutions and extreme conditions. Future research should also develop methodologies to consider local stakeholders and their preferences for energy systems