Review of Researches on Techno-Economic Analysis and Environmental Impact of Hybrid Energy Systems

Hybrid energy systems, which are combinations of two or more renewable and non-renewable energy sources, have been identified as a viable mechanism to address the limitations of a single renewable energy source, utilized for electricity generation. In view of this, several research works have been carried out to determine the optimal mix of different renewable and non-renewable energy resources used for electricity generation. This paper presents a comprehensive review of the optimization approaches proposed and adopted by various authors in the literature for optimal sizing of hybrid energy systems. It is observed that the objective functions - considered by a large percentage of researchers to optimize the sizing of hybrid energy systems - are cost minimization of the generated electricity, system reliability enhancement and environmental pollution reduction. Other factors covered in the literature are equally discussed in this article. Similarly, simulation and optimization software used for the same purpose are covered in the paper. In essence, the main aim of this paper is to provide a scope into the works that have been carried out in the field of hybrid energy systems, used for electricity generation with the view to informing researchers and members of the public alike, on trends in methods applied in optimal sizing of hybrid energy systems. It is believed that the information provided in this paper is very crucial in advancing research in the field

Techno-economic assessment of energy storage systems in multi-energy microgrids utilizing decomposition methodology

Renewable resources and energy storage systems integrated into microgrids are crucial in attaining sustainable energy consumption and energy cost savings. This study conducts an in-depth analysis of diverse storage systems within multi-energy microgrids, including natural gas and electricity subsystems, with a comprehensive focus on techno-economic considerations. To achieve this objective, a methodology is developed, comprising an optimization model that facilitates the determination of optimal storage system locations within microgrids. The model considers various factors, such as operating and emission costs of both gas and electricity subsystems, and incorporates a sensitivity analysis to calculate the investment and maintenance costs associated with the storage systems. Due to the incorporation of voltage and current relations in the electricity subsystem as well as gas pressure and flow considerations in the natural gas subsystem, the developed model is classified as a mixed-integer nonlinear programming model. To address the inherent complexity in solving, a decomposition approach based on Outer Approximation/Equality Relaxation/Augmented Penalty is developed. This study offers scientific insights into the costs of energy storage systems, potential operational cost savings, and technical considerations of microgrid operation. The results of the developed decomposition approach demonstrate significant advantages, including reduced solving time and a decreased number of iterations

Africa-EU Renewable Energy Research and Innovation Symposium 2018 (RERIS 2018)

This open access book presents the proceedings of the 2nd Africa-EU Renewable Energy Research and Innovation Symposium (RERIS 18), held in Maseru, Lesotho in January 2018. The symposium aimed to foster research cooperation on renewable energy between Africa and Europe – in academia, as well as the private and public sectors. Addressing thematic areas such as • Grid-connected renewable energy; • Decentralised renewable and household energy solutions; • Energy socioeconomics; and • Promotion of energy research, innovation, education and entrepreneurship, the book brings together contributions from academics and practitioners from the EU and Africa to enable mutual learning and knowledge transfer – a key factor in boosting sustainable development in the African renewable energy market. It also plays a significant role in promoting African renewable energy research, which helps to secure energy supply in both rural and urban areas and to increase generation capacities and energy system resilience. This book is an invaluable resource for academics and professionals across the renewable energy spectrum

Techno-economic feasibility of hybrid hydro-FPV systems in Sub-Saharan Africa under different market conditions

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Techno-Economic Feasibility Study of Autonomous Hybrid AC/DC Microgrid System

Distributed generation technology based on diesel generators often has been considered as a viable solution to providing power to remote areas, but the sky‐rocketing of diesel fuel price and the increasing cost of delivery to such remote sites have called for providing a sustainable solution that is environmentally friendly, economical, affordable, and easily accessible. To this end, the use of locally available energy resources is accepted as a sustainable solution in providing electricity for rural and remote settlements. The system cost of wind and solar energy systems is continuously decreasing because of the increase in the acceptance and deployment of the energy systems based on these renewable energy resources. A standalone hybrid AC/DC electric power system is designed, modeled, simulated, and optimized in HOMER Pro. HOMER is a Hybrid Optimization Model of Electric Renewable that enables the comparison of electric and thermal power production technologies across an extensive variety of applications. Both cycle‐charging and load‐following dispatched strategies are investigated. Plausible selected system components ratings are chosen for the simulation to ensure that there is enough search space for HOMER Pro to obtain an optimal system configuration. Net present cost (NPC) is used as an economic metric to assess the optimal configuration that is technically feasible

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

Design optimization of grid-connected PV-Hydrogen for energy prosumers considering sector-coupling paradigm: Case study of a university building in Algeria

Integrating sector coupling technologies into Hydrogen (H2) based hybrid renewable energy systems (HRES) is becoming a promising way to create energy prosumers, despite the very little research work being done in this largely unexplored field. In this paper, a sector coupling strategy (building and transportation) is developed and applied to a grid-connected PV/battery/H2 HRES, to maximise self-sufficiency for a University campus and to produce power and H2 for driving electric tram in Ouargla, Algeria. A multi-objective size optimization problem is solved as a single objective problem using the ε-constraint method, in which the cost of energy (COE) is defined as the main objective function to be minimized, while both loss of power supply probability (LPSP) and non-renewable usage (NRU) are defined as constraints. Particle swarm optimization and HOMER software are then employed for simulation and optimization purposes. Prior to the two scenarios investigated, a sensitivity study is performed to determine the effects of H2 demand by tram and NRU on the techno-economic feasibility of the proposed system, followed by a new reliability factor introduced in the optimization, namely loss of H2 supply probability (LHSP). The results of the first scenario show that by setting NRUmax = 100%, the system without H2 provides the best solution with COE of 0.016 $/kWh that reaches grid parity and has 13$/kWh. In the second scenario, it is also observed that an increased number of trams (i.e. increased H2 demands) causes a significant reduction in LHSP, COE, NRU and CO2 emissions. It is thus concluded that the grid/PV combination is the optimal choice for the studied system when considering economic aspects. However, taking into account the growing requirements of future energy systems, grid-connected PV with H2 will be the best solution

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