Performance analysis of various hybrid renewable energy systems using battery,hydrogen, and pumped hydro‐based storage units

The aim of this research is to analyze the techno‐economic performance of hybrid renewable energy system (HRES) using batteries, pumped hydro‐based, and hydrogen‐based storage units at Sharurah, Saudi Arabia. The simulations and optimization process are carried out for nine HRES scenarios to determine the optimum sizes of components for each scenario. The optimal sizing of components for each HRES scenario is determined based on the net present cost (NPC) optimization criterion. All of the nine optimized HRES scenarios are then evaluated based on NPC, levelized cost of energy, payback period, CO₂ emissions, excess electricity, and renewable energy fraction. The simulation results show that the photovoltaic (PV)‐diesel‐battery scenario is economically the most viable system with the NPC of US$2.70 million and levelized cost of energy of US$0.178/kWh. Conversely, PV‐diesel‐fuel cell system is proved to be economically the least feasible system. Moreover, the wind‐diesel‐fuel cell is the most economical scenario in the hydrogen‐based storage category. PV‐wind‐diesel‐pumped hydro scenario has the highest renewable energy fraction of 89.8%. PV‐wind‐diesel‐pumped hydro scenario is the most environment‐friendly system, with an 89% reduction in CO₂ emissions compared with the base‐case diesel only scenario. Overall, the systems with battery and pumped hydro storage options have shown better techno‐economic performance compared with the systems with hydrogen‐based storage.     

Techno-economic study of off-grid hybrid photovoltaic/battery and photovoltaic/battery/fuel cell power systems in Kunming,China

The objective of this study is to evaluate the technical and economic feasibility of stand-alone hybrid photovoltaic (PV)/battery and PV/battery/fuel cell (FC) power systems for a community center comprising 100 households in Kunming by using the Hybrid Optimization Model for Electric Renewable (HOMER) software. HOMER is used to define the optimum sizing and techno-economic feasibility of the system equipment based on the geographical and meteorological data of the study region. In this study, different hybrid power systems are analyzed to select the optimum energy system while considering total net present cost (NPC) and levelized cost of energy (COE). The results showed that the optimal hybrid PV/battery system comprised 500 kW PV modules, 1200 7.6-kWh battery units, and 500 kW power converters. The proposed system has an initial cost of $6,670,000, an annual operating cost of $82,763/yr, a total NPC of $7,727,992, and a levelized COE of $1.536/kWh. While the PV/battery/FC power system is possible, the cost increases were due to the investment cost of the FC system. The optimal PV/battery/FC system has an initial cost of $6,763,000, an annual operating cost of $82,312/yr, a total NPC of $7,815,223, and a levelized COE of $1.553/kWh.     

A novel methodology for scrutiny of autonomous hybrid renewable energy system

The optimal design of a hybrid system with different configurations including renewable generation is presented in this paper. A novel multi‐objective function consisting of 6 different objectives of hybrid system is reported using GA, PSO, and TLBO to decide the optimal configurations of parameters. The technical (loss of power supply probability, renewable factor), economical (cost of energy, penalty and fuel consumption), and social (job creation, human development index, and particular matter) features are investigated as objectives simultaneously for optimal design of hybrid system. The different objective indices namely cost of energy, loss of power supply probability, particular matter, human development index, job creation, and renewable factor indices are considered. The newly invented particular matter factor for design consideration of hybrid system directly shows the human health impacts, while pollutant emission is measured in the hybrid system design. The optimum values of objective indices are decided on the basis of the minimum value of multi‐objective function. The distinct cases from I to VI of hybrid system are examined for optimal configuration including different combinations of PV, wind, biomass, diesel generator, and battery bank. The resulting analysis of each case reveals that the performance of TLBO is better than PSO, and PSO is better than GA in all respect through new multi‐objective function and found case I is more efficient solution.     

Multi‐objective exergoeconomic optimization of an Integrated Solar Combined Cycle System using evolutionary algorithms

In this study, a multi‐objective optimization scheme is developed and applied for an Integrated Solar Combined Cycle System that produces 400 MW of electricity to find solutions that simultaneously satisfy exergetic as well as economic objectives. This corresponds to a search for the set of Pareto optimal solutions with respect to the two competing objectives. The optimization process is carried out by a particular class of search algorithms known as multi‐objective evolutionary algorithms. An example of decision‐making has been presented and a final optimal solution has been introduced. The analysis shows that optimization process leads to 3.2% increasing in the exergetic efficiency and 3.82% decreasing of the rate of product cost. Finally, sensitivity analysis is carried out to study the effect of changes in the Pareto optimal solutions to the system important parameters, such as interest rate, fuel cost, solar operation period, and system construction period. Copyright © 2010 John Wiley & Sons, Ltd.     

Determination of optimal operating conditions for a polymer electrolyte membrane fuel cell stack: optimal operating condition based on multiple criteria

A methodology for optimal control of the polymer electrolyte membrane fuel cell (PEMFC) with multiple criteria is presented here. In this regard, thermoelectric objectives and thermoeconomic objective are considered, simultaneously. The proposed fuel cell is a 1200 W Ballard PEMFC namely Nexa? power module. The net power density and exergetic efficiency of the PEMFC are maximized, and the unit cost of the generated power is minimized in a multi‐objective optimization procedure using the NSGA‐II (non‐dominated sorting genetic algorithm). Operating temperature and pressure, air stoichiometric coefficient at the cathode and the current density are considered as controlling parameters in order to acquire optimal performance of the PEMFC. A set of optimal solution namely the Pareto frontier is obtained, and a final optimal solution is selected from available solutions located on the Pareto frontier using the fuzzy decision‐making process based on the Bellman–Zadeh approach. Results are compared with corresponding results obtained previously in single objective optimization scenarios. It has been shown that the optimal operating condition obtained based on the multiple criteria approach has least deviation from the ideal features of the fuel cell in comparison to the corresponding optimal solution obtained in conventional single‐objective optimization approaches. Copyright © 2013 John Wiley & Sons, Ltd.     

Analysis of PV/wind potential in the Canadian residential sector through high‐resolution building energy simulation

Rising fuel prices and global warming are two major issues that concern people today. In this paper, the effect that the integration of the hybrid photovoltaic (PV)/wind‐turbine generation can have on conservation of energy and reduction of greenhouse gases (GHGs) has been studied. Base‐case energy demands were calculated using building energy simulation software and then the residential buildings were equipped with the PV/wind‐turbine electricity generation devices. The results show that the integration of those equipments can reduce both cost of fuel and GHG emissions to a fair amount. Copyright © 2008 John Wiley & Sons, Ltd.     

Decarbonising city bus networks in Ireland with renewable hydrogen

This paper presents techno-economic modelling results of a nationwide hydrogen fuel supply chain (HFSC) that includes renewable hydrogen production, transportation, and dispensing systems for fuel cell electric buses (FCEBs) in Ireland. Hydrogen is generated by electrolysers located at each existing Irish wind farm using curtailed or available wind electricity. Additional electricity is supplied by on-site photovoltaic (PV) arrays and stored using lithium-ion batteries. At each wind farm, sizing of the electrolyser, PV array and battery is optimised system design to obtain the minimum levelised cost of hydrogen (LCOH). Results show the average electrolyser capacity factor is 64% after the integration of wind farm-based electrolysers with PV arrays and batteries. A location-allocation algorithm in a geographic information system (GIS) environment optimises the distributed hydrogen supply chain from each wind farm to a hypothetical hydrogen refuelling station in the nearest city. Results show that hydrogen produced, transported, and dispensed using this system can meet the entire current bus fuel demand for all the studied cities, at a potential LCOH of 5–10 €/kg by using available wind electricity. At this LCOH, the future operational cost of FCEBs in Belfast, Cork and Dublin can be competitive with public buses fuelled by diesel, especially under carbon taxes more reflective of the environmental impact of fossil fuels.     

Simulation and optimization of stand-alone hybrid renewable energy systems

Stand-alone hybrid renewable energy systems usually incur lower costs and demonstrate higher reliability than photovoltaic (PV) or wind systems. The most usual systems are PV–Wind–Battery and PV–Diesel–Battery. Energy storage is usually in batteries (normally of the lead-acid type). Another possible storage alternative, such as hydrogen, is not currently economically viable, given the high cost of the electrolyzers and fuel cells and the low efficiency in the electricity–hydrogen–electricity conversion. When the design of these systems is carried out, it is usually done resolve an optimization problem in which the Net Present Cost (NPC) is minimized or, in some cases, in relation to the Levelized Cost of Energy (LCE). The correct resolution of this optimization problem is a complex task because of the high number of variables and the non-linearity in the performance of some of the system components. This paper revises the simulation and optimization techniques, as well as the tools existing that are needed to simulate and design stand-alone hybrid systems for the generation of electricity.     

An investigation of optimum PV and wind energy system capacities for alternate short and long‐term energy storage sizing methodologies

The goal of this study is to find the optimal sizes of renewable energy systems (RES) based on photovoltaic (PV) and/or wind systems for three energy storage system (ESS) scenarios in a micro‐grid; (1) with pumped hydro storage (PHS) as a long‐term ESS, (2) with batteries as a short‐term ESS, and (3) without ESS. The PV and wind sizes are optimally determined to accomplish the maximum annual RES fraction (F_RES ) with electricity cost lower than or equal to the utility tariff. Furthermore, the effect of the use of battery and PHS on the electricity cost and F_RES are studied. A university campus on a Mediterranean island is selected as a case study. The results show that PV‐wind hybrid system of 8 MW wind and 4.2 MW PV with 89.5 MWh PHS has the highest F_RES of 88.0%, and the highest demand supply fraction as 42.6%. Moreover, the results indicate that the economic and technical parameters of RESs are affected significantly by the use of ESSs depending on the type and the capacity of both the RES and the ESS.     

Optimal sizing of grid-connected photovoltaic energy system in Saudi Arabia

Resource optimization is a major factor in the assessment of the effectiveness of renewable energy systems. Various methods have been utilized by different researchers in planning and sizing the grid-connected PV systems. This paper analyzes the optimal photovoltaic (PV) array and inverter sizes for a grid-connected PV system. Unmet load, excess electricity, fraction of renewable electricity, net present cost (NPC) and carbon dioxide (CO₂) emissions percentage are considered in order to obtain optimal sizing of the grid-connected PV system. An optimum result, with unmet load and excess electricity of 0%, for serving electricity in Makkah, Saudi Arabia is achieved with the PV inverter size ratio of R = 1 with minimized CO₂ emissions. However, inverter size can be downsized to 68% of the PV nominal power to reduce the inverter cost, and hence decrease the total NPC of the system.     

Implementing of the multi-objective particle swarm optimizer and fuzzy decision-maker in exergetic, exergoeconomic and environmental optimization of a benchmark cogeneration system

Multi-objective optimization for design of a benchmark cogeneration system namely as the CGAM cogeneration system is performed. In optimization approach, Exergetic, Exergoeconomic and Environmental objectives are considered, simultaneously. In this regard, the set of Pareto optimal solutions known as the Pareto frontier is obtained using the MOPSO (multi-objective particle swarm optimizer). The exergetic efficiency as an exergetic objective is maximized while the unit cost of the system product and the cost of the environmental impact respectively as exergoeconomic and environmental objectives are minimized. Economic model which is utilized in the exergoeconomic analysis is built based on both simple model (used in original researches of the CGAM system) and the comprehensive modeling namely as TTR (total revenue requirement) method (used in sophisticated exergoeconomic analysis). Finally, a final optimal solution from optimal set of the Pareto frontier is selected using a fuzzy decision-making process based on the Bellman-Zadeh approach and results are compared with corresponding results obtained in a traditional decision-making process. Further, results are compared with the corresponding performance of the base case CGAM system and optimal designs of previous works and discussed.     

Wind-photovoltaic capacity coordination for a time-of-use rate industrial user

As the capacity of wind and photovoltaic (PV) generation systems increases, wind-PV capacity coordination for a time-of-use (TOU) rate industrial user may become an important problem. This coordination can maximise the economic benefits of investing in a wind generation system and a PV generation system. An evolutionary particle swarm optimisation approach to solve the wind-PV capacity coordination for a TOU rate industrial user is proposed. A benefit-cost ratio (BCR) is used to evaluate the economic benefit of investing in wind and PV generation systems for a TOU rate industrial user. The optimal contract capacities and the optimal installed capacities of the wind and PV generation systems for a TOU rate industrial user are obtained. The BCR of investing in wind and PV generation systems are maximised. Test results illustrate the merits of the proposed approach and help determine the impact of changes in electricity cost and capital cost on wind-PV capacity coordination for a TOU rate industrial user.     

Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach

Recently, the increasing energy demand has caused dramatic consumption of fossil fuels and unavoidable raising energy prices. Moreover, environmental effect of fossil fuel led to the need of using renewable energy (RE) to meet the rising energy demand. Unpredictability and the high cost of the renewable energy technologies are the main challenges of renewable energy usage. In this context, the integration of renewable energy sources to meet the energy demand of a given area is a promising scenario to overcome the RE challenges. In this study, a novel approach is proposed for optimal design of hybrid renewable energy systems (HRES) including various generators and storage devices. The ε-constraint method has been applied to minimize simultaneously the total cost of the system, unmet load, and fuel emission. A particle swarm optimization (PSO)-simulation based approach has been used to tackle the multi-objective optimization problem. The proposed approach has been tested on a case study of an HRES system that includes wind turbine, photovoltaic (PV) panels, diesel generator, batteries, fuel cell (FC), electrolyzer and hydrogen tank. Finally, a sensitivity analysis study is performed to study the sensibility of different parameters to the developed model.     

Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design

This paper presents a probabilistic multiobjective framework for optimal distributed energy resources (DERs) planning in the distribution electricity networks. The proposed model is from the distribution company (DISCO) viewpoint. The projected formulation is based on nonlinear programming (NLP) computation. The proposed design attempts to achieve a trade-off between minimizing the monetary cost and minimizing the emission of pollutants in presence of the electrical load as well as electricity market prices uncertainties. The monetary cost objective function consists of distributed generation (DG) investment and operation cost, payment toward loss compensation as well as payment for purchased power from the network. A hybrid fuzzy C-mean/Monte-Carlo simulation (FCM/MCS) model is used for scenario based modeling of the electricity prices and a combined roulette-wheel/Monte-Carlo simulation (RW/MCS) model is used for generation of the load scenarios. The proposed planning model considers six different types of DERs including wind turbine, photovoltaic, fuel cell, micro turbine, gas turbine and diesel engine. In order to demonstrate the performance of the proposed methodology, it is applied to a primary distribution network and using a fuzzified decision making approach, the best compromised solution among the Pareto optimal solutions is found.     

Optimal sizing of autonomous hybrid energy system using supply‐demand‐based optimization algorithm

Hybrid energy systems (HESs) comprising photovoltaic (PV) arrays and wind turbines (WTs) are remarkable solutions for electrifying remote areas. These areas commonly fulfil their energy demands by means of a diesel genset (DGS). In the present study, a novel computational intelligence algorithm called supply‐demand‐based optimization (SDO) is applied to the HES sizing problem based on long‐term cost analysis. The effectiveness of SDO is investigated, and its performance is compared with that of the genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), grasshopper optimization algorithm (GOA), flower pollination algorithm (FPA), and big‐bang‐big‐crunch (BBBC) algorithm. Three HES scenarios are implemented using measured solar radiation, wind speed, and load profile data to electrify an isolated village located in the northern region of Saudi Arabia. The optimal design is evaluated on the basis of technical (loss of power supply probability [LPSP]) and economic (annualized system cost [ASC]) criteria. The evaluation addresses two performance indicators: surplus energy and the renewable energy fraction (REF). The results reveal the validity and superiority of SDO in determining the optimal sizing of an HES with a higher convergence rate, lower ASC, lower LPSP, and higher REF than that of the GA, PSO, GWO, GOA, FPA, and BBBC algorithms. The performance analysis also reveals that an HES comprising PV arrays, WTs, battery banks, and DGS provides the best results: 238.7 kW from PV arrays, 231.6 kW from WTs, 192.5 kWh from battery banks, and 267.6 kW from the DGS. The optimal HES exhibits a high REF (66.4%) and is economically feasible ($104 323.10/year) and environmentally friendly. The entire load demand of the area under study is met without power loss (LPSP = 0%).     

Optimal sizing of photovoltaic systems based green hydrogen refueling stations case study Oman

Green hydrogen reduces carbon dioxide emission, advances the dependency on fossil fuels and improves the economy of the energy sector, especially in developing countries. Hydrogen is required for the green transportation sector and many other industrial applications. However, the high cost of green hydrogen production reduces the fast development of renewable energy projects based on hydrogen production. So, sizing by optimization is required to determine the optimum solutions for green hydrogen production. In this context, this paper aims to analyze three methods that can be developed and implemented for the production of green hydrogen for refueling stations using photovoltaic (PV) systems. Techno-economic models are adopted to calculate the Levelized Hydrogen Cost (LHC) for the PV grid-connected system, stand-alone PV system with batteries, and stand-alone PV system with fuel cells. The photovoltaic systems based green hydrogen refueling stations are optimized using Homer software. The optimization results of the Net Profit Cost (NPC), and the LHC permit the comparison of the three cases and the selection of the optimal solution. The analysis has shown that a 3 MWp grid-connected PV system represents a promising green hydrogen production at an LHC of 5.5 €/kg. The system produces 58 615 kg of green hydrogen per year reducing carbon dioxide emission by 8209 kg per year. The LHC in the stand-alone PV system with batteries, and stand-alone PV system with fuel cells are 5.74 €/kg and 7.38 €/kg, respectively.     

Determination of the optimum hybrid renewable power generating systems for Kavakli campus of Kirklareli University,Turkey

This study is to search for possibilities of supplying the load demand of Kavakli campus of Kirklareli University with solar energy and the fuel cell power generating system (electrolyzer/hydrogen tank/fuel cell) by using the HOMER software due to the fact that hybrid power systems with renewables can significantly reduce emissions which are caused by utilization of non-renewable power sources. In this study, various hybrid systems will be examined and compared among themselves considering cost of energy (COE), renewable fraction, total net present cost (NPC) and hydrogen production. Additionally, this study will seek whether a fuel cell can be integrated into the hybrid systems. According to the study results, the grid connected systems appear cost-effective as expected. Although the grid-connected photovoltaic (PV) hybrid system has the lowest COE and NPC, the grid-connected PV/fuel cell hybrid system with COE, 0.294$/kWh has a slightly higher cost than the optimum one. It is strongly believed that this system may be chosen because it is a cleaner system and its emissions are fairly low.     

Micrositing under practical constraints addressing the energy‐noise‐cost trade‐off

Wind energy is running well ahead of its peers to deal with the demand–supply and environmental crisis due to fossil fuels. However, continuous exploitation of land led wind farms built in close proximity of dwellings of human beings, restricted zones, causing adverse effect on health and the environment. In this work, using a widely used model for wake and acoustic model (ISO‐9613‐2), the optimal number and locations of turbines in a farm has been determined while meeting several conflicting objectives such as noise propagation, energy, and cost. An index‐based decomposition and repair strategy (iDRS) using different indices for grid locations and performing repair on chromosomes to enhance the performance of convergence has been proposed as solution methodology. Comparing with a well‐established case study, the methodology is applied next to another realistic case, where the effects of the presence of practical constraints on the optimal layout are demonstrated. A designer can select a layout from several choices from the obtained Pareto set of solutions based on the permissible noise limits, cost obligations, and the extent of harnessed energy.     

Development of an integrated multi‐objective optimization model for determining the optimal solar incentive design

To promote the deployment of the solar photovoltaic (PV) system from the long‐term perspective, the solar PV industry in many countries still needs the financial support from the government despite its remarkable growth and price reductions in the last decade. Many countries with this financial burden on their government budget, however, are planning to reduce or to expire the financial support step by step. To bring the solar PV market to its full maturity, it is crucial to improve the solar policies and to sustain the financial support with acceptable and reasonable prices, which can maximize the benefits for the investors while minimizing the incentive budget for the government. Towards this end, this study aimed to develop an integrated multi‐objective optimization (iMOO) model for determining the optimal solar incentive design from the perspectives of the investor and the government. A Microsoft Excel‐based iMOO model was developed using life cycle cost analysis, genetic algorithm, and Pareto optimal solutions. The developed Microsoft Excel‐based iMOO model was applied to six target regions to verify its effectiveness in determining the optimal solar incentive design. As a result, it was shown that depending on the various characteristics (e.g., solar radiation, electricity price, and installation cost) of a region, the optimal solar incentive design can be differently determined with a reasonable and acceptable level using the developed iMOO model. Among the six target regions, Newark required the lowest incentive budget of $US10,648.41 whereas Oklahoma City required the highest incentive budget of $US20,648.73 to offer their optimal solar incentives. The model developed in this study can help both the investor and the government in a decision‐making process and provide some solutions and insights for planning solar policies and strategies. Copyright © 2017 John Wiley & Sons, Ltd.     

Hybrid optimization algorithm for modeling and management of micro grid connected system

In this paper, a hybrid optimization algorithm is proposed for modeling and managing the micro grid (MG) system. The management of distributed energy sources with MG is a multi-objective problem which consists of wind turbine (WT), photovoltaic (PV) array, fuel cell (FC), micro turbine (MT) and diesel generator (DG). Because, perfect economic model of energy source of the MG units are needed to describe the operating cost of the output power generated, the objective of the hybrid model is to minimize the fuel cost of the MG sources such as FC, MT and DG. The problem formulation takes into consideration the optimal configuration of the MG at a minimum fuel cost, operation and maintenance costs as well as emissions reduction. Here, the hybrid algorithm is obtained as artificial bee colony (ABC) algorithm, which is used in two stages. The first stage of the ABC gets the optimal MG configuration at a minimum fuel cost for the required load demand. From the minimized fuel cost functions, the operation and maintenance cost as well as the emission is reduced using the second stage of the ABC. The proposed method is implemented in the Matlab/Simulink platform and its effectiveness is analyzed by comparing with existing techniques. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.