Conference Paper

Optimum Unit Sizing of Stand-Alone PV-WT-Battery Hybrid System Components Using Jaya

Authors:
  • Institute of Space Technology KICSIT Campus
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Abstract

Renewable energy sources (RESs) are considered as reliable and green electric power generations. Photovoltaic (PV) and wind turbine (WT) are used to provide electricity in remote areas. The optimum unit sizing of hybrid RESs components is a vital challenge in a stand-alone system. This paper presents Jaya algorithm for optimum unit sizing of a PV-WT-Battery hybrid system to fulfill the consumer's load at minimal cost. The reliability of the system is considered by a maximum allowable loss of power supply probability (LP SPmax). The results obtained from the Jaya algorithm show that the PV-WT-Battery hybrid system is the most economical and cost-effective solution for all proposed LP SPmax values as compared to PV-Battery and WT-Battery systems.

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... Considering the aforementioned advantages, many countries are getting encouragement to opt for RESs instead of FFs. For instance, a bill in 2018 was signed by California Governor Brown, Meta-heuristic algorithms with hybridized schemes and other optimization methodologies are widely adopted for energy management [16][17][18][19] and the unit sizing problem [20][21][22][23][24][25][26][27][28][29]. The Jaya optimization scheme was used for the optimal sizing of HRESs to minimize TAC [20,21]. ...
... For instance, a bill in 2018 was signed by California Governor Brown, Meta-heuristic algorithms with hybridized schemes and other optimization methodologies are widely adopted for energy management [16][17][18][19] and the unit sizing problem [20][21][22][23][24][25][26][27][28][29]. The Jaya optimization scheme was used for the optimal sizing of HRESs to minimize TAC [20,21]. In [22], Ghorbani et al. used a hybrid approach by combining the features of the GA with particle swarm optimization (PSO) for the optimal sizing of the PV-WT-battery system of a house in an SA environment, located in Tehran, Iran. ...
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An increase in the world's population results in high energy demand, which is mostly fulfilled by consuming fossil fuels (FFs). By nature, FFs are scarce, depleted, and non-eco-friendly. Renewable energy sources (RESs) photovoltaics (PVs) and wind turbines (WTs) are emerging alternatives to the FFs. The integration of an energy storage system with these sources provides promising and economical results to satisfy the user's load in a stand-alone environment. Due to the intermittent nature of RESs, their optimal sizing is a vital challenge when considering cost and reliability parameters. In this paper, three meta-heuristic algorithms: teaching-learning based optimization (TLBO), enhanced differential evolution (EDE), and the salp swarm algorithm (SSA), along with two hybrid schemes (TLBO + EDE and TLBO + SSA) called enhanced evolutionary sizing algorithms (EESAs) are proposed for solving the unit sizing problem of hybrid RESs in a stand-alone environment. The objective of this work is to minimize the user's total annual cost (TAC). The reliability is considered via the maximum allowable loss of power supply probability (LPSP max) concept. The simulation results reveal that EESAs provide better results in terms of TAC minimization as compared to other algorithms at four LPSP max values of 0%, 0.5%, 1%, and 3%, respectively, for a PV-WT-battery hybrid system. Further, the PV-WT-battery hybrid system is found as the most economical scenario when it is compared to PV-battery and WT-battery systems.
... T. Wanjekeche et al. have used the particle swarm optimization algorithm for multi-objective function to obtain the optimal sizing of hybrid stand-alone micro-grid system based on wind, PV and storage batteries to ensure the required power electricity demand for the specified location habitants and the water supply plant [20]. A. Khan et al. have proposed a Jaya algorithm for finding the optimum sizing of a PV-WT-Battery hybrid system to fulfill the consumer power demand and the at minimal cost [21]. X. Wang et al. have presented a methodology for designing a hybrid renewable energy system consists of solar, wind and diesel generator as a backup resource as well as battery storage, where a receding horizon optimization strategy has been used and applied to a single-family residential home [22]. ...
... S is salvage value of hybrid system. Salvage values of each system unit was given by equation (21). Where f is salvage value of each system component ($/m 2 ), g is inflation rate and & is size estimated by during optimization (m 2 ): ...
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... However, hybrid energy system depends on several factors such as: site location, resources intensity, electrical load and even the seasonal variations in consumption profile...etc. All this makes the techno-economic optimal sizing of the hybrid system components a vital challenge [1], [16]. The optimization technique, information and optimal sizing methods for PV/Diesel hybrid systems can be found in [17]. ...
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Currently, the use of decentralized hybrid systems based on renewable energy is an economical and practical option for residential applications in remote and rural areas. The optimal sizing of hybrid systems depends extensively on the load data and the potential of used resources. Demand side management DSM is a solution that makes it possible to manage consumption profile in terms of time and amont of consumed energy. This paper focuses on the optimum and economic design of an off grid PV/Battery/Diesel hybrid system. The simulation was carried out by HOMER software under the real load profile and climatic conditions. Firstly, load management effect on optimal system sizing is simulated, the strategic conservation measures are implemented by improving equipments energy efficiency. Secondly, the effect of other strategies such as strategic conservation, load shifting and peak clipping with 30% variation from the original load profile is also simulated. The technical, financial and environmental results of the optimal hybrid system for different strategies are discussed with comparison, including net present cost (NPC), energy cost (CoE), renewable fraction (RF) and CO2 emissions. Based on the obtained results, the advantages of the DSM is demonstrated firstly by the dayli consumption and peak load reduction, secondy by the reduction of system components and cost, which leads to a reduction in system costs from 11,159 $ to 6,279 $, from 0.244 $/kWh to 0.240 $/kWh for cost of energy and from 705.486 kg/yr to 522.454 kg/yr in term of annual CO2 emissions.
... 15: if x1new ≺ x2old then 16: select x1new as a new solution in PF; 17: end if 18: else 19: Sort and extract the BCS using FMF approach. 20: end if 21: end for 22: end for 23: t + +; 24: end while 25 been used. It is also required to consider the conflicting nature of the three objective functions as explained in Section II. ...
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... We consider a hybrid PV-WT-battery system, which is more ecofriendly and cost-effective than other hybrid systems utilizing diesel generators. The contributions listed below are an extension of our previous work [32]: ...
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... Limited reserves of fossil fuels and environmental protection issues have raised the concern to integrate renewable energy resources (RESs) into the grid. Wind power plants and solar photovoltaic are amongst the most common RESs [5,6]. A lot of research work has been done on classical OPF, which consider only thermal generators. ...
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... We consider the hybrid PV-WT-Battery system, which is more eco-friendly and costeffective as compared to other hybrid systems that utilize generators. Thus, the contributions listed below are an extension of our previous work [207]: ...
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Electrification to rural and remote areas with limited or no access to grid connection is one of the most challenging issues in developing countries like Colombia. Due to the recent concerns about the global climatic change and diminishing fuel prices, searching for reliable, environmental friendly and renewable energy sources to satisfy the rising electrical energy demand has become vital. This study aims at analyzing the application of photovoltaic (PV) panels, wind turbines and diesel generators in a stand-alone hybrid power generation system for rural electrification in three off-grid villages in Colombia with different climatic characteristics. The areas have been selected according to the “Colombia’s development plan 2011–2030 for non-conventional sources of energy”. First, different combinations of wind turbine, PV, and diesel generator are modeled and optimized to determine the most energy-efficient and cost-effective configuration for each location. HOMER software has been used to perform a techno-economic feasibility of the proposed hybrid systems, taking into account net present cost, initial capital cost, and cost of energy as economic indicators.
Article
In this paper, a reliable methodology incorporated mine blast algorithm (MBA) is applied to solve the optimal sizing of a hybrid system consisting of photovoltaic modules, wind turbines and fuel cells (PV/WT/FC) to meet a certain load of remote area in Egypt. The main objective of the optimal sizing process is to achieve the minimum annual cost of the system with load coverage. The sizing process is performed optimally based on real measured data for solar radiation, ambient temperature and wind velocity recorded by the solar radiation and meteorological station located at national research institute of astronomy and geophysics, Helwan city, Egypt. Three other meta-heuristic optimization techniques, particle swarm optimization, cuckoo search and artificial bee colony are applied to solve the problem and the results are compared with those obtained by the proposed methodology. A power management strategy that regulates the power flow between each system component is also presented. The obtained results show that; applying the proposed methodology will save about 24.8% in the annual total cost of the proposed system compared with PSO, 8.956% compared with CS and 11.5576% compared with ABC. The proposed algorithm based on MBA is candidate for solving the presented optimization problem of optimal sizing the hybrid PV/WT/FC system.
Chapter
This chapter introduces teaching-learning-based optimization (TLBO) algorithm and its elitist and non-dominated sorting multiobjective versions. Two examples of unconstrained and constrained benchmark functions and an example of a multiobjective constrained problem are presented to demonstrate the procedural steps of the algorithm.
Article
A new approach for optimal combinations of Hybrid Renewable Energy Systems (HRESs) is proposed, for diesel-free remote communities and related decision-making problems. The objective of the design is to satisfy the load with total cost’s minimization considering related constraints, where all analytical equations are given. The proposed system is a DC configuration for renewable energy integration in which Wind Turbines (WTs) are connected to the DC bus and the battery system removes the fluctuations in the DC bus. The WT is assumed to be well controlled to obtain the power curve and provide proper electricity to the grid. It is found that WT and Photovoltaic (PV) systems should be considered simultaneously on diesel-free generation islands to achieve a reliable and optimized configuration. A novel strategy is introduced for determining the range of batteries, which can be determined using the renewable energy potential to satisfy the load. It is also demonstrated that WTs are an essential power component in real HRES installations. It is illustrated that the range of WT operation must be first taken into account in real installations. Increasing WT fraction from an initial value, for instance 8%, to a desired value of about 33%, can lead to significant reductions in total cost as well as in the number of PV systems and batteries, even if the WT cost is significantly higher than that of PVs. A WT fraction higher than approximately 50% should not be considered. Finally, discussions are extended to consider the optimum WT fraction in a HRES.
Article
This paper presents a persuasive smart energy management system (PSEMS) which incorporates the peculiarities of the developing economies, for low/medium income earners, under the flat pricing regime. The PSEMS uses an algorithm based on mild modified intrusive genetic algorithm (MMIGA), with and without user preferences and considers grid status, while meeting the minimum demand criteria in terms of load allocation and cost optimization. Four budgetary conditions were used as case study. Results show that the dynamically generated demand (based on budget constraint) as obtained by PSEMS and the optimal dispatch as evaluated by MMIGA closely matches. Daily allocation efficiency in the range of 98.32–99.60% was obtained without users’ preference, while 98.76–99.67% was obtained with users’ preference, under the four budgetary conditions. The PSEMS allows the residential end user to make decisions regarding electricity consumption thus minimizing electricity bill and the use of electricity.
Article
Hybrid photovoltaic (PV)–wind turbine (WT) systems with battery storage have been introduced as a green and reliable power system for remote areas. There is a steady increase in usage of hybrid energy system (HES) and consequently optimum sizing is the main issue for having a cost-effective system. This paper evaluates the performance of different evolutionary algorithms for optimum sizing of a PV/WT/battery hybrid system to continuously satisfy the load demand with the minimal total annual cost (TAC). For this aim, all the components are modeled and an objective function is defined based on the TAC. In the optimization problem, the maximum allowable loss of power supply probability is also considered to have a reliable system, and three well-known heuristic algorithms, namely, particle swarm optimization (PSO), tabu search (TS) and simulated annealing (SA), and four recently invented metaheuristic algorithms, namely, improved particle swarm optimization (IPSO), improved harmony search (IHS), improved harmony search-based simulated annealing (IHSBSA), and artificial bee swarm optimization (ABSO), are applied to the system and the results are compared in terms of the TAC. The proposed methods are applied to a real case study and the results are discussed. It can be seen that not only average results produced by ABSO are more promising than those of the other algorithms but also ABSO has the most robustness. Also considering set to 5%, the PV/battery is the most cost-effective hybrid system, and in other values, the PV/WT/battery is the most cost-effective systems.
Article
Teaching–Learning-Based Optimization (TLBO) algorithms simulate the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. In this paper, the basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self motivated learning. Performance of the improved TLBO algorithm is assessed by implementing it on a range of standard unconstrained benchmark functions having different characteristics. The results of optimization obtained using the improved TLBO algorithm are validated by comparing them with those obtained using the basic TLBO and other optimization algorithms available in the literature.
Article
Taking into account oil depletion, increasing population, and increasing energy demand, electrical power generation has entered into a new phase of evolution, which can be characterized mainly by increasing concerns about climate change, by a transition from a hydrocarbon-based economy, and by an efficient utilization of energy. In this sense, it seems that alternative energies have gathered considerable momentum since 1970s oil crisis. Moreover, Earth seems to have enough power to cover World’s electrical power demand but not by a single source; for this reason, recent researches have been carried out in order to design in an optimal way system’s configuration. Nevertheless, because of the randomized nature of alternative energy sources, electrical load profile, as well as the non-linear response of system components, to mention a few, is not an easy to assess the hybrid energy system performance; therefore, hybrid energy system designing has been a complex task. For this reason, the aim of this paper is to present a brief review about the sizing methodologies developed in the recent years.
Article
It has become imperative for the power and energy engineers to look out for the renewable energy sources such as sun, wind, geothermal, ocean and biomass as sustainable, cost-effective and environment friendly alternatives for conventional energy sources. However, the non-availability of these renewable energy resources all the time throughout the year has led to research in the area of hybrid renewable energy systems. In the past few years, a lot of research has taken place in the design, optimization, operation and control of the renewable hybrid energy systems. It is indeed evident that this area is still emerging and vast in scope. The main aim of this paper is to review the research on the unit sizing, optimization, energy management and modeling of the hybrid renewable energy system components. Developments in research on modeling of hybrid energy resources (PV systems), backup energy systems (Fuel Cell, Battery, Ultra-capacitor, Diesel Generator), power conditioning units (MPPT converters, Buck/Boost converters, Battery chargers) and techniques for energy flow management have been discussed in detail. In this paper, an attempt has been made to present a comprehensive review of the research in this area in the past one decade.
Article
A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the ‘Teacher Phase’ and the second part consists of the ‘Learner Phase’. ‘Teacher Phase’ means learning from the teacher and ‘Learner Phase’ means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.