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Optimum Unit Sizing of Stand-Alone PV-WT-Battery Hybrid System Components Using Jaya
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.
... 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. ...
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 ): ...
Photovoltaic and wind energy are the most promising as a future energy technology and can be classified as a clean sources of electric energy in the world. Size optimization of the hybrid renewable energy system play an important role in minimizing the total cost of the system (TCS) and suitable load supply. The main focus of this research is to develop the efficient approach for the optimization of hybrid renewable energy system composed by photovoltaic area, wind turbine (WT), diesel generator (DG) and battery bank (BB). For this purpose, this paper proposes a new metaheuristic technique called modified grey wolf optimizer (M-GWO) for minimize the TCS of the hybrid system considering power balanced between the components. The study of reliability with loss power supply probability (LPSP), the energy not supplied (ENS) and the reliability of the power supply (RPS) methods are demonstrated. For improving the high exploration and exploitation to find the global optimum and robustness of our new approach the obtained results by M-GWO are compared with Grey Wolf Optimizer (GWO) and particle swarm optimization (PSO) methods.
... 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]. ...
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. ...
In recent past, to meet the growing energy demand of electricity, integration of renewable energy resources (RESs) in an electrical network is a center of attention. Furthermore, optimal integration of these RESs make this task more challenging because of their intermittent nature. Therefore, in the present study power flow problem is treated as a multi-constraint, multi-objective optimal power flow (MOOPF) problem along with optimal integration of RESs. Whereas, the objectives of MOOPF are threefold: overall generation cost, real power loss of system and carbon emission reduction of thermal sources. In this work, a computationally efficient technique is presented to find the most feasible values of different control variables of the power system having distributed RESs. Whereas, the constraint satisfaction is achieved by using penalty function approach (PFA) and to further develop true Pareto front (PF), Pareto dominance method is used to categorize Pareto dominate solution. Moreover, to deal with intermittent nature of RES, probability density function (PDF) and stochastic power models of RES are used to calculate available power from RESs. Since, objectives of the MOOPF problem are conflicting in nature, after having the set of non-dominating solutions fuzzy membership function (FMF) approach has been used to extract the best compromise solution (BCS). To test the validity of developed technique, the IEEE-30 bus system has been modified with integration of RESs and final optimization problem is solved by using particle swarm optimization (PSO) algorithm. Simulation results show the achievement of proposed technique managing fuel cost value long with the optimal values of other objectives.
... 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]: ...
Renewable energy sources (RESs) are considered to be reliable and green electric power generation sources. Photovoltaics (PVs) and wind turbines (WTs) are used to provide electricity in remote areas. Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment. The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution. This paper proposes a hybrid algorithm of Jaya and a teaching-learning-based optimization (TLBO) named the JLBO algorithm for the optimal unit sizing of a PV-WT-battery hybrid system to satisfy the consumer's load at minimal total annual cost (TAC). The reliability of the system is considered by a maximum allowable loss of power supply probability (LPSPmax) concept. The results obtained from the JLBO algorithm are compared with the original Jaya, TLBO, and genetic algorithms. The JLBO results show superior performance in terms of TAC, and the PV-WT-battery hybrid system is found to be the most economical scenario. This system provides a cost-effective solution for all proposed LPSPmax values as compared with PV-battery and WT-battery systems.
... 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. ...
An improved multi-objective multi-verse optimization (IMOMVO) algorithm is proposed for solving multi-objective optimal power flow (MOOPF) problem with uncertain renewable energy sources (RESs). Cross pollination steps of flower pollination algorithm (FPA) along with crowding distance and non-dominating sorting approach is incorporated with the basic MOMVO algorithm to further enhance the exploration, exploitation and for well distributed Pareto-optimal solution. To confirm the effectiveness of the proposed IMOMVO algorithm, modified IEEE 30-bus system with three cases are implemented by considering the total generation cost minimization, active power losses, and emission. The simulation results obtained with IMOMVO is compared with MOMVO, NSGA-II, and MOPSO, which reveal the capability of the proposed IMOMVO in terms of solution optimality and distribution.
... 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]: ...
A smart city is an efficient, reliable, and sustainable urban center that facilitates its inhabitants with a high quality of life standards via optimal management of its resources. Energy management of smart homes (SHs) is one of the most challenging and demanding issues which needs significant effort and attention. Demand side management (DSM) in smart grid (SG) authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. In DSM, scheduling of appliances based on consumer-defined priorities is an important task performed by a home energy management controller (HEMC). However, user discomfort is caused by the scheduling of home appliances based on the demand response or limiting its time of use. Further, rebound peaks that are regenerated in the off-peak hours is also a major challenge in DSM. In addition, an increase in the world population is resulting in high energy demand; thus, causing a huge consumption of fossil fuels. This ultimately results in severe environmental problems for mankind and nature. Renewable energy sources (RESs) emerge as an alternative to the fossil sources. These RESs have the advantages of environmental friendliness and sustainability, which are incorporated in SHs via two modes: grid-connected (GC) or stand-alone (SA). The reliability concerns in RESs are usually met with the usage of hybrid RESs along with the integration of energy storage systems (ESS). The efficient usage of these components in the hybrid RESs requires an optimum unit sizing that achieves the objectives pertaining to cost minimization and reliability in SA mode. These are some of the main concerns of a decision maker. This thesis focuses on employing meta-heuristic techniques for efficient utilization of energy and RESs in SH. At first, an evolutionary accretive comfort algorithm (EACA) is developed based on four postulations which allows the time-varying priorities to be quantified in time and device-based features. Based on the input data, considering the appliances’ power ratings, its time of use, and absolute comfort derived from priorities, the EACA is able to generate an optimal energy consumption pattern which would give maximum satisfaction at a predetermined user budget. A cost per unit comfort index (χ) which relates the consumer expenditure to the achievable comfort is also demonstrated. To test the applicability of the proposed EACA, three budget scenarios of 1.5 $/day, 2.0 $/day, and 2.5$/day are performed. Secondly, a priority-induced DSM strategy based on the load shifting technique considering various energy cycles of an appliance is presented. The day-ahead load shifting technique is mathematically formulated and mapped with multiple knapsack problem (MKP) to mitigate the rebound peaks. The proposed autonomous HEMC embeds three meta-heuristic optimization techniques: genetic algorithm (GA), enhanced differential evolution (EDE), and binary particle swarm optimization (BPSO) along with the optimal stopping rule, which is used for solving the load shifting problem. Next, we integrate the RESs and ESS in a residential sector considering GC mode. The proposed optimized home energy management system minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of the electricity market. Here the appliances are classified into shiftable and non-shiftable categories, and a hybrid GA-BPSO (HGPO) scheme outperforms to other algorithms in terms of cost and a peak-to-average ratio (PAR). Finally, meta-heuristic schemes that do not depend on algorithmic-specific parameters are focused for RESs and ESS integration in a SA system. Preliminary, Jaya algorithm is used for finding an optimal unit sizing of RESs components, including photovoltaic (PV) panels, wind turbines (WTs), and fuel cell (FC) with an objective to reduce the consumer total annual cost. The methodology is applied to real solar irradiation and wind speed data taken for Hawksbay, Pakistan. Next, an improved Jaya and the learning phase as depicted in teaching learningbased optimization (TLBO), named JLBO algorithm for optimal unit sizing of a PV-WT-Battery hybrid system is also demonstrated for another site located in Rafsanjan, Iran. The system reliability is considered using the maximum allowable loss of power supply probability (LPSPmax) provided by the consumer. Thus, the thesis objectives achieved are to have a green, reliable, economical, and sustainable power supply in the SH.
In a stand-alone environment, a system comprising of non-renewable source, renewable energy sources (RESs), and energy storage systems like fuel cells (FCs) provide an effective and reliable solution to fulfill the user's load. In this paper, a diesel generator (DG), pho-tovoltaics (PVs), wind turbines (WTs) and FCs are modeled, optimally sized, and compared in three scenarios: PV-WT-FC-DG, PV-FC-DG, and WT-FC-DG in terms of environmental emission and total annual cost (TAC) for a home, located in Hawksbay, Pakistan. The optimal size of hybrid RESs and their components is achieved using a novel TAC minimization algorithm (TACMA). The TACMA achieves superior results in terms of TAC when it is compared to two algorithm-specific parameter-less schemes: Jaya and teaching learning-based optimization. Further, the PV-WT-FC-DG and PV-FC-DG hybrid systems are found as the most economical and nature-friendly scenarios, respectively.
In a hybrid energy system, fulfilling the user's electricity demand cost-effectively is an imperative task. In a stand-alone environment, diesel generator (DG) and renewable energy sources (RESs), including photovoltaics (PVs), wind turbines (WTs), and fuel cells (FCs) provide an effective and reliable solution to fulfill a household load. To achieve the economic and environmental aspects of hybrid RESs (HRESs), optimal sizing of components is indispensable. In this paper, the PV-WT-FC-DG hybrid system is considered, optimally sized, and modeled in terms of environmental emissions and total annual cost (TAC) values for Hawksbay, Pakistan. For optimal sizing, the teaching learning-based optimization (TLBO) algorithm is proposed that does not require any algorithm-specific parameter for its execution. The results reveal that TLBO fulfills the household load at minimum TAC.
Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission, consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this paper, we present priority-induced DSM strategy based on the load shifting tech-nique considering various energy cycles of an appliance. The day-ahead load shifting technique proposed is mathematically formulated and mapped to multiple knapsack problem to mitigate the rebound peaks. The autonomous energy management controller proposed embeds three meta-heuristic optimization techniques; genetic algorithm, enhanced differential evolu-tion, and binary particle swarm optimization along with optimal stopping rule, which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the results validate that the proposed DSM strategy successfully shifts the appliance operations to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also helps in reducing the peak load demand from the smart grid. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay.
A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack. Available from: https://www.researchgate.net/publication/323945280_A_priority-induced_demand_side_management_system_to_mitigate_rebound_peaks_using_multiple_knapsack [accessed Mar 26 2018].
Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.
A simple yet powerful optimization algorithm is proposed in this paper for solving the constrained and unconstrained optimization problems. This algorithm is based on the concept that the solution obtained for a given problem should move towards the best solution and should avoid the worst solution. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. The performance of the proposed algorithm is investigated by implementing it on 24 constrained benchmark functions having different characteristics given in Congress on Evolutionary Computation (CEC 2006) and the performance is compared with that of other well-known optimization algorithms. The results have proved the better effectiveness of the proposed algorithm. Furthermore, the statistical analysis of the experimental work has been carried out by conducting the Friedman’s rank test and Holm-Sidak test. The proposed algorithm is found to secure first rank for the ‘best’ and ‘mean’ solutions in the Friedman’s rank test for all the 24 constrained benchmark problems. In addition, for solving the constrained benchmark problems, the algorithm is also investigated on 30 unconstrained benchmark problems taken from the literature and the performance of the algorithm is found better.
This paper presents the performance of teaching–learning-based optimization (TLBO) algorithm to obtain the optimum geometrical dimensions of a robot gripper. Design of robot grippers is an important aspect of the overall robot performance and the most demanding process in any robot system to match the need for the production requirement. Robot grippers are the end effectors used to grasp and hold the objects. A gripper acts as the bridge between the robot arm and the environment around it. Five objectives, namely the difference between the maximum and minimum gripping forces, the force transmission ratio, the shift transmission ratio, length of all the elements of the gripper, and the effort of the gripper mechanism are considered as objective functions. The problem has seven design variables, including six variables as dimensions of the gripper and one variable as the angle between two links of the gripper. Three robot grippers are optimized. Computational results shows that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem.
The power supply of remote sites and applications at minimal cost and with low emissions is an important issue when discussing future energy concepts. This paper presents the modelling and optimization of a stand-alone hybrid energy system. The system consists of photovoltaic (PV) panels and a wind turbine as renewable power sources, a diesel generator for back-up power and batteries to store excess energy and to improve the system reliability. For storage the technologies of lithium-ion, lead-acid, vanadium redox-flow or a combination thereof have been considered. To be able to use different battery technologies at the same time, a battery management system (BMS) is needed. The presented BMS minimizes operation costs while taking into account different battery operating points and ageing mechanisms. The system is modelled and implemented in Matlab/Simulink. As input, the model uses data of the irradiation, wind speed and air temperature measured in 10 min intervals for 10 years in Aachen - Germany. The load is assumed to be that of a rural UMTS/GSM base station for telecommunication. For a time frame of 20 years, the performance is evaluated and the total costs have been determined. Using a genetic algorithm, component sizes and settings have then been varied and the system re-evaluated to minimize the overall costs. The optimization has been also done for a site in Quneitra - Syria which has very good solar radiation that allows for the comparison between two countries, as the weather data in the two countries differ greatly (different weather data). The optimization results show that using batteries in combination with the renewables is economical and ecological. However, the best solution is to combine redox-flow batteries with the renewables. In addition, a power supply system consisting only of batteries, PV and wind generators may be applicable as well to satisfy the power demand.
The development of novel solar power technologies is considered to be one of many key solutions toward fulfilling a worldwide increasing demand for energy. Rapid growth within the field of solar technologies is nonetheless facing various technical barriers, such as low solar cell efficiencies, low performing balance-of-systems (BOS), economic hindrances (e.g., high upfront costs and a lack of financing mechanisms), and institutional obstacles (e.g., inadequate infrastructure and a shortage of skilled manpower). The merits and de-merits of solar energy technologies are both discussed in this article. A number of technical problems affecting renewable energy research are also highlighted, along with beneficial interactions between regulation policy frameworks and their future prospects. In order to help open novel routes with regard to solar energy research and practices, a future roadmap for the field of solar research is discussed.
Two heuristic approaches based on particle swarm optimization (PSO), i.e., a PSO algorithm with adaptive inertia weight (PSOAIW) and a PSO algorithm with a constriction factor (PSOCF), are applied to the optimization of a hybrid system consisting of photovoltaic panels, a fuel cell, natural gas and the electrical grid to supply residential thermal and electrical loads. An economic model is developed and an economic analysis carried out for the grid-connected hybrid solar–hydrogen combined heat and power systems. The optimization seeks to achieve the minimum cost of the system with relevant constraints for residential applications. The optimization process is implemented and tested using actual data from northeastern Iran. Three other well-known meta-heuristic optimization techniques, namely the imperialist competition algorithm, genetic algorithm, and original particle swarm optimization, are applied to solve the problem and the results are compared with those obtained by the two heuristic approaches. The results show that the proposed PSOAIW and PSOCF algorithms achieve better results than other algorithms, and the hybrid solar-hydrogen system is the most cost-effective and reliable for satisfying residential energy demands in the near future.
PV and wind hybrid are found to be the most lucrative solution for the diminishing traditional energy sources. Whereas these alternatives sources of the energy have many remarkable rewards like cost of energy and feasibility etc. The attributes of these sources of being cost effective and stable are possible due to their complementary nature as compared to independent energy systems. Therefore, these systems have admirable capability to meet energy crisis up to some extent. The proposed work gives the idea about various configuration, control strategy, techno-economic analysis and social effect. The findings of comprehensive review will help for further improvements in hybrid system design and control with respect to practical system implementation. This paper also presents a case study of remote area Barwani, India and results are compared using Homer and PSO. The resulting analysis reveals that configurations of hybrid system are the most techno-economical feasible solution concerning COE, renewable fraction, maximum renewable penetration, levelized cost, operating cost, mean electrical efficiency, and emission amongst various hybrid system configurations using PSO as compared to HOMER.
This study aims to supply electricity energy via a hybrid PV(photovoltaic)-Diesel-Battery for a summer house which is located remotely from the grid connection in Kilis province of southern Turkey (Latitude: 36° 43', Longitude: 37° 07', Altitude: 664 m). Summer houses are widespread in this area, and it is impossible for these buildings to benefit from grid connection. Optimal sizing of hybrid PV-Diesel-Battery systems prove to be very economical as an energy source for these houses. The optimization demonstrated that the lowest investment cost was 12,400 Turkish liras, and renewable energy part of the proposed system was calculated as 79%. In this respect, the system was optimally sized with a PV of 3 kW, a diesel generator of 1 kW, a converter of 2 kW and 6 units of battery. PV panels and diesel generator generate 4248 and 773 kW h/year, respectively, which amounts to 5021 kW h/year. This is a Hybrid PV-Diesel-Battery system. It consists of renewable (PV System) and non-renewable (Diesel Generator) energy sources. Therefore, this paper focuses on optimal sizing of the proposed system. Total annual energy demand is 2353.8 kW h/year, and the remaining amount is stored in the batteries. Total renewable production divided by load, total renewable production divided by generation, and renewable energy part of the proposed system are 116.38%, 84.6%, and %79, respectively. In addition to its cost effective energy supply, the system is also environment-friendly. Hybrid PV-Diesel-Battery system prevented 44.024 tCO2, 123.39 kg of CO, 13.471 kg of UHCs, 9.16 kg of PM, 90.52 kg of SO2 and 1100.33 kg of NOx. This study can also be adapted for other cities and regions.
Solar photovoltaic (PV) system is one of the matured solar-to-electricity conversion technologies with a great potential for residential applications. For wider adoption of PV systems, there is a need for an accurate sizing and economic assessment tool to inform decision makers. In this study, we propose a new optimization model based on integer programming for the adoption of stand-alone PV systems in the residential sector. The proposed model not only determines the optimal number of PV modules and batteries but also assesses the economic feasibility of the system through annualized cost. The model takes into account site-specific data in finding the optimal sizes. The effectiveness of the proposed model is assessed through a case study in Bursari, Nigeria. The result obtained from the model reveals that establishing a solar PV system is not only environmentally friendly but also about 30% cheaper than the diesel generators that are currently in use at Bursari. In particular, annually about 361 USD per residential building could be saved if the proposed system is used to replace the diesel generators. Finally, this study also provides valuable managerial insights about the effect of several parameters on the performance of the proposed PV system.
The major contributor to global warming is human-generated greenhouse gases (GHGs) emissions that pollute the air. GHGs emissions are a global issue dominated by emission of carbon dioxide (CO2). Notably, CO2 accounts for an estimated 77% of GHGs and thus has a huge impact on the environment. The capture, sequestration, and utilization of CO2 emissions from flue gas are now becoming familiar worldwide. These methods are a promising solution to promote sustainability for the benefit of future generations. Previously, many researchers have focused on capturing and storing CO2; however, less effort has been spent on finding ways to utilize flue gas emissions. Moreover, several issues must be overcome in the field of carbon capture and sequestration (CCS) technology, especially regarding the cost, capacity of storage and the durability of the storage reservoir. In addition, this paper addresses new technology in carbon capture and sequestration. To make CCS technology more feasible, this paper suggests a sustainable method combining CCS and biofuel production using CO2 as a feedstock. This method offers many advantages, such as CO2 emission mitigation and energy security through the production of renewable energy. Due to the many advantages of biofuels, the conversion of CO2 into biofuel is a best practice and may provide a solution to pollution while encouraging sustainability practises.
In this paper, a method for designing hybrid electricity generation systems is presented. It is based on the Modified Electric System Cascade Analysis method. The Power Pinch analysis is used as a guideline for development of an isolated power supply system, which consists of photovoltaic panels, wind turbines and energy storage units. The design procedure uses a simulation model, developed using MATLAB/SIMULINK and applies the developed algorithms for obtaining an optimal design. A validation of the Modified Electric System Cascade Analysis method is performed by comparing the obtained results with those from the Homer Pro software. The procedure takes as inputs hourly wind speed, solar radiation, demands, as well as cost data, for the generation and storage facilities. It is also applied to minimise the loss of power supply probability and to minimize the number of storage units. The algorithm has been demonstrated with a case study on a site in Oujda city, with daily electrical energy demand of 18.7 kWh, resulting in a combination of photovoltaic panels, wind turbine and batteries at minimal cost. The results from the Modified Electric System Cascade Analysis and HOMER Pro show that both tools successfully identified the optimal solution with difference of 0.04% in produced energy, 5.4% in potential excess of electricity and 0.07% in the cost of the energy.
In an off-grid hybrid wind-diesel-battery system, the diesel generator is often not utilised efficiently, therefore compromising its lifetime. In particular, the general rule of thumb of running the diesel generator at more than 40% of its rated capacity is often unmet. This is due to the variation in power demand and wind speed which needs to be supplied by the diesel generator. In addition, the frequent start-stop of the diesel generator leads to additional mechanical wear and fuel wastage. This research paper proposes a novel control algorithm which optimises the operation of a diesel generator, using genetic algorithm. With a given day-ahead forecast of local renewable energy resource and load demand, it is possible to optimise the operation of a diesel generator, subjected to other pre-defined constraints. Thus, the utilisation of the renewable energy sources to supply electricity can be maximised. Usually, the optimisation studies of a hybrid system are being conducted through simple analytical modelling, coupled with a selected optimisation algorithm to seek the optimised solution. The obtained solution is not verified using a more realistic system model, for instance the physical modelling approach. This often led to the question of the applicability of such optimised operation being used in reality. In order to take a step further, model-based design using Simulink is employed in this research to perform a comparison through a physical modelling approach. The Simulink model has the capability to incorporate the electrical and mechanical (Simscape) physical characteristics into the simulation, which are often neglected by other authors when performing such study. Therefore, hybrid system simulation models are built according to the system proposed in the work. Finally, sensitivity analyses are performed as a mean of testing the designed hybrid system’s robustness against wind and load forecast errors.
To deal with increasing energy consumption in the residential sector, distributed energy resources including photovoltaic (PV), fuel cell, etc. have been paid more and more attention. In this study, an optimization model is developed for the PV/Fuel Cell/Battery based residential energy system. While guaranteeing reliable system operation, the model may determine the optimal running strategies with annual running cost or annual CO2 emissions as the objective function to be minimized. In addition, besides the energy flows among the equipments within the hybrid energy system, the economic information including electricity tariff and natural gas price, as well as some policy issues (e.g., buy-back price) are also accounted. As the results of the model, besides the optimal electric and thermal balances, the rational utilization forms of PV module, fuel cell and battery can be also deduced. To verify the viability of the proposed approach, a numerical example is implemented and analyzed. The optimal operating strategies are deduced and compared from different perspectives. Furthermore, the results demonstrate that the PV module mainly contributes to the environmental performance of the assumed hybrid energy system, while the battery may be beneficial from the economic viewpoint.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.