Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research,a modified fractional order proportional integral derivate(FOPID)control method is proposed for the photovoltaic(PV)and thermoelectric generator(TEG)combined hybrid renewable energy system.The faster tracking and steady-state output are aimed at the suggested maximum power point tracking(MPPT)control technique.The derivative order number(μ)value in the improved FOPID(also known as PIλDμ)control structure will be dynamically updated utilizing the value of change in PV array voltage output.During the transient,the value ofμis changeable;it’s one at the start and after reaching the maximum power point(MPP),allowing for strong tracking characteristics.TEG will use the freely available waste thermal energy created surrounding the PVarray for additional power generation,increasing the system’s energy conversion efficiency.A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit.The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation(P&O)type MPPT control method.The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source.There is also a better control action and a faster response.

Optimal Economical Sizing of Grid-Connected Hybrid Renewable Energy System

In this paper, an optimized model is proposed to find the best values for decision variables to optimize the grid connected hybrid renewable energy system which consists of photovoltaic panels, wind turbines and battery bank for electrification to North-east region of Afghanistan to meet winter power shortages of the area. In the proposed model, there are three decision variables namely, the total area occupied by the set of PV panels, total swept area by the rotating turbines＇ blades, and the number of batteries. GA （genetic algorithm） is defined to find the optimal values of the decision variables. The objective of this research is to minimize the LCC （life cycle cost） of the hybrid renewable energy system, and ensuring at the same time systems reliability level which is measured in terms of LPSP （loss of power supply probability）.

Techno-economic analysis of a hybrid renewable energy system integrated with productive activities in an underdeveloped rural region of eastern Indonesia

The Southwest Maluku region in eastern Indonesia is considered a frontier,outermost and underdeveloped region.Its inhabitants live on isolated islands,including the residents of Mahaleta Village,where only 9.4%of the community have limited access to electricity.This study aimed to design an economically feasible hybrid renewable energy(RE)system based on solar and wind energy to integrate with the productive activities of the village.The study developed conceptual schemes to meet the demand for electricity from the resi-dential,community,commercial and productive sectors of the village.The analysis was performed using a techno-economic approach.The hybrid system was designed using the HOMER Pro optimization function,and cold-storage and dryer systems were designed to support related productive activities.The optimized design of the hybrid RE system comprised 271.62 kW of solar photovoltaics,80 kW of wind turbines and a 1-MWh lead-acid battery.We found that the hybrid RE system would only be economically feasible with a full-grant incentive and an electricity tariff of$0.0808/kWh.However,the productive activity schemes were all economically feasible,with a cold-storage cost of$0.035/kg and a drying cost of$0.082/kg.Integrating the hybrid RE system with productive activities can improve the economic feasibility of the energy system and create more jobs as well as increase income for the local community.

Multi-Objective Sizing of Solar-Wind-Hydro Hybrid Power System with Doubled Energy Storages Under Optimal Coordinated Operational Strategy

More and more attention has been paid to the high penetration of renewable energy in recent years.The randomness and intermittency of solar and wind energy make it an inevitable trend that renewables are coupled with energy storage technologies.Pumped hydro storage(PHS)is the most widelyused storage form in the power grid but the capacity is limited by geographic conditions.The concentrated solar power(CSP)plant with a thermal energy storage(TES)system can realize easier grid connections and effective peak shaving.Therefore,this paper proposes a solar-wind-hydro hybrid power system with PHS-TES double energy storages,and investigates the optimal coordinated operational strategy and multi-objective sizing.The optimal sizing problem which considers the minimum levelized cost of energy(LCOE)and loss of power supply probability(LPSP)as objectives is solved by multi-objective particle swarm optimization.Moreover,the seasonal uncertainties of renewables are considered by applying a scenario-based analysis using Kmeans clustering.Finally,a case study reveals the effectiveness of the coordinated operational strategy and double energy storages from the perspectives of economy and reliability.The comparisons of optimal sizing results show that the PV-WindCSP-PHS system decreases the LCOE by 19.1%compared to a PV-Wind-CSP system under the same LPSP,and reduces the LPSP compared to PV-Wind-PHS systems with limited reservoir capacity,which indicates that the proposed system with double energy storages has better economy and reliability performance compared to single storage.

Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System

Considering the increasing integration of renewable energies into the power grid,batteries are expected to play a key role in the challenge of compensating the stochastic and intermittent nature of these energy sources.Besides,the deployment of batteries can increase the benefits of a renewable power plant.One way to increase the profits with batteries studied in this paper is performing energy arbitrage.This strategy is based on storing energy at low electricity price moments and selling it when electricity price is high.In this paper,a hybrid renewable energy system consisting of wind and solar power with batteries is studied,and an optimization process is conducted in order to maximize the benefits regarding the dayahead production scheduling of the plant.A multi-objective cost function is proposed,which,on the one hand,maximizes the obtained profit,and,on the other hand,reduces the loss of value of the battery.A particle swarm optimization algorithm is developed and fitted in order to solve this non-linear multi-objective function.With the aim of analyzing the importance of considering both the energy efficiency of the battery and its loss of value,two more simplified cost functions are proposed.Results show the importance of including the energy efficiency in the cost function to optimize.Besides,it is proven that the battery lifetime increases substantially by using the multi-objective cost function,whereas the profitability is similar to the one obtained in case the loss of value is not considered.Finally,due to the small difference in price among hours in the analyzed Iberian electricity market,it is observed that low profits can be provided to the plant by using batteries just for arbitrage purposes in the day-ahead market.

A Hybrid of Grey Wolf Optimization and Genetic Algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System

In this paper,a hybrid of grey wolf optimization(GWO)and genetic algorithm(GA)has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy system.It was named as hybrid of grey wolf optimization and genetic algorithm(HGWOGA).HGWOGA was applied to this hybrid problem through three procedures.First,the balance between the exploration and the exploitation process was done by grey wolf optimizer algorithm.Then,we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning processes.At last,mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local minima.MATLAB code was designed to implement the proposed methodology.The result of this algorithm is compared with the results of iteration method,GWO,GA,artificial bee colony(ABC)and particle swarm optimization(PSO)techniques.The results obtained by this algorithm are better when compared with those mentioned in the text.

Optimal Hybrid Power System Using Renewables for a Household in the UK

The aim of this study is to find an optimal design for a distributed hybrid renewable energy system(HRES) for a residential house in the UK. The hybrid system, which consists of wind turbines, PV arrays, a biodiesel generator, batteries and converters, is designed to meet the known dynamic electrical load of the house and make use of renewable energy resources available locally. Hybrid Optimization Model for Electric Renewables(HOMER) software is used for this study. Different combinations of wind turbines, PV arrays, a biodiesel generator and batteries are evaluated and compared using the NPC(Net Present Cost) method to find the optimal solutions. The HRES is modeled, simulated and optimized using HOMER. The results showed that the wind-biodiesel engine-battery system was the best with the lowest NPC(USD 60254) and the lowest COE(Cost of Energy, USD 0.548/k Wh) while the second best system added PV arrays. This study gives evidence of the key contribution wind turbines make to HRES due to abundant wind resources in the UK, especially in Wales.

Consideration of some optimization techniques to design a hybrid energy system for a building in Cameroon

To solve the problem energy deficit encountered in developing countries,Hybrid Renewable Energy System(HRES)appears to be a very good solution.The paper presents the optimal design of a hybrid renewable energy system considering the technical i.e Loss of Power Supply Probability(LPSP),economic i.e Cost of Electricity(COE)and Net Present Cost(NPC)and environmental i.e Total Greenhouse gases emission(TGE)aspects using Particle Swarm Optimization(PSO),hybrid Particle Swarm Optimization-Grey Wolf Optimization(PSOGWO),hybrid Grey-Wolf Optimization-Cuckoo Search(GWOCS)and Sine-Cosine Algorithm(SCA)for a Community multimedia center in MAKENENE,Cameroon;where inhabitants have to spend at times 3 to 4 days of blackout.Seven configurations(Scenarios)of hybrid energy systems including PV,WT,Battery and Diesel generator are analyzed considering an average daily energy load of 50.22 kWh with a peak load of 5.6 kW.Four values of the derating factor i.e 0.6,0.7,0.8 and 0.9 are used in this analysis and the best value is 0.9.Scenario 3 with LPSP,COE,NPC,TGE and RF of 0.003%,0.15913$/kWh,46953.0485$,2.3406 kg/year and 99.8%respectively when using GWOCS is found to be the most appropriate for the Community multimedia center.The optimal Scenario is obtained for a system comprising of 18 kW of P_(pv-rated)corresponding to 69 solar panels,3 days of AD corresponding to a total battery capacity of 241 kWh and 1 of N_(dg).

Intelligent Energy Management Strategy and Sizing Methodology for Hybrid Systems in Isolated Regions

In this study,a comprehensive approach is presented for the sizing and management of hybrid renewable energy systems(HRESs)that incorporate a variety of energy sources,while emphasizing the role of artificial neural networks(ANNs)in system management.For optimal sizing of an HRES,the monthly average method wherein historical weather data are used to calculate the monthly averages of solar irradiance and wind speed,offering a well-balanced strategy for system sizing.This ensures that the HRES is appropriately scaled to meet the actual energy requirements of the specified location,avoiding the pitfalls of over-and under-sizing,and thereby enhancing the operational efficiency.Furthermore,the study details a cutting-edge strategy that employs ANNs for managing the inherent complexities of HRESs.It elaborates on the design,modeling,and control strategies for the HRES components by utilizing Matlab/Simulink for implementation.The findings demonstrate the proficiency of the ANN-based power manager in determining the operational modes guided by a specifically designed flowchart.By integrating ANN-driven energy management strategies into an HRES,the proposed approach marks a significant advancement in system adaptability,precision control,and efficiency,thereby maximizing the effective utilization of renewable resources.

Combined cold,heat and power(CCHP)systems and fuel cells for CCHP applications:a topological review

As the basis for the study,this manuscript was written at a time when the energy crisis is affecting most parts of the world and most es-pecially the prevailing and rampant electricity crisis in most developing countries.As a result,50 combined cooling,heating and power(CCHP)systems studies were reviewed,which included the internal combustion engine(ICE),Stirling engine,biomass,micro turbine,solar and biogas,photovoltaic(PV)and gas turbine,wind turbine,PV and micro-turbine,solid-oxide and phosphoric-acid fuel cells(FCs),ICE and thermoelectric generator,low-temperature(LT)polymer electrolyte membrane(PEM),inlet air throttling gas turbine,ground source heat pump(GSHP)micro gas turbine and PV,ICE and GSHP,ICE with dehumidification and refrigeration,5-kW PEM FC,thermoelectric cooler and LT-PEM FC,Stirling engine and molten carbonate FC,thermo-acoustic organic Rankine cycle,solar-thermal,geothermal,integrated energy systems,power-and heat-storage systems,energy-conversion systems,thermodynamic and thermo-economic optimization strategies,working fluids based on hydrogen,helium as well as ammonia,H_(2)O,CO_(2) etc.Of these reviewed CCHP systems,FC-based CCHP systems were of the greatest interest,particularly the PEM FC.Consequently,FCs were further investigated,whereby the seven popular types of FCs identified and classified were summarily compared with each other,from which the PEM FC was preferred due to its practical popularity.However,PEM FCs,like all FCs,are susceptible to the fuel-starvation phenomenon;therefore,six FC-assisted schemes were examined,from which the FC assisted with the supercapacitor and battery technique was the most widely applied.In sum,the significance of the study entails assorted CCHP systems,FCs,their highlights,their applications and their pros and cons in a single reference document that anyone can easily use to holistically understand the characteristics of the CCHP systems.The study concludes with our perspective,by which we formulate and propose an alternative innovative unique CCHP system model under research,which is based exclusively on green tech-nologies:FCs,lithium-ion battery,ultracapacitor,thermoelectricity and an energy-management system using MATLAB■.