Two heuristic approaches for the optimization of grid-connected hybrid solar–hydrogen systems to supply residential thermal and electrical loads

Maleki, Akbar; Khajeh, Morteza Gholipour; Rosen, Marc A.

doi:10.1016/j.scs.2017.06.023

Cited by 68 publications

(13 citation statements)

References 66 publications

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“…LCC of photovoltaic (PV) Just like WT system, the same procedure is utilized to measure that life cycle cost of the PV system. 45,46 LCC of fuel cell (MT)…”

Section: Lcc Of Wtsmentioning

confidence: 99%

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Combination of Side‐Blotched Lizard and Chaos Game Optimization based distributed energy management for microgrid system

Logeswaran

Rajesh

Shajin

et al. 2021

Int Trans Electr Energ Syst

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In the last decade, numerous energy management techniques have been presented. All of them have common objectives of minimizing the cost, PAR, and carbon emissions. In this manuscript, an optimal energy management (EM) on grid connected micro grid (MG) choosing energy scheduling with low emission and cost using hybrid technique is proposed. The proposed system is combination of Side-Blotched Lizard Algorithm (SBLA) and Chaos Game Optimization (CGO) Algorithm; thus it is called SBLA-CGO technique. The micro grid system consists of Photo-Voltaic (PV) system, Wind Turbine (WT), Battery Storage (BS), and Fuel cell (FC). The needed load demand of the grid connected MG system is continuously measured by SBLA method. Perfect combination of MG is increased via CGO along forecasted load demand circumstance. Moreover, the renewable energy (RE) predicting errors are assessed twice by micro grid EM to diminish the control. Several renewable energy source (RES) are considered by MG scheduling process to reduce the cost of electricity utilizing first method. The second method consists of balancing the power flow (PF) and reducing the effects of forecast errors according to rule given as programmed power reference. The major purpose of proposed method is assessed through the incorporation of FC, variation of hourly power of electrical network, cost of operation through preservation of system of microgrid linked to the network. According to RES, the power requirement and SOC of storage elements are the conditions. Batteries are List of Symbols and Abbreviations: η PV , efficiency of comparative units of PV; p PV , PV array of output power; p MPV , under standard condition the rated power of PV; n, maximum size of PV panels; gt, faculty of global irradiance of tilted plane; p WG , optimum unit of wind power; v Rated , v cut and v Ci , rated wind speed, minimal wind speed, cut out and cut in wind speed; η Charge , efficiency of battery charging; e RES T ð Þ, the overall energy produced; e Load , overall provided energy; c PV , c WT , c FC , annualized cost of system is expressed as c, the annual cost of the PV, WT and FC; c GS , c GP , overall cost of electricity; C wt , unit cost of the WTs; A wt , wind turbine generator blades swept area; C MntÀwt , each WT annual maintenance cost; C wt , sum of the WT installation fee and the WT price; C MntÀFC , FC annual maintenance cost; C HMÀFC , hourly maintenance cost of FC; N Bt , number of batteries; C MntÀBt , annual maintenance cost of battery; ACO

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“…LCC of photovoltaic (PV) Just like WT system, the same procedure is utilized to measure that life cycle cost of the PV system. 45,46 LCC of fuel cell (MT)…”

Section: Lcc Of Wtsmentioning

confidence: 99%

“…The capital costs of fuel cell are computed as below under initial cost

{CC}_{MT}

, 45,46

{CC}_{FC} goodbreak= N_{FC} {PW}_{FC} . \frac{j {(1 + j)}^{n}}{{(1 + j)}^{n} - 1} .

…”

Section: Energy Management Configurations With Mg Connected Systemmentioning

confidence: 99%

Combination of Side‐Blotched Lizard and Chaos Game Optimization based distributed energy management for microgrid system

Logeswaran

Rajesh

Shajin

et al. 2021

Int Trans Electr Energ Syst

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show abstract

“…Optimization of load allocation in both sufficient and insufficient supplies of energy has been done using the GA [42]. GA has been applied to different sources of power supplies to residential and non residential buildings such as renewable energy [43], gas engine combined cooling, heating and power system (CCHPS) [44], grid-connected hybrid solar-wind-hydrogen CHP system [45] and grid-connected hybrid solar-hydrogen combined heat and power systems [46]. This leads to cost minimization [47].…”

Section: Optimizationmentioning

confidence: 99%

Smart Review of the Application of Genetic Algorithm in Construction and Housing

Okagbue¹,

Peter²,

Akinola³

et al. 2020

IJATCSE

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Genetic algorithm (GA) is an example of evolutionary algorithms that are bio-inspired computational methods. GA has been applied to numerous fields. It has been applied in different aspects of construction and building but that is scarcely any review that documents it. The paper reviewed the application of GA in construction and building. It was revealed that energy management is the major area of application which are further subdivided into load scheduling, prediction, and optimization. Other nonenergy applications are pricing, environment, and construction design or real estate. The review presents research information to researchers. The information can assist in the optimization of construction processes which can reduce the construction time and costs, ensure optimal allocation and use of energy, prediction of energy demands and supply in houses and incorporation of sustainability in construction and management of real estate.

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“…From a problem type perspective, service deployment problem is the bin packing problem. There are many existing method to solve the bin packing problem, such as the Simulated Annealing(SA) [28], [29], the genetic algorithm (GA) [30]- [34], particle swarm optimization (PSO) [23], [35], [36]. Among them, the simulated annealing algorithm has a strong local search ability.…”

Section: Related Workmentioning

confidence: 99%

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

et al. 2020

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This study considers a service-deployment problem of C4ISR(Command, Control, Communication, Computer, Intelligence, Surveillance and Reconnaissance) system under service-oriented architecture. In order to make C4ISR sufficiently agile to meet the challenges brought by diverse combat missions and highly adversarial combat processes, this paper adopts a distributed deployment method to deploy the service processes that constitute C4ISR in multiple data centers. Considering the service information requirements and the location of specific data, a deployment constraint for a service is proposed. Further, in order to improve resource utilization and reduce communication overhead, this paper establishes a distributed service deployment model facing the characteristics of C4ISR. For the large search space and complex constraints of the optimal problem, a multi-objective genetic simulated annealing algorithm is proposed by designing the neighboring individual generation rules and combining the simulated annealing mechanism and the genetic algorithm to enhance the ability of both local search and global search. The simulation results show that this method can produce a high-quality C4ISR service-deployment scheme effectively.

show abstract

Two heuristic approaches for the optimization of grid-connected hybrid solar–hydrogen systems to supply residential thermal and electrical loads

Cited by 68 publications

References 66 publications

Combination of Side‐Blotched Lizard and Chaos Game Optimization based distributed energy management for microgrid system

Combination of Side‐Blotched Lizard and Chaos Game Optimization based distributed energy management for microgrid system

Smart Review of the Application of Genetic Algorithm in Construction and Housing

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

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