A novel multi-objective spiral optimization algorithm for an innovative solar/biomass-based multi-generation energy system: 3E analyses, and optimization algorithms comparison

Cao, Yan; Rad, Hima Nikafshan; Jamali, D.H.; Hashemian, Nasim; Ghasemi, Amir

doi:10.1016/j.enconman.2020.112961

Cited by 110 publications

(23 citation statements)

References 57 publications

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“…From Table V IPBO was able to achieve an improvement as high as 0.0272% and 0.0766% in cost for case 1 and case 2, respectively. VIII it can be seen that IPBO was able to achieve best cost and best compromise solution as compared to MSA [15], FFA [21], PSOGSA [38], MBFA [35], SOA [24], PSO [17], MOPSO [41], DE [31], and MODE/PSO [42], respectively. Other PBO variants PBO, PBO-CM and PBO-CU were also successful in achieving comparable cost and compromise solutions.…”

Section: Ieee 6-unit Test Systemmentioning

confidence: 99%

“…The outcomes of these problems are beneficial to initiate different demand response actions and demand side flexibility assessment [7][8][9][10]. Several prominent optimizations algorithms that tried to solve these problems include: Genetic algorithm (GA) [11], simulated annealing (SA) [12], differential evolution (DE) [13,14], moth swarm optimization algorithm (MSA) [15], spider monkey optimization (SMO) [16], particle swarm optimization (PSO) [17,18], grey wolf optimizer (GWO) [19], gravitational search algorithm (GSA), fire fly algorithm (FFA) [20,21], harmony search algorithm (HSA) [22,23], spiral optimization algorithm (SOA) [24], squirrel search algorithm (SSA) [25], harris hawks optimization (HHO) [26], sine-cosine algorithm (SCA) [27], artificial bee colony (ABC) [28], bacterial forging algorithm (BFA) [29], flower pollination algorithm (FPA) [30], differential evolution (DE) [31], modified flower pollination algorithm (FPA) [32], , Fluid search optimization (FSO) [33], improved ABC (IABC) [34], modified BFA (MBFA) [35], whale optimization algorithm (WOA) [36], hybrid hierarchical evolution (HHE) [37], hybrid particle swarm gravitational search algorithm (PSOGSA) [38], chaos turbo PSO (CTPSO) [39], new global PSO (NGPSO) [40], multiobjective PSO (MOPSO) [41], multi-objective DE based PSO (MODE/PSO) [42] quantum inspired glowworm swarm optimization (QGSO) [43], combination of cont...…”

Section: Introductionmentioning

confidence: 99%

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Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

et al. 2021

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This paper proposes a novel improved polar bear optimization (IPBO) algorithm and employs it along with polar bear optimization (PBO) and chaotic population-based variants of polar bear optimization algorithm to solve combined economic emission dispatch (CEED) problem. PBO is a meta-heuristic technique inspired by the hunting mechanisms of polar bears in harsh arctic regions based only on their sense of sight. Polar bears in nature exhibits hunting of prey not only on their sight but also on their keen sense of smell. Hence, a novel improved variant of PBO which enhances its operation by equipping it with tracking capabilities utilizing polar bears sense of smell has been proposed in this study. The validity of novel IPBO is tested through 5 benchmark functions and 140 units Korean ED problem. Furthermore, the impact of different population initialization methods is also observed on the capabilities of conventional PBO. The proposed chaotic population based PBO, improved PBO (IPBO) and PBO are employed to solve IEEE 3 unit and 6-unit CEED problem. CEED is a multi-objective power system optimization problem with conflicting objectives of cost and emission. The simulations performed undertake each objective individually as well as collectively. The results achieved by each technique are analyzed statistically through Wilcoxon rank sum test (WRST), probability density function and cumulative density function. Both the statistical and numerical analysis of results showcase the strength of each solution technique as well as their ability to improve cost and emissions in the solution of CEED problem.

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Section: Ieee 6-unit Test Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

et al. 2021

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“…Exergy destruction cost rate is calculated as Ref. 60:

{\dot{normalC}}_{D} = c_{F} {\dot{normalI}}_{normalF, normalk}

in which c F illustrates the cost of fuel.…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…Total cost rate for kth element is expressed as Ref. 60:

{\overset{Z}{true}}_{normalk}^{CI + OM} = {\dot{normalZ}}_{k}^{CI} + {\dot{normalZ}}_{k}^{OM} .

…”

Section: Mathematical Modelingmentioning

confidence: 99%

Thermodynamic and thermoeconomic analyses of an ejector/booster enhanced heat pump system with zeotropic mixture

2020

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Summary In the current investigation, a novel ejector‐enhanced heat pump system was presented and comprehensively evaluated using zeotropic mixtures. In the suggested plant, a booster was added to the conventional ejector‐enhanced system to expand its performance. Moreover, the suggested plant was modeled in a way to operate zeotropic mixtures, in order to maximize the productivity of the system. The plant was investigated with regard to energy, exergy, and thermoeconomic analyses to have a deep insight of the performance characteristics, that is, exergy efficiency, COP, and unit price of the product. To this end, a parametric evaluation was conducted to predict the performance characteristics of the plant by changing the value of different input variables. The results of exergy analysis indicated that the maximum exergy annihilation (7.784 kW) was associated with the ejector. The proposed system was also compared with a conventional system, and the findings unfolded that the COP of the proposed system surpassed the conventional system by up to 25%. Moreover, the outcomes of thermoeconomic analysis showed that the unit product cost of the proposed system is 133.596 $/GJ. The outcomes further demonstrated that the highest performance of the plant was concluded using R‐600/R‐143a mixture fluid with R‐600 mass fraction of 0.45. The sensitivity evaluation further indicated that, by enhancing the condenser temperature, exergetic efficiency and heating load increased while COP and unit cost of product decreased. Moreover, there was an optimum local point, in terms of exergetic efficiency (29.5%), when the booster pressure ratio is about 1.4. In addition, at the ejector pressure lift of 1.15, the exergetic efficiency was maximized locally. The heating load of the system, however, increased continually with increasing pressure lift. The proposed system showed a decent functionality with respect to thermodynamic and thermoeconomic criteria.

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“…Researchers have been continuously seeking new ways to design novel multigeneration renewable‐based energy systems to address various energy demands, for example, cooling, heating, electricity, and fuel demands 1 . However, the systems designed should be practical with high efficiency, at the same time, for which a reasonable budget should be allocated 2 .…”

Section: Introductionmentioning

confidence: 99%

Exergoeconomic and environmental optimisations of multigeneration biomass‐based solid oxide fuel cell systems with reduced CO ₂ emissions

2021

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Summary In this research, four multigeneration systems comprising of a syngas‐fuelled solid oxide fuel cell, double‐effect absorption chiller (first configuration), a thermoelectric generator unit (second system), solid oxide electrolyser cell (third configuration), and fuel synthesis unit (fourth configuration) are proposed for cooling, heating, electricity, and hydrogen/synthetic methane production. In Configurations 1 to 3, 25% of the discharged CO2 is reinjected into the gasifier, whereas an additional portion is reused in Configuration 4 for synthetic methane production. Energy, exergy, exergoeconomic, and environmental (4E) analyses are conducted comprehensively. Besides, optimisation based on the genetic algorithm is applied to optimise the system through four different modes, for example, maximum efficiency, minimum product, cost, and cost‐rate. The results show that the second system has the highest exergy efficiency and power output while having the minimum product cost. Configurations 3 and 4 are capable of fuel production, while Configuration 4 concludes the least levelised CO2 emissions; however, having the highest total product cost. The proposed systems have some advantages and drawbacks, so selecting a suitable system depends on the perspective of decision‐makers.

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A novel multi-objective spiral optimization algorithm for an innovative solar/biomass-based multi-generation energy system: 3E analyses, and optimization algorithms comparison

Cited by 110 publications

References 57 publications

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

Thermodynamic and thermoeconomic analyses of an ejector/booster enhanced heat pump system with zeotropic mixture

Exergoeconomic and environmental optimisations of multigeneration biomass‐based solid oxide fuel cell systems with reduced CO ₂ emissions

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A novel multi-objective spiral optimization algorithm for an innovative solar/biomass-based multi-generation energy system: 3E analyses, and optimization algorithms comparison

Cited by 110 publications

References 57 publications

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm

Thermodynamic and thermoeconomic analyses of an ejector/booster enhanced heat pump system with zeotropic mixture

Exergoeconomic and environmental optimisations of multigeneration biomass‐based solid oxide fuel cell systems with reduced CO 2 emissions

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Exergoeconomic and environmental optimisations of multigeneration biomass‐based solid oxide fuel cell systems with reduced CO ₂ emissions