A cascade hybrid PSO feed-forward neural network model of a biomass gasification plant for covering the energy demand in an AC microgrid

Chiñas-Palacios, Cristian; Vargas‐Salgado, Carlos; Águila-León, Jesús; Hurtado-Pérez, Elías

doi:10.1016/j.enconman.2021.113896

Cited by 42 publications

(16 citation statements)

References 49 publications

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“…The hybridisation of ML models with other ML techniques or advanced statistical methods has shown great potential in the wider energy sector [85]. More recently, researchers have also looked towards hybrid models to model thermochemical processes [74,86,87].…”

Section: Hybrid Machine Learning Modelsmentioning

confidence: 99%

Machine learning methods for modelling the gasification and pyrolysis of biomass and waste

Ascher

Watson

You

2022

Renewable and Sustainable Energy Reviews

111

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Section: Hybrid Machine Learning Modelsmentioning

confidence: 99%

Machine learning methods for modelling the gasification and pyrolysis of biomass and waste

Ascher

Watson

You

2022

Renewable and Sustainable Energy Reviews

111

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“…In addition, HOMER has the advantage of being able to integrate storage systems [35][36][37]. Moreover, much scientific work uses HOMER to simulate renewable energy systems [38][39][40][41][42]. It is used to solve rural electrification problems where grid power is expensive or insufficient, in scenarios where the software calculates the most cost-effective and techno-economically renewable energy alternative [43].…”

Section: Introductionmentioning

confidence: 99%

Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island

et al. 2022

Self Cite

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The decarbonization of the electric generation system is fundamental to reaching the desired scenario of zero greenhouse gas emissions. For this purpose, this study describes the combined utilization of renewable sources (PV and wind), which are mature and cost-effective renewable technologies. Storage technologies are also considered (pumping storage and mega-batteries) to manage the variability in the generation inherent to renewable sources. This work also analyzes the combined use of renewable energies with storage systems for a total electrification scenario of Grand Canary Island (Spain). After analyzing the natural site’s resource constraints and focusing on having a techno-economically feasible, zero-emission, and low-waste renewable generation mix, six scenarios for 2040 are considered combining demand response and business as usual. The most optimal solution is the scenario with the maximum demand response, consisting of 3700 MW of PV, around 700 MW of off-shore wind system, 607 MW of pump storage, and 2300 MW of EV batteries capacity. The initial investment would be EUR 8065 million, and the LCOE close to EUR 0.11/kWh, making the total NPC EUR 13,655 million. The payback is 12.4 years, and the internal rate of return is 6.39%.

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“…A hybrid artificial neural network (ANN) and PSO model were used to solve the biomass gasification plant (BGP) problem. This model was used to estimate the amount of biomass that was used to produce the required syngas, which is needed to meet the energy demand [14]. To enhance power exchange, the two-round fuzzy-based speed (TRFS) algorithm followed Stackelberg's game theory, and the Quasi-oppositional Symbiotic Organism Search Algorithm was used in a multi-MG environment to study the power exchange problem [15].…”

Section: Introductionmentioning

confidence: 99%

CHP-Based Economic Emission Dispatch of Microgrid Using Harris Hawks Optimization

Tiwari¹,

Dubey²,

Pandit³

et al. 2022

Fluids

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In this paper, the economically self-sufficient microgrid is planned to provide electric power and heat demand. The combined heat and power-based microgrid needs strategic placement of distributed generators concerning optimal size, location, and type. As fossil fuel cost and emission depend mainly on the types of distributed generator units used in the microgrid, economic emission dispatch is performed for an hour with a static load demand and multiple load demands over 24 h of a day. The TOPSIS ranking approach is used as a tool to obtain the best compromise solution. Harris Hawks Optimization (HHO) is used to solve the problem. For validation, the obtained results in terms of cost, emission, and heat are compared with the reported results by DE and PSO, which shows the superiority of HHO over them. The impact of renewable integration in terms of cost and emission is also investigated. With renewable energy integration, fuel cost is reduced by 18% and emission is reduced by 3.4% for analysis under static load demand, whereas for the multiple load demands over 24 h, fuel cost is reduced by 14.95% and emission is reduced by 5.58%.

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A cascade hybrid PSO feed-forward neural network model of a biomass gasification plant for covering the energy demand in an AC microgrid

Cited by 42 publications

References 49 publications

Machine learning methods for modelling the gasification and pyrolysis of biomass and waste

Machine learning methods for modelling the gasification and pyrolysis of biomass and waste

Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island

CHP-Based Economic Emission Dispatch of Microgrid Using Harris Hawks Optimization

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