Advances in Theoretical and Computational Energy Optimization Processes

Volume 1

Edited by
August 2020
422 pages
  • ISBN978-3-03936-638-5 (Hardback)
  • ISBN978-3-03936-639-2 (PDF)

This book is a reprint of the Special Issue Advances in Theoretical and Computational Energy Optimization Processes that was published in

This book is part of the book set Advances in Theoretical and Computational Energy Optimization Processes

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Environmental & Earth Sciences

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes.

  • Hardback
License and Copyright
© 2020 by the authors; CC BY-NC-ND license
smart communities; user comfort levels; renewable energy consumption rate; complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); energy difference (ED); energy entropy (EE); hybrid energy feature extraction; ship-radiated noise (S-RN); rail transit; train control; energy-efficient driving strategy; steep downhill segment; local optimization; cogeneration; technical viability; apartment building; Delphi; analytical hierarchy process; fuzzy technique for order of preference by similarity to ideal solution techniques; renewable energy (RE) resources; sustainable energy planning; appliance scheduling techniques; bacterial foraging algorithm (BFA); energy management system; energy optimization algorithms; grasshopper optimization algorithm (GOA); smart grid; demand-side management; economic demand response model; consumer utility function; electricity market restructuring; maximum power tracking (MPPT); wind speed forecasting; wind energy system (WES); state feedback controller; selective catalyst reduction system; emission control; marine Diesel engine; urea; ammonia; CMB receptor model; effective variance weighted least squares algorithm; enhanced sampling Monte Carlo simulation; empirical mode decomposition; support vector regression; genetic algorithm; mold level; continuous cast; shaft generator; DFIG; shipboard; power; control; reservoir heterogeneity; enhanced geothermal system; electricity generation; performance; influence; electricity demand; emissions; LEAP model; fossil fuels; renewable energy; transmission line; meteorological parameter; quasi-dynamic thermal rating (QDR); transmission congestion; multi-objective optimization; artificial neural network; NPSHr prediction; cavitation optimization; CFD; power shortage; electric heating load; electric water heater; demand response; virtual energy storage (VES), virtual state of charge (VSOC); redundant residential microgrid (RR-microgrid); optimal scheduling; virtual energy storage system (VESS); non-dominate sorting genetic algorithm II (NSGA-II); analytic hierarchy process (AHP); free acidity; NIRS; partial least squares; waste cooking oil; off-grid Solar PV power generation; remote rural regions; economic feasibility; CO2 mitigation; Pakistan; complex structural well; mixed well pattern; productivity evaluation; semi-analytical model; well location optimization; reactor coolant pump; coast-down characteristics; geometrical parameters; multiple linear regression; transition process; biomass energy; site selection; optimization; MCDM; FMCDM; FAHP; TOPSIS; reliability indices; wind farms; Sequential Monte Carlo Simulation; Malaysia; hybrid PV–diesel generator systems; digital resource management; energy management; reheat steam temperature; temporal feature selection; delay order prediction; deep neural network; genetic algorithm; carbon emissions; economic growth; energy; renewable energy; Fully Modified Ordinary Least Square (FMOLS); dynamic panel cointegration model; smart grid; demand response; load scheduling; home energy management; enhanced differential evolution; hybrid gray wolf-modified enhanced differential evolutionary algorithm; coalbed methane thermal recovery; thermal stimulation interaction; heat-gas-coal model; modeling and simulation; multi-energy system; economic dispatch; load replaceability; multi-energy conversion; permanent magnet linear synchronous motor (PMLSM); extended state observer (ESO); predictive function control (PFC); composite control; robustness; non-singular terminal sliding mode control (NTSMC); finite-time observer (FTO); mismatched/matched disturbance/uncertainties; permanent magnet synchronous motor (PMSM); aeroelastic effect; pretwisting method; power loss; optimization model; pretwist angle; plug-in hybrid electric bus; energy management; Q-learning; limited state space; Hardware-in-Loop (HIL) simulation; electricity model; power plants prospectives; Mexican prospectives; islanded multi-microgrids; real-time power dispatch; multi-agent; consensus algorithm; vortex structure; energy loss; entropy production; self-priming pump; chemical looping combustion; power production; carbon capture; internally circulating reactor; reactor design; fluidization; techno-economics; computational fluid dynamics; filtered two-fluid model; coarse-grid simulations; monthly energy-trade scheduling; time-sequence simulation method; feasibility; fairness; consumption of renewable energy; demand response; household electrical equipment; real-time electricity price; incentive mechanism; capacity constraint; reliability evaluation; park power supply system; power-to-gas; peak regulation compensation; ancillary service; wind/photovoltaic generation consumption; microgrid; optimization; voltage and frequency regulation; dynamic response enhancement; salp swarm optimization algorithm; power quality; n/a