Optimisation Models and Methods in Energy Systems
Abstract
Challenging problems arise in all segments of energy industries—generation, transmission, distribution and consumption. Optimization models and methods play a key role in offering decision/policy makers better information to assist them in making sounder decisions at different levels, ranging from operational to strategic planning.
Keywords
mixed integer linear programming; fuzzy set theory; stochastic programming; mixed integer linear programing; variable renewable power; generation efficiency; optimization; flexibility option; portfolio analysis; firefighting; semi-mean-absolute deviation model; component outage; energy network; predicted mean vote (PMV); generation expansion planning; building microgrid; demand side management; stochastic robust optimization; oil storage plants; long-term forecasting; multi-criteria decision making (MCDM); life cycle cost; graph theory; scenario-based multistage stochastic programming; optimal power generation mix; heating ventilation and air-conditioning (HVAC); intermittent sources; electric-power structure adjustment; technique for the order of preference by similarity to the ideal solution (TOPSIS); integrated energy system; Markov chain Monte Carlo; nondominated sorting genetic algorithm (NSGA); domino effect; energy system management model; electrical distribution systems; microgrid operation; influence diagram; net demand; wind power forecasting; energy conservation and emissions reduction; feasible operation region; meshed topology; occupancy-based control; islanded microgrids; combined heat and power; multi-objective optimization; re-optimization and reschedulingISBN
9783039211197, 9783039211180Publisher website
www.mdpi.com/booksPublication date and place
2019Classification
History of engineering and technology
Pages
192
Except where otherwise noted, this item's license is described
as
https://creativecommons.org/licenses/by-nc-nd/4.0/