A linear programming approach for the optimal planning of a future energy system. Potential contribution of energy recovery from municipal solid wastes

Future energy and environment management using fuzzy linear programming including energy recovered from urban solid wastes (Case study: Iran country)

“…Currently Iran does not have strict regulations about this problem but in the future, it would be helpful to consider CO 2 emission as well. NOMENCLATURE X 1 amount of electricity generated from solar thermal collectors for domestic use X 2 amount of electricity generated from solar thermal collectors for agricultural use X 3 amount of electricity generated from PV for domestic use X 4 amount of electricity generated from PV for commercial use X 5 amount of electricity generated from PV for industrial use X 6 amount of electricity generated from PV for agricultural use X 7 amount of electricity generated from PV for public use X 8 amount of electricity generated from PV for lighting of roads & squares X 9 amount of electricity generated from CSP for domestic use X 10 amount of electricity generated from CSP for commercial use X 11 amount of electricity generated from CSP for industrial use X 12 amount of electricity generated from CSP for agricultural use X 13 amount of electricity generated from CSP for public use X 14 amount of electricity generated from CSP for lighting of roads & squares X 15 amount of electricity generated from wind for domestic use X 16 amount of electricity generated from wind for commercial use X 17 amount of electricity generated from wind for industrial use X 18 amount of electricity generated from wind for agricultural use X 19 amount of electricity generated from wind for public use X 20 amount of electricity generated from wind for lighting of roads & squares X 21 amount of electricity generated from biomass for domestic use X 22 amount of electricity generated from biomass for commercial use X 23 amount of electricity generated from biomass for industrial use X 24 amount of electricity generated from biomass for agricultural use X 25 amount of electricity generated from biomass for public use X 26 amount of electricity generated from biomass for lighting of roads & squares X 27 amount of electricity generated from geothermal for domestic use X 28 amount of electricity generated from geothermal for commercial use X 29 amount of electricity generated from geothermal for industrial use X 30 amount of electricity generated from geothermal for agricultural use X 31 amount of electricity generated from geothermal for public use X 32 amount of electricity generated from geothermal for lighting of roads & squares X 33 amount of electricity generated from nuclear for domestic use X 34 amount of electricity generated from nuclear for commercial use X 35 amount of electricity generated from nuclear for industrial use X 36 amount of electricity generated from nuclear for agricultural use X 37 amount of electricity generated from nuclear for public use X 38 amount of electricity generated from nuclear for lighting of roads & squares X 39 amount of electricity generated from energy recovery from MSW for domestic use X 40 amount of electricity generated from energy recovery from MSW for commercial use X 41 amount of electricity generated from energy recovery from MSW for industrial use X 42 amount of electricity generated from energy recovery from MSW for agricultural use…”

“…However, renewable energy sources (RES) are clean and will not disappear in the future. Most recently, Xydis and Koroneos , who used linear programming to find an optimal solution to the question “how much electricity should be generated from different renewable sources to meet different needs in a country,” have proposed a model. However, in this article, everything is deterministic and there are no limitations on the amount of resources which are used to generate electricity.…”

Maximizing the Value of Residential Projects Using Fuzzy Rule Based Linear Programming

“…Finally, the decisions that must be made are subject to certain requirements and restrictions of a system which are called constraints (Smith, Taşkın 2008). LP has been widely used in optimizing complex systems, such as those arising in marketing (Stapleton et al 2003), finance (Benati, Rizzi 2007), energy (Xydis, Koroneos 2012), product design (Seibi, Sawaqed 2002), transportation (Luathep et al 2011), production planning and control (Rasmussen 2013;Doganis, Sarimveis 2007), chemistry (Rossi et al 2009), medicine (Mangasarian et al 1994), telecommunications (Sirdey, Maurice 2008), sports (Soleimani-Damaneh et al 2011), and military (Tucker et al 1998). Some attempts have been made to develop linear programming models for optimization problems in construction industry.…”

Wind Energy Integration through District Heating. A Wind Resource Based Approach

“…However, in this case, these calculations were included within the wind resource analysis. Adding up the electrical losses (internal interconnection medium voltage losses and transformer losses) and the wind turbine technical availability losses, a fixed percentage for the proposed wind farm, usually provided from the wind turbine manufacturer, it is possible to calculate the exergetic efficiency of the WF ( Figure 5) [23][24][25][26][27]. The electrical losses were estimated taking into account the fact that the wind farm is planned to be 18.15 MW, and therefore the overall electrical losses will be specified from the medium voltage losses for the interconnection of the wind turbines and the distribution power station (20/150 kV 25 MVA transformer) losses.…”