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Top1. Introduction
With sharp the rise in energy demand and resulting increased pollution, the issues of energy conservation and green power gained much attention to the researchers. Cogeneration or combined heat and power technology proves to be a promising alternative with its greater conversion efficiency than traditional generation method as it harnesses heat that would otherwise be wasted. Cogeneration is a sequential generation of two different forms of useful energy from a single primary energy source such as natural gas, typically electrical and thermal energy. The heat production capacity of most co-generation units depends on the power generated, and vice versa. The mutual dependency of multiple demand and heat–power capacity of those units introduces complexities in the integration of co-generation units into the economic dispatch (ED) problem.
Although a lot of works (Rooijers, & Amerongen, 1994; Guo, Henwood, & Ooijen, 1996; Song, Chou, & Stonham, 1999; Wong, & Algie, 2002; Su, & Chiang, 2004; Vasebi, Fesanghary, & Bathaee, 2007; Piperagkas, Anastasiadis, & Hatziargyriou, 2011; Sudhakaran, & Slochanal, 2003; Hosseini, Jafarnejad, Behrooz, & Gandomi, 2011) have been done in the field of cogeneration stating the objective of ED is to schedule the outputs of the online generating units so that the fuel cost of generation can be minimized, while simultaneously satisfying all system equality and inequality constraints.
Non-linear optimization methods, such as dual and quadratic programming (QP) (Rooijers, & Amerongen, 1994), lagrangian relaxation (Sashirekha, Pasupuleti, Moin, & Tan, 2013), gradient descent approaches, Lagrangian relaxation (LR) (Guo, Henwood, & Ooijen, 1996), were applied for solving combined heat power economic dispatch (CHPED) problem. However, these methods cannot handle non-convex fuel cost function of the generating units.