Society-based Grey Wolf Optimizer for large scale Combined Heat and Power Economic Dispatch problem considering power losses

Abstract

Combined Heat and Power Economic Dispatch (CHPED) reflects a momentous optimization and operation problem in the power systems for optimally allocating the produced heat and power to the committed Power-only (PO), Heat-only (HO), and CHP units. In this work, a new society-based optimization algorithm using social hierarchy of grey wolves, namely Society-based Grey Wolf Optimizer (SGWO) is proposed for the CHPED problem. This scheme divides the group of wolves to some societies so that each society has its own leader. These leaders follow the dominant wolf and also guide their societies in which the classification of wolves is based on the social hierarchy. Moreover a novel attacking and hunting the prey is suggested to change the effects of dominant, ordinate, and subordinate wolves in their new roles in societies. In order to verify the capabilities of the SGWO, simulations are conducted through two large-scale CHPED problems with the related challenges like Valve-Point Loading Effect (VPLE) and Prohibited Zones (PZ) of PO units, mutual dependency of produced heat and power of CHP units, and especially transmission power losses. Moreover, it is evaluated on twenty-three standard functions to verify its stability on different low- and high-dimensional functions. Comparisons based on the obtained solutions by different methods demonstrate the robustness and superior performance of the presented technique to fast provide better optimum point (more economical benefits) and solution quality meeting all equality and inequality constraints. In addition, the results of CHPED indicate that ≈0.5% reduction in the production cost results in up to $2.6−$34×106 increase in Annual Cost Saving (ACS). Also, the transmission losses and PZs of POs can increase the production cost by about 1.4% which leads to over $1.3×107 reduction in ACS in comparison with ignoring them. Moreover, this condition increases the computational time by about 35% while the proposed method can still be up to 13−26 times faster than the other analysed algorithms.