Solving multi-zone combined heat and power economic emission dispatch problem considering wind uncertainty by applying grasshopper optimization algorithm

Economic Dispatch is one of the most important issues in power system operation, as a complex optimization problem consisting of some objective functions constrained by various limits. To increase the energy efficiency and reduce environmental concerns, combined heat and power technologies are in the spotlight. To this end, combined heat and power economic emission dispatch is one of the most challenging concepts in power system studies. This paper aims to examine the application of grasshopper optimization algorithm with the binary approach for solving the mentioned problems, including the valve point loading effect, ramp-rate limits, prohibited zones, and transmission losses. In this study, the multi-zone combined heat and power economic emission dispatch problem is investigated and, to evaluate the ability of the mentioned algorithm, a new case study system is introduced and simulated. The results show that the applied method reduces the fuel cost and emission in the range of 0.00234% to 0.69% and 16.978% to 86.3423%, respectively. Also, a new 48-unit test system is introduced considering wind power uncertainty. The effectiveness of the wind farms in reducing fuel costs and emission, is proved by 5.95% to 6.12% and 42.13% to 54.01%, respectively, compared to the case without wind farms.

Automated summary

Economic dispatch is a complex optimization problem in power system operation. This study focuses on the economic emission dispatch in combined heat and power systems and investigates the application of the grasshopper optimization algorithm with the binary approach. It considers various constraints such as valve point loading effect, ramp-rate limits, prohibited zones, and transmission losses. The results show that the applied method reduces fuel costs and emissions significantly. Additionally, wind farms are proven effective in reducing fuel costs and emissions.