اﯾﺠﺎد ﯾﮏ ﺟﻬﺶ در اﻟﮕﻮرﯾﺘﻢ ﮔﺮگ ﺧﺎﮐﺴﺘﺮي ﺑﺮاي ﺣﻞ ﻣﺴﺌﻠﻪ ﺗﻮزﯾﻊ اﻗﺘﺼﺎدي-زﯾﺴﺖﻣﺤﯿﻄﯽ ﻧﯿﺮوﮔﺎهﻫﺎي ادﻏﺎم ﺷﺎﻣﻞ ﺣﺮارﺗﯽ و ﺑﺎدي

Apply a Mutation in Gray Wolf Optimization Algorithm to Solve the Economic-Environmental Dispatch Problem of Integrated Power Plants Including Thermal and Wind

در اﯾﻦ ﻣﻘﺎﻟﻪ ﻧﺴﺨﻪ ﺟﻬﺶ ﯾﺎﻓﺘﻪ دﯾﻨﺎﻣﯿﮑﯽ اﻟﮕﻮرﯾﺘﻢ ﺑﻬﯿﻨﻪﺳﺎزي ﮔﺮگ ﺧﺎﮐﺴﺘﺮي ﺑﺮاي ﺣﻞ ﻣﺴﺌﻠﻪ ﭘﺨﺶ ﺑﺎر اﻗﺘﺼﺎدي- زﯾﺴﺖﻣﺤﯿﻄﯽ ﺳﯿﺴﺘﻢ ﻗﺪرت اﺳﺘﺎﻧﺪارد 40 واﺣﺪي ﺑﻪ ﻫﻤﺮاه دو ﻣﺰرﻋﻪ ﺑﺎدي ﭘﯿﺸﻨﻬﺎد ﺷﺪه اﺳﺖ. ﻟﺬا ﺗﺎﺑﻊ ﻫﺪﻓﯽ ﺟﺎﻣﻊ از ﻫﺰﯾﻨﻪ-ﻫﺎي ﺑﻬﺮهﺑﺮداري ﮐﻪ ﺗﺮﮐﯿﺒﯽ از ﻫﺰﯾﻨﻪﻫﺎي ﻣﺴﺘﻘﯿﻢ اﻧﺮژي ﺑﺎد، ﻫﺰﯾﻨﻪ ﺟﺮﯾﻤﻪ ﺗﺨﻤﯿﻦ ﺑﯿﺶ از ﺣﺪ، ﻫﺰﯾﻨﻪ ﺟﺮﯾﻤﻪ ﺗﺨﻤﯿﻦ ﮐﻤﺘﺮ از ﺣﺪ، ﻫﺰﯾﻨﻪ واﺣﺪ ﺣﺮارﺗﯽ و ﻫﺰﯾﻨﻪ آﻻﯾﻨﺪﮔﯽ، اراﺋﻪ ﺷﺪه اﺳﺖ. ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻣﺎﻫﯿﺖ ﺗﺼﺎدﻓﯽ ﺳﺮﻋﺖ ﺑﺎد ﺗﻮان ﺗﻮﻟﯿﺪي ﺗﻮﺳﻂ ﺗﻮرﺑﯿﻦ-ﻫﺎي ﺑﺎدي ﻏﯿﺮﻗﺎﺑﻞ ﭘﯿﺶﺑﯿﻨﯽ اﺳﺖ، ﺑﻨﺎﺑﺮاﯾﻦ از ﺗﺎﺑﻊ ﺗﻮزﯾﻊ اﺣﺘﻤﺎل وﯾﺒﻮل ﺑﺮاي ﻣﺪلﺳﺎزي ﺗﻮان ﻣﺰرﻋﻪﻫﺎي ﺑﺎد در اﯾﻦ ﻣﻘﺎﻟﻪ اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. ﻫﺰﯾﻨﻪ ﺑﻬﺮهﺑﺮداري ﻣﺰرﻋﻪ ﺑﺎدي ﺑﻪﺻﻮرت اﺣﺘﻤﺎﻻﺗﯽ در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﺪه اﺳﺖ ﺗﺎ ﺳﻨﺎرﯾﻮﻫﺎي ﺑﺎد ﺑﺎ اﺣﺘﻤﺎل ﭘﺎﯾﯿﻦ ﺗﺎﺛﯿﺮ ﮐﻤﺘﺮي در ﻫﺰﯾﻨﻪ ﻧﻬﺎﯾﯽ داﺷﺘﻪ ﺑﺎﺷﻨﺪ. ﺷﺒﯿﻪﺳﺎزيﻫﺎ در ﻗﺎﻟﺐ ﺳﻪ ﺑﺨﺶ اﻧﺠﺎم ﺷﺪه اﺳﺖ و ﺑﻪﻣﻨﻈﻮر اﻋﺘﺒﺎرﺳﻨﺠﯽ ﺑﺎ ﻣﺮﺟﻊﻫﺎي دﯾﮕﺮ ﻣﻮرد ﻣﻘﺎﯾﺴﻪ واﻗﻊ ﺷﺪه اﺳﺖ. ﻧﺘﺎﯾﺞ ﺣﺎﺻﻞ ﺷﺪه از ﺑﻬﯿﻨﻪﺳﺎزيﻫﺎ در ﻫﺮ ﺳﻪ ﺳﻨﺎرﯾﻮ و ﻣﻘﺎﯾﺴﻪ آن ﺑﺎ اﻟﮕﻮرﯾﺘﻢﻫﺎي ﻫﻮﺷﻤﻨﺪ ﺗﺎﺋﯿﺪي ﺑﺮ ﻋﻤﻠﮑﺮد ﺑﻬﺘﺮ و دﻗﺖ ﺑﺎﻻﺗﺮ اﻟﮕﻮرﯾﺘﻢ ﭘﯿﺸﻨﻬﺎدي ﻧﺴﺒﺖ ﺑﻪ ﻧﺴﺨﻪ اﺻﻠﯽ اﻟﮕﻮرﯾﺘﻢ ﮔﺮگ ﺧﺎﮐﺴﺘﺮي و ﻫﻤﭽﻨﯿﻦ ﺳﺎﯾﺮ اﻟﮕﻮرﯾﺘﻢﻫﺎ دارد.

In this paper, a dynamic mutant version of the gray wolf optimization algorithm (MGWO) is proposed to solve the economic-environmental dispatch (E-ED) problem of a standard 40-unit power system with two wind farms. Thus, a comprehensive objective function of operating costs is presented, which is a combination of wind energy costs, over-estimated penalty costs, under-estimated penalty costs, thermal unit costs and emission costs. Due to the random nature of wind speed, the power generated by wind turbines is unpredictable. Therefore, the Weibull probability distribution function has been used to model the wind farm power in this paper. The cost of operating a wind farm is considered probabilistic so that low-probability wind scenarios have less effect on the total operation cost. The simulations are performed in the form of three section and the optimization results are compared with several meta-heuristic algorithm results for validation. The results of the optimizations in all three scenarios and its comparison with other algorithms confirm the better performance and higher accuracy of the proposed MGWO algorithm than the original version of the gray wolf algorithm (GWO) as well as other algorithms.