ﺑﺮﻧﺎﻣﻪ رﯾﺰي ﺗﺼﺎدﻓﯽ دو ﻣﺮﺣﻠﻪ اي ﻫﻤﺰﻣﺎن اﻧﺮژي و رزرو در رﯾﺰﺷﺒﮑﻪ ﻫﺎي ﻫﻮﺷﻤﻨﺪ ﻣﺒﺘﻨﯽ ﺑﺮ ﺑﻬﯿﻨﻪ ﺳﺎزي ﭼﻨﺪ ﻫﺪﻓﻪ

Two-Stage Stochastic Programming for Simultaneous Energy and Reserve Management in Smart Micro-Grids Based on a Multi-Objective Optimization

ﭼﮑﯿﺪه: در اﯾﻦ ﻣﻘﺎﻟﻪ، ﯾﮏ ﻣﺪل ﺑﺮﻧﺎﻣﻪ رﯾﺰي ﺗﺼﺎدﻓﯽ دو ﻣﺮﺣﻠﻪ اي ﻣﺒﺘﻨﯽ ﺑﺮ ﺑﻬﯿﻨﻪ ﺳﺎزي ﭼﻨﺪ ﻫﺪﻓﻪ ﺑﻪ ﻣﻨﻈﻮر ﺑﻬﺮه ﺑﺮداري ﺑﻬﯿﻨﻪ رﯾﺰﺷﺒﮑﻪ ﻫﻮﺷﻤﻨﺪ ﺑﺎ ﻫﺪف ﮐﻤﯿﻨﻪ ﺳﺎزي ﻫﺰﯾﻨﻪ ﺑﻬﺮه ﺑﺮداري و آﻻﯾﻨﺪه ﻫﺎي زﯾﺴﺖ ﻣﺤﯿﻄﯽ ﺑﺎ ﺣﻀﻮر ﻣﻨﺎﺑﻊ ﺗﺠﺪﯾﺪﭘﺬﯾﺮ و ﭘﺎﺳﺨﮕﻮﯾﯽ ﺑﺎر ﭘﯿﺸﻨﻬﺎد ﺷﺪه اﺳﺖ. در ﻣﺪل ﭘﯿﺸﻨﻬﺎدي، ﺧﻄﺎي ﭘﯿﺶ ﺑﯿﻨﯽ ﺗﻮان ﻣﻨﺎﺑﻊ ﺗﺠﺪﯾﺪﭘﺬﯾﺮ ﺑﻪ وﺳﯿﻠﻪ ﺗﻮاﺑﻊ ﭼﮕﺎﻟﯽ اﺣﺘﻤﺎل ﻣﺪﻟﺴﺎزي ﺷﺪه و ﺑﺮﻧﺎﻣﻪ ﻫﺎي ﭘﺎﺳﺦ ﺑﺎر، ﺟﻬﺖ ﭘﻮﺷﺶ ﻋﺪم ﻗﻄﻌﯿﺖ ﺗﻮان ﻣﻨﺎﺑﻊ ﺗﺠﺪﯾﺪﭘﺬﯾﺮ اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. در ﻣﺪل ﻣﺴﺄﻟﻪ ﻓﺮض ﻣﯽ ﺷﻮد ﮐﻪ ﺑﻬﺮه-ﺑﺮدار رﯾﺰﺷﺒﮑﻪ ﺑﺮاي ﻣﺪﯾﺮﯾﺖ ﺑﻬﯿﻨﻪ ﺷﺒﮑﻪ، در دو ﺣﻮزه ﺑﻬﺮه ﺑﺮداري در وﺿﻌﯿﺖ ﭘﺎﯾﻪ و ﺣﻮزه ﻣﺮﺑﻮط ﺑﻪ ﺳﻨﺎرﯾﻮﻫﺎي ﻣﺨﺘﻠﻒ ﺑﺮاي ﺗﻮﻟﯿﺪ ﻣﻨﺎﺑﻊ ﺗﺠﺪﯾﺪﭘﺬﯾﺮ، ﺗﺼﻤﯿﻢ ﮔﯿﺮي ﻣﯽ ﮐﻨﺪ. وﺿﻌﯿﺖ ﭘﺎﯾﻪي رﯾﺰ ﺷﺒﮑﻪ، اﺷﺎره ﺑﻪ ﺣﺎﻟﺘﯽ اﺳﺖ ﮐﻪ ﺗﻮان ﻣﻨﺎﺑﻊ ﺗﺠﺪﯾﺪ ﭘﺬﯾﺮ ﺑﺮاﺑﺮ ﺑﺎ ﻣﻘﺎدﯾﺮ ﭘﯿﺶ ﺑﯿﻨﯽ ﺷﺪه در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﻮﻧﺪ. ﺑﺮاي ﺣﻞ ﻣﺴﺄﻟﻪ، از روش ﺑﻬﯿﻨﻪ ﺳﺎزي ﭼﻨﺪﻫﺪﻓﻪ ازدﺣﺎم ذرات )MOPSO( و ﺑﺮاي اﺳﺘﺨﺮاج ﺧﺮوﺟﯽ از ﻓﻀﺎي ﭘﺮﺗﻮ، از روش TOPSIS اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. ﻣﺪل ﭘﯿﺸﻨﻬﺎدي ﺑﺮ روي ﯾﮏ رﯾﺰﺷﺒﮑﻪ ﻫﻮﺷﻤﻨﺪ ﻧﻤﻮﻧﻪ ﭘﯿﺎده ﺳﺎزي ﺷﺪه و ﻧﺘﺎﯾﺞ ﻋﺪدي ﻧﺸﺎن دﻫﻨﺪه ﮐﺎراﯾﯽ ﻣﺪﯾﺮﯾﺖ ﺳﻤﺖ ﺗﻘﺎﺿﺎ در ﮐﺎﻫﺶ ﻫﺰﯾﻨﻪ ﻫﺎ، آﻻﯾﻨﺪﮔﯽ و ﭘﻮﺷﺶ ﻋﺪم ﻗﻄﻌﯿﺖ ﻧﺎﺷﯽ از ﺗﻮان ﺗﻮﻟﯿﺪي ﻣﻨﺎﺑﻊ ﺑﺎدي و ﺧﻮرﺷﯿﺪي ﻣﯽ ﺑﺎﺷﺪ.

In this paper, a two-stage stochastic programming model based on a multi-objective optimization has been proposed for optimal operation of smart micro-grid (MG) aiming at minimizing operational costs and environmental emissions in presence of renewable resources and demand response. In the presented model, the forecasting error of the renewable resources productions is modeled by probability density functions and demand response has been implemented to cover the uncertainty of the renewable resources. Here, it is assumed that the MG operator decides on two stages for optimum management of its network; first stage is the operation in the base state and the second one is pertaining to the domains of different scenarios for generation of renewable resources. The base state of the micro-grid refers to the situation in which the active power productions of renewables are equal to the predicted values. To solve the problem, Multi-Objective Particle Swarm Optimization method has been used and TOPSIS technique has been applied to extract the output from the Pareto Frontier. The proposed approach is applied to a typical MG and the numerical results show the efficiency of demand side management in reducing costs and environmental emissions as well as covering the uncertainty resulting from renewables.