15053

1440 دكتري

دكتري

قدرت

اصفهان : دانشگاه صنعتي اصفهان

1397

هفده،[167]ص.: مصور، جدول، نمودار

مهدي معلم

محمدحسين منشئي، غلامرضا يوسفي

حامد نريماني، محمدامين لطيفي

1398/06/23

كتابنامه

مهندسي برق

مهندسي برق و كامپيوتر

1398/06/27

2550542

Stochastic Load Factor Based Energy Management with Equilibrium Selection Capability in the Game Theory Framework Azin Shojaei Berjouei Azin shojaei@ec iut ac ir Date of Submission December 23 2018 Doctor of Philosophy Thesis Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Supervisor Dr Mehdi Moallem moallem@cc iut ac ir 1st Advisor Dr Mohammad Hossein Manshaei manshaei@cc iut ac ir 2nd Advisor Dr Gholamreza Yousefi Yousefi@cc iut ac ir Department Graduate Program Coordinator Dr Gholamreza Yousefi Isfahan University of Technology Isfahan 84156 83111 IranAbstract In this thesis a new distributed stochastic load factor LF based energy management approach isproposed in the Stackelberg game framework The game s leader is the distribution company DISCO thatparticipates in the energy management game with two energy pricing mechanisms In the first energy pricingmechanism DISCO calculates the day ahead energy pricing through maximizing its profit which is formulatedas a stochastic conditional value at risk optimization problem In the second one a new pricing scheme isdesigned which discriminates individual customer s day ahead energy price based on his her contribution to theLF improvement Customer s contribution is measured via the Shapley Value Method SVM Energymanagement Game s followers are price taker customers who participate in the distribution grid operationprograms by holding a common constraint The real time uncertainty of renewable energies is tackled bycustomers objective stochastic formulation in which day ahead energy procurement and real time scheduling ofthe energy storages and flexible loads are jointly decided Depending on the number of game s equilibrium theproper learning algorithms are designed for different structure of the customer s objective function Theconvergence condition of the proposed energy management algorithms is investigated and proved Numericalanalysis confirms the effectiveness of the proposed stochastic energy management approach Key Words Smart grid Shapley Value K means clustering Stackelberg Game Load Factor Stochasticenergy management Introduction Many researchers have recently proposed the distributed and automated Demand SideManagement DSM algorithms in the context of the smart grid One of the main issues andopen area in the field of DSM is the uncertainty management of the demand side integratedrenewable energies and decreasing its impact on the efficient performance and economy ofthe system Different approaches have addressed the energy management challenge in thepresence of flexible loads energy storage devices and renewable energy sources The work Liu et al 2016 proposed a day ahead optimal bidding strategy for a microgrid consisting ofrenewable energies storages and dispatchable energy resources through a hybridstochastic robust optimization model In Nguyen et al 2015 a real time home energymanagement of the thermal loads considering renewable energy and price uncertainty isproposed in a stochastic scenario based optimization framework The authors of Marzbandet al 2017 devised a day ahead stochastic bidding strategy for a home microgrid withintegrated flexible loads energy storage and renewable energy In work Nikmehr et al 2017 an optimal stochastic scenario based scheduling of the network microgrid through

مهدي معلم

محمدحسين منشئي، غلامرضا يوسفي

حامد نريماني، محمدامين لطيفي