AbstractsBusiness Management & Administration

The energy needs of all sectors of our modern societies are constantly increasing. Indicatively,annual worldwide demand for electricity has increased ten-fold within the last 50 years. Thus, energyefficiency has become a major target of the research community. The ongoing research efforts are focusedon two main threads, i) optimizing efficiency and reliability of the power grid and ii) improvingenergy efficiency of individual devices / systems. In this thesis we explore the use of optimizationand game theory techniques towards both goals.Stable and economic operation of the power grid calls for electricity demand to be uniformly distributedacross a day. Currently, the price of electricity is fixed throughout a day for most users. Givenalso the highly correlated daily schedules of users, this leads to unbalanced distribution of demand.However, the recent development of low-cost smart meters enables bidirectional communication betweenthe electricity operator and each user, and hence introduces the option of dynamic pricing anddemand adaptation (a.k.a. Demand Response - DR). Dynamic pricing motivates home users to modifytheir electricity consumption profile so as to reduce their electricity bill. Eventually, users by movingdemand out of peak consumption periods lead to a more balanced total demand pattern and a morestable grid.A DR scheme has to balance the contradictory interests of the utility operator and the users.On the one hand, the operator wants to minimize electricity generation cost. On the other hand,each user aims to maximize a utility function that captures the trade-off between timely executionof demands and financial savings. In this thesis we focus on designing efficient DR schemes for theresidential sector. Initially, we introduce a realistic model of user’s response to time-varying pricesand identify the operating constraints of home appliances that make optimal demand scheduling NPHard.Thus, we devise an optimization-based dynamic pricing mechanism and demonstrate how itcan be implemented as a day-ahead DR market. Our numerical results underline the potential ofresidential DR and verify that our scheme exploits DR benefits more efficiently compared to existingones.The large number of home users though and the fact that the utility operator generally lacks the know-how of designing and applying dynamic pricing at such a large scale introduce the need fora new market entity. Aggregators act as intermediaries that coordinate home users to shift or evencurtail their demands and then resell this service to the utility operator. In this direction, we introducea three-level hierarchical model for the smart grid market and we devise the corresponding pricingmechanism for each level. The operator seeks to minimize the smart grid operational cost and offersrewards to aggregators toward this goal. Aggregators are profit-maximizing entities that competeto sell DR services to the operator. Finally, end-users are also self-interested and seek to optimizethe tradeoff between earnings and discomfort. Based on…