Demand Response Management in Smart Grid Networks: a Two-Stage Game-Theoretic Learning-Based Approach

In this diploma thesis, the combined problem of power company selection and Demand Response Management in a Smart Grid Network consisting of multiple power companies and multiple customers is studied via adopting a distributed learning and game-theoretic technique. Each power company is characterized by its reputation and competitiveness. The customers who act as learning automata select the most appropriate power company to be served, in terms of price and electricity needs’ fulfillment, via a distributed learning based mechanism. Given customers\u27 power company selection, the Demand Response Management problem is formulated as a two-stage game theoretic optimization framework, where at the first stage the optimal customers\u27 electricity consumption is determined and at the second stage the optimal power companies’ pricing is calculated. The output of the Demand Response Management problem feeds the learning system in order to build knowledge and conclude to the optimal power company selection. A two-stage Power Company learning selection and Demand Response Management (PC-DRM) iterative algorithm is proposed in order to realize the distributed learning power company selection and the two-stage distributed Demand Response Management framework. The performance of the proposed approach is evaluated via modeling and simulation and its superiority against other state of the art approaches is illustrated

A game theoretic approach for load-shifting in the smart grid

In this paper, load-shifting within the context of smart grid demand response is investigated for an electricity market composed of a single energy provider and multiple consumers. The problem is formulated as a Stackelberg game in which the provider, acting as leader, moves first and offers price discounts across a finite time horizon to motivate consumers to shift their energy consumptions from peak periods. The consumers, acting as followers, respond by deciding if and how they shift their consumption from their nominal demand. In this model, the aim of the energy provider is to maximize its profits, while the consumers aim to minimize their total costs related to both the energy consumption and inconvenience of deviating from the nominal demand. Within this setting, a procedure is proposed to obtain equilibrium outcomes and managerial insights are derived by investigating the impact of various factors, including consumer types and market diversity, on the interactions between the energy provider and its customers. Our results show that price discounts may provide significant leverage for achieving lower peak-to-average ratios while improving the service provider's profits. Our results demonstrate that when load-shifting is sacrificial for the consumers, equilibrium discounts and server provider profits not only depend on the size of the demand (market depth) but also the composition and the number of consumers (market breadth)