Visible to the public Energy Demand Scheduling Based on Game Theory for Microgrids

TitleEnergy Demand Scheduling Based on Game Theory for Microgrids
Publication TypeConference Paper
Year of Publication2018
AuthorsChouikhi, S., Merghem-Boulahia, L., Esseghir, M.
Conference Name2018 IEEE International Conference on Communications (ICC)
ISBN Number978-1-5386-3180-5
KeywordsBuildings, constrained optimization problem, consumer demand management, consumers, consumption scheduling, demand side management, demand-side management systems, distributed energy demand scheduling approach, distributed power generation, electricity grids, energy consumption, game theory, Human Behavior, interesting challenges, Metrics, Microgrids, minimal information exchange, minimal interactions, Minimization, modern grids, opportunity, optimisation, Optimization, policy-based governance, pubcrawl, renewable energy sources, Resiliency, scheduling, Smart Grid Consumeer Privacy, smart grid consumer privacy, Smart grids, smart power grids, Task Analysis, total energy cost

The advent of smart grids offers us the opportunity to better manage the electricity grids. One of the most interesting challenges in the modern grids is the consumer demand management. Indeed, the development in Information and Communication Technologies (ICTs) encourages the development of demand-side management systems. In this paper, we propose a distributed energy demand scheduling approach that uses minimal interactions between consumers to optimize the energy demand. We formulate the consumption scheduling as a constrained optimization problem and use game theory to solve this problem. On one hand, the proposed approach aims to reduce the total energy cost of a building's consumers. This imposes the cooperation between all the consumers to achieve the collective goal. On the other hand, the privacy of each user must be protected, which means that our distributed approach must operate with a minimal information exchange. The performance evaluation shows that the proposed approach reduces the total energy cost, each consumer's individual cost, as well as the peak to average ratio.

Citation Keychouikhi_energy_2018