Distributed economic dispatch of embedded generation in smart grids. (September 2015)

• Record Type: Journal Article
• Authors: Dibangoye, J.; Doniec, A.; Fakham, H.; Colas, F.; Guillaud, X.
• Is Part Of: Engineering applications of artificial intelligence. Volume 44(2015:Aug.)
• Page Start: 64
• Page End: 78
• Publication Date: 2015-09
• Subjects: Smart grid -- Distributed stochastic unit commitment problem -- Multi-agent planning -- Information preserving; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.05.007
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In a smart grid context, the increasing penetration of embedded generation units leads to a greater complexity in the management of production units. In this paper, we focus on the impact of the introduction of decentralized generation for the unit commitment (UC) problem. Unit commitment problems consist in finding the optimal schedules and amounts of power to be generated by a set of generating units in response to an electricity demand forecast. While this problem has received a significant amount of attention, classical approaches assume that these problems are centralized and deterministic. However, these two assumptions are not realistic in a smart grid context. Indeed, finding the optimal schedules and amounts of power to be generated by multiple distributed generator units is not trivial since it requires to deal with distributed computation, privacy, stochastic planning, etc. In this paper, we focus on smart grid scenarios where the main source of complexity comes from the proliferation of distributed generating units. In solving this issue, we consider distributed stochastic unit commitment problems. We introduce a novel distributed gradient descent algorithm which allows us to circumvent classical assumptions. This algorithm is evaluated through a set of experiments on real-time power grid simulator.