Robust optimal demand-side management in smart grids

Smart grids (SGs) are experiencing an increasing growth due to their economic, social and environmental benefits. The concept of SG has recently gained significant attention from the research community due to its ability to effectively integrate distributed energy resources (DER) including renewable energy sources (RES), energy storage systems (ESS) and the demand side management (DSM) programs. A SG can change the operation paradigm of the electric grid to ensure an efficient and sustainable electricity supply with lower losses and greater reliability and security. Despite these potential benefits, the massive penetration of DERs in SGs may impose new challenges to the system design and functioning. A substantial challenge arises from system uncertainties due to forecast errors. For instance, the inherent intermittency of RESs, the unpredictable changes in users’ electricity demand, and the volatility of the dynamic electricity price in electricity markets can inject considerable amounts of uncertainty into the electric grid. Facing these challenges, this thesis investigates the integration of DERs and DSM programs as great sources of flexibility and essential elements for effective supply-demand balancing into SGs in the presence of uncertainty. Firstly, we present a comprehensive classification, review and analysis of existing approaches and findings for DSM to highlight key features and components of energy management systems for more flexible and intelligent grids. We provide a definition of DSM and introduce the reader to the functionalities and achievements of DSM applications in SGs. We then focus on the state-of-the-art decision-making and control approaches for DSM, followed by a comprehensive description of demand side applications detailed for smart users, distribution networks and transmission networks. Afterwards, we characterize our novel methodologies presented in this thesis in two main parts including centralized and decentralized/distributed approaches. In the first part, we present five novel robust centralized DSM approaches for the optimal scheduling of residential microgrids (MGs) comprising a number of interconnected end-use consumers with various types of electrical loads, RESs, ESSs, and plug-in electric vehicles (PEVs). The general objective of the optimal scheduling is minimizing the expected electricity cost while satisfying device/comfort/contractual constraints of the system under the uncertainties on RES generation and users’ electricity demand. In addition, we deal with the conservativeness of the proposed approaches for different scenarios in terms of the cost saving, the peak-to-average ratio (PAR), and the constraints’ violation rate. The proposed robust DSM approaches allow the decision maker (i.e., the energy manager of the system) to make a satisfactory trade-off between the electricity cost and constraints’ violation rate considering the system technical limits and the users’ comfort. We validate the effectiveness of the proposed approaches on several simulated case studies and provide comparisons and discussions on the results. In the second part, we explore decentralized and distributed DSM approaches for the coordinated optimal charge control of PEVs in SGs. In particular, we develop a novel fully distributed control strategy for the optimal charging of large-scale PEV fleets aiming at the minimization of the aggregated charging cost and battery degradation, while satisfying the PEVs’ individual load requirements and the overall grid congestion limits. The proposed resolution algorithm requires a minimal shared information between PEVs that communicate only with their neighbors without relying on a central aggregator. Thus, it guarantees the PEV users’ privacy. We validate the proposed approach on numerical experiments with a large number of PEVs to demonstrate the ability of the approach in finding a global optimum solution with a favorable computational efficiency. Moreover, we present a new robust decentralized framework for day-ahead charge control of PEV fleets under uncertainties on the dynamic electricity price and the inelastic loads demand. The main objective of this work is minimizing both the overall charging cost and the aggregated battery degradation cost of PEVs while preserving the robustness of the solution against perturbations in the uncertain parameters. In addition, power congestion limits of the overall capacity of the distribution network and the PEVs’ individual needs such as charge level requirements and battery degradation cost are taken into account.