Data-Driven Demand Management for Smart Grid - Samadi Kouhi, Mikhak (Université d'Ottawa / University of Ottawa, 2022)
The concept of the smart grid has been proposed to modernize the power grid with efficient and comprehensive monitoring systems as well as autonomous and self-healing technologies. Demand response (DR) and demand side management (DSM) are two aspects of the smart grid. The first is used to control the demand and supply, and the peak-to-average ratio (PAR) of a distribution network, and the second is used to manage a site's energy consumption efficiently. This thesis focuses on reducing the need for importing extra electricity from resources outside the local distribution network using DR, DSM. First, a demand management model is described to optimize customer energy usage and consider their comfort within a sequential optimization model. A multi-layer and multi-objective optimization system is proposed at the energy consumption level to consider customer comfort and experience. The cluster-based sequential management approach is presented to improve customer comfort via appliance scheduling. To quantify thermal comfort, a thermodynamic solution is used for the heating ventilation, and air conditioning (HVAC) system to schedule thermal load and eliminate customer inconvenience on room temperature. Customer inconvenience refers to a condition that the use of an appliance does not meet the preferences of the customer. Moreover, the satisfaction of electric vehicle charging, constrained by minimum cost, and the preferred usage time for the non-interruptible deferrable loads are considered in this model. Due to the uncertain demand profile of users, stochastic solutions for demand response problems enable utility companies to address the uncertainties in the customers' energy consumption. A stochastic DR approach is presented between an aggregator and residential customers during peak load periods, and the optimal outputs of customers and aggregator are determined. This probabilistic demand response management model uses a mixed-strategy Stackelberg game to maximize the profit of total energy reduction for the aggregator and to maximize the reward of demand reduction for customers. The proposed solution reduces the demand, PAR, and the overall energy costs for both customers and the grid while maintaining customer comfort. To perform a secure and robust energy trading model with high scalable decentralized supervision, a mixed-strategy stochastic game model is integrated with energy blockchain to address uncertainties in DR contributions. This model utilizes the processing hardware of customers for block mining, stores customer DR agreements as distributed ledgers, and offers a smart contract and consensus algorithm for energy transaction validation. A novel consensus algorithm compatible with a DR problem is presented to incentivize customers to contribute to DR events and collaborate in block mining to gain monetary profits. The results demonstrate the security and robustness of the consensus algorithm for detecting malicious activities. In summary, this thesis proposes schemes that control grid demand and minimize energy usage while preserving user comfort, security, and economic fairness.
Keywords: Smart Grid