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UBC Theses and Dissertations

Demand side management for the future smart grid Samadi Dinani, Pedram


To achieve a high level of reliability and robustness in power systems, the grid is usually designed for the peak demand rather than the average demand. This usually results in an under-utilized system. Demand side management (DSM) programs can be adopted to shape the load pattern of the users to better utilize the available power generation capacity and to prevent installing new generation and transmission infrastructures. In this thesis, we propose different algorithms for DSM. First, we focus on the problem of maximizing the social welfare of the users. We consider a scenario where the users are equipped with automated control units and are able to make price-responsive decisions. We propose a Vickrey-Clarke-Groves (VCG) mechanism to maximize the social welfare of the users. Subsequently, we focus on developing a novel automated load scheduling algorithm to minimize the energy expenses of the user. The proposed algorithm takes into account the effects of the load uncertainties in future time slots. Moreover, the operational constraints of different types of appliances including must-run appliances, and interruptible and non-interruptible controllable appliances are studied. Next, we study how the utility company can set price values for different times of a day such that the peak-to-average ratio (PAR) of the load is minimized. We also consider the effects of the uncertainty regarding the price-responsiveness of the users. To simulate the likely behavior of the users in response to different price values for different times of the day, we propose the use of a system simulator unit. We propose two pricing algorithms based on stochastic approximation aiming to minimize the PAR of the aggregate load. Finally, we consider systems with high penetration of renewable energy resources. To tackle the reverse power flow problem associated with these systems, we propose a joint load scheduling and trading algorithm. This algorithm encourages the users to sell their excess generation to their neighboring users which mitigates the reverse power flow problem.

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Attribution-NonCommercial-NoDerivs 2.5 Canada