Distributed Demand-Side Optimization in the Smart Grid

The modern power grid is facing major challenges in the transition to a low-carbon energy sector. The growing energy demand and environmental concerns require carefully revisiting how electricity is generated, transmitted, and consumed, with an eye to the integration of renewable energy sources. The envisioned smart grid is expected to address such issues by introducing advanced information, control, and communication technologies into the energy infrastructure. In this context, demand-side management (DSM) makes the end users responsible for improving the efficiency, reliability and sustainability of the power system: this opens up unprecedented possibilities for optimizing the energy usage and cost at different levels of the network. The design of DSM techniques has been extensively discussed in the literature in the last decade, although the performance of these methods has been scarcely investigated from the analytical point of view. In this thesis, we consider the demand-side of the electrical network as a multiuser system composed of coupled active consumers with DSM capabilities and we propose a general framework for analyzing and solving demand-side management problems. Since centralized solution methods are too demanding in most practical applications due to their inherent computational complexity and communication overhead, we focus on developing efficient distributed algorithms, with particular emphasis on crucial issues such as convergence speed, information exchange, scalability, and privacy. In this respect, we provide a rigorous theoretical analysis of the conditions ensuring the existence of optimal solutions and the convergence of the proposed algorithms. Among the plethora of DSM methods, energy consumption scheduling (ECS) programs allow to modify the user’s demand profile by rescheduling flexible loads to off-peak hours. On the other hand, incorporating dispatchable distributed generation (DG) and distributed storage (DS) into the demand-side of the network has been shown to be equally successful in diminishing the peak-to-average ratio of the demand curve, plus overcoming the limitations in terms users’ inconvenience introduced by ECS. Quite surprisingly, while the literature has mostly concentrated on ECS techniques, DSM approaches based on dispatchable DG and DS have not attracted the deserved attention despite their load-shaping potential and their capacity to facilitate the integration of renewable sources. In this dissertation, we will this gap and devise accurate DSM models to study the impact of dispatchable DG and DS at the level of the end users and on the whole electricity infrastructure. With this objective in mind, we tackle several DSM scenarios, starting from a deterministic day-ahead optimization with local constraints and culminating with a stochastic day-ahead optimization combined with real-time adjustments under both local and global requirements. Each task is complemented by defining appropriate network and pricing models that enable the implementation of the DSM paradigm in realistic energy market environments. In this regard, we design both user-oriented and holistic-based DSM optimization frameworks, which are respectively applicable to competitive and externally regulated market scenarios. Numerical results are reported to corroborate the presented distributed schemes. On the one hand, the users’ electricity expenditures are consistently reduced, which encourages their active and voluntary participation in the proposed DSM programs; on the other hand, this results in a lower generation costs and enhances the robustness of the whole grid.