Energy Management Optimization In Smart Distribution Grid

Power grid system is an important milestone for human beings to step into the age of electric-ity from the age of machinery. Power grid consists of power generation equipments, transmission equipments, distribution equipments, etc., to provide a safe and reliable power source to power consumers. However, with the aging power equipment, growth of electricity demand, and the development of renewable energy technologies, reliable, stable, and highly efficient power tech-nology has become a significant research topic. Smart grid is considered to be the next generation of power grid technology to tackle the existing challenges. Equipped with the next generation of communications infrastructure and real-time measurement facilities, smart grid will improve reli-ability, stability and efficiency. In addition, smart grid has the ability of preventing and responding to internal and external crises of power grid. Smart grid will effectively deal with the power trans-portation and scheduling process uncertainty, provide renewable clean energy, optimize power grid system, improve the quality and reliability, manage unpredictable events of grid operation, and reduce operational risks.In smart grid, energy management optimization will be one kind of key technologies and frontier research topics. Combined with advanced metering infrastructure and communication infrastructures, smart grid emerges as a closed-loop feedback control system. It is available to design control and optimization methods to significantly enhance the energy-efficient, reliability, and stability of power grid. We can classify energy management optimization problems into two kinds:"macro" issues and "micro" issues. It depends on whether the agents in smart distributed grid affect each others in a real time manner. As a typical example of cyber-physics systems, smart grid has to face the challenges of security issues. Based on the state-of-the-art study, this dissertation studies on energy management optimization in smart distribution grid. Firstly, we provide a brief review of the background, overview, and related works on energy management optimization of smart distributed grid. The main contributions are summarized as follows:1. Study on residential load schedule with price uncertainty. Three indices, i.e., the power consumption expense, the robustness of schedule subject to uncertain electricity price and the satisfaction of customer, are taken into full consideration. We propose to optimize si-multaneously the three indices via convex optimization. Considering the characteristics of appliance, we model the residential load scheduling problem and finally formulate a mixed integer quadratic optimization problem. Both the convex optimization method and the re-laxation technique are utilized to tackle the hybrid optimization problem, and we provide a gap between the optimal result and the final result of our proposed algorithm. In addition, we design an iterative learning algorithm to simultaneously explore the user preference and determine the weight between cost and satisfaction.2. Study on meeting scheduling in commercial buildings. We consider the optimal meeting scheduling problem in a commercial building over a fixed period of time, with the objectives of minimizing the cost of energy consumption by the air-conditioning system and possibly achieving more balanced power distribution. By considering a set of realistic factors, includ-ing the eligible time slots of attendees and energy consumption characteristics of meeting rooms, this problem is formulated as a constrained mixed-integer linear program, which then can be solved by an optimization solver, e.g., CPLEX. However, because the compu-tation complexity increases dramatically with the problem size, a fast heuristic algorithm is proposed. The numerical simulations verify that the heuristic algorithm produces a near-optimal result and can reduce 28.5%of the energy consumption cost with lower computation complexity.3. Study on demand response management with multiple utility companies. Different from most existing studies that focus on the scenario with a single utility company, this section studies DRM with multiple utility companies. First, the interaction between utility compa-nies and residential users is modeled as a two-level game. That is, the competition among the utility companies is formulated as a non-cooperative game, while the interaction among the residential users is formulated as an evolutionary game. Then, we prove that the proposed strategies are able to make both games converge to their own equilibrium. In addition, the strategies for the utility companies and the residential users are implemented by distribut-ed algorithms. Illustrative examples show that the proposed scheme is able to significantly reduce peak load and demand variation.4. Study on impacts of unreliable communication and anti-jamming algorithm. Due to the vul- nerabilities of communication channels, especially the wireless networks, communication is not perfect in practice and will be threatened by kinds of attacks, among which jamming at-tack is deemed as the primary one. In this section, we consider jamming attack in the wireless communication of a smart microgrid. Firstly, the DRM performance degradation induced by jamming attack is fully studied and explicitly presented, by considering both packet loss ratio and load estimation error. Then, a modified regret matching based anti-jamming algorithm is proposed to enhance the quality of communication, and the performance of DRM. Final-ly, numerical results are presented to illustrate the impacts of unreliable communication on DRM, as well as the performance of the proposed anti-jamming algorithm.The conclusions are drawn with future work at the end of the dissertation.