Thesis - Open Access
Master of Science (MS)
Department / School
Electrical Engineering and Computer Science
The electric power industry has undergone significant changes in response to the environmental concerns during the past decades. Nowadays, due to the integration of different distributed energy systems in the smart grid, the balancing between power generation and load demand becomes a critical problem. Specifically, due to the intermittent nature of renewable energy sources (RESs) , power system optimization becomes significantly complicated. Due to the uncertain nature of RESs, the system may fail to ensure the power quality which may cause increased operating costs for committing costly reserve units or penalty costs for curtailing load demands. This dissertation presents three projects to study the optimization and control for smart grid and smart community. First, optimal operation of battery energy storage system (BESS) in grid-connected microgrid is studied. Near optimal operation/allocation of the BESS is investigated with the consideration of battery lifetime characteristics. Approximate dynamic programming (ADP) is proposed to solve optimal control policy for time-dependent and finite-horizon BESS problems and performance comparison is done with classical dynamic programming approach. The results show that the ADP can optimize the system operation under different scenarios to maximize the total system revenue. Second, optimal operation of the BESS in islanded microgrid is also studied. Specifically, a new islanded microgrid model is formulated based on Markov decision process. A computationally efficient ADP approach is proposed to solve this energy optimization problem, and achieve near minimum operational cost efficiently. Simulation results show that the proposed ADP can achieve 100% and at least 98% of optimality for deterministic and stochastic case studies, respectively. The performance of the proposed ADP approach also achieved 18:69 times faster response than that of the traditional DP approach for 0:5 million of data samples. Third, a demand side management technique is proposed for the optimization of residential demands with financial incentives. A new design of comfort indicator is proposed considering both thermal and other electric appliances based on consumers’ comfort level. The proposed approach is compared with two existing demand response approaches for both 10-houses and 100-houses simulation studies. For both cases, the proposed approach outperformed the existing approaches in terms of reward incentives and comfort levels.
Library of Congress Subject Headings
Microgrids (Smart power grids)
Electric power systems -- Management.
Renewable energy sources.
Includes bibliographical references (90-96)
Number of Pages
South Dakota State University
Das, Avijit, "Efficient Energy Optimization for Smart Grid and Smart Community" (2017). Electronic Theses and Dissertations. 1735.