Decision-making techniques for smart grid energy management
This thesis has contributed to the design of suitable decision-making techniques for energy management in the smart grid with emphasis on energy efficiency and uncertainty analysis in two smart grid applications. First, an energy trading model among distributed microgrids (MG) is investigated, aiming to improve energy efficiency by forming coalitions to allow local power transfer within each coalition. Then, a more specific scenario is considered that is how to optimally schedule Electric Vehicles (EV) charging in a MG-like charging station, aiming to match as many as EV charging requirements with the uncertain solar energy generation. The solutions proposed in this thesis can give optimal coalition formation patterns for reduced power losses and achieve optimal performance for the charging station. First, several algorithms based on game theory are investigated for the coalition formation of distributed MGs to alleviate the power losses dissipated on the cables due to power transfer. The seller and buyer MGs can make distributed decisions whether to form a coalition with others for energy trading. The simulation results show that game theory based methods that enable cooperation yield a better performance in terms of lower power losses than a non-cooperative approach. This is because by forming local coalitions, power is transferred within a shorter distance and at a lower voltage. Thus, the power losses dissipated on the transmission lines and caused by power conversion at the transformer are both reduced. However, the merge-and-split based cooperative games have an inherent high computational complexity for a large number of players. Then, an efficient framework is established for the power loss minimization problem as a college admissions game that has a much lower computational complexity than the merge-and-split based cooperative games. The seller and buyer MGs take the role of colleges and students in turn and apply for a place in the opposite set following their preference lists and the college MGs’ energy quotas. The simulation results show that the proposed framework demonstrates a comparable power losses reduction to the merge-and-split based algorithms, but runs 700 and 18000 times faster for a network of 10 MGs and 20 MGs, respectively. Finally, the problem of EV charging using various energy sources is studied along with their impact on the charging station’s performance. A multiplier k is introduced to measure the effect of solar prediction uncertainty on the decision-making process of the station. A composite performance index (the Figure of Merit, FoM) is also developed to measure the charging station’s utility, EV users charging requirements and the penalties for turning away new arrivals and for missing charging deadlines. A two-stage admission and scheduling mechanism is further proposed to find the optimal trade-off between accepting EVs and missing charging deadlines by determining the best value of the parameter k under various energy supply scenarios. The numerical evaluations give the solution to the optimization problem and show that some of the key factors such as shorter and longer deadline urgencies of EVs charging requirements, stronger uncertainty of the prediction error, storage capacity and its initial state will not affect significantly the optimal value of the parameter k.