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