Intelligent Energy Management for Microgrids with Renewable Energy, Storage Systems, and Electric Vehicles

The evolution of smart grid or smart microgrids represents a significant paradigm shift for future electrical power systems. Recent trends in microgrid systems include the integration of renewable energy sources (RES), energy storage systems (ESS), and plug-in electrical vehicles (PEV or EV). However, these integration trends bring with then new challenges for the design of intelligent control and management system. Traditional generation scheduling paradigms rely on the perfect prediction of future electricity supply and demand. They can no longer apply to a microgrid with intermittent renewable energy sources. To mitigate these problems, a massive and expensive energy storage can be deployed, which also need vast land area and sophisticated control and management. Electrical vehicles can be exploited as the alternative to the large and expensive storage. On the other hand, the use of electrical vehicles introduces new challenges due to their unpredictable presence in the microgrid. Furthermore, the utility and ancillary industries gradually adding sensors and power aware, intelligent functionality to home appliances for the efficient use of energy. Hence, the future smart microgrid stability and challenges are primarily dependent on the electricity consumption patterns of the home appliances, and EVs. Recently, demand side management (DSM) has emerged as a useful method to control or manipulate the user demand for balancing the generation and consumption. Unfortunately, most of the existing DSM systems solve the problem partially either using ESS to store RES energy or RES and ESS to charging and discharging of electrical vehicles. Hence, in this thesis, we propose a centralized energy management system which jointly optimizes the consumption scheduling of electrical vehicles and home appliances to reduce the peak-hour demand and use of energy produced from the RESs. In the proposed system, EVs store energy when generation is high or during off-peak periods, and release it when the demand is high compared to the generation. The centralized system, however, is an offline method and unable to produce a solution for a large-scale microgrid. Further, the real-time implementation of the centralized solution requires continuous change and adjustment of the energy generation as well as load forecast in each time slot. Thereby, we develop a game theoretic mechanism design to analyze and to get an optimal solution for the above problem. In this case, the game increases the social benefit of the whole community and conversely minimizes each household's total electricity price. Our system delivers power to each customer based on their real-time needs; it does not consider pre-planned generation, therefore the energy cost, uncertainty, and instability increase in the production plant. To address these issues, we propose a two-fold decentralized real-time demand side management (RDCDSM) which in the first phase (planning phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flat curve for its forecasted future demand. Then, in the second stage (i.e., allocation phase), customers play another repeated game with mixed strategy to mitigate the deviation between the immediate real-time consumption and the day-ahead predicted one. To achieve this, customers exploit renewable energy and energy storage systems and decide optimal strategies for their charging/discharging, taking into account their operational constraints. RDCDSM will help the microgrid operator better deals with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer. Now, it is envisioned that the presence of hundreds of microgrids (forms a microgrid network) in the energy system will gradually change the paradigms of century-old monopolized market into open, unbundled, and competitive market which accepts new supplier and admits marginal costs prices for the electricity. To adapt this new market scenario, we formulate a mathematical model to share power among microgrids in a microgrid network and minimize the overall cost of the electricity which involves nonlinear, nonconvex marginal costs for generation and T&D expenses and losses for transporting electricity from a seller microgrid to a buyer microgrid