الفهرس 
Chapter 1 IntroductionOnly 14 pages are availabe for public view
Abstract
In the recent decades, the renewable energy sources (RESs) have been highly utilized and the distributed generators (DGs) have been connected to the main grid profusely to enhance the power system reliability. This has led to the emergence of the microgrid (MG) concept where MGs represent the perfect solution to fully exploit the benefits of the RESs and the DGs in general. The integration of neighboring MGs into a multi-microgrid (MMG) system enhances the operation of the MGs either in grid connected mode or islanded mode. In this thesis, energy management (EM) strategies for both grid connected and islanded MMG systems are proposed.
This thesis suggests an EM system (EMS) for grid connected MMG systems to minimize the daily operating cost of such systems. The developed DA schedule is based on a two-stage hierarchical EM scheme. The first stage is the MG optimization that minimizes the cost utilizing the demand response programs (DRP) and the battery energy storage system (BESS) in each individual MG. In the first stage, The MG controller (MGC) determines the DA schedule of the dispatchable DG (DDG) and BESS in each MG, the new load profile after applying DRP, and the shortage/surplus power in each MG. The second stage is the MMG optimization which minimizes the cost by allowing power exchange among individual MGs to satisfy the total shortage of the MMG system. The MMG controller (MMGC) satisfies the remaining shortage amount either by purchasing power from the main grid or by operating the community MG (CMG) resources. The proposed EM scheme is formulated and solved using two optimization models: (1) deterministic optimization, (2) stochastic optimization. In the deterministic model, the RESs generation and load demand are assumed to be well known. In the stochastic model, the uncertainties associated with the RESs and the load demand are considered. To handle these uncertainties, a set of scenarios for the renewable generation and load demand are generated based on historical data using the Monte Carlo simulation (MCS) technique. To reduce the computational burden, these scenarios are reduced using the fast forward selection (FFS) technique.
An EMS is also proposed for islanded MMG systems to minimize the load shedding in a cost-effective manner using a two-stage hierarchical EM scheme. In the MG optimization stage, the load shedding in each MG is minimized utilizing the BESS, the DDG, and the DRP. Hence, the MGC informs the MMGC with the shortage/surplus power. In the MMG optimization stage, the amount of energy shortage in the MMG system is reduced by power exchange among MGs. The remaining amount of shortage can be reduced by the aid of the CMG resources.
The proposed optimization models for grid connected and islanded MMG are nonlinear complex optimization problems which can be handled more easily by the meta-heuristic optimization techniques. In this thesis, a new socio inspired meta-heuristic algorithm called the political optimizer (PO) is used to solve the proposed optimization models. To prove the effectiveness of this optimizer in handling the proposed models, we also solved the optimization problems using the well-known particle swarm optimization (PSO) technique and their results are compared. Finally, the proposed EMSs are tested using a three-MG system to prove their effectiveness in minimizing the operating cost of grid connected MMG systems and minimizing the load shedding of islanded MMG systems.