الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Energy storage systems (ESSs) are usually used to perform a single function in most sizing and energy management studies related to microgrids (MGs) such as supply/demand matching or fluctuations attenuation of the renewable energy sources (RESs) or reliability enhancement. This thesis presents an effective methodology to use the ESSs, especially battery storage units (BSUs), to perform multi-function including supply/demand matching and energy arbitrage. The first function (supply/demand matching) is to store the surplus power during the high-generation periods from the RESs and supply the deficit during the low-generation periods from the RESs. The second function (energy arbitrage) is to purchase the power from the utility grid (UG) during the off-peak periods (low price) and sell it during the on-peak periods (high price) resulting in a profit. This is done according to a system policy containing all possible scenarios to fully utilize the BSUs to maximize the benefit.
In the proposed work, the optimal sizing and the optimal energy management of the MG resources including wind turbines (WTs), photovoltaic system (PV), BSUs, and diesel generator units (DUs) are obtained. The main objectives of the proposed study in the sizing phase are: 1) minimizing the total costs of the MG, 2) minimizing the harmful gas emissions, and 3) minimizing the accumulated power difference between the generation from RESs and the demand. On the other hand, the main objective of the proposed study in the energy management phase is minimizing the hourly costs and hence minimizing the total operation daily costs of MG.
To obtain realistic results, the uncertainties of several parameters such as wind speed, solar irradiance, and temperature are considered in the study. In addition, the two modes of operation of the MG (grid-connected and islanded) and the demand response (DR) are also considered.
In the two phases, the problem is formulated as a constrained nonlinear optimization problem and is solved using two metaheuristic optimization algorithms, Moth-Flame Optimization (MFO) and Hybrid Firefly and Particle Swarm Optimization (HFPSO). Moreover, the uncertainties in the different parameters are considered by using the Latin Hypercube Sampling (LHS) method to generate samples of wind speed, solar irradiance, and temperature.