الفهرس | Only 14 pages are availabe for public view |
Abstract Nowadays, the power system has been expanded to include several renewable technologies which entail variability and uncertainty in the operation and planning processes of the power system. One of the key issues in the planning process of electric power systems is generation expansion planning (GEP) which aims at selecting the most suitable technology, size, location, and timing for the building of additional plant capacity while taking into account economical capabilities and technical power system constraints. In this thesis, wind energy assessment via searching for optimal parameters estimation of the wind speed Weibull distribution and wind power curve model are presented by several analytical and heuristic optimization techniques. The parameters estimation and wind power curve model are carried out based on per year real wind speed data that are collected from Zafaranah and Shark El-Ouinate sites in Egypt. Also, a new framework to study the GEP in a multi-stage horizon with reliability constrained is presented to minimize the capital investment costs, salvage value cost, operation and maintenance, and outage cost under several constraints over short and long-term planning horizons. In this context, several modern meta-heuristic optimization algorithms are employed and assessed which are particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), Aquila optimizer (AO) algorithm, bald eagle search (BES) and honey badger algorithm (HBA). The system reliability is incorporated as well where the expected energy not served (EENS), loss of load probability (LOLP), and loss of load expectation (LOLE) are estimated. Added to that, the probabilistic production cost simulation is involved using the Equivalent Energy Function (EEF) and Effective Load Distribution Curve (ELDC). |