ECONOMIC ENERGY ALLOCATION FOR A POWER SYSTEM CONSIDERING TECHNO-ECONOMIC CONSTRAINTS

Abstract

During the past few decades, rapid progress in reducing the cost of photovoltaic (PV) energy has been achieved. At the megawatt (MW) to gigawatt (GW) scale, large PV systems are connected to the electricity grid to provide power during the daytime. These large numbers of PV cells can be installed on sites with optimal solar radiation and other logistical considerations. However, the electricity produced by the PV power plant has to be transmitted and distributed by the grid, which leads to more power losses. With the widespread commissioning of large-scale solar PV power plants connected to the grid, it is crucial to have an optimal energy allocation between the conventional and the PV power plants. The electricity cost represents the most significant part of the budget of the power distribution companies, which can reach many countries billions of dollars. This optimal energy allocation is used to minimize the electricity cost from the point of view of buyers (distribution companies) rather than sellers (owners of power plants, i.e., investors). However, some constraints have to be considered and met, such as water demand, network limitations, and contractual issues such as minimum-take energy. The main contribution of this thesis is developing an optimization model for the energy-economic allocation of conventional and large-scale solar PV power plants. The developed model is generic and could apply to any country or electricity system having the same conditions. Furthermore, Al-Kharsaah power plant in Qatar and Al-Dhafra in the UAE will be discussed as two cases to validate the claimed contribution. For Al-Kharsaah and Al-Dhafra cases, the cost reduction percentage were 1.65% and 6.5% respectively. This is due to the different in size and energy price. In addition, the COVID-19 pandemic has brought several global challenges, one of which is meeting the electricity demand. Millions of people are confined to their homes, in each of which a reliable electricity supply is needed to support teleworking, e-commerce, and electrical appliances such as HVAC, lighting, fridges, water heaters, etc. Furthermore, electricity is also required to operate medical equipment in hospitals and perhaps temporary quarantine hospitals/shelters. Electricity demand forecasting is a crucial input into decision-making for electricity providers. This thesis discusses the impact of the COVID-19 pandemic on Qatar's electricity demand and forecasting. The results and findings will help decision-makers and planners manage future electricity demand and support distribution networks' preparedness for emerging situations. The forecasting part will be used as a supporting tool for the proposed optimization model. The input for this model is the amount of energy that has to be distributed between the different power plants. Therefore, a good forecasting model and technique will result in a better economic energy allocation.