Development of an Intelligent Power Quality and Stability Framework for Large-Scale Sustainable Integration of Renewable Energy Sources into Saudi Electricity Systems

The ever-increasing demand for electric power in combination with contemporary oil-based electric power generation has a detrimental effect on the environment. Accordingly, limited resources for conventional generators also impose a threat. Consequently, a global preference towards renewable based power generation (RES) such as PV and wind are adopted. Nevertheless, power generation in RESs is unpredictable leading to economical limitation and technical challenges. Hence, to circumvent these issues the concept of energy management with the implementation of energy storage system is highly recommended. Recent advancements in energy storage technologies and distributed renewable energy generations have contributed widely to the increasing deployment of microgrids (MGs) in todays power networks. One of the major technical concerns in MG applications is to improve the power quality of the system subjected to a disturbance; particularly the problem sometimes becomes very challenging in case of unknown disturbances with significantly higher magnitude. Hence intelligent control approaches are vital to cope with the problem. In this work, two intelligent decoupled battery energy storage-based controllers are proposed to improve the power quality of a MG system; in particular, voltage and frequency restoration to steady-state conditions are targeted. The MG system under consideration consists of three distributed generators, a conventional synchronous generator (SG), a photovoltaic power system, and wind power turbines, integrated with a hybrid energy storage system (HESS). The HESS consists of two energy storage technologies, which are a super-capacitors energy storage system (SCESS) and battery energy storage system (BESS). The SCESS shall be controlled to act in case of critical disturbances where fast responses are required whereas the BESS will be controlled to support the MG system in case of low-level disturbances. The proposed control approach for the HESS is based on hybrid differential evolution optimization (DEO) and artificial neural networks (ANNs). The parameters of the two controllers shall be optimized under a wide range of disturbances. The obtained input and output sets are consequently used to train the ANNs in order to perform an online tuning for the controllers parameters. Finally, the proposed DEO-ANN approach shall be evaluated under random low and high magnitude disturbances. The MG system responses during disturbances shall be assessed with and without the action of the proposed decoupled controllers as compared with the benchmark PID controller in order to verify the effectiveness and robustness of the proposal. The prosperous completion of this research will facilitate the operation of grid connected RES-microgrids particularly to the Kingdoms grid, under low and critical disturbances by improving the MG power quality and restore the system to steady-state conditions. In accordance the significance of BESS-SCSS hybridized technology and its corresponding optimal sizing strategy will inherently provide an insight to power system planners and operators to design and evolve to a techno-economic as well as a reliable power system network. Furthermore, additional consideration of Kingdoms environmental and climatic conditions will provide an extensive insight for realization and deployment of suitable energy storages technologies corresponding to their feasible technical characteristics to circumvent specific power system deficiencies due to RES.