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Highlights: • Meteorological data processing of kandla (India) has been considered for simulation work. • Probabilistic multi-state model of pumped storage hydro plant has been developed. • Reliability of proposed system has been evaluated using analytical technique and MCS. • The proposed system has been compared with PV-Wind-battery system. Abstract The storage system is promising as a crucial factor for maintaining the reliability of standalone microgrids for off-grid communities of India which depends on renewable energy sources. The pumped storage hydro (PSH) system is emerging as a source of green energy and overcomes the drawbacks of electrochemical energy storage devices in view of economic and environmental aspects which are the main concern across the world. The meteorological data of Kandla, India has been considered for simulation work. The mathematical models for the renewable energy sources based on meteorological data processing have been developed in the present work. In this work, a probabilistic upper reservoir multi-state model (M S M UR ) of PSH plant has been developed for incorporating realistic situation using analytical technique. The reliability evaluation of PV-Wind energy system incorporating PSH has been carried out. For reliability evaluation, indices like Loss of load expectation (LOLE) and Expected energy not served (EENS) are considered in this paper. Analytical technique and Monte Carlo Simulation are used for reliability assessment, and the results of both methods are compared in this work. This study demonstrates that the proposed system based on PSH is more reliable and environmental friendly than battery storage system. [ABSTRACT FROM AUTHOR]

• Various optimization techniques in AGC are critically reviewed. • Role of demand side management in AGC is also addressed. • Impact of electric vehicle integration to the grid is studied. Load and generation must be balanced for the system to be stable, which implies frequency deviation must be minimal. Whenever load perturbation takes place in the network, frequency diverges from its nominal value and unplanned exchange of power leads to unfortunate consequences. This will cause the mal-operation of electrical equipment such as change in speed, low efficiency, vibrations, harmonics, inaccuracy etc. Automatic Generation Control (AGC) provides a signal to alter the actual output power of multiple generators within a certain range based on changes in system frequency. For this purpose, several control techniques were employed in the system. This paper focuses on various approaches in the domain of automatic generation control for microgrid based hybrid power system. AI based techniques shows progressive results as compared to other classical and cascaded approaches. Machine learning and deep learning based controlling schemes has also been studied and study shows that these techniques provides loftier performance in comparison of other AI methods due to the availability of large number of trained data sets of controlled variable. Further massive data of controlled variables can be used to restore the system after cyber-attack. It is also addressed how to integrate electric vehicles with the power grid for Load Frequency Control. At the end of this article, some research gap such as role of demand response, protection and control issues, resilient controllers for cyber threats etc. are also addressed which will be useful for researcher working in this area. [ABSTRACT FROM AUTHOR]