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This paper addresses the transparent and realistic optimum day-ahead (DA) scheduling for a hybrid power system by explicitly considering the uncertainties. The basic components of the hybrid power system include conventional thermal generators, wind farm, and solar photovoltaic (PV) modules. A set of batteries is available for energy storage and/or discharge. The most critical problem in operating a wind farm or solar PV module is that these renewable energy resources cannot be dispatched in the same manner as conventional plants, because they involve climatic factors such as wind velocity and solar irradiation. This paper proposes the optimal scheduling strategy taking into account the impact of uncertainties in wind, solar PV, and load forecasts, and provides the best-fit DA schedule by minimizing both DA and real-time adjustment costs including the revenue from renewable energy certificates. This strategy consists of a genetic algorithm (GA)-based scheduling and a two-point estimate-based probabilistic real-time optimal power flow. The simulation for the IEEE 30-bus system with the GA and two-point estimate method, and the GA and Monte Carlo simulation have been obtained to test the effectiveness of the proposed scheduling strategy.Index Terms-Battery storage, day-ahead (DA) scheduling, hybrid power system, real-time (RT) adjustment price, renewable energy certificates (RECs), solar energy, wind energy.

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a b s t r a c tIn practice, the real time economic dispatch is performed in every 5e15 min intervals with the static snapshot forecast data. During the period between two consecutive schedules, generators participate in managing power imbalance, based on participation factors from previous economic dispatch. In modern power system with considerable renewable energy resources that have high variability, this conventional approach may not adequately accommodate the economic implication of the said variability. This paper proposes the evaluation of 'best-fit' participation factors by considering the minute-to-minute variability of solar, wind and load demand, for a scheduling period. The voltage, reactive power limit and line flow constraints are included for all minute-to-minute sub-intervals. Since 'best-fit' participation factors are evaluated only once, i.e., at the start of scheduling interval, the dimensionality of optimization problem remains the same as that of conventional approach. The proposed approach is suggested for sequential as well as dynamic variants. The proposed real time economic dispatch approaches are tested on IEEE 30 bus and 118 bus test systems considering variability in renewable energy sources and load demand.

This paper proposes the optimization of renewable energy resources (RERs) in the hybrid energy systems in a sustainable hybrid energy system. The behavior of renewable energy is uncertain and it is difficult for static optimization methods to optimize the uncertain non-stationary distributed energy resources in the hybrid system. A multi-objective based on the stochastic technique for optimizing total system losses and operating cost is formulated for the hybrid energy system. The proposed objective function aims to minimize the system losses and the total operating cost of RERs in different locations of the grid. In this paper, a next generation of grid connected RERs and load demand is proposed by considering the variability and uncertainty. Here, a robust stochastic approach is proposed by using the various probability distribution functions to represent the statistics of RERs. The simulation results of this paper handle the system operations under uncertainty. The proposed approach is tested on IEEE 37 node distribution system. The simulation results show the effectiveness of the proposed optimization approach in the hybrid energy system.

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