Optimal scheduling of thermal-wind-solar power system with storage

S. Surender Reddy

Renewable Energy, 2017, vol. 101, issue C, 1357-1368

Abstract: The incorporation of renewable energy resources (RERs) into electrical grid is very challenging problem due to their intermittent nature. This paper solves an optimal scheduling problem considering the hybrid generation system. The primary components of hybrid power system include conventional thermal generators, wind farms and solar photovoltaic (PV) modules with batteries. The main critical problem in operating the wind farm or solar PV plant is that these RERs cannot be scheduled in the same manner as conventional generators, because they involve climate factors such as wind velocity and solar irradiation. This paper proposes a new strategy for the optimal scheduling problem taking into account the impact of uncertainties in wind, solar PV and load demand forecasts. The simulation results for IEEE 30 and 300 bus test systems with Genetic Algorithm (GA) and Two-Point Estimate Method (2PEM) have been obtained to test the effectiveness of the proposed optimal scheduling strategy. Results for sample systems with GA and two-point estimate based optimal power flow, and GA and Monte Carlo Simulation (MCS) have been obtained to ascertain the effectiveness of proposed method. Some of the results are also compared with the Interior Point method. From the simulation studies, it can be observed that with a marginal increase in the cost of day-ahead generation schedule, a substantial reduction in real time mean adjustment cost is obtained.

Keywords: Energy storage; Load forecast uncertainty; Optimal scheduling; Renewable Energy Resources; Solar energy system; Wind energy system (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (39)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:101:y:2017:i:c:p:1357-1368

DOI: 10.1016/j.renene.2016.10.022

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