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Power system optimization, especially in the field of operation, has experienced essential changes in recent years regarding the introduction of the concept of the hybrid energy system (HES). As a result, the optimal power flow (OPF) problem, as the cornerstone of power system operation studies, has faced new challenges regarding the modeling of these complicated systems. In this article, the OPF in a HES is proposed using the hybrid particle swarm optimization-genetic algorithm (HPSO-GA) approach considering the fuel cost as the main objective and the emission cost as the primary objective function. As the mentioned objectives are counterintuitive, to obtain the optimal point, they should be considered simultaneously. To overcome this problem, the Pareto optimal front (POF) is applied and the multi-criteria technique for order performance by similarity to ideal solution (TOPSIS) is used simultaneously. The results of the simulation case study on an improved IEEE 14-bus standard system confirmed the performance of the proposed method against the mathematical method by applying MATLAB optimization. [ABSTRACT FROM AUTHOR]

This article presents a novel linear programming (LP) based two-stage stochastic approach for microgrids (MGs) under uncertainties. In this regard, the day-ahead programming of dispatchable resources in MG was modeled, considering the uncertainties in demand loads, and upstream network electricity price. Moreover, the inherent stochastic nature of wind and solar resources as well as the environmental aspects were modeled to find a realistic solution. Also, real-time pricing (RTP), demand response (DR) program considering the energy storage system (ESS) as a DR option was implemented on an MG as a smart customer. The extensive form of the two-stage stochastic recourse model was properly implemented for dispatchable and non-dispatchable resources. Furthermore, scenario generation and reduction procedures were realized with autoregressive and moving average (ARMA) model-based time-series (TS), and backward reduction (BR) method by the Kantorovich distance (KD), respectively. The simulations on a grid-connected MG, including micro-turbine (MT), fuel-cell (FC), wind-turbine (WT), photovoltaic module (PV), and ESS were reported in operational cases based on one month of real recorded data for wind speed, solar irradiance, demand load, and upstream network electricity price. The results for the next day in real-time confirmed the accuracy of the developed optimization methodology. [ABSTRACT FROM AUTHOR]

Nowadays, penetration of DG resources into the power systems due to their advantages is rapidly increasing. Islanding performance is one of the most disadvantages of using DGs, which should be considered, because this option may lead to negative effects on the protection, performance, and operation management of distributed systems implementation. An island must be detected in less than 2 sec based on IEEE STD 1547-2003 standard, and DG resources must be disconnected from distribution network immediately. In this paper, a novel hybrid algorithm to identify the islanding has been proposed based on the passive parameters (ROCOV and ROCORP) and capacitor bank connection strategy. In the incompatibility conditions of the large-scale power, by combining two parameters i.e. ROCOV and ROCORP and investigating their changes, the islanding is carefully and quickly identified. This strategy is completely verified in the mismatch conditions of small-scale power by applying the capacitor bank connection strategy. The presented approach is tested on two case studies and the detailed results were completely reported. As it was shown, the proposed approach is able to distinguish between islanding and the other events. [ABSTRACT FROM PUBLISHER]