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Distribution automation is well recognized as an effective solution to enhance the reliability and efficiency of these grids in a timely manner. This paper introduces an effective probabilistic operation framework for the automated distribution networks (ADNs) incorporating the plug-in electric vehicles (PEVs) charging/discharging schemes in the presence of different renewable energy sources (RESs). To this end, this paper pursues four different strategic approaches. Firstly, an effective fuzzy based probabilistic method is proposed to model the forecast error in the wind and solar units well as the load demand through the cloud theory. Secondly, an appropriate framework is devised to model the PEVs random behaviour considering their essential parameters such as the charging/discharging rate and arrival/departure time to/from the parking lots (PLs), the discharging level at driving mode on the road and the effects of battery degradation. As the third goal, an appropriate objective function which can consider automation indices including the social welfare and reliability is considered. Since the operation problem is a nonlinear continuous non-numerical problem, it requires an applicable and effective optimization algorithm which is regarded as the fourth goal of this paper. In this regard, a new θ-modified bat algorithm is introduced to find the optimal solution of the problem. The proposed model is simulated and examined on the IEEE 69-bus standard test system wherein results reveal the effectiveness and applicability of the proposed operation management framework. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid efficient stochastic framework for optimal operation of automated distribution grids considering the social, economic and technical priorities. To this end, optimal feeder reconfiguration is regarded as a key solution for altering the network topology and improving the above targets. Considering the wide popularity and high penetration of the plug-in hybrid electric vehicles in the future grids, three different charging schemes including controlled charging, uncontrolled charging and smart charging are introduced and compared with each other. In addition, different types of non-dispatchable renewable power sources including wind turbines and solar panels are considered to let the analysis be more practical. In order to capture the uncertainties existing in the nature of the problem, a hybrid data driven approach based on machine learning and unscented transform is proposed. The machine learning based approach, i.e. support vector machine, will help to estimate the standard deviation of the uncertain parameters. In order to make a global search in the multi-objective space, a new optimization method based on flower pollination algorithm and a new three-stage modification method are introduced to solve the problem. The quality and capability of the proposed framework are examined on an IEEE test system. • Assessing the social-economic-technical aspects of optimal switching in the automated grids. • Proposing three different charging schemes for plug-in hybrid electric vehicles in the automated grids. • Proposing a hybrid stochastic framework based on support vector machine and unscented transform. • Proposing an effective optimization method based on modified flower pollination algorithm. [ABSTRACT FROM AUTHOR]

In this paper optimal designing of two hybrid photovoltaic/wind turbine (PV/WT) systems with different storage include battery and hydrogen is presented with objective of minimising cost of energy generation (COEG) and considering reliability constraint as deficit power probability of the system using improved salp swarm optimisation algorithm (ISSOA) for commercial centre area, for Iran country. Different hybrid systems are compared in view of cost and reliability and the optimal size is determined. The results show that the PV/WT with battery storage has lower COEG than PV/WT with hydrogen storage to supply the commercial centre and also its reliability is better than the system integrated with hydrogen. The average cost of per kW of load supply for the PV/WT system with battery and hydrogen storage is 0.216$ and 0.610$, respectively. Superiority of ISSOA is confirmed in comparison with conventional SSOA and particle swarm optimisation in hybrid systems designing. [ABSTRACT FROM AUTHOR]