Conference Paper

Probabilistic Approach to Assess the Performance of Grid-Connected PV Systems

Conference: Int. Conf. on Probabilistic Methods Applied to Power Systems (PMAPS 2002), At Naples, Italy.
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    ABSTRACT: Ancillary services associated to a power system are those services that support the Transmission System Operator to ensure the requirements concerning the safe, security and reliability of the power system. Real-time behaviour of these services can be assessed using the adequacy indices of generation system. The main objective of this paper is to analyse the evolution in time of hourly values of tertiary reserve margin and hourly values of adequacy indices associated to a hybrid system that contain a conventional unit and a photovoltaic system. The hybrid system is involved in a study in order to supply differed load profiles, characterised by the same amount of demanded energy.
    2012 International Conference and Exposition on Electrical and Power Engineering (EPE 2012); 01/2012
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    ABSTRACT: In this paper, security constrained generation scheduling (SCGS) problem for a grid incorporating thermal, wind and photovoltaic (PV) units is formulated. The formulation takes into account the stochastic nature of both wind and PV power output and imbalance charges due to mismatch between the actual and scheduled wind and PV power outputs. A hybrid technique in which the basic elements are a genetic algorithm (GA) with artificial neural network (ANN) and a priority list (PL) is used to minimize the total operating costs while satisfying all operational constraints considering both conventional and renewable energy generators. Numerical results are reported and discussed based on the simulation performed on the IEEE 24-bus reliability test system. The results demonstrate the efficiency of the proposed approach to reduce the total production cost for real time operation. Moreover, the results verified that the proposed approach can be applied to different problem dimensions and can score more favorably compared with analytical techniques.
    Electric Power Systems Research 11/2014; 116:284–292. DOI:10.1016/j.epsr.2014.06.017 · 1.60 Impact Factor
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May 20, 2014