Due to the statistical uncertainty of loads and power sources found in smart grids, effective computational tools for probabilistic load flow analysis and planning are now becoming indispensable. In this research, we describe a unified simulation framework that allows quantifying the probability distributions of a set of observation variables as well as evaluating their sensitivity to potential variations in the power demands. The proposed probabilistic technique relies on the generalized Polynomial Chaos algorithm and on a region-wise aggregation/description of the time-varying load profiles. It is shown how detailed statistical distributions of some important figures of merit, which includes voltage unbalance factor in distribution networks, can be calculated with a two-orders of magnitude acceleration compared to standard Monte Carlo analysis. In addition, it is highlighted how the associated sensitivity analysis is of guidance for the optimal allocation and planning of new loads.
This paper presents a framework to analyze the problem of real-time management of Smart Grids. For this purpose, the energy management is integrated with the power system through a telecommunication system. The use of Multiagent Systems (MAS) leads the proposed algorithm to find the best-integrated solution, taking into consideration the operating scenario and the system characteristics. With this framework it was possible to evaluate the design of the energy management and the impact of the algorithm developed in the MAS. In the same way, the data sent from the power system to be used for energy management have a direct impact on his behavior. The proposed framework is tested with the help of a microgrid, so the results may be replicated.
This paper deals with the problem of real-time management of Smart Grids. For this sake, 1 the energy management is integrated with the power system through a telecommunication system.
2The use of Multiagent Systems leads the proposed algorithm to find the best-integrated solution, 3 taking into consideration the operating scenario and the system characteristics. The proposed 4 technique is tested with the help of an academic microgrid, so the results may be replicated.
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