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  • 1
    Language: English
    In: Energy, 01 August 2013, Vol.57, pp.116-124
    Description: Southeast European power transmission system is modeled by analyzing the system as an evolving grid, which is continually upgrading in order to satisfy the increasing load demand and certain reliability requirements. We adopt a model (known as OPA model) which satisfies two requirements. First, the model is based on probabilistic line outages and overloads, and it models the network using DC load flow and linear programming dispatch of generation. Second, the model includes systematic upgrading of the production and transmission capacities of the electric-power system. The results show the most vulnerable transmission lines of the Southeast European power transmission system that need to be upgraded. Moreover, the results indicate that the electric power system of Southeast Europe is functioning well with 20% excess of electricity generation. In comparison with the actual excess, it can be concluded that merging the electric power systems of the separate countries in the region into a common trade will produce a great economic benefit in investment of new generation capacities.
    Keywords: Southeast European Power Transmission System ; Network Long-Term Evolution Modeling ; Power Transmission Reliability ; OPA Model ; Environmental Sciences ; Economics
    ISSN: 0360-5442
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  • 2
    Language: English
    In: Energy, 2013, Vol.57, pp.116-124
    Description: Southeast European power transmission system is modeled by analyzing the system as an evolving grid, which is continually upgrading in order to satisfy the increasing load demand and certain reliability requirements. We adopt a model (known as OPA model) which satisfies two requirements. First, the model is based on probabilistic line outages and overloads, and it models the network using DC load flow and linear programming dispatch of generation. Second, the model includes systematic upgrading of the production and transmission capacities of the electric-power system. The results show the most vulnerable transmission lines of the Southeast European power transmission system that need to be upgraded. Moreover, the results indicate that the electric power system of Southeast Europe is functioning well with 20% excess of electricity generation. In comparison with the actual excess, it can be concluded that merging the electric power systems of the separate countries in the region into a common trade will produce a great economic benefit in investment of new generation capacities. ; p. 116-124.
    Keywords: Models ; Electric Power ; Trade ; Linear Programming ; Electricity
    ISSN: 0360-5442
    Source: AGRIS (Food and Agriculture Organization of the United Nations)
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  • 3
    Language: English
    In: Energy, 15 November 2016, Vol.115, pp.1688-1700
    Description: A number of recent studies use deep belief networks (DBN) with a great success in various applications such as image classification and speech recognition. In this paper, a DBN made up from multiple layers of restricted Boltzmann machines is used for electricity load forecasting. The layer-by-layer unsupervised training procedure is followed by fine-tuning of the parameters by using a supervised back-propagation training method. Our DBN model was applied to short-term electricity load forecasting based on the Macedonian hourly electricity consumption data in the period 2008–2014. The obtained results are not only compared with the latest actual data, but furthermore, they are compared with the predicted data obtained from a typical feed-forward multi-layer perceptron neural network and with the forecasted data provided by the Macedonian system operator (MEPSO). The comparisons show that the applied model is not only suitable for hourly electricity load forecasting of the Macedonian electric power system, it actually provides superior results than the ones obtained using traditional methods. The mean absolute percentage error (MAPE) is reduced by up to 8.6% when using DBN, compared to the MEPSO data for the 24-h ahead forecasting, and the MAPE for daily peak forecasting is reduced by up to 21%.
    Keywords: Electricity Load Forecasting ; Neural Networks ; Deep Belief Networks ; Restricted Boltzmann Machines ; Short-Term Forecasting ; Hourly Electricity Load ; Environmental Sciences ; Economics
    ISSN: 0360-5442
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