Browsing by Author "Franco, John Fredy"

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Integrating artificial neural networks and cellular automata model for spatial-temporal load forecasting
(2023) Franco, John Fredy
The long-term distribution planning should include an understanding of consumer behavior and needs to develop strategic expansion alternatives that meet the future demand. The magnitude of growth along with the place where and when it will be developed are determined by the spatial load forecasting. Thus, this paper proposes a spatial-temporal load forecasting method to recognize and predict development patterns using historical dynamics and determine the development of consumers and electric load in small areas. An artificial neural network is integrated to a cellular automaton method to establish transition rules, based on land-use preferences, neighborhood states, spatial constraints, and a stochastic disturbance. The main feature is the incorporation of temporality, as well as taking advantage of geospatial-temporal data analytics to calibrate and validate a holistic and integral framework. Validation consists of measuring the spatial error pattern during the training and testing phase. The performance of the method is assessed in the service area of an Ecuadorian power utility. The knowledge extraction from large-scale data, evaluating the sensitivity of parameters and spatial resolution was carried out in reasonable times. It is concluded that adequate normalization and use of temporality in the spatial factors improve the error in the spatial-temporal load forecasting.
Optimal subtransmission switching using a reliability simulation-based multi-objective optimization model
(2022) Banegas Dutan, Stalin Fernando
The growth of subtransmission network aims at satisfying load growth, maintaining a contingency level, and providing a high quality and reliable electricity service. Utilities direct the investments to reinforce this system and thus a meshed network with multiple-point feeding to the transmission system arises. At this point, an efficient alternative to achieve these objectives is to carry out a diagnosis of the network architecture and, taking advantage of the switching capability, to plan the switching of the subtransmission lines. An optimal subtransmission switching approach is proposed based on constrained multi-objective optimization that deals with energy losses and reliability, in addition to using information on the characteristics of loads and generation. A simulation-based optimization framework is constructed using the non-dominated genetic classification algorithm NSGA-II in the optimization phase and reliability assessment during simulation phase. As a result, a set of non-dominated solutions approximating the Pareto front is obtained, which allows the planner to make decisions based on its priorities and needs. The performance of the proposal is assessed with a real subtransmission system of an Ecuadorian power utility. This approach to the operational planning of a meshed subtransmission network constitutes a powerful decision-making tool that could be adopted by distribution utilities.