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With the advent of restructuring electricity sector and smart grids, combined with the increased variability and uncertainty associated with electricity market prices (EMP) signals and players' behavior, together with the growing integration of renewable energy sources, enhancing prediction tools are required for players and different regulators agents to face the non-stationarity and stochastic nature...
Renewable energies are in constant growth and evolution, being a clean way to provide the energy required for the sustainable development of human society. In this context, energy storage systems are a key factor in the integration of renewable generation, because through them, the flexibility of the power system can be increased. Lead-acid batteries have been extensively used to provide electricity...
This paper explores different modelling techniques for representing electrochemical energy storage devices in insular power grid applications. Particular attention is given to Thevenin based and not Thevenin based models. A case study involving two insular power systems with renewable generation are used to stand out the performance of the selected battery technologies: Lithium-ion (Li-ion), Nickel-Cadmium...
The constant increment in the penetration of renewable energy sources requires the improvement of the flexibility of the power system. Due to its high generation cost, it is expected that insular power systems experience an important increment in the power generation from renewable energy sources and, under this perspective, an energy storage system (ESS) could be the key factor to meet this goal...
In this paper a probabilistic model to solve the economic dispatch (ED) problem considering the uncertainty introduced by power sources, such as wind and solar, is presented. Assuming the forecasting error to be modeled by a beta probability distribution function (PDF), the proposed methodology presented in this paper allows the incorporation of this PDF in the optimization model, obtaining the PDF...
Due to increasing integration of renewable generation into the electrical framework in last decades, the mathematical techniques required for the optimal day-ahead scheduling needs to be continuously improved, specifically for modeling the variability of these sources. In this paper, a method for producing a new solution for the stochastic unit commitment (UC) problem from the analysis of each scenario...
In this paper, a management strategy to be used in the scheduling of insular electrical systems with energy storage system (EES) is presented. Integration of ESS allows reducing generation costs and greenhouse gases (GHG) emissions, as well as improving wind power penetration levels. The methodology presented in this paper is particularly useful for those systems provided with battery ESS, due to...
The main problem in integration of renewable power sources to the electricity grid is the uncertainty introduced by the power forecasting process in the optimal scheduling problem, which can considerably increase the generation cost. This problem has been widely analyzed using scenario generation/reduction methodologies. However, the consideration of a reduced number of scenarios can limit the capabilities...
Environmental problems related to the conventional generators have motivated governmental policies all over the world in order to incorporate alternative power sources to reduce greenhouse gas (GHG) emissions and fossil-fuel consumption. On the one hand, wind power generation can increase GHG emissions of the others conventional generators connected to the system. On the other hand, wind energy is...
This paper presents a review of short-term hydro scheduling tools reported by the scientific community, presenting different methodologies and models published over the last 20 years about operations, scheduling and optimization of hydro systems, in competitive electricity markets, considering the short-term horizon, i.e., between one day and one week, with the goal of maximizing profits and reducing...
This paper present a novel hybrid approach based on a combination of wavelet transform (WT), evolutionary particle swarm optimization (EPSO) and adaptive-network-based inference system (ANFIS), for short-term wind power forecasting in Portugal. The accuracy of the wind power forecasting attained with the proposed approach is evaluated, reporting its proficiency from a real-world case study in comparison...
This paper presents a hybrid evolutionary intelligent approach, based on a combination of evolutionary particle swarm optimization (EPSO) with an adaptive-network-based fuzzy inference system (ANFIS), for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses challenges due to its intermittency and volatility...
This paper proposes evolutionary particle swarm optimization (EPSO) combined with an adaptive-network-based fuzzy inference system (ANFIS) for short-term electricity prices forecasting. In a deregulated framework, producers and consumers require short-term price forecasting to derive their bidding strategies to the electricity market. Accurate forecasting tools are required for producers to maximize...
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