129,419 research outputs found
State-of-the-art Methods and software tools for short-term prediction of wind energy production
International audienceThe installed wind energy capacity in Europe today is 20 GW, while the projections for 2010 according to the Kyoto protocol and the EC directives is up to 40-60 GW. The large-scale integration of wind energy emerges the use of advanced operational tools for short-term forecasting of the wind production in the next hours up to the next 2-7 days. End-users (independent power producers, electric companies, transmission system operators, etc) recognize the contribution of wind prediction for a secure and economic operation of the power system. Especially, in a liberalized electricity market, prediction tools enhance the position of wind energy compared to other forms of dispatchable generation. The paper presents in detail the state-of the-art on the methods, the software tools and the relevant R&D projects for wind power forecasting. The paper finally presents experience by end-users that run operationally such prediction systems today as stand-alone applications or interfaced to EMS/DMS systems. The paper reviews the related literature on wind power prediction. Emphasis is given on operational tools such as WPPT, Prediktor, Zephyr, Previento, SIPREÓLICO, LocalPred, More-Care etc. The various models or tools are classified using criteria like: · The type of implemented approach i.e. timeseries (neural networks, ARMA etc) or physical. · The specific spatial scale focused by the models (regional, wind park scale, micro-scale). · The on-line performance of the prediction tools and their coupling to Energy Management Systems
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An Innovative Control Framework for District Heating Systems: Conceptualisation and Preliminary Results
This paper presents a holistic innovative solution for the transformation of the current district heating and cooling systems to automated more efficient systems. A variety of technological advancements have been developed and integrated to support the effective energy management of future district heating and cooling sector. First, we identify and discuss the main challenges and needs that are in line with the EU objectives and policy expectations. We give an overview of the main parts that our solution consists of, with emphasis on the forecasting tools and an advanced control system that addresses unit commitment and economic load dispatch problems. The proposed control approach employs distributed and scalable optimisation algorithms for optimising the short-term operations of a district heating and cooling plant subject to technical constraints and uncertainties in the energy demand. To test the performance and validate the proposed control system, a district heating plant with multiple energy generation units and real-life heat load data were used. Simulation experiments were also used to evaluate the benefits of using thermal storage units in district heating systems. The results show that the proposed method could achieve significant cost savings when energy storage is employed. The proposed control strategy can be applied for both operating optimally district heating plants with storage and supporting investment planning for new storage units
Short-Term Load Forecasting: The Similar Shape Functional Time Series Predictor
We introduce a novel functional time series methodology for short-term load
forecasting. The prediction is performed by means of a weighted average of past
daily load segments, the shape of which is similar to the expected shape of the
load segment to be predicted. The past load segments are identified from the
available history of the observed load segments by means of their closeness to
a so-called reference load segment, the later being selected in a manner that
captures the expected qualitative and quantitative characteristics of the load
segment to be predicted. Weak consistency of the suggested functional similar
shape predictor is established. As an illustration, we apply the suggested
functional time series forecasting methodology to historical daily load data in
Cyprus and compare its performance to that of a recently proposed alternative
functional time series methodology for short-term load forecasting.Comment: 22 pages, 6 Figures, 1 Tabl
Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study
Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG(Lab)(2)) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications
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