The Experts below are selected from a list of 26565 Experts worldwide ranked by ideXlab platform

Hamidreza Zareipour - One of the best experts on this subject based on the ideXlab platform.

  • Electricity Price Forecasting for Operational Scheduling of Behind-the-Meter Storage Systems
    IEEE Transactions on Smart Grid, 2018
    Co-Authors: Hamed Chitsaz, Hamidreza Zareipour, Payam Zamani-dehkordi, Palak Parikh
    Abstract:

    Electricity Price Forecast plays a key role in strategic behavior of participants in competitive electricity markets. With the growth of behind-the-meter energy storage, Price Forecasting becomes important in energy management and control of such small-scale storage systems. In this paper, a Forecasting strategy is proposed for real-time electricity markets using publicly available market data. The proposed strategy uses high-resolution data along with hourly data as inputs of two separate Forecasting models with different Forecast horizons. Moreover, an intra-hour rolling horizon framework is proposed to provide accurate updates on Price predictions. The proposed Forecasting strategy has the capability to detect Price spikes and capture severe Price variations. The real data from Ontario’s electricity market is used to evaluate the performance of the proposed Forecasting strategy from the statistical point of view. The generated Price Forecasts are also applied to an optimization platform for operation scheduling of a battery energy storage system within a grid-connected micro-grid in Ontario to show the value of the proposed strategy from an economic perspective.

  • a new feature selection technique for load and Price Forecast of electrical power systems
    IEEE Transactions on Power Systems, 2017
    Co-Authors: Oveis Abedinia, Nima Amjady, Hamidreza Zareipour
    Abstract:

    Load and Price Forecasts are necessary for optimal operation planning in competitive electricity markets. However, most of the load and Price Forecast methods suffer from lack of an efficient feature selection technique with the ability of modeling the nonlinearities and interacting features of the Forecast processes. In this paper, a new feature selection method is presented. An important contribution of the proposed method is modeling interaction in addition to relevancy and redundancy, based on information-theoretic criteria, for feature selection. Another main contribution of the paper is proposing a hybrid filter-wrapper approach. The filter part selects a minimum subset of the most informative features by considering relevancy, redundancy, and interaction of the candidate inputs in a coordinated manner. The wrapper part fine-tunes the settings of the composite filter.

  • economic impact of electricity market Price Forecasting errors a demand side analysis
    IEEE Transactions on Power Systems, 2010
    Co-Authors: Hamidreza Zareipour, Claudio A Canizares, Kankar Bhattacharya
    Abstract:

    Several techniques have been proposed in the literature to Forecast electricity market Prices and improve Forecast accuracy. However, no studies have been reported examining the economic impact of Price Forecast inaccuracies on Forecast users. Therefore, in this paper, the application of electricity market Price Forecasts to short-term operation scheduling of two typical and inherently different industrial loads is examined and the associated economic impact is analyzed. Using electricity market Price Forecasts as the expected next-day electricity Prices, optimal operating schedules and the associated costs are determined for each load. These costs are compared with those of a ?perfect? Price Forecast scenario in which actual Prices are used to determine the operating schedules. Numerical results and discussions are provided based on Price Forecasts with different error characteristics.

S J P S Mariano - One of the best experts on this subject based on the ideXlab platform.

  • daily operation optimization of a hybrid energy system considering a short term electricity Price Forecast scheme
    Energies, 2019
    Co-Authors: Pedro Bento, Hugo Nunes, Jose Pombo, Maria Do Rosario Calado, S J P S Mariano
    Abstract:

    The scenario where the renewable generation penetration is steadily on the rise in an increasingly atomized system, with much of the installed capacity “sitting” on a distribution level, is in clear contrast with the “old paradigm” of a natural oligopoly formed by vertical structures. Thereby, the fading of the classical producer–consumer division to a broader prosumer “concept” is fostered. This crucial transition will tackle environmental harms associated with conventional energy sources, especially in this age where a greater concern regarding sustainability and environmental protection exists. The “smoothness” of this transition from a reliable conventional generation mix to a more volatile and “parti-colored" one will be particularly challenging, given escalating electricity demands arising from transportation electrification and proliferation of demand-response mechanisms. In this foreseeable framework, proper Hybrid Energy Systems sizing, and operation strategies will be crucial to dictate the electric power system’s contribution to the “green” agenda. This paper presents an optimal power dispatch strategy for grid-connected/off-grid hybrid energy systems with storage capabilities. The Short-Term Price Forecast information as an important decision-making tool for market players will guide the cost side dispatch strategy, alongside with the storage availability. Different scenarios were examined to highlight the effectiveness of the proposed approach.

Tarjei Kristiansen - One of the best experts on this subject based on the ideXlab platform.

  • a time series spot Price Forecast model for the nord pool market
    International Journal of Electrical Power & Energy Systems, 2014
    Co-Authors: Tarjei Kristiansen
    Abstract:

    Abstract We present three relatively simple spot Price Forecast models for the Nord Pool market based on historic spot and futures Prices including data for inflow and reservoir levels. The models achieve a relatively accurate Forecast of the weekly spot Prices. The composite regression model achieves a mean absolute percentage error (MAPE) of around 7.5% and under-Forecasts the actual spot Price by some 1.4 NOK/MW h in the sample period. Out of sample testing achieves a MAPE of around 7.4% including a match of the actual spot Price. A myopic model using the previous week’s spot Price as a predictor for the next week’s spot Price achieves a MAPE of 7.5% and under-Forecasts the actual spot Price by some 0.9 EUR/MW h. A futures model using the futures Price for next week as a predictor for next week’s spot Price achieves a MAPE of 5.3% and over-Forecast the actual spot Price by some 4.3 EUR/MW h.

Palak Parikh - One of the best experts on this subject based on the ideXlab platform.

  • Electricity Price Forecasting for Operational Scheduling of Behind-the-Meter Storage Systems
    IEEE Transactions on Smart Grid, 2018
    Co-Authors: Hamed Chitsaz, Hamidreza Zareipour, Payam Zamani-dehkordi, Palak Parikh
    Abstract:

    Electricity Price Forecast plays a key role in strategic behavior of participants in competitive electricity markets. With the growth of behind-the-meter energy storage, Price Forecasting becomes important in energy management and control of such small-scale storage systems. In this paper, a Forecasting strategy is proposed for real-time electricity markets using publicly available market data. The proposed strategy uses high-resolution data along with hourly data as inputs of two separate Forecasting models with different Forecast horizons. Moreover, an intra-hour rolling horizon framework is proposed to provide accurate updates on Price predictions. The proposed Forecasting strategy has the capability to detect Price spikes and capture severe Price variations. The real data from Ontario’s electricity market is used to evaluate the performance of the proposed Forecasting strategy from the statistical point of view. The generated Price Forecasts are also applied to an optimization platform for operation scheduling of a battery energy storage system within a grid-connected micro-grid in Ontario to show the value of the proposed strategy from an economic perspective.

Rui Shan - One of the best experts on this subject based on the ideXlab platform.

  • applying time series analysis builds stock Price Forecast model
    Mathematical Models and Methods in Applied Sciences, 2009
    Co-Authors: Jun Zhang, Rui Shan
    Abstract:

    Time series analysis is a theory that used random process and mathematical statistics theory to analyze time .It is apply comprehensive to national economy macroeconomic adjustment and control, area complex development plan, enterprise operating management, market potential Forecasting, weather hydrology prediction. It is an important means for estimation and Forecast. The stock Price has very deep effect to the economic benefits of the nation and the macro-economy policy. So people pay close attention to it. In this article, SSE composite index of one year is fitted two kinds of time series models, then Forecast in short-time. Comparing the estimated valve with the true valve, the result is the relative error is small. So I think the model is suited to the data. At last, compare the two models.