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Special Issue "Wind Energy, Load and Price Forecasting towards Sustainability"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (30 September 2017)

Special Issue Editor

Guest Editor
Prof. Dr. João P. S. Catalão

Departamento de Engenharia Eletrotécnica e de Computadores, Universdade Beira Interior, Covilha 6201-001, Portugal
Website | E-Mail
Interests: power system operations and planning; wind and price forecasting; distributed renewable generation; demand response and smart grids

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Sustainability Special Issue on “Wind Energy, Load and Price Forecasting towards Sustainability”.

Wind Energy is one of the fastest growing sources of electricity worldwide. However, the availability of Wind Energy is not known in advance, so its large-scale integration in the power supply poses serious challenges. In order to conveniently address those challenges, Wind Energy forecasting plays a major role. Load forecasting has also recently acquired renewed interest with the advent of the smart grid, especially due to the growing focus on demand side-management activities. Likewise, Price forecasting is of major importance in the energy industry, both for companies and customers. Hence, in this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the fields of Wind Energy forecasting, Load forecasting and Price forecasting, towards increased overall sustainability of the energy system from supply to demand sides.

Prof. João P. S. Catalão
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Forecasting
  • Wind Energy
  • Load
  • Electricity Prices
  • Sustainability

Published Papers (10 papers)

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Research

Open AccessArticle Short Term Wind Power Prediction Based on Improved Kriging Interpolation, Empirical Mode Decomposition, and Closed-Loop Forecasting Engine
Sustainability 2017, 9(11), 2104; doi:10.3390/su9112104
Received: 30 September 2017 / Revised: 9 November 2017 / Accepted: 10 November 2017 / Published: 16 November 2017
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Abstract
The growing trend of wind generation in power systems and its uncertain nature have recently highlighted the importance of wind power prediction. In this paper a new wind power prediction approach is proposed which includes an improved version of Kriging Interpolation Method (KIM),
[...] Read more.
The growing trend of wind generation in power systems and its uncertain nature have recently highlighted the importance of wind power prediction. In this paper a new wind power prediction approach is proposed which includes an improved version of Kriging Interpolation Method (KIM), Empirical Mode Decomposition (EMD), an information-theoretic feature selection method, and a closed-loop forecasting engine. In the proposed approach, EMD decomposes volatile wind power time series into more smooth and well-behaved components. To enhance the performance of EMD, Improved KIM (IKIM) is used instead of Cubic Spline (CS) fitting in it. The proposed IKIM includes the von Karman covariance model whose settings are optimized based on error variance minimization using an evolutionary algorithm. Each component obtained by this EMD decomposition is separately predicted by a closed-loop neural network-based forecasting engine whose inputs are determined by an information-theoretic feature selection method. Wind power prediction results are obtained by combining all individual forecasts of these components. The proposed wind power forecast approach is tested on the real-world wind farms in Spain and Alberta, Canada. The results obtained from the proposed approach are extensively compared with the results of many other wind power prediction methods. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
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Open AccessArticle Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators
Sustainability 2017, 9(11), 2065; doi:10.3390/su9112065
Received: 27 September 2017 / Revised: 27 October 2017 / Accepted: 6 November 2017 / Published: 10 November 2017
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Abstract
Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price
[...] Read more.
Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA) method and Radial Basis Function Neural Network (RBFN). To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO) is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
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Open AccessArticle Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model
Sustainability 2017, 9(11), 1990; doi:10.3390/su9111990
Received: 27 September 2017 / Revised: 26 October 2017 / Accepted: 28 October 2017 / Published: 31 October 2017
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Abstract
Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited
[...] Read more.
Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
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Open AccessArticle Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes
Sustainability 2017, 9(11), 1972; doi:10.3390/su9111972
Received: 29 September 2017 / Revised: 21 October 2017 / Accepted: 22 October 2017 / Published: 28 October 2017
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Abstract
Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Users of
[...] Read more.
Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Users of electronic devices sometimes consume fluctuating amounts of electricity generated from smart-grid infrastructure owned by the government or private investors. However, frequent imbalance is noticed between the demand and supply of electricity, hence effective planning is required to facilitate its distribution among consumers. Such effective planning is stimulated by the need to predict future consumption within a short period. Although several interesting classical techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT) model to address the lacuna of enormous predictive error faced by the state-of-the-art models. The PSA-DT is based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model outperforms the state-of-the-art models in terms of accuracy to a near-zero error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
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Open AccessArticle Impact of Wind Electricity Forecasts on Bidding Strategies
Sustainability 2017, 9(8), 1318; doi:10.3390/su9081318
Received: 16 June 2017 / Revised: 18 July 2017 / Accepted: 25 July 2017 / Published: 1 August 2017
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Abstract
The change in the generation mix from conventional electricity sources to renewables has important implications for bidding behaviour and may have an impact on prices. The main goal of this work is to discover the role played by expected wind production, together with
[...] Read more.
The change in the generation mix from conventional electricity sources to renewables has important implications for bidding behaviour and may have an impact on prices. The main goal of this work is to discover the role played by expected wind production, together with other relevant factors, in explaining the day-ahead market price through a data panel model. The Spanish market, given the huge increase in wind generation observed in the last decade, has been chosen for this study as a paradigmatic example. The results obtained suggest that wind power forecasts are a new key determinant for supply market participants when bidding in the day-ahead market. We also provide a conservative quantification of the effect of such trading strategies on marginal prices at an hourly level for a specific year in the sample. The consequence has been an increase in marginal price to levels higher than what could be expected in a context with notable wind penetration. Therefore, the findings of this work are of interest to practitioners and regulators and support the existence of a wind risk premium embedded in electricity prices to compensate for the uncertainty of wind production. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
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Open AccessArticle A Joint Evaluation of the Wind and Wave Energy Resources Close to the Greek Islands
Sustainability 2017, 9(6), 1025; doi:10.3390/su9061025
Received: 28 April 2017 / Revised: 5 June 2017 / Accepted: 7 June 2017 / Published: 15 June 2017
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Abstract
The objective of this work is to analyze the wind and wave energy potential in the proximity of the Greek islands. Thus, by evaluating the synergy between wind and waves, a more comprehensive picture of the renewable energy resources in the target area
[...] Read more.
The objective of this work is to analyze the wind and wave energy potential in the proximity of the Greek islands. Thus, by evaluating the synergy between wind and waves, a more comprehensive picture of the renewable energy resources in the target area is provided. In this study, two different data sources are considered. The first data set is provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the ERA-Interim project and covers an 11-year period, while the second data set is Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO) and covers six years of information. Using these data, parameters such as wind speed, significant wave height (SWH) and mean wave period (MWP) are analyzed. The following marine areas are targeted: Ionian Sea, Aegean Sea, Sea of Crete, Libyan Sea and Levantine Sea, near the coastal environment of the Greek islands. Initially, 26 reference points were considered. For a more detailed analysis, the number of reference points was narrowed down to 10 that were considered more relevant. Since in the island environments the resources are in general rather limited, the proposed work provides some outcomes concerning the wind and wave energy potential and the synergy between these two natural resources in the vicinity of the Greek islands. From the analysis performed, it can be noticed that the most energetic wind conditions are encountered west of Cios Island, followed by the regions east of Tinos and northeast of Crete. In these locations, the annual average values of the wind power density (Pwind) are in the range of 286–298.6 W/m2. Regarding the wave power density (Pwave), the most energetic locations can be found in the vicinity of Crete, north, south and southeast of the island. There, the wave energy potential is in the range of 2.88–2.99 kW/m. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
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Open AccessArticle A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool
Sustainability 2017, 9(5), 864; doi:10.3390/su9050864
Received: 7 March 2017 / Revised: 14 May 2017 / Accepted: 16 May 2017 / Published: 20 May 2017
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Abstract
During the last decades, thanks to supportive policies of countries and a decrease in installation costs, total installed capacity of wind power has increased rapidly all around the world. The uncertain and variable nature of wind power has been a problem for transmission
[...] Read more.
During the last decades, thanks to supportive policies of countries and a decrease in installation costs, total installed capacity of wind power has increased rapidly all around the world. The uncertain and variable nature of wind power has been a problem for transmission system operators and wind power plant owners. To solve this problem, numerous wind power forecast systems have been developed. Unfortunately none of them can obtain absolutely accurate forecasts yet. Thus, researchers assumed that wind power generation is a stochastic process and they proposed a stochastic programming approach to solve problems arising from the uncertainty of wind power. It is well known that representing stochastic process by possible scenarios is a major issue in the stochastic programming approach. Large numbers of scenarios can represent a stochastic process accurately, but it is not easy to solve a stochastic problem that contains a large number of scenarios. For this reason scenario reduction methods have been introduced. Finally, the quality of this reduced scenario set must be at an acceptable level to use them in calculations. All of these reasons have encouraged authors to develop a wind power scenario tool that can generate and reduce the scenario set and test the quality of it. The developed tool uses historical data to model wind forecast errors. Scenarios are generated around 24 day-ahead point wind power forecasts. A fast forward reduction algorithm is used to reduce the scenario set. Two metrics are proposed to assess the quality of the reduced scenario set. Site measurements are used to test the developed wind power scenario tool. Results showed that the tool can generate and reduce the scenario set successfully and the proposed metrics are useful to assess the quality. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
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Open AccessArticle Assessing the Potential Benefits and Limits of Electric Storage Heaters for Wind Curtailment Mitigation: A Finnish Case Study
Sustainability 2017, 9(5), 836; doi:10.3390/su9050836
Received: 11 April 2017 / Revised: 10 May 2017 / Accepted: 12 May 2017 / Published: 16 May 2017
Cited by 1 | PDF Full-text (3518 KB) | HTML Full-text | XML Full-text
Abstract
Driven by policy changes and technological advancement, wind power installations are booming as compared to any other types of power generation. However, the increased penetration of renewable generation in the power systems has resulted in high level of curtailment. Advanced energy storage technologies
[...] Read more.
Driven by policy changes and technological advancement, wind power installations are booming as compared to any other types of power generation. However, the increased penetration of renewable generation in the power systems has resulted in high level of curtailment. Advanced energy storage technologies have been increasingly scrutinized as a feasible mitigation option in smart grids. This paper investigates the potential of mitigating wind generation curtailment via aggregating the domestic electric storage heaters. The key findings show that aggregating domestic thermal storages is a viable option for curtailment mitigation, but with the indispensable caution that mitigation potential significantly saturates as the share of wind generation escalates beyond a certain limit. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
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Open AccessArticle Stochastic Prediction of Wind Generating Resources Using the Enhanced Ensemble Model for Jeju Island’s Wind Farms in South Korea
Sustainability 2017, 9(5), 817; doi:10.3390/su9050817
Received: 10 April 2017 / Revised: 11 May 2017 / Accepted: 11 May 2017 / Published: 14 May 2017
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Abstract
Due to the intermittency of wind power generation, it is very hard to manage its system operation and planning. In order to incorporate higher wind power penetrations into power systems that maintain secure and economic power system operation, an accurate and efficient estimation
[...] Read more.
Due to the intermittency of wind power generation, it is very hard to manage its system operation and planning. In order to incorporate higher wind power penetrations into power systems that maintain secure and economic power system operation, an accurate and efficient estimation of wind power outputs is needed. In this paper, we propose the stochastic prediction of wind generating resources using an enhanced ensemble model for Jeju Island’s wind farms in South Korea. When selecting the potential sites of wind farms, wind speed data at points of interest are not always available. We apply the Kriging method, which is one of spatial interpolation, to estimate wind speed at potential sites. We also consider a wind profile power law to correct wind speed along the turbine height and terrain characteristics. After that, we used estimated wind speed data to calculate wind power output and select the best wind farm sites using a Weibull distribution. Probability density function (PDF) or cumulative density function (CDF) is used to estimate the probability of wind speed. The wind speed data is classified along the manufacturer’s power curve data. Therefore, the probability of wind speed is also given in accordance with classified values. The average wind power output is estimated in the form of a confidence interval. The empirical data of meteorological towers from Jeju Island in Korea is used to interpolate the wind speed data spatially at potential sites. Finally, we propose the best wind farm site among the four potential wind farm sites. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
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Open AccessFeature PaperArticle Wind Speed for Load Forecasting Models
Sustainability 2017, 9(5), 795; doi:10.3390/su9050795
Received: 25 April 2017 / Revised: 7 May 2017 / Accepted: 8 May 2017 / Published: 10 May 2017
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Abstract
Temperature and its variants, such as polynomials and lags, have been the most frequently-used weather variables in load forecasting models. Some of the well-known secondary driving factors of electricity demand include wind speed and cloud cover. Due to the increasing penetration of distributed
[...] Read more.
Temperature and its variants, such as polynomials and lags, have been the most frequently-used weather variables in load forecasting models. Some of the well-known secondary driving factors of electricity demand include wind speed and cloud cover. Due to the increasing penetration of distributed energy resources, the net load is more and more affected by these non-temperature weather factors. This paper fills a gap and need in the load forecasting literature by presenting a formal study on the role of wind variables in load forecasting models. We propose a systematic approach to include wind variables in a regression analysis framework. In addition to the Wind Chill Index (WCI), which is a predefined function of wind speed and temperature, we also investigate other combinations of wind speed and temperature variables. The case study is conducted for the eight load zones and the total load of ISO New England. The proposed models with the recommended wind speed variables outperform Tao’s Vanilla Benchmark model and three recency effect models on four forecast horizons, namely, day-ahead, week-ahead, month-ahead, and year-ahead. They also outperform two WCI-based models for most cases. Full article
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
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