Wind Energy Forecasting at Different Time Horizons with Individual and Global Models

This paper has been presented at: 14th IFIP International Conference on Artificial Intelligence Applications and InnovationsIn this work two different machine learning approaches have been studied to predict wind power for different time horizons: individual and global models. The individual approach constructs a model for each horizon while the global approach obtains a single model that can be used for all horizons. Both approaches have advantages and disadvantages. Each individual model is trained with data pertaining to a single horizon, thus it can be specific for that horizon, but can use fewer data for training than the global model, which is constructed with data belonging to all horizons. Support Vector Machines have been used for constructing the individual and global models. This study has been tested on energy production data obtained from the Sotavento wind farm and meteorological data from the European Centre for Medium-Range Weather Forecasts, for a 5 × 5 grid around Sotavento. Also, given the large amount of variables involved, a feature selection algorithm (Sequential Forward Selection) has been used in order to improve the performance of the models. Experimental results show that the global model is more accurate than the individual ones, specially when feature selection is used.The authors acknowledge financial support granted by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R

An energy credit based incentive mechanism for the direct load control of residential HVAC systems incorporation in day-ahead planning

The increasing operational complexity of power systems considering the higher renewable energy penetration and changing load characteristics, together with the recent developments in the ICT field have led to more research and implementation efforts related to the activation of the demand side. In this manner, different direct load control (DLC) and indirect load control concepts have been developed and DLC strategies are considered as an effective tool for load serving entities (LSEs) with several real-world application examples. In this study, a new DLC strategy tailored for residential air-conditioners (ACs) participating in the day-ahead planning, based on offering energy credits to the enrolled end-users is proposed. The mentioned energy credits are then used by residential end-users to lower their energy procurement costs during peak-price periods. The strategy is formulated as a stochastic mixed-integer linear programming (MILP) model considering uncertainties related to weather conditions. The outcomes regarding the end-user comfort level and economic benefits are also analyzed

Power quality assessment of wind turbines and comparison with conventional legal regulations: A case study in Turkey

Renewable energy sources have been investigated for use instead of conventional fossil fuels in many areas. Among these renewable energy sources, wind energy has come into prominence owing to the fact that it is a clean, sustainable and cost-effective type of energy. However, the connection of large wind farms to the grid may cause problems in terms of power quality due to the variability of the energy extracted from the wind. The mentioned power quality problems are generally taken into consideration after the grid integration of wind farms. However, the precautions that can be taken by means of the assessments before the installation of the turbines represent an easier and more economic way. In this study, the possible effects of the grid connected wind turbines on the power quality characteristics have been defined and the MATLAB based models have been constructed so as to calculate these effects. Particularly, fast voltage variations that are difficult to model due to their relations with the human factor have been analyzed in detail. It has been aimed that the models are suitable for use in practice while utilizing various standards such as IEC 61400-21 and IEC 61000-4-15 in order to setup the models. The analyses of the implementations that represent constraints for exploiting the wind resources in Turkey have been realized in terms of production and consumption with a case study. The realized calculations present the applicability of the model to grid conditions with different characteristics. It is also presented that the wind energy penetration can be increased without deteriorating the power quality of the grid with the use of the proposed model.Wind turbines Power quality Flickermeter

An EMD-ANN based prediction methodology for DR driven smart household load demand

This study proposes a model for the prediction of smart household load demand influenced by a dynamic pricing demand response (DR) program. Price-based DR programs have a considerable impact on household demand pattern due to the expected choice of customers or their home energy management systems (HEMSs) to use more energy in low price periods in order to reduce their electricity procurement cost. Many studies in the literature have dealt with power prediction, but the authors are prior in the field attempting to include the impact of different DR strategies on load demand prediction of smart households. The proposed methodology is expected to be valuable for utilities, retailers, aggregators, etc., in order to evaluate the success of their price-based DR strategies and predict adverse effects such as power peaks in normally off-peak periods and stress of infrastructure

Comprehensive optimization model for sizing and siting of DG units, EV charging stations and energy storage systems

The sizing and siting of renewable resources-based distributed generation (DG) units has been a topic of growing interest, especially during the last decade due to the increasing interest in renewable energy systems and the possible impacts of their volatility on distribution system operation. This paper goes beyond the existing literature by presenting a comprehensive optimization model for the sizing and siting of different renewable resources-based DG units, electric vehicle charging stations, and energy storage systems within the distribution system. The proposed optimization model is formulated as a second order conic programming problem, considering also the time-varying nature of DG generation and load consumption, in contrast with the majority of the relevant studies that have been based on static values

Demand response driven load pattern elasticity analysis for smart households

The recent interest in smart grid vision enables several smart applications in different parts of the power grid structure, where specific importance should be given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart households, constitute a vital point to take into account both in system planning and operation phases. In this study, the assessment of the impacts of pricing based DR strategies on smart household load pattern variations is provided. The household load data sets are acquired from a provided model of a smart household, including appliance scheduling. Then, an artificial neural network (ANN) approach based on Wavelet Transform (WT) is employed for the forecasting of responsive residential load behaviors to different pricing schemes. From the literature perspective this study contributes by considering DR impacts on load pattern forecasting, being a very useful tool for market participants such as aggregators in future pool-based market structures, or for load serving entities to discuss potential change requirements in existing DR strategies, or even to effectively plan new ones

Compressive spatio-temporal forecasting of meteorological quantities and photovoltaic power

This paper presents a solar power forecasting scheme, which uses spatial and temporal time series data along with a photovoltaic (PV) power conversion model. The PV conversion model uses the forecast of three different variables, namely, irradiance on the tilted plane, ambient temperature, and wind speed, in order to estimate the power produced by a PV plant at the grid connection terminals. The forecast values are obtained using a spatio-Temporal method that uses the data recorded from a target meteorological station as well as data of its surrounding stations. The proposed forecasting method exploits the sparsity of correlations between time series data in a collection of stations. The performance of both the PV conversion model and the spatio-Temporal algorithm is evaluated using high-resolution real data recorded in various locations in Italy. Comparison with other benchmark methods illustrates that the proposed method significantly improves the solar power forecasts, particularly over short-Term horizons