Energy Production Forecast Research Articles

Cost-optimal Control of Photovoltaic Systems with Battery Storage under Variable Electricity Tariffs

In this paper, domestic homes with a photovoltaic system, a battery storage and variable electricity tariffs are considered. First, a control oriented model of the system is developed. Second, three controllers are designed with the aim to minimize costs for the user, and at the same time help the energy provider maintaining grid stability, by proper battery load management. A base-level controller serves as performance reference for the advanced optimization based controllers (a nonlinear controller with full system knowledge and a linear controller) which utilize forecasts of energy production and consumption. Performance evaluation was done in simulation using real-world measurement data over a full year. Results show that significant cost reductions can be achieved with the nonlinear controller while the linear one suffers from imprecise system knowledge.

Short time ahead wind power production forecast

An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast.

Daily forecast of solar thermal energy production for heat storage management

Solar energy offers a renewable source of power but its fluctuating nature raises concerns about the electrical grid balancing. Network regulators have to estimate the upcoming production to match supply with demand; consequently, power plant operators may be asked to provide accurate forecasts. Planning the thermal or electrical output of solar power plants is thus highly required to ensure a stable power chain supply. This paper presents a solution that couples a meteorological model with a solar power plant performance model. The power output is predicted 24 h ahead in the case of a solar Fresnel power plant. The required Direct Normal Irradiance is inferred from the global horizontal irradiance; the thermal production is evaluated from an optical and thermal model. Our approach has been validated on a 1000 m2 Fresnel power plant, paving the way for model-based storage strategy.

Model and Procedures for Reliable near Term Wind Energy Production Forecast

Accurate and reliable prediction of wind energy production is important for the operational management of wind farm as well as ensuring the stability of electrical grid integrated with renewable wind energy. This paper describes a new method that aims to reliably predict wind energy production based on weather forecast data. With this new method, an aerodynamic model is firstly used to predict the distribution of wind speed with elevation by use of 24-hour ahead forecasted wind speed data at three-hour intervals (which is available with common weather forecast provider). The aerodynamic model considers the influence of factors such as ground topology, types of land cover, etc. on the wind speed distribution. Based on this model, wind speed at 10-minutes time interval is simulated, which is subsequently used together with the turbine production curve to predict energy production within the next 24 hours. The model and procedures for forecasting wind energy production are validated on a 100kW utility scale wind turbine. Comparison with alternative wind energy forecast procedures show that this new model-based forecast method provide more accurate prediction of wind energy production for different seasons. Major advantages of this model include that it is based on data commonly available from weather forecast providers and is applicable to different terrain conditions.

Wind and Photovoltaic Large-Scale Regional Models for Hourly Production Evaluation

This work presents two large-scale regional models used for the evaluation of normalized power output from wind turbines and photovoltaic power plants on a European regional scale. The models give an estimate of renewable production on a regional scale with 1h resolution, starting from a mesoscale meteorological data input and taking in account the characteristics of different plants technologies and spatial distribution. An evaluation of the hourly forecasted energy production on a regional scale would be very valuable for the transmission system operators when making the long-term planning of the transmission system, especially regarding the cross-border power flows. The tuning of these regional models is done using historical meteorological data acquired on a per-country basis and using publicly available data of installed capacity.

Meteorological Forecasting for renewable energy plants. A case study of two energy plants in Spain

<p class="R-AbstractKeywords"><span lang="EN-US">Energy resources are the engines that drive every economy [1], [4], [14], Therefore, it is necessary to develop their exploitation in a friendlier, environmentally and sustainable way indeed it is a critically needed nowadays. Then, it is necessary to improve efficiency and optimize renewable energy in order that replace polluting energy sources. This work aims to relate the use of forecasting on meteorological variables such as wind speed, wind direction, solar radiation, among others, obtained by mathematical models implemented on computer to forecast energy production in renewable energies plants. It has been implemented and automated one of the most used models by the scientific community in this field, WRF (Weather Research and Forecasting Model). WRF is a next generation mesoscale model, designed to serve as a tool for meteorological research in addition to provide forecasts in operational regime. This research introduce the topic of energy forecast, mainly of renewable energy, focusing on wind and solar energy, basing the study on a better forecasting of meteorological variables in order to use as income in energy production forecast. A case study in two Spanish renewable energy plants is exposed</span><span lang="EN-US">. </span></p>

Photovoltaic energy production forecast using support vector regression

Forecasting models for photovoltaic energy production are important tools for managing energy flows. The aim of this study was to accurately predict the energy production of a PV plant in Italy, using a methodology based on support vector machines . The model uses historical data of solar irradiance, environmental temperature and past energy production to predict the PV energy production for the next day with an interval of 15 min. The technique used is based on $$\nu $$?-SVR, a support vector regression model where you can choose the number of support vectors. The forecasts of energy production obtained with the proposed methodology are very accurate, with the $$R^{2}$$R2 coefficient exceeding 90 % . The quality of the predicted values strongly depends on the goodness of the weather forecast, and the $$R^{2}$$R2 value decreases if the predictions of irradiance and temperature are not very accurate.

2012 JRC Wind Status Report

The wind energy sector uses a mature yet continuously improving technology. The reliability of wind turbines and the efficiency of energy capture are both high, yet continue to improve with new designs, new components and new materials. Even non-technological aspects such as project management, better forecasts of wind energy production and better risks assessment are contributing to increasing the competitiveness of wind power and reducing its overall cost. This is sometimes achieved even if some cost areas can occasionally increase, e.g. capital costs due to the better quality of components.To further increase reliability, scientists and designers require better knowledge of loads, load effects and electrical effects in the mechanical and electrical parts of the turbine. They also require materials and basic components (e.g. electronic components of the electricity subsystems) that can stand higher temperatures and perform better at normal operating temperatures. In some areas reliability is improved through more redundancy of equipment, in particular in control and power electronics.The technological aspects of wind energy include: Wind turbine components: blades, forgings (e.g. main shafts), castings (e.g. hubs, bearing housings and bed plates), pitch, control and yaw systems, generator, gearbox, bearings and shafts, and power electronics. Design for manufacture, transport and installation; turbine assembly. Offshore foundations design and manufacture; and foundations, cable and turbine installation. Substations: switchgear, transformers, cables, circuit breakers, etc.The world wind market and the European offshore sectors are continuously growing although European onshore wind growth is less significant. Overall, the global annual market remains stable at around 40 GW of installed capacity, but this figure hides huge variations between individual markets. Cumulative installed capacity reached 240 GW at the end of 2011 and is expected to reach 285 GW at the end of the current year (2012).The 2011 manufacturers market continued to be dominated by Vestas and, as in 2010, it included four Chinese firms among the top-ten. However, the changes in the Chinese market are very significant as the 2010 market leader Sinovel (and 2nd worldwide) is now 7th worldwide. European firms, General Electric (US) and Suzlon (India) have a truly international reach whereas Chinese firms are still confined to the Chinese market.Capital expenditure (CapEx) or cost for wind installations vary greatly worldwide: unit costs in some countries can be triple those in other countries. Overall, 2011 CapEx for onshore wind averaged 1 580 €/kW, if Chinese and Indian installations are excluded (due to different cost methodologies), and the offshore figure reached 3 500 €/kW although from a low installed capacity basis. Operational costs (OpEx) are also very different onshore (21 €/MWh) and offshore (32 €/MWh, figures depend on assumptions such as capacity factors). There is a clear trend to lower prices in both CapEx and OpEx, possibly steeper in onshore than offshore OpEx and with very significant potential in offshore CapEx.This report presents a snapshot of the current situation of the wind sector from a technology and market perspective, and a detailed analysis of the economics of wind. It is the first of a series of annual reports which will not only include annual developments but also specific, one-off research into technology aspects of the wind sector.

Optimal Control of a Network of Power Microgrids Using the Pontryagin's Minimum Principle

The optimal control of the power flows in a network of microgrids (MGs) is presented. The problem is solved using the mathematical formalization of the optimal control based on the Pontryagin's minimum principle (PMP). The objective is to deliver an optimal control strategy for the minimization of the power flows among MGs, and to maintain the storage system operating around a given reference value. This study proposes an original formulation based on the PMP that may be viewed as a preliminary continuous time attempt to model and control the exchange of power in a network of MGs. Its main originality is the use and the exchange of information and forecast of energy production and consumption on the whole set of MGs, to improve the overall quality of the power management, and energy storage. A method based on the PMP is developed to solve the corresponding constrained optimal control problem in an almost exclusively analytical way and thus, to calculate the optimal control. To prove the viability of the proposed approach, an example has been solved for the case of four MGs collaborating in a network.

Probabilistic forecasts of solar irradiance using stochastic differential equations

Probabilistic forecasts of renewable energy production provide users with valuable information about the uncertainty associated with the expected generation. Current state‐of‐the‐art forecasts for solar irradiance have focused on producing reliable point forecasts. The additional information included in probabilistic forecasts may be paramount for decision makers to efficiently make use of this uncertain and variable generation. In this paper, a stochastic differential equation framework for modeling the uncertainty associated with the solar irradiance point forecast is proposed. This modeling approach allows for characterizing both the interdependence structure of prediction errors of short‐term solar irradiance and their predictive distribution. Three different stochastic differential equation models are first fitted to a training data set and subsequently evaluated on a one‐year test set. The final model proposed is defined on a bounded and time‐varying state space with zero probability almost surely of events outside this space. Copyright © 2014 John Wiley & Sons, Ltd.

Wind Energy Forecast in Complex Sites with a Hybrid Neural Network and CFD based Method

The wind is an intermittent renewable energy source and the energy production forecast is a fundamental activity for many reasons (grid regulation, maintenance, etc.).In this work a hybrid method (based on weather forecast data, neural networks and computational fluid dynamics) and a pure neural network approach are compared in a complex terrain site.The post processing of real production data has been discovered to be a key activity. Treatment and filtering of data spreading out from the supervisory control and data acquisition system are fundamental both for training and testing methods reliability.

Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity

This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV) plant. The model is called HIstorical SImilar MIning (HISIMI) model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model.

Crop Wind Energy Experiment (CWEX): Observations of Surface-Layer, Boundary Layer, and Mesoscale Interactions with a Wind Farm

Perturbations of mean and turbulent wind characteristics by large wind turbines modify fluxes between the vegetated surface and the lower boundary layer. While simulations have suggested that wind farms could significantly change surface fluxes of heat, momentum, momentum, moisture, and CO2 over hundreds of square kilometers, little observational evidence exists to test these predictions. Quantifying the influences of the “turbine layer” is necessary to quantify how surface fluxes are modified and to better forecast energy production by a wind farm. Changes in fluxes are particularly important in regions of intensely managed agriculture where crop growth and yield are highly dependent on subtle changes in moisture, heat, and CO2. Furthermore, speculations abound about the possible mesoscale consequences of boundary layer changes that are produced by wind farms. To address the lack of observations to answer these questions, we developed the Crop Wind Energy Experiment (CWEX) as a multiagency, multiuniversity field program in central Iowa. Throughout the summer of 2010, surface fluxes were documented within a wind farm test site and a 2-week deployment of a vertically pointing lidar quantified wind profiles. In 2011, we expanded measurements at the site by deploying six flux stations and two wind-profiling lidars to document turbine wakes. The results provide valuable insights into the exchanges over a surface that has been modified by wind turbines and a basis for a more comprehensive measurement program planned for the summer in 2014.

Hybrid Predictive Models for Accurate Forecasting in PV Systems

The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error.

Systematic Approaches to Ensure Correct Representation of Measured Multi-Irradiance Module Performance in PV System Energy Production Forecasting Software Programs

Photovoltaic (PV) systems are gradually becoming larger to better leverage economies of scale. Still, project size and financing are governed by the confidence that investors have in forecasted energy production. Default settings of PV module models in the two prevalent production forecasting software programs only poorly reproduce measured data for standard multicrystalline module types. To address this issue, this study presents and applies systematic approaches to minimize differences between modeled and measured PV module efficiencies at multiple irradiance intensities. Optimized models considerably improve the agreement between model and measurement and have a significant impact on energy forecasts.

One day-ahead forecasting of energy production in solar photovoltaic installations: An empirical study

This paper presents a flexible and easy-to-use methodological approach to the forecasting of energy production in solar photovoltaic (PV) installations, using time series analysis and neural networks. The aim is to develop and validate a one day-ahea

One day-ahead forecasting of energy production in solar photovoltaic installations

This paper presents a flexible and easy-to-use methodological approach to the forecasting of energy production in solar photovoltaic (PV) installations, using time series analysis and neural networ...

An assessment of the role mass market demand response could play in contributing to the management of variable generation integration issues

The penetration of wind and solar generating resources is expected to dramatically increase in the United States over the coming years. It is widely understood that large scale deployment of these types of renewable energy sources (e.g., wind, solar) that have variable and less predictable production characteristics than traditional thermal resources poses integration challenges for bulk power system operators. At present, bulk power system operators primarily utilize strategies that rely on existing thermal generation resources and improved wind and solar energy production forecasts to manage this uncertainty; a host of additional options are also envisioned for the near future including demand response (DR). There are well-established bodies of research that examine variable generation integration issues as well as demand response potential; but, the existing literature that provides a comparative assessment of the two neither treats this topic comprehensively nor in a highly integrated fashion. Thus, this paper seeks to address these missing pieces by considering the full range of opportunities and challenges for mass market DR rates and programs to support integration of variable renewable generation.

Optimizing energy production with integrated CCS technology for CO2 emissions mitigation in the Canadian oil sands industry

Abstract The forecasted energy production of oil sands operations in Alberta in the year 2030 were optimised under CO2 emissions constraints, using a mixed integer linear optimisation model. The model features a variety of technologies (with and without CO2 capture), including coal and natural gas power plants, IGCC, and oxyfuel plants. Hydrogen production technologies are steam methane reforming and coal gasification. The optimization is executed at increasing CO2 emissions reduction levels, yielding unique infrastructures that satisfy the energy demands of the oil sands industry at minimal cost. The economic and environmental impacts of the optimally chosen technologies on the forecasted operations of the oil sands industry in 2030 are thus determined. The maximum CO2 emissions reduction attainable by using CCS in the oil sands industry in 2030 is 39% with respect to a business-as-usual baseline. This CO2 reduction results in an energy cost increase of roughly 20% for synthetic crude and 2% for bitumen production. CO2 reductions ranging from 0–35% can be attained by optimising the energy infrastructures, yielding energy production cost reductions between 9%–18%. The maximum CO2 intensity reduction is 46% for synthetic crude and less than 3% for bitumen. Energy conversion and CO2 capture account for the bulk of the energy costs for synthetic crude whereas transport and storage combined contribute between 2.6% and 5% over the entire range of CO2 reductions. The optimal energy production technologies are strongly dependent on the CO2 reduction targets. Power production without capture, predominantly NGCC and supercritical coal technology, is optimal at CO2 reduction levels of up to 30%. At higher CO2 reductions, only NGCC with capture and Oxyfuel plants are optimal. H2 production via coal gasification is optimal for CO2 reduction levels of 35% and lower. Above 35% reduction, steam methane reforming with capture is the dominant technology.

An evaluation of errors in US energy forecasts: 1982–2003

Planners, policy-makers, and the private sector rely on energy forecasts to help make policy and investment decisions. In the US, the federal Department of Energy (through the Energy Information Administration and its predecessors) has conducted national forecasts of energy production and consumption for decades. This paper explores US energy forecasts in order to uncover potential systemic errors in US forecasting models. We apply an error decomposition technique to forecasts within each major energy sector (commercial, industrial, residential, and transportation) made during the period 1982 to 2003. We find that low errors for total energy consumption are concealing much larger sectoral errors that cancel each other out when aggregated. For example, 5-year forecasts made between 1982 and 1998 demonstrate a mean percentage error for total energy consumption of 0.1%. Yet, this hides the fact that the industrial sector was overestimated by an average of 5.9%, and the transportation sector was underestimated by an average of 4.5%. We also find no evidence that forecasts within each sector have improved over the two decades studied here.