This paper presents a set of methods for estimating the renewable energy generation downstream of a measurement device using real-world measurements. First, we present a generation disaggregation scheme where the only information available for estimation is the micro-synchrophasor measurements obtained at the substation or feeder head. We then propose two strategies in which we use measurements from the substation as well as a proxy solar irradiance measurement. Using these two measurement points, we first propose a multiple linear regression strategy, in which we estimate a relationship between the measured reactive power and the load active power consumption, which are then used in disaggregation. Finally, we expand this strategy to strategically manage the reconstruction errors in the estimators. We simultaneously disaggregate the solar generation and load. We show that it is possible to disaggragate the generation of a 7.5 megawatt photovoltaic site with a root-mean-squared error of approximately 450 kilowatts.
A common method for predicting the output of PV systems is a model-based approach @cite_3 @cite_10 , which uses a model, composed of the design characteristics of the PV array and the irradiance measurements, to estimate PV generation. In order to understand this approach, and how the proposed methodology in this paper offers a significant improvement for the purpose of real-time estimation of PV generation, we briefly discuss its limiting factors. The critical input to these models is the plane-of-array (POA) irradiance. The POA irradiance on a tilted plane, @math , whose tilt is @math degrees from the horizontal plane can be calculated using Equation @cite_14
@cite_14 @cite_10 @cite_3
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We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power measurements collected from 2014. The accuracy of the model is determined using computing error statistics such as mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative MBE (rMBE), mean percentage error (MPE) and relative RMSE (rRMSE). This work provides findings on how forecasts from individual inverters will improve the total solar power generation forecast of the PV system. An important issue for the growth and management of grid-connected photovoltaic (PV) systems is the possibility to forecast the power output over different horizons. In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kWP grid-connected PV plant installed in Italy. Different combinations of the time series of produced PV power and measured meteorological variables were used as inputs of the ANN. Several statistical error measures are evaluated to estimate the accuracy of the forecasting methods. A decomposition of the standard deviation error has been carried out to identify the amplitude and phase error. The skewness and kurtosis parameters allow a detailed analysis of the distribution error. This paper proposes a method for selecting explanatory variables for global solar radiation forecasting. The weather conditions affect generation output of renewable energy significantly. As a result, smart grids increase the uncertainties caused by the power injections of photovoltaic (PV) and or wind power generation. To smooth smart grid operation, it is necessary to select the predicted input variables for the forecasting model. In this paper, how to select explanatory variables in the forecasting model of PV generation is discussed because Japan gives much higher priority to PV generation in the framework of future energy policy. The global solar radiation is one of the most important variables in dealing with PV generation output forecasting. This paper focuses on the relationship between the global solar radiation and its explanatory variables. The proposed method makes use of the CART (Classification and Regression Trees) algorithm of data mining method to select the explanatory or input variables in the forecasting model of global solar radiation. CART has the function to give priority to explanatory variables through an index called Variable Importance. The proposed method was applied to real data of global solar radiation in Tokyo, Japan. In this paper, a method for estimating power injected into an electrical distribution system from spatially distributed residential PV systems using data from ground-based weather stations is described. This method was developed as part of the High Penetration PV Deployment Project at the Arizona Public Service (APS). Verification of this predictive method is also described in this paper. Finally, correlation statistics and power production ramp rates are calculated for irradiance based on measured power within the APS study area using two different sets of weather station measurements. The method has potential applications to real-time or forecasted power estimation on distribution circuits in addition to the development of broader guidelines on the optimal number and location of irradiance sensors for power estimation in distribution circuits with high penetrations of PV. In this paper, we tackle the problem of power prediction of several photovoltaic (PV) plants spread over an extended geographic area and connected to a power grid. The paper is intended to be a comprehensive study of one-day ahead forecast of PV energy production along several dimensions of analysis: 1) The consideration of the spatio-temporal autocorrelation, which characterizes geophysical phenomena, to obtain more accurate predictions. 2) The learning setting to be considered, i.e., using simple output prediction for each hour or structured output prediction for each day. 3) The learning algorithms: We compare artificial neural networks, most often used for PV prediction forecast, and regression trees for learning adaptive models. The results obtained on two PV power plant datasets show that: taking into account spatio temporal autocorrelation is beneficial; the structured output prediction setting significantly outperforms the nonstructured output prediction setting; and regression trees provide better models than artificial neural networks. Abstract Global radiation measured on fixed-tilt, south-facing planes (40° and vertical) and a 2-axis tracker at NREL’s Solar Radiation Research Lab. in Golden, CO is compared to predictions from ten transposition models, in combination with either optimal or suboptimal input data of horizontal irradiance. Suboptimal inputs are typically used in everyday engineering calculations, for which the necessary data are usually unavailable for the site under scrutiny, and must be estimated in some way. The performance of the transposition models is first evaluated for ideal conditions when optimal data are used. In this specific case, it is found that the Gueymard and Perez models provide the best estimates of global tilted irradiance under clear skies in particular. The performance of four direct diffuse separation models is also evaluated. Their predictions of direct and diffuse radiation appear biased in most cases, with a model-dependent magnitude. Finally, the performance of the resulting combinations of separation and transposition models is analyzed in a variety of situations. When only global irradiance is known, the accuracy of the tilted irradiance predictions degrades significantly, and is mainly conditioned by the local performance of the direct diffuse separation method. For the south-facing vertical surface, inaccuracies in the ground reflection calculations becomes another key factor and significantly increase the prediction error. The Reindl transposition algorithm appears to perform best in this case. When using suboptimal input data for the prediction of plane-of-array irradiance on a moderately tilted plane (40°S) or a 2-axis tracking plane, the Hay, Reindl and Skartveit models are less penalized than others and tend to perform better. It is concluded that further research should be conducted to improve the overall process of predicting irradiance on tilted planes in realistic situations where no local high-quality irradiance or albedo measurements are available. We examine the potential for geographic smoothing of solar photovoltaic (PV) electricity generation using 13 months of observed power production from utility-scale plants in Gujarat, India. To our knowledge, this is the first published analysis of geographic smoothing of solar PV using actual generation data at high time resolution from utility-scale solar PV plants. We use geographic correlation and Fourier transform estimates of the power spectral density (PSD) to characterize the observed variability of operating solar PV plants as a function of time scale. Most plants show a spectrum that is linear in the log–log domain at high frequencies f, ranging from to (slopes of −1.23 and −1.56), thus exhibiting more relative variability at high frequencies than exhibited by wind plants. PSDs for large PV plants have a steeper slope than those for small plants, hence more smoothing at short time scales. Interconnecting 20 Gujarat plants yields a spectrum, reducing fluctuations at frequencies corresponding to 6 h and 1 h by 23 and 45 , respectively. Half of this smoothing can be obtained through connecting 4–5 plants; reaching marginal improvement of 1 per added plant occurs at 12–14 plants. The largest plant (322 MW) showed an spectrum. This suggests that in Gujarat the potential for smoothing is limited to that obtained by one large plant. The rapid growth of solar Photovoltaic (PV) technology has been very visible over the past decade. Such increase in the integration of solar generation has brought attention to the forecasting issues. This paper presents a new approach to tackle the long-term forecasting challenge and accordingly reduce the uncertainty of the PV forecast, which would accordingly help facilitate its integration into the electric power grid. The new method includes a set of pre- and post-processes that will be undertaken before the data is fed to the forecasting model and after the forecast is obtained. Using the proposed method, the historical solar PV radiation data, which is non-stationary, is converted to a set of stationary data which will accordingly allow utilization of a larger set of data for forecasting. Numerical simulations exhibit the performance of the proposed method. Abstract With the increasing use of large-scale grid-connected photovoltaic system, accurate forecast approach for the power output of photovoltaic system has become an important issue. In order to forecast the power output of a photovoltaic system at 24-hour-ahead without any complex modeling and complicated calculation, an artificial neural network based approach is proposed in this paper. The improved back-propagation learning algorithm is adopted to overcome shortcomings of the standard back-propagation learning algorithm. Similar day selection algorithm based on forecast day information is proposed to improve forecast accuracy in different weather types. Forecasting results of a photovoltaic system show that the proposed approach has a great accuracy and efficiency for forecasting the power output of photovoltaic system. A set of three methods are proposed for forecasting amounts of global solar radiation. The forecasts are used to predict the output power of photovoltaic systems installed in power systems and control the output of other generators to meet the electricity demand. The methods forecast two-day-ahead, one-day-ahead and three-hour-ahead global solar radiation, respectively, based on weather information using descriptive statistics, factor analysis and binary trees. The main focus is on estimation of the anticipated variations in the forecasts, finding relevant input variables or signal components, and dealing with the nonlinearity of the relationship between the global solar radiation and the input variables. The methods have been tested on the meteorological and global solar radiation data obtained at an observation site, and the results show that they are promising to be used to keep the balance between demand and supply of electric power in the near future power systems with a large number of photovoltaic systems installed. In last decade, the higher penetration of renewable energy resources (RES) in energy market was encouraged by implementing the energy polices in several developed and developing countries due to increasing environmental concerns. Among wide range of RES, Photovoltaic (PV) electricity generation get higher attention by researcher, energy policy makers and power production companies due to its economic and environmental benefits. Therefore, a large PV penetration was observed in energy market with rapid growth in the last decade. The PV output power is highly uncertain due to several meteorological factors such as temperature, wind speed, cloud cover, atmospheric aerosol levels and humidity level. The inherent variability of PV output power creates different issues directly or indirectly for power grid such as power system control and reliability, reserves cost, dispatchable and ancillary generation, grid integration and power planning. Therefore, there is need to accurately forecast the PV output over the spectrum of forecast horizon at different chronological scales. In this paper, a comprehensive and systematic review of PV output power forecast models were provided. This review covers the different factors affecting PV forecast, PV output power profile and performance matrices to evaluate the forecast model. The critical analysis regressive and artificial intelligence based forecast models are also presented. In addition, the potential benefits of hybrid techniques for PV forecast models are also thoroughly discussed. In this paper, we develop an algorithm for PV(Photo Voltaic) generation forecasting on the day before. In this algorithm, we build database of solar radiation patterns by analyzing past 5 years data from the weather center but deal with February data only. Therefore, we can estimate the amount of PV power generation forecasting in February from this algorithm. This algorithm will facilitate the renewable energy development and give useful information for RES providers to manage their RES power generation schedule. We hope our proposal could help the PV module providers effectively manage their PV generation plan. Abstract This paper proposes a stochastic and dynamic mixed-integer linear program (SD-MILP) for optimal coordinated bidding of a risk-averse profit-maximizing hydropower producer. The day-ahead, intra-day, and real-time markets are considered. To model and predict day-ahead, intra-day, and real-time prices, the Holt–Winter (HW) and the Generalized Autoregressive Conditional Heteroscedastic (GARCH) predictors are combined using a proposed Markov switch. The discrete behavior of intra-day and real-time prices is modeled as different Markov states. The proposed Markov-based HW–GARCH model with a standard scenario generation-reduction technique is used to capture the uncertainty in day-ahead, intra-day, and real-time prices. The time-dependent conditional value at risk (T-CVaR) is proposed to model the risk of trading in different considered markets. The convex combination of the expected profit and T-CVaR is used as the objective of SD-MILP. The Markov-based HW–GARCH is modeled in Matlab and the SD-MILP is coded in GAMS. The Markov-based HW–GARCH predictor and the SD-MILP are used to develop the bidding curve of a three-reservoir hydropower producer using the electricity prices from the Nordic power market. To further examine the developed models a seven-reservoir hydropower producer is also studied. For these two cases, the coordinated bidding curves are derived and discussed. An accurate forecast of the exploitable energy from Renewable Energy Sources, provided 24 h in advance, is becoming more and more important in the context of the smart grids, both for their stability issues and the reliability of the bidding markets. This work presents a comparison of the PV output power day-ahead forecasts performed by deterministic and stochastic models aiming to find out the best performance conditions. In particular, we have compared the results of two deterministic models, based on three and five parameters electric equivalent circuit, and a hybrid method based on artificial neural network. The forecasts are evaluated against real data measured for one year in an existing PV plant located at SolarTechlab in Milan, Italy. In general, there is no significant difference between the two deterministic models, being the three-parameter approach slightly more accurate (NMAE three-parameter 8.5 vs. NMAE five-parameter 9.0 ). The artificial neural network, combined with clear sky solar radiation, generally achieves the best forecasting results (NMAE 5.6 ) and only few days of training are necessary to provide accurate forecasts. Abstract In this paper, a model for the synthetic generation of 1-min global solar radiation data starting from the daily clearness index is presented. The model is constructed by treating the process generating a normalized form of the 1-min clearness index sequence as a Markov process. Two sets of three-year global solar radiation data taken at 1-min intervals from two locations in Japan are used to construct the Markov transition matrices. Because different days have different statistical characteristics due to different weather conditions, the days in the data set are first clustered into groups based on the daily clearness index values. Transition matrices are then formed for each group and consequently used to synthetically generate 1-min global solar radiation data. Second-order Markov models are selected based on the partial autocorrelation functions of the measured data. The statistical characteristics of the measured and synthetic data sets are found to be in close agreement thus confirming the validity of the model. Realistic time-resolved data on occupant behaviour, presence and energy use are important inputs to various types of simulations, including performance of small-scale energy systems and buildings' indoor climate, use of lighting and energy demand. This paper presents a modelling framework for stochastic generation of high-resolution series of such data. The model generates both synthetic activity sequences of individual household members, including occupancy states, and domestic electricity demand based on these patterns. The activity-generating model, based on non-homogeneous Markov chains that are tuned to an extensive empirical time-use data set, creates a realistic spread of activities over time, down to a 1-min resolution. A detailed validation against measurements shows that modelled power demand data for individual households as well as aggregate demand for an arbitrary number of households are highly realistic in terms of end-use composition, annual and diurnal variations, diversity between households, short time-scale fluctuations and load coincidence. An important aim with the model development has been to maintain a sound balance between complexity and output quality. Although the model yields a high-quality output, the proposed model structure is uncomplicated in comparison to other available domestic load models. The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications. Both linear and nonlinear models have been proposed for forecasting the power output of photovoltaic systems. Linear models are simple to implement but less flexible. Due to the stochastic nature of the power output of PV systems, nonlinear models tend to provide better forecast than linear models. Motivated by this, this paper suggests a fairly simple nonlinear regression model known as multivariate adaptive regression splines (MARS), as an alternative to forecasting of solar power output. The MARS model is a data-driven modeling approach without any assumption about the relationship between the power output and predictors. It maintains simplicity of the classical multiple linear regression (MLR) model while possessing the capability of handling nonlinearity. It is simpler in format than other nonlinear models such as ANN, k-nearest neighbors (KNN), classification and regression tree (CART), and support vector machine (SVM). The MARS model was applied on the daily output of a grid-connected 2.1kW PV system to provide the 1-day-ahead mean daily forecast of the power output. The comparisons with a wide variety of forecast models show that the MARS model is able to provide reliable forecast performance. The renewable energy generation such as solar and wind will constitute an important part of the next generation grid. As the variations of renewable sources may not match the time distribution of load, energy storage is essential for grid stability. Supplemented with energy storage, we investigate the feasibility of integrating solar photovoltaic (PV) panels and wind turbines into the grid. To deal with the fluctuation in both the power generation and demand, we borrow the ideas from stochastic network calculus and build a stochastic model for the power supply reliability with different renewable energy configurations. To illustrate the validity of the model, we conduct a case study for the integration of renewable energy sources into the power system of an island off the coast of Southern California. Performance of the hybrid system under study is assessed by employing the stochastic model, e.g., with a set of system configurations, the long-term expected Fraction of Time that energy Not-Served (FTNS) of a given period can be obtained. The presence of distributed generation (DG), represented by photovoltaic generation and wind generation, brings new challenges to distribution network operation. To accommodate the integration of DG, this study proposes a bi-level optimisation model to determine the optimal installation site and the optimal capacity of battery energy storage system (BESS) in distribution network. The outer optimisation determines the optimal site and capacity of BESS aiming at minimising total net present value (NPV) of the distribution network within the project life cycle. Then optimal power flow (OPF) and BESS capacity adjustment are implemented in the inner optimisation. OPF optimises the scheduling of BESS and network losses. On the basis of optimal scheduling of BESS, a novel capacity adjustment method is further proposed to achieve the optimal BESS capacity considering battery lifetime for minimising the NPV of BESS. Finally, the proposed method is performed on a modified IEEE 33-bus system and proven to be more effective comparing with an existing method without BESS capacity adjustment.