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Due the high penetration of intermittent renewable energy sources (IRESs), transmission line currents show large fluctuations and thus significant uncertainty. This makes it difficult to operate a power system without violating transmission capacity constraints. This paper evaluates the dynamic line ratings (DLRs) of overhead lines based on changes in the line current owing to the high penetration of intermittent renewable energy sources. In particular, by focusing on extremely large (but rare) forecasting errors in the intermittent renewable energy source output, which are generally inevitable in most forecasting methods, a model for representing the forecasting error in line with current variation due to intermittent renewable energy source output is developed. The model is based on a shape parameter that represents the equivalent current variation required for the same temperature increase as that due to the extremely large forecasting error. Finally, based on the annual minute-by-minute irradiance data, preventive control of the transmission network with dynamic line ratings is evaluated using worst-case parameter values.

Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and analytical paper is largely focused on a statistical analysis of forecasting errors based on more than one hundred papers on wind generation forecasts. Factors affecting the magnitude of forecasting errors are presented and discussed. Normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE) have been selected as the main error metrics considered here. A new and unique error dispersion factor (EDF) is proposed, being the ratio of nRMSE to nMAE. The variability of EDF depending on selected factors (size of wind farm, forecasting horizons, and class of forecasting method) has been examined. This is unique and original research, a novelty in studies on errors of power generation forecasts in wind farms. In addition, extensive quantitative and qualitative analyses have been conducted to assess the magnitude of forecasting error depending on selected factors (such as forecasting horizon, wind farm size, and a class of the forecasting method). Based on these analyses and a review of more than one hundred papers, a unique set of recommendations on the preferred content of papers addressing wind farm generation forecasts has been developed. These recommendations would make it possible to conduct very precise benchmarking meta-analyses of forecasting studies described in research papers and to develop valuable general conclusions concerning the analyzed phenomena.

Power generation using wind has been extensively utilised, with substantial capacity add-on worldwide, during recent decades. The wind power energy sector is growing, and has turned into a great source of renewable power production. In the past decades of the 21st century, the capacity of installed wind energy has almost doubled every three years. This review paper presents the crucial facets and advancement strategies that were approved and adopted by the Government of India for intensifying the country’s own power safety, by the appropriate use of existing power sources. From India’s viewpoint, wind energy is not only utilized for power production but also to provide power in a more economical way. The particulars of India’s total energy production, contributions of numerous renewable sources and their demand are also encompassed in this paper. After an exhaustive review of the literature, detailed facts have been identified about the present position of wind energy, with an emphasis on government achievements, targets, initiatives, and various strategic advances in the wind power sector. Wind power potential is discussed, which can assist renewable power companies to select efficient and productive locations. All analyses carried out in this paper will be incredibly valuable to future renewable energy investors and researchers. The current scenario of wind power production in India is also paralleled with that of other globally prominent countries.

Accurate forecasting is a crucial task for energy management systems (EMSs) used in microgrids. Despite forecasting models destined to EMSs having been largely investigated, the analysis of criteria for the practical execution of this task, in the framework of an energy management algorithm, has not been properly investigated yet. On such a basis, this paper aims at exploring the effect of daily forecasting frequency on the performance of rolling-horizon EMSs devised to reduce demand uncertainty in microgrids by adhering to a reference planned profile. Specifically, the performance of a sample EMS, where the forecasting task is committed to a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), has been studied under different daily forecasting frequencies, revealing a representative trend relating the forecasting execution frequency in the EMS and the reduction of uncertainty in the electrical demand. On the basis of such a trend, it is possible to establish how often is convenient to repeat the forecasting task for obtaining increasing performance of the EMS. The obtained results have been generalized by extending the analysis to different test scenarios, whose results have been found coherent with the identified trend.

In this paper, we deal with a renewable-powered mini-grid, connected to an unreliable main grid, in a Joint Chance Constrained (JCC) programming setting. In several rural areas in Africa with low energy access rates, grid-connected mini-grid system operators contend with four different types of uncertainties: forecasting errors of solar power and load; frequency and outages duration from the main-grid. These uncertainties pose new challenges to the classical power system’s operation tasks. Three alternatives to the JCC problem are presented. In particular, we present an Individual Chance Constraint (ICC), Expected-Value Model (EVM) and a so called regular model that ignores outages and forecasting uncertainties. The JCC model has the capability to guarantee a high probability of meeting the local demand throughout an outage event by keeping appropriate reserves for Diesel generation and battery discharge. In contrast, the easier to handle ICC model guarantees such probability only individually for different time steps, resulting in a much less robust dispatch. The even simpler EVM focuses solely on average values of random variables. We illustrate the four models through a comparison of outcomes attained from a real mini-grid in Lake Victoria, Tanzania. The results show the dispatch modifications for battery and Diesel reserve planning, with the JCC model providing the most robust results, albeit with a small increase in costs.

Purpose: The study aims to analyze forecast errors for various time series generated by a 3PL logistics operator across 10 distribution channels managed by the operator. Design/methodology/approach: This study examines forecasting errors across 10 distribution channels managed by a 3PL operator using Google Cloud AI forecasting. The R environment was used in the study. The research centered on analyzing forecast error series, particularly decomposition analysis of the series, to identify trends and seasonality in forecast errors. Findings: The analysis of forecast errors reveals diverse patterns and characteristics of errors across individual channels. A systematic component was observed in all analyzed household appliance channels (seasonality in all channels, and no significant trend identified only in Channel 10). In contrast, significant trends were identified in one pharmaceutical channel (Channel 02), while no systematic components were detected in the remaining channels within this group. Research limitations: Logistics operations typically depend on numerous variables, which may affect forecast accuracy. Additionally, the lack of information on the forecasting models, mechanisms (black box), and input data limits a comprehensive understanding of the sources of errors. Value of the paper: The study highlights the valuable insights that can be derived from analyzing forecast errors in the time series within the context of logistics operations. The findings underscore the need for a tailored forecasting approach for each channel, the importance of enhancing the forecasting tool, and the potential for improving forecast accuracy by focusing on trends and seasonality. The findings also emphasize that customized forecasting tools can significantly enhance operational efficiency by improving demand planning accuracy and reducing resource misallocation. This analysis makes a significant contribution to the theory and practice of demand forecasting by logistics operators in distribution networks. The research offers valuable contributions to ongoing efforts in demand forecasting by logistics operators.

When the number of observed elections is low, subnational data can be used to perform electoral forecasts. Turgeon and Rennó (2012) applied this solution and proposed three forecasting models to analyze Brazilian presidential elections (1994-2006). The models, adapted from forecasting models of American and French presidential elections, considers economic and political factors. We extend their analysis to the recent presidential elections in Brazil (2010, 2014 and 2018) and find that the addition of the three recent elections does not improve the accuracy of our forecast models although it strengthens the relationship between the explanatory variables and vote for the incumbent. We also find that models based on the popularity of the incumbent outperform those based on trial-heat polls and that electoral forecast models can survive earthquake elections like the 2018 election that led to the unexpected rise of “outsider” and extremist candidate Jair Bolsonaro.

Hydropower is the largest source of renewable energy in the world and currently dominates flexible electricity production capacity. However, climate variations remain major challenges for efficient production planning, especially the annual forecasting of periodically variable inflows and their effects on electricity generation. This study presents a model that assesses the impact of forecast quality on the efficiency of hydropower operations. The model uses ensemble forecasting and stepwise linear optimisation combined with receding horizon control to simulate runoff and the operation of a cascading hydropower system. In the first application, the model framework is applied to the Dalälven River basin in Sweden. The efficiency of hydropower operations is found to depend significantly on the linkage between the representative biannual hydrologic regime and the regime actually realised in a future scenario. The forecasting error decreases when considering periodic hydroclimate fluctuations, such as the dry–wet year variability evident in the runoff in the Dalälven River, which ultimately increases production efficiency by approximately 2% (at its largest), as is shown in scenarios 1 and 2. The corresponding potential hydropower production is found to vary by 80 GWh/year. The reduction in forecasting error when considering biennial periodicity corresponds to a production efficiency improvement of about 0.33% (or 13.2 GWh/year).

Power systems with a high wind power share are characterized by low rotational inertia and weak frequency regulation, which can easily lead to frequency safety problems. Providing virtual inertia for large-scale wind turbines to participate in frequency regulation is a solution, but virtual inertia is related to wind power output prediction. Due to wind power prediction errors, the system inertia is reduced and there is even a risk of instability. In this regard, this article proposes a unit commitment model that takes into account the constraints of sharp changes in frequency caused by wind power prediction errors. First, the expressions of the equivalent inertia, adjustment coefficient, and other frequency influence parameters of the frequency aggregation model for a high proportion wind power system are derived, revealing the mechanism of the influence of wind power prediction power and synchronous machine start stop status on the frequency modulation characteristics of the system. Second, the time domain expression of the system frequency after the disturbance is calculated by the segment linearization method, and the linear expressions of “frequency drop speed and frequency nadir” constraints are derived to meet the demand of frequency regulation in each stage of the system. Finally, a two-stage robust optimization model based on a wind power fuzzy set is constructed by combining the effects of wind power errors on power fluctuation and frequency regulation capability. The proposed model is solved through affine decision rules to reduce its complexity. The simulation results show that the proposed model and method can effectively improve the frequency response characteristics and increase the operational reliability of high-share wind power systems.

Flood forecasting plays an essential role in enhancing the safety of residents downstream and preventing or reducing economic losses. One critical issue in flood risk assessment is the determination of the probability distribution of forecast errors. Several investigations, which have been carried out to analyze the influence of the uncertainty in real-time operation or water resources management, assumed that the relative forecast error was approximately normally distributed. This study investigates whether the flood forecast error follows the normal distribution. Several distributions were fitted to the flood error series, and their performances were analyzed using the data from Three Gorges Reservoir (TGR) and Muma River. Then, the most appropriate distribution was selected. Results show that the assumption of normal distribution is not justified for the flood forecast error series of TGR and Muma River. The use of normal distribution for estimating flood risk may lead to incorrect results.