Recommended Practices for the Implementation of Wind Power Forecasting Solutions : Part 1: Forecast Solution Selection Process

This is the first part of a series of three recommended practices that deal with the development and operation of renewable energy forecasting solutions in the power market. The first part “Forecast Solution Selection Process”, which is the current document, deals with the selection and background information necessary to collect and evaluate when developing or renewing a forecasting solution for the power market

A review of deterministic error scores and normalization techniques for power forecasting algorithms

The evaluation of the performance of forecasting algorithms in the area of power forecasting of regenerative power plants is the basis for model comparison. There are a multitude of different forms of evaluation scores, which, however, do not seem to be universally applied. In this article, we want to broaden the understanding for the function and relationship of different error scores in the area of deterministic error scores. A categorization by normalization technique is introduced, which simplifies the process of choosing the appropriate error score for an application. A number of popular error scores are investigated in a case study which details the development of error scores given different forms of error distributions. Furthermore, the behavior of different error scores on a real-world wind farm data set is analyzed. A correlation analysis between the evaluated scores gives insights on how these scores relate to each other. Properties and notes on the applicability of the presented scores are detailed in a discussion. Finally, an outlook on future work in the area of probabilistic error scores is given

Active congestion quantification and reliability improvement considering aging failure in modern distribution networks

The enormous concerns of climate change and traditional resource crises lead to the increased use of distributed generations (DGs) and electric vehicles (EVs) in distribution networks. This leads to significant challenges in maintaining safe and reliable network operations due to the complexity and uncertainties in active distribution networks, e.g., congestion and reliability problems. Effective congestion management (CM) policies require appropriate indices to quantify the seriousness and customer contributions to congested areas. Developing an accurate model to identify the residual life of aged equipment is also essential in long-term CM procedures. The assessment of network reliability and equipment end-of-life failure also plays a critical role in network planning and regulation. The main contributions of this thesis include a) outlining the specific characteristics of congestion events and introducing the typical metrics to assess the effectiveness of CM approaches; b) proposing spatial, temporal and aggregate indices for rapidly recognizing the seriousness of congestion in terms of thermal and voltage violations, and proposing indices for quantifying the customer contributions to congested areas; c) proposing an improved method to estimate the end-of-life failure probabilities of transformers and cables lines taking real-time relative aging speed and loss-of-life into consideration; d) quantifying the impact of different levels of EV penetration on the network reliability considering end-of-life failure on equipment and post-fault network reconfiguration; and e) proposing an EV smart charging optimization model to improve network reliability and reduce the cost of customers and power utilities. Simulation results illustrate the feasibility of the proposed indices in rapidly recognizing the congestion level, geographic location, and customer contributions in balanced and unbalanced systems. Voltage congestion can be significantly relieved by network reconfiguration and the utilization of the proposed indices by utility operators in CM procedures is also explained. The numerical studies also verify that the improved Arrhenius-Weibull can better indicate the aging process and demonstrate the superior accuracy of the proposed method in identifying residual lives and end-of-life failure probabilities of transformers and conductors. The integration of EV has a great impact on equipment aging failure probability and loss-of-life, thus resulting in lower network reliability and higher cost for managing aging failure. Finally, the proposed piecewise linear optimization model of the EV smart charging framework can significantly improve network reliability by 90% and reduce the total cost by 83.8% for customers and power utilities