As a response to rapidly increasing penetration of wind power generation in modern electric power grids, accurate prediction models are crucial to deal with the associated uncertainties. Due to the highly volatile and chaotic nature of wind power, employing complex intelligent prediction tools is necessary. Accordingly, this paper proposes a novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements. The decomposed signal is provided as input to a hybrid forecasting model built on a bagging neural network (BaNN) combined with K-means clustering. Moreover, a new intelligent optimization method named ChB-SSO is applied to automatically tune the BaNN parameters. The performance of the proposed forecasting framework is tested using different seasonal subsets of real-world wind farm case studies (Alberta and Sotavento) through a comprehensive comparative analysis against other well-known prediction strategies. Furthermore, to analyze the effectiveness of the proposed framework, different forecast horizons have been considered in different test cases. Different error assessment criteria were used and the obtained results demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.

This article presents a robust chance-constrained optimization framework for the optimal operation management of an energy hub (EH) in the presence of electrical, heating, and cooling demands, and renewable power generation. The proposed strategy can be used for optimal decision making of operators of EHs or energy providers. The electrical energy storage device in the studied EH can handle the fluctuations in operating points raised by such uncertainties. In order to model the hourly demands and renewable power generation uncertainties, a robust chance-constrained close-to-real-time model is adopted in this article. The considered EH in this study follows a centralized framework and the EH operator is responsible for the optimal operation of the hub assets based on the day-ahead scheduling. A thorough analysis of energy flows with different carriers is presented. In addition, a numerical stability test regarding the selection of the time step size is performed to guarantee the solution's time resolution independence, occurring in previous studies.

Virtual Power Plants (VPP) have emerged as a way to coordinate and control the growing number of Distributed Energy Resources (DERs) within power systems. Typically, VPP models have focused on financial or commercial outcomes and have not considered the technical constraints of the distribution system. The objective of this paper is the development of a technical VPP (TVPP) operational model to optimize the scheduling of a diverse set of DERs operating in a day-ahead energy market, considering grid management constraints. The effects on network congestion, voltage profiles and power losses are presented and analyzed. In addition, the thermal comfort of the consumers is considered and the trade-offs between comfort, costs and technical constraints are presented. The model quantifies and allocates the benefits of the DER operation to the owners in a fair and efficient manner using the Vickrey Clarke Grove mechanism. This paper develops a stochastic mixed-integer linear programming (MILP) model and various case studies are thoroughly examined on the IEEE 119 bus test system. Results indicate that electric vehicles provide the largest marginal contribution to the TVPP, closely followed by solar PV units. Also, the results show that the operations of the TVPP improve financial metrics and increase consumer engagement while improving numerous technical operational metrics. The proposed TVPP model is shown to improve the ability of the system to incorporate DERs, including those from commercial buildings.