Abstract
This paper investigates the contribution of hydrogen electrolysers (HEs) as highly controllable loads in the context of the Frequency Containment Reserve (FCR), in future operation scenarios on the Iberian Peninsula (IP). The research question is whether HEs can mitigate system insecurity regarding frequency or Rate of Change of Frequency (RoCoF) in critical periods of high renewable energy penetration (i.e. low system inertia), due to the fact that these periods will coincide with high volume of green hydrogen production. The proposed simulation platform for analysis consists of a simplified dynamic model developed in MATLAB/Simulink. The results obtained illustrate how HEs can outperform conventional generators on the provision of FCR. It is seen that the reference incident of 1GW loss in the IP in a 2040 low inertia scenario does not lead to insecure values of either frequency or Rate of Change of Frequency (RoCoF). On the other hand, an instantaneous loss of inverter-based resources (IBR) generation following a short-circuit may result in RoCoF violating security thresholds. The obtained results suggest that the HEs expected to be installed in the IP in 2040 may contribute to reduce RoCoF in this case, although this mitigation may be insufficient. The existing FCR mechanism does not fully exploit the fast-ramping capability of HEs; reducing measurement acquisiton delay would not improve results.

Abstract
This paper presents a model predictive control (MPC) framework for battery energy storage systems (BESS) management considering models for battery degradation, system efficiency and V-I characteristics. The optimization framework has been tested for microgrids with different renewable generation and load mix considering several operation strategies. A comparison for one-year simulations between the proposed model and a naïve BESS model, show an increase in computation times that still allows the application of the framework for real-time control. Furthermore, a trade-off between financial revenue and reduced BESS degradation was evaluated for the yearly simulation, considering the degradation model proposed. Results show that a conservative BESS usage strategy can have a high impact on the asset's lifetime and on the expected system revenues, depending on factors such as the objective function and the degradation threshold considered.

Abstract
Traditional power grids, being highly centralized in terms of generation, economy, and operation, continually employed probabilistic methods to account for load uncertainties. In modern smart grids (SG), rapid proliferation of non-dispatchable generation (physical decentralization) and liberal markets (market decentralization) leads to dismantling of the centralized paradigm, with operation being performed by several decentralized agents. Handling uncertainty in this new paradigm is aggravated due to 1) a vastly increased number of uncertainty sources, and 2) decentralized agents only having access to local data and limited information on other parts of the grid. A major problem identified in modern and future SGs is the need for fully decentralized optimal operation techniques that are computationally efficient, highly accurate, and do not jeopardize data privacy and security of individual agents. Machine learning (ML) techniques, being successors to traditional probabilistic methods are identified as a solution to this problem. In this paper, a conceptual model is constructed for the transition from a fully centralized operation of a SG to a decentralized one, proposing the transition scheme between the two paradigms. A novel ML algorithm for fully decentralized operation is proposed, formulated, implemented, and tested. The proposed algorithm relies solely on local historical data for local agents to accurately predict their optimal control actions without knowledge of the physical system model or access to historical data of other agents. The capability of cloud-based cooperative information exchange was augmented through a new concept of s-index activation codes, being encoded vectors shared between agents to improve their operation without sharing raw information. The algorithm is tested on a modified IEEE 24-bus test system and synthetically generating historical data based on typical load profiles. A week-ahead high-resolution (15 minute) fully decentralized operation case is tested. The algorithm is shown to guarantee less than 0.1% error compared to a centralized solution and to outperform a neural network (NN). The algorithm is exceptionally accurate while being highly computationally efficient and has great potential as a versatile model for fully decentralized operation of SGs.

Abstract
The rapid proliferation of Electric Vehicles (EVs) creates an inherent link between the previously independent transport and power sectors. This is especially relevant in the smart cities paradigm, which focuses on optimizing resource management using modern software tools and communication infrastructures. The optimal management of energy resources is of key importance, and with mobile EVs playing a pivotal role in smart city power flows, the coordination of energy management systems (EMSs) at their parking locations can bear global benefits. In this study the coordination between home energy management systems (HEMSs) and EV parking lot management systems (PLEMS) is proposed, modeled, and simulated, as a new contribution to earlier studies. The EMSs coordinate through partially sharing individual EV schedules and without sharing private information. Missing information is completed through public cloud repositories and services. The HEMS and PLEMS are implemented using mixed-integer linear programming (MILP). The proposed coordination framework is computationally implemented and simulated based on a real-life case study. The results show that the proposed EMS coordination framework is both technically beneficial for power grids and economically beneficial for EV owners.

Abstract
The need to take into account and explicitly model forecast uncertainty is today at the heart of many scientific and applied enterprises. For instance, the ever-increasing accuracy of weather forecasts has been driven by the development of ensemble forecasts, where a large number of forecasts are generated either by generating forecasts from different models or by repeatedly perturbing the initial conditions of a single forecast model. Importantly, this approach provides robust estimates of forecast uncertainty, which supports human judgement and decision-making. Although weather forecasts and their uncertainty are also crucial for the weather-to-power conversion for RES forecasting in system operation, power trading and balancing, the industry has been reluctant to adopt ensemble methods and other new technologies that can help manage highly variable and uncertain power feed-ins, especially under extreme weather conditions. In order to support the energy industry in the adaptation of uncertainty forecasts into their business practices, the IEA Wind Task 36 has started an initiative in collaboration with the Max Planck Institute for Human Development and Hans-Ertel Center for Weather Research to investigate the existing barriers in the industry to the adoption of such forecasts into decision processes. In the first part of the initiative, a forecast game was designed as a demonstration of a typical decision-making task in the power industry. The game was introduced in an IEA Wind Task 36 workshop and thereafter released to the public. When closed, it had been played by 120 participants. We will discuss the results of our first experience with the experiment and introduce some new features of the second generation of experiments as a continuation of the initiative. We will also discuss specific questions that emerged when we started and after analysing the experiments. Lastly we will discuss the trends we found and how we will fit these into the overall objective of the initiative which is to provide training tools to demonstrate the use and benefit of uncertainty forecasts by simulating decision scenarios with feedback and allowing people to learn from experience, rather than reading articles, how to use such forecasts.

Abstract
The papers in this special section focus on advances in renewable energy forecasting, predictability, business models, and applications in the power industry. During the last 25 years, research has been conducted for developing renewable energy source (RES) forecasting algorithms, especially for wind and solar energy, seeking an improvement of predictability and uncertainty forecasting products. Research on wave energy forecasting is also being conducted, although this technology is not at the same maturity levels of wind and solar energy technologies. Furthermore, the number of companies selling forecasting services has proliferated and the reliability and availability of the services have improved. Currently, power system operators and electrical energy traders use weather and power forecasts embedded in their decision-making processes. Despite all this research and adoption by the energy industry, deterministic forecasts are still predominant in utility practice mainly due to: i) lack of understanding and standardization of uncertainty forecast products; and ii) high computational time associated with stochastic and robust optimization approaches. Moreover, proven business cases are also needed to demonstrate the benefits of uncertainty forecasts to end-users.