As the world transitions towards variable renewable energy sources such as wind and solar, the demand for flexibility in the power system is increasing. Additionally, the electrification of transportation is driving up electricity demand. To address these challenges, electricity storage has emerged as a crucial solution. This study focuses on integrating battery electricity storage systems (BESS) in fast electric vehicle charging stations (EVCS) to provide ancillary services to the grid. The objective is to develop day-ahead bidding and scheduling algorithms tailored for infrastructure owners considering market and technical requirements specific to frequency regulation services in East-Denmark (DK2). A seasonal auto-regressive integrated moving average (SARIMA) model is also implemented to forecast ancillary service prices. Through a month-long test period, the developed method is compared to the optimal solution using perfect price information. Our findings offer insights into the benefit of multi-market strategies and ancillary service provision for fast EVCS and BESS owners, contributing to the understanding of sustainable energy integration and grid stability.

One of the challenges associated with the green transition involves advances in digitization and the integration of intelligent demand-response services to improve energy effi-ciency and unlock on-the-fly adaptability of electrical consumers. This paper presents a cloud-based optimization framework that relies on synergies between the Internet of Things (loT) and Digital Twin (DT) technology to provide energy-saving services in the wastewater pump station located in Bornholm, Denmark. The real-time collected data from the pump station establish the backbone of the DT, enabling high-fidelity simulations of system behaviors under different conditions and assessing the effectiveness of various energy -saving strategies. Moreover, the proposed methodology incorporates a Model Predictive Control (MPC) combined with the inflow forecasts to unlock energy optimization potential. Finally, the implementation of a pruned Neural Network Imitator (NNI) executed on an IoT gateway shows significant improvement in operating the pumps in the most efficient zone by emulating the functionality of MPC, tailoring control strategies for real-time efficiency improvement.

With the increased penetration of renewable energy sources, the power grid moves towards more distributed and uncertain power generation. In order to cost-effectively counter-balance the renewables' variability, demand side response has emerged as an attractive candidate technology over the recent years. Moreover, with Internet of Things (IoT) infrastructure implemented, demand side response can be controlled over the internet. Demand side response then offers an opportunity to use these connected assets to improve the stability of the power grid in return for payments from the grid operator. As a specific example of the demand response, this paper demonstrates how wastewater pumping stations with storage tanks can be used to provide ancillary services. The technical requirements that assets need to fulfill in order to participate in the frequency containment reserve (FCR-D) services are first described. The ancillary service is then explained and evaluated which technical aspects are the most relevant and challenging for wastewater pump stations. Moreover, an approach to calculate the flexible energy and power available at the pumping station is proposed for a real-world station in Bornholm Island, Denmark.