Tariff-based regulatory sandboxes for EV smart charging: Impacts on the tariff and the power system in a national framework

Electrification of private transport is a fundamental step for decarbonizing mobility. Electric vehicles (EV) can be a burden for the power system if vehicle-grid integration is not implemented by design. Market-based smart charging projects are effective, but their massive diffusion is limited. A fundamental instrument toward a large adoption of smart charging is the inclusion of smart charging-oriented measures in regulatory sandboxes, conveniently acting on electricity tariff. This paper presents a set of possible toolboxes for smart charging to show the potential that regulatory measures can have on steering the infrastructure deployment and the charging activity. Each proposed toolbox addresses a specific charging mode, including domestic, workplace, and public access charging. Proposed measures are target-oriented and evaluated based on their environmental, technical, and economic impacts. These include the carbon footprint of the electricity used for EV charging, the impact in terms of peak power withdrawal from the public grid and the charging cost born by EV users. Additionally, the assessment about the impact of prospected measures on the electricity tariffs' income is provided. Results show the possibility of reducing the evening EV-related peak load by 30% to 50% via home smart charging. Also, a 10% decrease in carbon footprint is achieved by valley-filling with work charging. Charging at the destination can reduce the system cost for the new distribution infrastructure, dropping the number of new dedicated connection points for public charging. The cost of incentives is partially repayable considering the additional EV penetration fostered by the reduced charging costs

E-mobility scheduling for the provision of ancillary services to the power system

In the present paper, the charging of an electric vehicles fleet is scheduled in order to provide power balance regulation to the electric grid. The starting of e-cars charging can be optimized according to predetermined limits set by users, in order to adjust the power exchange profile of the aggregated fleet. Ancillary services are sold on the Ancillary Services Market. Therefore, an analysis of the Italian Ancillary Services Market is provided to check the performance of the strategy proposed according to the actual market requirements. In this way, useful figures are provided to the reader in order to evaluate the technical and economic viability of the approach

Ancillary services markets in europe: Evolution and regulatory trade-offs

The generating power mix has deeply changed in the last years. Especially in Europe, the electric systems are evolving towards a more decentralized architecture, widely penetrated by renewable and distributed energy resources. These resources usually present less predictability and fast dynamics. To avoid jeopardizing the power system, they should be effectively included in the Ancillary Services Markets (ASMs), which procure the resources for balancing and safely dispatching the system. This work presents a meta-analysis of the evolution of ASMs and the underlying regulatory trade-offs. The evolutions are analyzed modularly to clearly investigate the benefit of each one. The analysis aims to provide an evaluation of each trend regarding the architecture, the services, and the products of the ASMs, based on the level of agreement of the two main counterparties: the system operator who manages the system and the Balancing Services Provider that delivers the service. The outcome is a ranking of the possible regulatory evolutions, with the win–win situations at the top and the cases that imply drawbacks for a counterparty at the bottom. The ranking can represent a guideline for regulatory authorities as well as market operators or players to realize effective market arrangements. Finally, a survey of European countries describes the effort of each one towards each evolution

BESS modeling: Investigating the role of auxiliary system consumption in efficiency derating

Large-scale Battery Energy Storage System (BESS) capacity installed for stationary applications is rising in the first decades of 21st century. Business models related to BESS highly depend on BESS lifetime. BESS lifetime can be preserved only if accurate thermal management of the assets allows to keep it at design temperature. Auxiliary systems' needs for cooling and heating the BESS cannot be disregarded while modeling the real-world operation of these facilities. In this paper we propose an improved protocol for organic modeling of large-scale BESS grid-connected. We assess the share of losses and the operational efficiency related to the provision of ancillary services to the network by BESS in different seasons and different working conditions. We highlight that BESS efficiency increases in case the system is constantly exploited, avoiding time idle or at low power. The model proposed, with respect to standard techniques, allows to better represent BESS performance. Indeed, just by disregarding the losses related to thermal management of the assets (as it is for standard modeling techniques), errors committed are up to 10%

Modeling and Analysis of BESS Operations in Electricity Markets: Prediction and Strategies for Day-Ahead and Continuous Intra-Day Markets

In recent years, the global energy sector has seen significant transformation, particularly in Europe, with a notable increase in intermittent renewable energy integration. Italy and the European Union (EU) have been among the leaders in this transition, with renewables playing a substantial role in electricity generation as of the mid-2020s. The adoption of Battery Energy Storage Systems (BESS) has become crucial for enhancing grid efficiency, sustainability, and reliability by addressing intermittent renewable sources. This paper investigates the feasibility and economic viability of batteries in wholesale electricity markets as per EU regulation, focusing on the dynamics of very different markets, namely the Day-Ahead Market (DAM) based on system marginal price and the Cross-Border Intra-day Market (XBID) based on continuous trading. A novel model is proposed to enhance BESS operations, leveraging price arbitrage strategies based on zonal price predictions, levelized cost of storage (LCOS), and uncertain bid acceptance in continuous trading. Machine learning and deep learning techniques are applied for price forecasting and bid acceptance prediction, respectively. This study finds that data-driven techniques outperform reference models in price forecasting and bid acceptance prediction (+7-14% accuracy). Regarding market dynamics, this study reveals higher competitiveness in the continuous market compared to the DAM, particularly with increased risk factors in bids leading to higher profits. This research provides insights into compatibility between continuous markets and BESS, showing substantial improvements in economic profitability and the correlation between risk and profits in the bidding strategy (EUR +9 M yearly revenues are obtained with strategic behavior that reduces awarded energy by 60%)