Electric power systems are moving toward more decentralized models, where energy generation is performed by small and distributed power plants, often from renewables. With the gradual phase out from fossil fuels, however, Distribution Energy Resources (DERs) are expected to take over in the provision of all regulation services required to operate the grid. To this purpose, the opening of national Ancillary Service Markets (ASMs) to DERs is considered an essential passage. In order to allow this transition to happen, current opportunities and barriers to market participation of DERs must be clearly identified. In this work, a comprehensive review is provided of the state-of-the-art of research on DER integration into ASMs. The topic at hand is analyzed from different perspectives. First, the current situation and main trends regarding the reformation processes of national ASMs are analyzed to get a clear picture of the evolutions expected and adjustment required in the future, according to the scientific community. Then, the focus is moved to the strategies to be adopted by aggregators for the effective control and coordination of DERs, exploring the challenges posed by the uncertainties affecting the problem. Coordination schemes between transmission and distribution system operators, and the implications on the grid infrastructure operation and planning, are also investigated. Finally, the review deepens the control capabilities required for DER technologies to perform the needed control actions.

The appearance of new single-phase intensive and often coincident loads, such as the electric vehicle private charging stations (i.e., residential or private parking lots of shopping malls or workplaces), can affect the operation of low voltage (LV) distribution networks. Unbalances among the three-phase loading are extremely common, even if the end-users of a given LV network have similar behaviors. Due to the coincidence of such loads, excessive voltage drops may often appear, altering the quality of supply voltage provided to the LV end-users. This work evaluates the effects of residential and other charging stations located in parking lots of shopping malls or workplaces on the quality of supply voltage of LV grids. Then, a decentralized approach is proposed, based on the multi-agent system, which allows the different flexibility resources available on the grid to act together to improve the network quality. The agents cooperate to regulate the network nodal voltage, taking into account the characteristics of the resources they are controlling and the needs of their owners. A case study derived from a real portion of the Italian distribution system demonstrates the validity of the approach in solving the network operation criticalities.

In the present paper, a tool is proposed to optimally schedule the charging requests of a fleet of carsharing Electric Vehicles (EVs) in an urban area, to enable their participation in the Ancillary Service Market. The centralized scheduler minimizes the imbalance of an EV fleet with respect to the power commitment declared in the Day-Ahead Market, providing also tertiary reserve and power balance control to the grid. The regulation is carried out by optimizing the initial charging time of each vehicle, according to a deadline set by the carsharing operator. To this purpose, a nature-inspired optimization is adopted, implementing innovative hybridizations of the Artificial Bee Colony algorithm. The e-mobility usage is simulated through a topology-aware stochastic model based on carsharing usage in Milan (Italy) and the Ancillary Services requests are modeled by real data from the Italian electricity market. The numerical simulations performed confirmed the effectiveness of the approach in identifying a suitable schedule for the charging requests of a large EV fleet (up to 3200 units), with acceptable computational effort. The benefits on the economic sustainability of the E-carsharing fleet given by the participation in the electricity market are also confirmed by an extensive sensitivity analysis.

As the adoption of electric vehicles accelerates, understanding the impact of public charging on the power grid is crucial. However, today, a notable gap exists in the literature regarding approaches capable of accurately estimating the expected influence of e-mobility power demand on electrical grids, especially at medium and low voltage levels. To fill this gap, in this study, a procedure is proposed to estimate the power demand patterns of public car parks in a 2030 scenario. To this end, data collected from real-life car parks in Italy are used in Monte Carlo simulations, where probabilistic daily power demand curves are created with different maximum charging powers (from 7.4 kW to ultra-fast charging). The results highlight high variability in the power demand depending on the location and type of car park. City center car parks exhibit peak demand during morning hours, linked to commercial activities, while car parks near railway stations and hospitals show demand patterns aligned with transportation and healthcare needs. Business area car parks, in contrast, have a more pronounced demand during work hours on weekdays, with much lower activity during weekends. This study also demonstrates that, in some situations, ultra-fast charging can increase peak power demand from the grid by up to 210%. Given their contribution to the existing literature, the power demand patterns from this research constitute a valuable starting point for future studies aimed at quantitatively assessing the impact of e-mobility on the power system. In addition, they can effectively support decision-makers in optimally designing the e-mobility recharge infrastructure.

In an effort to improve the stability and secure operation of the grid, regulatory bodies are opening Ancillary Services Markets participation to Distributed Energy Resources (DERs), energy storage systems, and demand response. Within this framework, this study proposes a model that simulates the coordinated operation of an aggregate of power plants, including non-dispatchable DERs and, as regulating units, Combined Heat and Power (CHP) generation and electrochemical energy storage systems. A Monte Carlo procedure is adopted to realistically create a population of aggregation scenarios. The real-time operation of the DER portfolio is managed through a Heuristic Greedy-Indexing logic, which allows the Aggregator to select the optimal control action to implement according to the technical and economic quantities characterizing the market and the grid. The techno-economic performance of the proposed algorithm is evaluated by simulating its interaction with the electricity markets. Finally, a sensitivity analysis is performed to analyze the profitability in different scenarios. The novel mathematical model proposed showed to be effective in managing a complex problem like the one at hand with an acceptable computational effort. The numerical results obtained confirmed that the aggregated participation in the market could provide interesting economic returns, especially if a CHP unit is involved as regulating unit, while the feasibility of the batteries adoption is still limited by the actual cost of the technology.