E-Mobility Scheduling for the Provision of Ancillary Services to the Power System

Gulotta, Francesco; Rancilio, Giuliano; Blaco, A.; Bovera, Filippo; Merlo, Marco; Matteucci, Matteo; Falabretti, Davide

doi:10.18178/ijeetc.9.5.349-355

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…On average, in 85.4% of cases, a zero imbalance is registered during the AS provision (median value equal to 94%) and, for the remaining time, the imbalance is higher than 5% of the corresponding AS requests (Imb(t AS ) > 0.05 •P AS (t AS )) only in 10% of cases, with a median value equal to 6%. This result has been achieved thanks to the implementation of the approach based on Equation (7), which allows to schedule the EVs' charging also according to the AS requests expected in the upcoming time steps.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In this regard, in studies [7,22], the authors presented a preliminary architecture to enable the e-mobility participation in the market for the AS provision, focusing also on the undesirable effects caused to the distribution grid. However, neither the details of the implemented optimization algorithm nor the approach adopted to simulate EV usage have been reported.…”

Section: Related Workmentioning

confidence: 99%

“…In the outlined framework, the present paper proposes a novel optimization procedure based on a hybridization of the Artificial Bee Colony algorithm aimed to schedule the charging requests of an EV carsharing fleet [7]. The initial charging time of each vehicle is coordinated by adopting a Centralized Control Architecture (CCA).…”

Section: Introductionmentioning

confidence: 99%

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A Nature-Inspired Algorithm to Enable the E-Mobility Participation in the Ancillary Service Market

Falabretti

Gulotta

2022

Energies

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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.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Nature-Inspired Algorithm to Enable the E-Mobility Participation in the Ancillary Service Market

Falabretti

Gulotta

2022

Energies

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“…2. It is designed to accurately simulate three categories of trips and the corresponding charging requests [24]. In particular, the most common charging modes are modeled: i) residential, ii) workplace/offices, and iii) shopping mall charging [25].…”

Section: Modeling Of the Ev Usagementioning

confidence: 99%

Impact of Electrical Vehicle Private Charging Stations on the Quality of the Low Voltage Network Supply

Pisano

Ruggeri

Soma

et al. 2023

IEEE Open J. Power Energy

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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.

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“…However, these methods require large computational effort to reach an optimal solution, with high modeling complexity; heuristic or meta-heuristic methods, on the other hand, are easier to implement and can give more feasible results for real-time operation. An example of these techniques is seen in [15], where hill-climbing algorithms are used to evaluate the optimal dispatch of a virtual power plant, or in [16], where a large fleet of electric vehicles is aggregated and managed by a centralized heuristic scheduler to provide Ancillary Services (ASs) to the power system.…”

mentioning

confidence: 99%

Participation of Aggregated DERs to the Ancillary Services Market: A Monte Carlo Simulation-Based Heuristic Greedy-Indexing Model

Falabretti

Gulotta

Spinelli³

2022

Energies

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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.

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E-Mobility Scheduling for the Provision of Ancillary Services to the Power System

Cited by 14 publications

References 11 publications

A Nature-Inspired Algorithm to Enable the E-Mobility Participation in the Ancillary Service Market

A Nature-Inspired Algorithm to Enable the E-Mobility Participation in the Ancillary Service Market

Impact of Electrical Vehicle Private Charging Stations on the Quality of the Low Voltage Network Supply

Participation of Aggregated DERs to the Ancillary Services Market: A Monte Carlo Simulation-Based Heuristic Greedy-Indexing Model

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