Remuneration Sensitivity Analysis in Prosumer and Aggregator Strategies by Controlling Electric Vehicle Chargers

Abstract

The efficient use of energy resources is profoundly changing power grid regulation and policy. New forms of power generation coupled with storage and the presence of new, increasingly flexible loads such as electric vehicles enable the development of multi-agent planning systems based on new forms of interaction. For instance, consumers can take advantage of flexibility by interacting directly with the grid or through aggregators that bridge the gap between these end-users and traditional centralised markets. This paper aims to provide insight into the benefits for aggregators and end-users from a financial perspective by proposing a methodology that can be applied to different scenarios. End-users may provide flexibility services related to private vehicle charging stations or battery storage systems. The paper will analyse different remuneration levels for end-users by highlighting the most beneficial scenarios for aggregators and end-users and providing evidence on potential conflict of interests. The numerical results show that some consumers may benefit more from aggregation. This is because if taken individually, consumption habits do not allow the same flexibility when considering clusters of consumers with different behaviour. It is also shown that there are cases in which consumers do not seem to benefit from the presence of intermediate parties. We provide extensive numerical results to gain insight for better decision making.

Optimal Charging Control of Electric Vehicle Fleets Based on Demand Aggregation and User-Oriented Disaggregation Respecting Data Privacy

“…The data points represent the energy demand during the drive per km for distinct values of the ambient temperature. The resulting polynomial coefficients α, β, γ, ϵ, ζ, and θ of the functions in (19) and (20) are defined and listed in Table IV.…”

“…This enables aggregation entities to define and implement smart charging strategies for EVs. Prior research showed how to design smart charging strategies [12]- [20] to reduce the overall energy cost, avoid grid congestions, circumvent peak loads due to the charging processes, and provide frequency regulation services [6], [21].…”

Schedulable capacity assessment method for PV and storage integrated fast charging stations with V2G

“…Equation (9) indicates that the EV increases the charging power in response to the downward scheduling of the grid in this scheduling interval, but its charging power cannot be so large that the battery SOC exceeds the upper limit, otherwise it will damage the battery life. Combining the power constraint (10) leads to the formula (11) for the threshold value of the downward SC.…”

“…EVs usually participate in grid dispatch through aggregators. [11] presents the mathematical formulation of the different prosumers and proposes the aggregation strategies, providing a guideline for the payment options of future users to be involved in aggregator schemes. A crucial step in realizing the interaction of EVs with the grid is the quick and accurate forecast of the schedulable capacity.…”

Frequency Regulation of Electric Vehicle Aggregator Considering User Requirements with Limited Data Collection

“…The EV aggregator manages a population of EVs in a centralized system [8,9]. The centralized system employs a control center to collect the information from all available EVs and controls when and at what rate each EV will charge or discharge [10,11]. Although the regulation capacity of an individual EV is small, large-scale aggregated EVs provide considerable regulation capacity and rapid responding speed for regulating the power system frequency [12].…”