Open AccessA Planning Model for an Electric Vehicle Aggregator Providing Ancillary Services to an Unbalanced Distribution Network Considering Contract Design
Author(s) -
Ammar Muqbel,
Ali T. Al-Awami,
Adnan S. Al-bukhaytan
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3368038
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Recent advancements in battery technology have made them more economically viable than ever before, making them suitable for various grid-scale applications. Due to their rapid response, batteries are attractive for providing ancillary services (AS), such as frequency regulation and reserve services, to the bulk power grid. On the other hand, an electric vehicle (EV) is viewed as a moving battery; consequently, EVs are also suitable for those services. This research proposes a linear planning model for an Electric Vehicle Aggregator (EVA) within a distribution network (DN) to offer Ancillary Services (AS) to the bulk power grid. The model takes into account contract design by identifying optimal incentives or charging tariffs that Electric Vehicle (EV) owners would be willing to pay the EVA for charging their vehicles. Additionally, the model takes into account the size of the electric vehicle (EV) fleet as a crucial planning factor for EV aggregation that depends on the energy pricing set by the EVA. The proposed model is developed to maximize the overall profit of bidding capacities in the energy and AS markets while supporting the operation of an unbalanced DN by maintaining the DN limits. Simulation results and sensitivity analyses on the model have been carried out to support the investment model and investigate the change in the optimal solution across different case studies. Simulations show that the optimal charging tariff (β) is (0.02 $/kWh)when considering scenarios where Distribution Network (DN) limits, such as thermal and voltage constraints, are ignored for all EV participation versus β relations. When including the DN, the optimal payoffs vary based on the relationship between the charging tariff and the number of participating EVs.