Search

• This paper studies the new problem, EVs' provision of flexible ramping products (FRPs). • The dispatching capability of EV fleets is disclosed through the feasible operating region in variable spaces. • A cloud-edge collaborated market structure for the electricity market with FRPs is proposed to facilitate a large amount of EVAs integrated with protected privacy. • A distributed optimal model combining the EVA's bidding subproblem and the market clearing subproblem is established. • The distributed optimal model is solved based on the modified analytical target cascading (ATC), in which the convergence performance is improved through heuristic constraints. • Time-coupled multi-period (TCMP) operation mode is implemented in the market to maximize the dispatching potential of EV fleets. More flexible ramping service is required due to the increase of renewable power generation in power systems. Electric vehicles (EVs) could provide such flexible ramping products (FRPs) at low cost while participating in the electricity market through aggregation. However, EVs' dispatching capability cannot be fully utilized without the right incentives. This paper addresses a distributed optimal model developed between EV aggregators (EVAs) and the independent system operator (ISO). To make such concept, a cloud-edge collaborated market structure is adopted. At the edge level, EVAs assess the dispatching capability and solve the market bidding subproblem. At the cloud level, ISO solves the market clearing subproblem considering system economy and security. The overall problem is solved by the analytical target cascading (ATC) method. Heuristic constraints are also introduced into the model to improve convergence performance. The model is tested on a modified IEEE 30-bus system. Results demonstrate that the proposed method can incentivize EVAs with different owners to shift load and provide FRPs accurately, meanwhile reducing the cost and increasing the consumption of renewable energy effectively. [ABSTRACT FROM AUTHOR]

• Electric vehicle aggregator capacity estimation method is proposed for LFC. • Robust mixed H 2 /H ∞ -SOF controller, synthesized via ILMI for LFC with EVs. • A comparison between mixed H 2 /H ∞ -SOF and only H ∞ -SOF. • Case studies of single and 3 area systems with different scenarios simulated. • Time delays and parameter uncertainties and load variation are considered. Besides being new loads that would call for further capacity from fossil-fuel based Conventional Sources (CS), managing Electric Vehicles (EVs) smartly i.e. using Vehicle-to-Grid (V2G), holds the opportunity to be used for Load Frequency Control (LFC). However, there exists uncertainty in available dispatch capacity and time delay linked to charging/discharging control of individual EVs. Assessing this uncertain capacity and related delay of its response for LFC is presented first in this paper. Next, for the EVs participating in multi-objective LFC (V2G) along with CSs, a robust mixed H 2 /H ∞ controller based on static output feedback is adopted. The system stability, i.e. bounding frequency and tie-line power to their nominal values in the presence of internal and external disturbance, is ensured by providing better reference tracking to both EVs and CSs. Case studies of a single and three-area system with EVs are tested for LFC, where the efficacy of the mixed H 2 /H ∞ is also endorsed by the comparison with only H ∞ controller. The results showed that the EVs and the adopted controller can respond effectively to the scenarios i.e. load variation, parameters uncertainties, effects of dead bands and time delays of both controller and EVs aggregator responses. It caused a decrease in the capacity burden on CS while this study open ways for the future grid to be pollution-free. [ABSTRACT FROM AUTHOR]