Search

Funding Information: Arsalan Najafi would like to acknowledge the support by Polish National Agency for Academic Exchange for the grant No. PPN/ULM/2020/1/00196 . Publisher Copyright: © 2022 The Author(s) This paper proposes a novel bi-level strategic energy trading framework to minimize the operation cost of the distribution network (DN) interacting with peer-to-peer (P2P) transactive energy hubs with electric vehicles. A distribution system operator at the upper level minimizes its total cost from purchasing electricity in the wholesale market, generating with its own microturbines, and selling electricity to the energy hubs. Each transactive energy hub at the lower level reacts to the offer price received from the upper level, interacting with the other energy hubs. Each energy hub has a parking lot to harvest the benefit from asynchronous storage of electricity in other energy hubs stemming from the difference between the arrival or departure times of the electric vehicles. A single-leader multi-follower game approach is developed to model the DN-energy hubs game structure. Then, an iterative model is proposed to find the equilibrium point between the leader and the followers, while the distributed problemof the interaction between the followers at the LL is solved by the Alternating Direction Method of Multipliers (ADMM). Numerical results for the IEEE 33-bus test system with two energy hubs show the effectiveness of the proposed transactive model between the energy hubs and the DN.

Utilizing multi-carrier energies such as wind energy, electric vehicle (EV) and battery banks are a significant step toward a cleaner production. Hence, This paper proposes a stochastic-based decision-making framework for the efficient short-term management of smart energy hub (EH) in restructured power systems with high penetration of renewable energy. The electrical and natural gas carriers are the input of smart EH, while the electricity and heat demands are considered as the outputs. The EH, including battery storage system (BSS) and EV fleet, is managed in the regulation market and day-ahead (DA) horizons. The energy hub operator makes optimal decisions regarding the natural gas network and energy supply for the thermal and electricity customers. An optimal self-scheduling model is developed to take into account the day ahead (DA) and regulation markets (RM) and decisions regarding generations of electrical and thermal devices as well as EV aggregator decisions. The primary goal of the proposed frameworkis minimizing the cost of procuring electricity and heat energy carriers in DA and regulation markets, including upward/downward regulations via a stochastic mixed-integer linear programming (MILP) approach. In order to get the uncertainty around the exact outcomes of RM prices, wind generation, and EV patterns, the model also takes into account the conditional value at risk (CVaR) term. The proposed formulation is examined by applying to a smart EH. Results show the effectiveness and usefulness of the proposed framework in managing the smart EHs efficiently. (c) 2020 Elsevier Ltd. All rights reserved.