Open AccessInterval–stochastic optimisation for transactive energy management in energy hubs
Author(s) -
Alipour Manijeh,
Abapour Mehdi,
Tohidi Sajjad
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2020.0524
Subject(s) - transactive memory , stochastic programming , computer science , scheduling (production processes) , grid , interval (graph theory) , mathematical optimization , operations research , energy market , demand response , energy (signal processing) , stochastic modelling , electric power system , distributed computing , power (physics) , renewable energy , electricity , engineering , electrical engineering , business , mathematics , knowledge management , statistics , geometry , physics , combinatorics , quantum mechanics , finance
With the growing penetration of distributed energy resources and energy hubs, the transactive energy market appears as a modern power market that facilitates coordinated operation and end‐to‐end energy trading. An energy hub can actively participate in the day‐ahead and real‐time markets for fulfilling various goals. This study proposes an interval–stochastic optimisation model based upon the transactive energy methodology for the scheduling of energy hubs in coupled electrical and thermal systems. Transactive energy technology is employed for the energy exchange management among the hub, consumers and the power grid. In the proposed model, the day‐ahead stage's uncertainties are modelled by using interval optimisation to avoid stochastic programming challenges in problems with lots of uncertainties. Further, the stochastic optimisation is employed in the real‐time stage as stochastic programming challenges will be conducted by updating the forecasts. By envisaging the uncertainties quiddity and the most of technical constraints of a hub, the real‐life situation is created. In addition, most of linearised constraints are applied to achieve reliable outcomes in a shorter time. Numerical simulations confirm the effectiveness and applicability of the proposed model.