Asset Details
Do you wish to reserve the book?
Multi‐Objective Energy Management for an Integrated Energy System With Small Modular Reactors Considering Uncertainty
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Multi‐Objective Energy Management for an Integrated Energy System With Small Modular Reactors Considering Uncertainty
Do you wish to request the book?
Multi‐Objective Energy Management for an Integrated Energy System With Small Modular Reactors Considering Uncertainty
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Multi‐Objective Energy Management for an Integrated Energy System With Small Modular Reactors Considering Uncertainty
2026
Overview
An integrated energy system (IES) can alleviate energy crises, promote multi‐energy complementarity, and enhance finer‐grained energy development. Nuclear power is clean and efficient, mainly when using small modular reactors (SMRs), which increase power generation, improve system flexibility, and promote a low‐carbon economy. This paper proposes a bi‐layer scheduling framework for a SMR‐connected integrated energy system (SMR‐IES) to optimize operating cost, carbon emissions, and average demand‐side flexibility during the peak period index. The first layer optimizes the multi‐objective operation of SMR‐IES using a hybrid of the improved augmented ε ‐constraint method and the modified technique for order preference by similarity to the ideal solution approach. This framework incorporates a ladder‐type carbon trading mechanism alongside a multi‐energy demand response program with a comprehensive user satisfaction index to account for carbon emissions throughout the entire process while enhancing demand‐side flexibility for the SMR‐IES. The second layer handles uncertainties using the information gap decision theory approach. The proposed method can determine a scheduling operation with predicted renewable energy sources, load, and energy price errors while keeping optimal objective values within acceptable bounds not higher than 35% of the nominal optimal values ( β = 0.35). Moreover, the proposed method offers a more efficient approach to managing uncertainty than stochastic and robust optimization methods.
