Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Research on Operation Optimization of Energy Systems
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Research on Operation Optimization of Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5825

Special Issue Editors

Dr. You Lv

E-Mail Website
Guest Editor
School of Control and Computer Engineering, North China Electric Power University, Beijing, China
Interests: power big data analysis and deep learning technology; power equipment condition monitoring and fault diagnosis; advanced energy systems analysis and modeling
Dr. Guorui Ren

E-Mail Website
Guest Editor
School of Control and Computer Engineering, North China Electric Power University, Beijing, China
Interests: wind energy evaluation; optimization and intelligent control; energy storage; virtual power plant operation

Special Issue Information

Dear Colleagues,

Energy is vital to economic and social development, as well as people's well-being. In the face of increasingly severe global issues, such as climate change, environmental challenges, and energy resource constraints, it is of great significance to improve energy utilization efficiency, promote clean and low-carbon energy development, and significantly reduce carbon dioxide emission intensity and pollutant emission levels in order to ease the energy crisis and improve the environment.

This Special Issue aims to present the most recent advances related to the optimization of all types of energy systems, including the theory, design, modelling, control, and application of systems that are involved in the optimization of energy generation and utilization.

Topics of interest for publication include, but are not limited to:

  • Modelling and optimization of power generation systems;
  • Flexibility enhancement of traditional power generation processes;
  • Condition monitoring of equipment in energy systems;
  • Intelligent control of key parameters in power generation systems;
  • Economic operation, pollution emissions and carbon reduction during the energy generation process;
  • Optimal dispatch of multi-energy systems;
  • Energy storage capacity optimization in different scenarios;
  • Optimization of integrated energy systems;
  • Cost and efficiency evaluation of energy systems;
  • Novel applications of optimized energy systems;
  • Software and design related to energy system optimization.

Dr. You Lv
Dr. Guorui Ren
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • power generation optimization
  • pollution emission reduction
  • flexibility enhancement
  • condition monitoring
  • intelligent control
  • optimal dispatch
  • energy storage
  • integrated energy systems
  • cost and efficiency evaluation
  • novel applications

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 2947 KiB  
Article
Impact of Electricity Price Expectation in the Planning Period on the Evolution of Generation Expansion Planning in the Market Environment
by Xian Huang and Kun Liu
Energies 2023, 16(8), 3328; https://0-doi-org.brum.beds.ac.uk/10.3390/en16083328 - 08 Apr 2023
Cited by 1 | Viewed by 1094
Abstract
With the continuous promotion of China’s electricity market reform, the introduction of competition in the power generation market provides a new research direction for the generation expansion planning (GEP) problem, which is of great significance in the promotion of the optimization of the [...] Read more.
With the continuous promotion of China’s electricity market reform, the introduction of competition in the power generation market provides a new research direction for the generation expansion planning (GEP) problem, which is of great significance in the promotion of the optimization of the power energy structure. In the context of marketization, the electricity price expectation during the planning period is a key factor of GEP for independent power generation groups. There is some literature showing that the electricity price expectation in the planning period can be estimated according to certain laws of market supply and demand, while it seems to us that a future Pay as Bid (PAB) mechanism is better to determine the electricity price expectation. In this paper, to explore the impact of these two different electricity price formation mechanisms on the evolution of the generation market, a multi-agent framework is first established to describe the interaction process among the generation market agents; then, a GEP model for independent power generation groups is developed in the market competition environment, and four representative scenarios are finally designed for detailed comparative studies. Based on these case studies, the conclusion can be summarized as: (1) the PAB bidding mechanism has a lower electricity price and higher market installed capacity almost all the time during the whole planning period for all four scenarios; (2) it is more important that PAB can reduce the impact of parameter uncertainty in the laws of market supply and demand, which can obtain more reliable and reasonable results regarding the long-term evolution of the generation market. Full article
(This article belongs to the Special Issue Research on Operation Optimization of Energy Systems)
►▼ Show Figures

Figure 1

18 pages, 4405 KiB  
Article
Two-Stage Optimal Scheduling of Highway Self-Consistent Energy System in Western China
by Yujiang Ye, Ruifeng Shi, Yuqin Gao, Xiaolei Ma and Di Wang
Energies 2023, 16(5), 2435; https://0-doi-org.brum.beds.ac.uk/10.3390/en16052435 - 03 Mar 2023
Cited by 1 | Viewed by 1002
Abstract
Under the background of “carbon peaking and carbon neutrality goals” in China, the Highway Self-Consistent Energy System (HSCES) with renewable energy as the main body has become a key research object. To study the operational status of the HSCES in a specific region [...] Read more.
Under the background of “carbon peaking and carbon neutrality goals” in China, the Highway Self-Consistent Energy System (HSCES) with renewable energy as the main body has become a key research object. To study the operational status of the HSCES in a specific region and realize the economically optimal operation of the HSCES, an HSCES model in a low-load, abundant-renewable-energy and no-grid scenario is established, and a two-stage optimal scheduling method for the HSCES is proposed. Moreover, in the day-ahead stage, uncertainty optimization scenarios are generated by Latin hypercube sampling, and a definition of the self-consistent coefficient is proposed, which is used as one of the constraints to establish a day-ahead economic optimal scheduling model. Through the case comparison analysis, the validity of the day-ahead scheduling model is confirmed and the optimal day-ahead scheduling plan is attained. Furthermore, in the intra-day stage, an intra-day rolling optimization method is proposed, which can effectively track the day-ahead scheduling plan and reduce the impact of forecast errors and energy fluctuations by coordinating the unit output within the HSCES system. It is verified that the HSCES can operate economically and safely in Western China, and self-consistently, without grid support. Full article
(This article belongs to the Special Issue Research on Operation Optimization of Energy Systems)
►▼ Show Figures

Figure 1

15 pages, 3170 KiB  
Article
Power Grid Structure Performance Evaluation Based on Complex Network Cascade Failure Analysis
by Di Zhang, Limin Jia, Jin Ning, Yujiang Ye, Hao Sun and Ruifeng Shi
Energies 2023, 16(2), 990; https://0-doi-org.brum.beds.ac.uk/10.3390/en16020990 - 16 Jan 2023
Cited by 7 | Viewed by 1577
Abstract
A safe and stable operation power system is very important for the maintenance of national industrial security and social economy. However, with the increasing complexity of the power grid topology and its operation, new challenges in estimating and evaluating the grid structure performance [...] Read more.
A safe and stable operation power system is very important for the maintenance of national industrial security and social economy. However, with the increasing complexity of the power grid topology and its operation, new challenges in estimating and evaluating the grid structure performance have received significant attention. Complex network theory transfers the power grid to a network with nodes and links, which helps evaluate the system conveniently with a global view. In this paper, we employ the complex network method to address the cascade failure process and grid structure performance assessment simultaneously. Firstly, a grid cascade failure model based on network topology and power system characteristics is constructed. Then, a set of performance evaluation indicators, including invulnerability, reliability, and vulnerability, is proposed based on the actual functional properties of the grid by renewing the power-weighted degree, medium, and clustering coefficients according to the network cascade failure. Finally, a comprehensive network performance evaluation index, which combines the invulnerability, reliability, and vulnerability indicators with an entropy-based objective weighting method, is put forward in this study. In order to confirm the approach’s efficacy, an IEEE-30 bus system is employed for a case study. Numerical results show that the weighted integrated index with a functional network could better evaluate the power grid performance than the unweighted index with a topology network, which demonstrates and validates the effectiveness of the method proposed in this paper. Full article
(This article belongs to the Special Issue Research on Operation Optimization of Energy Systems)
►▼ Show Figures

Figure 1

11 pages, 1708 KiB  
Article
Prediction of NOx Emissions from a Coal-Fired Boiler Based on Convolutional Neural Networks with a Channel Attention Mechanism
by Nan Li, You Lv and Yong Hu
Energies 2023, 16(1), 76; https://0-doi-org.brum.beds.ac.uk/10.3390/en16010076 - 21 Dec 2022
Cited by 4 | Viewed by 1266
Abstract
This paper presents a small and efficient model for predicting NOx emissions from coal-fired boilers. The raw data collected are processed by the min–max scale method and converted into a multivariate time series. The overall model’s architecture is mainly based on building blocks [...] Read more.
This paper presents a small and efficient model for predicting NOx emissions from coal-fired boilers. The raw data collected are processed by the min–max scale method and converted into a multivariate time series. The overall model’s architecture is mainly based on building blocks consisting of separable convolutional neural networks and efficient channel attention (ECA) modules. The experimental results show that the model can learn good representations from sufficient data covering different operation conditions. These results also suggest that ECA modules can improve the model’s performance. The comparative study shows our model’s strong performance compared to other NOx prediction models. Then, we demonstrate the effectiveness of the model proposed in this paper in terms of predicting NOx emissions. Full article
(This article belongs to the Special Issue Research on Operation Optimization of Energy Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop