Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment

Topic Information

Dear Colleagues,

The rapid development of novel energy technologies and equipment, including renewable energy, energy storage, green hydrogen, energy production, and energy conversion and consumption devices, provides opportunities for smart grids to achieve the objectives of economic security, reliability, flexibility, and low carbon. Moreover, technological advancements cannot only control energy flow but also supply an energy load via alternative sources. However, it is difficult to adapt traditional methods to the increasingly complex and changing energy environment and ensure that they meet the requirements of rapid response and intelligent decision making. Therefore, this topic focuses on utilizing the latest innovative techniques and energy equipment to guarantee the intelligent and effective operation, control, and planning of smart grids. The goals of this Topic are as follows:

1) investigate accurate models of energy systems and equipment and explore the impact of energy equipment on energy systems;

2) coordinate the control of multiple types of energy equipment to achieve the safe, economical, reliable, flexible, and environmental operation of smart grids;

3) develop advanced energy management strategies and intelligent planning schemes to improve energy efficiency;

4) apply advanced optimization technologies and/ or artificial intelligence methods for the intelligent and effective operation, control, and planning of smart grids;

5) and realize synergy among multiple energy sources to improve the flexibility of smart grids.

Topics of interest include but are not limited to the following:

1. The advanced modeling of energy systems and equipment;
2. Efficient energy management strategies for smart grids;
3. The intelligent control of multiple types of equipment for the safe operation of smart grids;
4. The planning of multiple types of energy production, conversion, and consumption devices;
5. Advanced and effective methods for the operation, control, and planning of smart grids;
6. Machine learning and deep learning for the intelligent operation of smart grids;
7. Control strategies for intelligent switch and protection equipment, the design of renewable energy inverters, and power electronic topologies;
8. High-voltage transmission technology and the technological innovation of HVDC transmission;
9. Strategies for the safe and stable operation of smart grids under extreme weather.

Dr. Pengfei Zhao
Prof. Dr. Sheng Chen
Dr. Yunqi Wang
Dr. Liwei Ju
Dr. Zhengmao Li
Dr. Minglei Bao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electricity electricity	-	4.8	2020	27.2 Days	CHF 1000	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Forecasting forecasting	2.3	5.8	2019	24.2 Days	CHF 1800	Submit
Processes processes	2.8	5.1	2013	14.4 Days	CHF 2400	Submit
Smart Cities smartcities	7.0	11.2	2018	25.8 Days	CHF 2000	Submit
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400	Submit

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Published Papers (5 papers)

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All Electricity Energies Forecasting Processes Smart Cities Sustainability

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18 pages, 3501 KiB  

Open AccessArticle

A Bi-Level Reactive Power Optimization for Wind Clusters Integrating the Power Grid While Considering the Reactive Capability

by Xiping Ma, Wenxi Zhen, Rui Xu, Xiaoyang Dong and Yaxin Li

Energies 2024, 17(16), 3910; https://doi.org/10.3390/en17163910 (registering DOI) - 8 Aug 2024

Viewed by 111

Abstract

With the integration of large-scale wind power clusters into the power system, wind farms play a crucial role in grid reactive power regulation. However, the range of its reactive power remains uncertain, posing challenges in formulating a viable program for regulating reactive power [...] Read more.

With the integration of large-scale wind power clusters into the power system, wind farms play a crucial role in grid reactive power regulation. However, the range of its reactive power remains uncertain, posing challenges in formulating a viable program for regulating reactive power to ensure the safe and cost-effective operation of the power system. Based on this, this paper develops a bi-level reactive power optimization for wind clusters integrating the power grid while considering the reactive capability. Firstly, this paper carries out a refined analysis of the wind power clusters, taking into account the characteristics of different areas to estimate the exact value of the reactive power capability in wind power clusters. Secondly, a bi-level reactive power optimization model is established. The upper-layer optimization aims to minimize active losses and voltage deviation in power system operation, while the lower-layer optimization focuses on maximizing reactive power margin utilization in wind farms. To solve this bi-level optimization model, an improved artificial fish swarm algorithm (AFSA) is employed, which decouples real variables and integer variables to enhance the optimization ability of the algorithm. Finally, the effectiveness of our proposed optimization strategy and algorithm is validated through the simulation results. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 4840 KiB  

Open AccessArticle

Cyber Insurance for Energy Economic Risks

by Alexis Pengfei Zhao, Faith Xue Fei and Mohannad Alhazmi

Smart Cities 2024, 7(4), 2042-2064; https://doi.org/10.3390/smartcities7040081 - 27 Jul 2024

Viewed by 286

Abstract

The proliferation of information and communication technologies (ICTs) within smart cities has not only enhanced the capabilities and efficiencies of urban energy systems but has also introduced significant cyber threats that can compromise these systems. To mitigate the financial risks associated with cyber [...] Read more.

The proliferation of information and communication technologies (ICTs) within smart cities has not only enhanced the capabilities and efficiencies of urban energy systems but has also introduced significant cyber threats that can compromise these systems. To mitigate the financial risks associated with cyber intrusions in smart city infrastructures, this study introduces a two-stage hierarchical planning model for ICT-integrated multi-energy systems, emphasizing the economic role of cyber insurance. By adopting cyber insurance, smart city operators can mitigate the financial impact of unforeseen cyber incidents, transferring these economic risks to the insurance provider. The proposed two-stage optimization model strategically balances the economic implications of urban energy system operations with cyber insurance coverage. This approach allows city managers to make economically informed decisions about insurance procurement in the first stage and implement cost-effective defense strategies against potential cyberattacks in the second stage. Utilizing a distributionally robust approach, the study captures the emergent and uncertain nature of cyberattacks through a moment-based ambiguity set and resolves the reformulated linear problem using a dynamic cutting plane method. This work offers a distinct perspective on managing the economic risks of cyber incidents in smart cities and provides a valuable framework for decision making regarding cyber insurance procurement, ultimately aiming to enhance the financial stability of smart city energy operations. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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17 pages, 4387 KiB  

Open AccessArticle

A Stochastic Model Predictive Control Method for Tie-Line Power Smoothing under Uncertainty

by Molin An, Xueshan Han and Tianguang Lu

Energies 2024, 17(14), 3515; https://doi.org/10.3390/en17143515 - 17 Jul 2024

Viewed by 341

Abstract

With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model predictive control (SMPC) method [...] Read more.

With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model predictive control (SMPC) method is proposed for tie-line power smoothing using a novel data-driven linear power flow (LPF) model that enhances efficiency by updating parameters online instead of retraining. The scenario method is then employed to simplify the objective function and chance constraints. The stability of the proposed model is demonstrated theoretically, and the performance analysis indicates positive results. In the one-day case study, the mean relative error is only 1.1%, with upper and lower quartiles of 1.4% and 0.2%, respectively, which demonstrates the superiority of the proposed method. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 1991 KiB  

Open AccessArticle

Building a Sustainable Future: A Three-Stage Risk Management Model for High-Permeability Power Grid Engineering

by Weijie Wu, Dongwei Li, Hui Sun, Yixin Li, Yining Zhang and Mingrui Zhao

Energies 2024, 17(14), 3439; https://doi.org/10.3390/en17143439 - 12 Jul 2024

Viewed by 409

Abstract

Under the background of carbon neutrality, it is important to construct a large number of high-permeability power grid engineering (HPGE) systems, since these can aid in addressing the security and stability challenges brought about by the high proportion of renewable energy. Construction and [...] Read more.

Under the background of carbon neutrality, it is important to construct a large number of high-permeability power grid engineering (HPGE) systems, since these can aid in addressing the security and stability challenges brought about by the high proportion of renewable energy. Construction and engineering frequently involve multiple risk considerations. In this study, we constructed a three-stage comprehensive risk management model of HPGE, which can help to overcome the issues of redundant risk indicators, imprecise risk assessment techniques, and irrational risk warning models in existing studies. First, we use the fuzzy Delphi model to identify the key risk indicators of HPGE. Then, the Bayesian best–worst method (Bayesian BWM) is adopted, as well as the measurement alternatives and ranking according to the compromise solution (MARCOS) approach, to evaluate the comprehensive risks of projects; these methods are proven to have more reliable weighting results and a larger sample separation through comparative analysis. Finally, we established an early warning risk model on the basis of the non-compensation principle, which can help prevent the issue of actual risk warning outcomes from being obscured by some indicators. The results show that the construction of the new power system and clean energy consumption policy are the key risk factors affecting HPGE. It was found that four projects are in an extremely high-risk warning state, five are in a relatively high-risk warning state, and one is in a medium-risk warning state. Therefore, it is necessary to strengthen the risk prevention of HPGE and to develop a reasonable closed-loop risk control mechanism. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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23 pages, 3144 KiB  

Open AccessArticle

Coordinated Optimization of Hydrogen-Integrated Energy Hubs with Demand Response-Enabled Energy Sharing

by Tasawar Abbas, Sheng Chen, Xuan Zhang and Ziyan Wang

Processes 2024, 12(7), 1338; https://doi.org/10.3390/pr12071338 - 27 Jun 2024

Viewed by 536

Abstract

The energy hub provides a comprehensive solution uniting energy producers, consumers, and storage systems, thereby optimizing energy utilization efficiency. The single integrated energy system’s limitations restrict renewable absorption and resource allocation, while uncoordinated demand responses create load peaks, and global warming challenges sustainable [...] Read more.

The energy hub provides a comprehensive solution uniting energy producers, consumers, and storage systems, thereby optimizing energy utilization efficiency. The single integrated energy system’s limitations restrict renewable absorption and resource allocation, while uncoordinated demand responses create load peaks, and global warming challenges sustainable multi-energy system operations. Therefore, our work aims to enhance multi-energy flexibility by coordinating various energy hubs within a hydrogen-based integrated system. This study focuses on a cost-effective, ecologically sound, and flexible tertiary hub (producer, prosumer, and consumer) with integrated demand response programs, demonstrating a 17.30% reduction in operation costs and a 13.14% decrease in emissions. Power-to-gas technology enhances coupling efficiency among gas turbines, boilers, heat pumps, and chillers. A mixed-integer nonlinear programming model using a GAMS BARON solver will achieve the optimal results of this study. The proposed model’s simulation results show reduced energy market costs, total emissions, and daily operation expenses. Full article

(This article belongs to the Topic Intelligent, Flexible, and Effective Operation of Smart Grids with Novel Energy Technologies and Equipment)

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