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Energies
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Planning and Operation of Integrated Renewable Energy Distribution System
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Planning and Operation of Integrated Renewable Energy Distribution System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (25 February 2025) | Viewed by 6533

Special Issue Editors

Dr. Fengzhang Luo

E-Mail Website
Guest Editor
Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Interests: distribution system analysis; evaluation and optimization planning; low-carbon distribution system and intelligent distribution system; integrated energy distribution system planning and operation
Dr. Guoqiang Zu

E-Mail Website
Guest Editor
State Grid Tianjin Electric Power Research Institute, Tianjin, China
Interests: distribution system planning, analysis and operation; AC-DC hybrid distribution system; vehicle to grid (V2G)
Dr. Tianyu Zhang

E-Mail Website
Guest Editor
State Grid Tianjin Economic and Technology Research Institute, Tianjin, China
Interests: distribution system planning; analysis and operation; AC-DC hybrid distribution system; electric vehicles to grid

Special Issue Information

Dear Colleagues,

The increasing penetration of intermittent and stochastic renewable energy brings great challenges for distribution networks. This Special Issue aims to present the most recent advances related to the current research and future development in integrated renewable energy distribution system: planning and operation.

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

  • Form and development trend of future distribution system;
  • Integrated renewable energy distribution system planning;
  • Integrated renewable energy distribution system operation;
  • Reliability and resilience assessment;
  • Optimization;
  • Digital twins;
  • EV orderly charging and V2G;
  • Load-demand response;
  • Renewable energy consumption;
  • DC distribution system technology;
  • Power electronics applications

Dr. Fengzhang Luo
Dr. Guoqiang Zu
Dr. Tianyu Zhang
Guest Editors

Manuscript Submission Information

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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

  • integrated renewable energy distribution system planning
  • reliability and resilience assessment
  • integrated renewable energy distribution system operation
  • optimization
  • digital twins
  • EV orderly charging and V2G
  • load demand response
  • virtual power plant
  • renewable energy consumption
  • DC distribution system technology
  • power electronics applications

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Published Papers (6 papers)

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Research

22 pages, 2522 KiB  
Article
Distributed Risk-Averse Optimization Scheduling of Hybrid Energy System with Complementary Renewable Energy Generation
by Yanbo Jia, Bingqing Xia, Zhaohui Shi, Wei Chen and Lei Zhang
Energies 2025, 18(6), 1405; https://doi.org/10.3390/en18061405 - 12 Mar 2025
Viewed by 71
Abstract
Large-scale penetration of renewable energy generation brings various challenges to the power system in terms of safety, reliability, economy and flexibility. The development of large-scale, high-security energy-storage technology can effectively address these challenges and improve the capabilities of power systems in power-supply guarantee [...] Read more.
Large-scale penetration of renewable energy generation brings various challenges to the power system in terms of safety, reliability, economy and flexibility. The development of large-scale, high-security energy-storage technology can effectively address these challenges and improve the capabilities of power systems in power-supply guarantee and flexible adjustment. This paper proposes a novel distributed risk-averse optimization scheduling model of a hybrid wind–solar–storage system based on the adjustability of the storage system and the complementarity of renewable energy generation. The correlation of wind power and photovoltaic generation is quantified based on a Copula function. A risk-averse operation optimization model is proposed using conditional value at risk to quantify the uncertainty of renewable energy generation. A linear formulation of conditional value at risk under typical scenarios is developed by Gibbs sampling the joint distribution and Fuzzy C-Means clustering algorithm. A distributed solution algorithm based on an alternating-direction method of multipliers is developed to derive the optimal scheduling of hybrid wind–solar–storage system in a distributed manner. Numerical case studies based on IEEE 34-bus distribution network verify the effectiveness of the proposed model in reducing the uncertainty impact of renewable energy generation on an upstream grid (the overall amount of renewable energy generation sent back to the upstream grid has decreased about 80.6%) and ensuring the operational security of hybrid wind–solar–storage system (overall voltage deviation within 5.6%). Full article
(This article belongs to the Special Issue Planning and Operation of Integrated Renewable Energy Distribution System)
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20 pages, 3748 KiB  
Article
Micro-Energy Grid Energy Utilization Optimization with Electricity and Heat Storage Devices Based on NSGA-III Algorithm
by Junchao Yang and Li Li
Energies 2024, 17(22), 5563; https://doi.org/10.3390/en17225563 - 7 Nov 2024
Viewed by 792
Abstract
With the implementation of policies to promote renewable energy generation on the supply side, a micro-energy grid, which is composed of different electricity generation categories such as wind power plants (WPPs), photovoltaic power generators (PVs), and energy storage devices, can enable the local [...] Read more.
With the implementation of policies to promote renewable energy generation on the supply side, a micro-energy grid, which is composed of different electricity generation categories such as wind power plants (WPPs), photovoltaic power generators (PVs), and energy storage devices, can enable the local consumption of renewable energy. Energy storage devices, which can overcome the challenges of an instantaneous balance of electricity on the supply and demand sides, play an especially key role in making full use of generated renewable energy. Considering both minimizing the operation costs and maximizing the renewable energy usage ratio is important in the micro-energy grid environment. This study built a multi-objective optimization model and used the NSGA-III algorithm to obtain a Pareto solution set. According to a case study and a comparative analysis, NSGA-III was better than NSGA-II at solving the problem, and the results showed that a higher renewable generation ratio means there is less electricity generated by traditional electricity generators like gas turbines, and there is less electricity sold into the electricity market to obtain more benefits; therefore, the cost of the system will increase. Energy storage devices can significantly improve the efficiency of renewable energy usage in micro-energy grids. Full article
(This article belongs to the Special Issue Planning and Operation of Integrated Renewable Energy Distribution System)
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18 pages, 8897 KiB  
Article
Flexibility-Oriented AC/DC Hybrid Grid Optimization Using Distributionally Robust Chance-Constrained Method
by Yue Chen, Qiuyu Lu, Kaiyue Zeng, Yinguo Yang and Pingping Xie
Energies 2024, 17(19), 4902; https://doi.org/10.3390/en17194902 - 30 Sep 2024
Viewed by 765
Abstract
With the increasing integration of stochastic sources and loads, ensuring the flexibility of AC/DC hybrid distribution networks has become a pressing challenge. This paper aims to enhance the operational flexibility of AC/DC hybrid distribution networks by proposing a flexibility-oriented optimization framework that addresses [...] Read more.
With the increasing integration of stochastic sources and loads, ensuring the flexibility of AC/DC hybrid distribution networks has become a pressing challenge. This paper aims to enhance the operational flexibility of AC/DC hybrid distribution networks by proposing a flexibility-oriented optimization framework that addresses the growing uncertainties. Notably, a comprehensive evaluation method for operational flexibility assessment is first established. Based on this, this paper further proposes a flexibility-oriented operation optimization model using the distributionally robust chance-constrained (DRCC) method. A customized solution method utilizing second-order cone relaxation and sample average approximation (SAA) is also introduced. The results of case studies indicate that the flexibility of AC/DC hybrid distribution networks is enhanced through sharing energy storage among multiple feeders, adaptive reactive power regulation using soft open points (SOPs) and static var compensators (SVCs), and power transfer between feeders via SOPs. Full article
(This article belongs to the Special Issue Planning and Operation of Integrated Renewable Energy Distribution System)
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22 pages, 2748 KiB  
Article
Active Distribution Network Expansion Planning Based on Wasserstein Distance and Dual Relaxation
by Jianchu Liu, Xinghang Weng, Mingyang Bao, Shaohan Lu and Changhao He
Energies 2024, 17(12), 3005; https://doi.org/10.3390/en17123005 - 18 Jun 2024
Cited by 3 | Viewed by 752
Abstract
In the future, a high proportion of distributed generations (DG) will be integrated into the distribution network. The existing active distribution network (ADN) planning methods have not fully considered multiple uncertainties, differentiated regulation modes or the cost of multiple types of interconnection switches. [...] Read more.
In the future, a high proportion of distributed generations (DG) will be integrated into the distribution network. The existing active distribution network (ADN) planning methods have not fully considered multiple uncertainties, differentiated regulation modes or the cost of multiple types of interconnection switches. Meanwhile, it is difficult to solve large-scale problems at small granularity. Therefore, an expansion planning method of ADN considering the selection of multiple types of interconnection switches is proposed. Firstly, a probability distribution ambiguity set of DG output and electrical-load consumption based on the Wasserstein distance is established for dealing with the issue of source-load uncertainty. Secondly, a distributionally robust optimization model for collaborative planning of distribution network lines and multiple types of switches based on the previously mentioned ambiguity set is established. Then, the original model is transformed into a mixed integer second-order cone programming (SOCP) model by using the convex relaxation method, the Lagrangian duality method and the McCormick relaxation method. Finally, the effectiveness of the proposed method is systematically verified using the example of Portugal 54. The results indicate that the proposed method raises the annual net profit by nearly 5% compared with the traditional planning scheme and improves the reliability and low-carbon nature of the planning scheme. Full article
(This article belongs to the Special Issue Planning and Operation of Integrated Renewable Energy Distribution System)
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19 pages, 1918 KiB  
Article
Power Supply Reliability Analysis of Distribution Systems Considering Data Transmission Quality of Distribution Automation Terminals
by Fengzhang Luo, Nan Ge and Jing Xu
Energies 2023, 16(23), 7826; https://doi.org/10.3390/en16237826 - 28 Nov 2023
Cited by 4 | Viewed by 1389
Abstract
A distribution automation system is the integration of physical power distribution systems and information systems. Its information system guarantees the safe operation and reliable power supply of physical systems by monitoring, collecting and transmitting information. In the information system, the remote terminal unit [...] Read more.
A distribution automation system is the integration of physical power distribution systems and information systems. Its information system guarantees the safe operation and reliable power supply of physical systems by monitoring, collecting and transmitting information. In the information system, the remote terminal unit of distribution automation is the hub of the information system, connecting it to the physical power system. Considering the unreliability of terminal information transmission in the information system, this paper aims to build a model to quantitatively evaluate the impact of unreliable transmission information on the power supply reliability of distribution systems. Firstly, the m-segment and n-connection unit model of distribution feeders is established, and then, the power supply reliability indices in the process of handling feeder terminal unit error are analyzed and calculated under the configuration modes of “three-remote” and “two-remote” of remote terminals. Then, considering the impact of a transmission error in the information system, the reliability index calibration model under the condition of unreliable information transmission is established. Finally, a case study is presented to illustrate how the proposed model is implemented. Full article
(This article belongs to the Special Issue Planning and Operation of Integrated Renewable Energy Distribution System)
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23 pages, 3157 KiB  
Article
Implementation of Deep Learning-Based Bi-Directional DC-DC Converter for V2V and V2G Applications—An Experimental Investigation
by Mohan Krishna Banda, Sreedhar Madichetty and Shanthi Kumar Nandavaram Banda
Energies 2023, 16(22), 7614; https://doi.org/10.3390/en16227614 - 16 Nov 2023
Cited by 1 | Viewed by 1439
Abstract
Growth in renewable energy systems, direct current (DC) microgrids, and the adoption of electric vehicles (EVs) will substantially increase the demand for bi-directional converters. Precise control mechanisms are essential to ensure optimal performance and better efficiency of these converters. This paper proposes a [...] Read more.
Growth in renewable energy systems, direct current (DC) microgrids, and the adoption of electric vehicles (EVs) will substantially increase the demand for bi-directional converters. Precise control mechanisms are essential to ensure optimal performance and better efficiency of these converters. This paper proposes a deep neural network (DNN)-based controller designed to precisely control bi-directional converters for vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) applications. This control technique allows the converter to quickly attain new reference values, enhancing performance and efficiency by significantly reducing the overshoot duration. To train the DNN controller, large synthetic data are used by performing simulations for various sets of conditions, and the results are validated with a hardware setup. The real-time performance of the DNN controller is compared with a conventional proportional–integral (PI)-based controller through simulated results using MATLAB Simulink (version 2023a) and with a real-time setup. The converter attains a new reference of about 975 μs with the proposed control technique. In contrast, the PI controller takes about 220 ms, which shows that the proposed control technique is far better than the PI controller. Full article
(This article belongs to the Special Issue Planning and Operation of Integrated Renewable Energy Distribution System)
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