Integration of Distributed Energy Resources in Smart Grids

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 January 2025 | Viewed by 3504

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, Tongji University, Shanghai 200092, China
Interests: robust economic model predictive control; flexible demand response and the design of control strategies for the smart grids

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Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: energy system economics; transportation electrification; artificial intelligence in power systems
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Special Issue Information

Dear Colleagues,

Increased urban electric loads driven by the electrification of the transport sector, cooling/heating technologies, various forms of distributed energy storage, advancements in distributed generations, demand side responses, etc., will fundamentally transform the operational paradigm of future urban energy infrastructure. In particular, in the future, flexibility and resilience will not necessarily be delivered through asset redundancy at the national level, but through the smart control of multi-energy systems at the local district level, by making use of local backup generation, energy storage, demand side response technologies, and the control of local urban energy infrastructure. Distributed energy resources (DERs) will constitute the cornerstone of the future inner-city smart grids, in which the security of supply will be delivered by local resources at the district level. To support such a paradigm shift, the large-scale integration of DERs through intelligent and sophisticated coordinative  actions are required. In this context, it is important to fully understand the interactions between different DERs, find out how to intensify the synergies across different energy vectors, enable them to support one another, and investigate the mechanism based on how different energy vectors can co-support the operation of smart grids. Additionally, since smart control is the core used to arouse the synergies between various DERs, it is significant to explore effective control strategies to realize the optimal dispatch of DERs aimed at enhancing the efficiency of urban energy systems.

Dr. Zihang Dong
Prof. Dr. Yujian Ye
Guest Editors

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Keywords

  • energy system flexibility and resilience enhancement through Distributed Energy Resources (DER)
  • demand side response and energy management of DERs
  • advanced Information and communication technologies supporting the integration of DERs in smart grids
  • prediction algorithms, communication approaches, control strategies and business model in Virtual Power Plant (VPP)
  • generation planning and market design for integrated energy systems
  • multi-energy system integration, covering electricity, gas, heat, cold, transport, information and hydrogen systems, etc.
  • Artificial Intelligence-based approaches for local energy system modelling
  • advanced control strategies for the coordination of numerous/heterogeneous DERs
  • smart scheduling and routing of Electric Vehicles (EV) for improving power system operation
  • modelling of local energy systems, including smart buildings, micro-grids, industrial parks, etc.

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Published Papers (3 papers)

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Research

16 pages, 401 KiB  
Article
Task Offloading in Real-Time Distributed Energy Power Systems
by Ningchao Wu, Xingchuan Bao, Dayang Wang, Song Jiang, Manjun Zhang and Jing Zou
Electronics 2024, 13(14), 2747; https://doi.org/10.3390/electronics13142747 - 12 Jul 2024
Cited by 1 | Viewed by 553
Abstract
The distributed energy power system needs to provide sufficient and flexible computing power on demand to meet the increasing digitization and intelligence requirements of the smart grid. However, the current distribution of the computing power and loads in the energy system is unbalanced, [...] Read more.
The distributed energy power system needs to provide sufficient and flexible computing power on demand to meet the increasing digitization and intelligence requirements of the smart grid. However, the current distribution of the computing power and loads in the energy system is unbalanced, with data center loads continuously increasing, while there is a large amount of idle computing power at the edge. Meanwhile, there are a large number of real-time computing tasks in the distributed energy power system, which have strict requirements on execution deadlines and require reasonable scheduling of multi-level heterogeneous computing power to meet real-time computing demands. Based on the aforementioned background and issues, this paper studies the real-time service scheduling problem in a multi-level heterogeneous computing network of distributed energy power systems. Specifically, we consider the divisibility of tasks in the model. This paper presents a hierarchical real-time task-scheduling framework specifically designed for distributed energy power systems. The framework utilizes an orchestrating agent (OA) as the execution environment for the scheduling module. Building on this, we propose a hierarchical selection algorithm for choosing the appropriate network layer for real-time tasks. Further, we develop two scheduling algorithms based on greedy strategy and genetic algorithm, respectively, to effectively schedule tasks. Experiments show that the proposed algorithms have a superior success rate in scheduling compared to other current algorithms. Full article
(This article belongs to the Special Issue Integration of Distributed Energy Resources in Smart Grids)
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15 pages, 2222 KiB  
Article
Feature Extraction Approach for Distributed Wind Power Generation Based on Power System Flexibility Planning Analysis
by Sile Hu, Jiaqiang Yang, Yuan Wang, Chao Chen, Jianan Nan, Yucan Zhao and Yue Bi
Electronics 2024, 13(5), 966; https://doi.org/10.3390/electronics13050966 - 2 Mar 2024
Cited by 1 | Viewed by 857
Abstract
This study addresses the integral role of typical wind power generation curves in the analysis of power system flexibility planning. A novel method is introduced for extracting these curves, integrating an enhanced K-means clustering algorithm with advanced optimization techniques. The process commences [...] Read more.
This study addresses the integral role of typical wind power generation curves in the analysis of power system flexibility planning. A novel method is introduced for extracting these curves, integrating an enhanced K-means clustering algorithm with advanced optimization techniques. The process commences with thorough data cleaning, filtering, and smoothing. Subsequently, the refined K-means algorithm, augmented by the Pearson correlation coefficient and a greedy algorithm, clusters the wind power curves. The optimal number of clusters is ascertained through the silhouette coefficient. The final stage employs particle swarm and whale optimization algorithms for the extraction of quintessential wind power output curves, essential for flexibility planning in power systems. This methodology is validated through a case study involving wind power output data from a new energy-rich provincial power grid in North China, spanning from 1 January 2019, to 31 December 2022. The resultant curves proficiently mirror wind power fluctuations, thereby laying a foundational framework for power system flexibility planning analysis. Full article
(This article belongs to the Special Issue Integration of Distributed Energy Resources in Smart Grids)
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22 pages, 4372 KiB  
Article
A Multi-Agent Deep-Reinforcement-Learning-Based Strategy for Safe Distributed Energy Resource Scheduling in Energy Hubs
by Xi Zhang, Qiong Wang, Jie Yu, Qinghe Sun, Heng Hu and Ximu Liu
Electronics 2023, 12(23), 4763; https://doi.org/10.3390/electronics12234763 - 24 Nov 2023
Cited by 1 | Viewed by 1375
Abstract
An energy hub (EH) provides an effective solution to the management of local integrated energy systems (IES), supporting the optimal dispatch and mutual conversion of distributed energy resources (DER) in multi-energy forms. However, the intrinsic stochasticity of renewable generation intensifies fluctuations in the [...] Read more.
An energy hub (EH) provides an effective solution to the management of local integrated energy systems (IES), supporting the optimal dispatch and mutual conversion of distributed energy resources (DER) in multi-energy forms. However, the intrinsic stochasticity of renewable generation intensifies fluctuations in the system’s energy production when integrated into large-scale grids and increases peak-to-valley differences in large-scale grid integration, leading to a significant reduction in the stability of the power grid. A distributed privacy-preserving energy scheduling method based on multi-agent deep reinforcement learning is presented for the EH cluster with renewable energy generation. Firstly, each EH is treated as an agent, transforming the energy scheduling problem into a Markov decision process. Secondly, the objective function is defined as minimizing the total economic cost while considering carbon trading costs, guiding the agents to make low-carbon decisions. Lastly, differential privacy protection is applied to sensitive data within the EH, where noise is introduced using energy storage systems to maintain the same gas and electricity purchases while blurring the original data. The experimental simulation results demonstrate that the agents are able to train and learn from environmental information, generating real-time optimized strategies to effectively handle the uncertainty of renewable energy. Furthermore, after the noise injection, the validity of the original data is compromised while ensuring the protection of sensitive information. Full article
(This article belongs to the Special Issue Integration of Distributed Energy Resources in Smart Grids)
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