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Electronics
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Integration of Distributed Energy Resources in Smart Grids
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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: closed (15 January 2025) | Viewed by 6353

Special Issue Editors

Dr. Zihang Dong

E-Mail Website
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
Prof. Dr. Yujian Ye

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: energy system economics; transportation electrification; artificial intelligence in power systems
Special Issues, Collections and Topics in MDPI journals

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 (6 papers)

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Research

21 pages, 6718 KiB  
Article
A Two-Stage Distributionally Robust Optimization Model for Managing Electricity Consumption of Energy-Intensive Enterprises Considering Multiple Uncertainties
by Jiale Li, Zhaobin Du, Liao Yuan, Yuanping Huang and Juan Liu
Electronics 2024, 13(24), 5058; https://doi.org/10.3390/electronics13245058 - 23 Dec 2024
Viewed by 528
Abstract
Energy-intensive enterprises (EIEs), as vital demand-side flexibility resources, can significantly enhance the power system’s ability to regulate demand by participating in demand response (DR). This helps alleviate supply pressures during tight demand–supply conditions, ensuring the system’s safe and stable operation. However, due to [...] Read more.
Energy-intensive enterprises (EIEs), as vital demand-side flexibility resources, can significantly enhance the power system’s ability to regulate demand by participating in demand response (DR). This helps alleviate supply pressures during tight demand–supply conditions, ensuring the system’s safe and stable operation. However, due to the current level of electricity management in EIEs, their participation in demand response has disrupted the continuity of production to some extent, which may hinder the sustainability of demand-side management mechanisms. To address this issue, this paper proposes a two-stage distributionally robust optimization (DRO) model for managing production electricity in EIEs, considering multiple uncertainties. First, a production electricity load model based on the state task network (STN) is developed, reflecting the characteristics of industrial production lines. Next, a two-stage DRO model for day-ahead and intra-day electricity management is formulated, integrating an uncertainty set for distributed generation output based on the Wasserstein distance and probabilistic constraints for the day-ahead DR capacity. Finally, a cement plant in western China is used as a case study to validate the effectiveness of the proposed model. The results show that the proposed model effectively guides EIE in participating in DR while optimizing electricity costs, enabling cost savings of up to 27.7%. Full article
(This article belongs to the Special Issue Integration of Distributed Energy Resources in Smart Grids)
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26 pages, 1474 KiB  
Article
Joint Optimization of Renewable Energy Utilization and Multi-Energy Sharing for Interconnected Microgrids with Carbon Trading
by Jieqi Rong, Weirong Liu, Nvzhi Tang, Fu Jiang, Rui Zhang and Heng Li
Electronics 2024, 13(24), 4995; https://doi.org/10.3390/electronics13244995 - 19 Dec 2024
Viewed by 566
Abstract
Connecting microgrids can promote the sharing of multi-energy sources, reduce carbon emissions, and enhance the consumption of renewable energy. However, the uncertainty of renewable energy and the coupling of multiple energy sources makes energy management difficult in connected microgrids. To address the challenges, [...] Read more.
Connecting microgrids can promote the sharing of multi-energy sources, reduce carbon emissions, and enhance the consumption of renewable energy. However, the uncertainty of renewable energy and the coupling of multiple energy sources makes energy management difficult in connected microgrids. To address the challenges, a dual-layer energy management framework for interconnected microgrids is proposed in this paper. In the bottom layer, a load scheduling problem within one microgrid is formulated to maximize the utilization of renewable energy, which is solved by an improved gray wolf algorithm with fast convergence and effective optimum seeking. In the upper layer, a distributed energy dispatch strategy is proposed to coordinate the energy sources for multiple microgrids to achieve multi-energy sharing with carbon trading. Combining the load scheduling and energy dispatching, the overall energy utilization is improved, and the operation cost and carbon emission are reduced. The simulation results on real-world datasets validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Integration of Distributed Energy Resources in Smart Grids)
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21 pages, 6514 KiB  
Article
Optimal Regulation Strategy of Distribution Network with Photovoltaic-Powered Charging Stations Under Multiple Uncertainties: A Bi-Level Stochastic Optimization Approach
by Nanxing Chen, Zhaobin Du and Wei Du
Electronics 2024, 13(23), 4600; https://doi.org/10.3390/electronics13234600 - 21 Nov 2024
Viewed by 637
Abstract
In order to consider the impact of multiple uncertainties on the interaction between the distribution network operator (DNO) and photovoltaic powered charging stations (PVCSs), this paper proposes a regulation strategy for a distribution network with a PVCS based on bi-level stochastic optimization. First, [...] Read more.
In order to consider the impact of multiple uncertainties on the interaction between the distribution network operator (DNO) and photovoltaic powered charging stations (PVCSs), this paper proposes a regulation strategy for a distribution network with a PVCS based on bi-level stochastic optimization. First, the interaction framework between the DNO and PVCS is established to address the energy management and trading problems of different subjects in the system. Second, considering the uncertainties in the electricity price and PV output, a bi-level stochastic model is constructed with the DNO and PVCS targeting their respective interests. Furthermore, the conditional value-at-risk (CVaR) is introduced to measure the relationship between the DNO’s operational strategy and the uncertain risks. Next, the Karush–Kuhn–Tucker (KKT) conditions and duality theorem are utilized to tackle the challenging bi-level problem, resulting in a mixed-integer second-order cone programming (MISCOP) model. Finally, the effectiveness of the proposed regulation strategy is validated on the modified IEEE 33-bus system. Full article
(This article belongs to the Special Issue Integration of Distributed Energy Resources in Smart Grids)
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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 793
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 1087
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 1718
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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