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Analysis of Electricity Distribution Network and Electricity Markets

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (21 April 2023) | Viewed by 4380

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Special Issue Editors

Dr. Jiajia Yang

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Guest Editor

School of Electrical Engineering and Telecommunications, Faculty of Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: power system operation and optimization; electricity markets; demand response schemes; DER; and renewable generation integration
Special Issues, Collections and Topics in MDPI journals

Dr. Yunqi Wang

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Guest Editor

Department of Data Science and AI, Monash University, Melbourne, VIC 3800, Australia
Interests: smart grids; power systems optimisation; energy and low-carbon management; renewable and sustainable systems; electric vehicles
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Dr. Zhengmao Li

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Guest Editor

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: optimal planning and operation of multi-energy systems; resilience in multi-energy systems; uncertainties handling in multi-energy systems; stochastic/robust optimization; multi-energy ship; optimization methods
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Special Issue Information

Dear Colleagues,

Electricity networks have been undergoing a radical transition from mono-directional to bi-directional systems with the development of new technologies and structures. Meanwhile, the increasing penetration of distributed energy resources (DERs)—such as rooftop photovoltaic (PV) systems and diesel generators—into distribution networks has significantly changed the energy trading pattern in traditional electricity markets. Through effective energy trading, all participants in distribution networks—including DERs, prosumers, and virtual power plants—can obtain much higher flexibility, reliability, and energy utilization efficiency. Moreover, these stakeholders could also contribute to increasing social welfare in wholesale electricity markets. In this regard, it is urgent and worthwhile to conduct a comprehensive analysis of the new electricity distribution networks and markets for higher operation flexibility and social welfare. However, there are huge challenges to designing efficient and appropriate market structures with increasing penetration levels of DERs, necessitating innovative ideas and research in this promising direction. This Special Issue invites a wide range of authors from academic, industrial, policy-making and consultancy backgrounds to present and develop analyses, solutions, and technologies in the field of electricity distribution networks and electricity markets.

This Special Issue solicits original and novel research papers addressing the following topics (this list, however, is by no means exhaustive):

Planning and operation of electricity distribution networks with a high penetration of diverse DERs;
Customized demand-side management and smart home management to enhance the flexibility of the electricity distribution network;
Novel market architecture and mechanisms for local and distribution-level electricity markets and relevant coordination with wholesale markets;
Market participation of prosumers, DERs, and virtual power plants at electricity markets;
Market clearing and pricing in different-level electricity markets;
Optimization strategies for electricity market integration of active electricity distribution networks;
Decentralized peer-to-peer energy and service trading and distributed ledger technology;
Advanced information and computing technologies and data analytics to support the implementation of DERs, and different-level electricity markets.

Dr. Jiajia Yang
Dr. Yunqi Wang
Dr. Zhengmao Li
Guest Editors

Manuscript Submission Information

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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

distribution network
electricity market
distributed energy resource
decentralization electricity market
virtual power plant
ancillary service

Published Papers (3 papers)

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Research

12 pages, 2791 KiB

Open AccessArticle

Topology Identification of Low-Voltage Distribution Network Based on Deep Convolutional Time-Series Clustering

by Qingzhong Ni and Hui Jiang

Energies 2023, 16(11), 4274; https://doi.org/10.3390/en16114274 - 23 May 2023

Cited by 4 | Viewed by 1213

Abstract

Accurate topology relationships of low-voltage distribution networks are important for distribution network management. However, the topological information in Geographic Information System (GIS) systems for low-voltage distribution networks is prone to errors such as omissions and false alarms, which can have a heavy impact on the effective management of the networks. In this study, a novel method for the identification of topology relationships, including the user-transformer relationship and the user-phase relationship, is proposed, which is based on Deep Convolutional Time-Series Clustering (DCTC) analysis. The proposed DCTC method fuses convolutional autoencoder and clustering layers to perform voltage feature representation and clustering in a low-dimensional feature space simultaneously. By jointly optimizing the clustering process via minimizing the sum of the reconstruction loss and clustering loss, the proposed method effectively identifies the network topology relationships. Analysis of examples shows that the proposed method is correct and effective. Full article

(This article belongs to the Special Issue Analysis of Electricity Distribution Network and Electricity Markets)

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12 pages, 2221 KiB

Open AccessArticle

Power Quality Disturbance Classification Based on Parallel Fusion of CNN and GRU

by Jiajun Cai, Kai Zhang and Hui Jiang

Energies 2023, 16(10), 4029; https://doi.org/10.3390/en16104029 - 11 May 2023

Cited by 2 | Viewed by 1079

Abstract

Effective identification of complex power quality disturbances (PQDs) is the premise and key to improving power quality issues in the current complex power grid environment. However, with the increasing application of solid-state switches, nonlinear devices, and multi-energy system generation, the power grid disturbance signals are distorted and complicated. This increases the difficulty of PQDs identification. To address this issue, this paper presents a novel method for power quality disturbance classification using a convolutional neural network (CNN) and gated recurrent unit (GRU). The CNN consists of convolutional blocks, some of which come with a squeeze-and-excitation block (SE), and is used to extract the short-term features from PQDs, where the convolutional block is used to capture the spatial information from PQDs and the SE is used to enhance the feature extraction capability of the convolutional neural network. The GRU network is designed to capture the long-term feature from PQDs, and an attention mechanism connected to GRU’s hidden states at different times is proposed to improve the GRU’s feature capture ability in long-term sequences. The CNN and GRU are parallelly arranged to perceive the same PQDs in two different views, and the feature information extracted from them is fused and transmitted to the Softmax activation layer for classification. Based on MATLAB-Simulink, a typical multi-energy-source system is constructed to analyze PQDs, and twelve PQDs are simulated to validate the proposed method. The simulation results show that the proposed method has higher classification accuracy in both single and hybrid disturbances and significant advantages in noise immunity. Full article

(This article belongs to the Special Issue Analysis of Electricity Distribution Network and Electricity Markets)

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20 pages, 9213 KiB

Open AccessArticle

Optimal Ultra-Local Model Control Integrated with Load Frequency Control of Renewable Energy Sources Based Microgrids

by Abualkasim Bakeer, Gaber Magdy, Andrii Chub, Francisco Jurado and Mahmoud Rihan

Energies 2022, 15(23), 9177; https://doi.org/10.3390/en15239177 - 3 Dec 2022

Cited by 11 | Viewed by 1333

Abstract

Since renewable energy sources (RESs) have an intermittent nature, conventional secondary frequency control, i.e., load frequency control (LFC), cannot mitigate the effects of variations in system frequency. Thus, this paper proposes incorporating ultralocal model (ULM) control into LFC to enhance microgrid (µG) frequency stability. ULM controllers are regarded as model-free controllers that yield high rejection rates for disturbances caused by load/RES uncertainties. Typically, ULM parameters are set using trial-and-error methods, which makes it difficult to determine the optimal values that will provide the best system performance and stability. To address this issue, the African vultures optimization algorithm (AVOA) was applied to fine-tune the ULM parameters, thereby stabilizing the system frequency despite different disturbances. The proposed LFC controller was compared with the traditional secondary controller based on an integral controller to prove its superior performance. For several contingencies, the simulation results demonstrated that the proposed controller based on the optimal ULM coupled with LFC could significantly promote RESs into the µG. Full article

(This article belongs to the Special Issue Analysis of Electricity Distribution Network and Electricity Markets)

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