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Steady-State Operation, Disturbed Operation and Protection of Power Networks

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (20 June 2020) | Viewed by 14048

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Prof. Dr. François Vallée

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Power Electrical Engineering Unit, University of Mons, Boulevard Dolez 31 7000 Mons, Belgium
Interests: energy saving; water and wastewater minimisation; optimisation of energy supply networks; waste to energy; integration of renewable energy sources; systems modelling; process synthesis; process operation
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Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to a Special Issue of Energies on “Steady-State Operation, Disturbed Operation and Protection of Power Networks”. With the ongoing energy transition, Distributed Energy Resources (DERs) and new loads (e.g., electric vehicles, EVs) are emerging in modern power systems, which is highly impacting the operation of the latter. Indeed, in addition to the increased uncertainty in power system management, DERs (as well as EVs) can significantly affect the power quality level (by harmonics, unbalance, etc.) and contribute in multiple manners (depending on the interface) to the fault currents. Many algorithms and tools have been developed over the last years to ensure safe operation of the system while fostering the integration of renewable energy-based generation. Moreover, the current advances in Artificial Intelligence (AI, e.g., deep learning) and the actual computation resources offer new prospects for related research.

This Special Issue will deal with novel optimization, forecasting, and computation techniques that improve the operation and protection of modern power systems. Topics of interest for publication include, but are not limited to the following:

Optimization of operation of power systems;
Impact of DERs and storage devices on power system operation;
Application of AI techniques for an improved power system operation;
Recent developments in protective equipment;
New improved coordination schemes among protective devices;
Power quality matters in modern power systems;
Control methods of power electronics;
New forecasting methods;
Integration and impact of electric vehicles

Prof. Dr. François Vallée
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

distributed energy resources
power quality
smart grid
storage
forecast
electric vehicles
power electronics
protection schemes & devices

Published Papers (5 papers)

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Research

17 pages, 1588 KiB

Open AccessArticle

Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities

by Zacharie De Grève, Jérémie Bottieau, David Vangulick, Aurélien Wautier, Pierre-David Dapoz, Adriano Arrigo, Jean-François Toubeau and François Vallée

Energies 2020, 13(18), 4892; https://doi.org/10.3390/en13184892 - 18 Sep 2020

Cited by 23 | Viewed by 2859

Abstract

Renewable Energy Communities consist in an emerging decentralized market mechanism which allows local energy exchanges between end-users, bypassing the traditional wholesale/retail market structure. In that configuration, local consumers and prosumers gather in communities and can either cooperate or compete towards a common objective, such as the minimization of the electricity costs and/or the minimization of greenhouse gas emissions for instance. This paper proposes data analytics modules which aim at helping the community members to schedule the usage of their resources (generation and consumption) in order to minimize their electricity bill. A day-ahead local wind power forecasting algorithm, which relies on state-of-the-art Machine Learning techniques currently used in worldwide forecasting contests, is in that way proposed. We develop furthermore an original method to improve the performance of neural network forecasting models in presence of abnormal wind power data. A technique for computing representative profiles of the community members electricity consumption is also presented. The proposed techniques are tested and deployed operationally on a pilot Renewable Energy Community established on an Medium Voltage network in Belgium, involving

2.25

M

W

of wind and 18 Small and Medium Enterprises who had the possibility to freely access the results of the developed data modules by connecting to a dedicated web platform. We first show that our method for dealing with abnormal wind power data improves the forecasting accuracy by 10% in terms of Root Mean Square Error. The impact of the developed data modules on the consumption behaviour of the community members is then quantified, by analyzing the evolution of their monthly self-consumption and self-sufficiency during the pilot. No significant changes in the members behaviour, in relation with the information provided by the models, were observed in the recorded data. The pilot was however perturbed by the COVID-19 crisis which had a significant impact on the economic activity of the involved companies. We conclude by providing recommendations for the future set up of similar communities. Full article

(This article belongs to the Special Issue Steady-State Operation, Disturbed Operation and Protection of Power Networks)

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15 pages, 1336 KiB

Open AccessFeature PaperArticle

Deep Reinforcement Learning-Based Voltage Control to Deal with Model Uncertainties in Distribution Networks

by Jean-François Toubeau, Bashir Bakhshideh Zad, Martin Hupez, Zacharie De Grève and François Vallée

Energies 2020, 13(15), 3928; https://doi.org/10.3390/en13153928 - 1 Aug 2020

Cited by 21 | Viewed by 3333

Abstract

This paper addresses the voltage control problem in medium-voltage distribution networks. The objective is to cost-efficiently maintain the voltage profile within a safe range, in presence of uncertainties in both the future working conditions, as well as the physical parameters of the system. Indeed, the voltage profile depends not only on the fluctuating renewable-based power generation and load demand, but also on the physical parameters of the system components. In reality, the characteristics of loads, lines and transformers are subject to complex and dynamic dependencies, which are difficult to model. In such a context, the quality of the control strategy depends on the accuracy of the power flow representation, which requires to capture the non-linear behavior of the power network. Relying on the detailed analytical models (which are still subject to uncertainties) introduces a high computational power that does not comply with the real-time constraint of the voltage control task. To address this issue, while avoiding arbitrary modeling approximations, we leverage a deep reinforcement learning model to ensure an autonomous grid operational control. Outcomes show that the proposed model-free approach offers a promising alternative to find a compromise between calculation time, conservativeness and economic performance. Full article

(This article belongs to the Special Issue Steady-State Operation, Disturbed Operation and Protection of Power Networks)

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12 pages, 1086 KiB

Open AccessArticle

Impact of Lossy Compression Techniques on the Impedance Determination

by Maik Plenz, Marc Florian Meyer, Florian Grumm, Daniel Becker, Detlef Schulz and Malcom McCulloch

Energies 2020, 13(14), 3661; https://doi.org/10.3390/en13143661 - 16 Jul 2020

Cited by 2 | Viewed by 1585

Abstract

One of the essential parameters to measure the stability and power-quality of an energy grid is the network impedance. Including distinct resonances which may also vary over time due to changing load or generation conditions in a network, the frequency characteristic of the impedance is an import part to analyse. The determination and analysis of the impedance go hand in hand with a massive amount of data output. The reduction of this high-resolution voltage and current datasets, while maintaining the fidelity of important information, is the main focus of this paper. The presented approach takes measured impedance datasets and a set of lossy compression procedures, to monitor the performance success with known key metrics. Afterwards, it continually compares the results of various lossy compression techniques. The innovative contribution is the combination of new and existing procedures as well as metrics in one approach, to reduce the size of the impedance datasets for the first time. The approach needs to be efficient, suitable, and exact, otherwise the decompression results are useless. Full article

(This article belongs to the Special Issue Steady-State Operation, Disturbed Operation and Protection of Power Networks)

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Graphical abstract

21 pages, 4346 KiB

Open AccessArticle

Security Assessment and Coordinated Emergency Control Strategy for Power Systems with Multi-Infeed HVDCs

by Qiufang Zhang, Zheng Shi, Ying Wang, Jinghan He, Yin Xu and Meng Li

Energies 2020, 13(12), 3174; https://doi.org/10.3390/en13123174 - 19 Jun 2020

Cited by 2 | Viewed by 2048

Abstract

Short-circuit faults in a receiving-end power system can lead to blocking events of the feed-in high-voltage direct-current (HVDC) systems, which may further result in system instability. However, security assessment methods based on the transient stability (TS) simulation can hardly catch the fault propagation phenomena between AC and DC subsystems. Moreover, effective emergency control strategies are needed to prevent such undesired cascading events. This paper focuses on power systems with multi-infeed HVDCs. An on-line security assessment method based on the electromagnetic transient (EMT)-TS hybrid simulation is proposed. DC and AC subsystems are modeled in EMTDC/PSCAD and PSS/E, respectively. In this way, interactions between AC and DC subsystems can be well reflected. Meanwhile, high computational efficiency is maintained for the on-line application. In addition, an emergency control strategy is developed, which coordinates multiple control resources, including HVDCs, pumped storages, and interruptible loads, to maintain the security and stability of the receiving-end system. The effectiveness of the proposed methods is verified by numerical simulations on two actual power systems in China. The simulation results indicate that the EMT-TS hybrid simulation can accurately reflect the fault propagation phenomena between AC and DC subsystems, and the coordinated emergency control strategy can work effectively to maintain the security and stability of systems. Full article

(This article belongs to the Special Issue Steady-State Operation, Disturbed Operation and Protection of Power Networks)

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17 pages, 6383 KiB

Open AccessArticle

Peak Shaving through Battery Storage for Low-Voltage Enterprises with Peak Demand Pricing

by Vasileios Papadopoulos, Jos Knockaert, Chris Develder and Jan Desmet

Energies 2020, 13(5), 1183; https://doi.org/10.3390/en13051183 - 5 Mar 2020

Cited by 11 | Viewed by 3736

Abstract

The renewable energy transition has introduced new electricity tariff structures. With the increased penetration of photovoltaic and wind power systems, users are being charged more for their peak demand. Consequently, peak shaving has gained attention in recent years. In this paper, we investigated the potential of peak shaving through battery storage. The analyzed system comprises a battery, a load and the grid but no renewable energy sources. The study is based on 40 load profiles of low-voltage users, located in Belgium, for the period 1 January 2014, 00:00–31 December 2016, 23:45, at 15 min resolution, with peak demand pricing. For each user, we studied the peak load reduction achievable by batteries of varying energy capacities (kWh), ranging from 0.1 to 10 times the mean power (kW). The results show that for 75% of the users, the peak reduction stays below 44% when the battery capacity is 10 times the mean power. Furthermore, for 75% of the users the battery remains idle for at least 80% of the time; consequently, the battery could possibly provide other services as well if the peak occurrence is sufficiently predictable. From an economic perspective, peak shaving looks interesting for capacity invoiced end users in Belgium, under the current battery capex and electricity prices (without Time-of-Use (ToU) dependency). Full article

(This article belongs to the Special Issue Steady-State Operation, Disturbed Operation and Protection of Power Networks)

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