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Machine Learning and Optimization with Applications of Power System III

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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 (31 October 2023) | Viewed by 19571

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Prof. Dr. Hongseok Kim

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Guest Editor

Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea
Interests: power system with optimal power flow; energy storage; machine learning for energy big data and forecasting; energy trading; microgrids
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Special Issue Information

Dear Colleagues,

This Special Issue is focused on machine learning and optimization techniques that can be applied for power system operation, such as energy data analytics, time series energy forecasting, renewable energy markets, energy storage systems (ESS), microgrids, and distribution networks. Modern power systems face new challenges due to the high penetration of renewable generation, and thus, prediction and control are essential for grid reliability. Thanks to the massively deployed energy IoT sensors and energy big data, machine learning, including deep learning, is being actively applied to predict renewable generation and electric loads. The accurate forecasting of PV and wind power is also of prime importance for strategic bidding in renewable energy markets. Deep learning techniques including recurrent neural networks (RNN), long short-term memory (LSTM), and convolution neural networks (CNN) are expected to improve the prediction accuracy of time series energy data.

Nevertheless, forecasting errors are unavoidable, and mitigating the variability of the grid requires other techniques. Indeed, ESS plays a key role in controlling the grid under volatile generation and loads and is widely deployed for peak cut frequency regulation, bidding in renewable energy markets, demand response, etc. Multiple small-scale ESS units can also be aggregated and collectively controlled as one virtual unit. Finally, it is desirable to optimally operate distribution networks and/or microgrids with the aforementioned distributed energy resources; optimal power flow possibly combined with peer-to-peer energy trading is also of great interest.

In this Special Issue, new theoretical and/or practical research results using machine learning and optimization techniques with the application of power systems are solicited. Pilot programs and field tests considering regional requirements are also welcome. The preferred topics include but are not limited to:

Energy data analytics and forecasting;

Deep learning (RNN, LSTM, CNN, etc.) for load and renewable generation prediction;

Deep reinforcement learning for stochastic control;

ESS operation considering uncertainty, frequency regulation, demand response, and/or battery degradation;

Demand response;

Energy bidding and game theory in renewable energy markets;

Pilot programs and field tests;

Microgrid optimization and simulator development;

Optimal power flow in distribution networks;

Virtual power plants.

Prof. Dr. Hongseok Kim
Guest Editor

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Keywords

Energy data analytics and forecasting
Deep learning (RNN, LSTM, CNN, etc.) for load and renewable generation prediction
Deep reinforcement learning for stochastic control
ESS operation considering uncertainty, frequency regulation, demand response, and/or battery degradation
Demand response
Energy bidding and game theory in renewable energy markets
Pilot programs and field tests
Microgrid optimization and simulator development
Optimal power flow in distribution networks
Virtual power plants

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Related Special Issues

Machine Learning and Optimization with Applications of Power System in Energies (10 articles)
Machine Learning and Optimization with Applications of Power System II in Energies (11 articles)

Published Papers (8 papers)

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Research

23 pages, 591 KiB

Open AccessArticle

Optimal Power Flow Management for a Solar PV-Powered Soldier-Level Pico-Grid

by Tawanda Kunatsa, Herman C. Myburgh and Allan De Freitas

Energies 2024, 17(2), 459; https://doi.org/10.3390/en17020459 - 17 Jan 2024

Cited by 2 | Viewed by 1230

Abstract

Users ought to decide how to operate and manage power systems in order to achieve various goals. As a result, many strategies have been developed to aid in this regard. Optimal power flow management is one such strategy that assists users in properly operating and managing the supply and demand of power in an optimal way under specified constraints. However, in-depth research on optimal power flow management is yet to be explored when it comes to the supply and demand of power for the bulk of standalone renewable energy systems such as solar photovoltaics, especially when it comes to specific applications such as powering military soldier-level portable electronic devices. This paper presents an optimal power flow management modelling and optimisation approach for solar-powered soldier-level portable electronic devices. The OPTI toolbox in MATLAB is used to solve the formulated nonlinear optimal power flow management problem using SCIP as the solver. A globally optimal solution was arrived at in a case study in which the objective function was to minimise the difference between the power supplied to the portable electronic device electronics and the respective portable electronic device power demands. This ensured that the demand for solar-powered soldier-level portable electronic devices is met at all times in spite of the prohibitive case scenarios’ circumstances under the given constraints. This resolute approach underscores the importance placed on satisfying the demand needs of the specific devices while navigating and addressing the limitations posed by the existing conditions or constraints. Soldiers and the solar photovoltaic user fraternity at large will benefit from this work as they will be guided on how to optimally manage their power systems’ supply and demand scenarios. The model developed herein is applicable to any demand profile and any number of portable electronic device and is adaptable to any geographical location receiving any amount of solar radiation. Full article

(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System III)

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18 pages, 1137 KiB

Open AccessArticle

Denoising Masked Autoencoder-Based Missing Imputation within Constrained Environments for Electric Load Data

by Jaeik Jeong, Tai-Yeon Ku and Wan-Ki Park

Energies 2023, 16(24), 7933; https://doi.org/10.3390/en16247933 - 6 Dec 2023

Viewed by 1307

Abstract

With recent advancements in data technologies, particularly machine learning, research focusing on the enhancement of energy efficiency in residential, commercial, and industrial settings through the collection of load data, such as heat, electricity, and gas, has gained significant attention. Nevertheless, issues arising from hardware- or network-related problems can result in missing data, necessitating the development of management techniques to mitigate these challenges. Traditional methods for missing imputation face difficulties when operating in constrained environments characterized by short data collection periods and frequent consecutive missing. In this paper, we introduce the denoising masked autoencoder (DMAE) model as a solution to improve the handling of missing data, even in such restrictive settings. The proposed DMAE model capitalizes on the advantages of the denoising autoencoder (DAE), enabling effective learning of the missing imputation process, even with relatively small datasets, and the masked autoencoder (MAE), allowing for learning in environments with a high missing ratio. By integrating these strengths, the DMAE model achieves an enhanced performance in terms of missing imputation. The simulation results demonstrate that the proposed DMAE model outperforms the DAE or MAE significantly in a constrained environment where the duration of the training data is short, less than a year, and missing values occur frequently with durations ranging from 3 h to 12 h. Full article

(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System III)

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17 pages, 1580 KiB

Open AccessArticle

Multidimensional Feature-Based Graph Attention Networks and Dynamic Learning for Electricity Load Forecasting

by Chaokai Huang, Ning Du, Jiahan He, Na Li, Yifan Feng and Weihong Cai

Energies 2023, 16(18), 6443; https://doi.org/10.3390/en16186443 - 6 Sep 2023

Cited by 1 | Viewed by 1045

Abstract

Electricity load forecasting is of great significance for the overall operation of the power system and the orderly use of electricity at a later stage. However, traditional load forecasting does not consider the change in load quantity at each time point, while the information on the time difference of the load data can reflect the dynamic evolution information of the load data, which is a very important factor for load forecasting. In addition, the research topics in recent years mainly focus on the learning of the complex relationships of load sequences in time latitude by graph neural networks. The relationships between different variables of load sequences are not explicitly captured. In this paper, we propose a model that combines a differential learning network and a multidimensional feature graph attention layer, it can model the time dependence and dynamic evolution of load sequences by learning the amount of load variation at different time points, while representing the correlation of different variable features of load sequences through the graph attention layer. Comparative experiments show that the prediction errors of the proposed model have decreased by 5–26% compared to other advanced methods in the UC Irvine Machine Learning Repository Electricity Load Chart public dataset. Full article

(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System III)

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20 pages, 6649 KiB

Open AccessArticle

Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants

by Taeseop Park, Keunju Song, Jaeik Jeong and Hongseok Kim

Energies 2023, 16(14), 5293; https://doi.org/10.3390/en16145293 - 11 Jul 2023

Cited by 5 | Viewed by 2400

Abstract

Machine learning-based time-series forecasting has recently been intensively studied. Deep learning (DL), specifically deep neural networks (DNN) and long short-term memory (LSTM), are the popular approaches for this purpose. However, these methods have several problems. First, DNN needs a lot of data to avoid over-fitting. Without sufficient data, the model cannot be generalized so it may not be good for unseen data. Second, impaired data affect forecasting accuracy. In general, one trains a model assuming that normal data enters the input. However, when anomalous data enters the input, the forecasting accuracy of the model may decrease substantially, which emphasizes the importance of data integrity. This paper focuses on these two problems. In time-series forecasting, especially for photovoltaic (PV) forecasting, data from solar power plants are not sufficient. As solar panels are newly installed, a sufficiently long period of data cannot be obtained. We also find that many solar power plants may contain a substantial amount of anomalous data, e.g., 30%. In this regard, we propose a data preprocessing technique leveraging convolutional autoencoder and principal component analysis (PCA) to use insufficient data with a high rate of anomaly. We compare the performance of the PV forecasting model after applying the proposed anomaly detection in constructing a virtual power plant (VPP). Extensive experiments with 2517 PV sites in the Republic of Korea, which are used for VPP construction, confirm that the proposed technique can filter out anomaly PV sites with very high accuracy, e.g., 99%, which in turn contributes to reducing the forecasting error by 23%. Full article

(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System III)

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18 pages, 914 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Dynamic DNR and Solar PV Smart Inverter Control Scheme Using Heterogeneous Multi-Agent Deep Reinforcement Learning

by Se-Heon Lim and Sung-Guk Yoon

Energies 2022, 15(23), 9220; https://doi.org/10.3390/en15239220 - 5 Dec 2022

Cited by 2 | Viewed by 2133

Abstract

The conventional volt-VAR control (VVC) in distribution systems has limitations in solving the overvoltage problem caused by massive solar photovoltaic (PV) deployment. As an alternative method, VVC using solar PV smart inverters (PVSIs) has come into the limelight, which can respond quickly and effectively to solve the overvoltage problem by absorbing reactive power. However, the network power loss, that is, the sum of line losses in the distribution network, increases with reactive power. Dynamic distribution network reconfiguration (DNR), which hourly controls the network topology by controlling sectionalizing and tie switches, can also solve the overvoltage problem and reduce network loss by changing the power flow in the network. In this study, to improve the voltage profile and minimize the network power loss, we propose a control scheme that integrates the dynamic DNR with volt-VAR control of PVSIs. The proposed control scheme is practically usable for three reasons: Primarily, the proposed scheme is based on a deep reinforcement learning (DRL) algorithm, which does not require accurate distribution system parameters. Furthermore, we propose the use of a heterogeneous multiagent DRL algorithm to control the switches centrally and PVSIs locally. Finally, a practical communication network in the distribution system is assumed. PVSIs only send their status to the central control center, and there is no communication between the PVSIs. A modified 33-bus distribution test feeder reflecting the system conditions of South Korea is used for the case study. The results of this case study demonstrates that the proposed control scheme effectively improves the voltage profile of the distribution system. In addition, the proposed scheme reduces the total power loss in the distribution system, which is the sum of the network power loss and curtailed energy, owing to the voltage violation of the solar PV output. Full article

(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System III)

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18 pages, 4190 KiB

Open AccessArticle

Adaptive Power Flow Prediction Based on Machine Learning

by Jingyeong Park, Daisuke Kodaira, Kofi Afrifa Agyeman, Taeyoung Jyung and Sekyung Han

Energies 2021, 14(13), 3842; https://doi.org/10.3390/en14133842 - 25 Jun 2021

Cited by 2 | Viewed by 2913

Abstract

Power flow analysis is an inevitable methodology in the planning and operation of the power grid. It has been performed for the transmission system, however, along with the penetration of the distributed energy resources, the target has been expanded to the distribution system as well. However, it is not easy to apply the conventional method to the distribution system since the essential information for the power flow analysis, say the impedance and the topology, are not available for the distribution system. To this end, this paper proposes an alternative method based on practically available parameters at the terminal nodes without the precedent information. Since the available information is different between high-voltage and low-voltage systems, we develop two various machine learning schemes. Specifically, the high-voltage model incorporates the slack node voltage, which can be practically obtained at the substation, and yields a time-invariant model. On the other hand, the low voltage model utilizes the deviation of voltages at each node for the power changes, subsequently resulting in a time-varying model. The performance of the suggested models is also verified using numerical simulations. The results are analyzed and compared with another power flow scheme for the distribution system that the authors suggested beforehand. Full article

(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System III)

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26 pages, 6120 KiB

Open AccessArticle

A Deep Learning Approach for Peak Load Forecasting: A Case Study on Panama

by Bibi Ibrahim and Luis Rabelo

Energies 2021, 14(11), 3039; https://doi.org/10.3390/en14113039 - 24 May 2021

Cited by 26 | Viewed by 3841

Abstract

Predicting the future peak demand growth becomes increasingly important as more consumer loads and electric vehicles (EVs) start connecting to the grid. Accurate forecasts will enable energy suppliers to meet demand more reliably. However, this is a challenging problem since the peak demand is very nonlinear. This study addresses the research question of how deep learning methods, such as convolutional neural networks (CNNs) and long-short term memory (LSTM) can provide better support to these areas. The goal is to build a suitable forecasting model that can accurately predict the peak demand. Several data from 2004 to 2019 was collected from Panama’s power system to validate this study. Input features such as residential consumption and monthly economic index were considered for predicting peak demand. First, we introduced three different CNN architectures which were multivariate CNN, multivariate CNN-LSTM and multihead CNN. These were then benchmarked against LSTM. We found that the CNNs outperformed LSTM, with the multivariate CNN being the best performing model. To validate our initial findings, we then evaluated the robustness of the models against Gaussian noise. We demonstrated that CNNs were far more superior than LSTM and can support spatial-temporal time series data. Full article

(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System III)

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13 pages, 636 KiB

Open AccessArticle

Cloud Energy Storage System Operation with Capacity P2P Transaction

by Jungsub Sim, Minsoo Kim, Dongjoo Kim and Hongseok Kim

Energies 2021, 14(2), 339; https://doi.org/10.3390/en14020339 - 9 Jan 2021

Cited by 15 | Viewed by 2890

Abstract

Research on energy storage systems (ESS) is actively aiming to mitigate against the unreliability of renewable energy sources (RES), and ESS operation and management has become one of the most important research topics. Since installing ESS for each user requires high investment cost, a study on cloud ESS gains attention recently. Cloud ESS refers to an ESS that is logically shared by multiple users as if they have their own ESS in their premises. In this paper, we propose a new cloud ESS sharing technique that allows capacity P2P transactions among users. Since cloud ESS is a virtual facility that is linked to an actual ESS, it is easy for users to sell the unused storage capacity to other users or to buy additional capacity from other users during operation. We also propose a system that encourages users to completely entrust the cloud ESS operator and share the extra benefit with the operator and other users. To verify the proposed method, we demonstrate the benefit of capacity P2P transaction based on real year-round data of users. Full article

(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System III)

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