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Special Issue "Machine Learning and Optimization with Applications of Power System"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: closed (15 February 2019)

Special Issue Editor

Guest Editor
Dr. Hongseok Kim

Electronic Engineering, Sogang University, Seoul, Korea
Website | E-Mail
Phone: +82-2-705-7989
Interests: power system with optimal power flow; energy storage; machine learning for energy big data and forecasting; energy trading; microgrids

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 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 be also 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

Prof. Hongseok Kim
Guest Editor

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 1800 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

  • 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

Published Papers (10 papers)

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Research

Open AccessArticle
Data Requirements for Applying Machine Learning to Energy Disaggregation
Energies 2019, 12(9), 1696; https://doi.org/10.3390/en12091696
Received: 18 January 2019 / Revised: 5 April 2019 / Accepted: 30 April 2019 / Published: 5 May 2019
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Abstract
Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment have been rather limited, possibly because the commonly used datasets are not [...] Read more.
Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment have been rather limited, possibly because the commonly used datasets are not adequate for NILM research. In this study, we report the findings from a newly collected dataset that contains 10 Hz sampling data for 58 houses. The dataset not only contains the aggregate measurements, but also individual appliance measurements for three types of appliances. By applying three classification algorithms (vanilla DNN (Deep Neural Network), ML (Machine Learning) with feature engineering, and CNN (Convolutional Neural Network) with hyper-parameter tuning) and a recent regression algorithm (Subtask Gated Network) to the new dataset, we show that NILM performance can be significantly limited when the data sampling rate is too low or when the number of distinct houses in the dataset is too small. The well-known NILM datasets that are popular in the research community do not meet these requirements. Our results indicate that higher quality datasets should be used to expedite the progress of NILM research. Full article
(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System)
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Open AccessArticle
Online Building Load Management Control with Plugged-in Electric Vehicles Considering Uncertainties
Energies 2019, 12(8), 1436; https://doi.org/10.3390/en12081436
Received: 15 February 2019 / Revised: 31 March 2019 / Accepted: 7 April 2019 / Published: 15 April 2019
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Abstract
Robust operation of load management control for a building is important to account for the uncertainty in demand as well as any distributed sources connected to the building. This paper discussed an online load management control solution using distributed energy storage (DES) while [...] Read more.
Robust operation of load management control for a building is important to account for the uncertainty in demand as well as any distributed sources connected to the building. This paper discussed an online load management control solution using distributed energy storage (DES) while considering uncertainties in demand as well as DES to reduce peak demand for economic benefit. In recent years’ demand-side management (DSM) solutions using DES such as stationary energy management system (BESS) and plugged-in electric vehicles (PEV) have been popularised. Most of these solutions resort to deterministic load forecast for the day ahead energy scheduling and do not consider the uncertainties in demand and DES making these solutions vulnerable to uncertainties. This study presents an online density demand forecast, k-means clustering of PEV groups and stochastic optimisation for robust operation of BESS and PEV for a building. The proposed method accounts for uncertainties in demand and uncertainties due to mobile energy storage as presented by PEVs. For a case study, we used data obtained from an industrial site in South Korea. The verified results as compared to other methods with a deterministic approach prove the solution is efficient and robust. Full article
(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System)
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Open AccessFeature PaperArticle
Mixed Integer Quadratic Programming Based Scheduling Methods for Day-Ahead Bidding and Intra-Day Operation of Virtual Power Plant
Energies 2019, 12(8), 1410; https://doi.org/10.3390/en12081410
Received: 14 February 2019 / Revised: 1 April 2019 / Accepted: 9 April 2019 / Published: 12 April 2019
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Abstract
As distributed energy resources (DERs) proliferate power systems, power grids face new challenges stemming from the variability and uncertainty of DERs. To address these problems, virtual power plants (VPPs) are established to aggregate DERs and manage them as single dispatchable and reliable resources. [...] Read more.
As distributed energy resources (DERs) proliferate power systems, power grids face new challenges stemming from the variability and uncertainty of DERs. To address these problems, virtual power plants (VPPs) are established to aggregate DERs and manage them as single dispatchable and reliable resources. VPPs can participate in the day-ahead (DA) market and therefore require a bidding method that maximizes profits. It is also important to minimize the variability of VPP output during intra-day (ID) operations. This paper presents mixed integer quadratic programming-based scheduling methods for both DA market bidding and ID operation of VPPs, thus serving as a complete scheme for bidding-operation scheduling. Hourly bids are determined based on VPP revenue in the DA market bidding step, and the schedule of DERs is revised in the ID operation to minimize the impact of forecasting errors and maximize the incentives, thus reducing the variability and uncertainty of VPP output. The simulation results verify the effectiveness of the proposed methods through a comparison of daily revenue. Full article
(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System)
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Open AccessArticle
Implementation of Optimal Scheduling Algorithm for Multi-Functional Battery Energy Storage System
Energies 2019, 12(7), 1339; https://doi.org/10.3390/en12071339
Received: 1 February 2019 / Revised: 1 April 2019 / Accepted: 2 April 2019 / Published: 8 April 2019
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Abstract
Energy storage system (ESS) can play a positive role in the power system due to its ability to store, charge and discharge energy. Additionally, it can be installed in various capacities, so it can be used in the transmission and distribution system and [...] Read more.
Energy storage system (ESS) can play a positive role in the power system due to its ability to store, charge and discharge energy. Additionally, it can be installed in various capacities, so it can be used in the transmission and distribution system and even at home. In this paper, the proposed algorithm for economic optimal scheduling of ESS linked to transmission systems in the Korean electricity market is proposed and incorporated into the BESS (battery energy storage system) demonstration test center. The proposed algorithm considers the energy arbitrage operation through SMP (system marginal price) and operation considering the REC (renewable energy certification) weight of the connected wind farm and frequency regulation service. In addition, the proposed algorithm was developed so that the SOC (state-of-charge) of the ESS could be separated into two virtual SOCs to participate in different markets and generate revenue. The proposed algorithm was simulated and verified through Matlab and loaded into the demonstration system using the Matlab “Runtime” function. Full article
(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System)
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Open AccessArticle
Privacy-Preserving Electricity Billing System Using Functional Encryption
Energies 2019, 12(7), 1237; https://doi.org/10.3390/en12071237
Received: 16 February 2019 / Revised: 23 March 2019 / Accepted: 25 March 2019 / Published: 1 April 2019
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Abstract
The development of smart meters that can frequently measure and report power consumption has enabledelectricity providers to offer various time-varying rates, including time-of-use and real-time pricing plans. High-resolution power consumption data, however, raise serious privacy concerns because sensitive information regarding an individual’s lifestyle [...] Read more.
The development of smart meters that can frequently measure and report power consumption has enabledelectricity providers to offer various time-varying rates, including time-of-use and real-time pricing plans. High-resolution power consumption data, however, raise serious privacy concerns because sensitive information regarding an individual’s lifestyle can be revealed by analyzing these data. Although extensive research has been conducted to address these privacy concerns, previous approaches have reduced the quality of measured data. In this paper, we propose a new privacy-preserving electricity billing method that does not sacrifice data quality for privacy. The proposed method is based on the novel use of functional encryption. Experimental results on a prototype system using a real-world smart meter device and data prove the feasibility of the proposed method. Full article
(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System)
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Open AccessArticle
Impedance Estimation with an Enhanced Particle Swarm Optimization for Low-Voltage Distribution Networks
Energies 2019, 12(6), 1167; https://doi.org/10.3390/en12061167
Received: 14 February 2019 / Revised: 18 March 2019 / Accepted: 21 March 2019 / Published: 26 March 2019
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Abstract
Many researchers in recent years have studied voltage deviation issues in distribution networks. Characterizing the impedance between consuming nodes in a network is the key to controlling the network voltage. Existing impedance estimation methods are faced with three challenges: time synchronized measurement, a [...] Read more.
Many researchers in recent years have studied voltage deviation issues in distribution networks. Characterizing the impedance between consuming nodes in a network is the key to controlling the network voltage. Existing impedance estimation methods are faced with three challenges: time synchronized measurement, a generalization of the network model, and convergence of the optimization for objective functions. This paper extends an existing impedance estimation algorithm by introducing an enhanced particle swarm optimization (PSO). To overcome this method’s local optimum problem, we propose adaptive inertia weights. Also, our proposed method is based on a new general model for a low voltage distribution network with non-synchronized measurements. In the case study, the improved impedance estimation algorithm realizes better accuracy than the existing method. Full article
(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System)
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Open AccessArticle
Practical Operation Strategies for Energy Storage System under Uncertainty
Energies 2019, 12(6), 1098; https://doi.org/10.3390/en12061098
Received: 27 February 2019 / Revised: 18 March 2019 / Accepted: 18 March 2019 / Published: 21 March 2019
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Abstract
Recent advances in battery technologies have reduced the financial burden of using the energy storage system (ESS) for customers. Peak cut, one of the benefits of using ESS, can be achieved through proper charging/discharging scheduling of ESS. However, peak cut is sensitive to [...] Read more.
Recent advances in battery technologies have reduced the financial burden of using the energy storage system (ESS) for customers. Peak cut, one of the benefits of using ESS, can be achieved through proper charging/discharging scheduling of ESS. However, peak cut is sensitive to load-forecasting error, and even a small forecasting error may result in the failure of peak cut. In this paper, we propose a two-phase approach of day-ahead optimization and real-time control for minimizing the total cost that comes from time-of-use (TOU), peak load, and battery degradation. In day-ahead optimization, we propose to use an internalized pricing to manage peak load in addition to the cost from TOU. The proposed method can be implemented by using dynamic programming, which also has an advantage of accommodating the state-dependent battery degradation cost. Then in real-time control, we propose a concept of marginal power to alleviate the performance loss incurred from load-forecasting error and mimic the offline optimal battery scheduling by learning from load-forecasting error. By exploiting the marginal power, real-time ESS charging/discharging power gets close to the offline optimal battery scheduling. Case studies show that under load-forecasting uncertainty, the peak power using the proposed method is only 22.4% higher than the offline optimal peak power, while the day-ahead optimization has 76.8% higher peak power than the offline optimal power. In terms of profit, the proposed method achieves 77.0% of the offline optimal profit while the day-ahead method only earns 19.6% of the offline optimal profit, which shows the substantial improvement of the proposed method. Full article
(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System)
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Open AccessArticle
A Hybrid Neural Network Model for Power Demand Forecasting
Energies 2019, 12(5), 931; https://doi.org/10.3390/en12050931
Received: 1 February 2019 / Revised: 2 March 2019 / Accepted: 6 March 2019 / Published: 10 March 2019
Cited by 1 | PDF Full-text (5409 KB) | HTML Full-text | XML Full-text
Abstract
The problem of power demand forecasting for the effective planning and operation of smart grid, renewable energy and electricity market bidding systems is an open challenge. Numerous research efforts have been proposed for improving prediction performance in practical environments through statistical and artificial [...] Read more.
The problem of power demand forecasting for the effective planning and operation of smart grid, renewable energy and electricity market bidding systems is an open challenge. Numerous research efforts have been proposed for improving prediction performance in practical environments through statistical and artificial neural network approaches. Despite these efforts, power demand forecasting problems remain to be a grand challenge since existing methods are not sufficiently practical to be widely deployed due to their limited accuracy. To address this problem, we propose a hybrid power demand forecasting model, called (c, l)-Long Short-Term Memory (LSTM) + Convolution Neural Network (CNN). We consider the power demand as a key value, while we incorporate c different types of contextual information such as temperature, humidity and season as context values in order to preprocess datasets into bivariate sequences consisting of <Key, Context[1, c]> pairs. These c bivariate sequences are then input into c LSTM networks with l layers to extract feature sets. Using these feature sets, a CNN layer outputs a predicted profile of power demand. To assess the applicability of the proposed hybrid method, we conduct extensive experiments using real-world datasets. The results of the experiments indicate that the proposed (c, l)-LSTM+CNN hybrid model performs with higher accuracy than previous approaches. Full article
(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System)
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Open AccessArticle
Online Evaluation Method for Low Frequency Oscillation Stability in a Power System Based on Improved XGboost
Energies 2018, 11(11), 3238; https://doi.org/10.3390/en11113238
Received: 18 October 2018 / Revised: 7 November 2018 / Accepted: 16 November 2018 / Published: 21 November 2018
Cited by 1 | PDF Full-text (2914 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Low frequency oscillation in an interconnected power system is becoming an increasingly serious problem. It is of great practical significance to make online evaluation of actual power grid’s stability. To evaluate the stability of the power system quickly and accurately, a low frequency [...] Read more.
Low frequency oscillation in an interconnected power system is becoming an increasingly serious problem. It is of great practical significance to make online evaluation of actual power grid’s stability. To evaluate the stability of the power system quickly and accurately, a low frequency oscillation stability evaluation method based on an improved XGboost algorithm and power system random response data is proposed in this paper. Firstly, the original input feature set describing the dynamic characteristics of the power system is established by analyzing the substance of low frequency oscillation. Taking the random response data of power system including the disturbance end time feature and the dynamic feature of power system as the input sample set, the wavelet threshold is applied to improve its effectiveness. Secondly, using the eigenvalue analysis method, different damping ratios are selected as threshold values to judge the stability of the system low-frequency oscillation. Then, the supervised training with improved XGboost algorithm is performed on the characteristics of stability. On this basis, the training model is obtained and applied to online low frequency oscillation stability evaluation of a power system. Finally, the simulation results of the eight-machine 36-node test system and Hebei southern power grid show that the proposed low frequency oscillation online evaluation method has the features of high evaluation accuracy, fast evaluation speed, low error rate of unstable sample evaluation, and strong anti-noise ability. Full article
(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System)
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Open AccessArticle
A New Input Selection Algorithm Using the Group Method of Data Handling and Bootstrap Method for Support Vector Regression Based Hourly Load Forecasting
Energies 2018, 11(11), 2870; https://doi.org/10.3390/en11112870
Received: 10 September 2018 / Revised: 17 October 2018 / Accepted: 21 October 2018 / Published: 23 October 2018
PDF Full-text (3693 KB) | HTML Full-text | XML Full-text
Abstract
Electric load forecasting is indispensable for the effective planning and operation of power systems. Various decisions related to power systems depend on the future behavior of loads. In this paper, we propose a new input selection procedure, which combines the group method of [...] Read more.
Electric load forecasting is indispensable for the effective planning and operation of power systems. Various decisions related to power systems depend on the future behavior of loads. In this paper, we propose a new input selection procedure, which combines the group method of data handling (GMDH) and bootstrap method for support vector regression based hourly load forecasting. To construct the GMDH network, a learning dataset is divided into training and test datasets by bootstrapping. After constructing GMDH networks several times, the inputs that appeared frequently in the input layers of the completed networks were selected as the significant inputs. Filter methods based on linear correlation and mutual information (MI) were employed as comparison methods, and the performance of hybrids of the filter methods and the proposed method were also confirmed. In total, five input selection methods were compared. To verify the performance of the proposed method, hourly load data from South Korea was used and the results of one-hour, one-day and one-week-ahead forecasts were investigated. The experimental results demonstrated that the proposed method has higher prediction accuracy compared with the filter methods. Among the five methods, a hybrid of an MI-based filter with the proposed method shows best prediction performance. Full article
(This article belongs to the Special Issue Machine Learning and Optimization with Applications of Power System)
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