Research

27 pages, 12751 KiB

Open AccessArticle

LMI-Based Model Predictive Current Control for an LCL-Filtered Grid-Connected Inverter under Unexpected Grid and System Uncertainties

by Yubin Kim, Thuy Vi Tran and Kyeong-Hwa Kim

Electronics 2022, 11(5), 731; https://doi.org/10.3390/electronics11050731 - 26 Feb 2022

Cited by 9 | Viewed by 2240

Abstract

To guarantee a system stability and reliable operation of an inductor-capacitor-inductor (LCL)-filtered grid-connected inverter (GCI) under unexpected grid and system uncertainties, a linear matrix inequality (LMI)-based model predictive control (MPC) is presented in this paper. Even though the conventional MPC scheme is constructed [...] Read more.

To guarantee a system stability and reliable operation of an inductor-capacitor-inductor (LCL)-filtered grid-connected inverter (GCI) under unexpected grid and system uncertainties, a linear matrix inequality (LMI)-based model predictive control (MPC) is presented in this paper. Even though the conventional MPC scheme is constructed by a simple concept, it is difficult to determine an optimized weighting matrix of the MPC cost function against parameter discrepancies. To overcome this problem, the MPC scheme is combined with LMI-based optimization. The system states are estimated by the LMI-based current-type observer in the stationary reference frame to implement the proposed scheme. Additionally, the MPC scheme is combined with the disturbance observer to eliminate offset error, which improves the reference tracking performance. In comparison with the other studies, the proposed control method ensures high robust control performance under grid voltage imbalance, parameter uncertainty, and frequency variation. In addition, the proposed approach achieves a robust active damping even for the grid impedance variation without the need of considering further damping method. The control design step is systematic and straightforward. Furthermore, unlike the conventional schemes, the proposed controller does not require an integral term and the 2nd harmonic compensation term to obtain a good reference tracking performance under grid imbalanced condition, which contributes to the reduction of the controller complexity by decreasing the order of the controller model. To verify the effectiveness of the proposed LMI-based MPC control scheme, the simulation and experiments are carried out by using prototype three-phase GCI. The comprehensive simulation and experimental results clearly demonstrate the robustness of the proposed current controller under various adverse test conditions with unexpected grid and system uncertainties. Full article

(This article belongs to the Special Issue Integration of Distributed Energy Resources (DERs) in Power Grid: Challenges and Solutions)

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16 pages, 1996 KiB

Open AccessCommunication

Utility DERMS for Active Management of Emerging Distribution Grids with High Penetration of Renewable DERs

by Luka Strezoski and Izabela Stefani

Electronics 2021, 10(16), 2027; https://doi.org/10.3390/electronics10162027 - 21 Aug 2021

Cited by 12 | Viewed by 5421

Abstract

Operational and planning challenges caused by ever-increasing integration of electronically coupled renewable distributed energy resources (DERs) have become a reality all over the globe. These challenges range from technical constraint violations to malfunctional setting and coordination of the protective equipment and inaccurate operational [...] Read more.

Operational and planning challenges caused by ever-increasing integration of electronically coupled renewable distributed energy resources (DERs) have become a reality all over the globe. These challenges range from technical constraint violations to malfunctional setting and coordination of the protective equipment and inaccurate operational planning. Moreover, to enable the preconditions for the integration of high penetration of renewable DERs, utilities are faced with potentially huge investment requirements in strengthening the grid assets. However, recent advances in specialized software solutions for integration and active management of high penetration of DERs could turn these challenges into operational and monetary benefits. Hence, if planned, managed, and operated in an optimal way, the high penetration of DERs could be a valuable resource for increasing the efficiency of the overall management of distribution grids. Utility distributed energy resource management systems (utility DERMSs) aim to provide all of these capabilities integrated into a single software solution. In this paper, a utility DERMS concept is introduced, and the capabilities of state-of-the-art utility DERMS solutions for helping the key stakeholders to pave the way towards stable, optimal, and secure emerging distribution systems with high penetration of electronically coupled renewable DERs are explored. Full article

(This article belongs to the Special Issue Integration of Distributed Energy Resources (DERs) in Power Grid: Challenges and Solutions)

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13 pages, 3507 KiB

Open AccessArticle

A Study on the Power Reserve of Distributed Generators Based on Power Sensitivity Analysis in a Large-Scale Power System

by Dongmin Kim, Jung-Wook Park and Soo Hyoung Lee

Electronics 2021, 10(7), 769; https://doi.org/10.3390/electronics10070769 - 24 Mar 2021

Cited by 4 | Viewed by 1624

Abstract

Converter-based generators (CBGs) that use renewable energy sources (RESs) are replacing traditional aging coal and nuclear power generators. Increasing the penetration of CBGs into the entire power generation process reduces both the inertia constant of the power system and the total amount of [...] Read more.

Converter-based generators (CBGs) that use renewable energy sources (RESs) are replacing traditional aging coal and nuclear power generators. Increasing the penetration of CBGs into the entire power generation process reduces both the inertia constant of the power system and the total amount of power reserves. Additionally, RESs are very intermittent and it is difficult to predict changes in them. These problems, due to CBGs using RESs, pose new challenges to net–load balancing. As a solution, this paper proposes a virtual multi-slack (VMS) droop control that secures the stability and efficiency of system operation by controlling the output of CBGs distributed in various regions. The VMS droop control makes it possible to increase the inertia constant of the power system and to respond quickly and appropriately to load changes through the proposed VMS droop control based on power sensitivity. It is also proposed that the process selects proper power reserves of CBGs for stable VMS droop control. To verify the effectiveness of the proposed VMS droop control and the proper power reserve selection method for CBGs, several case studies were performed using a real Korean power system. Full article

(This article belongs to the Special Issue Integration of Distributed Energy Resources (DERs) in Power Grid: Challenges and Solutions)

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13 pages, 1740 KiB

Open AccessArticle

Backup Capacity Planning Considering Short-Term Variability of Renewable Energy Resources in a Power System

by Deukyoung Lee, Dongjun Lee, Hanhwi Jang and Sung-Kwan Joo

Electronics 2021, 10(6), 709; https://doi.org/10.3390/electronics10060709 - 18 Mar 2021

Cited by 3 | Viewed by 2203

Abstract

Increasing renewable energy penetration rate in a power grid leads to an increase in the variability of the generated energy, which increases the system integration cost. To handle the output variations in the generation, it is necessary to secure sufficient flexible resources, such [...] Read more.

Increasing renewable energy penetration rate in a power grid leads to an increase in the variability of the generated energy, which increases the system integration cost. To handle the output variations in the generation, it is necessary to secure sufficient flexible resources, such as energy storage units. Flexible resources can adjust the output quickly, which helps to increase the system flexibility. However, the electricity generation cost of the flexible resources is usually high. Because the renewable energy expansion policy is being implemented worldwide, it is necessary to evaluate the ability to manage the short-term variations of the renewable energy outputs to obtain a cost-effective long-term plan. In this study, the variability of renewable energy in Korea over the past five years was analyzed. Additionally, the backup capacity is determined to manage the variability of renewable energy output. The backup capacity is affected by system flexibility. In general, increasing system flexibility decreases the backup capacity and increases the total electricity production cost. In this study, a backup capacity planning method is proposed considering the short-term variability of renewable energy output and flexibility deficit in a power system. The numerical results illustrated the effectiveness of the proposed backup capacity planning method. Full article

(This article belongs to the Special Issue Integration of Distributed Energy Resources (DERs) in Power Grid: Challenges and Solutions)

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10 pages, 3181 KiB

Open AccessFeature PaperArticle

Clustering of Load Profiles of Residential Customers Using Extreme Points and Demographic Characteristics

by Hyun Cheol Jeong, Minseok Jang, Taegon Kim and Sung-Kwan Joo

Electronics 2021, 10(3), 290; https://doi.org/10.3390/electronics10030290 - 26 Jan 2021

Cited by 16 | Viewed by 2233

Abstract

In this paper, a systematic method is proposed to cluster the energy consumption patterns of residential customers by utilizing extreme points and demographic characteristics. The extreme points of the energy consumption pattern enable effective clustering of residential customers. Additionally, demographic characteristics can be [...] Read more.

In this paper, a systematic method is proposed to cluster the energy consumption patterns of residential customers by utilizing extreme points and demographic characteristics. The extreme points of the energy consumption pattern enable effective clustering of residential customers. Additionally, demographic characteristics can be used to determine an effective extreme point for the clustering algorithm. The K-means-based features selection method is used to classify energy consumption patterns of residential customers into six types. Furthermore, the type of energy consumption pattern can be identified depending on the characteristics of residential customers. The analytical results of this paper show that the extreme points are effective in clustering the energy consumption patterns of residential customers. Full article

(This article belongs to the Special Issue Integration of Distributed Energy Resources (DERs) in Power Grid: Challenges and Solutions)

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20 pages, 5213 KiB

Open AccessArticle

Impact of Revised Time of Use Tariff on Variable Renewable Energy Curtailment on Jeju Island

by Jinyeong Lee, Jaehee Lee and Young-Min Wi

Electronics 2021, 10(2), 135; https://doi.org/10.3390/electronics10020135 - 10 Jan 2021

Cited by 6 | Viewed by 2750

Abstract

Jeju Island announced the “Carbon Free Island (CFI) Plan by 2030” in 2012. This plan aims to replace conventional generators with distributed energy resources (DERs) up to a level of 70% by 2030. Akin to Jeju Island, as DERs have been expanded in [...] Read more.

Jeju Island announced the “Carbon Free Island (CFI) Plan by 2030” in 2012. This plan aims to replace conventional generators with distributed energy resources (DERs) up to a level of 70% by 2030. Akin to Jeju Island, as DERs have been expanded in islanded power systems, variable renewable energy (VRE) has become a significant component of DERs. However, VRE curtailment can occur to meet power balance, and VRE curtailment generally causes energy waste and low efficiency, so it should be minimized. This paper first presents a systematic procedure for estimating the annual VRE curtailment for the stable operation of the islanded power systems. In this procedure, the VRE curtailment is estimated based on the power demand, the grid interconnection, the capacity factor of VRE, and conventional generators in the base year. Next, through the analysis of the hourly net load profile for the year in which the VRE curtailment is expected to occur, a procedure was proposed to find the season and hour when VRE curtailment occurs the most. It could be applied to revised Time-of-Use (ToU) tariff rates as the most cost-effective mitigation method of VRE curtailment on the retail market-side. Finally, price elasticity of electricity demand was presented for applying the revised ToU tariff rate scenarios in a specific season and hour, which found that VRE curtailment occurred the most. Considering self- and cross-price elasticity of electricity, revised ToU tariff rate scenarios were used in a case study on Jeju Island. Eventually, it was confirmed that VRE curtailment could be mitigated when the revised ToU tariff rates were applied, considering the price elasticity of demand. Full article

(This article belongs to the Special Issue Integration of Distributed Energy Resources (DERs) in Power Grid: Challenges and Solutions)

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18 pages, 4001 KiB

Open AccessArticle

Analysis of Challenges Due to Changes in Net Load Curve in South Korea by Integrating DERs

by Chi-Yeon Kim, Chae-Rin Kim, Dong-Keun Kim and Soo-Hwan Cho

Electronics 2020, 9(8), 1310; https://doi.org/10.3390/electronics9081310 - 14 Aug 2020

Cited by 1 | Viewed by 2758

Abstract

The development of Distributed Energy Resources (DERs) is essential in accordance with the mandatory greenhouse gas (GHG) emission reduction policies, resulting in many DERs being integrated into the power system. Currently, South Korea is also focusing on increasing the penetration of renewable energy [...] Read more.

The development of Distributed Energy Resources (DERs) is essential in accordance with the mandatory greenhouse gas (GHG) emission reduction policies, resulting in many DERs being integrated into the power system. Currently, South Korea is also focusing on increasing the penetration of renewable energy sources (RES) and EV by 2030 to reduce GHGs. However, indiscriminate DER development can give a negative impact on the operation of existing power systems. The existing power system operation is optimized for the hourly net load pattern, but the integration of DERs changes it. In addition, since ToU (Time-of-Use) tariff and Demand Response (DR) programs are very sensitive to changes in the net load curve, it is essential to predict the hourly net load pattern accurately for the modification of pricing and demand response programs in the future. However, a long-term demand forecast in South Korea provides only the total amount of annual load (TWh) and the expected peak load level (GW) in summer and winter seasons until 2030. In this study, we use the annual photovoltaic (PV) installed capacity, PV generation, and the number of EV based on the target values for 2030 in South Korea to predict the change in hourly net load curve by year and season. In addition, to predict the EV charging load curve based on Monte Carlo simulation, the EV users’ charging method, charging start time, and State-of-Charge (SoC) were considered. Finally, we analyze the change in hourly net load curve due to the integration of PV and EV to determine the amplification of the duck curve and peak load time by year and season, and present the risks caused by indiscriminate DERs development. Full article

(This article belongs to the Special Issue Integration of Distributed Energy Resources (DERs) in Power Grid: Challenges and Solutions)

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17 pages, 4188 KiB

Open AccessArticle

Day-Ahead Forecasting for Small-Scale Photovoltaic Power Based on Similar Day Detection with Selective Weather Variables

by Shree Krishna Acharya, Young-Min Wi and Jaehee Lee

Electronics 2020, 9(7), 1117; https://doi.org/10.3390/electronics9071117 - 09 Jul 2020

Cited by 15 | Viewed by 3799

Abstract

As photovoltaic (PV) power plants are an essential component of modern smart grids, the PV generation forecasting of such plants has recently been gaining interest. The forecasting results of PV power often suffer from large errors because of unusual weather conditions. In a [...] Read more.

As photovoltaic (PV) power plants are an essential component of modern smart grids, the PV generation forecasting of such plants has recently been gaining interest. The forecasting results of PV power often suffer from large errors because of unusual weather conditions. In a learning-based forecasting model, the forecasting accuracy can be enhanced by using carefully selected data for training rather than all the data without any screening. That is, using a training set that only contains information obtained from similar days can help enhance the accuracy of learning-based PV forecasting. This paper proposes a forecasting method for small-scale PV generation. This method is based on long short-term memory; further, it detects similar days considering the different impacts of weather variables on PV power according to the day. This method can address issues caused by unnecessary learning from non-similar historical days. The simulation results demonstrate that the proposed method exhibits better performance than do existing similar day detection methods. Full article

(This article belongs to the Special Issue Integration of Distributed Energy Resources (DERs) in Power Grid: Challenges and Solutions)

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12 pages, 3304 KiB

Open AccessArticle

A Context-Aware IoT and Deep-Learning-Based Smart Classroom for Controlling Demand and Supply of Power Load

by Prabesh Paudel, Sangkyoon Kim, Soonyoung Park and Kyoung-Ho Choi

Electronics 2020, 9(6), 1039; https://doi.org/10.3390/electronics9061039 - 23 Jun 2020

Cited by 13 | Viewed by 4185

Abstract

With the demand for clean energy increasing, novel research is presented in this paper on providing sustainable, clean energy for a university campus. The Internet of Things (IoT) is now a leading factor in saving energy. With added deep learning for action recognition, [...] Read more.

With the demand for clean energy increasing, novel research is presented in this paper on providing sustainable, clean energy for a university campus. The Internet of Things (IoT) is now a leading factor in saving energy. With added deep learning for action recognition, IoT sensors implemented in real-time appliances monitor and control the extra usage of energy in buildings. This gives an extra edge on digitizing energy usage and, ultimately, reducing the power load in the electric grid. Here, we present a novel proposal through context-aware architecture for energy saving in classrooms, combining Internet of Things (IoT) sensors and video action recognition. Using this method, we can save a significant amount of energy usage in buildings. Full article

(This article belongs to the Special Issue Integration of Distributed Energy Resources (DERs) in Power Grid: Challenges and Solutions)

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