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15 pages, 3784 KiB

Open AccessArticle

Realistic Load Modeling for Efficient Consumption Management Using Real-Time Simulation and Power Hardware-in-the-Loop

by Pedro Faria and Zita Vale

Energies 2023, 16(1), 338; https://doi.org/10.3390/en16010338 - 28 Dec 2022

Viewed by 1610

By empowering consumers and enabling them as active players in the power and energy sector, demand flexibility requires more precise and sophisticated load modeling. In this paper, a laboratory testbed was designed and implemented for surveying the behavior of laboratory loads in different [...] Read more.

By empowering consumers and enabling them as active players in the power and energy sector, demand flexibility requires more precise and sophisticated load modeling. In this paper, a laboratory testbed was designed and implemented for surveying the behavior of laboratory loads in different network conditions by using real-time simulation. Power hardware-in-the-loop was used to validate the load models by testing various technical network conditions. Then, in the emulation phase, the real-time simulator controlled a power amplifier and different laboratory equipment to provide a realistic testbed for validating the load models under different voltage and frequency conditions. In the case study, the power amplifier was utilized to supply a resistive load to emulate several consumer load modeling. Through the obtained results, the errors for each load level and the set of all load levels were calculated and compared. Furthermore, a fixed consumption level was considered. The frequency was changed to survey the behavior of the load during the grid’s instabilities. In the end, a set of mathematical equations were proposed to calculate power consumption with respect to the actual voltage and frequency variations. Full article

(This article belongs to the Special Issue Smart Energy Systems: Control and Optimization)

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11 pages, 16503 KiB

Open AccessArticle

PV Penetration under Market Environment and with System Constraints

by Aris Dimeas and George Kiokes

Energies 2022, 15(22), 8673; https://doi.org/10.3390/en15228673 - 18 Nov 2022

Viewed by 780

Abstract

The installed capacity of PVs in the distribution grid is affected not only by network constraints, but also by the economic viability of the related investments. Depending on the market participation models, this is determined critically by the Day Ahead Market (DAM) prices. [...] Read more.

The installed capacity of PVs in the distribution grid is affected not only by network constraints, but also by the economic viability of the related investments. Depending on the market participation models, this is determined critically by the Day Ahead Market (DAM) prices. Increasing RES installations in a country usually results in a long term drop in the market prices and, as a consequence, a reduction in the income of the PVs investors and possible market cannibalization. This paper models the effect of large-scale penetration of PVs on the market prices and identifies the optimal penetration level for the viability of PV projects. The optimal penetration is highly related to the installation of new PVs and this is a parameter for the analysis. Therefore, the paper identifies different penetration costs for the different installation cost. Furthermore, the PV network hosing capacity can be increased by distribution network reinforcements. Therefore, in the paper, the investments for enhancement of the distribution grid are assessed with respect to market prices and are analyzed at the macroscopic level. Again, the analysis considers different costs for network reinforcements. Full article

(This article belongs to the Special Issue Smart Energy Systems: Control and Optimization)

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35 pages, 2635 KiB

Open AccessArticle

Energy Management Simulation with Multi-Agent Reinforcement Learning: An Approach to Achieve Reliability and Resilience

by Kapil Deshpande, Philipp Möhl, Alexander Hämmerle, Georg Weichhart, Helmut Zörrer and Andreas Pichler

Energies 2022, 15(19), 7381; https://doi.org/10.3390/en15197381 - 08 Oct 2022

Cited by 5 | Viewed by 1900

Abstract

The share of energy produced by small-scale renewable energy sources, including photovoltaic panels and wind turbines, will significantly increase in the near future. These systems will be integrated in microgrids to strengthen the independence of energy consumers. This work deals with energy management [...] Read more.

The share of energy produced by small-scale renewable energy sources, including photovoltaic panels and wind turbines, will significantly increase in the near future. These systems will be integrated in microgrids to strengthen the independence of energy consumers. This work deals with energy management in microgrids, taking into account the volatile nature of renewable energy sources. In the developed approach, Multi-Agent Reinforcement Learning is applied, where agents represent microgrid components. The individual agents are trained to make good decisions with respect to adapting to the energy load in the grid. Training of agents leverages the historic energy profile data for energy consumption and renewable energy production. The implemented energy management simulation shows good performance and balances the energy flows. The quantitative performance evaluation includes comparisons with the exact solutions from a linear program. The computational results demonstrate good generalisation capabilities of the trained agents and the impact of these capabilities on the reliability and resilience of energy management in microgrids. Full article

(This article belongs to the Special Issue Smart Energy Systems: Control and Optimization)

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20 pages, 2376 KiB

Open AccessArticle

Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models

by Tadeusz A. Grzeszczyk and Michal K. Grzeszczyk

Energies 2022, 15(5), 1852; https://doi.org/10.3390/en15051852 - 02 Mar 2022

Cited by 8 | Viewed by 1852

Abstract

There is a lot of research on the neural models used for short-term load forecasting (STLF), which is crucial for improving the sustainable operation of energy systems with increasing technical, economic, and environmental requirements. Neural networks are computationally powerful; however, the lack of [...] Read more.

There is a lot of research on the neural models used for short-term load forecasting (STLF), which is crucial for improving the sustainable operation of energy systems with increasing technical, economic, and environmental requirements. Neural networks are computationally powerful; however, the lack of clear, readable and trustworthy justification of STLF obtained using such models is a serious problem that needs to be tackled. The article proposes an approach based on the local interpretable model-agnostic explanations (LIME) method that supports reliable premises justifying and explaining the forecasts. The use of the proposed approach makes it possible to improve the reliability of heuristic and experimental neural modeling processes, the results of which are difficult to interpret. Explaining the forecasting may facilitate the justification of the selection and the improvement of neural models for STLF, while contributing to a better understanding of the obtained results and broadening the knowledge and experience supporting the enhancement of energy systems security based on reliable forecasts and simplifying dispatch decisions. Full article

(This article belongs to the Special Issue Smart Energy Systems: Control and Optimization)

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17 pages, 3383 KiB

Open AccessArticle

Consumer-Driven Demand-Side Management Using K-Mean Clustering and Integer Programming in Standalone Renewable Grid

by Muhammad Ahsan Ayub, Hufsa Khan, Jianchun Peng and Yitao Liu

Energies 2022, 15(3), 1006; https://doi.org/10.3390/en15031006 - 29 Jan 2022

Cited by 9 | Viewed by 1612

Abstract

Many countries have larger land areas and scattered communities. Therefore, to electrify them, small standalone power systems are the more preferred and cost-efficient solution as compared to utility grid extensions. The main objective of a standalone power system is to supply cleaner, cheaper, [...] Read more.

Many countries have larger land areas and scattered communities. Therefore, to electrify them, small standalone power systems are the more preferred and cost-efficient solution as compared to utility grid extensions. The main objective of a standalone power system is to supply cleaner, cheaper, and uninterrupted electricity. However, for standalone power systems, demand-side management always remains a challenging task. In this paper, a load scheduling algorithm driven by K-mean clustering and linear integer programming to schedule consumers’ appliances for the upcoming day is proposed. In addition, the basic power to run the necessary appliances is kept available in the system all the time. Furthermore, to assist the consumer in every situation, the battery storage system and the overall system size reduction are also taken into consideration. Consumer input is also used in scheduling the appliances. The proposed method is evaluated on the publicly available real-world dataset; the simulation results demonstrate that the proposed approach performs better, due to which the reliability and continuity of the system are increased. Full article

(This article belongs to the Special Issue Smart Energy Systems: Control and Optimization)

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24 pages, 4592 KiB

Open AccessArticle

Electrical Load Demand Forecasting Using Feed-Forward Neural Networks

by Eduardo Machado, Tiago Pinto, Vanessa Guedes and Hugo Morais

Energies 2021, 14(22), 7644; https://doi.org/10.3390/en14227644 - 16 Nov 2021

Cited by 16 | Viewed by 2249

Abstract

The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and [...] Read more.

The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated with these changes. The recent advances of computational technologies and the ever-growing data availability allowed the development of sophisticated and efficient algorithms that can process information at a very fast pace. In this sense, the use of machine learning models has been gaining increased attention from the electricity sector as it can provide accurate forecasts of system behaviour from energy generation to consumption, helping all the stakeholders to optimize their activities. This work develops and proposes a methodology to enhance load demand forecasts using a machine learning model, namely a feed-forward neural network (FFNN), by incorporating an error correction step that involves the prediction of the initial forecast errors by another FFNN. The results showed that the proposed methodology was able to significantly improve the quality of load demand forecasts, demonstrating a better performance than the benchmark models. Full article

(This article belongs to the Special Issue Smart Energy Systems: Control and Optimization)

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21 pages, 3625 KiB

Open AccessArticle

An Optimized Adaptive Protection Scheme for Numerical and Directional Overcurrent Relay Coordination Using Harris Hawk Optimization

by Muhammad Irfan, Abdul Wadood, Tahir Khurshaid, Bakht Muhammad Khan, Ki-Chai Kim, Seung-Ryle Oh and Sang-Bong Rhee

Energies 2021, 14(18), 5603; https://doi.org/10.3390/en14185603 - 07 Sep 2021

Cited by 14 | Viewed by 1624

Abstract

The relay coordination problem is of dire importance as it is critical to isolate the faulty portion in a timely way and thus ensure electrical network security and reliability. Meanwhile a relay protection optimization problem is highly constraint and complicated problem to be [...] Read more.

The relay coordination problem is of dire importance as it is critical to isolate the faulty portion in a timely way and thus ensure electrical network security and reliability. Meanwhile a relay protection optimization problem is highly constraint and complicated problem to be addressed. To fulfill this purpose, Harris Hawk Optimization (HHO) is adapted to solve the optimization problem for Directional Over-current Relays (DOCRs) and numerical relays. As it is inspired by the intelligent and collegial chasing and preying behavior of hawks for capturing the prey, it shows quite an impressive result for finding the global optimum values. Two decision variables; Time Dial Settings (TDS) and Plug Settings (PS) are chosen as the decision variables for minimization of overall operating time of relays. The proposed algorithm is implemented on three IEEE test systems. In comparison to other state-of-the-art nature inspired and traditional algorithms, the results demonstrate the superiority of HHO. Full article

(This article belongs to the Special Issue Smart Energy Systems: Control and Optimization)

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16 pages, 2980 KiB

Open AccessArticle

Intelligent Dynamic Pricing Scheme for Demand Response in Brazil Considering the Integration of Renewable Energy Sources

by Diego B. Vilar and Carolina M. Affonso

Energies 2021, 14(16), 4839; https://doi.org/10.3390/en14164839 - 09 Aug 2021

Cited by 2 | Viewed by 1824

Abstract

This paper proposes a novel dynamic pricing scheme for demand response with individualized tariffs by consumption profile, aiming to benefit both customers and utility. The proposed method is based on the genetic algorithm, and a novel operator called mutagenic agent is proposed to [...] Read more.

This paper proposes a novel dynamic pricing scheme for demand response with individualized tariffs by consumption profile, aiming to benefit both customers and utility. The proposed method is based on the genetic algorithm, and a novel operator called mutagenic agent is proposed to improve algorithm performance. The demand response model is set by using price elasticity theory, and simulations are conducted based on elasticity, demand, and photovoltaic generation data from Brazil. Results are evaluated considering the integration effects of renewable energy sources and compared with other two pricing strategies currently adopted by Brazilian utilities: flat tariff and time-of-use tariff. Simulation results show the proposed dynamic tariff brings benefits to both utilities and consumers. It reduces the peak load and average cost of electricity and increases utility profit and load factor without the undesirable rebound effect. Full article

(This article belongs to the Special Issue Smart Energy Systems: Control and Optimization)

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Review

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34 pages, 3466 KiB

Open AccessReview

A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective

by Trinadh Pamulapati, Muhammed Cavus, Ishioma Odigwe, Adib Allahham, Sara Walker and Damian Giaouris

Energies 2023, 16(1), 289; https://doi.org/10.3390/en16010289 - 27 Dec 2022

Cited by 11 | Viewed by 3188

Abstract

The energy sector is undergoing a paradigm shift among all the stages, from generation to the consumer end. The affordable, flexible, secure supply–demand balance due to an increase in renewable energy sources (RESs) penetration, technological advancements in monitoring and control, and the active [...] Read more.

The energy sector is undergoing a paradigm shift among all the stages, from generation to the consumer end. The affordable, flexible, secure supply–demand balance due to an increase in renewable energy sources (RESs) penetration, technological advancements in monitoring and control, and the active nature of distribution system components have led to the development of microgrid (MG) energy systems. The intermittency and uncertainty of RES, as well as the controllable nature of MG components such as different types of energy generation sources, energy storage systems, electric vehicles, heating, and cooling systems are required to deploy efficient energy management systems (EMSs). Multi-agent systems (MASs) and model predictive control (MPC) approaches have been widely used in recent studies and have characteristics that address most of the EMS challenges. The advantages of these methods are due to the independent characteristics and nature of MAS, the predictive nature of MPC, and their ability to provide affordable, flexible, and secure MG operation. Therefore, for the first time, this state-of-the-art review presents a classification of the MG control and optimization methods, their objectives, and help in understanding the MG operational and EMS challenges from the perspective of the energy trilemma (flexibility, affordability, and security). The control and optimization architectures achievable with MAS and MPC methods predominantly identified and discussed. Furthermore, future research recommendations in MG-EMS in terms of energy trilemma associated with MAS, MPC methods, stability, resiliency, scalability improvements, and algorithm developments are presented to benefit the research community. Full article

(This article belongs to the Special Issue Smart Energy Systems: Control and Optimization)

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