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Novel Energy Management Approaches in Microgrid Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 20 March 2025 | Viewed by 8849

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Special Issue Editors

Dr. Dimitrios A. Tsiamitros

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Guest Editor

Department of Electrical & Computer Engineering, University of Western Macedonia, Kozani, Greece
Interests: distributed generation; smart grids; microgrids; electromagnetic compatibility; power system simulation

Dr. Dimitrios Stimoniaris

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Guest Editor

Department of Electrical & Computer Engineering, University of Western Macedonia, Kozani, Greece
Interests: distributed generation; smart grids; microgrids

Special Issue Information

Dear Colleagues,

Due to large amounts of distributed generation (DG) connected lately to the low-voltage (LV) and medium-voltage (MV) networks, many important issues have risen: (a) some parameters of the grid cannot be measured accurately, (b) more renewable energy units are difficult to connect to the grid, (c) regular maintenance activities are now difficult or even prohibited due to the thermal capability or to voltage values, and (d) electricity grid protection has become more complicated now. Therefore, reliable, cost-effective communication and control schemes are needed in order to ensure the stable operation of the grid and keep the power quality indices inside their limits. These energy management schemes should ensure: (a) reliable measurement, (b) interoperability of different communication protocols, (c) new control schemes and techniques of distributed generation with low cost intervention and legal compatibility, and (d) that the new devices on the grid must be taken into account, such as energy storage, electric vehicles and their charging systems.

Dr. Dimitrios A. Tsiamitros
Dr. Dimitrios Stimoniaris
Guest Editors

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Keywords

distributed generation (DG) control
energy management system interoperability
storage system control
electric vehicles to grid (V2G) and grid to electric vehicle (G2V) schemes
network operations in cases of increased DGs
microgrid management systems

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Published Papers (6 papers)

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Research

20 pages, 1959 KiB

Open AccessArticle

Predictive Analytics for Energy Efficiency: Leveraging Machine Learning to Optimize Household Energy Consumption

by Piotr Powroźnik and Paweł Szcześniak

Energies 2024, 17(23), 5866; https://doi.org/10.3390/en17235866 - 22 Nov 2024

Abstract

This paper presents a novel machine learning framework useful for optimizing energy consumption in households. Home appliances have a great potential to optimize electricity consumption by mitigating peaks in the grid load or peaks in renewable energy generation. However, such functionality of home appliances requires their users to change their behavior regarding energy consumption. One of the criteria that could encourage electricity users to change their behavior is the cost of energy. The introduction of dynamic energy prices can significantly increase energy costs for unsuspecting consumers. In order to be able to make the right decisions about the process of electricity use in households, an algorithm based on machine learning is proposed. The presented proposal for optimizing electricity consumption takes into account dynamic changes in energy prices, energy production from renewable energy sources, and home appliances that can participate in the energy optimization process. The proposed model uses data from smart meters and dynamic price information to generate personalized recommendations tailored to individual households. The algorithm, based on machine learning and historical household behavior data, calculates a metric to determine whether to send a notification (message) to the user. This notification may suggest increasing or decreasing energy consumption at a specific time, or may inform the user about potential cost fluctuations in the upcoming hours. This will allow energy users to use energy more consciously or to set priorities in home energy management systems (HEMS). This is a different approach than in previous publications, where the main goal of optimizing energy consumption was to optimize the operation of the power system while taking into account the profits of energy suppliers. The proposed algorithms can be implemented either in HEMS or smart energy meters. In this work, simulations of the application of machine learning with different characteristics were carried out in the MATLAB program. An analysis of machine learning algorithms for different input data and amounts of data and the characteristic features of models is presented. Full article

(This article belongs to the Special Issue Novel Energy Management Approaches in Microgrid Systems)

18 pages, 4921 KiB

Open AccessArticle

Optimisation of Integrated Heat Pump and Thermal Energy Storage Systems in Active Buildings for Community Heat Decarbonisation

by Zaid Al-Atari, Rob Shipman and Mark Gillott

Energies 2024, 17(21), 5310; https://doi.org/10.3390/en17215310 - 25 Oct 2024

Viewed by 546

Abstract

The electrification of residential heating systems, crucial for achieving net-zero emissions, poses significant challenges for low-voltage distribution networks. This study develops a simulation model to explore the integration of heat pumps within active building systems for community heating decarbonisation. The model optimises heat pump operations in conjunction with thermal energy storage units to reduce peak demand on low-voltage networks by using real-time measured electricity demand data and modelled heat demand data for 76 houses. The study employs an algorithm that adjusts thermal storage charging and discharging cycles to align with off-peak periods. Three scenarios were simulated: a baseline with unoptimised heat pumps, a fixed threshold model, and an active building model with daily optimised thresholds. The results demonstrate that the active building model achieves a 21% reduction in peak demand on the low-voltage substation compared to the baseline scenario; it also reduces the total electrical energy consumption by 12% and carbon emissions by 17%. The fixed threshold scenario shows a 16% improvement in peak demand reduction, but it also shows an increase in energy consumption and emissions. These findings highlight the potential of active buildings to enhance the efficiency and sustainability of residential energy systems, marking a significant step toward decarbonising residential heating while maintaining grid stability. Full article

(This article belongs to the Special Issue Novel Energy Management Approaches in Microgrid Systems)

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22 pages, 6571 KiB

Open AccessFeature PaperArticle

Integrated Optimal Energy Management of Multi-Microgrid Network Considering Energy Performance Index: Global Chance-Constrained Programming Framework

by Mohammad Hemmati, Navid Bayati and Thomas Ebel

Energies 2024, 17(17), 4367; https://doi.org/10.3390/en17174367 - 1 Sep 2024

Viewed by 773

Abstract

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed. Full article

(This article belongs to the Special Issue Novel Energy Management Approaches in Microgrid Systems)

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35 pages, 5814 KiB

Open AccessArticle

A Cost-Effective Energy Management Approach for On-Grid Charging of Plug-in Electric Vehicles Integrated with Hybrid Renewable Energy Sources

by Mohd Bilal, Pitshou N. Bokoro, Gulshan Sharma and Giovanni Pau

Energies 2024, 17(16), 4194; https://doi.org/10.3390/en17164194 - 22 Aug 2024

Viewed by 1118

Abstract

Alternative energy sources have significantly impacted the global electrical sector by providing continuous power to consumers. The deployment of renewable energy sources in order to serve the charging requirements of plug-in electric vehicles (PEV) has become a crucial area of research in emerging nations. This research work explores the techno-economic and environmental viability of on-grid charging of PEVs integrated with renewable energy sources in the Surat region of India. The system is designed to facilitate power exchange between the grid network and various energy system components. The chosen location has contrasting wind and solar potential, ensuring diverse renewable energy prospects. PEV charging hours vary depending on the location. A novel metaheuristic-based optimization algorithm, the Pufferfish Optimization Algorithm (POA), was employed to optimize system component sizing by minimizing the system objectives including Cost of Energy (COE) and the total net present cost (TNPC), ensuring a lack of power supply probability (LPSP) within a permissible range. Our findings revealed that the optimal PEV charging station configuration is a grid-tied system combining solar photovoltaic (SPV) panels and wind turbines (WT). This setup achieves a COE of USD 0.022/kWh, a TNPC of USD 222,762.80, and a life cycle emission of 16,683.74 kg CO₂-equivalent per year. The system also reached a 99.5% renewable energy penetration rate, with 3902 kWh/year of electricity purchased from the grid and 741,494 kWh/year of energy sold back to the grid. This approach could reduce reliance on overburdened grids, particularly in developing nations. Full article

(This article belongs to the Special Issue Novel Energy Management Approaches in Microgrid Systems)

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11 pages, 4067 KiB

Open AccessArticle

Distributed Generation Control Using Ripple Signaling and a Multiprotocol Communication Embedded Device

by Evangelos Boutsiadis, Nikolaos Pasialis, Nikolaos Lettas, Dimitrios Tsiamitros and Dimitrios Stimoniaris

Energies 2023, 16(22), 7604; https://doi.org/10.3390/en16227604 - 16 Nov 2023

Cited by 1 | Viewed by 1165

Abstract

Remotely performing real-time distributed generation control and a demand response is a basic aspect of the grid ancillary services provided by grid operators, both the transmission grid operators (TSOs) and distribution grid operators (DNOs), in order to ensure that voltage, frequency and power loads of the grid remain within safe limits. The stochastic production of electrical power to the grid from the distributed generators (DGs) from renewable energy sources (RES) in conjunction with the newly appeared stochastic demand consumers (i.e., electric vehicles) hardens the efforts of the DNOs to keep the grid’s operation within safe limits and prevent cascading blackouts while staying in compliance with the SAIDI and SAIFI indices during repair and maintenance operations. Also taking into consideration the aging of the existing grid infrastructure, and making it more prone to failure year by year, it is yet of great significance for the DNOs to have access to real-time feedback from the grid’s infrastructure—which is fast, has low-cost upgrade interventions, is easily deployed on the field and has a fast response potential—in order to be able to perform real-time grid management (RTGM). In this article, we present the development and deployment of a control system for DG units, with the potential to be installed easily to TSO’s and DNO’s substations, RES plants and consumers (i.e., charging stations of electric vehicles). This system supports a hybrid control mechanism, either via ripple signaling or through a network, with the latter providing real-time communication capabilities. The system can be easily installed on the electric components of the grid and can act as a gateway between the different vendors communication protocols of the installed electrical equipment. More specifically, a commercially available, low-cost board (Raspberry Pi) and a ripple control receiver are installed at the substation of a PV plant. The board communicates in real-time with a remote server (decision center) via a 5G modem and with the PV plants inverters via the Modbus protocol, which acquires energy production data and controls the output power of each inverter, while one of its digital inputs can be triggered by the ripple control receiver. The ripple control receiver receives on-demand signals with the HEDNO, triggering the digital input on the board. When the input is triggered, the board performs a predefined control command (i.e., lower the inverter’s power output to 50%). The board can also receive control commands directly from the remote server. The remote server receives real-time feedback of the acquired inverter data, the control signals from the ripple control receiver and the state and outcome of each performed control command. Full article

(This article belongs to the Special Issue Novel Energy Management Approaches in Microgrid Systems)

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25 pages, 3423 KiB

Open AccessArticle

Load Frequency Control in Two-Area Multi-Source Power System Using Bald Eagle-Sparrow Search Optimization Tuned PID Controller

by T. Dharma Raj, C. Kumar, Panos Kotsampopoulos and Hady H. Fayek

Energies 2023, 16(4), 2014; https://doi.org/10.3390/en16042014 - 17 Feb 2023

Cited by 17 | Viewed by 3999

Abstract

For power system engineers, automated load frequency control (LFC) for multi-area power networks has proven a difficult problem. With the addition of numerous power generation sources, the complexity of these duties becomes even more difficult. The dynamic nature of linked power networks with varied generating sources, such as gas, thermal, and hydropower plants, is compared in this research. For the study to be more accurate, frequency and tie-line power measurements are used. For precise tuning of proportional-integral-derivative (PID) controller gains, the Bald Eagle Sparrow search optimization (BESSO) technique was used. The BESSO algorithm was created by combining the characteristics of sparrows and bald eagles. The performance of BESSO is determined by comparing its findings to those acquired using traditional approaches. In terms of Integral Time Absolute Error (ITAE), which is the most important criterion used to reduce system error, the findings presented in this study indicate the effectiveness of the BESSO-PID controller. Finally, sensitivity analysis and stability analysis proved the robustness of the developed controller. The settling times associated with the tie-line power flow, frequency variation in area-1, and frequency variation in area-2, respectively, were 10.4767 s, 8.5572 s, and 11.4364 s, which were all less than the traditional approaches. As a result, the suggested method outperformed the other strategies. Full article

(This article belongs to the Special Issue Novel Energy Management Approaches in Microgrid Systems)

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