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This paper proposes a multi-agent system for energy management in a microgrid for smart home applications, the microgrid comprises a photovoltaic source, battery energy storage, electrical loads, and an energy management system (EMS) based on smart agents. The microgrid can be connected to the grid or operating in island mode. All distributed sources are implemented using MATLAB/Simulink to simulate a dynamic model of each electrical component. The agent proposed can interact with each other to find the best strategy for energy management using the java agent development framework (JADE) simulator. Furthermore, the proposed agent framework is also validated through a different case study, the efficiency of the proposed approach to schedule local resources and energy management for microgrid is analyzed. The simulation results verify the efficacy of the proposed approach using Simulink/JADE co-simulation.
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Optimizing Microgrid Energy Management Systems with Variable Renewable Energy Penetration: Analysis of Data Loss Effects
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This study presents a multi-layered microgrid system with an optimization-based energy management system, where the impact of renewable energy penetration and data loss in battery command is investigated. Data loss in battery command can cause voltage instability, energy supply loss, and increased operational costs in microgrid systems, especially in electricity markets. The simulation results show that on average, more data loss results in higher operational costs, but there are situations where less data loss can be more detrimental to microgrid operation than higher levels of data loss. This research provides valuable insights into the effects of data loss in battery command and its potential economic impact on microgrid operation.
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Das, S., Wang, Z. (2023). Optimizing Microgrid Energy Management Systems with Variable Renewable Energy Penetration: Analysis of Data Loss Effects. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2023, Volume 2. FTC 2023. Lecture Notes in Networks and Systems, vol 814. Springer, Cham. https://doi.org/10.1007/978-3-031-47451-4_26
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A novel approach to energy management with power quality enhancement in hydrogen based microgrids through numerical simulation.
1. Introduction
- The proposed research design offers notable returns over traditional microgrid design approaches, including the following: Emphasizing the significance of proposed design and energy management optimization in MG systems involves developing a comprehensive cost-driven approach to optimize DERs. This approach considers the integration of hydrogen ( H 2 ) storage and electrolyzer within a grid-tied operation mode.
- The problem formulation integrates economic and environmental considerations, particularly focusing on the utilization of an electrolyzer for hydrogen ( H 2 ) production to FCs. The analysis explores the ideal scale for various resources in the energy system operation, emphasizing the potential for greater financial returns through increased integration of RESs.
- Rule-based fuzzy logic consistently outperforms traditional hydrogen-based microgrid design methods in terms of availability, present cost, power efficacy, and power quality enhancement.
- In the context of hydrogen-based microgrid context, the PSO method revolutionized the construction of MG architecture, streamlining the initial design phase and elevating system performance. Economic competence for the hydrogen-based microgrid is enhanced by proper energy management. PSO converges power within the constraints and improves the power quality, ultimately with the expected lowest cost.
- The obtained outcomes show that the proposed method is vigorous and beats the other traditional techniques in terms of faster performance, lowest costs, inferior level of harmonics, improved battery state of charge (SoC), more FC load power, and increased overall efficacy.
2. System Model
2.1. pv power management, 2.2. wind power management, 2.3. hydrogen power management, 2.4. grid integration, 3. energy management system, 4. simulation and results, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest, nomenclature.
| HBESS | Hydrogen-Based Energy Storage Systems | NSRDB | National Solar Radiation Database |
| HMGs | Hybrid Microgrids | NOCT | Nominal Operating Cell Temperature |
| FCs | Fuel Cells | WT | Wind Turbine |
| RESs | Renewable Energy Resources | HT | Hydrogen Tank |
| EMS | Energy Management System | Hydrogen (kg) | |
| DERs | Distributed Energy Resources | Carbon dioxide (kg) | |
| ESS | Energy Storage systems | PSO | Particle Swarm Optimization |
| SoC | State of Charge | MPPT | Maximum Power Point Tracking |
| IMES | Integrated Management System | NP-Cost | Net Present Cost ($) |
| PEMFC | Proton Exchange Membrane Fuel cell | NP- | Net present cost of solar ($) |
| BESS | Battery Energy Storage System | NP- | Net present cost of wind ($) |
| PV | Photovoltaic | NP- | Net present cost of fuel cell ($) |
| Solar Irradiation ( ) | Flow rate values oxygen ( ) | ||
| Ambient temperature (°C) | Hydrogen-based electrolyzer flow rates ( ) | ||
| Cell temperature (°C) | Hydrogen-based fuel cell flow rates ( ) | ||
| Area of solar ( ) | Electrolyzer cell flow rates of oxygen ( ) | ||
| Temperature coefficient ( ) | Fuel cell flow rates of oxygen ( ) | ||
| Output power of a solar panel (kW) | Minimum hydrogen energy ( ) | ||
| Number of cells | Maximum hydrogen energy ( ) | ||
| and | Wind velocities (m/s) | Precise carbon emission (kg/kWh) | |
| Reference height (m) | Emission penalty ($/kg) | ||
| Output power of wind turbine (kW) | Power generated from solar (kW) | ||
| Cut-in velocity ( ) | Power generated from wind (kW) | ||
| Cut-off speed ( ) | Power generated from fuel cells (kW) | ||
| Volume of hydrogen ( ) | Hydrogen quantity by electrolyzers (kg) | ||
| . | Volume of oxygen ( ) | Hydrogen quantity by fuel cells (kg) | |
| Flow rate values of hydrogen ( ) | , | Efficiencies of corresponding inverters (%) | |
| Efficiency of electrolyzer (%) | |||
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Click here to enlarge figure
| Ref. | EMS Strategy | Contribution | Constraints |
|---|---|---|---|
| [ ] | Moth-Flame Optimization (MFO) | An Integrated Management System (IMES) has been devised to oversee energy management. Achieved 1.287% reduction in the operational cost. Accomplished its objectives within an impressive time frame of under 150 min. | Taking power and cost under consideration while taking much time about to more than 150 min. |
| [ ] | Amended Penguin Optimization Algorithm (APOA) | This research focuses on reducing steady-state error and improvement in power quality. Addressing Sag and Swell situations are discussed for enhancement of systems efficiency. | Power balancing only. |
| [ ] | African Vulture’s Optimization Algorithm (AVO) | Techno-economic goals have been addressed comprehensively. Assessing daily operational expenses, including operational costs. BESS performance improved by using AVO. | Battery performance and operational cost. |
| [ ] | Marine Predator Algorithm (MPA) | Introduces a centralized control system designed to manage large-scale distributed energy resources. To improve the microgrid performance, an adaptive dynamic voltage restorer (ADVR) with PID based on MPA has been utilized. This research includes a comparative analysis with two other distinct methods. | Power maintenance condition is under consideration. |
| [ ] | SAC and XGBoost | A methodology has been devised that leverages the SAC algorithm as the decision-making tool for Home Energy Management Systems (HEMS). The comprehensive evaluation included an assessment of internal system factors as well as external environmental aspects. | Charging and discharging of electric vehicles, battery constraints and operational appliances limitations. |
| [ ] | Quantum Teaching Learning-Based Optimization (QTLBO) | A daily optimal scheduling framework has been developed and implemented. Reduced the standard deviation in stochastic scenarios to a minimum value. | Power balancing and considering only global values for different sources. |
| [ ] | Mixed Integer Linear Programming (MILP) | This study introduces an approach for storage systems, offering a new perspective on energy storage solutions. A techno-economic approach primarily focuses on future homes as self-sufficient entities. | Balancing of power and storage only. |
| Wind Speed (m/s) | Electrical Load (p.u.) | |
|---|---|---|
| 422 | 6.45 | 0.6211 |
| Unit Type | Solar Panel ($/kW) | Wind System ($/kW) | FCs ($/kW) | Electrolyzer ($/kW) |
|---|---|---|---|---|
| Capital Cost | 1000 | 950 | 600 | 150 |
| Solar Panel (kW) | Wind System (kW) | FCs (kW) | Electrolyzer (kW) | Cost (T $) |
|---|---|---|---|---|
| 700 | 670 | 900 | 440 | 25 |
| Solar Panel ($/kW) | Wind System ($/kW) | FCs ($/kW) | Electrolyzer ($/kW) | Total Cost after Assessment $ | Time or Total Number of Iterations | Voltage Convergence | Power Quality Improvement | |
|---|---|---|---|---|---|---|---|---|
| Ref. [ ] | 360 | 200 | 150 | - | - | 3500 s | No | No |
| Ref. [ ] | 5 | 5 | 5 | 30 | - | 1408 s | No | No |
| Ref. [ ] | 60 | - | 2 | 45 | - | - | No | No |
| Ref. [ ] | 770 | 900 | 907 | 439 | to | 200 iterations | No | No |
| In this research | 700 | 670 | 900 | 440 | to | 100 iterations | Yes | Yes |
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Share and Cite
Qamar, H.G.M.; Guo, X.; Ghith, E.; Tlija, M. A Novel Approach to Energy Management with Power Quality Enhancement in Hydrogen Based Microgrids through Numerical Simulation. Appl. Sci. 2024 , 14 , 7607. https://doi.org/10.3390/app14177607
Qamar HGM, Guo X, Ghith E, Tlija M. A Novel Approach to Energy Management with Power Quality Enhancement in Hydrogen Based Microgrids through Numerical Simulation. Applied Sciences . 2024; 14(17):7607. https://doi.org/10.3390/app14177607
Qamar, Hafiz Ghulam Murtza, Xiaoqiang Guo, Ehab Ghith, and Mehdi Tlija. 2024. "A Novel Approach to Energy Management with Power Quality Enhancement in Hydrogen Based Microgrids through Numerical Simulation" Applied Sciences 14, no. 17: 7607. https://doi.org/10.3390/app14177607
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Utilizing Ai for Efficient Energy Management: An Analysis of Tehran
21 Pages Posted: 29 Aug 2024
AmirAli Saifoddin Asl
Tehran University
Ali Majnoon
In order to better control Tehran's energy use and make predictions about it, this study looked at using advanced machine learning and deep learning models. In order to achieve such high projected accuracy, the study used a wide variety of models, such as Neural Networks, Random Forest Regression, and Gradient Boosting. The study's dataset included information from 2000 to 2022. With an R-squared value of 0.9835 and an MSE of 0.0165, the Random Forest Regression model demonstrated exceptional performance. This means that 98.35% of the variance in power consumption was explained by the model. Feature engineering was crucial in improving the effectiveness of the model, with temperature-related variables and fuel use being identified as the most influential predictors. The study utilized Sequential Least Squares Programming (SLSQP) to optimize the models, which led to the discovery of circumstances that reduced electricity consumption to a minimum of 1.09 million kWh. The correlation matrix research has substantiated the robust connections between environmental elements and energy usage, offering valuable insights for forthcoming energy management techniques. In summary, the research effectively showcased the capability of AI-driven models to enhance energy efficiency and mitigate carbon emissions in metropolitan environments such as Tehran.
Keywords: Energy Management, Machine Learning, Deep Learning, Electricity Consumption Forecasting, Artificial Intelligence
Suggested Citation: Suggested Citation
AmirAli Saifoddin Asl (Contact Author)
Tehran university ( email ), do you have a job opening that you would like to promote on ssrn, paper statistics, related ejournals, artificial intelligence ejournal.
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