Hardware-in-the-Loop Validation of an Energy Management System for LV Distribution Networks with Renewable Energy Sources

Kelm, Paweł; Wasiak, I.; Mieński, R.; Wędzik, Andrzej; Szypowski, Michał; Pawełek, R.; Szaniawski, Krzysztof

doi:10.3390/en15072561

Cited by 11 publications

(6 citation statements)

References 25 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, the use of AI in attack vectors may increase in the coming years. This shift might be consistent with the growing (noncriminal) use of AI across a range of fields and daily applications [48][49][50][51][52][53]. The novel approach described in this article was created to evaluate the security risks associated with various communication networks of energy management and control system infrastructures.…”

Section: Discussionmentioning

confidence: 72%

Computational Study of Security Risk Evaluation in Energy Management and Control Systems Based on a Fuzzy MCDM Method

Alhakami

2023

Processes

View full text Add to dashboard Cite

Numerous cyberattacks on connected control systems are being reported every day. Such control systems are subject to hostile external attacks due to their communication system. Network security is vital because it protects sensitive information from cyber threats and preserves network operations and trustworthiness. Multiple safety solutions are implemented in strong and reliable network security plans to safeguard users and companies from spyware and cyber attacks, such as distributed denial of service attacks. A crucial component that must be conducted prior to any security implementation is a security analysis. Because cyberattack encounters in power control networks are currently limited, a comprehensive security evaluation approach for power control technology in communication networks is required. According to previous studies, the challenges of security evaluation include a power control process security assessment as well as the security level of every control phase. To address such issues, the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) based on multiple criteria decision-making (MCDM) is presented for a security risk assessment of the communication networks of energy management and control systems (EMCS). The methodology focuses on quantifying the security extent in each control step; in order to value the security vulnerability variables derived by the protection analysis model, an MCDM strategy incorporated as a TOPSIS is presented. Ultimately, the example of six communication networks of a power management system is modelled to conduct the security evaluation. The outcome validates the utility of the security evaluation.

show abstract

Section: Discussionmentioning

confidence: 72%

Computational Study of Security Risk Evaluation in Energy Management and Control Systems Based on a Fuzzy MCDM Method

Alhakami

2023

Processes

View full text Add to dashboard Cite

show abstract

“…The authors used mixed integer linear programming (MILP) as an example of microgrid cost optimization to validate the model. RS-485 serial communication technology establishes a connection between the energy management system (EMS) and the generators to regulate the battery charging state 45 . The objective is to minimize operating costs while maximizing economic benefits.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model

Alsulami,

Abu Al-Haija,

Alturki

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Microgrids are small-scale energy system that supplies power to homes, businesses, and industries. Microgrids can be considered as a trending technology in energy fields due to their power to supply reliable and sustainable energy. Microgrids have a mode called the island, in this mode, microgrids are disconnected from the major grid and keep providing energy in the situation of an energy outage. Therefore, they help the main grid during peak energy demand times. The microgrids can be connected to the network, which is called networked microgrids. It is possible to have flexible energy resources by using their enhanced energy management systems. However, connection microgrid systems to the communication network introduces various challenges, including increased in systems complicity and noise interference. Integrating network communication into a microgrid system causes the system to be susceptible to noise, potentially disrupting the critical control signals that ensure smooth operation. Therefore, there is a need for predicting noise caused by communication network to ensure the operation stability of microgrids. In addition, there is a need for a simulation model that includes communication network and can generate noise to simulate real scenarios. This paper proposes a classifying model named Noise Classification Simulation Model (NCSM) that exploits the potential of deep learning to predict noise levels by classifying the values of signal-to-noise ratio (SNR) in real-time network traffic of microgrid system. This is accomplished by initially applying Gaussian white noise into the data that is generated by microgrid model. Then, the data has noise and data without noise is transmitted through serial communication to simulate real world scenario. At the end, a Gated Recurrent Unit (GRU) model is implemented to predict SNR values for the network traffic data. Our findings show that the proposed model produced promising results in predicting noise. In addition, the classification performance of the proposed model is compared with well-known machine learning models and according to the experimental results, our proposed model has noticeable performance, which achieved 99.96% classification accuracy.

show abstract

“…The first setup was a conventional PHIL setup and the second was a simplified setup based on a quasi-dynamic PHIL approach, which involves fast and continuously steady-state load flow calculations. With a focus on distribution networks with a high penetration of renewable energy sources, the work in [17] used real-time digital simulation and hardwarein-the-loop methods for the validation of prototype controllers. The work in [18] used PHIL to validate a voltage regulation scheme for distribution networks with renewables.…”

Section: Related Literaturementioning

confidence: 99%

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

Faria

Vale

2022

Energies

View full text Add to dashboard Cite

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.

show abstract

Hardware-in-the-Loop Validation of an Energy Management System for LV Distribution Networks with Renewable Energy Sources

Cited by 11 publications

References 25 publications

Computational Study of Security Risk Evaluation in Energy Management and Control Systems Based on a Fuzzy MCDM Method

Computational Study of Security Risk Evaluation in Energy Management and Control Systems Based on a Fuzzy MCDM Method

Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model

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

Contact Info

Product

Resources

About