An Interoperable EMS for the Provision of Grid Services with Hybrid Energy Storage Systems

Unamuno, Eneko; Polat, Hakan; Cabezuelo, D.; Galarza, J.; Anta, Adolfo; Toutain, E.; Geury, Thomas; Hegazy, Omar

doi:10.1109/iecon49645.2022.9968746

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…In this section two relatively different use cases are described. They will be used in subsequent sections to compare the performance of the proposed MLBEMS versus an updated version of the RBEMS proposed in [25]. These use cases represent the Pan-European grid scenario and an EV charging station with PV generation.…”

Section: Use Case Descriptionsmentioning

confidence: 99%

“…Using a standard frequency support algorithm presented in [25], the power demand from the HESS is given in Fig. 2.…”

Section: A Use Case 1: Pan-european Gridmentioning

confidence: 99%

“…The aim of this paper is to compare the improved interoperable rule-based EMS (RBEMS) developed in [25], to machine learning-based EMS (MLBEMS). MLBEMS aims to include the consumed life of the batteries and provide a power-sharing such that the overall system lifetime is improved and hence the operational costs are reduced.…”

Section: Introductionmentioning

confidence: 99%

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Development and Comparison of Rule- and Machine Learning-Based EMS for HESS Providing Grid Services

Polat,

Unamuno,

Cabezuelo

et al. 2024

IEEE Access

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In this paper, a smart machine-learning-based energy management system (MLBEMS) is developed for a hybrid energy storage system (HESS). This HBESS consists of batteries with high-energy (HE) and high-power (HP) characteristics, to provide grid-supporting services. The aim of the MLBEMS is to improve the overall battery lifetime and achieve state-of-charge (SoC) balancing for two different use cases (UC). UC1 involves enhanced frequency regulation for the Pan-European grid, while UC2 pertains to an electric vehicle (EV) charging station with photovoltaic (PV) generation. The designed MLBEMS is compared with a rule-based energy management system (RBEMS) from the literature with similar use cases. To ensure optimal power sharing between the battery modules, an optimization model is created using real battery aging data. Using a genetic algorithm, optimal power sharing is achieved for various initial SoC conditions. The generated dataset is subsequently utilized to train a machine-learning regression model, and the resulting prediction function is imported into MATLAB/Simulink. In UC1, MLBEMS achieved a 39.3% better SoC balancing compared to RBEMS, along with 36.5% and 22.6% higher battery lifetimes for HE and HP batteries, respectively. Similarly, for UC2, MLBEMS achieved a 68.5% improvement in SoC balancing, along with 53.6% and 45.8% higher battery lifetimes for HE and HP batteries, respectively.

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Section: Use Case Descriptionsmentioning

confidence: 99%

“…Using a standard frequency support algorithm presented in [25], the power demand from the HESS is given in Fig. 2.…”

Section: A Use Case 1: Pan-european Gridmentioning

confidence: 99%

See 1 more Smart Citation

Development and Comparison of Rule- and Machine Learning-Based EMS for HESS Providing Grid Services

Polat,

Unamuno,

Cabezuelo

et al. 2024

IEEE Access

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“…Energy storage systems in the power grid can play a big role in the optimal and reliable operation of the system. Energy storage systems can improve the voltage profile, maximize the penetration level of renewable energy, minimize carbon dioxide emissions, reduce the power losses and minimize the operation cost of the power system at both transmission and distribution levels [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Pharmacophore-Modeling-Based Optimal Placement and Sizing of Large-Scale Energy Storage Stations in a Power System including Wind Farms

Fayek,

Rusu

2023

Applied Sciences

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The world is targeting fully sustainable electricity by 2050. Energy storage systems have the biggest role to play in the 100% renewable energy scenario. This paper presents an optimal method for energy storage sizing and allocation in a power system including a share of wind farms. The power system, which is used as a test system, is a modified version of the IEEE 39 bus system. The optimization is applied using novel pharmacophore modeling (PM), which is compared to state-of-the-art techniques. The objective of the optimization is to minimize the costs of power losses, peak demand and voltage deviation. The PM optimization is applied using two methods, namely, weighting factor and normalization. The optimization and simulation are applied in the DIgSILENT power factory software application. The results show that normalization of PM optimization drives the power system to less cost in terms of total power losses by up to 29% and voltage deviation by up to 4% and better covers peak demand than state-of-the-art optimization techniques.

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Assessing the Impact of EV Charging and Discharging Profiles on T-Type Active Front End Charger Lifetime

Polat,

Hosseinabadi,

Chakraborty

et al. 2023

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society

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An Interoperable EMS for the Provision of Grid Services with Hybrid Energy Storage Systems

Cited by 3 publications

References 10 publications

Development and Comparison of Rule- and Machine Learning-Based EMS for HESS Providing Grid Services

Development and Comparison of Rule- and Machine Learning-Based EMS for HESS Providing Grid Services

Pharmacophore-Modeling-Based Optimal Placement and Sizing of Large-Scale Energy Storage Stations in a Power System including Wind Farms

Assessing the Impact of EV Charging and Discharging Profiles on T-Type Active Front End Charger Lifetime

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