Optimal Energy Storage System Operation for Peak Reduction in a Distribution Network Using a Prediction Interval

Kodaira, Daisuke; Jung, Won-Wook; Han, Sekyung

doi:10.1109/tsg.2019.2949573

Cited by 44 publications

(43 citation statements)

References 26 publications

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“…Peak shaving applications are investigated in [4]- [5] for planning purposes, to examine the location, sizing and costbenefit of the ESSs. In addition, peak shaving services provided to distribution grids using BESSs are proposed in [6]- [11] for operational purposes using optimization methods. These services are provided by minimizing the daily peak power [6]- [9] or the square of the power drawn from the feeder [10].…”

Section: Introductionmentioning

confidence: 99%

Energy Management and Control of a Flywheel Storage System for Peak Shaving Applications

Tziovani

Hadjidemetriou

Charalampous

et al. 2021

IEEE Trans. Smart Grid

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Peak shaving applications provided by energy storage systems enhance the utilization of existing grid infrastructure to accommodate the increased penetration of renewable energy sources. This work investigates the provision of peak shaving services from a flywheel energy storage system installed in a transformer substation. A lexicographic optimization scheme is formulated to define the flywheel power set-points by minimizing the transformer power limit violations and the flywheel energy losses. Convex functions that represent the flywheel power losses and its maximum power are derived and integrated in the proposed scheme. A two-level hierarchical control framework is introduced to operate the transformer-flywheel-system in a way that handles prediction errors and modelling inaccuracies. At the higher level, a model predictive controller is developed that solves the lexicographic optimization scheme using linear programming. At the lower-level, a secondary controller corrects the power set-points of the model predictive controller using realtime measurements. A software platform has been developed for integrating the proposed controllers in an experimental setup to test their effectiveness in a realistic testbed setting, and the flywheel system characteristics are experimentally identified. Simulation and experimental results validate and verify the modelling, identification, control and operation of a real flywheel system for peak shaving services.

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Section: Introductionmentioning

confidence: 99%

Energy Management and Control of a Flywheel Storage System for Peak Shaving Applications

Tziovani

Hadjidemetriou

Charalampous

et al. 2021

IEEE Trans. Smart Grid

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“…These point prediction methods have been applied in the field of temperature prediction, including back-propagation neural network [19,20], artificial neural network [21], gray model [22], genetic algorithm [23,24], and support vector machine [25,26]. However, point prediction only provides one prediction result at each target point, which lacks uncertainty estimation [27,28]. For concrete temperature data in the construction period, due to the existence of data noise in monitored temperature data, the complex and changeable external factors, as well as the subjective uncertainty of sample selection, the point prediction method experiences difficulty in quantifying the uncertainties of the concrete temperature prediction values.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

et al. 2020

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Joint grouting simulation is an important aspect of arch dam construction simulation. However, the current construction simulation model simplifies the temperature factors in joint grouting simulation, which leads to the difference between the simulation results and the actual construction schedule. Furthermore, the majority of existing temperature prediction research is based on deterministic point predictions, which cannot quantify the uncertainties of the prediction values. Thus, this study presents a real-time construction simulation method coupling a concrete temperature field interval prediction model to address these problems. First, a real-time construction simulation model is established. Secondly, this paper proposes a concrete temperature interval prediction method based on the hybrid-kernel relevance vector machine (HK-RVM) with the improved grasshopper optimization algorithm (IGOA). The hybrid-kernel method is adopted to ensure the prediction accuracy and generalization ability of the model. Additionally, the improved grasshopper optimization algorithm (IGOA), which utilizes the tent chaotic map and cosine adaptive method to improve the algorithm performance, is developed for the parameter optimization of HK-RVM. Thirdly, concept drift detection based on variable window technology is proposed to update the prediction model. Finally, an arch dam project in China is used as a case study, by which the superiority and applicability of the proposed method are proven.

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“…Peak shaving applications are investigated in [3]- [5] for planning purposes, to examine the location, sizing and the cost-benefit of the ESSs in distribution feeders. Optimization methods to provide peak shaving services are proposed in [6]- [9]. The methods proposed in [6]- [7] aim to reduce the peak load of the feeder for the next day by scheduling the charging/discharging of a battery energy storage system (BESS).…”

Section: Introductionmentioning

confidence: 99%

“…Optimization methods to provide peak shaving services are proposed in [6]- [9]. The methods proposed in [6]- [7] aim to reduce the peak load of the feeder for the next day by scheduling the charging/discharging of a battery energy storage system (BESS). A quadratic multi-objective optimization scheme is presented in [8] that minimizes both the square of the power drawn from the feeder to achieve peak shaving and the BESS life-cycle cost to increase the BESS lifetime.…”

Section: Introductionmentioning

confidence: 99%

Modelling and energy management of a flywheel storage system for peak shaving applications

Tziovani

Hadjidemetriou

Charalampous

et al. 2020

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

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Peak shaving applications provided by energy storage systems are sustainable solutions for enhancing the existing capacity of distribution feeders and transformers in order to maintain their safe and reliable operation under an increased penetration of renewable energy sources and load demand growth. This work investigates the integration of a flywheel energy storage system installed in a feeder of a distribution network to provide peak shaving services. An empirical model is defined to determine the energy losses of a prototype flywheel system using an experimental setup. Furthermore, a multiobjective optimization scheme is proposed to minimize the flywheel energy losses along with the violated peak power of the feeder. Three different objective functions for applying peak shaving are presented and their efficiency is investigated in the simulation results. Finally, the impact of the flywheel energy losses on the peak shaving application of the distribution feeder is examined using a prototype and a commercial-grade flywheel energy storage system.

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Optimal Energy Storage System Operation for Peak Reduction in a Distribution Network Using a Prediction Interval

Cited by 44 publications

References 26 publications

Energy Management and Control of a Flywheel Storage System for Peak Shaving Applications

Energy Management and Control of a Flywheel Storage System for Peak Shaving Applications

Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

Modelling and energy management of a flywheel storage system for peak shaving applications

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