Dimensioning battery energy storage systems for peak shaving based on a real-time control algorithm

Lange, Christopher; Rueß, Alexandra; Nuß, Andreas; Öchsner, Richard; März, Martin

doi:10.1016/j.apenergy.2020.115993

Cited by 30 publications

(6 citation statements)

References 26 publications

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“…In [14], Lange et al developed a model to simulate the behavior of the BESS when providing peak shaving. The model developed uses Look-Up Tables (LUT) to model the efficiency of converters and the capability limits of the BESS.…”

Section: Bess Models and Case Studies From The Literaturementioning

confidence: 99%

Battery Energy Storage System Performance in Providing Various Electricity Market Services

Jaffal,

Guanetti,

Rancilio

et al. 2024

Batteries

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The Battery Energy Storage System (BESS) is one of the possible solutions to overcoming the non-programmability associated with these energy sources. The capabilities of BESSs to store a consistent amount of energy and to behave as a load by releasing it ensures an essential source of flexibility to the power system. Nevertheless, BESSs have some drawbacks that pose limitations to their utilization. Indeed, effectively managing the stored and released energy is crucial, considering the degradation of performance associated with these systems over time. The substantial capital expenditure (CAPEX) required to install these systems represents a current constraint, impeding their broader adoption. This work evaluates a techno-economic analysis of a 2MW/2MWh BESS providing multiple services, namely participating in capacity and balance markets. The analysis is based on a BESS model implemented in SIMULINK, adopting online data gathered from a Lithium Iron Phosphate (LFP) battery facility. The model evaluates the auxiliary power consumption, state-of-charge (SoC), state of health (SoH), and the round-trip efficiency (RTE) of the overall system. The analysis is based on three price profiles: 2019 (Business-As-Usual), 2020 (COVID-19), and 2022 (Gas Crisis). Furthermore, this work conducts a case study to analyze the behavior of the BESS. It entails a sensitivity analysis, specifically evaluating the influence of CAPEX and upward bid price on the economic viability of the project. The results show a strong relation between the CAPEX variation and the Internal Rate of Return (IRR) of the project.

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Section: Bess Models and Case Studies From The Literaturementioning

confidence: 99%

Battery Energy Storage System Performance in Providing Various Electricity Market Services

Jaffal,

Guanetti,

Rancilio

et al. 2024

Batteries

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“…In this paper, a heuristic strategy is proposed that controls the BESS setpoints in a real-time to flatten the grid power. This strategy utilises power thresholds to control the BESS as done previously in [24,25]. However, the coordination optimization of multiple BESS is considered by conducting online OPF to determine the multiple BESS dispatch with minimum losses.…”

Section: Real-time Operation Strategymentioning

confidence: 99%

“…In [24], a management algorithm is proposed that utilises a heuristic rule‐based approach in controlling the BESS in real‐time for peak shaving in islanded microgrids. Another BESS heuristic control approach is introduced in [25] for peak shaving by dimensioning BESS parameters. Both studies utilise power threshold in controlling the BESS power for peak‐shaving.…”

Section: Introductionmentioning

confidence: 99%

Distributed battery energy storage systems operation framework for grid power levelling in the distribution networks

et al. 2021

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The integration of battery energy storage systems (BESS) in the electrical grid is accelerating to mitigate the challenges associated with the rapid deployment of low carbon technologies (LCTs). This work investigates the ability of BESS to provide the power networks with important ancillary services such as peak shaving and grid power levelling through two case studies conducted in collaboration with Northern Ireland's distribution network operator. A powerful approach consisting of two strategies is developed to operate the BESS powerfully to enhance the operation of the distribution network. The first strategy is day‐ahead scheduling that aims to dispatch the distributed BESS to smooth the grid power and mitigate voltage and lines stresses. A powerful demand forecasting algorithm is utilised to efficiently apply the day‐ahead scheduling. The second strategy is a real‐time operation to flatten the grid power which can be used separately or to adjust the results obtained from the day‐ahead strategy against the forecasting errors. The proposed approach was validated using real measurements and applied to an 11 kV distribution network located in Northern Ireland, UK. The BESS expected revenues from the participation in different services available in the island of Ireland are quantified and the degradation is considered.

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“…These forecasts use production planning data and weather forecasts to predict load curves for the electricity, heating and cooling sectors. They can be used to generate an operating strategy for the EGI components in order, for example, to charge or discharge storage facilities in an optimized manner, to run energy plants at the optimal operating point and to reduce load peaks [9,12].…”

Section: Introductionmentioning

confidence: 99%

Increasing Energy Efficiency and Flexibility by Forecasting Production Energy Demand Based on Machine Learning

Trenz

Hoffmann

Lange

et al. 2023

Lecture Notes in Mechanical Engineering

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The ability of manufacturing companies to compete depends strongly on the efficient use of production resources and the flexibility to adapt to changing production conditions. Essential requirements for the energetic infrastructure (EGI) result from the production itself, e.g., security of supply, efficiency and peak shaving. Since production always takes priority and must not be disturbed, the flexibility potential in terms of energy efficiency lies primarily in the EGI. Based on this, strategies will be developed that support companies in increasing their efficiency and flexibility by optimizing the configuration and operation of the EGI, while production processes are reliably supplied and not adapted. This is reached with intelligent operation strategies for the heating and cooling network based on forecasts, the use of energy storage systems, and the coupling of energy sectors. This paper presents an approach for energy forecasts used for the optimization of operation strategies. Hence, an energy-forecast-tool was developed, which is used for the prediction of electrical and thermal loads depending on the expected production. Therefore, machine learning models are trained with past weather, energy, and production data. Using production planning data and weather forecasts, the model can predict energy demands as input for an EGI optimization.

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Dimensioning battery energy storage systems for peak shaving based on a real-time control algorithm

Cited by 30 publications

References 26 publications

Battery Energy Storage System Performance in Providing Various Electricity Market Services

Battery Energy Storage System Performance in Providing Various Electricity Market Services

Distributed battery energy storage systems operation framework for grid power levelling in the distribution networks

Increasing Energy Efficiency and Flexibility by Forecasting Production Energy Demand Based on Machine Learning

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