Optimal ESS size calculation for ramp rate control of grid-connected microgrid based on the selection of accurate representative days

Tahir, Hira; Park, Dong-Hwan; Park, Su-Seong; Kim, Rae-Young

doi:10.1016/j.ijepes.2022.108000

Cited by 22 publications

(8 citation statements)

References 44 publications

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“…The objective function (5) aims to minimize the total cost C total associated with the G&TEP model. This includes the investment costs of the power generation equipment C inv shown in equation ( 6), operational costs C ope shown in Equation ( 8), DR costs C dr shown in Equation ( 9), costs of curtailing renewable energy resources C newcurt shown in Equation (10), and costs of load loss C loadloss shown in Equation (11). Moreover, Equation ( 7) calculates the discount rate for the investment costs.…”

Section: Objectivementioning

confidence: 99%

“…This module was designed to simulate long-term operational conditions in an uncertain environment [9]. However, the complex nature of generation and transmission expansion planning (G&TEP) problems, which often span a large geographical area over several decades, presents a significant challenge in integrating a long-term operation simulation model into the planning process [10,11]. To maintain computational tractability, prevalent G&TEP models typically adopt a lower temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

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A closed‐loop representative day selection framework for generation and transmission expansion planning with demand response

Liu,

Li,

Liu

et al. 2024

Energy Conversion and Econom

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In power systems with a high proportion of renewable energy resources (RES), the inherent stochasticity and volatility of RES necessitate careful consideration in power system planning. Scenario analysis is commonly employed to address the stochastic nature in power system planning. Existing studies generally adopt an open‐loop structure, where representative days are selected first and planning decisions are subsequently made. However, this method may not accurately represent the operating status of a system owing to changes in the power generation structure during the planning process. To address this limitation, this paper introduces a closed‐loop framework for representative day selection within the context of generation and transmission expansion planning (G&TEP), incorporating demand response (DR). The framework comprises three layers: representative day selection, planning decisions, and long‐term operational simulation. Initially, an approach for selecting representative days is proposed by combining the clustering and optimization‐based methods. Subsequently, a G&TEP model that incorporates DR is presented in the second layer. Lastly, the framework encompasses a three‐layer closed‐loop structure, enabling dynamic adjustments and enhancements to the representative day selection process to ensure optimality. Case studies on the reliability and operational test system of a power grid with large‐scale renewable integration (XJTU‐ROTS) demonstrate the effectiveness of our proposed framework.

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Section: Objectivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A closed‐loop representative day selection framework for generation and transmission expansion planning with demand response

Liu,

Li,

Liu

et al. 2024

Energy Conversion and Econom

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“…Arabali A., 2014 [36] Awad A., [37] Zhang Y., [38] Alsaidan I., 2017 [39] Zhang Y., [40] Fioriti D., [41] Amini M., 2021 [42] Tahir H., [43] The optimal size of the battery storage in the study [15] was determined, taking into account the depth of discharge to extend the life of the battery storage. This article discusses the firefly algorithm to achieve optimal scheduling that minimizes the overall cost of generation.…”

Section: Limitations Of the Power Grid Limitations Of The Bess Refmentioning

confidence: 99%

Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation

et al. 2022

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The modeling of battery energy storage systems (BESS) remains poorly researched, especially in the case of taking into account the power loss due to degradation that occurs during operation in the power system with a large penetration of generation from renewables and stochastic load from electric vehicles (EV). Meanwhile, the lifetime varies considerably from the manufacturer’s claim due to different operating conditions, and also depends on the level of renewable energy sources (RES) penetration, cyclic operation, temperature, discharge/charge rate, and depth of discharge. Choosing a simplistic approach to the degradation model can lead to unreliable conclusions in choosing the best management strategy and significant investment and operating costs. Most existing BESS models in stationary applications either assume zero degradation costs for storage or simplify battery life to a linear function of depth of discharge (DOD), which can lead to additional error in estimating the cost of BESS degradation. The complexity of constructing a lifetime model of BESS is due to the presence of nonlinear degradation of BESS at the beginning and at the end of the lifetime, as well as the difficulty in obtaining a large amount of experimental data that are close to the real-world operating conditions for the construction of most models. This article analyzes the features of BESS that are specific to their operation in microgrids in terms of the influence of the main stress factors on the degree of BESS degradation. This study also provides a review of existing models for assessing battery degradation.

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“…However, the quantification of the parametric uncertainties could have added an additional layer of insight and understanding to the associated multi-criteria decision-making analyses. Tahir et al [25] have also sought to optimally size energy storage systems, whilst taking into account the ramp rate control of the wider MG integrating the battery energy storage system. They have used representative days to reduce the cost expensiveness of the operation of the system.…”

Section: Literature Reviewmentioning

confidence: 99%

Quantifying the effects of forecast uncertainty on the role of different battery technologies in grid-connected solar photovoltaic/wind/micro-hydro micro-grids: An optimal planning study

Mohseni

Brent

2022

Journal of Energy Storage

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Optimal ESS size calculation for ramp rate control of grid-connected microgrid based on the selection of accurate representative days

Cited by 22 publications

References 44 publications

A closed‐loop representative day selection framework for generation and transmission expansion planning with demand response

A closed‐loop representative day selection framework for generation and transmission expansion planning with demand response

Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation

Quantifying the effects of forecast uncertainty on the role of different battery technologies in grid-connected solar photovoltaic/wind/micro-hydro micro-grids: An optimal planning study

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