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Proposed model with weighted parameters for microgrid management: Incorporating diverse load profiles, assorted tariff policies, and energy storage devices

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  • Carvalho, Diego B.
  • Bortoni, Edson da C.

Abstract

This study presents an optimization-based model with weighted and adjustable parameters, incorporating both deterministic and stochastic variants to enable a smooth transition between control strategies for microgrid management. The motivation to conduct this study arises from an identified gap in the literature on microgrid management, especially as not many studies tackle non-scheduled loads under different energy market policies. Considering this factor, the research objective is to ascertain the technical and economic advantages that the proposed approach offers to a microgrid characterized by a load profile that undergoes significant variations based on the day of the week. A Model Predictive Control (MPC)-based control system is employed as the system manager, using key performance indicators to assess the model’s effectiveness. The control strategy, implemented at the reference signal generator level, is designed to minimize operational costs and curb the Energy Storage System (ESS) degradation. The results demonstrate that the deterministic variant of the proposed model provides a significant quantitative return for the microgrid, especially in more stable load profiles. Additionally, the deterministic variant is a method that allows elucidating a range of values for the weighted parameters of the proposed model, which will be used in the approach stochastic variant. Conversely, the stochastic variant stands out by offering more pronounced benefits to the microgrid in scenarios with abrupt load profiles. Both versions of the model under study exhibit remarkable performance compared to the benchmark models.

Suggested Citation

  • Carvalho, Diego B. & Bortoni, Edson da C., 2024. "Proposed model with weighted parameters for microgrid management: Incorporating diverse load profiles, assorted tariff policies, and energy storage devices," Energy, Elsevier, vol. 296(C).
    • Handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224007485
    DOI: 10.1016/j.energy.2024.130976
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    2. Gjorgievski, Vladimir Z. & Velkovski, Bodan & Markovski, Blagoja & Cundeva, Snezana & Markovska, Natasa, 2024. "Energy community demand-side flexibility: Energy storage and electricity tariff synergies," Energy, Elsevier, vol. 313(C).
    3. Gao, Jinling & Maalla, Allam & Li, Xuetao & Zhou, Xiao & Lian, Kong, 2024. "Comprehensive model for efficient microgrid operation: Addressing uncertainties and economic considerations," Energy, Elsevier, vol. 306(C).

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