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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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FEATURED ARTICLE

Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
Issue 2/2022

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  2/2023 - 3

Risk-based Optimal Bidding and Operational Scheduling of a Virtual Power Plant Considering Battery Degradation Cost and Emission

AKKAS, O. P. See more information about AKKAS, O. P. on SCOPUS See more information about AKKAS, O. P. on IEEExplore See more information about AKKAS, O. P. on Web of Science, CAM, E. See more information about CAM, E. on SCOPUS See more information about CAM, E. on SCOPUS See more information about CAM, E. on Web of Science
 
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Download PDF pdficon (1,631 KB) | Citation | Downloads: 562 | Views: 919

Author keywords
distributed power generation, energy management, power system planning, renewable energy sources, risk analysis

References keywords
power(41), energy(29), virtual(26), plant(23), scheduling(12), optimal(12), jijepes(9), stochastic(7), risk(7), markets(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2023-05-31
Volume 23, Issue 2, Year 2023, On page(s): 19 - 28
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.02003
Web of Science Accession Number: 001009953400003
SCOPUS ID: 85164345741

Abstract
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A virtual power plant (VPP) is a system combining various types of distributed energy resources (DERs) to provide a reliable power system operation. It provides the advantage of making changes in generation according to variety, price, and demand conditions with bringing renewable energy sources (RES) in a single portfolio and using their flexibility. In this study, it is tried to find optimal bidding and operational scheduling of a VPP containing Wind Power Plant (WPP), Photovoltaic Power Plant (PVPP), Heat-Only Unit (HOU), Battery Energy Storage System (BESS), Combined Heat and Power Plant (CHPP), and electrical/thermal demands and participating in the day-ahead electricity market in 24-h time interval. It is aimed to maximize profit and minimize emissions with considering the battery cost. A stochastic model is formed by considering the uncertainty arising from RES. In addition, CVaR (Conditional Value at Risk) as a risk measurement technique is applied against the risk arising from low profit scenarios. The proposed optimization problem formulated as a stochastic Mixed Integer Nonlinear Programming (MINLP) model and is solved by solver LINDO in GAMS (General Algebraic Modelling System). The case studies are implemented to show the applicability and effectiveness of the presented model.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 1,593 TCR
SCOPUS® Citations for all references: 1,992 TCR

Web of Science® Average Citations per reference: 46 ACR
SCOPUS® Average Citations per reference: 57 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-07-18 21:58 in 228 seconds.




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