DOAJ Open Access 2023

Scalable Distributed Optimization Combining Conic Projection and Linear Programming for Energy Community Scheduling

Mohammad Dolatabadi Alberto Borghetti Pierluigi Siano

Abstrak

In this paper, a new method to address the scheduling problem of a renewable energy community while considering network constraints and users' privacy preservation is proposed. The method decouples the optimization solution into two interacting procedures: conic projection (CP) and linear programming (LP) optimization. A new optimal CP method is proposed based on local computations and on the calculation of the roots of a fourth-order polynomial for which a closed-form solution is known. Computational tests conducted on both 14-bus and 84-bus distribution networks demonstrate the effectiveness of the proposed method in obtaining the same quality of solutions compared with that by a centralized solver. The proposed method is scalable and has features that can be implemented on microcontrollers since both LP and CP procedures require only simple matrix-vector multiplications.

Topik & Kata Kunci

Production of electric energy or power. Powerplants. Central stations Renewable energy sources

Penulis (3)

Mohammad Dolatabadi

Alberto Borghetti

Pierluigi Siano

Format Sitasi

APA MLA BibTeX

Dolatabadi, M., Borghetti, A., Siano, P. (2023). Scalable Distributed Optimization Combining Conic Projection and Linear Programming for Energy Community Scheduling. https://doi.org/10.35833/MPCE.2022.000783

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.35833/MPCE.2022.000783
Akses: Open Access ✓