Título Capacity Market for Distribution System Operator – with Reliability Transactions – Considering Critical Loads and Microgrids
Autores Mohamed A.A. , SABILLON ANTUNEZ, CARLOS FRANCISCO, Golriz A. , Lavorato M. , Rider M.J. , Venkatesh B.
Publicación externa No
Medio IEEE Trans Power Delivery
Alcance Article
Naturaleza Científica
Cuartil JCR 2
Cuartil SJR 1
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137597518&doi=10.1109%2fTPWRD.2022.3200869&partnerID=40&md5=2c70cf0e08e347e58190bf44ab9ca33e
Fecha de publicacion 01/04/2023
ISI 000966487200001
Scopus Id 2-s2.0-85137597518
DOI 10.1109/TPWRD.2022.3200869
Abstract Conventional distribution system (DS) asset planning methods consider energy only from transmission systems (TS) and not from distributed energy resources (DER), leading to expensive plans. Newer transactive energy DS (TEDS) asset planning models, built on capacity market mechanisms, consider energy from both TS and DERs, leading to lower-cost plans and maximizing social welfare. However, in both methods the cost of higher reliability requirements for some users are socialized across all users, leading to lower social welfare. In this paper, a novel transactive energy capacity market (TECM) model is proposed for DS asset planning. It builds on TEDS incremental capacity auction models by provisioning for critical loads to bid and receive superior reliability as a service. The TECM model considers these reliability transactions, in addition, to selling energy transactions from TS and DERs, buying energy transactions from loads, and asset upgrade transactions from the network operator. The TECM model allows for islanded microgrids and network reconfiguration to maximize social welfare. The TECM model is assessed on several case studies, demonstrating that it achieves higher social welfare and a lower plan cost. IEEE
Palabras clave Commerce; Costs; Electric power system planning; Electric power transmission; Energy resources; Capacity planning; Critical zones; Distribution systems; Energy; Flexible capacity; Load modeling; Power
Miembros de la Universidad Loyola

Change your preferences Gestionar cookies