Pregled bibliografske jedinice broj: 1254289
A multiobjective-based approach for demand-side management in smart distribution grids
A multiobjective-based approach for demand-side management in smart distribution grids // 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech)
Split, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2020. str. 1-6 doi:10.23919/splitech49282.2020.9243715 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1254289 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
A multiobjective-based approach for demand-side
management in smart distribution grids
Autori
Almeida, Vitor A.C.C. ; da Silva, Igor R. S. ; Rabelo, Ricardo de A. L. ; Rodrigues, Joel J.P.C. ; Solic, Petar
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
2020 5th International Conference on Smart and Sustainable Technologies (SpliTech)
/ - : Institute of Electrical and Electronics Engineers (IEEE), 2020, 1-6
Skup
5th International Conference on Smart and Sustainable Technologies (SpliTech 2020)
Mjesto i datum
Split, Hrvatska, 23.09.2020. - 26.09.2020
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
ant colony optimization, demand-side management, multi-objective, smart grids
Sažetak
Demand-side load management methods need to find a compromise between utility and consumer interests. However, oftentimes these entities have conflicting objectives, particularly in price-based demand response programs. This paper considers the demand-side management problem from the perspective of both the energy service provider and its consumers in two steps: a local and decentralized load schedule optimization, followed by a system-wide demand profile optimization. A multi-objective optimization model is formulated for the load scheduling problem to minimize consumer expenses and the cost associated with inconvenient load shifting. Then, the proposed approach to improve the aggregated demand profile by reducing the peak-to-average ratio is described. Ant colony optimization algorithms were implemented and compared with other heuristics. Numerical results based on simulated load profiles show that ant algorithms were able to improve the aggregated demand load profile.
Izvorni jezik
Engleski
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split
Profili:
Petar Šolić
(autor)