2020
Authors
Tarjano, C; Pereira, V;
Publication
CoRR
Abstract
2020
Authors
Fang, D; Zou, M; Harrison, G; Djokic, SZ; Ndawula, MB; Xu, X; Hernando-Gil, I; Gunda, J;
Publication
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Abstract
2020
Authors
Zhao P.; Gu C.; Huo D.; Shen Y.; Hernando-Gil I.;
Publication
IEEE Transactions on Industrial Informatics
Abstract
Energy hub system (EHS) incorporating multiple energy carriers, storage, and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation. This article proposes an innovative distributionally robust optimization model to operate EHS with an energy storage system (ESS), considering the multimodal forecast errors of photovoltaic (PV) power. Both battery and heat storage are utilized to smooth PV output fluctuation and improve the energy efficiency of EHS. This article proposes a novel multimodal ambiguity set to capture the stochastic characteristics of PV multimodality. A two-stage scheme is adopted, where 1) the first stage optimizes EHS operation cost, and 2) the second stage implements real-time dispatch after the realization of PV output uncertainty. The aim is to overcome the conservatism of multimodal distribution uncertainties modeled by typical ambiguity sets and reduce the operation cost of EHS. The presented model is reformulated as a tractable semidefinite programming problem and solved by a constraint generation algorithm. Its performance is extensively compared with widely used normal and unimodal ambiguity sets. The results from this article justify the effectiveness and performance of the proposed method compared to conventional models, which can help EHS operators to economically consume energy and use ESS wisely through the optimal coordination of multienergy carriers.
2020
Authors
Heleno, M; Sehloff, D; Coelho, A; Valenzuela, A;
Publication
APPLIED ENERGY
Abstract
This paper models the role of electricity tariffs on the long-term adoption of photovoltaic and storage technologies as well as the consequent impact on the distribution grid. An adoption model that captures the economic rationality of tariff-driven investments and considers the stochastic nature of individual consumers' decisions is proposed. This model is then combined with a probabilistic load flow to evaluate the long-term impacts of the adoption on the voltage profiles of the distribution grid. To illustrate the methodology, different components of the electricity tariffs, including solar compensation mechanisms and time differentiation of Time-of-Use (ToU) rates, are evaluated, using a case study involving a section of a medium-voltage network with 118 nodes.
2020
Authors
Waqar, A; Ahmad, T; Aamir, M; Ali, M; Habib, HUR; Zahid, M;
Publication
PROCEEDINGS OF 2020 17TH INTERNATIONAL BHURBAN CONFERENCE ON APPLIED SCIENCES AND TECHNOLOGY (IBCAST)
Abstract
2020
Authors
Habib, HUR; Wang, SR; Waqar, A; Farhan, BS; Kotb, KM; Kim, YS;
Publication
IEEE ACCESS
Abstract
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