8 results found
Moreira A, Heleno M, Valenzuela A, 2021, A Risk-Averse Approach for Distribution Grid Expansion Planning, ENERGIES, Vol: 14
Alvarado D, Moreira A, Moreno R, et al., 2019, Transmission Network Investment With Distributed Energy Resources and Distributionally Robust Security, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 34, Pages: 5157-5168, ISSN: 0885-8950
Moreira A, Fanzeres B, Strbac G, 2018, Energy and reserve scheduling under ambiguity on renewable probability distribution, ELECTRIC POWER SYSTEMS RESEARCH, Vol: 160, Pages: 205-218, ISSN: 0378-7796
Moreira A, Pozo D, Street A, et al., 2017, Reliable Renewable Generation and Transmission Expansion Planning: Co-Optimizing System's Resources for Meeting Renewable Targets, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 32, Pages: 3246-3257, ISSN: 0885-8950
Moreira A, strbac G, Moreno R, et al., 2017, A Five-Level MILP Model for Flexible Transmission Network Planning under Uncertainty: A Min-Max Regret Approach, IEEE Transactions on Power Systems, Vol: 33, Pages: 486-501, ISSN: 0885-8950
The benefits of new transmission investment significantly depend on deployment patterns of renewable electricity generation that are characterized by severe uncertainty. In this context, this paper presents a novel methodology to solve the transmission expansion planning (TEP) problem under generation expansion uncertainty in a min-max regret fashion, when considering flexible network options and n 1 security criterion. To do so, we propose a five-level mixed integer linear programming (MILP) based model that comprises: (i) the optimal network investment plan (including phase shifters), (ii) the realization of generation expansion, (iii) the co-optimization of energy and reserves given transmission and generation expansions, (iv) the realization of system outages, and (v) the decision on optimal post-contingency corrective control. In order to solve the fivelevel model, we present a cutting plane algorithm that ultimately identifies the optimal min-max regret flexible transmission plan in a finite number of steps. The numerical studies carried out demonstrate: (a) the significant benefits associated with flexible network investment options to hedge transmission expansion plans against generation expansion uncertainty and system outages, (b) strategic planning-under-uncertainty uncovers the full benefit of flexible options which may remain undetected under deterministic, perfect information, methods and (c) the computational scalability of the proposed approach.
Moreira A, Street A, Arroyo JM, 2015, An Adjustable Robust Optimization Approach for Contingency-Constrained Transmission Expansion Planning, IEEE Transactions on Power Systems, Vol: 30, Pages: 2013-2022, ISSN: 0885-8950
Moreira A, Street A, Arroyo JM, 2015, Energy and reserve scheduling under correlated nodal demand uncertainty: An adjustable robust optimization approach, International Journal of Electrical Power and Energy Systems, Vol: 72, Pages: 91-98, ISSN: 0142-0615
This paper presents a nonparametric approach based on adjustable robust optimization to consider correlated nodal demand uncertainty in a joint energy and reserve scheduling model with security constraints. In this model, up- and down-spinning reserves provided by generators are endogenously defined as a result of the optimization problem. Adjustable robust optimization is used to characterize the worst-case load variation under a given user-defined uncertainty set. This paper differs from recent previous work in two respects: (i) nonparametric correlations between nodal demands are accounted for in the uncertainty set and (ii) based on the binary expansion linearization approach, a mixed-integer linear model is provided for the optimization related to the worst-case demand. The resulting problem is formulated as a trilevel program and solved by means of Benders decomposition. Empirical results suggest that the effect of nodal correlations can be effectively captured by the robust scheduling model.
Street A, Moreira A, Arroyo JM, 2014, Energy and Reserve Scheduling Under a Joint Generation and Transmission Security Criterion: An Adjustable Robust Optimization Approach, IEEE Transactions on Power Systems, Vol: 29, Pages: 3-14, ISSN: 0885-8950
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