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Sebastián Dávila

Publications and source records attributed to Sebastián Dávila.

2 recordsLinked to original sources

A Bilevel Optimization Model for Bottom-Up Coordination of Multiple Low-Voltage Energy Communities and the Medium-Voltage Network

The increasing penetration of distributed energy resources (DERs) is transforming low-voltage (LV) networks into active systems, including energy communities, whose generation, storage, and energy exchange activities require enhanced coordination with upstream medium-voltage (MV) networks. In the proposed Stackelberg structure, a local market operator aggregates multiple LV communities and acts as a single leader, determining DER operations and boundary energy exchanges, while the MV network serves as the follower, ensuring efficient system feasibility through an economic dispatch that includes both conventional and utility-scale PV generation. The proposed bottom-up coordination scheme concentrates discrete DER scheduling at the LV level while the MV level retains a convex continuous formulation, enabling an exact single-level reformulation via the Karush-Kuhn-Tucker (KKT) conditions. In addition, a distributed coordination algorithm that combines Lagrangian Dual Decomposition (LDD) with the Alternating Direction Method of Multipliers (ADMM) is developed to coordinate LV communities in parallel while preserving data confidentiality. The framework is validated using the IEEE 33-bus system at the MV level and six European 206-bus LV test feeders. Results indicate that the LDD-ADMM algorithm closely matches the exact reformulation, with an average relative deviation of 1.7e-4, with deviations confined to periods of scarcity for the cheap resource. Furthermore, leaders' decisions can induce operating conditions that increase followers' costs relative to their independently optimal dispatch, a pattern reinforced by comparison with a feasibility-based single-level relaxation that satisfies the required energy exchanges but fails to achieve a cost-efficient allocation of MV resources.

math.OC↗

Comparison of metaheuristics for the firebreak placement problem: a simulation-based optimization approach

The problem of firebreak placement is crucial for fire prevention, and its effectiveness at landscape scale will depend on their ability to impede the progress of future wildfires. To provide an adequate response, it is therefore necessary to consider the stochastic nature of fires, which are highly unpredictable from ignition to extinction. Thus, the placement of firebreaks can be considered a stochastic optimization problem where: (1) the objective function is to minimize the expected cells burnt of the landscape; (2) the decision variables being the location of firebreaks; and (3) the random variable being the spatial propagation/behavior of fires. In this paper, we propose a solution approach for the problem from the perspective of simulation-based optimization (SbO), where the objective function is not available (a black-box function), but can be computed (and/or approximated) by wildfire simulations. For this purpose, Genetic Algorithm and GRASP are implemented. The final implementation yielded favorable results for the Genetic Algorithm, demonstrating strong performance in scenarios with medium to high operational capacity, as well as medium levels of stochasticity

cs.AI↗