Anibal Tavares de Azevedo

Nonlinear Medium-Term Hydro-Thermal Scheduling With Transmission Constraints

In this paper, a nonlinear model for medium-term hydro-thermal scheduling problems with transmission constraints is presented. A nonlinear formulation of hydro power production functions of discharge and storage is used for proper representation of head variation in cascaded reservoirs. Transmission networks are formulated as a direct-current power flow model with support to load attainment over multiple levels representing peak and off-peak hours, enabling varying degrees of both network topology and load balance nodes representation over time stages. Sparse matrix structures that arise from the mathematical formulation allow fast decoupled computation of search directions in a primal-dual interior-point framework. Computational tests with very large case studies are presented. Analysis of numerical algorithm convergence is provided along with insightful optimal solution interpretative commentary.

Solving the 3D container ship loading planning problem by representation by rules and meta-heuristics

This paper formulates the 3D containership loading planning problem 3D CLPP and also proposes a new and compact representation to efficiently solve it. The key objective of stowage planning is to minimise the number of container movements and also the ships instability. The binary formulation of this problem is properly described and an alternative formulation called Representation by Rules is proposed. This new representation is combined with three metaheuristics - genetic algorithm, simulated annealing, and beam search - to solve the 3D CLPP in a manner that ensures that every solution analysed in the optimisation process is compact and feasible.

Pareto clustering search applied for 3D container ship loading plan problem

Pareto Clustering Search (PCS) is a hybrid method to solve multi-objective problems.PCS detects promising areas and applies local search heuristics only in these areas.We apply the PCS to solve the 3D Container ship Loading Plan Problem (CLPP).The PCS provides better solutions for the CLPP than mono-objective methods.Decision maker chooses the solution that best meets their interests in a situation. The 3D Container ship Loading Plan Problem (CLPP) is an important problem that appears in seaport container terminal operations. This problem consists of determining how to organize the containers in a ship in order to minimize the number of movements necessary to load and unload the container ship and the instability of the ship in each port. The CLPP is well known to be NP-hard. In this paper, the hybrid method Pareto Clustering Search (PCS) is proposed to solve the CLPP and obtain a good approximation to the Pareto Front. The PCS aims to combine metaheuristics and local search heuristics, and the intensification is performed only in promising regions. Computational results considering instances available in the literature are presented to show that PCS provides better solutions for the CLPP than a mono-objective Simulated Annealing.

Equivalent networks for medium-term hydro-thermal scheduling with variable head cascaded reservoirs and transmission constraints

In this paper we investigate the use of equivalent networks to reduce variable and constraint spaces of medium-term hydro-thermal scheduling problems with variable head cascaded reservoirs and transmission constraints. We take advantage of the fact that in DC power flow formulations, which are suitable for these problems, the Ward equivalent yields exact equivalent networks as far as topology is concerned. Numerical tests based on modified versions of the IEEE 14-bus and IEEE RTS-96 networks with hydro plants from the Brazilian power system illustrate how the consideration of such constraints significantly influences optimal cascaded reservoir operation over the planning horizon. Additional analysis shows how medium-term hydro-thermal scheduling benefits from equivalent networks in providing optimal transmission-constrained dispatches with lower computational requirements.

Optimal power flow models using network flow method

The network flow method is used in many areas and also in power system problems. The main application in power system is optimal power flow calculation, and this article presents three approaches complementing early developments: direct current optimal power flow, direct current optimal power flow with explicit losses and alternating current optimal power flow. We detail the mathematical formulation of each approach and test it to a simple system describing the constraint matrices. Then we compare the three approaches showing the advantages of each model.

Solving the 3D stowage planning problem integrated with the quay crane scheduling problem by representation by rules and genetic algorithm

Abstract The operational efficiency of a port depends on proper container movement planning, called “stowage planning”, especially because unloading and loading container ships demands time, and this has a cost. Thus, the optimization of operations through stages is important to avoid blockage activities. This paper proposes a framework for solving the 3D stowage planning (3D SP) problem for container ships integrated with the scheduling of quay cranes (SQC) problem. 3D SP and SQC problems are interrelated and combinatorial, justifying the applications of meta-heuristics like a genetic algorithm combined with simulation and representation by rules. The robustness of the developed approach is attested in problems with 30 ports, 1500 TEUs ship or 15 ports and 22,000 TEUs ship and two quay cranes. These studies showed that the addition of the SQC problem leads to a 45.82% increase in load/unload time for the 3D SP problem solution, on average. This could help the charterer to avoid paying charges to the shipowner due to its an extra unplanned use of the vessel. Additionally, the developed methodology also helps to explain a long term phenomena of continuous increasing in container ship capacity since 1950s.

R&D and Innovation Project Selection: Can Optimization Methods be Adequate?☆

Abstract This article proposes a comprehensive framework for R&D and innovation project selection under uncertainty and subject to real-world constraints applicable to the Brazilian electricity sector, using a combination of integer programming formulation and a PROMETHEE-based method. The objective is to contribute to this domain by offering an approach suitable for the challenges of this sector, but also applicable to other situations involving R&D and innovation investments under similar conditions. The manuscript presents applications using real data from an electricity company. It also compares the proposed method with similar approaches found in the literature such as PROMETHEE II and V. The application revealed the best performance of the proposed framework in dealing with the sectors regulatory constraints, which emphasize the companies’ accomplishment with R&D and innovation expenditures obligations. In this way, although R&D and innovation project selection is not a typical case of optimization, under some particular regional, sector-based or organization boundaries this can be a better solution.