Antonio Escobar

Multistage and coordinated planning of the expansion of transmission systems

In this paper, an efficient genetic algorithm (GA) is presented to solve the problem of multistage and coordinated transmission expansion planning. This is a mixed integer nonlinear programming problem, difficult for systems of medium and large size and high complexity. The GA presented has a set of specialized genetic operators and an efficient form of generation of the initial population that finds high quality suboptimal topologies for large size and high complexity systems. In these systems, multistage and coordinated planning present a lower investment than static planning. Tests results are shown in one medium complexity system and one large size high complexity system.

Transmission network expansion planning considering multiple generation scenarios

This paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering open access. The methodology finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with multiples generation scenarios. The model presented is solved using a specialized genetic algorithm. The methodology is tested in a system from the literature.

Transmission network expansion planning considering uncertainty in generation and demand

This paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering uncertainty in demand and generation. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The model presented results in an optimization problem that is solved using a specialized genetic algorithm. The results obtained for known systems from the literature show that cheaper plans can be found satisfying the uncertainty in demand and generation.

Multistage Transmission Expansion Planning via Network Partitioning and Principal Variables Identification

This paper presents a method to solve multistage transmission expansion planning problem (MTEP) for highly complex systems. The method includes three partition criteria to separate the transmission network into several subsystems and four active power injection mechanisms to keep information from the interaction between subsystems. A principal variables identification process is applied to each subsystem in order to modify the limits of addition of new elements according to the significance of each right-of-way. These new limits are used to solve the MTEP for the original network, which now has a reduced search space. The MTEP is represented by an efficient version of the linear disjunctive model (DM). The obtained results show an outstanding performance in terms of quality of the answer and computation time. The best solutions known for complex test systems are improved thanks to this method.

Multistage Security-Constrained HVAC/HVDC Transmission Expansion Planning With a Reduced Search Space

This paper proposes a new method to solve the multistage security-constrained transmission expansion planning problem, incorporating lines based on high-voltage alternating current (HVAC) and high-voltage direct current (HVDC) alternatives. A novel mixed-integer linear programming model, which incorporates transmission losses using a piecewise linearization, is presented. An efficient method to reduce the search space of the problem is developed to help in the solution process. Garvers 6-bus system and a modified Southern Brazilian system are used to show the precision and efficiency of the proposed approach. The tests are performed for cases with and without HVDC links and transmission losses. The results indicate that better expansion plans can be found by considering HVDC proposals in the expansion process. The promising trend of using HVDC lines in future networks to improve the reliability in the system is demonstrated.

Middle termed hydrothermal dispatch considering maintenance outages using heuristics

In this paper a solution model for a hydrothermal dispatch problem considering maintenance outages of the generation plants is proposed. The solution is based on an optimization model programmed in C++ that uses the solver CPLEX and heuristics.

Transmission expansion planning considering multiple generation scenarios and demand uncertainty

This paper shows a methodology for solving the Transmission Expansion Planning Problem (TEPP) when Multiple Generation Scenarios (MGS) and demand uncertainty are considered. MGS lead to multiple power flow patterns, as a result of the competitive environment in power systems. In this work, the different flow patterns are taken into account, in order to avoid future congestion of the transmission network and thus avoiding future load shedding. The solution to this problem is obtained by a specialized Chu-Beasley Genetic Algorithm (CBGA) which includes a new initialization strategy using non-linear interior point. A diversification stage is also included to spread the solutions in the search space and increase convergence capability. Generation and demand uncertainty are also considered in the mathematical model by allowing variations within a given range. This formulation allows for an important decrease in the cost of the expansion plans when compared to the traditional models with fixed generation and demand. Expansion plans for the 6-bus Garver system and the IEEE-24 bus system are found with this methodology, obtaining zero load shedding under any future generation scenario

Planeamiento de la transmisión utilizando punto interior no lineal y algoritmo genético de chu-beasley

This paper presents the transmission planning problem using linear and nonlinear interior point method. Operative problem is solved though linear interior point and the non-linear version is used for generating the initial population of the genetic algorithm and finding better solutions. The planning strategy is based on the specialized Chu-Beasley genetic algorithm.

Optimización matemática en superficies de desempeño de redes neuronales artificiales

Several optimization methods are used in this article in order to show the behavior on performance surfaces and to conclude about efficiency in neuronal networks training. These methods are: descendent gradient, Newton Raphson, conjugated gradient and its adaptations to multilayer networks: basic backpropagation and Levenberg-Marquardt.