Jesús M López-Lezama

Optimal Contract Pricing of Distributed Generation in Distribution Networks

This paper proposes a bilevel programming approach to determine the optimal contract price of dispatchable distributed generation (DG) units in distribution systems. Two different agents are considered in the model, namely, the distribution company (DisCo) and the owner of the DG. The former seeks the minimization of the payments incurred in attending the forecasted demand, while the latter seeks the maximization of his profit. To meet the expected demand, the DisCo has the option to purchase energy from any DG unit within its network and directly from the wholesale electricity market. A traditional distribution utility model with no competition among DG units is considered. The proposed model positions the DG owner in the outer optimization level and the DisCo in the inner one. This last optimization problem is substituted by its Karush-Kuhn-Tucker optimality conditions, turning the bilevel programming problem into an equivalent single-level nonlinear programming problem which is solved using commercially available software. Tests are performed in a modified IEEE 34-bus distribution network.

Market-driven security-constrained Transmission Network Expansion Planning

This paper presents a Bi-level Programming (BP) approach to solve the Transmission Network Expansion Planning (TNEP) problem. The proposed model is envisaged under a market environment and considers security constraints. The upper-level of the BP problem corresponds to the transmission planner which procures the minimization of the total investment and load shedding cost. This upper-level problem is constrained by a single lower-level optimization problem which models a market clearing mechanism that includes security constraints. Results on the Garvers 6-bus and IEEE 24-bus RTS test systems are presented and discussed. Finally, some conclusions are drawn.

Ubicación de Generación Distribuida para Minimización de Pérdidas Usando un Algoritmo Genético Híbrido

This paper presents a methodology for optimal Distributed Generation (DG) allocation using a hybrid genetic algorithm. In this case, the objective function is the minimization of power losses. The proposed algorithm involves an artificial neural network to evaluate the fitness function and a local search subroutine that allows the algorithm to explore a wider search space, and eventually, escape from local optimal solutions. The proposed approach was tested on a 34-bus system showing that the proper allocation of DG units allows the reduction of active power losses and the improvement of voltage profile.

Comparison of reactive power support in distribution networks provided by Capacitor Banks and distributed generators

This paper presents a comparison of reactive power support in distribution networks provided by switched Capacitor Banks (CBs) and Distributed Generators (DGs). Regarding switched CBs, a Tabu Search metaheuristic algorithm is developed to determine their optimal operation with the objective of reducing the power losses in the lines on the system, while meeting network constraints. On the other hand, the optimal operation of DGs is analyzed through an evolutionary Multi-Objective (MO) programming approach. The objectives of such approach are the minimization of power losses and operation cost of the DGs. The comparison of the reactive power support provided by switched CBs and DGs is carried out using a modified IEEE 34 bus distribution test system.

Optimal contract pricing of distributed generation under a competitive framework

A bilevel programming approach for the optimal contract pricing of distributed generation (DG) in distribution networks is presented. The outer optimization problem corresponds to the owner of the DG who must decide the contract price that would maximize his profits. The inner optimization problem corresponds to the distribution company (DisCo), which procures the minimization of the payments incurred in attending the expected demand while satisfying network constraints. The meet the expected demand the DisCo can purchase energy either form the transmission network through the substations or form the DG units within its network. The inner optimization problem is substituted by its Karush-Kuhn-Tucker optimality conditions, turning the bilevel programming problem into an equivalent single-level nonlinear programming problem which is solved using commercially available software.

A multi-objective programming approach for the optimal operation of distributed generation considering uncertainty

Due to the renewed interest in distributed generation (DG), the number of DG units incorporated in distribution systems has been rapidly increasing in the past few years. This situation requires new analysis tools for understanding system performance, and taking advantage of the potential benefits of DG. This paper presents an evolutionary multi-objective programming approach to determine the optimal operation of DG in distribution systems. The objectives are the minimization of the system power losses and operation cost of the DG units. The proposed approach also considers the inherent stochasticity of DG technologies powered by renewable resources. Some tests were carried out on the IEEE 34 bus distribution test system showing the robustness and applicability of the proposed methodology.

Flujo Óptimo Reactivo para Minimización de Pérdidas: una Nueva Propuesta de Función de Adaptación y Manejo de Restricciones

This work presents a fitness function and constraint handling approach applied to the problem of optimal reactive power dispatch. This problem aims at the reduction of power losses by deciding on voltage setpoints of generators, transformers taps and switchable capacitor and reactor banks. The fitness function consist of a product of sub-functions that represent permissible limits on system variables and that also includes a specific goal on power loss reduction. The main advantage of the proposed fitness function is the fact that the global optimum of the problem is known beforehand. A genetic algorithm was used to solve the ORPD with both fitness functions. This fitness function was implemented and compared with a traditional fitness function by means of a genetic algorithm for three study IEEE cases: 57, 118 and 300 bus systems. It was found that the proposed fitness function showed better performance than the traditional one, guaranteeing the system operation within the feasible region.

Metaheuristics Applied to the Optimal Location and Sizing of Distributed Generation in Power Distribution Systems

Resumen En este articulo se presenta una evaluacion de la ubicacion y dimensionamiento optimo de generacion distribuida en sistemas de distribucion de energia electrica. Para tal evaluacion se han considerado las siguientes tecnicas de busqueda: i) recocido simulado, ii) busqueda de entorno variable iii) algoritmo genetico y iv) algoritmo genetico hibrido. En el problema de ubicacion y dimensionamiento optimo se han considerado dos objetivos: la minimizacion de perdidas activas y el mejoramiento del perfil de tensiones. Para comparar la efectividad de los metodos implementados se utiliza un sistema de distribucion de prueba de 34 barras. La correcta ubicacion y dimensionamiento de la generacion distribuida permitio mejorar sustancialmente el perfil de tension de la red y reducir en un 80% las perdidas para el sistema de prueba. Palabras clave: generacion distribuida, algoritmos geneticos, entorno variable, recocido simulado Metaheuristics Applied to the Optimal Location and Sizing of Distributed Generation in Power Distribution Systems

Análisis de Vulnerabilidad de Sistemas de Potencia Mediante Programación Binivel

This paper presents a new technique for vulnerability analysis in power systems under multiple contingencies. The model under study is known as the “Terrorist Problem”. The main contribution of this work is the formulation and solution of the AC version of this problem, which allows identifying the most critical elements of the power system and gives early signals to the system operator regarding the vulnerability of the system. Furthermore, the AC version of the model allows obtaining more realistic results than the traditional DC model, due to the fact that such version considers the effect of reactive power and voltage magnitudes in the system buses. The proposed model is solved by means of a hybrid metaheuristic technique that combines genetic algorithms with local search. The results obtained using the IEEE RTS – 24 bus test system show the relevance and applicability of the proposed methodology.

Transmission network expansion planning considering weighted transmission loading relief nodal indexes

This paper presents a model and a solution approach for the transmission network expansion planning (TNEP) problem that integrates security constraints given by weighted transmission loading relief (WTLR) indexes. Such indexes integrate shift and power distribution factors and allow to measure the severity of overloads in normal conditions and under any single contingency. Furthermore, the inclusion of small-scale generation was considered as complementary to TNEP solutions. The proposed model was solved by means of the metaheuristic NSGA-II (Non-dominated Sorting Genetic Algorithm II), which enabled to find a set of solutions that represent a trade-off between the cost of the expansion plan and its security level. Several tests were performed on the 6-bus Garver system and the IEEE 24-bus reliability test system, thus showing the applicability of the proposed approach. It was found that the inclusion of small-scale generation in strategic nodes allows to reduce the cost of expansion plans and increases their level of security for single contingencies.