Transmission system expansion planning by a branch-and-bound algorithm

A branch and bound (B& B) algorithm using the DC model, to solve the power system transmission expansion planning by incorporating the electrical losses in network modelling problem is presented. This is a mixed integer nonlinear programming (MINLP) problem, and in this approach, the so-called fathoming tests in the B&B algorithm were redefined and a nonlinear programming (NLP) problem is solved in each node of the B& B tree, using an interior-point method. Pseudocosts were used to manage the development of the B&B tree and to decrease its size and the processing time. There is no guarantee of convergence towards global optimisation for the MINLP problem. However, preliminary tests show that the algorithm easily converges towards the best-known solutions or to the optimal solutions for all the tested systems neglecting the electrical losses. When the electrical losses are taken into account, the solution obtained using the Garver system is better than the best one known in the literature.

Morse basis expansion applied to diatomic molecules

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem

This paper analyses the impact of choosing good initial populations for genetic algorithms regarding convergence speed and final solution quality. Test problems were taken from complex electricity distribution network expansion planning. Constructive heuristic algorithms were used to generate good initial populations, particularly those used in resolving transmission network expansion planning. The results were compared to those found by a genetic algorithm with random initial populations. The results showed that an efficiently generated initial population led to better solutions being found in less time when applied to low complexity electricity distribution networks and better quality solutions for highly complex networks when compared to a genetic algorithm using random initial populations.

ENERGY GENERATION EXPANSION PLANNING MODEL CONSIDERING EMISSIONS CONSTRAINTS

The generation expansion planning (GEP) problem consists in determining the type of technology, size, location and time at which new generation units must be integrated to the system, over a given planning horizon, to satisfy the forecasted energy demand. Over the past few years, due to an increasing awareness of environmental issues, different approaches to solve the GEP problem have included some sort of environmental policy, typically based on emission constraints. This paper presents a linear model in a dynamic version to solve the GEP problem. The main difference between the proposed model and most of the works presented in the specialized literature is the way the environmental policy is envisaged. Such policy includes: i) the taxation of CO(2) emissions, ii) an annual Emissions Reduction Rate (ERR) in the overall system, and iii) the gradual retirement of old inefficient generation plants. The proposed model is applied in an 11-region to design the most cost-effective and sustainable 10-technology US energy portfolio for the next 20 years.

An implementation of modified scatter search algorithm to transmission expansion planning

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Análise do escoamento de HFC-134a em tubos capilares usando o modelo de dois fluidos

This work presents a numerical model to simulate refrigerant flow through capillary tubes, commonly used as expansion devices in refrigeration systems. The capillary tube is considered straight and horizontal. The flow is taken as one-dimensional and adiabatic. Steady state and thermodynamic equilibrium conditions are assumed. The two-fluid model, involving four conservation equations and considering the hidrodynamic nonequilibrium between the liquid and vapor phases is applied to the flow region. The pressure profiles and the mass flow rates given by the model are compared with experimental data.

Comparative studies on non-convex optimization methods for transmission network expansion planning

We have investigated and extensively tested three families of non-convex optimization approaches for solving the transmission network expansion planning problem: simulated annealing (SA), genetic algorithms (GA), and tabu search algorithms (TS). The paper compares the main features of the three approaches and presents an integrated view of these methodologies. A hybrid approach is then proposed which presents performances which are far better than the ones obtained with any of these approaches individually. Results obtained in tests performed with large scale real-life networks are summarized.

The δ expansion and the principle of minimal sensitivity

The δ-expansion is a nonperturbative approach for field theoretic models which combines the techniques of perturbation theory and the variational principle. Different ways of implementing the principle of minimal sensitivity to the δ-expansion produce in general different results for observables. For illustration we use the Nambu-Jona-Lasinio model for chiral symmetry restoration at finite density and compare results with those obtained with the Hartree-Fock approximation.

Highly excited states for the helium atom in the hyperspherical adiabatic approach

The hyperspherical adiabatic approach is used to obtain the highly excited series 1sns 1S e and 1s(n + 1)p 1P o of the helium atom. The introduction of appropriate asymptotic conditions at large values of the hyperspherical radius results in a stable algorithm that allows the calculation of the full atomic spectrum with precision of a few parts per million. Comparison with the variational calculations available in the literature shows that the accuracy of the results improves with increasing principal quantum number. We present the energies up to n = 31 which is the typical value used in multiphoton excitation experiments.

Optimized delta expansion for relativistic nuclear models: Renormalization of vacuum fluctuations

We use the optimized linear δ expansion and functional methods to study vacuum contributions in nuclear matter up to the lowest non-trivial order which includes exchange terms. We show that well known results (MFT, RHA and HF) can be easily reproduced when appropriate limits are taken. Neglecting vacuum contributions we explicitly show that the δ expansion goes beyond the traditional loop approximation previously used to study two loop vacuum contributions in nuclear matter. We then evaluate and renormalize vacuum exchange contributions showing that they are numerically very large, as predicted by the ordinary loop approximation.

Free expansion of attractive and repulsive Bose-Einstein condensed vortex states

A numerical study of the time-dependent Gross-Pitaevskii equation for an axially symmetric trap to obtain insight into the free expansion of vortex states of BEC is presented. As such, the ratio of vortex-core radius to radia rms radius xc/xrms(<1) is found to play an interesting role in the free expansion of condensed vortex states. the larger this ratio, the more prominent is the vortex core and the easier is the possibility of experimental detection of vortex states.

A constructive heuristic algorithm to Short Term Transmission Network Expansion Planning

In this paper a method for solving the Short Term Transmission Network Expansion Planning (STTNEP) problem is presented. The STTNEP is a very complex mixed integer nonlinear programming problem that presents a combinatorial explosion in the search space. In this work we present a constructive heuristic algorithm to find a solution of the STTNEP of excellent quality. In each step of the algorithm a sensitivity index is used to add a circuit (transmission line or transformer) to the system. This sensitivity index is obtained solving the STTNEP problem considering as a continuous variable the number of circuits to be added (relaxed problem). The relaxed problem is a large and complex nonlinear programming and was solved through an interior points method that uses a combination of the multiple predictor corrector and multiple centrality corrections methods, both belonging to the family of higher order interior points method (HOIPM). Tests were carried out using a modified Carver system and the results presented show the good performance of both the constructive heuristic algorithm to solve the STTNEP problem and the HOIPM used in each step.

A branch-and-bound algorithm for the multi-stage transmission expansion planning

This work presents a branch-and-bound algorithm to solve the multi-stage transmission expansion planning problem. The well known transportation model is employed, nevertheless the algorithm can be extended to hybrid models or to more complex ones such as the DC model. Tests with a realistic power system were carried out in order to show the performance of the algorithm for the expansion plan executed for different time frames. © 2005 IEEE.

Transmission network expansion planning in full open market considering security constraints

This paper presents a mathematical model and a methodology to solve the transmission network expansion planning problem with security constraints in full competitive market, assuming that all generation programming plans present in the system operation are known. The methodology let us find an optimal transmission network expansion plan that allows the power system to operate adequately in each one of the generation programming plans specified in the full competitive market case, including a single contingency situation with generation rescheduling using the security (n-1) criterion. In this context, the centralized expansion planning with security constraints and the expansion planning in full competitive market are subsets of the proposal presented in this paper. The model provides a solution using a genetic algorithm designed to efficiently solve the reliable expansion planning in full competitive market. The results obtained for several known systems from the literature show the excellent performance of the proposed methodology.