This paper presents a particle swarm optimization (PSO) as a tool for loss reduction study. This issue can be formulated as a nonlinear optimization problem. The proposed application consists of using a developed optimal power flow based on loss minimization function by expanding the original PSO. The study is carried out in two steps. First, by using the tangent vector technique, the critical area of the power system is identified under the point of view of voltage instability. Second, once this area is identified, the PSO technique calculates the amount of shunt reactive power compensation that takes place in each bus. The proposed approach has been examined and tested with promising numerical results using the IEEE 118-bus system.

The paper proposes a new test function to be used in an existent performance index for detection of proximity to a static voltage collapse point. This test function is based on a reduction of the load flow Jacobian with respect to the critical bus of a system. The test function is compared with known singular values and eigenvalues indices, and with other previously proposed test functions. A thorough analysis of the similarities, advantages, and disadvantages of all these indices and test functions is presented. The techniques are tested and compared on the IEEE 300 bus test system, showing the effect of system characteristics and limits in these indices and functions.

A long-term, multiarea, and multistage model for the supply/interconnections expansion planning of integrated electricity and natural gas (NG) is presented in this paper. The proposed Gas Electricity Planning (GEP) model considers the NG value chain, i.e., from the supply to end-consumers through NG pipelines and the electrical systems value chain, i.e., power generation and transmission, in an integrated way. The sources of NG can be represented by NG wells, liquefied natural gas (LNG) terminals and storages of NG and LNG. The electricity generation may be composed by hydro plants, wind farms, or thermal plants where the latter represent the link between the gas and the electricity chain. The proposed model is formulated as a mixed-integer linear optimization problem which minimizes the investment and operation costs to determine the optimal location, technologies, and installation times of any new facilities for power generation, power interconnections, and the complete natural gas chain value (supply/transmission/storage) as well as the optimal dispatch of existing and new facilities over a long range planning horizon. A didactic case study as well as the Brazilian integrated gas/electricity system are presented to illustrate the proposed framework.

In order to optimize their assets, electrical power distribution companies seek out various techniques to improve system operation and its different variables, like voltage levels, active power losses and so on. A few of the tools applied to meet these objectives include reactive power compensation, use of voltage regulators, and network reconfiguration. One target most companies aim at is power loss minimization; one available tool to do this is distribution system reconfiguration. To reconfigure a network in radial power distribution systems means to alter the topology changing the state of a set of switches normally closed (NC) and normally opened (NO). In restructured electrical power business, a company must also consider obtaining a topology as reliable as possible. In most cases, reducing the power losses is no guarantee of improved reliability. This paper presents a multiobjective algorithm to reduce power losses while improving the reliability index using the artificial immune systems technique applying graph theory considerations to improve computational performance and Pareto dominance rules. The proposed algorithm is tested on a sample system, 14-bus test system, and on Administración Nacional de Electricidad (ANDE) real feeder (CBO-01 23-kV feeder).

This paper integrates the natural gas and electricity networks in terms of power and gas optimal dispatch. It shows the fundamentals of natural gas network modeling including pipelines and compression stations. It also describes the equality constraint that models the energy transformation between gas and electric networks. A mathematical model of this problem is formulated as an optimization problem where the objective function is to minimize the integrated gas-electricity system operation cost and the constraints are the power system and natural gas pipeline equations and capacities. Case studies are presented integrating the IEEE-14 test system and Belgian calorific gas network. The integrated electricity-gas optimal power flow problem is solved using an hybrid approach which combines evolutionary strategy algorithm with Newton's and Interior point method. It hybrid approach fully takes the advantages of both evolutionary strategy optimization and classical methods (such as Newton's and interior point method) which the former is able to jump out of the local optimal point and the latter boats the local exploration ability within the neighborhood of the optimal. It increases the precision and quickens the convergence. The proposed model shows the importance of the integration of the two systems in terms of operation, planning, security and reliability.