Evaluation of the performance of space reduction technique using AC and DC models in Transmission Expansion problems

Transmission Expansion Planning (TEP) is anoptimization problem that has a non-convex and combinatorialsearch space so that several solution algorithms may converge tolocal optima. Therefore, many works have been proposed to solvethe TEP problem considering its relaxation or reducing its searchspace. In any case, relaxation and reduction approaches shouldnot compromise the quality of the final solution. This paper aimsat analyzing the performance of a search space technique using aConstructive Heuristic Algorithm (CHA) admitting that the TEPproblem is then solved using a Discreet Evolutionary ParticleSwarm Optimization (DEPSO). On one hand the reductionquality is performed by analyzing whether the optimal expansionroutes are included in the CHA constrained set and, on the otherhand, the relaxation quality of the DC model is analyzed bychecking if the optimal solution obtained with it violates anyconstraint using the AC model. The simulations were performedusing three different test systems. The results suggest that theproposed CHA provides very good results in reducing the TEPsearch space and that the adoption of the DC model originatesseveral violations if the full AC model is used to model theoperation of the power system

Multiyear and multi-criteria AC Transmission Expansion Planning model considering reliability and investment costs

One of the major concerns in Power Systems issurely related with their reliability. Long-term expansionplanning studies traditionally use the well-known deterministic"N-1" contingency criterion. However, this criterion is appliedbased on worst-case analyses and the obtained plan mayoriginate over-investments. Differently, probabilistic reliabilityapproaches can incorporate different type of uncertainties thataffect power systems. In this work, a long term multi-criteriaAC Transmission Expansion Planning model was developedconsidering two objectives - the probabilistic reliability indexExpected Energy Not Supplied (EENS) and the investment cost.The Pareto-Front associated with these two objectives wasobtained using Genetic Algorithms and the final solution wasselected using a fuzzy decision making function. This approachwas applied to the IEEE 24 Bus Test System and the resultsensure its robustness and efficiency

Comparative Analysis of Constructive Heuristic Algorithms for Transmission Expansion Planning

Transmission Expansion Planning (TEP) is a complex optimization problem that has the purpose of determining how the transmission capacity of a network should be enlarged, satisfying the increasing demand. This problem has combinatorial nature and different alternative plans can be designed so that many algorithms can converge towards local optima. This feature drives the development of tools that combine high robustness and low computational effort. This paper presents a comparative analysis and a detailed review of the main Constructive Heuristic Algorithms (CHA) used in the TEP problem. This kind of tools combine low computational effort with reasonable quality solutions and can be associated with other tools to use in a subsequent step in order to improve the final solution. CHAs proved to be very effective and showed good performance as the test results will illustrate

Static transmission expansion planning using Heuristic and metaheuristic techniques

This paper describes a hybrid tool to perform StaticTransmission Expansion Planning, STEP, studies and itsapplication to the Garver 6-Bus academic system and to theSouthern Brazilian Transmission equivalent real system. Thedeveloped STEP tool integrates two phases as follows. The firstone uses Constructive Heuristic Algorithms (CHA) to reduce thesearch space, and the second uses Particle Swarm Optimization(PSO) to identify the final solution. This hybridization betweenCHAs and PSO proved to be very effective and shows goodperformance to reduce the size of the STEP search space and toidentify good quality solutions. These are relevant issues giventhe combinatorial nature of investment problems leading to theexplosion of the number of alternative plans, one of the greatest difficulties faced in this planning problem