Optimal long term transmission expansion planning based on maximum principle

1988 ◽  
Vol 3 (4) ◽  
pp. 1494-1501 ◽  
Author(s):  
K.J. Kim ◽  
Y.M. Park ◽  
K.Y. Lee
Keyword(s):  
Maximum Principle ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion

scholarly journals An Enhanced Genetic Algorithm to Solve the Static and Multistage Transmission Network Expansion Planning

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Luis A. Gallego ◽  
Marcos J. Rider ◽  
Marina Lavorato ◽  
Antonio Paldilha-Feltrin
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Heuristic Algorithms ◽  
Initial Population ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Local Improvement ◽  
Linear Programming Problems

An enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem.

A survey of long term transmission expansion planning using cycles

2018 ◽  
Vol 11 (102) ◽  
pp. 5047-5055
Author(s):  
Pedro Pablo Cardenas A. ◽  
Laura Monica Escobar V. ◽  
Antonio Escobar Z.
Keyword(s):  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion

scholarly journals A two-stage stochastic MILP model for generation and transmission expansion planning with high shares of renewables

2020 ◽  
Author(s):  
Giovanni Micheli ◽  
Maria Teresa Vespucci ◽  
Marco Stabile ◽  
Cinzia Puglisi ◽  
Andres Ramos
Keyword(s):  
Power Systems ◽  
Planning Horizon ◽  
Production Costs ◽  
Energy Sources ◽  
Two Stage ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Flexible Resources

Abstract This paper is concerned with the generation and transmission expansion planning of large-scale energy systems with high penetration of renewable energy sources. Since expansion plans are usually provided for a long-term planning horizon, the system conditions are generally uncertain at the time the expansion plans are decided. In this work, we focus on the uncertainty of thermal power plants production costs, because of the important role they play in the generation and transmission expansion planning by affecting the merit order of thermal plants and the economic viability of renewable generation. To deal with this long-term uncertainty, we consider different scenarios and we define capacity expansion decisions using a two-stage stochastic programming model that aims at minimizing the sum of investment, decommissioning and fixed costs and the expected value of operational costs. To be computationally tractable most of the existing expansion planning models employ a low level of temporal and technical detail. However, this approach is no more an appropriate approximation for power systems analysis, since it does not allow to accurately study all the challenges related to integrating high shares of intermittent energy sources, underestimating the need for flexible resources and the expected costs. To provide more accurate expansion plans for power systems with large penetration of renewables, in our analysis, we consider a high level of temporal detail and we include unit commitment constraints on a plant-by-plant level into the expansion planning framework. To maintain the problem computationally tractable, we use representative days and we implement a multi-cut Benders decomposition algorithm, decomposing the original problem both by year and by scenario. Results obtained with our methodology in the Italian energy system under a 21-year planning horizon show how the proposed model can offer professional guidance and support in strategic decisions to the different actors involved in electricity transmission and generation.

scholarly journals Planning the Expansion of Long-Term Transmission Networks Using a Cycle-Based Formulation

2018 ◽  
Vol 13 (1) ◽  
pp. 237
Author(s):  
Pedro Pablo Cardenas Alzate ◽  
Laura Monica Escobar Vargas ◽  
Antonio Hernando Escobar Zuluaga
Keyword(s):  
Power Systems ◽  
Polynomial Solution ◽  
Planning Problem ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Solution Algorithms ◽  
Transmission Expansion ◽  
Complete Problems ◽  
System Graph

This paper presents a methodology to solve the long-term transmission expansion planning problem, using a formulation that uses mathematical expressions that are alternatives to the second Kirchhoff’s law and that are applied to the cycles critical of the system graph. The network transmission expansion planning problem of power systems is part of the socalled NP-complete problems, which belong to a category of problems that are dfficult to solve, for which polynomial solution algorithms are not known. The proposed methodology is applied to two test systems of the specialized literature with very good results.

Transmission Expansion Planning by using DC and AC Models and Particle Swarm Optimization

2012 ◽  
pp. 260-284 ◽  
Author(s):  
Santiago P. Torres ◽  
Carlos A. Castro ◽  
Marcos J. Rider
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Time Frame ◽  
Swarm Optimization ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Adaptive Pso

The Transmission Expansion Planning (TEP) entails to determine all the changes needed in the electric transmission system infrastructure in order to allow the balance between the projected demand and the power supply, at minimum investment and operational costs. In some type of TEP studies, the DC model is used for the medium and long term time frame, while the AC model is used for the short term. This chapter proposes a load shedding based TEP formulation using the DC and AC model, and four Particle Swarm Optimization (PSO) based algorithms applied to the TEP problem: Global PSO, Local PSO, Evolutionary PSO, and Adaptive PSO. Comparisons among these PSO variants in terms of robustness, quality of the solution, and number of function evaluations are carried out. Tests, detailed analysis, guidelines, and particularities are shown in order to apply the PSO techniques for realistic systems.

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