This paper proposes a method of solving transmission network expansion problem using LP-Based Particle Swarm Optimization. An optimal expansion plan for the existing transmission network is proposed. It formulates the design requirements of network planning as an operational planning model and is solved by an optimization algorithm such that an optimal planning scheme is obtained satisfying all constraints. Particle Swarm Optimization is a population- based search algorithm. The experimental results for linear programming technique, LP-based PSO and DC model are given. © 2006 IEEE.

Shanti K. Swarup

Department of Electrical Engineering

OPERATIONAL PLANNING MODELS

particle swarm optimization

TRANSMISSION NETWORK EXPANSION PLANNING

Algorithms

Constraint theory

Linear Programming

Mathematical models

Optimization

Problem solving

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Transmission expansion planning using LP-based particle swarm optimization

2006 IEEE Power India Conference

Proper investments for expansion of generation, transmission and distribution systems in an electric grid is a very important issue that rely on optimal expansion planning of the grid resources. Investments on transmission network influence those in generation and distribution side which motivates a co-optimization of all these different resources of a grid. The co-optimization based Generation - Transmission Expansion planning is a large scale, constrained, hard bound optimization problem. This research article proposes an Information exchange based Clustered Differential Evolution algorithm (IE-CDE) for solving the problem of expansion planning of generation and transmission resources in an electric grid. The proposed algorithm is first tested extensively on the CEC 2017 constrained optimization benchmark problems and the results are compared with those obtained by state-of-the art algorithms to investigate the efficiency of the proposed algorithm in solving challenging constrained optimization problems. Then the proposed algorithm IE-CDE is used to solve the challenging Generation-Transmission expansion planning problem (GT) on a test system called Garver system. The implementation is also extended to incorporate the expansion planning of demand management resources along with generation and transmission resources (GTD) on the same test system mentioned before as well as an additional one called IEEE 24 bus system. The results obtained by proposed IE-CDE on the GT and GTD expansion planning problems are compared with state of the art algorithms in the literature and the comparison reveal that the proposed method is able to find better solutions than the other algorithms yielding lower cost of expansion for the electric grid. The claim for superiority of the proposed method over others is also substantiated by statistical significance tests on the obtained results. © 2018 Elsevier B.V.

Swarm and Evolutionary Computation

Information exchange based clustered differential evolution for constrained generation-transmission expansion planning

This paper analyses the benefit of a multi-stage transmission expansion planning (TEP) over single stage TEP. The paper establishes the conditions (load growth and interest rate) under which the two formulations are equivalent, and when a multi-stage TEP formulation could lead to a better solution. The paper answers an important question of how the planning span shall be divided into stages when going for a multi-stage TEP. The paper further analyses the effect of high peak to average load ratio (PAR) in a multi-stage TEP. The single-stage and multi-stage TEP problems are formulated to minimize the total investment and operation cost over the planning period. The operating cost is calculated by dispatching the generators and loads in a double sided auction market where the supply and demand bids are assumed to be fixed over the planning period. The TEP problem has a mixed integer linear programming (MILP) formulation and is tested on 6 bus Garver System implemented in GAMS. © 2018 IEEE.

International Conference on Innovative Smart Grid Technologies, ISGT Asia 2018

Single-Stage vs Multi-Stage Transmission Expansion Planning in Electricity Markets: An MILP Approach

It is important to handle the transmission network expansion planning problem under restructured power systems to allow fair competition in the system. A scenario-based two-stage Transmission Network Expansion Planning (TNEP) is presented in the paper and solved using a meta-heuristic algorithm. Various scenarios, for market-driven power flow patterns is considered in a 6-bus Garver Test System, and Differential Evolution is employed to solve the traditional Transmission Network Expansion planning problems in each scenario. A decision making scheme based on risk analysis is employed to select a plan with minimum risk. In this paper it is assumed that the market players trade only via pool market model, where bid pricing affects the generator loading. © 2016 IEEE.

2016 National Power Systems Conference, NPSC 2016

A scenario-based transmission network expansion planning in electricity markets

IEE Proceedings - Generation, Transmission and Distribution

Constructive heuristic algorithm for the DC model in network transmission expansion planning

IEEE Transactions on Power Systems

Particle swarm optimization to solving the economic dispatch considering the generator constraints

Efficient linear programming algorithm for the transmission network expansion planning problem

International Journal of Electrical Power & Energy Systems

Optimal power flow using particle swarm optimization

A hierarchical decomposition approach for transmission network expansion planning