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.

Shanti K. Swarup

Department of Electrical Engineering

Commerce

decision making

Electric load flow

Electric power transmission

Electric power transmission networks

Evolutionary algorithms

Expansion

Heuristic algorithms

Optimization

Power markets

Risk analysis

Risk assessment

Differential Evolution

MARKET DRIVEN

MARKET PLAYERS

META HEURISTIC ALGORITHM

MINIMUM RISKS

restructured power systems

SCENARIO-BASED

TRANSMISSION NETWORK EXPANSION PLANNING

Electric power system planning

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

A scenario-based transmission network expansion planning in electricity markets

2016 National Power Systems Conference, NPSC 2016

With the advent of technological developments, a large amount of data is being created and made available at every time instance. This work tries to leverage this data for useful interpretations and derives resource investment planning strategies. Data analytics can be a useful tool to understand the optimal locations, for resource investments in the grid. The study can be helpful in reducing the search space in an optimal resource investment planning problem. As a precursor data analytics study could help in forming policies that could complement a planning problem. India is a growing economy and the electric demand of the country is also increasing. There is a need to attract more investment in the generation side to meet the future loads. It is important to statistically characterize the electricity prices to find the potential investment regions in the grid. This paper gives an overview of the restructured power industry in India, the various market segments in existence and gives a detailed statistical analysis of the Day Ahead Electricity Market prices using Minitab and Statistics toolbox of MATLAB. It also discusses the operational issues like congestion management scheme followed and their effect on the area prices, that could help in empirical resource planning and policy making. © 2018 IEEE.

2018 20th National Power Systems Conference, NPSC 2018

Data analytics for statistical characterization of prices and empirical resource investment planning in Indian electricity market

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

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.

2006 IEEE Power India Conference

Transmission expansion planning using LP-based particle swarm optimization

2013 IEEE Symposium on Swarm Intelligence (SIS)

Joint energy and spinning reserve dispatch in wind-thermal power system using IDE-SAR technique

2011 International Conference on Energy, Automation and Signal

Modified differential evolution optimization algorithm for multi-constraint optimal power flow

2011 IEEE Power and Energy Society General Meeting

Transmission expansion planning by meta-heuristic techniques: A comparison of Shuffled Frog Leaping Algorithm, PSO and GA

Power Systems

Electric Power System Planning

Journal	Data powered by Typeset2016 National Power Systems Conference, NPSC 2016
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open Access	No