Generation expansion planning for Tamil Nadu: a case study

Summary Developing countries like India necessitates installation of new power plants to meet the elevating electricity demand. Several single‐objective mathematical formulations are modeled and solved previously for short‐term and long‐term power generation expansion planning aiding power system planners to ensure optimal decision. The input data, such as forecasted demands, economic and technical data of the generating units are numerous and unmanageable to be analyzed manually. Modern power systems have faced major challenge in solving multiple objectives such as least cost and minimal greenhouse gas emissions simultaneously. This paper presents an application of multiobjective differential evolution algorithm to multistage, multiobjective generation expansion planning problem for Tamil Nadu, a state in India. The multiobjective generation expansion planning problem has been solved for short‐term (6 years) and long‐term (12 years) planning horizon by considering 8 different cases and also compared with Nondominated Sorting Genetic Algorithm version II.

Section: Formulation Of Mmgep Problemmentioning

confidence: 99%

Multistage multiobjective electricity generation expansion planning for Tamil Nadu considering least cost and minimal GHG emission

Bhuvanesh¹,

Christa²,

Subramanian³

et al. 2018

“…The dynamic behavior of 150‐MW HDGT was analyzed using this model, and the responses were validated . Careful modeling and control of HDGT plants are required to avoid inevitable shutdown in grid connected operation . In order to improve the dynamic and steady‐state response of higher‐order HDGT plants, attempts were made to develop proportional‐integral‐derivative (PID) controller and soft computing techniques–based controllers such as neural network, fuzzy logic, and neuro‐fuzzy technique …”

Section: Introductionmentioning

confidence: 99%

Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Iqbal

Xavier²

2020

Summary Analysis of higher‐order gas turbine plant in real time would be tedious and expensive. In order to overcome this complexity, reduced‐order model for 5001M heavy‐duty gas turbine rated 18.2 MW has been obtained by Routh approximation, clustering technique, modified pole clustering, eigen permutation, Mihailov criterion, and Padé approximation algorithms. The step responses are obtained using MATLAB/Simulink and compared based on time domain specifications and performance index criteria. It indicates that the mixed method, namely, Routh approximation–Padé approximation algorithm–based reduced‐order model, retains the original characteristics. Further, particle swarm optimization (PSO) algorithm has also been applied to develop an optimal reduced‐order model. Based on the dynamic response against the load disturbance and set point variations, PSO‐based reduced‐order model has been identified as an optimal reduced‐order model for heavy‐duty gas turbine. The reduced‐order model proposed in this paper will be suitable for analyzing the dynamic behavior of heavy‐duty gas turbine plants in real‐time environment.

“…Integration of renewable energy plants with an advanced gas turbine can be a viable solution for most of the stability issues . Recent survey on biomass gasifier power plants and a study of generation expansion plan for Tamilnadu, a southern state of India, suggested that the huge potential renewable sources can be converted into useful energy by proper modelling and implementing suitable control strategy . Gas turbine plant may cause severe instability problems under load fluctuation and may lead to an inevitable plant shutdown .…”

Section: Introductionmentioning

confidence: 99%

Model order reduction of heavy duty gas turbine power plants with field test parameters

Iqbal

Sarumathi²,

Jothi

et al. 2018

Summary Frequent load fluctuation and set point variations are the major issues in grid connected heavy duty gas turbine power plants. It may affect the stability of the plant and may lead to inevitable shutdown. Dynamic stability analysis of higher order heavy duty gas turbine in real‐time environment would be tedious and expensive. Various model order reduction techniques namely clustering technique‐Pade approximation (CT‐PA), modified clustering technique‐Pade approximation (MCT‐PA), and Mihailov criterion‐Pade approximation (MC‐PA) are proposed for identifying the reduced order model of 18.2 MW industrial heavy duty gas turbine with field test parameter. The step response of gas turbine plant against the load disturbance and set point variations is obtained using the reduced order models and compared with that of higher order system. The time domain specifications and error criteria have witnessed that the MC‐PA‐based reduced order model retains the original characteristics and yields an improved transient and steady‐state responses. Therefore, MC‐PA‐based reduced order model is identified as an effective reduced order model for the higher order gas turbine plant. The proposed reduced order model would be useful for analysing the behaviour of latest derivative speedtronic controlled gas turbine plant.