Optimal Allocation of STATCOMs and Wind-Based Distributed Generators using a Stochastic Mathematical Program

In the last two decades, large economies worldwide have been relying increasingly on renewable energy sources to reduce greenhouse emissions and dependence on fossil fuels. This has changed the topology of distribution systems adding complexity to their planning and operation. In this study, a new planning model is proposed to allocate static synchronous compensators (STATCOMs) and wind-based distributed generators (W-DGs), considering the stochastic nature of wind velocities and load demands. The proposed optimization model is a mixed-integer nonlinear program (MINLP), which simultaneously allocates STATCOMs and W-DGs. It minimizes the costs of power losses, investment, operation, and maintenance while maximizing the CO2 reduction rewards and power generation revenues. The Canadian 41-bus network with loads following the IEEE-RTS generic load model is used to test and validate the proposed planning approach. The achieved results demonstrate the effectiveness of such a planning approach in the allocation of STATCOMs and W-DGs. The installation of wind-based distributed generators (W-DGs) in the form of individual units to supply a few loads or in bulk to supply larger loads have increased. High penetration levels of W-DGs have altered the topology of distribution networks (DNs) from being unidirectional to multi-directional, i.e., active direction networks (ADNs). The government’s commitments to clean energy has led to an increase in the investments toward more use of renewable resources to generate clean energy with less environmental impacts. A case study is presented based on actual wind data obtained from Windsor Ontario region. The data is modeled using a Gamma distribution function to model the probabilities of wind speed. Genetic algorithm (GA) is utilized to solve the developed model to allocate and size the W-DGs and the STATCOMs.

Name thesis:degree_name

M.A.Sc.

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Windsor

Year dc:date.issued

2020

Author dc:creator

• Dalawir, Mahzan

Advisors dc:contributor.advisor

• Azab, Ahmed A.
• Azzouz, Maher A.

Contributors dc:contributor

• mita@uwindsor.ca

Licence

• http://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en_CA

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14776/9044

OAI identifier oai:identifier

oai:uwindsor.scholaris.ca:20.500.14776/9044