Stochastic programming models for planning wind based distributed generation in prosumers of energy mode
Date
2020-05
Authors
Runsewe, Temitope
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Abstract
This thesis formulates, solves and contrasts several stochastic programming models that a decision maker may use to determine the siting and sizing of distributed energy resources (DER) in a distributed generation (DG) system. This thesis focuses on
two major approaches: strategical and operational. In the strategical one, four models are designed to minimize the lifecycle cost of DG systems powered with wind energy and a substation considering the loss of load probability and thermal constraints. The models
are solved in three cities with high -medium -low wind speed profiles. Extensive data analysis is performed on the 9-year hourly wind speed data collected which permits to estimate the probability distribution for the power generation. Results from the strategical models show that the system designed using stochastic programming is highly reliable as it considers uncertainties in the wind speed. In the operational approach, two new models are proposed with the objective of minimizing the lifecycle cost of DG systems powered with wind energy and a substation using system nodes as prosumers. The first model
considers the loss of load probability and thermal constraints. The second one adds energy storage system (ESS) into the first model while considering just the thermal constraints and including 365 days across a year. The second model is solved for three distinct cases: a fixed battery capacity of 100MW, a fixed battery capacity of 250MW and a variable battery capacity. This thesis demonstrates that the operational models are tractable and can be solved using commercial solvers. It also assesses the benefit of
considering system nodes as prosumers using ESS.
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Keywords
Distributed generation, Prosumer
Citation
Runsewe, T. (2020). <i>Stochastic programming models for planning wind based distributed generation in prosumers of energy mode</i> (Unpublished thesis). Texas State University, San Marcos, Texas.