Thesis

A simple and effective approach for peak load shaving using battery storage systems and distributed generation

This thesis discusses the different strategies used to perform peak load shaving through the means of distributed generation and energy storage systems from the Utility’s perspective. Peak load shaving, sometimes referred to as load leveling or peak shifting, consists of the schemes used to eliminate the peaks and valleys in the load profile. This practice offers vast benefits to utilities in cost generation, line loss reduction, and volt support which is further discussed. Prior work for peak load shaving has been mainly focused on approaches such as linear and dynamic programming, and heuristic approaches such as particle-swarm optimization. The proposed algorithm here is based on a simple approach which compares the load profile with its average in a certain period and shares the charge/discharge among the energy storage devices based on defined weighting factors. In particular, the thesis focuses on the usage of Battery Energy Storage Systems (BESS) and distributed generations to accomplish this task. Results show that the proposed algorithm offers a simple, fast and effective way for peak-load shaving without heavy computational burdens often needed in other methods. As a result, it can be easily implemented in the Utility main substation for controlling the charge/discharge of storage devices throughout the distribution system.

Thesis (M.S., Electrical and Electronic Engineering)--California State University, Sacramento, 2014.

This thesis discusses the different strategies used to perform peak load shaving through the means of distributed generation and energy storage systems from the Utility’s perspective. Peak load shaving, sometimes referred to as load leveling or peak shifting, consists of the schemes used to eliminate the peaks and valleys in the load profile. This practice offers vast benefits to utilities in cost generation, line loss reduction, and volt support which is further discussed. Prior work for peak load shaving has been mainly focused on approaches such as linear and dynamic programming, and heuristic approaches such as particle-swarm optimization. The proposed algorithm here is based on a simple approach which compares the load profile with its average in a certain period and shares the charge/discharge among the energy storage devices based on defined weighting factors. In particular, the thesis focuses on the usage of Battery Energy Storage Systems (BESS) and distributed generations to accomplish this task. Results show that the proposed algorithm offers a simple, fast and effective way for peak-load shaving without heavy computational burdens often needed in other methods. As a result, it can be easily implemented in the Utility main substation for controlling the charge/discharge of storage devices throughout the distribution system.

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