Optimized operational strategy planning model for Smart Net Zero Community

Due to some complex technical problems, both power plant’s generators and local grid transmission lines do not have the potential to generate and transmit enough electricity to cover and meet all load demand during peak hours. Based on this technical difficulty, there is no solution for customers to use costly energy during peak hours and accordingly pay more money for their consumed electricity(Refer to electrical pricing scheme).In this case, in order to remedy above mentioned problem, smart solar communities as grid-friendly consumers, would be established with the purpose of supporting the local grids. This feature would be more important when local grids are in peak period hours. To this end, solar communities should shift their internal electric loads from peak load period to off-peak hours. Regardless of shifting the loads, they should either decrease overall amount of energy consumption or used dynamic load shedding/shaving methods. In this case, for managing load demand during peak hours, a community-scale optimal strategy planning model is necessary. Base on community-scale dynamic complexity and uncertainty, this model should be constructed based on inexact optimization techniques such as Interval Parameter linear Programming, (Mixed) Integer Linear Programming, Fuzzy Linear Programming or integration of above methods. Also, for considering all possibilistic and probabilistic distributions into community system, decision making methods such as Multi Stage Stochastic Programming, Superiority–Inferiority Base Programming as well as Chance–Constrains Programming should be incorporated into inexact planning models. The outcomes of project will be of importance to government agencies looking for solutions to climate change (such as NRCan), utilities looking for electrical load shaving and peak load shifting opportunities (such as electrical local distribution companies, THESL, and the Ontario Power Authority).

Faculty Supervisor:

Drs. Alan Fung & Kaamran Raahemifar

Student:

Nima Alibabaei

Partner:

S2E Technologies Inc.

Discipline:

Engineering

Sector:

Energy

University:

Ryerson University