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Online Optimization for Energy Managements with Capacity Constraints



Suli Zou, John Lygeros

There have been extensive studies on actively utilizing Electric Vehicles (EVs) for grid-level benefits, such as valley-filling, load balancing, and frequency regulation, to name a few. In the presence of renewable energy, EV charging control has the potential for facilitating the integration of wind power and solar power. In recent years, there has been an increasing interest in jointly controlling EVs, solar energy, and energy storage systems to achieve valley-filling, peaking shaving, and energy neutral design.

Due to local renewable energy and demand prediction, stochasticity is arising in the control scheme. The problem can be formulated as s stochastic optimization problem, which aims at effectively coordinating EV charging to minimize the system cost. The considered uncertainties emphasize the need for online algorithms.

The project will feature 60% of theory and 40% of simulations. Some basic knowledge on modeling, optimization, and mechanism design and a good level of mathematical maturity are required. Simulation examples are based on IEEE standard test systems. Validation of the designed algorithms will require coding skills (MATLAB or Python). For this project, I will schedule 1 meeting per week.


• Conduct both of the EV charging model and the renewable model when impos- ing a capacity constraint;

• Present the decentralized control scheme and offline algorithm to achieve EV charging strategy;

• Design online optimization algorithms;

• Simulate and analyze results.

Weitere Informationen

John Lygeros

Art der Arbeit: 60% of theory and 40% of simulations
Anzahl StudentInnen:
Status: open
Projektstart: March 2018
Semester: Spring 2018