Browsing by Author "Kahraman, Aysegul"

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Citation - WoS: 90
Citation - Scopus: 93
A predictive control strategy for optimal management of peak load thermal comfort energy storage and renewables in multi-zone buildings
(ELSEVIER, 2019) Emrah Biyik; Aysegul Kahraman; Biyik, Emrah; Kahraman, Aysegul
Buildings are responsible for about 40% of the global energy consumption where heating ventilation and air conditioning (HVAC) systems account for the most part of it. Continuous increase in the installation of new HVAC systems and higher penetration of renewables and energy storage in the building energy network require more sophisticated control approaches to realize the full potential of these systems. In this paper an optimal control framework to coordinate HVAC battery energy storage and renewable generation in buildings is developed. The controller aims to reduce peak load demand while achieving thermal comfort within industry standards. To facilitate this a simple lumped mathematical model that describes the zone transient thermal dynamics is structured with a minimal data from the building and is trained with actual thermal and electrical data. Next a model predictive control algorithm that takes into account building thermal dynamics battery state of charge renewable generation status and actual operational data and constraints is formulated to regulate HVAC demand battery power and building thermal comfort. The controller considers the changes in the outside dry-bulb air temperature electricity price required energy amount and comfort conditions simultaneously in order to find the proper optimal zone temperatures guaranteeing occupant comfort. The new controller was tested using data from a real building and preliminary results indicate that significant reduction in peak electrical power demand can be achieved by the proposed approach.
Stochastic Microgrid Control Problems: Effects of Load Distribution and Planning Horizon
(IEEE, 2019) Aysegul Kahraman; Onder Bulut; Emrah Biyik; Cuneyt Guzelis; Gokhan Demirkiran; Demirkiran, Gokhan; Guzelis, Cuneyt; Kahraman, Aysegul; Bulut, Onder; Biyik, Emrah
Microgrids enable the integration of distributed energy resources with high renewable penetration into the main power grid. In this study a microgrid problem that takes into account the stochastic nature of the net load defined as the difference between actual demand and renewable generation is studied. The problem is formulated as a Mixed Integer Linear Stochastic Optimization Programming and is solved under different net load distributions and planning horizons. Numerical results show that increasing variance causes a rise in total system cost for the approach that solves the stochastic problem by ignoring randomness (as in most real-life applications) as well as for the one that solves the problem with the true distribution. It is observed that enlarging the planning horizon also has similar effects.