Master Thesis MSTR-2020-25

BibliographyRamachandran Selvaraj, Sri Vishnu: Feasibility analysis of using Model Predictive Control in Demand-Side Management of residential building.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 25 (2020).
49 pages, english.

The energy systems are becoming smart recently with an increase in communication capabilities between producer, distributor and consumer. Also, many distributed renewable energy producers both in large and domestic scale are adding to the system day by day. Executing Smart Demand-Side Management (DSM) programs can help in providing financial benefits and stability of the energy system without compromising the comfort of end-users. Model Predictive Control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. Due to its ability to predict future events and generate optimal control, it is widely used in process industries since the 1980s and in recent years it is introduced in power systems. This motivates to study the economic feasibility of using MPC in executing DSM for Residential building, to optimize the power consumption costs and stability of the energy system in the presence of local renewable energy sources (E.g., PV system). The main contribution of this thesis work is to measure the economic benefit of using MPC on DSM of household electricity consumption. A detailed study of modeling the demand side, i.e the appliances of a smart home, along with the domestic energy generators is done in the initial part. Apart from the physical properties of the renewable energy generators, the influence of external factors like weather, dynamic-pricing of electricity and changing user preference is also considered in the model. This formulated model is used to perform simulation of the residential building to generate an optimized energy consumption schedule and calculate the resulting economic benefits. The periodic changes in weather forecast and dynamic-prices are fed into the simulation to improve the prediction accuracy of the system. Lastly, the model is evaluated on a physical implementation to analyze its performance. There are multiple findings as part of the result of this thesis, like the economic benefit of using such a system will encourage many users to participate in Demand response programs, this in turn will help in the reduction of pollution originating from non-renewable energy generators.

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Department(s)University of Stuttgart, Institute of Architecture of Application Systems
Superviser(s)Aiello, Prof. Marco
Entry dateDecember 16, 2020
   Publ. Institute   Publ. Computer Science