Autonomous optimal control for demand side management with resistive domestic hot water heaters using linear optimization

Significance Statement

The share of electricity generation from renewable sources has grown rapidly during the last decade: the installed photovoltaic peak capacity in Germany is currently 40 GWp, more than 50% of Germany’s average power consumption, however PV only provides about 6% of Germany’s electrical energy. I.e., renewable power fluctuates rapidly and only rarely approaches peak power. This introduces unprecedented variability into the power grid, adversely affecting grid stability and electricity markets. Nevertheless, the share of renewable power must increase substantially to achieve independence from dwindling fossil fuels and to mitigate climate change. Activating flexible electrical loads to respond to fluctuating generation seems the most promising and cost effective medium term strategy to integrate more renewables into the power grid. This strategy is called “Demand Side Management” (DSM).

Thermal appliances, particularly domestic hot water heaters (DHWHs), have by far the greatest DSM potential amongst domestic appliances as they have high power ratings, exceeding 2 kW, and large thermal storage capacities, around 10 kWh. Furthermore, they are ubiquitous, cheap, and easy to model. Finally, domestic hot water heaters can be retrofitted for DSM cheaply, requiring few resources with little adverse environmental effects.

The mainstream approach to DSM of distributed flexible loads is centralized control requiring two-way communication between each device and a central entity, the virtual power plant (VPP). This raises issues for 1) user privacy, 2) VPP and grid security, and 3) the availability of warm water when required.

In contrast, our approach relies on one-way communication of pseudo-costs in conjunction with local intelligent control: Information about the future cost of electricity is communicated to the devices via one of several cheap and robust communication technologies including ripple control, long wave broadcasting, power line communication, or any type of internet service. The local controller receives the pricing information and uses it to find optimal switching times by minimizing pseudo-costs based on a dynamical model of heater physics using predicted hot water draws as side conditions.

Our approach offers several advantages:1) Protection of user privacy 2) inherent security with respect to hacker attacks, 3) high quality of service via the use of local historic data to predict user behavior, 4) great flexibility with respect to control objectives. The pseudo-cost function can be shaped to achieve various objectives including: 1) maximizing usage of locally generated renewable power, 2) minimizing electricity costs, 3) minimizing energy use, 4) achieving a specific response to unusual power generation patterns. 

About the author

Peter Kepplinger received his M.S. in Mathematics from the University of Vienna, Austria, in 2012. Between 2008 and 2012 he worked as an application analyst for UC4 software and as a self-employed software developer.

Currently he is a research assistant at the University of Applied Sciences Vorarlberg and a student at the Doctoral Program in Engineering Sciences at the University of Innsbruck, Austria.

About the author

Gerhard Huber is a research associate at the illwerke vkw Endowed Professorship for Energy Efficiency at the Vorarlberg University for Applied Sciences, Austria. He received his M.S. in Energy and Environmental Management in 2007. He subsequently worked as an energy engineer in the food industry before joining the Vorarlberg University of Applied Science.

About the author

Dr. Jörg Petrasch received his M.S. and PhD in Mechanical Engineering from ETH Zurich. He subsequently worked as an R&D engineer in the chemical industry, as a consulting engineer, and as a management consultant. In 2009 he joined the faculty of the University of Florida, USA as Assistant Professor. Since 2012 he holds the position of illwerke vkw Professor for Energy Efficiency at the Vorarlberg University of Applied Science, Austria. His research interests are in the fields of renewable energies, demand side management, energy storage, as well as tomography based and numerical methods for energy applications.

Journal Reference

Energy and Buildings, Volume 100, 2015, Pages 50–55.

Peter Kepplinger, Gerhard Huber, Jörg Petrasch,

illwerke vkw Professorship for Energy Efficiency, Vorarlberg University of Applied Sciences, Dornbirn, Austria

Abstract

Electric domestic hot water heaters are well suited for demand side management, as they possess high nominal power ratings combined with large thermal buffer capacities. In this paper, their potential for demand side management via pseudo cost functions is studied. A fully mixed thermal model of the water heater is used to formulate the optimization problem as a binary integer program, whereas a multi-layer model is used to simulate actual system behavior. The current approach requires only one-way communication of a pseudo cost function, which may depend upon factors such as expected electricity prices, local grid load, and expected renewable electricity production. An optimal heating strategy is then determined on-site based on the expected demand and the pseudo cost function. The expected demand is found via a nearest-neighbor data-mining algorithm using historic data. The optimization algorithm is used in conjunction with long-term simulations assuming different user behavior patterns and optimization strategies. The current approach compares favorably with conventional night tariff-switched operation. Assuming typical user behavior and using real spot market electricity prices as the pseudo cost function leads to cost savings of approximately 12% and energy savings of approximately 4%. Higher energy savings of approximately 12% can be attained by setting the pseudo cost function constant resulting in energy-driven optimization.

Go To Energy and Buildings

 

Autonomous optimal control demand side management resistive domestic hot water heaters using linear optimization. Advances In Engineering

 

 

Check Also

Advancing Fusion Energy: High-Field REBCO Superconducting Magnets in the SPARC TFMC Program - Advances in Engineering

Advancing Fusion Energy: High-Field REBCO Superconducting Magnets in the SPARC TFMC Program