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.
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.
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