Assessment of optimization control strategies for energy management
Abstract: With the increasing demand for renewable energy sources, new systems are being developed to sustain future infrastructure, accommodating these new energy sources. One of the proposed solutions is to incorporate distributed energy resources to different households in order to provide local energy demands effectively. To enable large-scale integration of flexible energy resources, it is crucial to reduce end-user energy and power costs, which can be done by designing an optimization model objected to minimize the total electricity bill. In the scope of this Master thesis, the interest lies in investigating a control strategy to operate batteries, heat pumps, and other assets by producing the optimal setpoints using the designed optimization algorithm that takes, amongst others, market and weather data as well as customer behavior into account. The applied method for producing these setpoints is sensitivity analysis in linear programming, and heat pump scheduling has been investigated for performance evaluation of this technique. The results show that applying this method produces the optimal setpoints over the non-controllable electricity load range by utilizing a low number of optimizations, i.e. high computation-efficiency, and high accuracy. Consequently, the controller by having the given setpoints as the input can easily adjust the heat pump output power based on the real-time non-controllable electricity load without creating any peaks and extra costs for the customers.
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