Abstract
If CO2-emissions are to be reduced, the shares of renewable energy sources will have to be significantly increased. However, energy flexibility is required to cope with the increased share of renewable energy. Utilising it necessitates mathematical models of the operational response of energy flexible consumers. In this paper we present an accurate and general dynamic model of energy flexibility based on stochastic differential equations. The intuitive interpretation of the parameters is explained, to show the generality of the proposed model. To validate the approach, the parameters are estimated for three water towers and three buildings controlled by economic model predictive controllers. The model is then used to offer the energy flexibility on the current electricity market of Scandinavia, Nord Pool, using the so called “flexi orders”. Finally, the energy flexibility is used by controlling the demand of the water towers indirectly, through price signals designed based on the proposed model. Compared to having perfect foresight of electricity prices and future demand, between 63% and 98% of the potential savings were obtained in for these case studies. This shows that even without direct control of energy flexible systems, most of the potential can be reached under the current market conditions.
Highlights
Accompanying the ever increasing share of Renewable Energy Source(RESs) is the challenge of controlling energy generation and matching it to energy demand [1]
In this paper we present an accurate and general dynamic model of energy flexibility based on stochastic differential equations
The parameters are estimated for three water towers and three buildings controlled by economic model predictive controllers
Summary
Accompanying the ever increasing share of Renewable Energy Source(RESs) is the challenge of controlling energy generation and matching it to energy demand [1]. It is still expected that energy flexible systems will be automatically controlled [16], but in the end the owner has full autonomy This approach comes with its own challenges, such as what to do when no one wants to be flexible and the uncertainty of the response to changing prices. This have been investigated using a bottom-up approach for domestic households [17], with focus on thermal loads in [18] and activity patterns in [2] While this gives insights into the physical capabilities of particular appliances, it does not address the fundamental challenge, namely to estimate the expected response for a given sequence of prices. In [20] this topic was explored, where the relation between prices and change in demand was assumed to be linear and time-invariant This allows for easy interpretation of the energy flexibility through the step-response, termed the Flexibility Function.
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