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penetration of demand side management is large the effect of this load shift on the grid (and the energy price) needs to be investigated and integrated demand side-supply side simulations are necessary [21]. From figures 5(b) and 7(b) an increase in total energy use due to storage heat losses can be seen for all storage sizes and configurations. To obtain an environmental benefit the increase in energy use must be supplied by renewable energy sources. This can be obtained as the storage is charged at times different from the time of use if the energy price represents the availability of renewable energy sources which would otherwise be wasted. 4. Conclusions The effect of the active thermal energy storage model in an optimal control environment for demand side management has been investigated under different price scenarios. A reduction in consumer energy cost up to 20% was calculated for a dwelling conform with Belgian regulations on an average November day. This value however is valid with perfect weather and occupancy predictions and without model mismatch, and assuming constant heat production efficiency. For a price signal with a long period of constant minimal energy prices, such as the day-night tariff in Belgium, a significant difference in control profiles between the ideally stratified and the ideally mixed storage tank model is obtained. The storage size for which minimal energy cost is obtained was also calculated and was found to be larger for the ideally mixed model. From the optimisations using the time-of-use energy price less consumer benefit was obtained. Moreover due to the distinct short minimum cost period peak loads are not diminished but are likely to be more concentrated. The difference between the ideally stratified model and the ideally mixed model has also been evaluated. The stratified model outperforms the mixed model in all simulated cases. Only when the energy storage capacity is oversized significantly a comparable performance is expected. This raises issues for the implementation of model predictive control algorithms in real situations using sensible thermal energy storage since an actual storage in this configuration will have a behaviour in between the two ideal cases. The required storage control model will have to account for the imperfect stratification leading to non-convex optimization problems which are difficult to solve. 5. Acknowledgements This research was funded from the academisation of the study of industrial engineering at the catholic university college Limburg, KHLim. Nomenclature A Area [ m2] C Heat capacity[ J/K] C ̇ Heat capacity flow rate [ W/K] DSM Demand side management E Energy [ J] J Cost [e] p price of heat [e /J] Q ̇ Heat flow rate [ W] STES Sensible thermal energy storage t Time [ s] T Temperature [ K] TES Thermal energy storage U Co ̈effici ̈ent of heat transfer [ W/m2K] Greek symbols ε Effectiveness [ −] 9PDF Image | Energy cost reduction by optimal control of ideal sensible thermal energy storage
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