How to improve peak time coverage through a smart-controlled MCHP unit combined with thermal and electric storage systems

Ummenhofer, Christoph D.; Olsen, John; Page, John; Roediger, Tim

doi:10.1016/j.enbuild.2016.12.086

Cited by 8 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig. 7 (a) shows the evaluation of our proposed reformulation for the coupling constraint in (19) together with the a-posteriori feasibility validation of the common chance constraint between agent 1 and agent 2. We plot the obtained rh,1 + rc,2 using DS, CS, and MCS formulations.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…STGs have been studied implicitly in the context of micro combined heat and power systems, see [19], or general smart grids, e.g., see [20] and [21]. Building heat demand with a dynamical storage tank was considered in [22], whereas in [23] an adaptive-grid model for dynamic simulation of thermocline thermal energy storage systems was developed.…”

Section: Related Workmentioning

confidence: 99%

“…If (r h a,k ) i and (r c a,k ) j represent the current thermal radius of warm and cold wells of ATES system in agent i and j, respectively, then constraints(18) and(19) are equivalent.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic Energy Management for Building Climate Comfort in Smart Thermal Grids with Seasonal Storage Systems

Rostampour

Keviczky

2019

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

This paper presents an energy management framework for building climate comfort (BCC) systems interconnected in a grid via aquifer thermal energy storage (ATES) systems in the presence of two types of uncertainty (private and common). ATES can be used either as a heat source (hot well) or sink (cold well) depending on the season. We consider the uncertain thermal energy demand of individual buildings as a private uncertainty source and the uncertain common resource pool (ATES) between neighbors as a common uncertainty source. We develop a large-scale stochastic hybrid dynamical model to predict the thermal energy imbalance in a network of interconnected BCC systems together with mutual interactions between their local ATES. We formulate a finite-horizon mixed-integer quadratic optimization problem with multiple chance constraints at each sampling time, which is in general a non-convex problem and difficult to solve. We then provide a computationally tractable framework by extending the so-called robust randomized approach and offering a less conservative solution for a problem with multiple chance constraints. A simulation study is provided to compare completely decoupled, centralized and move-blocking centralized solutions. We also present a numerical study using a geohydrological simulation environment (MODFLOW) to illustrate the advantages of our proposed framework.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Energy Management for Building Climate Comfort in Smart Thermal Grids with Seasonal Storage Systems

Rostampour

Keviczky

2019

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

show abstract

“…Ummenhofer et al [20] carried out experimental tests of an mCHP unit with an ICE coupled with a thermal energy storage (TES) and an electric energy storage (EES). They focused on the performance of charging and discharging the EES to improve peak time coverage of the power grid.…”

Section: Review Of the Literaturementioning

confidence: 99%

Modelling of the Biomass mCHP Unit for Power Peak Shaving in the Local Electrical Grid

et al. 2019

View full text Add to dashboard Cite

In the article, the method and algorithm for a control strategy of the operation of a micro combined heat and power (mCHP) unit and for reducing the power consumption peaks (peak shaving) are proposed and analyzed. Two scenarios of the mCHP’s operation, namely with and without the control strategy, are discussed. For calculation purposes, a boiler fired with wood pellets coupled with a Stirling engine, manufactured by ÖkoFEN, was used. These results were used to analyze two scenarios of the control strategy. In this study, the operation of mCHP was simulated using the energyPRO software. The application of this control strategy to dispersed mCHP systems allows for a very effective “peak shaving” in the local power grid. The results of calculation using the new algorithm show that the electricity generated by the mCHP system covers the total demand for power during the morning peak and reduces the evening peak by up to 71%. The application of this method also allows for a better reduction of the load of conventional grids, substations, and other equipment.

show abstract

Managing Safety Risk by Manufacturers

2019

Electrochemical Power Sources: Fundamentals, Systems, and Applications

View full text Add to dashboard Cite

How to improve peak time coverage through a smart-controlled MCHP unit combined with thermal and electric storage systems

Cited by 8 publications

References 24 publications

Probabilistic Energy Management for Building Climate Comfort in Smart Thermal Grids with Seasonal Storage Systems

Probabilistic Energy Management for Building Climate Comfort in Smart Thermal Grids with Seasonal Storage Systems

Modelling of the Biomass mCHP Unit for Power Peak Shaving in the Local Electrical Grid

Managing Safety Risk by Manufacturers

Contact Info

Product

Resources

About