Demand Response of Ancillary Service From Industrial Loads Coordinated With Energy Storage

As one of the featured initiatives in smart grids, demand response is enabling active participation of electricity consumers in the supply/demand balancing process, thereby enhancing the power system's operational flexibility in a cost-effective way. Industrial load plays an important role in demand response because of its intense power consumption, already existing advanced monitoring, and control infrastructure, and its strong economic incentive due to the high energy costs. As typical industrial loads, cement plants are able to quickly adjust their power consumption rate by switching on/off the crushers. However, in the cement plant as well as other industrial loads, switching on/off the loading units only achieves discrete power changes, which restricts the load from offering valuable ancillary services such as regulation and load following, as continuous power changes are required for these services. In this paper, we overcome this restriction of poor granularity by proposing methods that enable these loads to provide regulation or load following with the support of an onsite energy storage system.

[1]  Guido Sand,et al.  A Mean Value Cross Decomposition Strategy for Demand-side Management of a Pulping Process , 2015 .

[2]  Gabriela Hug,et al.  Computational approaches for efficient scheduling of steel plants as demand response resource , 2016, 2016 Power Systems Computation Conference (PSCC).

[3]  Madeleine Gibescu,et al.  Deep learning for estimating building energy consumption , 2016 .

[4]  Federico Milano,et al.  The effect of time-of-use tariffs on the demand response flexibility of an all-electric smart-grid-ready dwelling , 2016 .

[5]  J. Zico Kolter,et al.  Large-scale probabilistic forecasting in energy systems using sparse Gaussian conditional random fields , 2013, 52nd IEEE Conference on Decision and Control.

[6]  Henrik Madsen,et al.  Facilitating Renewable Integration by Demand Response , 2014 .

[7]  Ian A. Hiskens,et al.  Frequency Regulation From Commercial Building HVAC Demand Response , 2016, Proceedings of the IEEE.

[8]  Eduard Muljadi,et al.  Economic dispatch for microgrid containing electric vehicles via probabilistic modelling , 2015, 2015 North American Power Symposium (NAPS).

[9]  Gabriela Hug,et al.  Optimal regulation provision by aluminum smelters , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[10]  Nicholas A. DiOrio,et al.  Economic Analysis Case Studies of Battery Energy Storage with SAM , 2015 .

[11]  Manfred Morari,et al.  Robust integer optimization and scheduling problems for large electricity consumers , 2012, 2012 American Control Conference (ACC).

[12]  Tariq Samad,et al.  Smart grid technologies and applications for the industrial sector , 2012, Comput. Chem. Eng..

[13]  Kostas Margellos,et al.  Capacity Controlled Demand Side Management: A Stochastic Pricing Analysis , 2016, IEEE Transactions on Power Systems.

[14]  Iiro Harjunkoski,et al.  Industrial Tools and Needs , 2016 .

[15]  Gregor Verbic,et al.  Aggregated Demand Response Modelling for Future Grid Scenarios , 2015, 1502.05480.

[16]  Qi Zhang,et al.  An adjustable robust optimization approach to scheduling of continuous industrial processes providing interruptible load , 2016, Comput. Chem. Eng..

[17]  Ram Rajagopal,et al.  Predictability, constancy and contingency in electric load profiles , 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[18]  Gabriela Hug,et al.  Bidding strategy in energy and spinning reserve markets for aluminum smelters' demand response , 2015, 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[19]  Leon Liebenberg,et al.  Load-shifting opportunities for a typical South African cement plant , 2011, 2011 Proceedings of the 8th Conference on the Industrial and Commercial Use of Energy.

[20]  B. Kirby,et al.  Providing Reliability Services through Demand Response: A Preliminary Evaluation of the Demand Response Capabilities of Alcoa Inc. , 2009 .

[21]  Feng Gao,et al.  Line loss reduction with distributed energy storage systems , 2012, IEEE PES Innovative Smart Grid Technologies.

[22]  Peter B. Luh,et al.  An Efficient Approach to Short-Term Load Forecasting at the Distribution Level , 2016, IEEE Transactions on Power Systems.

[23]  Jhi-Young Joo,et al.  Efficient Coordination of Wind Power and Price-Responsive Demand—Part I: Theoretical Foundations , 2011, IEEE Transactions on Power Systems.

[24]  Gabriela Hug,et al.  Industrial demand response by steel plants with spinning reserve provision , 2015, 2015 North American Power Symposium (NAPS).

[25]  Ignacio E. Grossmann,et al.  Optimization of steel production scheduling with complex time-sensitive electricity cost , 2015, Comput. Chem. Eng..

[26]  Cong Liu,et al.  Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power , 2011 .

[27]  Hoay Beng Gooi,et al.  Penetration Rate and Effectiveness Studies of Aggregated BESS for Frequency Regulation , 2016, IEEE Transactions on Smart Grid.

[28]  Gabriela Hug,et al.  Model predictive control of industrial loads and energy storage for demand response , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[29]  Marina Gonzalez Vaya,et al.  Self Scheduling of Plug-In Electric Vehicle Aggregator to Provide Balancing Services for Wind Power , 2016, IEEE Transactions on Sustainable Energy.

[30]  Pierre Pinson,et al.  Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy , 2016, IEEE Transactions on Power Systems.

[31]  Gabriela Hug,et al.  Cost-Effective Scheduling of Steel Plants With Flexible EAFs , 2017, IEEE Transactions on Smart Grid.

[32]  Ignacio E. Grossmann,et al.  Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets , 2015 .

[33]  A. Isaksson,et al.  Scheduling and energy - Industrial challenges and opportunities , 2015, Comput. Chem. Eng..

[34]  Adam Wierman,et al.  Pricing data center demand response , 2014, SIGMETRICS '14.

[35]  Zhi Chen,et al.  Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization , 2012, IEEE Transactions on Smart Grid.

[36]  Goran Andersson,et al.  Scheduling and Provision of Secondary Frequency Reserves by Aggregations of Commercial Buildings , 2016, IEEE Transactions on Sustainable Energy.

[37]  Wei Wei,et al.  Charging Strategies of EV Aggregator Under Renewable Generation and Congestion: A Normalized Nash Equilibrium Approach , 2016, IEEE Transactions on Smart Grid.

[38]  H. Heinimann.  Swiss Federal Institute of Technology (ETH) , 2002 .