Demand response potential evaluation for residential air conditioning loads

Residential air conditioning loads with energy storage characteristics can quickly participate in the demand response, making it an important demand response resource. It can improve resource utilisation and the flexibility of power grid operation through the effective regulation. However, the degree of residential air conditioning to participate in demand response is affected by the outdoor temperature, users' comfort settings, thermal storage and insulation properties of buildings. Moreover, the difficulty of assessing the demand response potential is further increased by the uncertainty of the influencing factors. To guide the residential air conditioners to participate in the power grid operation, the aggregated air conditioner model is established to describe the relationship among the total power, the external environment, and the indoor temperature. The demand response potential model is established from the amount and the duration of demand response. The effects of outdoor temperature, indoor temperature adjustment and the number of air conditioners participating in the response are quantitatively evaluated. Finally, the accuracy of the aggregated model and demand response potential model are verified by numerical simulation.

[1]  Benjamin F. Hobbs,et al.  Measuring the economic value of demand-side and supply resources in integrated resource planning models , 1993 .

[2]  D. Kirschen Demand-side view of electricity markets , 2003 .

[3]  S.E. Widergren,et al.  Modeling uncertainties in aggregated thermostatically controlled loads using a State queueing model , 2005, IEEE Transactions on Power Systems.

[4]  V. Ismet Ugursal,et al.  Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .

[5]  Duncan S. Callaway Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy , 2009 .

[6]  Ahmad Faruqui,et al.  The impact of informational feedback on energy consumption d A survey of the experimental evidence , 2010 .

[7]  Johanna L. Mathieu,et al.  Modeling and Control of Aggregated Heterogeneous Thermostatically Controlled Loads for Ancillary Services , 2011 .

[8]  Hosam K. Fathy,et al.  Modeling and control insights into demand-side energy management through setpoint control of thermostatic loads , 2011, Proceedings of the 2011 American Control Conference.

[9]  Leigh Tesfatsion,et al.  Intelligent Residential Air-Conditioning System With Smart-Grid Functionality , 2012, IEEE Transactions on Smart Grid.

[10]  Ning Lu,et al.  An Evaluation of the HVAC Load Potential for Providing Load Balancing Service , 2012, IEEE Transactions on Smart Grid.

[11]  C. Monteiro,et al.  Optimum residential load management strategy for real time pricing (RTP) demand response programs , 2012 .

[12]  Wei Zhang,et al.  Aggregated Modeling and Control of Air Conditioning Loads for Demand Response , 2013 .

[13]  Jochen Conrad,et al.  Demand Response potential of electrical heat pumps and electric storage heaters , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[14]  R. G. Harley,et al.  Model Predictive and Genetic Algorithm-Based Optimization of Residential Temperature Control in the Presence of Time-Varying Electricity Prices , 2013 .

[15]  Jacquelien M. A. Scherpen,et al.  Distributed Control of the Power Supply-Demand Balance , 2013, IEEE Transactions on Smart Grid.

[16]  Shuhui Li,et al.  Integrating Home Energy Simulation and Dynamic Electricity Price for Demand Response Study , 2014, IEEE Transactions on Smart Grid.

[17]  Matti Lehtonen,et al.  Demand response potential of residential HVAC loads considering users preferences , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[18]  Atila Novoselac,et al.  Demand response for residential buildings based on dynamic price of electricity , 2014 .

[19]  Tyrone L. Vincent,et al.  Potentials and Economics of Residential Thermal Loads Providing Regulation Reserve , 2014, ArXiv.

[20]  Ning Lu,et al.  A Demand Response and Battery Storage Coordination Algorithm for Providing Microgrid Tie-Line Smoothing Services , 2014, IEEE Transactions on Sustainable Energy.

[21]  Santiago Grijalva,et al.  Thermal energy storage for air conditioning as an enabler of residential demand response , 2014, 2014 North American Power Symposium (NAPS).

[22]  Lei Zheng,et al.  A Distributed Demand Response Control Strategy Using Lyapunov Optimization , 2014, IEEE Transactions on Smart Grid.

[23]  Tyrone L. Vincent,et al.  Aggregate Flexibility of Thermostatically Controlled Loads , 2015, IEEE Transactions on Power Systems.

[24]  Marija D. Ilic,et al.  Smart residential energy scheduling utilizing two stage Mixed Integer Linear Programming , 2015, 2015 North American Power Symposium (NAPS).

[25]  Kevin P. Schneider,et al.  Evaluating the Magnitude and Duration of Cold Load Pick-up on Residential Distribution Feeders Using Multi-State Load Models , 2016 .

[26]  Kameshwar Poolla,et al.  Identification of Virtual Battery Models for Flexible Loads , 2016, IEEE Transactions on Power Systems.

[27]  Farhad Kamyab,et al.  Demand Response Program in Smart Grid Using Supply Function Bidding Mechanism , 2016, IEEE Transactions on Smart Grid.

[28]  Ong Hang See,et al.  A review of residential demand response of smart grid , 2016 .

[29]  Gregor Verbic,et al.  A Fast Distributed Algorithm for Large-Scale Demand Response Aggregation , 2016, IEEE Transactions on Smart Grid.

[30]  P. Siano,et al.  Assessing the benefits of residential demand response in a real time distribution energy market , 2016 .

[31]  Fu Xiao,et al.  Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model , 2017 .

[32]  Wei Zhang,et al.  A Geometric Approach to Aggregate Flexibility Modeling of Thermostatically Controlled Loads , 2016, IEEE Transactions on Power Systems.