A Bi-Level Coordinated Optimization Strategy for Smart Appliances Considering Online Demand Response Potential

Demand response (DR) is counted as an effective method when there is a large-capacity power shortage in the power system, which may lead to peak loads or a rapid ramp. This paper proposes a bi-level coordinated optimization strategy by quantitating the DR potential (DRP) of smart appliances to descend the steep ramp and balance the power energy. Based on dynamic characteristics of the smart appliances, the mathematic models of online DRP are presented. In the upper layer, a multi-agent coordinated distribution method is proposed to allocate the demand limit to each agent from the dispatching center considering the online DRP. In the lower layer, an optimal smart appliances-controlling strategy is presented to guarantee the total household power consumption of each agent below its demand limit considering the consumers’ comfort and response times. Simulation results indicate the feasibility of the proposed strategy.

[1]  Yi Tang,et al.  A hierarchical charging strategy for electric vehicles considering the users' habits and intentions , 2015, 2015 IEEE Power & Energy Society General Meeting.

[2]  Hong Liu,et al.  A diagnostic method for distribution networks based on power supply safety standards , 2016 .

[3]  Leon M. Tolbert,et al.  A dynamic simulation tool for estimating demand response potential from residential loads , 2015, 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[4]  Alan Goodrich,et al.  Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections , 2014 .

[5]  Paul Denholm,et al.  Overgeneration from Solar Energy in California - A Field Guide to the Duck Chart , 2015 .

[6]  Mark O'Malley,et al.  A methodology for estimating the capacity value of demand response , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[7]  Leehter Yao,et al.  A Real-Time Charging Scheme for Demand Response in Electric Vehicle Parking Station , 2017, IEEE Transactions on Smart Grid.

[8]  Liuchen Chang,et al.  Identification and Estimation for Electric Water Heaters in Direct Load Control Programs , 2017, IEEE Transactions on Smart Grid.

[9]  References , 1971 .

[10]  Saifur Rahman,et al.  An Algorithm for Intelligent Home Energy Management and Demand Response Analysis , 2012, IEEE Transactions on Smart Grid.

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

[12]  Saifur Rahman,et al.  Grid Integration of Electric Vehicles and Demand Response With Customer Choice , 2012, IEEE Transactions on Smart Grid.

[13]  Yingying Chen,et al.  Optimal Dispatch of Air Conditioner Loads in Southern China Region by Direct Load Control , 2016, IEEE Transactions on Smart Grid.

[14]  Birgitte Bak-Jensen,et al.  Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks , 2017 .

[15]  Azah Mohamed,et al.  Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy , 2016 .

[16]  Shengnan Shao,et al.  An Approach to Demand Response for Alleviating Power System Stress Conditions due to Electric Vehicle Penetration , 2011 .

[17]  Luiz C. P. da Silva,et al.  Large-scale control of domestic refrigerators for demand peak reduction in distribution systems , 2013 .

[18]  Haiwang Zhong,et al.  Real-time demand response potential evaluation: A smart meter driven method , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

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

[20]  Xiao-Ping Zhang,et al.  Real-time Energy Control Approach for Smart Home Energy Management System , 2014 .