Flexible Demand Resource Pricing Scheme: A Stochastic Benefit-Sharing Approach

With the rapidly increased penetration of renewable generations, the incentive-based demand-side management (DSM) shows great value in alleviating the uncertainty and providing flexibility for a microgrid. However, how to price those demand resources becomes one of the most significant challenges for promoting the incentive-based DSM under microgrid environments. In this article, a flexible demand resource pricing scheme is proposed. Instead of using the utility function of end-users like most existing literature, the economic benefit of the flexible demand resources is evaluated by the operation performance enhancement of the microgrid, and correspondingly, the resource is priced based on a benefit-sharing approach. An iteration-based chance-constrained method is established to calculate the economic benefit and shared compensation for the demand resource providers. Meanwhile, the financial risks for the microgrid operator due to uncertain factors are mitigated by the chance-constrained criterion. The proposed scheme is examined by an experimental microgrid to illustrate its effectiveness.

[1]  Qianfan Wang,et al.  A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output , 2012, 2012 IEEE Power and Energy Society General Meeting.

[2]  Jamshid Aghaei,et al.  Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems) , 2013 .

[3]  Hamed Mohsenian Rad,et al.  Energy and Performance Management of Green Data Centers: A Profit Maximization Approach , 2013, IEEE Transactions on Smart Grid.

[4]  Bill Rose,et al.  Microgrids , 2018, Smart Grids.

[5]  Shahram Jadid,et al.  Stochastic multi-objective operational planning of smart distribution systems considering demand response programs , 2014 .

[6]  Zhongfu Tan,et al.  A two-stage scheduling optimization model and solution algorithm for wind power and energy storage system considering uncertainty and demand response , 2014 .

[7]  Ana Paula Barbosa-Póvoa,et al.  Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules , 2014 .

[8]  M. Javadi,et al.  Renewable energy resources short-term scheduling and dynamic network reconfiguration for sustainable energy consumption , 2015 .

[9]  Matti Lehtonen,et al.  A Medium-Term Decision Model for DisCos: Forward Contracting and TOU Pricing , 2015, IEEE Transactions on Power Systems.

[10]  Geza Joos,et al.  Multiobjective Optimization Dispatch for Microgrids With a High Penetration of Renewable Generation , 2015, IEEE Transactions on Sustainable Energy.

[11]  Weijen Lee,et al.  A stochastic microgrid operation scheme to balance between system reliability and greenhouse gas emission , 2015, 2015 IEEE/IAS 51st Industrial & Commercial Power Systems Technical Conference (I&CPS).

[12]  Wei Cao,et al.  Direct Load Control in Microgrids to Enhance the Performance of Integrated Resources Planning , 2014, IEEE Transactions on Industry Applications.

[13]  Mohammad Sadeghi,et al.  Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties , 2016 .

[14]  Giorgio Rizzoni,et al.  Residential Demand Response: Dynamic Energy Management and Time-Varying Electricity Pricing , 2016, IEEE Transactions on Power Systems.

[15]  Meng Liu,et al.  Power system dynamic economic dispatch with controllable air-conditioning load groups , 2016, 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[16]  Qiang Li,et al.  A Pattern and Method of Optimized Power Utilization Based on Consumers’ Interaction Capability , 2016 .

[17]  Sandro Macchietto,et al.  Optimal scheduling of energy storage for renewable energy distributed energy generation system , 2016 .

[18]  Iakovos Michailidis,et al.  Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage , 2016 .

[19]  John Baillieul,et al.  Control and Communication Protocols Based on Packetized Direct Load Control in Smart Building Microgrids , 2016, Proceedings of the IEEE.

[20]  Bo Zhao,et al.  Stochastic Optimal Operation of Microgrid Based on Chaotic Binary Particle Swarm Optimization , 2016, IEEE Transactions on Smart Grid.

[21]  J. Catalão,et al.  End-User Comfort Oriented Day-Ahead Planning for Responsive Residential HVAC Demand Aggregation Considering Weather Forecasts , 2017, IEEE Transactions on Smart Grid.

[22]  Long Zhao,et al.  The Impact of Time-of-Use (TOU) Rate Structure on Consumption Patterns of the Residential Customers , 2017, IEEE Transactions on Industry Applications.

[23]  Mahesh S. Illindala,et al.  Design and planning strategy for energy storage system in a shipboard dc hybrid power system , 2017, 2017 IEEE/IAS 53rd Industrial and Commercial Power Systems Technical Conference (I&CPS).

[24]  Mahesh S. Illindala,et al.  Comprehensive Protection Strategy for an Islanded Microgrid Using Intelligent Relays , 2015, IEEE Transactions on Industry Applications.

[25]  Xiaohua Xia,et al.  Optimal dispatch for a microgrid incorporating renewables and demand response , 2017 .

[26]  Zhao Yang Dong,et al.  Robust Operation of Microgrids via Two-Stage Coordinated Energy Storage and Direct Load Control , 2017, IEEE Transactions on Power Systems.

[27]  Wei Feng,et al.  Robust optimization for energy transactions in multi-microgrids under uncertainty , 2018 .

[28]  Mohammadreza Barzegaran,et al.  The impact of customers’ participation level and various incentive values on implementing emergency demand response program in microgrid operation , 2018 .

[29]  Fei Wang,et al.  Multi-Objective Optimization Model of Source–Load–Storage Synergetic Dispatch for a Building Energy Management System Based on TOU Price Demand Response , 2018, IEEE Transactions on Industry Applications.

[30]  Jun Zeng,et al.  A Potential Game Approach to Distributed Operational Optimization for Microgrid Energy Management With Renewable Energy and Demand Response , 2019, IEEE Transactions on Industrial Electronics.

[31]  Feng Zhu,et al.  Flexible Demand Resource Pricing Scheme: A Stochastic Benefit-Sharing Approach , 2019, 2019 IEEE/IAS 55th Industrial and Commercial Power Systems Technical Conference (I&CPS).