Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system

As the installed capacity of wind power is growing, the stochastic variability of wind power leads to the mismatch of demand and generated power. Employing the regulating capability of demand to improve the utilization of wind power has become a new research direction. Meanwhile, the micro combined heat and power (micro-CHP) allows residential consumers to choose whether generating electricity by themselves or purchasing from the utility company, which forms a residential hybrid energy system. However, the impact of the demand response with hybrid energy system contained micro-CHP on the large-scale wind power utilization has not been analyzed quantitatively. This paper proposes an operation optimization model of the residential hybrid energy system based on price response, integrating micro-CHP and smart appliances intelligently. Moreover, a novel load aggregation method is adopted to centralize scattered response capability of residential load. At the power grid level, a day-ahead stochastic economic dispatch model considering demand response and wind power is constructed. Furthermore, simulation is conducted respectively on the modified 6-bus system and IEEE 118-bus system. The results show that with the method proposed, the wind power curtailment of the system decreases by 78% in 6-bus system. In the meantime, the energy costs of residential consumers and the operating costs of the power system reduced by 10.7% and 11.7% in 118-bus system, respectively.

[1]  Tongquan Wei,et al.  Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home , 2013, IEEE Transactions on Smart Grid.

[2]  Jin Lin,et al.  A Versatile Probability Distribution Model for Wind Power Forecast Errors and Its Application in Economic Dispatch , 2013, IEEE Transactions on Power Systems.

[3]  Aidong Yang,et al.  Modelling and selection of micro-CHP systems for domestic energy supply: The dimension of network-wide primary energy consumption , 2014 .

[4]  Shuhui Li,et al.  Developing smart and real-time demand response mechanism for residential energy consumers , 2014, 2014 Clemson University Power Systems Conference.

[5]  Fangxing Li,et al.  Coupon-Based Demand Response Considering Wind Power Uncertainty: A Strategic Bidding Model for Load Serving Entities , 2016, IEEE Transactions on Power Systems.

[6]  Nicola Bianco,et al.  Economic optimization of a residential micro-CHP system considering different operation strategies , 2016 .

[7]  Giorgio Graditi,et al.  Comparison between two different operation strategies for a heat-driven residential natural gas-fired CHP system: Heat dumping vs. load partialization , 2016 .

[8]  Reza Ghorbani,et al.  A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture , 2016 .

[9]  Bart De Schutter,et al.  Demand Response With Micro-CHP Systems , 2011, Proceedings of the IEEE.

[10]  Enzo Sauma,et al.  Homeostatic control, smart metering and efficient energy supply and consumption criteria: A means to building more sustainable hybrid micro-generation systems , 2014 .

[11]  Hamdi Abdi,et al.  Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem , 2016 .

[12]  Enzo Sauma,et al.  Building sustainable energy systems: Homeostatic control of grid-connected microgrids, as a means to reconcile power supply and energy demand response management , 2014 .

[13]  Ehab F. El-Saadany,et al.  Overview of wind power intermittency impacts on power systems , 2010 .

[14]  Kyeong-Deok Moon,et al.  Home energy management system based on power line communication , 2010, IEEE Transactions on Consumer Electronics.

[15]  M. Carrion,et al.  A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem , 2006, IEEE Transactions on Power Systems.

[16]  Saeed Mohajeryami,et al.  A novel economic model for price-based demand response , 2016 .

[17]  Felisa M. Córdova,et al.  Sustainable Hybrid Energy Systems: An Energy and Exergy Management Approach with Homeostatic Control of Microgrids , 2015, ITQM.

[18]  Yunsi Fei,et al.  Smart Home in Smart Microgrid: A Cost-Effective Energy Ecosystem With Intelligent Hierarchical Agents , 2015, IEEE Transactions on Smart Grid.

[19]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[20]  Hantao Cui,et al.  Day-ahead coordinated operation of utility-scale electricity and natural gas networks considering demand response based virtual power plants , 2016 .

[21]  Hoay Beng Gooi,et al.  Corrective economic dispatch and operational cycles for probabilistic unit commitment with demand response and high wind power , 2016 .

[22]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[23]  Tianshu Wei,et al.  From passive demand response to proactive demand participation , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[24]  Chuanwen Jiang,et al.  Active robust optimization for wind integrated power system economic dispatch considering hourly demand response , 2016 .

[25]  Enzo Sauma,et al.  Business optimal design of a grid-connected hybrid PV (photovoltaic)-wind energy system without energy storage for an Easter Island's block , 2013 .

[26]  Amir Safdarian,et al.  A Distributed Algorithm for Managing Residential Demand Response in Smart Grids , 2014, IEEE Transactions on Industrial Informatics.

[27]  David C. Yu,et al.  An Economic Dispatch Model Incorporating Wind Power , 2008, IEEE Transactions on Energy Conversion.

[28]  Santiago Grijalva,et al.  Modeling for Residential Electricity Optimization in Dynamic Pricing Environments , 2012, IEEE Transactions on Smart Grid.

[29]  M. Sailaja Kumari,et al.  Demand response and pumped hydro storage scheduling for balancing wind power uncertainties: A probabilistic unit commitment approach , 2016 .

[30]  Bin Wang,et al.  Adjustable Robust Real-Time Power Dispatch With Large-Scale Wind Power Integration , 2015, IEEE Transactions on Sustainable Energy.

[31]  D. Kirschen,et al.  Quantifying the Effect of Demand Response on Electricity Markets , 2007, IEEE Transactions on Power Systems.

[32]  Fabio Rinaldi,et al.  Long-term performance analysis of an HT-PEM fuel cell based micro-CHP system: Operational strategies , 2015 .

[33]  Kankar Bhattacharya,et al.  Optimal Operation of Residential Energy Hubs in Smart Grids , 2012, IEEE Transactions on Smart Grid.

[34]  Hongbin Sun,et al.  Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty , 2016 .

[35]  R. Hanitsch,et al.  Technical and economical comparison of micro CHP systems , 2005, 2005 International Conference on Future Power Systems.