Energy management for voltage control in a net-zero energy house community considering appliance operation constraints and variety of households

Abstract A large-scale diffusion of photovoltaics (PVs) is expected to disturb the voltage control in an electric distribution network. When PVs generate more electricity than demand requires, the electric voltage may exceed its legal limit, and the PV generation is suppressed by the power conditioner. This paper evaluates the extent to which residential energy management resources (EMR) can contribute to controlling the distribution line voltage and reduce the amount of PV generation suppression while considering the variety among households due to family composition and appliance ownership and the operation constraints due to operation time restrictions, occupants’ comfort and control method (manual control or automatic control). This paper estimates EMR that can be provided by a washing machine, clothes dryer, dishwasher, rice cooker, electric pot, air conditioner and heat pump water heater. EMR varies among households and accounts for 3%–63% of annual household electricity demand. Although 9.9% of PV generation is suppressed when all detached houses have installed PVs in the assumed residential community, the EMR is able to decrease to no more than 3.0%; this reduction potential decreases by 23% owing to the above-mentioned operation constraints.

[1]  J. G. Slootweg,et al.  Responsiveness of residential electricity demand to dynamic tariffs: Experiences from a large field test in the Netherlands , 2016 .

[2]  Dirk Saelens,et al.  Assessing electrical bottlenecks at feeder level for residential net zero-energy buildings by integrated system simulation , 2012 .

[3]  Darren Robinson,et al.  A bottom-up stochastic model to predict building occupants' time-dependent activities , 2013 .

[4]  Stephen McLaughlin,et al.  Development of Low-Voltage Load Models for the Residential Load Sector , 2014, IEEE Transactions on Power Systems.

[5]  Kosuke Kurokawa,et al.  Estimation of Photovoltaic Module Temperature Rise , 1996 .

[6]  Geert Deconinck,et al.  Demand response flexibility and flexibility potential of residential smart appliances: Experiences from large pilot test in Belgium , 2015 .

[7]  Jan Schoormans,et al.  A real-life assessment on the effect of smart appliances for shifting households’ electricity demand , 2015 .

[8]  Ian Richardson,et al.  A high-resolution domestic building occupancy model for energy demand simulations , 2008 .

[9]  Firuz Zare,et al.  Overvoltage prevention in LV smart grid using customer resources coordination , 2013 .

[10]  Jasper Frunt,et al.  Load shifting potential of the washing machine and tumble dryer , 2016, 2016 IEEE International Energy Conference (ENERGYCON).

[11]  Y. Shimoda,et al.  Evaluation of city-scale impact of residential energy conservation measures using the detailed end-use simulation model , 2007 .

[12]  J. Torriti.  A review of time use models of residential electricity demand , 2014 .

[13]  Dirk Saelens,et al.  Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour , 2016 .

[14]  Yoshiyuki Shimoda,et al.  Evaluation of Behavior Model of Occupants in Home Based on Japanese National Time Use Survey , 2015, Building Simulation Conference Proceedings.

[15]  Lance Manuel,et al.  Effect of technology-enabled time-of-use energy pricing on thermal comfort and energy use in mechanically-conditioned residential buildings in cooling dominated climates , 2016 .

[16]  Takashi Ikegami,et al.  Development of the Optimum Operation Scheduling Model of Domestic Electric Appliances for the Supply-Demand Adjustment in a Power System , 2010 .

[17]  Mark Sumner,et al.  Power flow control for power and voltage management in future smart energy communities , 2014 .

[18]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[19]  Yacine Rezgui,et al.  ANN–GA smart appliance scheduling for optimised energy management in the domestic sector , 2016 .

[20]  M. E. Baran,et al.  Optimal sizing of capacitors placed on a radial distribution system , 1989 .

[21]  Veselin Rakocevic,et al.  Application-oriented modelling of domestic energy demand , 2014 .

[22]  Jukka Paatero,et al.  A model for generating household electricity load profiles , 2006 .

[23]  Edmundas Kazimieras Zavadskas,et al.  Importance of occupancy information when simulating energy demand of energy efficient house: A case study , 2015 .

[24]  Tsai-Hsiang Chen,et al.  Examination of major factors affecting voltage variation on distribution feeders , 2012 .

[25]  Jaume Salom,et al.  Stochastic model for electrical loads in Mediterranean residential buildings: Validation and applications , 2014 .

[26]  Koen Vanthournout,et al.  An automated residential demand response pilot experiment, based on day-ahead dynamic pricing , 2015 .

[27]  Wilfried van Sark,et al.  Smart charging of electric vehicles with photovoltaic power and vehicle-to-grid technology in a microgrid; a case study , 2015 .

[28]  Janaka Ekanayake,et al.  Capability of smart appliances to provide reserve services , 2015 .