Impact of residential electricity tariffs with variable energy prices on low voltage grids with photovoltaic generation

Abstract This paper presents an approach integrating simulation models for residential electricity demand with price elasticity and electricity generation from photovoltaic systems as well as for load flow analysis using Monte Carlo simulation in low voltage distribution grids. The price elasticity model, using metering data collected within a field test including approximately 1100 households, and the application of the approach for analysing the impact of alternative electricity tariffs on grid utilisation for different use cases constitute the main contribution. We compare a tariff using constant prices with tariffs using variable prices, one of which is directly derived from the central wholesale market, while another one is subject to sales-driven rules and regional renewable electricity availability. The results reveal that generation peaks lead to power flow reversions and an exceedance of the limit for voltage deviation in several hours for high photovoltaic installation rates and thus to more critical situations than demand peaks for the considered low voltage grid. While the considered tariffs can, generally, support grid stability, several drawbacks should be reduced in future tariff design: the considered sales-driven tariff may aggravate critical grid situations during demand peaks while a tariff, solely deriving its variable prices from a central wholesale market, may not be suitable to relieve critical grid situations during distributed generation peaks.

[1]  Valentin Bertsch,et al.  Layout Optimisation of Decentralised Energy Systems Under Uncertainty , 2013, OR.

[2]  Ein Rollenmodell zur Einbindung der Endkunden in eine smarte Energiewelt , 2013 .

[3]  A. Faruqui,et al.  Household response to dynamic pricing of electricity: a survey of 15 experiments , 2010 .

[4]  Yongxiu He,et al.  Residential demand response behavior analysis based on Monte Carlo simulation: The case of Yinchuan in China , 2012 .

[5]  Jean Mahseredjian,et al.  Load flow calculations in distribution systems with distributed resources. A review , 2011, 2011 IEEE Power and Energy Society General Meeting.

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

[7]  J. Torriti,et al.  Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy , 2012 .

[8]  Mark Rylatt,et al.  A simple model of domestic lighting demand , 2004 .

[9]  Andrew Peacock,et al.  Assessing the potential of residential demand response systems to assist in the integration of local renewable energy generation , 2014 .

[10]  Barbara Borkowska,et al.  Probabilistic Load Flow , 1974 .

[11]  S. Conti,et al.  Probabilistic load flow using Monte Carlo techniques for distribution networks with photovoltaic generators , 2007 .

[12]  Igor Papic,et al.  Assessment of maximum distributed generation penetration levels in low voltage networks using a probabilistic approach , 2015 .

[13]  Dominik Möst,et al.  Simulations in the Smart Grid Field Study MeRegioSimulationen im MeRegio Smart Grid Feldtest , 2010, it Inf. Technol..

[14]  J. S. Christensen,et al.  Probabilistic load flow calculation using Monte Carlo techniques for distribution network with wind turbines , 1998, 8th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.98EX227).

[15]  W. Fichtner,et al.  A high-resolution determination of the technical potential for residential-roof-mounted photovoltaic systems in Germany , 2014 .

[16]  Wolfgang Ketter,et al.  Demand side management—A simulation of household behavior under variable prices , 2011 .

[17]  Shi You,et al.  Indirect control for demand side management - A conceptual introduction , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[18]  H Markiewicz,et al.  Voltage Disturbances Standard EN 50160 - Voltage Characteristics in Public Distribution Systems , 2008 .

[19]  Jukka Paatero,et al.  Impacts of distributed photovoltaics on network voltages: Stochastic simulations of three Swedish low-voltage distribution grids , 2010 .

[20]  Rolf Witzmann,et al.  Abschätzung des Photovoltaik-Potentials auf Dachflächen in Deutschland , 2010 .

[21]  Guy R. Newsham,et al.  The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review , 2010 .

[22]  W. J. Bonwick,et al.  Structural modelling of energy demand in the residential sector: 1. Development of structural models , 1997 .

[23]  Tim Jackson,et al.  The value of reducing distribution losses by domestic load-shifting: a network perspective , 2009 .

[24]  W. El-Khattam,et al.  Investigating distributed generation systems performance using Monte Carlo simulation , 2006, IEEE Transactions on Power Systems.

[25]  G. Goldman,et al.  A Survey of Utility Experience with Real Time Pricing , 2004 .

[26]  Lennart Soder,et al.  Distribution network planning with a large amount of small scale photovoltaic power , 2013, 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).