The Effects of Residential Battery Storage on Grid Impact Indicators

Grid impact indicators hold valuable information about a prosumer when interpreted carefully. This makes them quite useful for benchmarking different demand side management schemes and studying active buildings. In this paper, the effects that battery storage has on five such indicators have been addressed in detail. Their values were calculated by analyzing 20 households with photovoltaics (PVs) for a period of one year. Measurement data of the households electricity consumption and PV generation were obtained from a publicly available database. To improve the scope of the analyses, three battery control strategies have been used to simulate the battery’s operation. The findings show that four of the five analyzed indicators follow certain patters when plotted as a function of the prosumer’s generation-to-consumption ratio. Similar patterns are also observed in the absolute changes in the value of all indicators when storage is integrated.

[1]  Andreas Sumper,et al.  Comparison of control strategies of residential PV storage systems , 2015 .

[2]  Georgios C. Christoforidis,et al.  Active power management in low voltage networks with high photovoltaics penetration based on prosumers’ self-consumption , 2018, Applied Energy.

[3]  F. Cucchiella,et al.  Photovoltaic energy systems with battery storage for residential areas: an economic analysis , 2016 .

[4]  Johan Driesen,et al.  Multiobjective Battery Storage to Improve PV Integration in Residential Distribution Grids , 2013, PES 2013.

[5]  Johan Driesen,et al.  Grid Impact Indicators for Active Building Simulations , 2015, IEEE Transactions on Sustainable Energy.

[6]  Goran Strbac,et al.  A MILP model for optimising multi-service portfolios of distributed energy storage , 2015 .

[7]  Michael A. Danzer,et al.  Optimal charge control strategies for stationary photovoltaic battery systems , 2014 .

[8]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[9]  Daniel Nilsson,et al.  Photovoltaic self-consumption in buildings : A review , 2015 .

[10]  Tomi Medved,et al.  Advanced peak shaving control strategies for battery storage operation in low voltage distribution network , 2017, 2017 IEEE Manchester PowerTech.

[11]  M. Stanley Whittingham,et al.  History, Evolution, and Future Status of Energy Storage , 2012, Proceedings of the IEEE.

[12]  Bruno Francois,et al.  Power Control Design of a Battery Charger in a Hybrid Active PV Generator for Load-Following Applications , 2011, IEEE Transactions on Industrial Electronics.

[13]  Goran Strbac,et al.  Economic analysis of energy storage business models , 2017, 2017 IEEE Manchester PowerTech.

[14]  A. Jossen,et al.  Economics of Residential Photovoltaic Battery Systems in Germany: The Case of Tesla’s Powerwall , 2016 .

[15]  Jaume Salom,et al.  Understanding net zero energy buildings: Evaluation of load matching and grid interaction indicators , 2011 .

[16]  Andreas I. Chrysochos,et al.  The impact of Photovoltaic Self-Consumption Rate on voltage levels in LV distribution grids , 2017, 2017 11th IEEE International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG).

[17]  V. Fernao Pires,et al.  Economic assessment of residential PV systems with self-consumption and storage in Portugal , 2017 .

[18]  James Marco,et al.  Techno-economic analysis of the viability of residential photovoltaic systems using lithium-ion batteries for energy storage in the United Kingdom , 2017 .

[19]  M. Webber,et al.  The impacts of storing solar energy in the home to reduce reliance on the utility , 2017, Nature Energy.

[20]  Karsten Voss,et al.  Load Matching and Grid Interaction of Net Zero Energy Buildings , 2010 .

[21]  Dong Hui,et al.  Battery Energy Storage Station (BESS)-Based Smoothing Control of Photovoltaic (PV) and Wind Power Generation Fluctuations , 2013, IEEE Transactions on Sustainable Energy.

[22]  Peter Lund,et al.  Options for improving the load matching capability of distributed photovoltaics: Methodology and application to high-latitude data , 2009 .