Green Neighbourhoods: The Role of Big Data in Low Voltage Networks’ Planning

In this chapter, we aim to illustrate the benefits of data collection and analysis to the maintenance and planning of current and future low voltage net- works. To start with, we present several recently developed methods based on graph theory and agent-based modelling for analysis and short- and long-term prediction of individual households electric energy demand. We show how maximum weighted perfect matching in bipartite graphs can be used for short-term forecasts, and then review recent research developments of this method that allow applications on very large datasets. Based on known individual profiles, we then review agent-based modelling techniques for uptake of low carbon technologies taking into account socio-demographic characteristics of local neighbourhoods. While these techniques are relatively easily scalable, measuring the uncertainty of their results is more challenging. We present confidence bounds that allow us to measure uncertainty of the uptake based on different scenarios. Finally, two case-studies are reported, describing applications of these techniques to energy modelling on a real low-voltage net- work in Bracknell, UK. These studies show how applying agent-based modelling to large collected datasets can create added value through more efficient energy usage. Big data analytics of supply and demand can contribute to a better use of renewable sources resulting in more reliable, cheaper energy and cut our carbon emissions at the same time.

[1]  Nilay Shah,et al.  Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies , 2017 .

[2]  Michael Nye,et al.  Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term , 2013 .

[3]  Tansu Alpcan,et al.  The importance of spatial distribution when analysing the impact of electric vehicles on voltage stability in distribution networks , 2015 .

[4]  Danica Vukadinovic Greetham,et al.  An innovation diffusion model of a local electricity network that is influenced by internal and external factors , 2017, 1703.05964.

[5]  Vijay Mahajan,et al.  Innovation diffusion: A deterministic model of space-time integration with physical analog☆ , 1977 .

[6]  Chao Sun,et al.  Nonlinear Predictive Energy Management of Residential Buildings with Photovoltaics & Batteries , 2016 .

[7]  Alexander Schrijver,et al.  Combinatorial optimization. Polyhedra and efficiency. , 2003 .

[8]  Siddharth Arora,et al.  Forecasting electricity smart meter data using conditional kernel density estimation , 2014, 1409.2856.

[9]  Spyros G. Tzafestas,et al.  Computational Intelligence Techniques for Short-Term Electric Load Forecasting , 2001, J. Intell. Robotic Syst..

[10]  Phil Blythe,et al.  A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts , 2015 .

[11]  Jeremy D. Watson,et al.  Impact of solar photovoltaics on the low-voltage distribution network in New Zealand , 2016 .

[12]  Elmar Kiesling,et al.  Agent-based simulation of innovation diffusion: a review , 2011, Central European Journal of Operations Research.

[13]  F. Bass.  A new product growth model for consumer durables , 1976 .

[14]  Peter Grindrod,et al.  A new error measure for forecasts of household-level, high resolution electrical energy consumption , 2014 .

[15]  Emrah Karakaya,et al.  Finite Element Method for forecasting the diffusion of photovoltaic systems: Why and how? , 2016 .

[16]  Kazunori Shinohara,et al.  Dynamic Innovation Diffusion Modelling , 2009 .

[17]  Ken R. McNaught,et al.  Design classes for hybrid simulations involving agent-based and system dynamics models , 2012, Simul. Model. Pract. Theory.

[18]  Benjamin K. Sovacool,et al.  Further reflections on vulnerability and resistance in the United Kingdom's smart meter transition , 2017, Energy Policy.

[19]  Wolf Fichtner,et al.  Analysing socioeconomic diversity and scaling effects on residential electricity load profiles in the context of low carbon technology uptake , 2016 .

[20]  Helmar Burkhart,et al.  An auction-based weighted matching implementation on massively parallel architectures , 2012, Parallel Comput..

[21]  Albert Molderink,et al.  A three-step methodology to improve domestic energy efficiency , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[22]  Lynne E. Parker,et al.  Energy and Buildings , 2012 .

[23]  Marek Brabec,et al.  A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers , 2008 .

[24]  Krzysztof Gajowniczek,et al.  Electricity forecasting on the individual household level enhanced based on activity patterns , 2017, PloS one.

[25]  Tao Hong,et al.  Probabilistic electric load forecasting: A tutorial review , 2016 .

[26]  Jennifer A. Scott,et al.  On the use of suboptimal matchings for scaling and ordering sparse symmetric matrices , 2015, Numer. Linear Algebra Appl..

[27]  S. Quoilin,et al.  Quantifying self-consumption linked to solar home battery systems: Statistical analysis and economic assessment , 2016 .

[28]  Matthew Rowe,et al.  A Peak Reduction Scheduling Algorithm for Storage Devices on the Low Voltage Network , 2014, IEEE Transactions on Smart Grid.

[29]  Peter Grindrod,et al.  Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data , 2016, IEEE Transactions on Smart Grid.

[30]  Will Gorman,et al.  The practicality of distributed PV-battery systems to reduce household grid reliance , 2017 .

[31]  Rayman Preet Singh,et al.  On hourly home peak load prediction , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[32]  Danica Vukadinovic Greetham,et al.  Electric vehicles and low-voltage grid: impact of uncontrolled demand side response , 2017 .

[33]  Rabikar Chatterjee,et al.  The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach , 1990 .

[34]  Marilyn A. Brown,et al.  Enhancing efficiency and renewables with smart grid technologies and policies , 2014 .

[35]  J. Munkres.  ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .

[36]  Masahiro Sugiyama,et al.  Climate change mitigation and electrification , 2012 .

[37]  M. F. Laguna,et al.  Adoption of innovations with contrarian agents and repentance , 2016, 1612.08949.

[38]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[39]  Laura HATTAM,et al.  Green neighbourhoods in low voltage networks: measuring impact of electric vehicles and photovoltaics on load profiles , 2016, 1606.07683.

[40]  N. Tomizawa,et al.  On some techniques useful for solution of transportation network problems , 1971, Networks.

[41]  Richard L. Morrill,et al.  The Shape of Diffusion in Space and Time , 1970 .

[42]  P. McSharry,et al.  A comparison of univariate methods for forecasting electricity demand up to a day ahead , 2006 .

[43]  C Swinerd,et al.  Simulating the diffusion of technological innovation with an integrated hybrid agent-based system dynamics model , 2014, J. Simulation.

[44]  E. Rogers.  Diffusion of Innovations , 1962 .

[45]  Nathaniel Charlton,et al.  Graph-based algorithms for comparison and prediction of household-level energy use profiles , 2013, 2013 IEEE International Workshop on Inteligent Energy Systems (IWIES).