smart grid consumer engagement
TRANSCRIPT
Geert-Jan van der Zanden MSc EMP 2011
The Smart Grid in Europe:
The Impact of Consumer Engagement on the Value of the European Smart Grid
Smart grids?
Image: http://theasiacareertimes.com/wp-content/uploads/2011/06/smartgrid.jpg
Power (IEA): 17% of GHG
Smart grid (EC):European CO2 - 9% Domestic consumption - 10%
The smart grid – Consumer engagement
Main research questions:
• Why is the smart grid in Europe not developing as quickly as expected?
• How can the development of the smart grid in Europe be facilitated by maximizing consumer engagement with the smart grid?
Sub-questions:
• What is the projected value of the smart grid in Europe?
• What impact does consumer engagement have on the value of the smart grid in Europe and how can it be maximized?
Thesis structure
Qualitative
Drivers & barriers
Consumers
Value (Business Case)
Consumer
Quantitative
Smart Grid
Behavioral Change Theories• Theory of Regulatory Engagement
• Transaction Theory• Social Comparison Theory
• Theory of Diffusion of Innovation • Theory of Affordances (expanded)
Methodology
Empirical EvidenceReviews of demand response tests by:
• Faruqui• Darby
• Ehrhardt-Martinez• Myself
Recommendations
Literature Research
Interviews
Blog Discussion
Forecasted investment in smart grid technology in Europe
• Goldman Sachs: €187 billion in next 30 years
• Smart Energy Demand Coalition: €120 billion by 2030
• Booz & Company: €90 billion by 2020
Source: Van der Zanden, GTM Research
Distribution Automation
Systems management and data security ICT
Distributed and Renewable electricity generation (DG/RES)
Advanced energy storage
Energy Management Services / HAN
EV charging infrastructure
Advanced Metering Infrastructure AMI
Demand response (DR) systems Demand response (DR) systems
smart grid technologies
Source: Van der Zanden, adapted from IEA – Technology Roadmap: Smart Grids, 2011
Generation Transmissionn
Distribution Industrial Service ResidentialTransmission lines
Transmission substation
Distribution lines
Distributiion substation
Trans-former
Drivers and barriers
Drivers and barriers
Consumer engagementfeedback
absolute reduction
demand response
2%-8%€3.6 billion - €18.2 billion p.a.
consumer bill
peak demand reduction
5%-15%€3 billion - €9 billion p.a.
in deferred capex
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Source: Van der Zanden, GTM Research
• Not very obvious for utilities
• Very clear for society
• High impact from consumer response but high uncertainty
• Important role for regulators
Smart grid business case
Behavioral Change Theories
• Theory of Regulatory Engagement• Transaction Theory• Social Comparison Theory • Theory of Diffusion of Innovation • Theory of Affordances (expanded)
Socio-environmental stimuli
Actor’s motivation and experience
Intrinsic action
possibilities
Actor’s capabilities
Gibson (1997)
Norman (1999)
Van der Zanden (2011)
Design
Plans / Individual goals
Education / incentivesSocietal goals
Desired Action
Theory of Affordances (Expanded)
Empirical evidence
Faruqui (2009): • Higher P, lower consumption• Dynamic pricing most effective• ‘Opt out’ has higher adoption rate than ‘opt in’• Response depends on enabling technologies, consumer segment, pre-pay/credit.
Darby (2006, 2009):• Not AMI (technology), but feedback changes consumption. • Direct feedback more effective than indirect feedback, but depends on quality of
information / interface and context.• Historic comparison seems more effective than social comparison.• Frequent, accurate billing with guidance on average use disaggregated by end-use
can be almost as effective as real disaggregated feedback.• Online feedback requires more effort from consumers.• Smart meters as communication-hub to build utility-consumer relationship?
Ehrhardt-Martinez (2010)• Frequent or real-time,
disaggregated feedback generates the highest savings.
• Enhanced billing is a very cost effective option.
• Programs focused on overall reduction are more effective (-10%) than those focused on peak load reduction (-3%)
Empirical evidence
Own findings (review of results of 24 widely varying European studies)
• Response depends on target consumer and context.
• Without educated consumers, real time feedback seems only marginally more effective than enhanced billing.
Actionable advice seems more important than real-time feedback.
• Social feedback seems effective.• Regular reminders contribute to
habit formation.
Empirical evidence
10 Recommendations for utilities
1 Focus on technology or financials only is not going to generate engagement.
2 Lower transaction costs for consumers through education or set-and-forget technology.
3 Segment consumers, no one-size-fits all.4 Give consumers easy feedback, actionable advice and insight in the big
picture.5 Leverage social networks.6 Collaborate with third party providers.7 ‘Opt-out’ rather than ‘opt-in’ programs.8 Introduce pre-paid alongside on-credit schemes.9 Underline the hedonic aspects of electricity savings.10 Stimulate two- or three-way interaction between utility and customers.
10 Recommendations for regulators
1 Communicate societal goals and targets. Educate and encourage consumers.
2 Policies that align utility goals with societal and consumer goals.3 Not smart meters, but accurate, timely feedback and advice should
be mandatory.4 ‘Opt-out’ of dynamic pricing, rather than ‘opt-in’.5 Promote overall reduction rather then peak load.6 Invest in behavioral change and formation of norms and values
around energy efficiency.7 Promote behavioral change before ee-retrofits and ee-appliances.8 Ensure clear, stable and supportive regulatory conditions. 9 Apply the integration principle.10 Accelerate R&D and sharing of results.
Q & A
Image: GEImage: GE
Thank you for your attention