Peaking Interest: How awareness drives the effectiveness of - PowerPoint PPT Presentation

Peaking Interest: How awareness drives the effectiveness of time-of-use electricity pricing Brian C. Prest Ph.D. Candidate, Duke University brian.prest@duke.edu USAEE 2017 Annual Conference November 13, 2017 Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Overview Time-of-use (TOU) electricity pricing: charge more during peak hours Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Overview Time-of-use (TOU) electricity pricing: charge more during peak hours Goal: reduce costs associated with timing of load Investment in “peaker” power plants Managing intermittent renewables (without expensive battery storage) Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Overview Time-of-use (TOU) electricity pricing: charge more during peak hours Goal: reduce costs associated with timing of load Investment in “peaker” power plants Managing intermittent renewables (without expensive battery storage) Literature shows consumers do respond, but only modestly Why? Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Overview Time-of-use (TOU) electricity pricing: charge more during peak hours Goal: reduce costs associated with timing of load Investment in “peaker” power plants Managing intermittent renewables (without expensive battery storage) Literature shows consumers do respond, but only modestly Why? Neoclassical idea: “Get the prices right” and peak load will be solved 1 Small responses indicate real costs of shifting ⇒ Charge correct prices to induce conservation Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Overview Time-of-use (TOU) electricity pricing: charge more during peak hours Goal: reduce costs associated with timing of load Investment in “peaker” power plants Managing intermittent renewables (without expensive battery storage) Literature shows consumers do respond, but only modestly Why? Neoclassical idea: “Get the prices right” and peak load will be solved 1 Small responses indicate real costs of shifting ⇒ Charge correct prices to induce conservation Behavioral idea: People are boundedly rational 2 Small responses indicate insufficient information or attention ⇒ Provide nudges, information, or automation Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Overview Time-of-use (TOU) electricity pricing: charge more during peak hours Goal: reduce costs associated with timing of load Investment in “peaker” power plants Managing intermittent renewables (without expensive battery storage) Literature shows consumers do respond, but only modestly Why? Neoclassical idea: “Get the prices right” and peak load will be solved 1 Small responses indicate real costs of shifting ⇒ Charge correct prices to induce conservation Behavioral idea: People are boundedly rational 2 Small responses indicate insufficient information or attention ⇒ Provide nudges, information, or automation So which is closer to the truth? Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

This study Theory ambiguous ⇒ agnostic, data-driven approach Let the data suggest the mechanism, not imposing assumptions Identify what factors drive heterogeneous responses Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

This study Theory ambiguous ⇒ agnostic, data-driven approach Let the data suggest the mechanism, not imposing assumptions Identify what factors drive heterogeneous responses Apply (and extend) new machine learning algorithm for estimating heterogeneous causal effects, designed by Athey and Imbens (2016) Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

This study Theory ambiguous ⇒ agnostic, data-driven approach Let the data suggest the mechanism, not imposing assumptions Identify what factors drive heterogeneous responses Apply (and extend) new machine learning algorithm for estimating heterogeneous causal effects, designed by Athey and Imbens (2016) Data: TOU pricing experiment on Irish households Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

This study Theory ambiguous ⇒ agnostic, data-driven approach Let the data suggest the mechanism, not imposing assumptions Identify what factors drive heterogeneous responses Apply (and extend) new machine learning algorithm for estimating heterogeneous causal effects, designed by Athey and Imbens (2016) Data: TOU pricing experiment on Irish households Testing > 150 dimensions of observable characteristics from survey data for heterogeneous responses Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Results Average TOU pricing effect : -9% peak consumption Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Results Average TOU pricing effect : -9% peak consumption Key Drivers of Heterogeneity : Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Results Average TOU pricing effect : -9% peak consumption Key Drivers of Heterogeneity : Key result : Unaware households don’t respond (-2%, insignif.) 1 Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Results Average TOU pricing effect : -9% peak consumption Key Drivers of Heterogeneity : Key result : Unaware households don’t respond (-2%, insignif.) 1 Low energy consuming households don’t respond 2 Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Results Average TOU pricing effect : -9% peak consumption Key Drivers of Heterogeneity : Key result : Unaware households don’t respond (-2%, insignif.) 1 Low energy consuming households don’t respond 2 Information amplifies effects up to 2 x (even conditional on awareness) 3 Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Results Average TOU pricing effect : -9% peak consumption Key Drivers of Heterogeneity : Key result : Unaware households don’t respond (-2%, insignif.) 1 Low energy consuming households don’t respond 2 Information amplifies effects up to 2 x (even conditional on awareness) 3 Consumers are extremely insensitive to the size of the price change 4 Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Results Average TOU pricing effect : -9% peak consumption Key Drivers of Heterogeneity : Key result : Unaware households don’t respond (-2%, insignif.) 1 Low energy consuming households don’t respond 2 Information amplifies effects up to 2 x (even conditional on awareness) 3 Consumers are extremely insensitive to the size of the price change 4 Conditional on above, nothing else matters (of 150+ characteristics) 5 Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Results Average TOU pricing effect : -9% peak consumption Key Drivers of Heterogeneity : Key result : Unaware households don’t respond (-2%, insignif.) 1 Low energy consuming households don’t respond 2 Information amplifies effects up to 2 x (even conditional on awareness) 3 Consumers are extremely insensitive to the size of the price change 4 Conditional on above, nothing else matters (of 150+ characteristics) 5 Implications : Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Results Average TOU pricing effect : -9% peak consumption Key Drivers of Heterogeneity : Key result : Unaware households don’t respond (-2%, insignif.) 1 Low energy consuming households don’t respond 2 Information amplifies effects up to 2 x (even conditional on awareness) 3 Consumers are extremely insensitive to the size of the price change 4 Conditional on above, nothing else matters (of 150+ characteristics) 5 Implications : These generally point towards behavioral explanations Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Results Average TOU pricing effect : -9% peak consumption Key Drivers of Heterogeneity : Key result : Unaware households don’t respond (-2%, insignif.) 1 Low energy consuming households don’t respond 2 Information amplifies effects up to 2 x (even conditional on awareness) 3 Consumers are extremely insensitive to the size of the price change 4 Conditional on above, nothing else matters (of 150+ characteristics) 5 Implications : These generally point towards behavioral explanations Can’t reliably predict awareness Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Results Average TOU pricing effect : -9% peak consumption Key Drivers of Heterogeneity : Key result : Unaware households don’t respond (-2%, insignif.) 1 Low energy consuming households don’t respond 2 Information amplifies effects up to 2 x (even conditional on awareness) 3 Consumers are extremely insensitive to the size of the price change 4 Conditional on above, nothing else matters (of 150+ characteristics) 5 Implications : These generally point towards behavioral explanations Can’t reliably predict awareness “Getting prices right” might not be sufficient Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017

Experimental Details: Treatment Treatment and Control Group Assignments Bi-Monthly Bill and Monthly Bill and In-Home Display Load Reduction Control Energy Statement Energy Statement (IHD) Incentive Tariff A 195 216 205 216 0 Tariff B 80 87 72 81 0 Tariff C 222 217 202 213 0 Tariff D 80 87 78 77 0 Control 0 0 0 0 678 Energy Statement Example Brian C. Prest (USAEE 2017) Peaking Interest November 13, 2017