This is Getting Complicated

Transcription

1 This is Getting Complicated Planning DR Resources in a World of DERs Shauna Jensen, Portland General Electric Robin Maslowski, Navigant

2 Demand Response Energy Efficiency Wind Customer-Controlled Storage

3 Adequacy Operations Reliability Economics Storage Flexibility Wind Distribution Energy Carbon Infrastructure

4 Industry Evolution Changing Regulatory Environment Increasing third-party competition Higher levels of interconnection requests from qualified facilities De-Carbonization & Climate Change Unable to reach decarbonization goals without distribution resources Bulk resource options increasingly untenable to stakeholders Intensified storms enhance benefits of small electrical islands Customer Expectations Internet of things experience Customizability Technology More customer options at lower prices Advanced utility infrastructure equipment

5 Planning Evolution Exogenous development Trend analysis Survey of existing resources Utility-driven goals Value analysis Likelihood of customer participation Grid accommodation Intentional design Technical potential Forecast Operational balance Proactive coordination Hosting capacity Locational value

6 Departmental Data & Granularity Needs Department Use Cases Granularity Study Outputs Integrated Resource Planning Load Forecasting Transmission & Distribution Planning Customer Programs Long-Term Economic Market dispatch Resource optimization Load forecast adjustment Long-Term Economic Energy demand planning Augment overlay Mid-Term Engineering Locational planning Infrastructure design Long-Term Business Program Design Development targets Aggregate system level Aggregate system level Annual penetration forecasts through 2050 per technology Hourly load shapes Interactive effects Annual average kwh impacts High & low scenarios Annual average kwh impacts High & low scenarios Feeder level Locational feeder-level forecasts through 2025 per technology Load shapes Confidence intervals Aggregated from customer level Annual penetration forecasts per technology Interactive effects High & low scenarios

7 The New World of DER Forecasting Resource Type Interactive Effects System- Level Forecast ( ) Scenario Development Hourly Load Shapes Feeder-Level Forecast ( ) Customer Segment Impact Type Scenarios Energy Efficiency (EE) Demand Response (DR) Solar Standalone Solar Solar + Storage Storage Customer- Controlled Utility- Controlled Electric Mobility Light-Duty (LD) Medium/ Heavy-Duty (MHD) Charging Residential Time of Use (TOU) Pricing

8 Unique Underlying Methodologies for DERs PGE-Specific Inputs Electricity Price based on TOU Penetration Zip-level PEV registrations for state of Oregon Demand Response CEC Carbon- Pricing Policy Oregon Clean Vehicle Rebate Adopter population 2 1 Rent/Own Scen. Credit Scen Illustration Only --- Electric Mobility 4 Adopter Population Source: Navigant Analysis 1. Bass, Frank (1969). "A new product growth model for consumer durables". Management Science 15 (5): p Sterman, John D. Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill p Solar & Storage Energy Efficiency

9 Definition of Interactive Effects How the presence of one DER might change another DER s load shape, beyond the simple addition of the two load shapes. * * necessarily

10 Types of Interactive Effects Interactive effects can influence participation and/or the net impact on system load, and be either direct or indirect influences. Participation Customers that participate in one DER may have higher propensity to participate in another DER Net Impact on System Load Interaction could increase or decrease net system load from what it would have been otherwise Direct Indirect E.g., presence of Solar influences load shape of Storage E.g., presence of EE influences customer s choice in size of Solar system

11 Interactive Effects Addressed in this Study Focused on the interactions that are likely to impact the forecasts the most, with the acknowledgement that some interactions are still too uncertain to quantify. Solar + Storage Captures interactions in impacts and participation for solar + storage at a customer site Light-Duty Vehicles + DR Explicitly accounts for Light-Duty Vehicle participation in Direct Load Control Pricing (TOU) + Other DER Scenario analysis examines influence of pricing on the other DERs, including other DR types

12 Scenarios Examined a Range of Drivers Technology / Driver Overall Effect EE Technology Costs Policies Carbon Prices Pricing Lower technology costs High Scenario More favorable policies for DER Higher carbon prices in electricity and gasoline Energy Trust of Oregon High Scenario Opt-out TOU participation DR % adder No change* Solar Low PV $ Increased marketing and tax credits continue Storage through 2050 EV Low Li-Ion $ Increased vehicle availability + vehicle production + marketing High carbon $ Opt-out residential TOU * Given no energy impacts estimated

13 Results Highlight Interactions between DERs TOU Pricing drives further adoption of Solar, Storage, and Solar + Storage by enhancing the value proposition to customers. Standalone Solar continues to be more economically attractive to customers than Solar + Storage into the future, even with opt-out TOU. MWh PGE System-Level Storage Forecast by Use Case (MWh) MWh Plug-in electric vehicles grows by more than 100x by 2050, dominated by battery electric lightduty and heavy-duty vehicles. 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 PGE System-Level LDV Energy Forecast (MWh) Solar + Customer Controlled Storage Solar + Utility Controlled Storage Customer Controlled Storage Utility Controlled Storage 500,000 - Solar + Utility-Controlled Storage significantly exceeds adoption of standalone Storage or Solar + Customer-Controlled Storage, even with opt-out TOU. BEV PHEV

14 Forecasting * Potential Problem Statement Business Drivers Granularity Measures/ Programs Potential Studies How many MWs of load reduction could be feasible from a full-scale DR portfolio rollout to all customer segments? Starting point for understanding program opportunities. Informs customer strategy and helps prioritize program focus and design efforts Customer segmentation >> geographic granularity Inclusive of all that might be viable and cost effective Scenarios Technical, Economic, Achievable Low, Base, High Forecasting How many MWs of load reduction do we actually anticipate on the grid at a systemand localized-level? Informs integrated resource planning, distribution resource planning, and customer strategy Geographic granularity >> customer segmentation Inclusive of those expected for actual adoption * necessarily

15 Be Prepared for Data Challenges Increased dimensionality and granularity increased data needs Hourly forecasting hourly data Hourly end use load shapes by customer segment Hourly DER impact shapes Feeder-level forecasting feeder-level data Baseline growth estimates Customer counts by customer segment Consumption by customer segment Customer-level propensity modeling customer-level data May trigger new sensitivity/privacy concerns Scope out approaches upfront for accessing data

16 Working Together Silo d Departments Different priorities Reliability Economic optimization Customer outreach Separate planning practices Peak Dispatch Top-down vs. Bottom-up Pilot vs. Short-term Portfolio vs. Long-Term Portfolio Even departments with regular interaction do not fully understand each other s work Jurisdictional boundaries are becoming foggy Increased need to understand capabilities of cross-function contributors Institutional inter-departmental education Function and methodology Language Iterative process to constantly update contributions Emotional intelligence in breaking silos

17 Start with Universal Need: Data Step 1: Data for all departments share common root Use the same consultant to produce DER and Flexible Load data that feeds processes in: Integrated Resource Planning (IRP) Distribution System Planning (DSP) Customer Strategy (CS) Load Forecasting (LF) Step 2: Define data uses for each department IRP: Dispatch profiles for resource optimization (top-down) DSP: Locational peaks (bottom-up), operational capabilities (top-down or bottom-up) CS: Likelihood of customer participation in programs (top-down and bottom-up) LF: Trajectory of load alteration due to DER and Flexible Load (top-down and bottom-up) Step 3: Fit data to each format without losing fidelity of common root Address differences in planning practices without changing underlying assumptions Common discrepancies: IRP, CS, and LF study low and high penetration scenarios (i.e., scenario drivers) T&D reliability events related to peak (i.e., confidence intervals and uncertainty)

18 This is Getting Complicated Planning DR Resources in a World of DERs Shauna Jensen, Portland General Electric Robin Maslowski, Navigant