Renewable Electric Storage Program Preliminary Results and Findings U LESS -

Similar documents
Solar + Storage. Behind and In front of the meter. Becca Gillespie UniEnergy Technologies

StorageVET Applications and Demonstration Energy Storage Valuation Workshop

Dynamic Pricing. June 2, 2016 Harvard s Electricity Policy Group

2014 Integral Analytics, Inc.

Bosch Energy Storage Solutions Smart integration of storage

Generation Technology Assessment & Production Cost Analysis

Laboratory for Energy Smart Systems (LESS)

Beyond LCOE: The Value of CSP with Thermal Energy Storage

Integrating Energy Efficiency and Demand Response

Permanent Load Shifting Program

Value of Energy Storage in Utility Applications. Ben Kaun Sr. Project Engineer SVLG Energy Committee Meeting November 21, 2013

Electrification and its Implications for the California Electricity System

Renewable sources of energy, dispatching priority and balancing the system, facts and options: Part I

Summary of Net Metered Projects as of June 30, Small Net Metered Projects as of 6/30/2016 Small Group Host Projects as of 6/30/2016

Chris Lotspeich Celtic Energy Inc. NESEA Building Energy 16 March 9 th, 2016

MODELING AND PERFORMANCE ANALYSIS OF A HYBRID BIOGAS-PHOTOVOLTAIC SYSTEM

Load Shift Working Group.

Benefit Analysis of Energy Storage: Case Study with the Sacramento Utility Management District

Update on Zero-Net Energy (ZNE) in California

ELECTRIC COST OF SERVICE AND RATE DESIGN STUDY

Smart Grids: How an Intelligent Utility Network will transform data into information to address key areas of importance to energy utilities

Benefit-Cost Analysis Summary Report

Nuclear-renewable hybrid energy systems for non-electric applications

Integrated Resource Plan

Cost-Benefit Analysis of the PSE&G Energy Efficiency Program 2014 Prospective

Benefits from Generation and Storage on the Distribution System

LONG ISLAND LONG ISLAND

Planned Islanding and Energy Storage at BC Hydro

PERFORMANCE EVALUATION, MONITORING AND ASSESSMENT OF UTILITY COMPANIES, AND DEVELOPMENT OF KEY PERFORMANCE INDICATORS.

Bundled Energy Solutions to Lower Operating Costs and Maximize ROI. Harvey Katzen Director of Bundled Energy Services ABM Building and Energy

Environmental Security Technology Certification Program (ESTCP) LARGE SCALE ENERGY STORAGE AND MICROGRIDS

Electric Reliability in Pennsylvania

Commercial and Industrial Use Tariffs. Codes 10, 20, 30, 40 and 50

Building Bridges to Net Zero. Microgrid and Battery Storage Technologies

Roadmap for Solar PV. Michael Waldron Renewable Energy Division International Energy Agency

Subtask 1: Economic of PV System Performance and Reliability

Traditional approach to modelling EE & DG

DATA ASSUMPTIONS AND DESCRIPTION OF STUDIES TO BE PERFORMED 2014 EGSL & ELL Integrated Resource Plans

Residential Wind Turbine Design Decision Support System $54 Variance

The Benefits of Dynamic Pricing of Default Electricity Service

Charles D. Corbin & Gregor P. Henze

The Value of Distributed Photovoltaics to Austin Energy and the City of Austin

An Economic Assessment of the Benefits of Storage in South Africa. by Jeremy Hargreaves, Energy & Environmental Economics

Energy Storage for micro- and minigrids in urban and rural Africa. Maxine Ghavi, Group SVP, Program Director Microgrid, ABB

The Role of Microgrids in Grid Modernization Initiatives Sima Seidi Principal Consultant, Microgrids and Distributed Energy Resources Tetra Tech

Storage Solutions in Developing Countries A Case Study on the Market Potential for Battery Storage in Tanzania

ERA OF BATTERY STORAGE

La regulación del incierto futuro de los sistemas eléctricos

Brent Gloy, April

Draft Results Capital Cost and Performance of New Entrant Peaking Unit

Including Advanced Energy Storage in Integrated Resource Planning: Cost Inputs and Modeling Approaches

Welcome. RETScreen s Clean Energy Policy Toolkit. Clean Energy Solutions Center Webinar 26 April 2013

Nuclear Power Economics and Markets

Statewide Pricing Pilot (SPP)

Integrated Planning Model (IPM ) Overview

Matt Gudorf UCI Energy Manager

Value of DER Implementation. ULI Electric Cities July 26, 2017 Stephen Wemple, Utility of the Future

Monitoring Analytics. Introduction. Operating Parameters. Generation Costs

Renewables: The True Costs. Michael Taylor and Eun Young So IRENA, Bonn, Germany 7 May 2015

Electricity Markets. Rapid Conference May 17, Mike Rencheck Rencheck Consulting LLC

NON SOLAR DISTRIBUTED GENERATION MARKET RESEARCH

Energy Storage Working Group Meeting July 23, 2013 NJDEP Hearing Room - Trenton, NJ 1:00 pm to 3:00 pm. Agenda

California Grid Operations: Current Conditions and Future Needs

Promoting the Use of GSHPs to Reduce Peak Demand and Improve Electric System Utilization

How the HOMER Pro Software Can Improve the Design and Development of Clean and Sustainable Energy Projects

Policy and Regulatory Approaches to Grid Integration of Renewable Energy in the US

Energy Storage and Distributed Energy Resources Phase 3 ( ESDER 3 ) Issue Paper

Modeling Distributed Generation Adoption Using Electric Rate Feedback Loops

Capacity Performance Training. June 24, 2015

SWARM. Increasing Households Internal PV Consumption and Offering FCR Power with Distributed Batteries. Energieinformatik 2015, Karlsruhe

Chilled Water Plant Redesign

Societal and Environmental Transformation The Case for SMART Malta

Revised Cal. P.U.C. Sheet No E Cancelling Revised Cal. P.U.C. Sheet No E

Combined Heat & Power (CHP) Program Impact Evaluation

ERCOT in 2018: Challenges & Opportunities

Day-ahead ahead Scheduling Reserve (DASR) Market

Applying PROBE for Congestion Analysis and Grid Planning

Solar Microgrid Feasibility Study City of Ann Arbor

The Business Case For Distributed Energy Storage

Economic Viability of Residential Microgrids in Ontario

CALMAC Study ID PGE0354. Daniel G. Hansen Marlies C. Patton. April 1, 2015

Description of EDC Data and Documents in the Additional Data Portion of the BGS Auction Data Room

Global Energy Storage Demand for a 100% Renewable Electricity Supply

ICA Demand Curve Development:

Green+ ELECTRICITY AUTHORITY OF CYPRUS. NER 300 European Program 2 nd call of proposals Zero Energy Mountains of Cyprus. Green

Grid-interactive Electric Thermal Storage (GETS) provides economic and environmental benefit

Welcome / Bienvenue. RETScreen Training Institute RETScreen 101 Introduction to Clean Energy Project Analysis

Monte-Carlo Optimization Framework for Assessing Electricity Generation Portfolios

Reliability Analysis using PLEXOS

Preliminary Assumptions for On-Shore Wind Technologies. Gillian Charles GRAC October 2, 2014

Christian Ohler, ABB Switzerland Corporate Research Efficiency versus Cost - a Fundamental Design Conflict in Energy Science

2016 Fall Reliability Conference MRO

This roadmap is a product of collaboration among three organizations the California Independent System Operator (ISO), the California Public

Demystifying Rooftop Solar

Evolution of the Grid in MISO Region. Jordan Bakke, David Duebner, Durgesh Manjure, Laura Rauch MIPSYCON November 7, 2017

PEAK DEMAND REDUCTION STRATEGY

PV Investments Cost Benchmarking & Gap Analysis LCOE Simulation & Sensitivity Analysis

2017 IRP Advisory Group. July 21, 2017 IRPAG

R e p l a c i n g D i e s e l G e n e r a t i o n w i t h R e n e w a b l e S o u r c e s i n N u n a v u t C o m m u n i t i e s : P r e - s e l e c

Strategic Plan. Renewable Energy. July 23, 2014

Transcription:

Renewable Electric Storage Program Preliminary Results and Findings U LESS - 6/3/216 1

Agenda Overview Study synopsis Model inputs billing structure Customer segments and load characteristics DER configurations, ES cost structure PV-ES economics; Preliminary results and findings through illustrative examples Value of ES across different applications Value of different ES configurations in different applications Effect of customer load characteristics on PV-ES economics Effect of cost structure on PV-ES economics PV-ES resiliency benefits vs. reduced NPVs (importance of state incentives) Conclusion of results 2

Study synopsis Metrics proposition for incentive level: NPV/kW or kwh NPV is not a simple function of ES size f( ) DER sizing ES application billing structure Customer load characteristics State incentives Energy bill management (EBM) Frequency regulation Resiliency NPV Bundle application (EBM+FR) Resiliency 3

Overview; model inputs, methodology, and objective Customer element Public critical segments DER configurations Pricing element Environment element Financial and economic analysis of PV-ES systems for following applications: Energy bill management (EBM) Bundle application (EBM+FR) Frequency regulation Resiliency Methodology; Mixed-integer optimization framework for daily economic dispatch Monte-Carlo simulation for blackout events Discounted cash flow analysis NJ weather characteristics PJM (ancillary & LMP) DER cost Major blackout events SAIFI CAIDI Assisting decision makers (NJ-BPU) in the process of assigning incentives for renewable ES systems 4

billing structure billing components: Delivery charges (kw and kwh for ) Assumption: All customers have elected Rider BGS-CIEP and will be charged according to PJM LMPs for kwh Supply charges (kw and kwh for ) Three s are considered in this study: 1 2 3 Different demand charge levels for different PSL Seasonal flat demand charge for PSL < 15 kw Seasonal TOU demand charges for PSL> 15kW Different demand charge levels for different PSL Seasonal flat demand charge Higher demand charges for PSL> 75kW Demand charge is not sensitive to PSL Seasonal flat demand charge ranks kwh charges: kw charges: > > 1 2 3 > > 1 2 3 5

Customer segments, and load characteristics Hospital Large office Small office Small hotel Annual consumption: 8,8 mwh Annual consumption: 6,7 mwh Annual consumption: 84 mwh Annual consumption: 757 mwh AVG daily load profile AVG daily load profile AVG daily load profile AVG daily load profile Load differentiation Prolonged peak hours High load level; similar load shape to small office Low load level; similar load shape to large office Different load shape; after hours peak Emergency functionality 6

Load data, DER configuration, and ES cost structure Electric load time-series: simulated data using DOE EnergyPlus (Weather file : NJ) Critical load: Fixed portion of each end-use e.g., 8% of cooling, 4% of lighting, and etc. configuration PV production level as a % of total consumption (8 %) ES Power rate: percentage of peak critical (5, 1 %) ES duration (.5,1,1.5,,5hrs) Electric storage c elements: Factory cost (4 $/kwh) Installation cost (47% of factory cost) Invertor cost (3 $/kw) Fixed O&M (18 $/kw) 7

Value of ES across different applications 8

CF Value of ES in different applications averaged over all scenario, all segments (all percentages are against base scenario where PV is only available; NO ES) On average bundle application provides the highest cash flow among all the applications Average growth in annual cash-flow ($) vs. base scenario Bundle EBM FR 34% 26% 29% Impact of rate structure on cash flow values; peak demand charge is the major player in PV-ES systems cash flows Average growth in annual cash-flow ($) vs. base scenario 1 2 3 Bundle EBM FR Bundle EBM FR Bundle EBM FR 44% 35% 32% 37% 16% 34% 22% 15% 21% kwh charges: kw charges: 1 1 > 2 2 > > > 3 3 Same order as in kw charges, not kwh Average energy and peak demand saving in bundle application vs. base Growth in energy saving (%) Growth in peak demand saving (%) 6% 25% 9

CF Value of different ES configurations in different applications averaged over all s, and segments % OF ANNUAL GROWTH OF CASH FLOW On average Bundle application provides the most cash flow among all applications On average low discharge durations ( 1 hr), FR provides the most cash flow On average high discharge durations ( 5hrs), Bundle and EBM CF converges toward each other Average growth in annual cash-flow Bundle EBM FR 6% 5% ES Cap 5% ES Cap 1% 4% 3% 2% 1% No ES % 5 1 15 2 25.5 1 1.5 2 2.5 3 3.5 4 4.5 5.5 1 1.5 2 2.5 3 3.5 4 4.5 5 ES CONFIGURATION ES discharge duration ES Size 1

NPV Value (5yr. horizon) of different ES configurations in bundle application and EBM for all segments NPV/kW ($) NPV/kW ($) NPV/kW ($) NPV/kW ($) Bundle application provides higher NPV/kW compared to EBM Increasing the duration of ES results in less NPV/kW because of the higher investment cost 5-5 Large office 3; Bundle vs. EBM ES Power rate 5% ES Power rate 1% 5-5 Small office 3; Bundle vs. EBM ES Power rate 5% ES Power rate 1% -1-1 -15-15 -2 ES duration ES Configuration ES duration -2 ES Configuration Large Office; EBM; 3 Large Office; Bundle; 3 Small Office; EBM; 3 Small Office; Bundle; 3 5-5 -1 Hospital 3; Bundle vs. EBM ES Power rate 5% ES Power rate 1% 5-5 -1 Small Hotel 3; Bundle vs. EBM ES Power rate 5% ES Power rate 1% -15-15 -2 ES Configuration -2 ES Configuration Hospital; EBM; 3 Hospital; Bundle; 3 Small Hotel; EBM; 3 Small Hotel; Bundle; 3 11

Effect of customer load characteristics on PV-ES economics 12

NPV/kW ($) NPV/kW ($) Effect of load level on PV-ES economics (NPV/kW in 5 yr. horizon) Small office V.S. Large office: Large office Annual consumption: 6,7 mwh AVG daily load profile Small office Annual consumption: 84 mwh AVG daily load profile Similar electricity load shape with different load levels results in close NPV/kW 4 2-2 -4-6 -8-1 -12-14 -16 ES power rate 5%; 3; Bundle Application 1 2 3 4 5 6 ES Duration (hr.) Large Office; ES Power rate 5% Small Office; ES Power rate 5% 4 2-2 -4-6 -8-1 -12-14 -16 ES power rate 1%; 3; Bundle Application 1 2 3 4 5 6 ES Duration (hr.) Large Office; ES Power rate 1% Small Office; ES Power rate 1% ~ 3% difference in NPV/kW 13

Effect of load shape on PV-ES economics (NPV/kW in 5 yr. horizon) NPV/kW ($) NPV/kW ($) Large office V.S. Hospital: Hospital Annual consumption: 8,8 mwh AVG daily load profile Large office Annual consumption: 6,7 mwh AVG daily load profile Load shape may significantly influence on PV-ES economics Longer peak duration in hospital results in less NPV/kW compared to large office; however in larger capacity of ES, NPV/kW values are getting closer in two segments ES power rate 5%; 3; Bundle Application ES power rate 1%; 3; Bundle Application 4 2-2 -4-6 -8-1 -12-14 -16 1 2 3 4 5 6 ES Duration (hr.) Hospital; ES Power rate 5% Large Office; ES Power rate 5% 4 2-2 -4-6 -8-1 -12-14 -16 1 2 3 4 5 6 ES Duration (hr.) Hospital; ES Power rate 1% Large Office; ES Power rate 1% 35% difference in NPV/kW 14

NPV/kW ($) NPV/kW ($) Effect of load shape on PV-ES economics (NPV/kW in 5 yr. horizon) Small office V.S. Small hotel: Small office Annual consumption: 84 mwh AVG daily load profile Small hotel Annual consumption: 757 mwh AVG daily load profile After hours peak in small hotel, causes lower NPV/kW in small systems (effect of load shape) High rated capacity enables to shave after hours peak and leads to closer NPV/kW values ES power rate 5%; 3; Bundle Application ES power rate 1%; 3; Bundle Application 2 2-2 1 2 3 4 5-2 1 2 3 4 5-4 -4-6 -6-8 -8-1 -1-12 -12-14 -14-16 ES Duration (hr.) -16 ES Duration (hr.) Small Office; ES Power rate 5% Small Hotel; ES Power rate 5% Small Office; ES Power rate 1% Small Hotel; ES Power rate 1% 3% difference in NPV/kW 6% difference in NPV/kW 15

Effect of cost structure on PV-ES economics 16

Effect of cost structure on PV-ES economics Recalling slide number 9 where overall results (averaged over all segments) were presented Impact of rate structure on cash flow values; peak demand charge is the major player in PV-ES systems cash flows Average growth in annual cash-flow ($) vs. base scenario 1 2 3 Bundle EBM FR Bundle EBM FR Bundle EBM FR 44% 35% 32% 37% 16% 34% 22% 15% 21% kwh charges: kw charges: 1 1 > 2 2 > > > 3 3 Same order as in kw charges, not kwh In the next slide we dig deep into all segments 17

NPV/kW ($) NPV/kW ($) NPV/kW ($) NPV/kW ($) NPV/kW (5yr. horizon) of bundle application for all segments across all s Storage system in 1 generates more value (NPV/kW); Demand charge ($/kw) in 1 is higher and the major ES value comes from peak demand shaving 4 2-2 -4-6 -8-1 -12-14 -16 ES Power rate 5% ES duration Large office ES Configuration ES Power rate 1% ES duration Large Office; Bundle; 1 Large Office; Bundle; 2 Large Office; Bundle; 3 4 2-2 -4-6 -8-1 -12-14 -16 ES duration ES Power rate 5% Small office ES Configuration ES Power rate 1% ES duration Small Office; Bundle; 1 Small Office; Bundle; 2 Small Office; Bundle; 3 4 2-2 -4-6 -8-1 -12-14 -16 Hospital Small Hotel ES Power rate 5% ES Power rate 1% 4 2 ES Power rate 5% ES Power rate 1% -2-4 -6-8 -1-12 -14 ES duration ES duration ES duration ES duration -16 ES Configuration ES Configuration Hospital; Bundle; 1 Hospital; Bundle; 2 Hospital; Bundle; 3 Small Hotel; Bundle; 1 Small Hotel; Bundle; 2 Small Hotel; Bundle; 3 18

Annual CF($) Annual CF($) Contribution of revenue streams across s Depending on cost structure, contribution of energy saving and peak saving may vary Storage system in 1 generates more value because of peak demand saving; 15, Annual CF ($); 1; Bundle; Small office; ES Cap 2 kw 1, 5, - 21% 21% 22% 36% 17% 16% 32% 35%.5 2 3.5 5 ES Duration Annual energy saving Cash Flow ($) Annual peak demand saving Cash Flow ($) Annual Net metering Cash Flow ($) Annual FR Cash Flow ($) 16% 12% 37% 35% 16% 9% 39% 36% 1, 8, 6, 4, 2, - 26% 21% 15% 38% Annual CF ($); 2; Bundle; Small office; ES Cap 2 kw 23% 17% 23% 37% 22% 13%.5 2 ES Duration 3.5 5 27% 38% 21% 1% 29% 4% Annual energy saving Cash Flow ($) Annual peak demand saving Cash Flow ($) Annual Net metering Cash Flow ($) Annual FR Cash Flow ($) 19

Effect of demand charge structure on daily dispatch; TOU vs. Flat ES systems under TOU demand charge tariffs (here 1) would generate more revenue through peak shaving 16 T O U 1- June 27 o n - p e a k 16 F l a t 2 -June 27 14 14 12 12 1 1 8 8 6 6 4 4 2 2 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 CustLoad PVES NetEnergyPurchase, ESL LESEND Original load PV to ES Net load ES to L SOC CustLoad PVES NetEnergyPurchase, ESL LESEND In both graphs; hour 2: net load in TOU: 678 kw Flat: 873 kw TOU vs. Flat: 12% reduction of net load >> 12% reduction in ramp-up capacity TOU helps to smooth out duck curve 2

PV-ES resiliency benefits vs. reduced NPVs (importance of state incentives) 21

PV-ES systems resiliency benefits vs. reduced NPVs (5yr. horizon); importance of BPU incentives for promoting resiliency NPV/kW ($) % served critical load In order to enhance resiliency and being financially feasible, state incentives are crucial; the bigger the ES systems, the less NPV, the higher resiliency Small Office - 3 Bundle 4 2 ES Power rate 5% ES Power rate 1% 9% 8% -2-4 -6-8 -1 3 1 7% 6% 5% 4% 3% -12 2% -14-16 ES duration 2 ES duration 1% % Owner point of view; noncritical facility Smaller systems Less state incentives needed NPV/kW; 3; Bundle ES Configuration % Served Critical Load during outage Owner point of view; critical facility (small office as a resemble of police station) Example: 7% of critical load has to be supplied in blackout events 22

NPV/kW ($) % served critical load NPV/kW % served critical load PV-ES systems resiliency benefits vs. reduced NPVs (5yr. horizon); similar behavior in other segments Similar behavior in other segments; ESs with longer duration are more resilient but generate less NPVs 4 2-2 -4-6 -8-1 -12-14 -16 ES duration Large office - 3 bundle ES Power rate 5% ES Configuration ES Power rate 1% ES duration 8% 7% 6% 5% 4% 3% 2% 1% % 2-2 -4-6 -8-1 -12-14 -16 ES duration Small hotel - 3 bundle ES Power rate 5% ES Configuration ES Power rate 1% ES duration 6% 5% 4% 3% 2% 1% % NPV/kW; 3; Bundle % Served Critical Load during outage NPV/kW; 3; Bundle % Served Critical Load during outage 23

Conclusion of results NPV is not a simple function of ES size f( ) DER sizing ES application billing structure Customer load characteristics On average bundle application provides the most cash flow among all applications Peak demand charge is the major player in PV-ES systems cash flows Increasing the duration of ES results in less NPV/kW because of the higher investment cost Similar electricity load shapes with different load levels results in close NPV/kW Load shape may significantly influence on PV-ES economics In order to enhance resiliency and being financially feasible, state incentives are crucial 24

Questions and Discussion Farbod Farzan: farbod_farzan@yahoo.com Khashayar Mahani: mahani.khashayar@gmail.com 25