ELECTRICITY STORAGE: APPLICATIONS AND BUSINESS CASES CERI Breakfast Overview Allan Fogwill, President & CEO NAIT – Edmonton October 17, 2019 Flagship Breakfast Overview Sponsor : www.ceri.ca
CANADIAN ENERGY RESEARCH INSTITUTE Overview Founded in 1975, the Canadian Energy Research Institute (CERI) is an independent, registered charitable organization specializing in the analysis of energy economics and related environmental policy issues in the energy production, transportation, and consumption sectors. Our mission is to provide relevant, independent, and objective economic and environmental research of energy issues to benefit business, government, academia and the public. CERI publications include: • Market specific studies • Geopolitical analyses • Quarterly market reports (crude oil, electricity and natural gas) In addition, CERI hosts a series of study overview events, executive briefings for organizations and an annual Petrochemicals Conference. 2
CORE FUNDERS 3
DONORS Ivey Foundation 4
AGENDA • Introduction • Energy storage applications: • Behind the fence applications • Energy arbitrage • Renewable energy firming • Results • Conclusions 5
ENERGY STORAGE: A RAPIDLY GROWING TECHNOLOGY 6
ENERGY STORAGE APPLICATIONS 7
APPLICATIONS – STORAGE TECHNOLOGY MATCHING 8
SCOPE AND OBJECTIVES OF THIS STUDY • Review current status of energy storage • Provide an outlook of future cost of select energy storage • Assess three energy storage applications for Canadian electricity systems 1. Bill malmanagement (Customer side) 2. Bulk energy arbitrage (Grid side) 3. Renewable energy firming • Use two metrics • Internal rate of return (IRR) (cases 1 & 2) • Levelized cost of electricity (LCOE) (case 3) • Methods • Application simulation models (cases 1 & 2) • Optimization model for renewable energy and storage sizing (case 3) 9
ENERGY STORAGE TECHNOLOGY CLASSIFCATION 10
COST OF ENERGY STORAGE TECHNOLOGIES • Cost decline due to technology learning: Rapid growth in energy storage will lead to decline in capital costs • Decline in costs are already observed • Some technologies are growing faster than the rest • Lithium and flow batteries are advancing at the fastest rate • Utility-scale Battery costs are expected to see around 8% per year cost decline over the next three years • Capital cost of a utility-scale lithium-ion battery storage capital costs are expected to decline by 52% between 2018 and 2030 • Cost of hydrogen fuel cells too are expected decline at a faster rate 11
FUTURE CAPITAL COSTS OF SELECT ENERGY STORAGE TECHNOLOGIES 12
APPLICATION 1: BILL MANAGEMENT USING BEHIND-THE-METER (BTM) ENERGY STORAGE • Commercial and industrial customers usually pay for facility demand charges according to the peak demands recorded during their billing periods. • The demand charges can amount to 50% of their total monthly electricity bill. • The demand charges can be decreased either by shaving the peak demands using ESS or through shifting some of the operations from on-peak to off-peak hours (aka demand response program) . 13
APPLICATION 1: BILL MANAGEMENT USING BEHIND-THE-METER (BTM) ENERGY STORAGE Demand Charge Reduction Shifting operations from Shaving the peak on- to off-peak hours demands using ESS (Response program) The storage device NOT straight-forward to stores energy during the implement off-peak hours to (many legal, technical, and commercial issues later discharge it within on- need to be addressed peak hours before the fact) Promoted by: Noticeable Storage Cost Reductions 14
APPLICATION 1: BILL MANAGEMENT USING BEHIND-THE-METER (BTM) ENERGY STORAGE • Four types of customers • Secondary school • Hotel • Hospital • Large office building • Small mall building • Five provinces (AB, BC, SK, ON, NB) • Mainly due to availability of complete rate structure information • Lithium-iron storage • Due to scalability and maturity • Simulation model to optimally size storage • Estimated the IRR of the application case • Both current costs and future cost of storage (2020, 2030, 2040) 15
SAMPLE BATTERY OPTIMAL SIZING FOR A SECONDARY SCHOOL IN AB (2025) E (kWh): 24 P (kW): 48 IRR: 17% 16
ECONOMIC ASSESSMENT OF ESS FOR BTM APPLICATIONS IN CANADIAN PROVINCES The load profiles of these facilities are collected from the public data available on OpenEI (OpenEI 2019) 17
APPLICATION 1: CONCLUDING REMARKS • Lithium-ion batteries are the most widely utilized storage technology, primarily because of their fast and powerful response to the demand making them an ideal candidate for this role. • The shape of the load profile of a facility is the primary factor controlling the amount of peak demand reduction achieved by ESS. Thus, any recommended size for the ESS will be specific to that facility. • Utility rate structure (primarily the difference between energy and peak demand charges) is the second important factor affecting the profitability of ESS for BTM applications. 18
APPLICATION 2: BULK ENERGY ARBITRAGE • Buy electricity when price is low and sell back when the price is high • Possible when an open energy market is available • Use Alberta market for the analysis • Electricity prices observed over last four years • Three storage technologies • CAES, Flow batteries, and Li-ion batteries • Simulation model with IRR analysis 19
OBSERVED ELECTRICITY PRICES 20
ENERGY ARBITRAGE (RESULTS) 21
CAPACITY PAYMENTS • Energy storage can provide capacity services and earn revenue • Estimated the capacity payments required to break even under current storage capital costs (assumed 2018 electricity prices) • Required annual capacity payment to breakeven: • CAES: $9000/MW • Li-ion: $253,000/MW • Flow batteries: $69,000/MW • Assume that both energy market revenues and capacity payments are stackable • Note that CAES technology requires lower annual capacity market payments due to its lower investment cost and longer project lifetime (30 years) • It is higher for flow and Li-ion, mainly because of the shorter project lifetime (16 years and 9 years respectively) 22
APPLICATION 2: CONCLUDING REMARKS • None of the technologies assessed are economically attractive with the current capital cost and Alberta under current costs when energy arbitrage is the only revenue stream • Higher spread between peak price and off-peak price improves economics for energy storage under energy arbitrage applications • Deploying the learning rate for future cost reduction shows that only flow batteries have the potential to reach positive IRR values (around 5%). • For Li-ion batteries, despite the similar overnight cost as compared to flow batteries, their IRR values remain negative by 2040, which is due to the low battery life cycle (9 years). • Because of the replacement cost, fuel cost, and mature technology (no potential reduction in capex), the storage using standalone CAES technology is not economically attractive (negative IRR value). 23
APPLICATION 3: RENEWABLE ENERGY FIRMING • Variable renewable energy firming is considered as a main application for energy storage under current policies and market conditions • Variable renewable such as wind and solar are intermittently available and not necessarily available when the system needs energy/capacity • Energy storage can be used to make variable renewables dispatchable • Assessed a case where co-located wind, solar PV, and energy storage systems makes a electricity generation system with: • 90% availability in peak demand periods • 60% availability in other times • Mimics a the reliability of a typical generating system 24
APPLICATION 3: RENEWABLE ENERGY FIRMING • All 10 Canadian provinces • 30 locations with good wind and solar PV resources per province • Wind resource availability: 30-42% • Solar PV resource availability: 12-16% • Storage technologies considered: • Battery storage (Li-iron and flow batteries) • Hydrogen fuel cells • Optimization model that runs at hourly resolution to estimate optimal generation and storage capacity • Estimated the LCOE at each of the 300 locations in 2020, 2030, and 2040 • Considered technology learning for storage and renewable capital costs • Select the rated capacity of the integrated electricity generation system to be 100MW 25
LEVELIZED COST OF ELECTRICITY BY INVESTMENT YEAR AND PROVINCE (all LCOE values are in cents/kWh) 2020 2030 2040 Province Mean Range Mean Range Mean Range AB 22 [19 - 23] 17 [16 - 18] 15 [14 - 15] MB 18 [16 - 19] 16 [15 - 18] 15 [14 - 16] NS 18 [16 - 21] 15 [13 - 17] 14 [12 - 16] ON 19 [18 - 26] 16 [15 - 21] 14 [13 - 19] • Depending on the province, LCOE reductions of up to 22% by 2030 and 32% by 2040 are possible due to technology learning 26
STORAGE UTILIZATION OVER TWO WEEKS IN SUMMER location with lowest LCOE in Ontario 27
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