Electric Capacity
Market Performance
with Generation
Investment and
Renewables
Cynthia Bothwell
Benjamin Hobbs
Johns Hopkins University
Work Supported in part by NSF grants OISE 1243482 (WINDINSPIRE) and ECCS 1230788
Outline
 Motivation
 Methodology



General
Data
Model
 Results



Without Renewables: Benchmark
Optimal Renewable Mix
Summary Findings
 Next
Steps
Motivation:
In light of existing market failures, how
does the introduction of intermittent
renewable generation sources (wind and solar)
affect electricity markets?
In particular, how do renewables affect the
performance of alternative energy & capacity
market designs?
Methodology:Overview
General approach:
1.
2.
Identify optimal generation mixes
Determine whether alternative market designs “support”
the optimal mixes (are investments sufficiently profitable
to support investment, but so profitable as to incent too
much?)
The optimal mix and participant profitability was
examined including a variety of renewable penetrations
to determine whether and how renewable generation
impacts markets.
Methodology & Data
 Modeled
Two Different Systems using hourly
actual load, wind, and solar data for one year.
Winter
Load Factor 70.8%
Wind CF
39.4%
Solar CF
21.5%
Gas Price
High
Data
Europe
 Modeled
Summer
55.8%
43.8%
21.5%
Low
Texas
new generation costs per EIA for CT
and CC. Included an existing baseload plant
with fixed cost 25% of new construction – limited
to 45% of the peak load. Examined renewables
with and without subsidies.
Methodology – The Model
1.
Determined an optimal investment mix to serve
each of the two systems by minimizing annual cost
subject to a reserve margin.
Minimize:
2.
FCsolar + FCwind + FCCT + FCCC + FCcoal + VCCT + VCCC + VCcoal – Subsidywind
Compared the cost for optimal operation with the
revenues collected using four different electricity
market mechanism:
1.
2.
3.
4.
Energy Only
Energy with Scarcity Pricing
Energy with capacity Market for dispatchable
capacity
Energy with capacity market for all generation with
intermittent contribution equal to the difference in the
peak load with and without the renewable resource.
Results: Before renewables
Lower gas price, lower cost: revenues close to cost
Natural gas generator revenues are less than costs
- Will not motivate new investment without capacity mechanism
Existing baseload revenues are greater than cost
- Motivation to modernize
- Higher costs to consumers
Results: Optimal Mix with Renewables
Reduction in portfolio cost
No solar in optimal mix without subsidy
Increase wind development optimal with high gas price
Natural gas generator revenues are still less than costs
Existing baseload revenues are still greater than cost but reduced
- Less motivation to modernize
- Motivation is removed if wind is built in excess of optimal due to subsidy
Summary Results
 Scarcity
pricing insufficient to meet reliability
targets
 Solar needs subsidies
 Optimal wind development depends on fuel prices
and may not guarantee profits
 Wind may receive economic rents – if
underdeveloped, subsidized, or higher capacity
factor
 Baseload capacity lacks incentives for
modernization when gas prices are low and
renewable investment is higher than the
economically optimal level (RPS or subsidy).
Key Findings
 Existing
depreciated baseload can represent a
larger distortion than renewables
 Capacity market distortions are sensitive to
varying factors which lead to different policy
solutions
 Generation adequacy determinations are also
important in setting capacity mechanisms
Next Steps

Current work:






Expanding cases with more actual data
Looking at reliability criteria – reserve margin
versus VOLL
Understanding magnitudes of market distortions
Consider alternative renewable capacity
contribution methods
Analyze market equilibrium investment and
operations under designs
Assess the resulting distortions, efficiency,
consumer impacts, and incentives for
renewable investment