POWER SYSTEM OPTIMIZATION FLEXIBLE POWER SYMPOSIUM, VAIL COLORADO

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1 POWER SYSTEM OPTIMIZATION FLEXIBLE POWER SYMPOSIUM, VAIL COLORADO Mikael Backman Director, Market Development Americas Wartsila Power Plants

2 The Value of Smart Power Generation Smart Power Generation can bring value on different levels of our industry. Power System Utility Portfolio Individual Project 3 Wärtsilä 30 April 2013 POWER PLANTS 2013

3 Agenda 2010 LTPP Analysis 2012 LTPP Analysis Conclusions

4 2010 LTPP Analysis Background Purpose: Find the value proposition of increased flexibility in the CAISO system by Model: Analysis by DNV KEMA using 2010 LTPP, comparing 5.5 GW of new gas plants to Wärtsilä (SPG) units. Co-optimization of energy (DA & RT) and all ancillary services (Reg up/down, load following, spin, nonspin). Analysis: Total cost of energy, A/S, Carbon and water with SPG vs current plans. High load and environmentally constrained scenarios used. Model run for WECC and CAISO results extracted. As a default, DR ($17,500/MWh) was used as back stop from the LTPP but also a scenario with unlimited GT capacity (@ $96/MWh) was analyzed.

5 Model Scenarios & Parameters 6 Wärtsilä 6 29 November 2011 Smart Power Generation in Future Power Systems

6 Day Ahead Results- Summary DR Backstop Unlimited GT Backstop

7 Main Conclusions, 2010 LTPP analysis 1. California will run out of A/S products on high variability days in With sufficient capacity of SPG injected, the system savings will be 10-13% on an annual basis compared to the current plans consisting of GT/GTCC plants. 3. Even compared to unlimited amounts of new GT s for A/S, the system savings will be ~4% annualized if Wartsila SPG is used. 4. Compared to the reference scenario, 300 to 900 MW less SPG capacity is needed to meet the resource adequacy criteria 5. SPG provides majority of upward ancillary services, which allows more efficient plants (CCGT) to operate in their design point; System Optimization Before SPG After SPG

8 Agenda 2010 LTPP Analysis 2012 LTPP Analysis Conclusions

9 Analysis update based on 2012 LTPP Contracted with Energy Exemplar to run modeling Scenario 1A chosen (early SONGS retirement) after discussions with CPUC and others Different assumptions created expectation of smaller opportunity for optimization (lower peak, lower gas price etc) New modifications done to base model that were not done for prior analysis. - All generators in CAISO without part load heat rates were assigned part load heat rates based on intelligence from GTPRO V23.0 for heat rate correction on part load A/S production. - Model modified to force all CAISO generators to adhere to start profiles to correct availability and heat rates for spin offers. (Model sees generators as capable of providing full capacity into Spin Reserve at full load heat rate, while they are off-line.) - These changes mirror similar modifications made by others, e.g., NREL (WECC model used for western wind integration studies) 10 Wärtsilä

10 Difference in Methods DNV Kema simulated 10 select days, estimate annual costs using probabilistic approach. Energy Exemplar simulated all hours for 365 days 11 Wärtsilä

11 Base vs. Flexible Capacity options Base Case: 2.5 GW of simple cycle (mostly aero) GTs GW GTCCs Flex Case: 2.5 GW of Wärtsilä 18V50SG GW Wärtsilä Flexicycle 12 Wärtsilä

12 Flexibility case reduces shortfalls w/ same capacity Need simulation applies - Peak monthly reserve requirements to every hour of that month - Relaxes some constraints (e.g., start costs) Purpose? To identify potential capacity shortfalls under worst case conditions. Base Case (7/22/2022) FLEX Case (7/22/2022) Flex Case yields 1.37 GW max shortfall, 50% less than 2.7 GW for Base Wärtsilä Flexible case reduces capacity shortfall by 50% (with no more capacity added by 2022 than in the Base Case indexed to GTs & GTCCs) 13 Wärtsilä

13 Flexible Case Optimization Results Energy + AS Base Flex Savings Direct Production Cost (BUSD/a) % Gross 1) savings at marginal cost (BUSD/a) % Flexible case yields a 6.93% decrease in annual ratepayer costs, where all generators paid at marginal cost. Flexible case reduces the # of starts/year for existing GTCC fleet by 20% (average of 29 starts/year in flex case vs. 36 in Base case. Flexible case increases capacity factor of existing GTCC fleet 6.1% (average 52.89% cf in Flex case vs % in Base case). Flexible case decreases CO2 generation by in-state assets by 1.1% (39.45 million short tons/year in Flex case vs Base case). 1) Direct generation * marginal price. Does not include all rate payer savings like surcharges etc 14 Wärtsilä

14 Detailed Results 15 Wärtsilä

15 Agenda 2010 LTPP Analysis 2012 LTPP Analysis Conclusions

16 Some interesting analysis UK Analysis, RedPoint More accurate information for market mechanism development Technology specific savings $870 million annual savings (5% of annual system operating cost) without any additional investment cost 54% lower system balancing costs CAISO Analysis, KEMA DNV Better information for future capacity mix planning Technology specific savings $890 million annual savings (11% of annual system operating cost) without any additional investment cost 50% lower system balancing costs Inertia Analysis, KEMA DNV Utilization of non-spinning resources for operational reserves Can speed replace inertia? 17 Wärtsilä Links to the studies:

17 With new, innovative mechanisms, we can for example.. Utilize non-spinning resources for operational products, not just contingency reserves thus reducing costs of spinning products like regulation up and LF up allowing steam based plants to be utilised more efficiently by running them at a higher load factor...reducing the cycling and start related costs from steam based plants...reducing the amount of thermal power needed for higher levels of renewable integration...etc 18 Wärtsilä

18 One example 50% RPS with overgeneration Base case - Hours 5000MW over 10h, 50,000 MWh, remaining MW, or 126,000 MWh. Total of 176,000 MWh - CO 2 generation (X) is proportional to GTCC generation 176,000 MWh at 55% efficiency, CO 2 =X*176,000 MWh Alternative case - Replace 50% of GTCC generation hours 7-17 w/ off-line, fast-start SPG, 25,000 MWh; remaining 14 hours same as base; total of 151,000 MWh -SPG 50% efficient, power to grid in 30s, shuts down CO 2 = X*(50,000 MWh * 0.5) + X*(126,000 MW * 1.05) = X*(25,000 MWh) + X*(132,300 MWh) = X*157,300 MWh 1.05 is adjustment factor for ½ of GTCC capacity replaced by SPG at 50% efficiency (52.5% vs 55%) % reduction in X = 100 * (176, ,300) / 176,000 = 10.6% Graph from Investigating a Higher Renewables Portfolio Standard in California ( 19 Wärtsilä

19 One example 50% RPS with overgeneration Allowing 50% of GTCC generation during hours 7-17 for the day illustrated in Fig 27 of E3 report to be supplanted by quick start, off-line SPG would - Reduce CO 2 generation 10% - Reduce overgeneration by 25,000 MWh per day - Provides reliability at lower cost (forecasts ) - With higher ramp capability, the system needs less gas capacity This would require market mechanisms to allow compensation for non-spin, offline resources to satisfy operational needs (5GW ramp hours 17-19). The mechanisms should be neutral, independent of technology, and based solely on capabilities. 20 Wärtsilä

20 Summary Both these analyses show that there is value to increased flexibility resulting in system optimization, even though the features enabling this can not be sold in the market today. There is a role for different types of gas generation and room for optimization. As a first step, the library of gas generation used for planning should be broadened to include different technologies. As discussed briefly, increased usage of fast start non-spin assets can further extract value from development in asset performance. 21 Wärtsilä

21 Thank you very much for the attention! Smart Power Generation 22 Wärtsilä 11 February 2014