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© S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 1 MIT Laboratory for Energy and the Environment Scenario-Based Multi-Attribute Tradeoff.

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Presentation on theme: "© S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 1 MIT Laboratory for Energy and the Environment Scenario-Based Multi-Attribute Tradeoff."— Presentation transcript:

1 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 1 MIT Laboratory for Energy and the Environment Scenario-Based Multi-Attribute Tradeoff Analysis: Vermont Public Service Board Presentation Stephen R. Connors Analysis Group for Regional Energy Analysis MIT Laboratory for Energy and the Environment One Amherst St. Room E Cambridge, MA , USA

2 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 2 Multi-Attribute Tradeoffs MIT Framework –Originated in the late 1980s (IRP) // Not a model Designed as Extensive/Inclusive Approach for Multi-Stakeholder/Controversy-Laden Decision Environments Multi-Attribute Multi-Objective Calculate every imaginable attribute (automatically) Few Decision Attributes/Numerous Performance Attributes Used in Two Modes: Exploratory/Learning Mode [Joint Fact-Finding, Rough Consensus on Long-Term Vision] Decision/Negotiated Settlement Mode [Choose among superior portfolios of option]

3 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 3 Whose Attributes? The Usual Suspects The Black Sheep Attributes Rates vs. Bills vs. Revenues Price/Rate Volatility Costs vs. Investments Continuous vs. Lumpy Expenditures Three Es: Efficiency, Equity, Employment The Incalculable Those Darn Externalities Indirect Economic Impacts Those Pesky Markets

4 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 4 Markets Are Our Friends Competition - Boston Style Whos in Charge?

5 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 5 You Want to Predict This? Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec $/MWh

6 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 6 AGREA NIMBYs » NUMBY Not Under My Backyard Originally for pipelines Equally good for carbon sequestration » NIMO Not In My Ocean Ocean Disposal of CO 2 » NOMH (gnome) Not On My Horizon Originally conceived for offshore wind Equally good for onshore-ridge wind … And, the Ability to Site Common NIMBYs » NIMBY Not in My Backyard » NOTE / NOPE Not Over There Either Not On Planet Earth » BANANA Build Absolutely Nothing Anywhere Near Anybody

7 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 7 Direct Stakeholder Input A Structured Dialogue...

8 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 8 Tradeoff Analysis (1) Scenario-Based Multi-Attribute Tradeoff Analysis is a scenario planning approach developed to facilitate dialogue and learning among multi-stakeholder audiences. »Large Number of Activities/Options (Multi-Option Strategies) »Large Number of Uncertainties (Multiple Futures, Scientific Uncertainty) »Large Number of Goal States/Attributes (Multiple Stakeholders, Conflicting Goals)

9 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 9 Tradeoff Analysis (3) Crafting Scenarios to Help Guide Policymakers A multi-option strategy for a given future is a scenario

10 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 10 Tradeoff Analysis (2) Features »Identifies Good and Bad Strategies »Identifies Competing/Complementary Sets of Options »Recognizes Different Deployment Schedules of Different Options »Use to Identify Robust/Flexible versus Optimal Strategies »Helps Facilitate Stakeholder Dialogues

11 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 11 Shandong Province Population 90 Million Area Size of Florida Installed Capacity (00) 18.5 GW (mostly coal)

12 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 12 Shandong Strategies Strategy Components (1008 strategies) »Existing Generation Additional Unit Retirements (2) FGD Retrofits (2) Use of Prepared Coals (3) »New Generation Baseload Technology Mix (7) Extra-Regional Generation (2) »End-Use Options Peak Load Management (2) Improved End-Use Efficiency (EUE) (3) (Note: The first option in any option-set is by definition the reference option.)

13 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 13 Tradeoff Analysis Results

14 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 14 So, What Does It Cost?

15 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 15 For the Strategy with Prepared Coal in Existing Units, Existing Unit Retirements and FGD Retrofits, Aggressive (20%) End-Use Efficiency, Peak Load Management and New Conventional Coal, Nuclear and Natural Gas Generation Ann. SO2 & PM10 Emissions <- Sulfur Reductions from Fuel Switch (from Reference Strategy) <- PM Reductions from Fuel Switch Reductions from Fuel Switch, End-Use Efficiency & New Generation Choice -> Reference Future

16 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 16 Key Results (Shandong) Significant cost-effective opportunities exist for reducing power plant criteria pollutants (SO 2, PM, NO x ), and reducing increases of greenhouse gas emissions (CO 2 ). The best performing strategies were a combination of the following options: »Use of prepared coal to reduce PM and SO 2 »Select retirement or emissions retrofits of existing generation »Implementation of peak load management and end-use efficiency programs »Addition of non-carbon emitting generation technologies »Do not forget the fuel supply infrastructure and markets

17 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 17 Demand-Side and CO2

18 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 18 Existing Units and PM10

19 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 19 Whats Required? Develop an infrastructure management perspective. What is the resulting vision? Explore options in greater detail Develop in-depth knowledge of energy consumption patterns and renewable energy resources Identify and implement essential robust options, and develop promising future flexible options.

20 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 20 Now Looking at Tougher Options Technology Development, Deployment and Use Renewable Resources… Non-dispatchable Variable across multiple time scales Temporal dynamics interact with other important dynamics (markets, consumer behavior) Decentralized Decisions Choice and use of distributed generation Energy efficiency options that use information technology to reduced demand (smart loads) Influenced by Market Prices and other Situational Aspects Example: Wind Resource Dynamics

21 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 21 Wind in Space and Time Source: Mass Renewable Energy Trust TrueWind Solutions Electricity Demand Generation from Wind

22 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 22 Logan Hotel Buzzards Bay Boston Nantucket Seasonal & Daily Variability Generation Summed by Month and Hour-of-Day (2004)

23 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 23 One Site for Many Years… Nantucket (Sleigh Ride?)

24 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 24 Operating Modes and Resource Portfolios Renewable Resource Variability Wind and Sun (Magnitude and Timing) Rainfall (Hydropower, Biomass) Fuel Markets Fuel Prices/Price Differentials (esp. Natural Gas) Infrastructure Investments (Pipelines/Storage/LNG) Conventional Generation Nuclear Availability, Hydro Potential Power Market Structure (Capacity Markets, Bid Rules) Power Grid Operations (Reliability/Contingency Practices) Energy Demands Demand Growth – Relative to Supply Growth Heating Degree Days/Cooling Degree Days

25 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 25 Identifying Robust Strategies A Robust Strategy has Robust and Flexible Options Commonalities and Differences

26 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 26 Closing Observations/Questions At Which Stage Are You? » Short/Medium-Term in the Context of a Long-Term Plan/Vision/Whatever » Are All the Major Factors Been Included/Considered? » Have the Major Organizational/ Institutional Factors Been Overlooked? (Brains and Bodies)

27 © S.R.Connors–AGREA/LFEE/MIT Feb 2007 – pg. 27 Have We Had It Too Easy? We need to develop the information, tools, implementable visions, and the policies and people to realize them.


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