Approaches, challenges and opportunities in urban energy systems modelling

Transcription

1 Approaches, challenges and opportunities in urban energy systems modelling Dr James Keirstead Workshop on Urban Energy and Carbon Modelling in Rapidly Urbanizing World IIASA, Laxenburg, Austria 10 th - 11 th March 2011

2 Outline Definitions Summary of major approaches Challenges and shortcomings Future opportunities

3 Definitions What is an energy system? the combined processes of acquiring and using energy in a given society or economy Jaccard (2005) Sustainable Fossil Fuels Key points: Combined processes Acquiring and using Society or economy

4 Definitions What about the urban part? Covered in Helga s session yesterday Key issue is direct versus indirect accounting and what this means for energy infrastructure Focus on urban service demands Energy is a derived demand so the combined processes of acquiring and using energy to satisfy the service demands of an urban area.

5 Review methodology Draft literature review of 30 urban energy system papers Lessons from SynCity modelling at Imperial College Full review paper in preparation

6 Approaches The literature suggests 4 main approaches Empirical: measuring and evaluating current urban energy consumption and related impacts Evaluation/simulation: bottom-up assessment of changes in demographics, technologies etc. Techno-economic optimization: designing integrated systems to an overall objective Transitions: focus on policy and development

7 Approaches: empirical IEA World Energy Report 2008 Cities represent 2/3 of global primary energy demand 71% of direct-energy related GHG emissions Dhakal (2009) on Chinese cities

8 Approaches: evaluation Parshall et al (in press) Spatially explicit model of energy demands used to analyse technical potential for co-generation

9 Approaches: optimization Many examples including Imperial s RTN model

10 Sample optimization results Gas District heat

11 Approaches: transitions Energy ladder versus energy transition Source: Chancel 2010

12 Challenges Some common themes from the literature Difficulty acquiring input data at appropriate spatial and temporal resolutions (and what does appropriate mean) Need to deal with uncertainty (especially for planning models) Agent heterogeneity (Drivers of demand variability? Implications for peak demands) Model formulation issues (e.g. LP models of non-linear systems, tradeoffs between fidelity and tractability)

13 Challenges: input data Multiple data sources required Common strategies: downscaling and use of proxies Source: Manfren et al (2011)

14 Challenges: service demands? Nissing and von Blottnitz (2010)

15 Challenges: uncertainty Sources of uncertainty Model inputs (prices, impacts, demand growth) Model complexity (links between inputs and outputs) Different stakeholder aims Various strategies Scenarios: enumerated by experts Multi-criteria analysis/multi-objective optimization Formal sensitivity and uncertainty analysis

16 Challenges: uncertainty Sensitivity analysis not widely employed And when it is, often informal one-at-a-time methods Interaction effects missing but important (e.g. spark gap) Source: Manfren et al (2011)

17 Challenges: agent heterogeneity Energy systems are at the meso-level characterised by two typical aspects, i.e. dynamics driven by interactions between actors, and heterogeneous characteristics of actors Schenk (2007) Policy-relevant modelling needs to inform customized solutions for different groups

18 Challenges: model formulation Optimization models are typically LPs But non-linear constraints (e.g. Renewable performance, CHP part loads) Balance of tractability with fidelity 1600 Solve time (seconds) y = 0.003x R² = Number of zones in city Simulation Source: Robinson et al (2007) Optimization Source: Imperial RTN

19 Opportunities Summary of key challenges Need highly disaggregated data Need to capture uncertainty (either inherent in problem or due to model formulation) Both require advanced computation Two * promising opportunities Activity-based modelling Parallel computation *And a third more speculative one

20 Activity-based modelling Land use and transportation literature has moved from aggregate to disaggregate models Regression, optimization, aggregate spatial (e.g. gravity or other physical proxies) Disaggregate (e.g. random utility choice) the value of more complex, behaviourally valid, microscopic models is not that one obtains microscopic forecasts, but that one obtains macroscopic forecasts based on microscopic principles (Timmermans 2003)

21 Activity-based modelling Does and doesn t solve the data problem Enables high spatial and temporal resolution demand simulation But models need to be parameterized from detailed transport surveys Fortunately these are available for many large cities Preferences may not be significantly different for similar cities Need to calculate marginal occupancy loads

22 Activity-based modelling

23 Activity-based modelling

24 Parallel computation Regardless of modelling approach, multiple runs are required to understand uncertainty For Monte Carlo-based analyses it could be 100s of times Parallel computation via dual core laptops, cheap grids, cloud computing Source:

25 Parallel computation Source: Keirstead and Shah (2011)

26 Examples Cisco s Planetary Skin Institute Google Earth Open Street Maps Vulcan (Purdue) DECC LLSOA electricity and gas data FOI request on energy consumption for public buildings Opportunities: the data deluge How to: Separate wheat from chaff Manipulate data efficiently Source: The Guardian,

27 Conclusions Four main approaches to urban energy systems analysis Of which three rely on numerical analysis or modelling Good recent progress but challenges remain: Input data, handling of uncertainty, diversity of urban populations, model size and tractability Promising solutions from Activity-based modelling Parallel computation Massive public-access data sets Why are we doing this modelling? To understand service demands and socio-technical systems needed to satisfy these