Large-Scale Simulation for Sustainable Energy Design

1. Technische Universität Berlin Energy sustainability through representative large-scale simulation : the logical and physical design of xeona International Conference on Sustainability Engineering and Science (ICSES) Auckland, New Zealand • 06–09 July 2004• www.nzses.org.nz Robbie Morrison 1, 2 Tobias Wittmann 1 Thomas Bruckner 1 1 Institute for Energy Engineering Technical University of Berlin Germany 2 Mathematical and Computing Sciences Victoria University of Wellington New Zealand Issue D

2. Authors Thomas Bruckner Tobias Wittmann Robbie Morrison

3. Network component (more later) Resource processing networked systems TECHNICAL ISSUES • Typical features of resource processing networked systems: • high capital cost — and often environmental cost — of infrastructure • limited natural entitlements — rivers, transmission corridors, gas fields, etc • subsystems which operate in (increasingly) volatile circumstances • plant performance which relates to context — ambient conditions, price, etc • decentralized decision-making —whether administered or market pricing • final demand is for services (rather that commodities) • strong implications for biophysical sustainability and societal functioning • The energy sector as a representative example

4. Windflow prototype, 500 kW Christchurch, NZ, 2003 Public interest performance ETHICAL ISSUES • Public interest is a normative concept • Resource processing networked systems should operate, evolve, and innovateto improve public interest performance: • whole-system financial cost • depletable resource use • greenhouse gas emissions • local environmental impacts • This presentation looks at the contribution that representative large-scale simulation can make to public interest policy development in the energy sector • Examples derive mostly from New Zealand

5. Motivation for modeling COMPLEX SYSTEMS • Complex multi-party systems defy simplistic analysis • Large-scale simulation provides an alternative to econometric modeling and system dynamics • Versatile model application/interpretation, briefly: • operational mode — scenario investigation • operational plus investment mode — system evolution experimentation • Potential for proactive use: • adaptive resource consents, for instance, for fresh water take (NZ issue) • model-based, not trigger-based, ring-fenced generation (NZ issue) • revenue redistribution among cooperating parties • Can generate important non-observable system metrics, for instance: • weather-normalized, inventory-corrected social energy efficiency

6. Simulation environments COMPUTER SCIENCE agent-based extension • Object-oriented: circa 1995 • Status: first use late-1995, extensive technology library • Category: high-resolution • Role: technical behavior in the presence of one internal decision-maker • License: GPL plus requests • Web: www.iet.tu-berlin.de/deeco deeco xeona • Object-oriented: circa 2004 • Status: alpha release planned for 2005 • Category: entity-oriented • Role: in addition, able to capture multi-participant domestic and commercial behavior • License: GPL plus requests

7. hydro-generator wholesale retail household commercial relationships Illustrative example time interval: ►one hour (say) time horizon: ►annual (operational) ►decade (plus investment) authority attribute exergy resources public interest system metrics time-series external circumstances

8. Overlaid networks Two foundation networks: ►mathematical graphs Commercial associations network: ► negotiation pathways ► bilateral contracts ►market-mediated relationships Physical and instrumental resources (PIR) network: ►stock and flow model ►also supports instrumental resources (including carbon permits and flow of funds) Optimal single interval operation: these arrangements allow use of linear or mixed integer (LP or MILP) methods to optimize subsystem operation: ►single operator (merit order) ►bid-informed market (stack order) Optimization informed simulation

9. Agent-based modeling All actors: bounded rationality ►limited processing power ►public information only Domestic actors: ► investment responses based on lifestyle classification Commercial actors: ► commercial motivation ► can call on external software and even human support (experimental economics) Future refinements: ► greater analytical sophistication ► learning and adaptation ► cooperation and coalition stability Under- recognized topic

10. Technical components End-use facilities: have received limited public policy attention to date Engineering plant: generalized entity Component characterization: ►input-output relationships (generalized efficiency) ► plant capacity constraints (lower, upper) ► cost/impact "creation" equations Context-dependent performance: ► environmental circumstances ► neighboring plant via "dialog" ► internal state, tracking operating history and inventory Network programming Support for heat transport and storage temperatures: ► engineering controllers mimicked to determine flo and return temperatures ► non-ideal storage modeled such that energy loss causes temperature decay Resource quality captured Improved technical realism

11. HVDC link, January 2004 Wind damage Policy issues (1)mostly large-scale Licensing: merits of licensing hydro-generator stack (bidding) models Carbon tax: efficacy assessment Market improvement: by simulation System (n−1) security: based on minimum cut (bottleneck) analysis Additionality assessment: for NZ Projects Mechanismemissions units (EU) allocation, using in situ analysis Intermittent renewables: whole of system evaluation Extreme event functioning: including dry cold winters

12. Policy issues (2)mostly dispersed Rewarded end-user responsiveness: various demand management initiatives Rebound: take-back effect from domestic efficiency investments Solar hot water support: merits of accelerated domestic solar hot water uptake Building performance: merits of tighter building standards Resource consent (RMA) process: consideration of alternatives Investment protection: distributed solutions tend to be vulnerable to upstream reinforcement Whole-system public interestperformance criteria (PIPC): ►financial cost ► depletable resource use ► greenhouse gas emissions ► local environmental impact Policy trade-offs may be required

13. Further subsystems miscellaneous components Huntly 1000 MW power station 25ºC max for river gas Some other parts of the jigsaw coal Waikato River neighborhood fuel cells (phosphoric acid) nuclear power electricity electricity low-levelwaste gas hot water ? high-level waste ?

14. Trade-off information forpolicy makers (single operator case) Trade-off line 200% Situation: Complex municipal energy system in northern Europe modeled using deeco everything 150% large solar + seasonal storage 100% Financial cost increase everything − large solar 50% small solar medium solar cogeneration + short distance heat grid 0% Source: Bruckner, Groscurth, and Kümmel (1997) oil-fired boilers +electricity imports gas heat-pumps + heat grid –50% Note: LHV is lower heating value 0% 10% 20% 30% 40% 50% Business as usual reference Depletable fuel savings (LHV)

15. 1 2 3 Key assumptions Preamble • extensive state describes prevailing plant duty and/or inventory • intensive state includes quantities like output voltage, flo and return temperatures, and stratified storage temperatures State orthogonality • extensive state selection has no influence on intensive state Cross-interval operation • extensive state selection covering storage is procedural rather than optimal • applies to single operator managed storage only Efficiency curve convexity • plant efficiency increases stepwise with plant duty • required where linear optimization is employed or where a global optimum must be guaranteed

16. Software design • Object-orientation (taken to include generic programming) • scientific programming — optimization solvers, ordinary differential equation solvers, implicit variables methods, and graph algorithms • orthodox object-oriented design and analysis (OODA) • multi-agent simulation techniques • Physical design • modularized software architecture • XML • for persistent storage and data exchange • UML • standardized visual language for design and documentation

17. Closure • Simulation is cheaper and faster than policy formulation by trial-and-error • Energy-services supply may well be headed toward smarter lighter networks and greater use of renewable and fuel-passive technologies • Large-scale simulation is indicated and other methods appear less suitable: • a single socially-motivated decision-maker is no longer appropriate • econometric methods struggle to capture technical possibilities • system dynamics struggles to capture network issues • Large-scale simulation may have application in other areas, such as the management of fresh water take (for hydro-generation, cooling, irrigation) • The method can yield important non-observable system metrics — essential for the proper auditing of policy efficacy

18. Selected references Bruckner, Thomas, Helmuth-M Groscurth, and Reiner Kümmel. 1997. Competition and synergy between energy technologies in municipal energy systems. Energy– The International Journal. 22(10): 1005–1014. Lindenberger, Dietmar, Thomas Bruckner, Helmuth-M Groscurth, and Reiner Kümmel. 2000. Optimization of solar district heating systems : seasonal storage, heat pumps, and cogeneration. Energy– The International Journal. 25(7): 591–608. Morrison, Robbie, Thomas Bruckner. 2002. High-resolution modeling of distributed energy resources using deeco : adverse interactions and potential policy conflicts. In – Sergio Ulgiati et al. (eds.). 2003. Proceedings of the 3rd International Workshop in Advances in Energy Studies — Reconsidering the Importance of Energy. Held at Porto Venere, Italy, 24–28 September 2002. Padova, Italy: Servizi Grafici Editoriali. 97–107. Morrison, Robbie, Tobias Wittmann, and Thomas Bruckner. 2003. Energy policy and distributed solutions : a model-based interpretation. Paper at the Australia New Zealand Society for Ecological Economics (ANZSEE) Think Tank. Held at University of Auckland, Auckland, New Zealand, 16 November 2003. Bruckner, Thomas, Robbie Morrison, Chris Handley, and Murray Patterson. 2003. High-resolution modeling of energy-services supply systems using deeco : overview and application to policy development. Annals of Operations Research. 121(1–4): 151–180. Lindenberger, Dietmar, Thomas Bruckner, Robbie Morrison, Helmuth-M Groscurth, and Reiner Kümmel. 2004. Modernization of local energy systems. Energy–The International Journal. 29(2): 245–256.