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Direct Policy Conditioning for reservoir operation

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Improving the protection of aquatic ecosystems by dynamically constraining reservoir operation via Direct Policy Conditioning

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Direct Policy Conditioning for reservoir operation

  1. 1. IFAC 2014 CAPE TOWN -­‐ZA Matteo Giuliani1, Andrea Castelletti1,2, Patrick M. Reed3 1 Dipartimento di Elettronica, Informazione, e Bioingegneria, Politecnico di Milano, Milano, Italy 2 Institute of Environmental Engineering ETH-Z, Zurich 3 Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY Modelling and Control of Water Systems Improving the protection of aquatic ecosystems by dynamically constraining reservoir operation via Direct Policy Conditioning
  2. 2. The fall of the social planner myth? SOCIAL PLANNER’S PARETO OPTIMAL Stakeholder 1’s utility Stakeholder 2’s utility utopia REALITY dominated unfeasible
  3. 3. A real world example Anghileri, D. et al. Journal of Water Resources Planning and Management, 139(5), 492–500, 2013 Hydropower reservoir Penstock Power plant Como city River Adda River Adda Legend Lario Lario catchment River Irrigated area Kilometers 0 5 10 20 30 40 50 UNCOORDINATED CENTRALIZED UNCOORDINATED CENTRALIZED Lake Como Lake Como r s 3 s 1 s 3 u 3 s 2 u 1 u 2 R2 3 (•) R2 R1 hydropower plant irrigated area H2 H3 H2 H1 H3 q 2 q 1 q 3 q 2 q 1 s 1 s 2 s 3 u 2 u 3 u 1 m 1 m 2 m (•) (•) m (•) u 3 (a) (b) r 3 (•) FIG. 3. The model scheme under uncoordinated (left) and centralized (right) 23 490’000 480’000 470’000 460’000 800 900 1000 1100 1200 1300 1400 1500 1600 Irrigation deficit [m3/s]2 Hydropower revenue [euro/day] H b a C4 C3 C6 CO2 CO1 UC C5 C4 C3 C2 C1 UC Lake Como Lake Como r s 1 s 2 u 1 u 2 R2 R1 R2 R1 hydropower plant irrigated area H2 H1 H3 H2 H1 H3 q 3 q 2 q 1 q 3 q 2 q 1 s 1 s 2 s 3 u 2 u 3 u 1 m 1 m 2 m (•) (•) m (•) (a) (b) r FIG. 3. The model scheme under uncoordinated (left) and centralized (right) man-agement. 23 UNCOORDINATED CENTRALIZED (SOCIAL PLANNER)
  4. 4. Efficiency vs acceptability: how to trade-off? Giuliani M. et al., Journal of Water Resources Planning and Management, 2014 acceptability efficiency utopia SOCIAL PLANNER INDIVIDUALISM acceptability of the social planner efficiency of individualism coordination mechanism design
  5. 5. Direct Policy Conditioning an approach to condition the individualistic control policy and push it towards a social welfare equilibrium
  6. 6. Direct Policy Conditioning an approach to condition the individualistic control policy and push it towards a social welfare equilibrium PRIMARY obj. SECONDARY obj. utopia SECONDARY’s OPTIMUM PRIMARY’s OPTIMUM
  7. 7. Direct Policy Conditioning COMPUTE THE SOCIAL PLANNER POLICIES 1 PRIMARY obj. SECONDARY obj. utopia SECONDARY’s OPTIMUM PRIMARY’s OPTIMUM
  8. 8. Direct Policy Conditioning GET INSIGHTS ON HOW TO CONDITION THE PRIMARY’S POLICY 1 2 COMPUTE THE SOCIAL PLANNER POLICIES PRIMARY obj. SECONDARY obj. utopia SECONDARY’s OPTIMUM PRIMARY’s OPTIMUM
  9. 9. GET INSIGHTS ON HOW TO CONDITION THE PRIMARY’S POLICY 1 2 COMPUTE THE CONSTRAINED PRIMARY POLICY Direct Policy Conditioning COMPUTE THE SOCIAL PLANNER POLICIES 3 PRIMARY obj. SECONDARY obj. utopia SECONDARY’s OPTIMUM PRIMARY’s OPTIMUM
  10. 10. GET INSIGHTS ON HOW TO CONDITION THE PRIMARY’S POLICY COMPUTE THE CONSTRAINED PRIMARY POLICY Direct Policy Conditioning Multi-objective optimization using the Direct Policy Search approach Policy parameters vectors Objectives values 1 2 3 PRIMARY obj. SECONDARY obj. utopia SECONDARY’s OPTIMUM PRIMARY’s OPTIMUM
  11. 11. 1 2 COMPUTE THE CONSTRAINED PRIMARY POLICY Direct Policy Conditioning Multi-objective optimization using the Direct Policy Search approach Input Variable Selection of the most relevant parameters in explaining the secondary objectives Policy parameters vectors Objectives values Subset of policy parameters to be conditioned 3 PRIMARY obj. SECONDARY obj. utopia SECONDARY’s OPTIMUM PRIMARY’s OPTIMUM
  12. 12. 1 2 Single-objective optimization of the primary objective with restricted constraints on the sensitive policy parameters Direct Policy Conditioning Multi-objective optimization using the Direct Policy Search approach Input Variable Selection of the most relevant parameters in explaining the secondary objectives Policy parameters vectors Objectives values Subset of policy parameters to be conditioned 3 PRIMARY obj. SECONDARY obj. utopia SECONDARY’s OPTIMUM PRIMARY’s OPTIMUM
  13. 13. CASE STUDY
  14. 14. The Susquehanna river system (a) (b) atomic power plant Baltimore Chester Fishery and boating Conowingo Muddy Run Marietta facility station Pennsylvania Maryland lateral inflow Susquehanna River Muddy Run inflow Lower Susquehanna River Maryland New York Pennsylvania Conowingo pond Chester Baltimore (b) Conowingo pond Marietta facility station atomic power plant Baltimore Muddy Run Chester Fishery and boating FERC environmental requirements Conowingo hydropower plant Pennsylvania Maryland lateral inflow Susquehanna River Muddy Run inflow River Maryland Chester Baltimore
  15. 15. DPC experimental setting 1 SOCIAL PLANNER POLICIES • POLICY: Gaussian Radial Basis function with 2 inputs (level & time) + 4 output (release outputs) + 4 basis functions: 32 parameters • OPTIMIZATION: Borg MOEA parameterized as in Hadka and Reed [2013] • NFE = 1,000,000 per replica • 30 replications to avoid dependence on randomness 1
  16. 16. Social Planner policies Giuliani. M. et al. Water Resources Research, 2014
  17. 17. DPC experimental setting SOCIAL PLANNER POLICIES • POLICY: Gaussian Radial Basis function with 2 inputs (level & time) + 4 output (release outputs) + 4 basis functions: 32 parameters • OPTIMIZATION: Borg MOEA parameterized as in Hadka and Reed [2013] • NFE = 1,000,000 per replica • 30 replications to avoid dependence on randomness INPUT VARIABLE SELECTION • Tree based iterative input selection [Galelli and Castelletti, 2013] 1 2
  18. 18. Input Variable Selection (a) Selected features and corresponding contribution in explaining the Environment objective 75 50 25 0 explained variance 2 ct 3 b 3 t 4 t 2 bw4 t 1 bbt 3 w4 4 w4 (b) Decision variables selected on the Pareto-optimal set 1 0.5 variable x1 x2 x3 u1 Gaussian Radial Basis Function [Giuliani et al. 2014] b = Basis radius c = Basis centre w = Network weights 60% explained variance
  19. 19. Input Variable Selection parameter value Reference p.: the best for the environment Lower bound p. : current situation
  20. 20. DPC experimental setting SOCIAL PLANNER POLICIES • POLICY: Gaussian Radial Basis function with 2 inputs (level & time) + 4 output (release outputs) + 4 basis functions: 32 parameters • OPTIMIZATION: Borg MOEA parameterized as in Hadka and Reed [2013] • NFE = 1,000,000 per replica • 30 replications to avoid dependence on randomness INPUT VARIABLE SELECTION • Tree based iterative input selection [Galelli and Castelletti, 2013] CONSTRAINED PRIMARY POLICY • Baseline policy with constraints on 8 policy parameters • Default Borg MOEA parameterization [Hadka and Reed 2013] • NFE = 100,000 per replication • 30 replications to avoid dependence on randomness • Historical horizon 1999 (drought) 1 2 3
  21. 21. DPC policies’ performance
  22. 22. DPC policies’ performance +18.6 x 106 US$/year + 36% +46% - 30% but ..
  23. 23. Conclusions § Direct Policy Conditioning as a coordination mechanism design § Preliminary results seem to be interesting in terms of improved perfomance of current operation in the Susquehanna rb § Weakness in the physical interpretation of the parameters: how to communicate the conditioning to the dam operator? § Sensitivity to the conditioning setting
  24. 24. THANKS
  25. 25. Programmed event supported by the TC EGU General Assembly, Vienna 12 April—17 April 2015 EGU General Assembly The EGU General Assembly 2015 will bring together geoscientists from all over the world into one meet-ing covering all disciplines of the Earth, Planetary and Space Sciences. Especially for young scientists the EGU aims to provide a forum to present their work and discuss their ideas with experts in all fields of geosciences. In the divisions Energy, Resources and the Environ-ment (ERE) and Hydrological Sciences (HS) the fol-lowing sessions are proposed: • Design and Operation of Combined Hydro/Wind/Solar Power Generation Systems: Computer Based Control and Optimization; • Design and Operation of Water Resource Systems: Computer Based Control and Optimization. Motivation Many environmental systems have been modified and are still being modified by human intervention. This intervention usually takes the form of the construction of additions to the system intended to change system behaviour to better serve the needs of society. This implies that these systems and their behaviour are being designed. They are no longer governed by natural processes alone. Therefore models of both the natural and the artificial part of the system will be needed. As the demands placed on water systems by society increase and are increasingly in conflict with each other, it will become harder to define goals for the modification of these systems and their behaviour. It will also become harder to design systems and oper-ating rules to satisfy these goals. The aim of these sessions is to bring together experts in the fields of water management, hydro-, solar-, and wind-power, control theory and operations research to discuss novel methods or novel ways of using tradi-tional methods to define and implement desired beha-viour for environmental systems. Design and Operation of Water Resource Systems: Computer Based Control and Optimization For control theory water systems pose some unique challenges because of the presence of large delays and very limited means of control. In fact for some sys-tems the limits on the size of the change that can be effected in a given time period necessitate the use of forecasts to anticipate on system behaviour. For oper-ations research the special challenge is the presence of incommensurable and conflicting optimization targets, the complex network of relations between stakeholders and the lack of one clear shared motivation amongst stakeholders. Moreover, a new awareness of more vari-ability in the climate on longer time scales and rapid social changes both pose new challenges for the decision making process. This implies a need for more frequent reconsideration of decisions and a shorter time scale for the decision process. This process will therefore need faster models, for instance simplified dynamic models of hydrological systems, statistical process emulators, surrogate models (e.g. linear or nonlinear regression) based on data to feed faster optimization algorithms. Currently the following people and institutions are in-volved in the preparation of this session: • Niels Schütze, Dresden University of Technology, Germany; • Andrea Castelletti, Politecnico di Milano, Italy; • Francesca Pianosi, University of Bristol, United Kingdom; • Renata Romanowicz, Institute of Geophysics, Polish Academy of Sciences, Warszawa; • Ronald van Nooijen and Alla Kolechkina, Delft University of Technology, Netherlands. Design and Operation of Combined Hy-dro/ Wind/Solar Power Generation Sys-tems: Computer Based Control and Op-timization In most locations the yield of wind power or solar power is uncertain. Hydropower seems an attractive means of providing backup power and storage of energy for fu-ture use. Combined schemes seem attractive, but will need automatic control to optimize their yield. Un-certainty about yield and future supply and demand is a key issue for the management of these combined schemes. They may also need special facilities for in-tegration in the current energy distribution infrastruc-ture. Currently the following people and institutions are in-volved in the preparation of this session: • Demetris Koutsoyiannis and Andreas Efstra-tiadis, National Technical University of Athens, Greece; • Andrea Castelletti, Politecnico di Milano, Italy; • Burlando Paolo, ETH Zürich, Zwitzerland; • Patrick Michael Reed, Cornell University, USA; • Alla Kolechkina and Ronald van Nooijen, Delft University of Technology, Netherlands. Key dates • Call for papers for EGU 2015: 15 October 2014 • Deadline for receipt of abstracts: 7 January 2015 • Letter of acceptance to key authors: 23 January 2015 • Conference: 12 April to 17 April 2015 in Vienna, Austria
  26. 26. Programmed event supported by the TC 26th IUGG General Assembly 2015, Earth and Environmental Sciences for Future Generations Prague, Czech Republic June 22 - July 2, 2015 IAHS Workshop Hw07 Announcement At the 26th IUGG General Assembly in Prague in 2015 there will be an IAHS work-shop on Control of Water Resource Systems Hw07. The workshop is being organized under the auspices of the International Commission on Water Resources Systems (ICWRS). Motivation Today it is rare to find a water resource sys-tem where the interaction with society can be ignored. Most systems consist of both nat-ural and manmade components and are gov-erned by both natural processes and processes within society. The interaction between soci-ety and the natural system is complex. An important part of this interaction consists of our attempts as humans to alter the system behaviour through the construction and ma-nipulation of structures such as wells, dams, pumps, weirs, gates, sluices and locks. In a changing world it can no longer be taken for granted that the operational rules for the manipulation of the manmade components of the water resource system will be appropriate over the whole life time of the infrastructure. This workshop is intended for presentations on the formulation and adaptation of operational rules for the automated manipulation of man-made components of water resource systems with changing boundary conditions, or, less formally, for presentations on computer con-trol of water resource systems in a world in flux. Convener team Currently the following people and institu-tions are involved in the preparation of this session. • Alla Kolechkina, Delft University of Technology, Netherlands • Ronald van Nooijen, Delft University of Technology, Netherlands • Andrea Castelletti, Politecnico di Mil-ano, Italy; 26th General Assembly of the Inter-national Union of Geodesy and Geo-physics (IUGG) A better understanding of the way in which our planet functions and of the effects of our actions on its behaviour is needed to provide for the needs of future generations. This Scientific Assembly to be held in Prague from 22 June to 2 July 2015 will provide an opportunity for scientists from all geophysical disciplines and from all countries to meet and exchange knowledge and ideas. The Assembly also will also give the participants the oppor-tunity to inform the general public and policy makers. Key dates for this workshop • Abstract submission open: September 2014 • Deadline for receipt of workshop ab-stracts: 31 January 2015 • Early bird registration deadline : 10 April 2015 • Standard fee registration deadline : 15 June 2015 • Conference: 22 June to 2 July in Prague, Czech Republic
  27. 27. TC 8.3 meeting – Wed 27 12:00 Dassen Room (Westin)

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