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Samira Fazlollahi 1,2 , Gwenaelle Becker 2 , Michel Guichard 2 , Francois Marechal 1 1 Industrial Energy Systems Laboratory, Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne, Switzerland (EPFL) 2 Veolia Environnement Recherche et Innovation (VERI), 291 avenue Dreyfous Ducas, 78520 Limay, France 1. Motivation & Objectives Develop a systematic procedure for design and operation optimization of district energy systems Developed model includes: • Energy integration • Multi-objectives • Process design • Daily thermal storage • GIS base network design 2. Methodology 1 The basic concept of the developed model is the decomposition of the problem into several parts (Figure 1). Three major steps are; I. Structuring phase for collecting and manipulating data II. Multi-objective nonlinear optimization 3 phase for optimizing the design and operation of an energy system. III. Post-Processing phase for detail evaluation of the Pareto frontier. III- Post-Processing phase Available equipment Energy demand Energy sources I-Data Structuring Figure1 . District energy system design framework implemented in Matlab Backup Technology c) Energy integration d) Thermal storage integration 5 Acknowledgments: The authors would like to acknowledge Veolia Environnement Recherche et Innovation (VERI) for the financial support . [3] S. Fazlollahi, P. Mandel, G. Becker, F. Marechal, Methods for multi-objective investment and operating optimization of complex energy systems, Energy 45 (2012) 12 – 22 [1] S. Fazlollahi, F. Marechal, Multi-objective, multi-period optimization of biomass conversion technologies using evolutionary algorithms and mixed integer linear programming (MILP), Applied Thermal Engineering (2011) Volume 50 Issue 2, Pages 1504-1513 [2] S. Fazlollahi, S. L. Bungener, G. Becker, F. Marechal, Multi-Objectives, Multi-Period Optimization of district energy systems: I-Selection of typical days, submitted to Computer Aided Chemical Engineering (2012) Solution B: 80% less Exergy losses after EI [4] S. Fazlollahi, G. Becker, F. Marechal, Multi-Objectives, Multi-Period Optimization of district energy systems: III-Networks design, submitted to ESCAPE23, 2013, Lappeenranta Finland a) Thermo-economic evaluation b) GIS base network design 4 •12% reduction in coal consumptions, •15.8% CO2 emissions reduction [5] S. Fazlollahi, S. L. Bungener, F. Marechal, Multi-Objective, Multi-Period Optimization of Renewable Technologies and storage system Using Evolutionary Algorithms and Mixed Integer Linear Programming (MILP), PSE, 2012, Singapore OP : Operating expenses, OPIN: operating cost including incomes, INV: annual investment cost, Total: Total cost, NetCO 2 : Net CO 2 emissions, GPC : total cost divided by supplied heat OP [10 6 €/an] OPIN [10 6 €/an] INV [10 6 €/an] Total [10 6 €/an] B 68 33 17 51 J 73 39 5 43 NetCO 2 [10 3 kg/MJ] CO 2 [10 3 kg/MJ] GPC [10 3 €/MJ th ] B −39 32.2 8.9 J −6 65 7.5 B: gas boilers, steam turbines, gasifier and biogas engines J: gas boilers, steam turbines and gas engines economical targets environmental impacts Pareto optimal Performances evaluation Final decision III-Post-processing phase Next iteration II-Master non-linear optimization (EMOO) Environomic objectives: OPEX, CAPEX, CO2 emissions Thermo-economic states of selected superstructure Data processing II.3-Slave MILP optimization (EIO) Next iteration Optimization includes: - Energy-integration - mass balance - networks design - thermal storage II.4-EE Environomic evaluation: Economical & environmental Post evaluation Thermo-economic Simulation Master optimization Evolutionary, multi objective algorithm II.1 EMOO II.2-TES Master set of decision variables: Type & maximum size of equipments, CO2 taxes Optimal system configuration and operation Sensitivity analysis Data base Data base Data base Data base 3. Preliminary results Design a district energy system for a city with 550,000 inhabitants 1 ; C) List of equipments 1 b) A list of available energy source 1 Equipment Reference: [MW th / el ] Ranges: [MW th / el ] β s [€/kW/an] α s [k€/an] O&M [€/Mw] Boiler (NG) Boiler (BM) Engine (NG) Engine (SNG) Gasifier Gas turbine Steam turbine ORC 42 th 42 th 5 el 5 el 20 bm 20 el 30 el 2 el [0 210] [0 210] [0 100] [0 50] [0 200] [0 200] [0 200] [0 20] 14 17 25 25 67 73 32 38.5 84 84 15 15 10 3 14 272 96 3.5 10.4 10 12 1 50 10 30 I- Data structuring a) Demand profile: typical days 2 Resources ∆ CO 2 : [kg/MJ] Price: [€/MJ] Electricity Natural Gas Biomass SNG 0.3071 0.0641 0 0 0.0198 0.0092 0.0036 0.0099 BM: Biomass, NG: Natural gas, Decision variables in the multi-objective optimization phase Optimization steps Variables Master Optimization: (EMOO) Thermo-economic simulation models: (ETM) Slave optimizer: (EIO) Type of equipments and their maximum available size. The corresponding thermodynamic states and the investment turnkey cost of equipment Utilization rate and the operation strategy of selected equipment II- Multi-objectives, multi-periods optimization results Respect to three objectives: i. the annual investment cost ii. the operating cost iii. the overall CO 2 emissions Solution B: Network design 1 2 3
