Combined Production Systems PDF

Smart Electricity Systems COMBINED PRODUCTION SYSTEMS Prof. Gianfranco Chicco Dipartimento Energia “Galileo Ferraris” Politecnico di Torino © Copyright Gianfranco Chicc...

Smart Electricity Systems COMBINED PRODUCTION SYSTEMS Prof. Gianfranco Chicco Dipartimento Energia “Galileo Ferraris” Politecnico di Torino © Copyright Gianfranco Chicco, 2019-2024  Combined production Cogeneration (or CHP, Combined Heat and Power) refers to simultaneous production of electricity and heat, obtained by using the same fuel source The effectiveness of cogeneration depends on the possibility of reaching energy savings and environmental benefits with respect to the separate production of electricity (from the electrical grid) and heat (through local boilers) in an economically competitive way Black box model: W (electricity) F (fuel) CHP Q (heat) Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Separate production vs. cogeneration Sankey diagrams separate production cogeneration 134 100 P. Mancarella, PhD thesis Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Combined production The black box contains some key elements:  the cogeneration prime mover  the auxiliary boiler (with possible supply through a different fuel) The outputs from the CHP system interact with the loads, with the electrical grid (in a bidirectional way), and in case with the district heating network, and in certain circumstances part of the heat (not forming the useful energy) can be withdrawn to the external ambient CHP Fy Wy electrical and thermal loads prime mover electrical grid FCHP Qy district heating FAB QAB external ambient auxiliary boiler Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Prime movers (commercial applications) Internal Combustion Engines (ICE), supplied with gas or diesel  sizes up to some dozens of MWe  excellent performance at partial loads  emissions from diesel units higher than those from gas units Gas turbines  sizes from about 5 MWe  relatively low emissions  efficiency decreasing at partial loads Microturbines  indicative sizes in the range 25-300 kWe  lower emissions with respect to ICE  efficiency decreasing at partial loads (but less than gas turbines) Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Control strategies of the prime movers The control strategies determine the possible prime mover operation at partial loads Some strategies:  Electricity (or heat) load-following: the cogenerator follows the evolution of the electrical (or thermal) load, determining the thermal (or electrical) output through the cogeneration ratio)  Peak shaving: the system is used to cover the peaks (typically of the electrical load; the thermal peaks are covered by the boiler)  Continuous operation: the cogenerator is kept OFF or ON at its nominal capacity (or another scheduled value), on the basis of economic considerations (e.g., OFF when buying electricity is cheap) Further strategies are based on the input energy prices and their variations Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Parameters cogeneration ratio, i.e., produced heat to produced electricity ratio Q  W electrical efficiency, ratio between the electricity output and the fuel thermal content input W W  F thermal efficiency, ratio between the thermal output and the fuel thermal content input Q Q  F Note: the fuel indicated for electrical and thermal efficiency calculations is the total fuel input to the prime mover Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Energy efficiency indicator: Primary Energy Saving (PES) Definition: F SP - F F the combined production PES  1- is convenient if F SP W Q + eSP t SP PES > 0  eSP electrical efficiency of the reference electricity production system  tSP thermal efficiency of the reference boiler Conventional evaluation of the energy saving for a cogenerator producing the same quantities of useful energy (electricity W and heat Q) by using the fuel F, with respect to the separate production (SP) requiring FSP kWht of fuel The reference values are defined by the regulatory bodies High efficiency cogeneration could require PES > 0.1 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Multi-generation Combined generation with multiple energy vectors (or energy carriers) to serve different types of demand (electricity, heat at different enthalpy levels, cooling, desiccant effect, desalination, …) Convenient with respect to separate production with equal demands Example with trigeneration  Combined generation of three energy vectors, e.g., electricity W, heat Q and cooling R  Typical acronyms are CCHP (Combined Cooling Heat and Power) or CHCP (Combined Heat Cooling and Power) Black-box model for trigeneration: W (electricity) F (fuel) CCHP Q (heat) R (cooling) G. Chicco, P. Mancarella, Distributed Multi-Generation: a Comprehensive View, Renewable and Sustainable Energy Reviews 13 (3) 2009, 535-551 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Trigeneration Combined generation of electricity, heat and cooling Convenient with respect to separate production with equal demands Sankey diagrams separate production trigeneration 60 (losses) 16 24 traditional (cooling) (losses) 100 system (fuel) 100 (fuel) 40 40 (electricity) 136 3 (electricity) (losses) 20 23 20 boiler (heat) 100 (fuel) (heat) 13 16 electric (fuel) (cooling) chiller P. Mancarella, PhD thesis Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Trigeneration One of the main aspects is the cooling generation mode:  Separate (or parallel): cooling unit not supplied by electricity or heat  Bottoming (or cascaded): cooling unit supplied by electricity or heat produced inside the trigeneration plant (with possible additional input from and outside source) Q electrical bottoming F R W F cooling side cogeneration side W cooling side R Q Q thermal F bottoming cogeneration side W F W cogeneration side Q cooling side R separate bottoming P. Mancarella, PhD thesis Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Trigeneration Primary Energy Saving Acronym TPES Extension of the PES used for cogeneration A reference value for cooling in separate production is needed:  defined by taking into account the COP SP of a reference electric chiller Since the electric chiller is fed by electricity, the reference value is obtained by considering the chain cSP  eSPCOPSP equivalent fuel electric chiller useful cooling input F input W output R  eSP COPSP equivalent fuel useful cooling input F output R  eSP COPSP G.Chicco and P.Mancarella, Trigeneration Primary Energy Saving Evaluation for Energy Planning and Policy Development, Energy Policy, Vol.35, No.12, 2007, pp. 6132–6144 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Trigeneration Primary Energy Saving Definition: F SP - F F TPES   1- F SP W Q R + + e SP t SP cSP  F overall fuel thermal energy input (including the fuel feeding an auxiliary boiler or a separate cooling machine)  FSP total fuel thermal energy input needed for the separate production of the same energy vectors W, Q and R in the trigeneration system  W net electricity output (including the electricity sold to the grid, excluding the electricity used to supply units inside the trigeneration system)  Q net heat output (excluding the thermal energy used to supply units inside the trigeneration system)  R net cooling energy output (excluding the energy used for cooling purposes inside the system) G.Chicco and P.Mancarella, Trigeneration Primary Energy Saving Evaluation for Energy Planning and Policy Development, Energy Policy, Vol.35, No.12, 2007, pp. 6132–6144 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Polygeneration systems fuel / GDS F EDS AGP – Additional Generation Block R DCN CHP – Combined Heat and Power DH DCN – District Cooling Network RES DH – District Heating storage / HDS EDS – Electricity Distribution System Q R Q Q W R GDS – Gas Distribution System AGP H R block Q HDS – Hydrogen Distribution Network local Q R H W Q Q user MG – Multi-Generation W W CHP H H block Q Q W – electricity W W Q – heat Q storage / HDS R – cooling RES H – hydrogen DH DCN F – Fuel W EDS F fuel / GDS P. Mancarella, G. Chicco, Distributed Multi-Generation Systems: Energy Models and Analyses (book), Nova Science Publisher, 2009 Smart Electricity Systems © Copyright Gianfranco Chicco, 2018-2024  Polygeneration Primary Energy Saving Definition: F SP - F p Fp PPES  SP  1- p F X    X , x D  xSP  The concepts seen for CHP and CCHP can be extended to the general case with a similar formulation  The superscript p indicates polygeneration  The entry X is the actual energy output of a generic energy vector  The set D contains the useful output (demand) of various types of energy from the poly-generation system  The key aspects are:  The possibility of identifying the useful energy output for each energy vector  An appropriate definition of the separate production efficiency for each energy vector G.Chicco and P.Mancarella, A unified model for energy and environmental performance assessment of natural gas-fueled poly-generation systems, Energy Conversion and Management, Vol. 49, No.8, August 2008, 2069-2077 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Energy Hubs Framework developed within the project “ Vision of Future Energy Networks” (ETH Zürich and partners), started in 2002 and now completed Focus set on the long-term evolution of the energy systems (time horizon of 30-50 years) Energy system structures revisited without considering the limitations provided by the actual constraints Multiple energy carriers are converted, conditioned and stored in centralised energy hubs Specific consideration of multiple energy carriers (other than electricity) in the form of energy interconnectors, enabling integrated transportation of different forms of energy (electrical, chemical, thermal) in one device M. Geidl, G. Koeppel, P. Favre-Perrod, B. Klöckl, G. Andersson, K. Fröhlich, Energy Hubs for the Future, IEEE Power and Energy Magazine, 2007, 5 (1): 25-30 Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  The Energy Hub model Matrix model containing topological and technical data T Input array: vi  éë Fi ,Wi , Qi , Ri ùû T Output array: vo  éë Fo ,Wo , Qo , Ro ùû For a given plant unit Y, the matrix model is: vYo  H Y × vYi æ FoY ö æ YFF YFW YFQ YFR ö æ Fi Y ö ç ÷ ç WQ ÷ ç ÷ ç WoY ÷ç WF Y WW Y Y WR Y ÷×ç Wi Y ÷ ÷ ç QF QW QQ QR ÷ ç Y Y Y ç QoY Y QiY ÷ ç ÷ ç ÷ ç ÷ è RoY ø è YRF YRW YRQ YRR ø è RiY ø Example for a CHP unit with electrical and thermal efficiencies: æ CHP ö æ ö æ ö ç Fi ÷ ç CHP0 ÷ ç 0 0 0 0 ÷ ç ÷ ç WoCHP ÷  ç W 0 0 0 ÷×ç 0 ÷ vCHP  H CHP × vCHP ç Qo ÷ ç Q 0 0 0 ÷ ç 0 ÷ o i ç ÷ ç ÷ è 0 ø è 0 0 0 0 ø ç ÷ è 0 ø M. Geidl, G. Koeppel, P. Favre-Perrod, B. Klöckl, G. Andersson, K. Fröhlich, Energy Hubs for the Future, IEEE Power and Energy Magazine, 2007, 5 (1): 25-30 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  The Energy Hub model - Examples Auxiliary Boiler (AB): æ ö æ F AB ö ç 0 ÷ æ 0 ö ç i ÷ ç 0 ÷ ç ÷ ç 0 0 0 ÷ ç AB ÷  ç 0 0 0 0 ÷×ç 0 ÷ voAB  H AB × viAB çQo ÷ çç t 0 0 0 ÷÷ ç 0 ÷ ç ÷ è 0 0 0 0 ø ç ÷ è 0 ø è 0 ø Absorption chiller (Water Absorption Refrigerator Group - WARG) æ ö æ ö ç 0 ÷ æ ö ç 0 ÷ ç 0 ÷ ç ÷ ç 0 0 0 0 ÷ ç ÷ç 0 0 0 0 ÷×ç 0 ÷ vWARG o  H WARG × vWARG i ç 0 ÷ çç 0 0 0 0 ÷÷ çQiWARG ÷ ç RWARG ÷ è 0 0 COPWARG 0 ø ç ÷ è o ø è 0 ø Electric chiller (Compression Electric Refrigerator Group - CERG) æ ö æ ö ç 0 ÷ æ ö ç 0 ÷ ç 0 ÷ ç ÷ çW CERG ÷ 0 0 0 0 ç ÷ç 0 0 0 0 ÷×ç i ÷ vCERG  H CERG × vCERG ç 0 ÷ çç 0 0 0 0 ÷÷ ç 0 ÷ o i ç RCERG ÷ è 0 COPCERG 0 0 ø ç ÷ è o ø è 0 ø G.Chicco and P.Mancarella, Matrix modelling of small-scale trigeneration systems and application to operational optimization, Energy, Vol. 34, No. 3, March 2009, pp. 261–273 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  The Energy Hub model Overall system: matrix representation vo  H × vi The matrix entries can be determined by visual inspection Example for a subsystem with CHP and WARG (with no auxiliary boiler nor grid connection) T é Input array vi  ë Fi ,0 ,0 ,0ûù T Output array vo  éë0,Wd , Qd ,Rd ùû The topology is represented by the dispatch factors aQQ CHP + a RQ CHP + a aQ CHP 1 Qa is the heat wasted to the ambient (not a useful output), obtained a posteriori after calculating all energy flows for a given control strategy Energy balance: Wd  W Fi é 0 0 0 0 ù ê W 0 0 0 ú Qd  Q a QQ Fi CHP H ê Q aQQCHP 0 0 0 ú ê ú R   a COP CHP WARG F ê ë Q a CHP RQ COP WARG 0 0 0 úû d Q RQ i G.Chicco and P.Mancarella, Matrix modelling of small-scale trigeneration systems and application to operational optimization, Energy, Vol. 34, No. 3, March 2009, pp. 261–273 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  The Energy Hub model Larger example with Electricity Distribution System (EDS) and Fuel Distribution System (FDS) EHP: electric heat pump AB: auxiliary boiler Inputs: Wi, Fi AC: absorption chiller Outputs: Wo, Qo, Ro EDS EHP ( , , ) I nput CHP ( , ) FDS (waste heat) ! " #!" # Output AB ( , , ) G.Chicco and P.Mancarella, Matrix modelling of small-scale trigeneration systems and application to operational optimization, Energy, Vol. 34, No. 3, March 2009, pp. 261–273 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  The Energy Hub model Plant coupling matrix H for the larger example (from visual inspection): vo  H × vi Assumption: the EHP operates in cooling mode, with Qe = 0 (split at the FDS output) (split at the EDS output) with: (split at the CHP output) (split at the AB output) G.Chicco and P.Mancarella, Matrix modelling of small-scale trigeneration systems and application to operational optimization, Energy, Vol. 34, No. 3, March 2009, pp. 261–273 Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  The Energy Hub model EDS EHP ( , , ) I nput CHP Inputs: Outputs: ( , ) Wi, Fi W o , Qo, R o FDS (waste heat) ! " #!" # Output AB ( , , ) with G.Chicco and P.Mancarella, Matrix modelling of small-scale trigeneration systems and application to operational optimization, Energy, Vol. 34, No. 3, March 2009, pp. 261–273 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Dependency between energy vectors Consider the dependency of the different energy vectors to provide the same service, e.g., electricity and gas as substitutable resources CBDR: Carrier-based Demand Response Nilufar Neyestani, Maziar Yazdani-Damavandi, Miadreza Shafie-khah, Gianfranco Chicco, João P.S. Catalão, Stochastic Modeling of Multienergy Carriers Dependencies in Smart Local Networks with Distributed Energy Resources, IEEE Trans. on Smart Grid, vol. 6, no. 4, July 2015, pp. 1748-1762 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Impact of the combined production on smart grids Multi-generation systems are used to cover the simultaneous demand of different energy vectors Coordinated operation of the units in the multi-generation system is needed to serve the demand variable during time with possible benefits on energy efficiency, environmental impact and reduction of the energy costs, with possible optimisation objectives:  minimum operation cost, taking into account the gas price r gGDSand the prices of electricity sold to the grid roEDSand bought from the grid riEDS {  min r gGDSFi + riEDSWi - roEDS Wo -Wd } with Wi × Wo -Wd   0  maximum TPES input demand output Efficient monitoring and control in real time is needed to make the management of the systems more effective Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024  Electricity shifting potential Reduce (or increase) the electricity exchanged with the grid (EDS) on the basis of proper incentives (e.g., to provide reserve services), by covering the shifted demand from fuel-based sources The multi-generation system can modify the electricity exchanged with the grid without modifying the user’s energy demand (i.e., with no impact on the user’s comfort) DR: Demand Response 8 electricity DR incentive The effectiveness of applying energy costs variation shifting potential (mu/kWhel) 7 0.14 electricity shifting is determined DR benefits costs and benefits (mu) 6 0.12 through cost-benefit analysis, by 0.10 considering the variation of the 5 energy costs with respect to the 4 0.08 incentives 3 0.06 The electricity shifting potential 2 non-profitabl e 0.04 is the maximum electricity 1 region 0.02 reduction available from the local 0 0 10 20 30 40 50 multi-generation system reduced electricity input from EDS (kWh el) P. Mancarella and G. Chicco, Real-time demand response from energy shifting in distributed multi-generation, IEEE Trans. on Smart Grid, Special Section on Real-Time Demand Response, vol. 4, no. 4, December 2013, pp. 1928–1938 Smart Electricity Systems © Copyright Gianfranco Chicco, 2017-2024 Multi-energy System with Electric Heat Pump Multi-energy system example with Electric Heat Pump (EHP) that can operate in heating and cooling modes, with the corresponding coefficients of performance (COPs) EDS: electrical distribution system EHP: electric heat pump FDS: fuel distribution system AB: auxiliary boiler CHP: combined heat and power AC: absorption chiller Dispatch factors included in the vector α P. Mancarella, G. Chicco and T. Capuder, Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services, Energy, Volume 161, October 2018, pp. 381-395. Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  Multi-energy System Optimisation Multi-generation system example with Electric Heat Pump (EHP) that can operate in heating and cooling modes, with the corresponding COPs Objective function (operational costs, in monetary units mu): f{ min r FDS Fi + r i EDS max{W i ,0} + r EDS o min{Wi ,0} }  Decision variables: x  Fi ,Wi , α T ,Wy , Qy , Qt , Re , Rw T AC gas EHP constant CHP gas price input boiler EDS dispatch factors multi-energy demand electricity price P. Mancarella and G.Chicco, Integrated energy and ancillary services provision in multi-energy systems, Proceedings of the IREP 2013, Rethymnon, Crete, Greece, 25-30 August 2013 Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  Initial Solution The EHP operates in cooling mode The multi-energy load does not change (comfort is not affected)  Fuel (natural gas) price 40 mu/MWh (mu: monetary unit)  Electricity prices: 50 mu/MWh for buying electricity from the EDS, 25 mu/MWh for selling electricity to the EDS first candidate to the reduction: EHP input from EDS P. Mancarella, G. Chicco and T. Capuder, Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services, Energy, Volume 161, October 2018, pp. 381-395. Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  EHP Input Reduction (from EDS) Part of the cooling load from the EHP output goes to the AC output The cooling load shifted away from the EHP output is supplied by the AC, which in turn is supplied by additional heat provided by the CHP CHP operation at maximum output changed Electricity input reduction 61.9 kWhel P. Mancarella, G. Chicco and T. Capuder, Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services, Energy, Volume 161, October 2018, pp. 381-395. Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  EDS Input Reduction The EDS input is reduced to zero The EHP cooling output is reduced and is compensated by the AC output The total electricity reduction is 68.4 kWhel changed P. Mancarella, G. Chicco and T. Capuder, Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services, Energy, Volume 161, October 2018, pp. 381-395. Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  Is it Possible to do More? Yes… by shifting all the EHP cooling output to the AC cooling output The 25 kWhel are available to be sent to supply the electrical demand, but since the electrical demand is already covered the excess is sent back to the EDS changed The total electricity reduction is 93.4 kWhel P. Mancarella, G. Chicco and T. Capuder, Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services, Energy, Volume 161, October 2018, pp. 381-395. Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  Energy Flows and Costs The total reduction (93.4 kWhel) is called electricity shifting potential (ESP) The curves are not exactly linear because of non-constant partial load efficiency changes in the ESP ESP energy flows extra costs P. Mancarella and G. Chicco, Real-time demand response from energy shifting in distributed multi-generation, IEEE Trans. on Smart Grid, Special Section on Real-Time Demand Response, vol. 4, no. 4, December 2013, pp. 1928–1938 Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  Availability and Exercise Fees Availability fee: expressed in mu/MW, given to remunerate the availability of the provider even though the service is not called to operate Exercise fee: expressed in mu/MWh, refers to the energy delivered to the EDS (or energy reduction from the EDS) if the service is called Three cases with the same costs determined before and constant availability fee (0.001 mu/kW/halfhour) P. Mancarella, G. Chicco and T. Capuder, Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services, Energy, Volume 161, October 2018, pp. 381-395. Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  Maximum Profit Electricity Reduction (MPER) input electricity reduction Benefit function at time interval h availability number of time intervals in 1 h fee exercise service called fee (=1) or not (=0) Profit function (benefits minus extra operation costs) at time interval h extra costs if the service is called MPER quantifies the electricity input reduction that corresponds to the maximum profit P. Mancarella, G. Chicco and T. Capuder, Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services, Energy, Volume 161, October 2018, pp. 381-395. Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  Profitability Map Constant availability fee (0.001 mu/kW/halfhour) The maximum profit MPER (filled points) changes with the exercise fee P. Mancarella, G. Chicco and T. Capuder, Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services, Energy, Volume 161, October 2018, pp. 381-395. Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  MPER Parametric Analysis For variable availability fees The maximum profit in general is not at the electricity shifting potential maximum input electricity reduction profit at maximum profit P. Mancarella, G. Chicco and T. Capuder, Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services, Energy, Volume 161, October 2018, pp. 381-395. Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  Break-even Conditions Availability fee (0.001 mu/kW/halfhour) The break-even condition (boundary of the profitable region) changes with the exercise fee P. Mancarella, G. Chicco and T. Capuder, Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services, Energy, Volume 161, October 2018, pp. 381-395. Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024  Break-even Conditions - Parametric Analysis Availability fee (0.001 mu/kW/halfhour) The non-profitable region is reduced by increasing the availability fee P. Mancarella, G. Chicco and T. Capuder, Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services, Energy, Volume 161, October 2018, pp. 381-395. Smart Electricity Systems © Copyright Gianfranco Chicco, 2019-2024