Optimization of the north Europe H2 distribution pipeline

Transcription

1 Optmzaton of the north Europe H2 dstrbuton ppelne Florence Boutemy a, Benjamn Grandgeorge b, Sona Garca Del Cerro c, Stéphane Houyou b, Bors Pachany d (a) AIR LIUIDE R&D, 1 chemn de la Porte des Loges, Jouy-en-Josas, FRANCE florence.boutemy@arlqude.com (b) (d) (c) AIR LIUIDE Large Industres, 280 bd du Souveran, 1160 Bruxelles, BELGIUM benjamn.grandgeorge@arlqude.com, stephane.houyou@arlqude.com AIR LIUIDE Ibérca de Gases, c/ San Norberto, 23, Madrd, SPAIN sona.garcadelcerro@arlqude.com AIR LIUIDE Engneerng, 4 rue des Fusllés, Vtry-sur-Sene, FRANCE bors.pachany@arlqude.com ABSTRACT: A method for cost optmzaton has been developed on AIR LIUIDE north Europe H2/ network. The method provdes a daly plannng of producton for all plants and compressors of the network gven customers demands. The method mnmzes producton varable cost on a quarter horzon of tme and ncludes the optmzaton of quantty-dependent prce structures of raw materals. Sgnfcant savngs are expected. If the case of H2/ rato changes due to H2 energy demand, the method adjusts the plants producton and mnmzes the cost of H2. KEYWORDS: optmzaton, varable cost, plannng, network, operatonal research 1. Introducton The paper provdes a method of plannng producton and optmzng costs across a number of H2/ producton plants sharng a common dstrbuton network. The paper llustrates the method on the AIR LIUIDE north Europe H2/ network. It wll present BINGO an AIR LIUIDE dedcated decson support tool. BINGO schedules the producton load whle mnmzng H2 & overall producton varable cost. Potental savngs wll be assessed. 2. Problem defnton Context Hydrogen supplers often produce or purchase H2 from a number of dfferent hydrogen sources for nstance large steam methane reformng (SMR), auto-thermal reformer (ATR) or recovery from H2 rch stream from varous ndustres. A common system of gas transmsson s the ppelne that connects multple producton plants to multple customers. Producton plannng s mportant to provde H2, and SYNGAS (a mx of H2 and ) to customers wth flexblty whle mantanng proftablty. H2/ demand s matched wth multple producton plants based on expected customers demands, plant avalabltes, effcences and varable costs. 1/11

2 The extenson of H2/ networks to a couple of mult-products plants combned wth the lberalzaton of energy prces makes an advanced tool essental to substtute manual coordnaton. The advanced tool manages manly natural gas (NG) and H2 purchase contracts. NG or mpure H2 contracts typcally nclude take-or-pay arrangements, varable prcng based on usage rates and spot market. Descrpton of AIR LIUIDE north Europe H2/ network AIR LIUIDE H2/ network s located n France, Belgum and the Netherlands. It conssts of 16 unts wth unque producton confguraton, connected to 900 km ppelne networks, supplyng H2, or SYNGAS (a mx of H2 and ) to ol & gas, chemcal and electrcal customers. The 16 unts of the H2/ network are of followng types: Type Man Feed Product ATR Natural gas H2,,SY1.1 and SY1.3 SMR Natural gas H2 and TCR Natural gas H2 Recovery Impure H2 H2 Vaporzer Lqud H2 H2 gas H2 supply* H2 H2 Fgure 1 Lst of the dfferent means of producton unts located along AIR LIUIDE HY network *Some H2 supply are not operated by AIR LIUIDE. Some compressors ncrease the H2 pressure before njecton to H2 networks. H2 networks are connected wth compressors and dspatchng valve. HY Benelux Network may be dvded nto 4 ndependent networks. - network 1; - network 2; - SY network; - H2 network (dvded nto 3 connected networks of dfferent pressure : H2 network HP, H2 network MP, H2 network LP) Some local customers are fed locally. Mult-products sources (ATR, SMR) lnk the producton of H2//SY snce and SY cannot be produced separately from H2. Rozenburg Rotterdam Dordrecht Bergen-op-Zoom NETHERLANDS Terneuzen Antwerpen Lllo Dunkerque Gent Brussels BELGIUM Genk Geleen FRANCE Llle Feluy Jemeppe Wazers Maubeuge PIPELINE: Hydrogen Carbon monoxde Hydrogen plant Hydrogen and carbon monoxde plant Fgure 2 Map of AIR LIUIDE H2/ Benelux network, /11

3 Structure of varable producton costs Plants varable producton costs nclude the costs of feed, utltes, 2 penaltes and revenues from byproducts (steam). Lnear or pecewse lnear functons relate H2//SY producton to quanttes consumed (feed, utltes) and produced by (2 and steam). Most of the tme, the unt-cost s fx or set-up for a day. Yet, n some cases the unt-cost of natural gas or H2 supply depends on prevous consumpton. For nstance, some natural gas contracts nclude a Take-or-Pay (TOP) clause whch means that a mnmum quantty of natural gas s pad to the suppler over a contractual perod whatever the consumpton s. Some rebates or penaltes may apply f the total quantty consumed over a contractual perod s out contractual bounds. Elements of decsons nvolved n the producton plannng All elements nvolved n network producton plannng are summarzed n the Fgure 3 below: Inputs - Producton: unts and compressors avalablty, unts capactes - Customer demand - Data: ndexes and prces, past consumpton Degrees of freedom Producton and Cost Models - Materal balance per source - Slot contracts model for H2 and NG supply - Calculaton of varable cost of producton - Level of producton H2/ flowrate per source - Network compressors & some unts on/off PRODUCTION PLANNING Outputs - Network balances - Connectons - Local demands Network - Physcal Constrants and Contractual lmts Fgure 3 Elements nvolved n producton plannng - Plannng of producton - On/Off for some unts and network compressors - Varable cost of producton per unt & compressors - Margnal cost of Network Constrants 3. Descrpton of the method Prncple The problem of producton plannng can be wrtten as an optmzaton problem: Mnmze the total varable producton cost whle satsfyng network constrants Total varable producton cost s the varable producton cost of H2 and over a perod of tme. Network constrants ensure that customers demand s satsfed wth the exstng confguraton and avalablty of unts compressors. Scope of the method The goal of the method s to dspatch the producton among sources n an economcal way. The level of plannng s called Tactc plannng. Strategc plannng that deals from network desgn to plannng of mantenance s excluded. Real-tme plannng that deals wth real-tme adjustments between plannng and 3/11

4 actual s also excluded. In the case of AIR LIUIDE H2/ network, Real-tme plannng and Strategc plannng are prepared separately. Pressure s not a man ssue when preparng Tactc plannng. Varable cost mnmzaton s the key ssue. The method nether ncludes pressure smulaton nor use pressure as a degree of freedom. Producton equals consumpton. Cost optmzaton based on natural gas spot purchase s also excluded from the scope. Long term strategy versus short term strategy One bottleneck to address s to generate an optmal producton plannng for current week although fnal prces of some feeds (NG, H2) or by-products (2) are def ned n a later tme. A later tme s the end of a contractual perod whch can range from one quarter to 2 years. Indeed some rebates or penaltes apply f the total quantty consumed over a contractual perod s out of contractual bounds. Therefore, mnmzng the cost of producton every day does not mean that the yearly cost of producton s mnmum. The cost structure of natural gas contract s llustrated below: Total Cost Total Cost = f(v) V1=TOP V2 V3 Fgure 4 Example of supply contract cost structure V= Total volume purchased on a perod A frst soluton to address the ssue would be to plan based on all the nformaton avalable untl the end contractual perods. Ths soluton would requre much tme for the user to capture the data, and to compute the results. The computaton tme would not be acceptable. An heurstc method has been desgned based on a basc strategy theory. A quarter (3 months) s a representatve perod of tme for ths problem, snce t s the shortest contractual perod. We assume that mnmzng the producton varable cost over a quarter s equvalent to mnmzng the producton varable cost over a longer perod, provded the contractual volumes (V1, V2, V3) defned for a perod longer than a quarter are manually allocated and adjusted We have defned two steps of optmzaton: 1. Plan on a quarter horzon to make tactc decsons wth a weekly granularty 2. Plan on a weekly horzon, gven tactc decson wth a daly granularty As t s detaled below, the method ncludes a predctve module that makes tactc decsons and allocates volume over a quarter. The predctve module makes proft of the full use of slots contracts and selects the cheapest combnaton of all slots of all contracts. In most cases, the cumulated volume consumed s beyond take-or-pay for all contracts. The predctve module transfers some recommendatons to the operatonal modules. Recommendatons consst n enforcng the consumpton of a mnmum volume of raw materals for some contracts. The operatonal module can dspatch the volume freely over the days of the week n order to mnmze the producton varable costs. The end-user may actvate/desactvate the recommendatons and changes the recommended value. 4/11

5 Producton Mode Total cost Past H2/ NG purchases week Expecteddemand ExpectedAvalabltes Expected Indexes/Prce week..week +1 Slot Contract data quarter volume TACTIC DECISIONS BINGO uarterly Plannng 12 tme-step AllocatedNG/H2 Volume and prce For the current week and slot contracts uarter volume Opton to free allocatedvalue week Expected demand Expectedavalabltes Indexes/Prce (otherthan slots ) Saturday.. Frday OPERATIONNAL DECISIONS BINGO Weekly Plannng 7 tme-step Producton Plann Global varable C week Optmzaton algorthm selecton Fgure 5 Calculaton steps for Producton Plannng The optmzaton problem ncludes non-lnear phenomena such as compressor energy consumpton or H2/ producton process. It also requres nteger varables to make on/off decsons for compressors and some unts and to model some feeds contracts. The nature of the optmzaton problem descrbed s a constraned mxed-nteger non lnear optmzaton. Non-lnear functons have been approxmated as lnear functons n order to rely on an ndustral commercal algorthm. Ths choce has been comforted by AIR LIUIDIE state-of-art of H2 process modellng and that must-do requrement on compressors on/off choces. Fnally, the optmzaton algorthm selected has the followng features: Varables Both nteger and real Constrants Lnear Objectve Lnear Nature Mxed nteger optmzaton under constrants Chosen algorthm Xpress-MP wth module for mxed-nteger resoluton and ODBC module for nteracton wth Excel-based nterface Number of varables 4050 Number of 8733 constrants Fgure 6 Features of the optmzaton algorthm 5/11

6 Objectve functon of the optmzaton problem The objectve functon s the mnmzaton of the decson crtera. In Producton Plannng Problem, t s the sum of all the costs encountered when producng H2, and SY. Global Varable Cost = sources ProductonCost + compressor s j Compresson Cost j Eq 1 Wth a generc cost functon Producton Cost 2, = Total Cost Eq 2 NGfuel, Ms, Elec, NGprocess, H 2Purchased, Steam, In optmzaton termnology, varable (n captal letters) are the results of the optmzaton, constrants are any relaton that lnks varable and parameters are constant of the problem. Constrants of the optmzaton problem 1. Network balances At each tme tme-step, and on all 4 networks, the global producton meets H2//SY demands snce the network has almost no storage capacty. For nstance on H2 network, for all tme-steps t, are flow rate. s a varable sources H 2, = H 2demand j customersj Eq 3 2. Sub-network balancess Two sub-networks are connected wth a couple of compressors that compress the flow to a hgher pressure and a dspatch that expand to a lower pressure. Source A MP network: 75 bar Expanson valve LP network: 20 bar MP HP/MP HP Source B HP network: 100 bar Fgure 7 Connecton between H2 networks The connecton has been modelled wth Boolean varables. 6/11

7 3. Plants and compressors constrants Every connecton between a plant, a compressor and the network are detaled. They are based on flow conservaton. For nstance: Local customer A low pressure TCR 1 PSA C1 Network C2 TCR 2 Ths unt connecton s descrbed as follows: Fgure 8 Connecton between a plant and network H2_ prod_ TCR1 H2_ demand_ locala H 2_ flare_ TCR1 = + H 2_ plant_ locala H2 _ prod_ TCR 2 + Network plantlocal A H 2_ flare_ TCR 2 = compc1 + compc2 + H 2_ plant_ locala Eq 4 Models of producton sources The producton sources processes have been modelled lnearly wth less than 20 equatons. Some producton unts are break-down nto several peces. For each tme-step, by-products and raw materals consumed are deducted from the fnal product. The set of equatons below s gven as an example. NGprocess NGfuel NG dmw elec = r = steamcoldbox = r = r = r dmw elec1 NGfuel = r NGprocess NGfuel + steam + r + r NGprocess NG elec2 + r NGfuel H2 + b H2 NGprocess H 2 steam H 2 H 2 + b + c B + b NGfuel StateEqupment NGprocess r and b are parameters (set by the user), (t) are flow rates, B StateEqupment (t) s a boolean varable that ndcates the state on/off of an equpment of the plant. (t) and B StateEqupment (t) are varable: Models of compressors Compressors have been modeled as pecewse lnear functon Power Consumpton kw Eq 5 State= off State= on 0 q mn Flowrate Nm 3 /h Fgure 9 Compressors consumpton model 7/11

8 P elec (t) = a elec comp (t) + b elec B state (t) Eq 6 q mn B state (t) = comp (t) = q pmn B state (t) Eq 7 P elec s the electrcal consumpton, a elec and b elec are parameters, B state (t) a boolean varable that ndcates the state on/off of the compressors, q mn the mnmum flow of the compressors and comp (t) the flow gong through the compressors. P elec (t), comp (t) and B state (t) are varables. Models of cost a) A unque cost defnton s used for most feeds, utltes and by-products (electrcty, demneralsed water, oxygen, steam ). For example, for all tme-steps t and all producton sources Cost Demneralsed Water (t)= Demneralsed Water(t) t prce Demneralsed Water(t) Eq 8 Cost Electrcty (t)= Electrcty(t) t prce Electrcty(t) Eq 9 Cost Steam (t) = Steam (t) t prce Steam (t) Eq 10 And fnally Total Cost = Cost t) Electrcty alltmestep Electrct y ( Eq 11 t s the length of a tme-step, (t) are flowrates, prce are prce per unt, Cost(t) are the cost or revenue by tme-step, TotalCost s the cumulated cost over the horzon of tme. (t), Cost(t) and TotalCost(t) are varable. Dfferent values for prces can be defned at each tme-step. Prces of revenues for nstance steam are negatve. b) Some feeds (NG or H2) have a more complex cost structure. They have been modelled by slots as detaled n fgure 4. At every run of the optmzaton, the cumulated volume consumed over the horzon tme s computed. For all contracts (ether natural gas or H2) wth a slot structure, V = t Eq 12 t the length of a tme-step NG NG alltmestep Total Cost NG =f (V NG ) Eq 13 f s the lnear functon descrbed n fgure 4. Other constrants of the optmzaton problem Some specfc constrants lnk the producton of dfferent unts. Others (such as hourly mnmum) apply on some natural gas contract. 4. Results AIR LIUIDE developed several prototypes BINGO based on the method below. Benefts BINGO proved to be a valuable support for cost management. It s a good-eye opener and helps to quantfy the mpact of some decsons. A lot of tme s saved not only n plannng generaton but also n decson takng snce the plannng generated by BINGO s reproducble unlke manual plannngs that are hard to rank. It s used as a reportng tool and knowledge captalzaton. Snce the network complexty ncreased 8/11

9 sgnfcantly durng BINGO developments, BINGO s a compulsory tool for varable costs management and optmzng. Beyond those soft savngs, several hard savngs pot entals were dentfed. Chess player strategy A case study was run from January 2006 to March Hand-made plannng strategy has been compared to BINGO recommendatons. Sgnfcant savngs have been demonstrated. Dscrepances between handmade and BINGO have been observed on man plants. As detaled n the Fgure 10 below, dscrepances are rregular from a week to another, even when H2 overall producton s smlar. Ths shows that BINGO takes full advantages of cumulatve effects (slots contracts). It ponts out the fact that no best practse could mprove network producton plannng optmzaton as well as BINGO does. Nm3/h H2 produced per week: BINGO - Hand-made W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 Plant A Plant B Source C Plant D Total H2 demand Fgure 10 Dfferences between BINGO plannng and Hand-Made plannng for H2 producton on man sources. Some swap are performed between H2/ sources. In partcular, between two H2 and plants, BINGO tends to favour the most expensve plant snce the overall combnaton produces more H2. The graph (correspondng to the producton of H2 n Fgure 11 below shows the benefts of such swap. Nm3/h produced per week BINGO - Hand-made W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 Plant A Plant B Fgure 11 Dfferences between BINGO Plannng and Hand-Made plannng for producton Moreover, fgure 11 shows that producng n a more economcal manner on a week mght not be an optmal strategy at the end of the quarter exactly lke a chess player may lose some pawns at the proft of others. 9/11

10 Delta Cost: (BINGO - Hand-made) plannng Tme n weeks Cost Savngs per week Cumulated Savngs Tme n Weeks Fgure 12 Delta Cost of producton between BINGO and hand-made plannng Natural gas / H2 supply cost management Another case study was run from January 2005 to March Followngs results were observed on a source A, whch s a source wth a hgh Take-Or-Pay constrant and hgh prce. Hand-made plannng recommends a constant settng pont for ths source whereas BINGO has found a strategy where the TOP s reached exactly. H2 supply from source A Volume purchase Hand-made BINGO TOP Tme n Week Fgure 13 Contract management Reference vs BINGO Compressors On the same case study run from January 2005 to March 2005, a strong dfference was compressors management. In fact, network coordnators do not nclude compressors cost n producton plannng. BINGO showed that optmzng compressors could save sgnfcant savngs. BINGO adjusts producton sources so that compressors are ether off ether at ther maxmum capacty. On Fgure 13, source A has two compressors operated by AIR LIUIDE. BINGO plannng would recommend producng at two rates of flow rates whch corresponds to 1 or 2 compressors on. 5. Concluson A method has been developed on AIR LIUIDE H2/ network to optmze the plannng of producton. Ths method s based on mxed-lnear programmng. Tests on a prototype have demonstrated the necessty of supportng plannng decson wth an advanced tool that allows optmzng varable costs on a long run. BINGO s n-lne wth AIR LIUIDE sustanable development polcy snce t ncreases the cost-effcency of the network, reduces compresson energy and takes nto account envronmental taxes (on fuel and 2 emsson) nto the producton plannng. 10/11

11 6. Acknowledgement The authors wsh to acknowledge to other people who contrbuted sgnfcantly to the project - Sponsor and end-users of BINGO: W. Lamberts, T. Van de Weghe - Research & Development: R. Majjad, E. Patay 7. Intellectual property Ths nventon s protected by a patent applcaton. 8. References Novel network producton plannng method, US Patent Applcaton, F. Boutemy, B. Pachany, T. Van de Weghe and S. Garca Del Cerro 11/11