Utility Systems Operation: Optimisation -Based Decision Making

Transcription

1 Utility Systes Operation: Optiisation -Based Decision Making Patricia Velasco-Garcia Petar Sabev Varbanov Harvey Arellano-Garcia Günter Wozny To cite this version: Patricia Velasco-Garcia Petar Sabev Varbanov Harvey Arellano-Garcia Günter Wozny. Utility Systes Operation: Optiisation -Based Decision Making. Applied Theral Engineering Elsevier (16) pp < /j.appltheraleng >. <hal > HAL Id: hal Subitted on 19 Feb 2013 HAL is a ulti-disciplinary open access archive for the deposit and disseination of scientific research docuents whether they are published or not. The docuents ay coe fro teaching and research institutions in France or abroad or fro public or private research centers. L archive ouverte pluridisciplinaire HAL est destinée au dépôt et à la diffusion de docuents scientifiques de niveau recherche publiés ou non éanant des établisseents d enseigneent et de recherche français ou étrangers des laboratoires publics ou privés.

2 Accepted Manuscript Title: Utility Systes Operation: Optiisation Based Decision Making Authors: Patricia Velasco-Garcia Petar Sabev Varbanov Harvey Arellano-Garcia Günter Wozny PII: S (11) DOI: /j.appltheraleng Reference: ATE 3593 To appear in: Applied Theral Engineering Received Date: 12 April 2011 Revised Date: 19 May 2011 Accepted Date: 31 May 2011 Please cite this article as: P. Velasco-Garcia P.S. Varbanov H. Arellano-Garcia Günter Wozny. Utility Systes Operation: Optiisation Based Decision Making Applied Theral Engineering (2011) doi: /j.appltheraleng This is a PDF file of an unedited anuscript that has been accepted for publication. As a service to our custoers we are providing this early version of the anuscript. The anuscript will undergo copyediting typesetting and review of the resulting proof before it is published in its final for. Please note that during the production process errors ay be discovered which could affect the content and all legal disclaiers that apply to the journal pertain.

3 Utility Systes Operation: Optiisation Based Decision Making Patricia Velasco-Garcia 1 Petar Sabev Varbanov 2* Harvey Arellano-Garcia 1 Günter Wozny 1 1 Berlin University of Technology Departent of Process Dynaics and Operation Straße des 17. Juni Berlin Gerany 2 Centre for Process Integration and Intensification CPI 2 Research Institute for Cheical and Process Engineering Faculty of Inforation Technology University of Pannonia Egyete utca Veszpré Hungary eail:varbanov@cpi.uni-pannon.hu; phone: Abstract Utility systes provide heat and power to industrial sites. The iportance of operating these systes in an optial way has increased significantly due to the unstable and in the long ter rising prices of fossil fuels as well as the need for reducing the greenhouse gas eissions. This paper presents an analysis of the proble for supporting operator decision-aking under conditions of variable stea deands fro the production processes on an industrial site. An optiisation odel has been developed where besides for running the utility syste also the costs associated with starting up the operating units have been odelled. The illustrative case study shows that accounting for the shutdowns and start-ups of utility operating units can bring significant cost savings. 1 Introduction Cheical plants operate in existing industrial sites where a nuber of production processes are grouped together and are supplied with power and heat by a site utility syste. Cobined Heat and Power (CHP) production also called cogeneration is typical for odern utility systes. Optiisation of energy systes has been researched using Process Integration and Matheatical Prograing ainly fro the viewpoint of process synthesis and scheduling [1 2]. However significantly less attention has been paid to optiising the operation of energy systes and especially accounting for such iportant dynaic effects as start-ups shutdowns and load variations. They are coon in utility systes due to the changes in process deands [3]. Another recent developent has been the integration of renewable energy sources into the energy supply and utility systes in particular also taking the advantage of joining additional energy users to the industry e.g. residential and coercial building 1

4 coplexes resulting in the so-called Locally Integrated Energy Sectors [4 5]. The ost significant challenge before integrating renewables is their varying availability [5]. When variations in the availability of renewable energy and in the agnitude of the user deands takes place the question arises whether it is worth activating or stopping certain devices. Currently utility syste operators still lack optiisation-based tools for aking such decisions. Many works have been published on synthesis and optiisation of utility systes where the odels feature cobinations of continuous (operation paraeters) and discrete variables (units selection switch on/off) leading to ixed-integer prograing (MIP) forulations. One of the first syste-level works in the field is that of Papoulias and Grossann [6]. They presented a MILP approach to utility syste synthesis for fixed site power and heat requireents. Solving MINLP probles usually involves high coputational effort. Thus such odels are unsuitable for real-tie utility anageent probles. However Prokopakis and Maroulis [7] have developed an approach to real-tie optiisation applying an adapted MINLP odel which could be solved spending reasonable tie and coputational effort. Several specification options are available using actual perforance data fro the equipent supplier detailed / approxiate odels or the concept of operation at equal increental cost. The latter is derived fro Lagrangian analysis of the optiisation proble and analytical expressions involving loads and cost coefficients. The ultiperiod operation paradig was introduced into the utility syste odel in [8]. The odel was forulated as a MILP including the operating and start-up/shutdown costs. In [9] a new iproved approach is presented to odel stea and gas turbines using the Willan s lines including turbine part-load perforance. Then optiisation of existing utility systes is carried out using Successive Mixed Integer Linear Prograing (SMILP). In [10] a new top-down procedure for retrofit of existing total sites was introduced. First a top-level analysis of the utility syste is perfored to identify the scope for energy savings. They cannot be always translated to cost savings via reduction of fuel consuption because the latter is also related to the power iport fro the grid via the aount of stea generated 2

5 and expanded through the stea turbines. Different path alternatives for the stea surplus utilisation are evaluated in ters of power generation efficiency. As a final result stea arginal prices for different stea headers are calculated at different rates of potential stea saving. Varbanov et al. [11] developed further the Top-Level Analysis ethodology using successive operational optiisation to evaluate the arginal stea prices. On industrial sites energy deands vary with tie. Several researchers have studied the optiisation of utility systes taking this into account. For instance in [12] Siulated Annealing was used for the synthesis of utility systes with variable deands. In [13] a odel and an optiisation approach were suggested to deal with utility deand variability due to seasonal deands and uncertainties. The uncertainties in energy deand are handled by defining deviation ranges for each operating period. The switch conditions are explicitly odelled using binary variables. However the ain reasons for the switching costs have not been clarified. By odelling turbine efficiency uncertainties different cases are considered and copared and the ean efficiencies odelled as polynoial functions of the stea flows through the turbines. In [3] the optiisation of stea-supply systes under variable energy deands was also studied. The variable trend of the heat-power deand in the tie is discretised in a piecewiseconstant anner by approxiating it with a set of rectangles whose heights correspond to the power/stea deands for the corresponding periods. A ore recent work [14] discusses the scheduling of utility syste operation integrated with the schedule of the reainder of the process operations. This has illustrated the iportance of accounting for the variation in the user energy deands. However what is still issing there is the appropriate siultaneous representation of the costs and tie constraints associated with the start-up and shut-down of key equipent types as boilers stea turbines gas turbines etc. The current paper is an attept to provide odel extensions for such operating units accounting for their start-up costs capable of both sufficient odelling accuracy and also siplicity so that they will be suitable for integration into large-scale optiisation odels. In the following sections the utility syste odel and the used optiisation procedure are 3

6 described. An industrial case study is also provided to illustrate the applicability of the approach. 2 Syste optiisation 2.1 Utility syste odel The ain coponents of a utility syste are boilers and the stea turbines. Stea boilers are odelled with a constant efficiency odel which has been accepted by the site engineers as providing sufficient accuracy for the considered exaple. A ore elaborate boiler odel can be eployed [9] should this be necessary. Stea turbines are odelled using Willan s lines with fixed coefficients. This ensures a good balance between odelling siplicity and precision. The coplete syste odel has been built following the ethodology presented in [9] and is presented in Appendix A. 2.2 General forulation issues The rigorous utility syste odel is non-linear ainly due to the bilinear equations it contains e.g. enthalpy balances (Appendix A). In operating the syste both discrete and continuous decisions are involved. The forer include the operation status of the devices (on/off) while the latter concern the values of variables corresponding to physical quantities such as flowrates teperatures enthalpies etc. Thus in general a MINLP (Mixed Integer Non-Linear Prograing) forulation would be necessary for the optiisation of the syste. Nevertheless MILP (Mixed Integer Linear Prograing) is coonly used for the optiisation of existing utility systes. The advantage of MILP is that it avoids the coputational probles inherent to MINLP solution algoriths. It is possible to apply however only if a linear odel can be obtained. Previous works on utility systes odelling obtain a linear odel by either erely fixing the perforance of the devices [6] or use the non-linear odels directly [15]. In the current work following [9] a siplified odel has been used for optiisation cobined with successive rigorous siulations thus taking advantage of the linear optiisation algoriths. This procedure is called Successive Mixed Integer Linear 4

7 Prograing (SMILP) which allows using a sequence of MILP runs for solving the nonlinear syste without losing precision. The non-linearities are taken into account during the rigorous siulations. For handling the variation of process energy deands the costs associated with starting-up certain operating units are included into those for running the utility syste. Thus decision support to syste operators is provided for optial operation. 2.3 Changes of the operating state A coon situation when running utility systes is that the rates of stea supply to and deand fro the site processes change with tie. This occurs on a regular basis due to process shut-downs and start-ups. The variations are ore frequent on sites including batch processes. As a result of changing the overall stea supply-deands pattern it usually becoes necessary to respond with changes in the operating specifications of the utility syste. For exaple a rise in the process stea deand leads to increased stea generation. The additional stea can be produced by different devices and it is necessary to decide which is the ost advantageous way to operate whether to increase the ass flow rate in a running boiler and/or to switch on other devices and if required - which of the. Furtherore increased stea generation ay also be an opportunity to produce additional power by expanding the stea through stea turbines. On the other hand a decrease of the stea requireents eans an excess of stea in the utility syste. It ay be ost cost-effective either to save fuel by switching off soe stea generator units or to generate additional power by increasing the stea ass flow rate through active stea turbines. Another option ight be to switch on a different stea turbine. The operating efficiency of a utility syste under varying stea deands is deterined by a nuber of factors aong which the following are ost significant: The direction of the change i.e. increase or decrease and the agnitudes of the current and the new deands. Reduced deands usually result in stea surplus which can be either utilised or saved. 5

8 The utility level e.g. at the HP MP or LP stea headers. Changing deands for lower-pressure stea allows ore power cogeneration than changing those for higherpressure. The expected duration of the new deands. The effect of this factor is especially interesting for deand increases since sustained higher deands usually justify activating ore devices (e.g. boilers and stea turbines) than in the case of shorter or transient deand increases. The changes in the operating states for utility syste coponents can be of two types. They can be either started up (activated) or shut down (deactivated). Norally starting up a unit involves the ore significant costs steing fro expenses for extra fuel consuption. In coparison shutting down an operating unit involves uch less costs ainly bound to personnel which are uch less significant and can be neglected for sipler cases. To evaluate the effect of changing the operating state and take into account the costs of possible operating unit start-ups a binary variable y CHANGE is defined for each operating unit. Its value is related to two other binary entities defined for each operating unit. The paraeter y CURRENT refers to the operation state for the current stea deand while the variable y NEW designates the operation state for the new deand. The binary variable y CHANGE is used for estiating the start-up cost incurred when the device changes its operation state fro idle to working. The other transition is not of interest since the ajor costs are when starting a unit and not when shutting it down. Therefore y CHANGE assues the value of 1 only if the unit features a state transition fro idle to active. The logic of setting the value of y CHANGE is illustrated in Table 1. Using the defined binary entities it can be judged if it is worth switching a device on or whether there ight be a better operation alternative. Equations (1) to (3) allow ipleenting this logic in the odel. y y = 0 (1) CURRENT CHANGE y y 0 (2) CHANGE NEW y y y 0 (3) CHANGE + CURRENT NEW 6

9 2.4 Estiation of the device start-up costs Variations in process deands involve start-ups shut-downs and interediate load variations. In this section the developed fraework for estiating the costs associated with starting-up equipent ites is described. For clarity siple ethods for estiating the start-up costs of the operating units are used. More elaborate ethods can be eployed under the sae fraework should the specific case require it. Starting-up a unit requires soe tie to heat up the device and to ake it operational. In ost cases soe fuel or stea is consued and this involves a certain extra cost in addition to that for regular continuous operation. This observation is the basis for the proposed start-up cost estiation procedure. In order to calculate the start-up cost for a stea boiler the fuel necessary to reach stea generation conditions for the water contained in the boiler dru could be used to obtain an estiate. It can be eventually suppleented by the heat capacity of the various other parts of the boiler including the casing. Thus for a given start-up duration the boiler start-up cost is deterined by the following equation: STupC BOILER = Price t (4) STup FUEL FUEL The introduced start-up duration paraeter t STup is different fro the new process stea deands duration. It reflects the duration of the tie period necessary to bring the boiler to the respective teperature at the specified fuel consuption. In the case of stea turbines the start-up cost is estiated using the ass flow-rate of stea passing through the turbine to heat it up fro cold state until it reaches the required teperature. The price of the stea consued for this purpose is obtained fro a financial balance through the coplete utility syste in the way described by Sith and Varbanov [16]. The turbine start-up cost is calculated as: STup STupC ST = STup STEAM PriceST IN tstup (5) Start-up costs for an operating unit are charged only if it changes its operating status fro idle (off) to active (on). Thus the introduced binary variable y CHANGE is ultiplied by the device 7

10 start up cost (a paraeter) reflecting the logic introduced above (see Table 1 and Eq. 1 to 3). The total start-up cost is expressed as a su overall operating units as follows: STupC = STupC UNIT y ) (6) ( CHANGEUNIT UNITS 2.5 General optiisation procedure The ain siplifications for obtaining a MILP forulation fro the rigorous odel include fixing the stea enthalpies in the syste coponents (stea headers stea turbine outlets) and optiising the flowrates. However for better precision each MILP run is followed by a siulation step using the rigorous odel. This procedure is iteratively repeated until no enthalpy variations occur. The successive MILP optiisation procedure is described in detail in [17]. 3 Case study In this section an industrially-based case study is presented to illustrate how the ethodology can be applied. The notation of the syste eleents follows the conventions of the industrial source site. Different scenarios are analysed for the deand changes starting fro a given initial operating state. Following is the general description of the syste. A ore detailed set of data for the operating units is provided in Appendix B. 3.1 Syste description Figure 1 shows the utility syste flowsheet. Boiler 1 burns natural gas to generate High Pressure (HP) stea while Boiler 2 (also gas-fired) produces Mediu Pressure (MP) stea. There is also a 40 MW gas turbine consuing natural gas operated at full load. Its flue gas goes through a two-pressure-level Heat Recovery Stea Generator (HRSG) with suppleentary firing to generate HP stea Low Pressure (LP) stea and heat up Boiler Feed Water (BFW) to be delivered to the Waste Incineration Plant (WIP). Specialised Heat Exchangers 1 and 2 (SHE1 SHE2) produce HP stea and heat up BFW for the WIP too. Stea coing fro the WIP can be directed to the HRSG the Specialised Heat Exchangers or be expanded through the letdown valve LD2. The flowrate of the water returned to the WIP 8

11 should slightly exceed the one of the received stea in order to cover the inherent losses (e.g. leaks). HP stea can be expanded through stea turbines ST1 ST2 letdown valve LD1 or the Turbocopressor driver TC. ST1 and ST2 have extraction outlets to the MP ain and exhaust to the LP ain. LD1 and TC exhaust to the MP ain. Turbopups (direct drive stea turbines) TP1 TP2 and TP3 and letdown valve LD3 expand MP stea to the LP header. There are HP MP and LP stea deands for process heating. The feed water pups can be run by the direct drivers (Turbopups 1 2 and 3) or by electric otors. Siilarly the power necessary for the air copression can be provided by the turbine of turbocopressor TC or by an electric otor. 3.2 Case study setup Assuptions In presenting the case studies the deands fro process stea users before the change occurs are denoted as current and new deand denotes the process stea deand after the change has occurred. Two different scenarios have been studied one for the case when WIP produces stea (Scenario 1) and another when it is not working (Scenario 2). The rate of condensate return is assued zero in both scenarios. The ain data of the scenarios are shown in Table 2. Since the optiisation outcoe depends on the duration of the new deand two different durations are used to investigate this influence within each scenario. In the flowsheet figures only the non-zero flowrates are shown. The costs for the initial state are given in /h which reflects that its duration is not considered in the cost calculation. On the other hand the objective function in the optiisation proble considers the duration of the new deands since in this case the operation cost depends on how long the new deands last. Therefore the costs in the optial configuration are given in. 9

12 3.2.2 Scenario 1 In this case the WIP is online and supplies additional stea to the utility syste. The current and new process stea deands for this case are given in Table 2. The flowsheet with the initial stea flowrates for the current scenario is shown in Figure 2. The HRSG heats up part of the 20 t/h stea coing fro the WIP to HP header condition. In addition it also heats the 20.2 t/h feed water returned to the WIP and another 64.8 t/h of HP stea. The two standalone boilers and special heat exchangers are idle. Fro all stea turbines only ST2 is in operation expanding 39.3 t/h stea to the MP ain and t/h stea to the LP ain thus generating 6.06 MW of electrical power. The utility syste operating cost is 1183 /h Scenario 2 The initial state for Scenario 2 is illustrated in Table 2 and Figure 3. Boiler 2 is active supplying stea to the MP ain. Turbine ST2 expands t/h stea to the MP ain and t/h stea to the LP ain. Stea turbine TP2 is also on generating 5.22 MW of electrical power. The operation cost is /h. 3.3 Results Scenario 1 The optiisation results for Scenario 1 are shown in Figure 4 and Figure 5. In the initial state only stea turbine ST2 is ON and the operation costs are 1183 /h. As a consequence of the increase of the stea deand in the MP (iddle pressure) ain fro 45 t/h to 60 t/h and in the LP (low pressure) ain fro 30 t/h to 60 t/h for 5 hours Boiler 2 and turbines ST1 TP1 and TP3 have been turned on and turbine ST2 has been switched off. The reason for this result is that ST1 is slightly ore efficient than ST2. Figure 4 shows these changes of the operation status in the different devices obtained by the optiisation. The cost of the operation during these hours adds up to This operation state can be copared with other operation states. For exaple if the stea is expanded through the letdowns instead of through the stea turbines despite the start up cost savings power for internal consuption should be bought fro the power grid increasing the total cost to which eans a cost difference of 12 % copared to the optiisation. 10

13 Another duration of the new deands of 30 in has also been analysed. In this case the optial configuration (Figure 5) shows that turbine ST1 was switched on and ST2 - off. The shorter duration of the deand increase and the saller nuber of devices involved in the status change result in the cost having a lower value of If an expansion through the letdowns is considered the total cost would be 1704 a 16% cost reduction is achieved by the optiisation. To copare the optial cases the specific cost in /h is estiated. When the new stea deands last 5 hours the specific cost is /h while the specific cost for a new deand duration of 30 in is 2830 /h. The specific cost is lower for a longer deand change because the start up cost is shared over a longer tie interval. Hence the longer the duration is the worthier is to start up a device because the start-up costs are easier to overcoe. The optiisation results regarding the changes of the device statuses the total and start-up costs are suarised in Table Scenario 2 Scenario 2 analyses the operation of the utility syste when there is no stea coing fro the Waste Incineration Plant. The optiisation results are shown in Figure 6 (5 h new deands duration) and Figure 7 (0.5 h new deands duration). The optiised operation for a load change during 5 h shows that Boiler 2 reains on and turbines ST1 and TP1 have been switched on while turbine ST2 has been turned off. The costs for this scenario are If an expansion through the letdowns is considered the total cost is then which eans a cost reduction of 8 % copared to the optiisation. In the case of the shorter duration the difference with the previous optiised state is that turbine TP2 reains on instead of switching on the turbine TP1. The costs add up to The coparison with a letdown expansion configuration gives a cost reduction of 21% where the total cost in this case is The specific cost for a duration of 5 h is 2328 /h and for a duration of 30 inutes is /h. As for the other scenario a higher specific cost is observed for a shorter duration here too. 11

14 The optiisation results concerning the changes of statuses total and start up cost for Scenario 2 are suarised in Table 4. 4 Conclusions and future work In this paper an optiisation ethodology has been presented for supporting operators in running utility systes in the ost efficient way accounting for varying stea deands. The ethod presented includes the forulation of a utility syste odel siulation and optiisation through atheatical prograing. The tool provides the optial utility syste operating specifications when a deand change occurs for a certain expected duration. The optial solution is found by iniising the operating costs which also include the start-up costs of various syste coponents. The rigorous non-linear optiisation odel is solved by a successive ixed integer linear prograing (SMILP) procedure where the non-linearities are taken into account with a rigorous siulation after each MILP optiisation step. The particular coputational ipleentation for the presented work has been in the MATLAB environent. However other tools could be used too for exaple Theroflow. The ethodology has been applied to a real-life case study thus deonstrating its validity and providing exaples of its use. The results indicate that by accounting for start-up costs sizable cost savings can be achieved up to 20 %. The optiisation odel and procedure described in the current paper present the first step in developing a fully-featured fraework for operational optiisation of utility systes under deand variations. The ain results fro this step are: The pattern for estiating device start-up costs has been developed Siple estiate odels for start-up costs of stea boilers and stea turbines have been forulated The further steps in the presented fraework should investigate further odel developent and iproveent of the optiisation procedure as detailed next. 12

15 4.1 Model developent In this work siple odels have been used to describe the utility syste coponents. For instance boilers are described using a constant-efficiency odel and stea turbines using Willan s lines with fixed linear coefficients. Future ipleentations of the current ethodology could use variable-efficiency boiler odel and stea turbine perforance coefficients depending on the turbine size and part-load for exaple as introduced by Varbanov et al. [9]. For siplicity the gas turbine has been assued to work at full load with a constant exhaust teperature and ass flow. The proposed ethodology can readily be extended to odelling the gas turbine part-load operation. Furtherore the odels should include also ore realistic and precise evaluation of the various cost ites besides the fuel costs the various effluent treatents and CO 2 capture should also be accounted for [18 19]. Considering the utility syste dynaics via dynaic siulation ay provide further insights into the syste behaviour. 4.2 Iproveents to the optiisation procedure In the start up cost calculation fuel ass flow (for boilers and special heat exchangers) and stea ass flow (for stea turbines and drivers) and the duration of the start-up process are used as an estiate of the start-up costs for the various equipent types. More accurate forulation of these start-up costs should be a further step towards creating a ore practical ethodology ipleentation. Also the horizon for the optiisation has to be increased and the odel should be able to optiise coplete schedules for utility systes looking several periods ahead copared with single-step optiisation in the anner defined in [14]. Also the variations in the process power deands as well as renewables availability should also be accounted for thus providing a fully-fledged tool to the process operators. One of the ajor challenges to achieve this lies in the appropriate introduction of the tie constraints associated with the device start-ups as they are generally quite diverse while a gas or a stea turbine can be started in a atter of inutes up to several hours a stea boiler start-up ay need up to 24 h and longer. If certain variations in the deand and supply are 13

16 shorter in any cases it ay be ore econoic not to stop the devices. A further challenge is to introduce and odel the hot standby ode for boilers. References [1] F. Friedler Process Integration Modelling and Optiisation for Energy Saving and Pollution Reduction. Cheical Engineering Transactions 18 (2009) pp [2] F. Friedler Process Integration Modelling and Optiisation for Energy Saving and Pollution Reduction. Applied Theral Engineering 30(16) (2010) pp [3] L. Halasz A.B. Nagy T. Ivicz F. Friedler and L.T. Fan Optial Retrofit Design and Operation of the Stea-Supply Syste of a Cheical Coplex. Applied Theral Engineering 22 (2002) pp [4] S. Perry J. Kleeš and I. Bulatov Integrating Waste and Renewable Energy to Reduce the Carbon Footprint of Locally Integrated Energy Sectors. Energy 33(10) (2008) pp [5] P. Varbanov and J. Kleeš Integration and Manageent of Renewables into Total Sites with Variable Supply and Deand. Coputers & Cheical Engineering (2011) doi: /j.copcheeng [6] S.A. Papoulias and I.E. Grossann A structural Optiisation Approach in Process Synthesis I. Utility Systes. Coputers & Cheical Engineering 7(6) (1983) pp [7] G.J. Prokopakis and Z.B. Maroulis Real-Tie Manageent and Optiisation of Industrial Utility Systes. Coputers & Cheical Engineering 20 (1996) pp [8] R.R. Iyer and I.E. Grossann Optial ultiperiod operational planning for utility systes. Coputers & Cheical Engineering 21(8) (1997) pp [9] P. Varbanov S. Doyle and R. Sith Modelling and Optiisation of Utility Systes. Che Eng Res Des Trans ICheE 82(A5) (2004) pp [10] Y. Makwana Y. R. Sith and X.X. Zhu A Novel Approach for Retrofit and Operations Manageent of Exiting Total Sites. Coputers & Cheical Engineering 22 (1998) pp [11] P.S. Varbanov S. Perry Y. Makwana X.X. Zhu and R. Sith Top-Level Analysis of Site Utility Systes. Che Eng Res Des Trans ICheE 82(A6) (2004)

17 [12] L.O.A. Maia L. A. V. de Carvalho and R.Y. Qassi Synthesis of utility systes by siulated annealing Coputers & Cheical Engineering 19(4) (1995) [13] K.P. Papalexandri E.N. Pistikopoulos and B. Kalitventzeff Modelling and Optiisation Aspects in Energy Manageent and Plant Operation with Variable Energy Deands- Application to Industrial Probles. Coputers & Cheical Engineering 22(9) (1998) pp [14] M.H. Agha R. Thery G. Hetreux A. Hait and J. Le Lann Integrated production and utility syste approach for optiizing industrial unit operations. Energy 35(2) (2010) pp [15] J.C. Bruno F. Fernandez F. Castells and I.E. Grossann A Rigorous MINLP Model for the Optial Synthesis and Operation of Utility Plants. Trans ICheE Part A Che Eng Res Des 76(March) (1998) pp [16] R. Sith and P. Varbanov What s the Price of Stea? Cheical Engineering Progress 101(7) (2005) pp [17] P. Varbanov 2004 Optiisation and Synthesis of Process Utility Systes PhD Thesis UMIST Manchester UK. [18] J.J. Kleeš Environental policy decision-aking support tools and pollution reduction technologies: a suary. Clean Technologies and Environental Policy 12 (6) (2010) pp [19] J. Kleeš I. Bulatov and T. Cockeril Techno-Econoic Modelling and Cost Functions of CO 2 Capture Processes. Coputers & Cheical Engineering 31(5-6) (2007) pp

18 Appendix A. Model features Stea turbine The net power generated is odelled according to the Willan s line W = A B (7) NET ST IN + The total power extracted fro the stea expansion is derived taking into account the echanical efficiency W TOT W = η NET M Fro the energy balance the enthalpy of the exhaust can be calculated as h ST OUT = hst IN W TOT ST IN Boiler The boiler aterial balance is: BL OUT = BL BFW IN BL BD (8) (9) (10) To calculate the necessary fuel heat in the boiler and then the necessary fuel ass flowrate : the preheat evaporation heat and superheat are estiated: Q Q Q BL PRE = BL BFW IN hpre (11) BL EVAP = BL OUT hevap (12) BL SH = BL OUT hsh (13) Q BL STEAM = QBL PRE + QBL EVAP + QBL SH Q (14) (15) (16) BL FUEL = BL FUEL = Q BL STEAM Q η BL BL FUEL NHV FUEL 16

19 Specialised Heat Exchanger Stea Superheating Stea coing fro the incineration plant is expanded and then superheated to atch the HP ain conditions. Q SH SH = SH ST WIP hsh (17) The fuel needed for superheating is calculated Q Q SH SH FUEL SH = (18) ηsh Water Preheating Soe boiler feed water is heated up to prespecified conditions and delivered to the incineration plant: Q SH PRE = SH BFW IN hpre (19) The fuel needed for the preheating section is: Q Q SH PRE FUEL PRE = (20) ηsh Letdown For isenthalpic valves both aterial and energy balances are linear: Desuperheater LD IN LD OUT = (21) h LD IN hld OUT = (22) Using ass balance and energy balance the desuperheating water ass flow and then the outlet ass flow are calculated. DSH IN DSH W = DSH OUT + (23) DSH IN hdsh IN + DSH W hdsh W = DSH OUT hdsh OUT DSH W (24) DSH IN ( hdsh IN hdsh OUT ) ( h h ) = (25) DSH OUT DSH W 17

20 Appendix B. Detailed case study data General Paraeters The specifications used for the case studies are given in Table B1 and following. Soe abbreviations: NG stands for natural gas RG for residual gas DA for deaerator and WIP for Waste Incineration Plant have been used. Boiler paraeters Boiler specifications are presented in Table B2. Boiler 1 (BL1) and Boiler 2 (BL2) have different capacity specifications. Special heat exchanger paraeters In Table B3 the Specialised Heat Exchanger paraeters are given. Both Specialised Heat Exchangers have the sae paraeters. GT and HRSG paraeters In Table B4 HRSG paraeters are specified. Stea turbine paraeters The specifications for the stea turbines ST1 (Stea turbine 1) and ST2 (Stea turbine 2) are provided in Table B5. Driver paraeters The paraeters for the drivers are given in Table B6. TC stands for Turbocopressor TP1 for Turbopup 1 TP2 for Turbopup 2 and TP3 for Turbopup 3. Pup paraeters The specifications for the pups are provided in the Table B7. 18

21 Noenclature General CRR [-] Condensate return rate T BFW [ C] Boiler feed water teperature T HP [ C] High pressure ain teperature P HP [bar] High pressure ain pressure P MP [bar] Mediu pressure ain pressure P LP [bar] Low pressure ain pressure Pr ice EXHAUST [ /MWh] Gas turbine exhaust price before suppleentary firing Pr ice FUEL [ /t] Fuel price Pr ice NG [ /t] Natural gas price Pr ice RG [ /t] Rest gas price Pr ice MKW [ /t] Make up water price Pr ice POWER [ /MWh] Power export price Pr ice W WIP [ /t] Price of the water turned back to the waste incineration plant Pr ice ST WIP [ /t] Price of the stea coing fro the waste incineration plant STupC [ ] Total start up cost t [h] New deand duration NEW DEMAND t STup [h] Start up duration y CURRENT [-] Unit current operating status y [-] Change on the status CHANGE y [-] Unit new operating status NEW Boilers η BL [-] Boiler efficiency 19

22 BL BFW IN MIN [t/h] Miniu boiler feed water inlet ass flow BL BFW IN MAX [t/h] Maxiu boiler feed water inlet ass flow BL BFW IN [t/h] Boiler feed water inlet ass flow [t/h] Fuel ass flow BL FUEL BL OUT [t/h] Stea outlet ass flow STup FUEL [t/h] Fuel ass flow used during start up of boiler or Specialised Heat Exchanger NHV FUEL [MWh/t] Net heating value STupC BOILER [ ] Boiler start-up cost HRSG T in [ C] Miniu teperature difference required Cp FG [MWh/t/C] Flue gas specific heat capacity FG [t/h] Flue gas ass flow HRSG BFW HP MIN [t/h] Miniu Boiler Feed Water (HP-HRSG) ass HRSG BFW HP MAX flow [t/h] Maxiu Boiler Feed Water (HP-HRSG) ass HRSG BFW LP MIN flow [t/h] Miniu Boiler Feed Water (LP-HRSG) ass HRSG BFW LP MAX flow [t/h] Maxiu Boiler Feed Water (LP-HRSG) ass HRSG ST HP MIN flow [t/h] Miniu HP stea ass flow HRSG ST HP MAX FG IN [t/h] Maxiu HP stea ass flow T [ C] Teperature of the flue gas at the HRSG inlet Specialised Heat Exchangers η SH [-] Specialised Heat Exchanger efficiency 20

23 SH ST IN MIN [t/h] Miniu stea inlet ass flow SH ST IN MAX [t/h] Maxiu stea inlet ass flow SH W IN MIN [t/h] Miniu water inlet ass flow SH W IN MAX [t/h] Maxiu water inlet ass flow Stea turbines A LP [MWh/t] Slope Willan's line (second wheel) A MP [MWh/t] Slope Willan's line (first wheel) B MP [MW] Intercept Willan's line (first wheel) B LP [MW] Intercept Willan's line (second wheel) η M 1 [-] Efficiency of the first wheel η M 2 [-] Efficiency of the second wheel ST IN MIN [t/h] Miniu inlet ass flow of stea for a stea ST IN MAX turbine [t/h] Maxiu inlet ass flow of stea for a stea ST OUT 1 MIN turbine [t/h] Miniu extraction ass flow of stea for a ST OUT 1 MAX stea turbine [t/h] Maxiu extraction ass flow of stea for a ST OUT 2 MIN stea turbine [t/h] Miniu outlet ass flow of stea for a stea ST OUT 2 MAX turbine [t/h] Maxiu outlet ass flow of stea for a stea turbine wheel of the stea turbine [t/h] Stea ass flow used during start up of stea STup STEAM turbines STupC ST [ ] Stea turbine start up cost Drivers 21

24 A [MWh/t] Slope Willan s line B [MW] Intercept Willan s line η DR [-] Driver efficiency DR IN MIN [t/h] Miniu inlet ass flow of stea for a driver DR IN MAX [t/h] Maxiu inlet ass flow of stea for a driver 22

25 List of Figures Figure 1. Utility syste flowsheet Figure 2. Initial State Scenario 1 Figure 3. Initial State Scenario 2 Figure 4. Optial operation Scenario 1 (5 h) Figure 5. Optial operation for Scenario 1 (30 in) Figure 6. Optial configuration Scenario 2 (5 h) Figure 7. Optial configuration Scenario 2 (30 in) 23

26 List of Tables Table 1. Possible cobinations for the binary variables Table 2. Stea deands for both Scenarios (t/h) Table 3. Optiisation results for Scenario 1 Table 4. Optiisation results for Scenario 2 Table B1. General paraeters TableB2. Boiler paraeters Table B3. Specialised Heat Exchanger paraeters Table B4. GT and HRSG paraeters Table B5. Stea turbine paraeters Table B6. Driver paraeters Table B7. Pup paraeters 24

27 Optiisation ethodology for decision aking on running utility systes Accounting for varying stea deands Optial operating specifications when a deand change occurs Operating costs include start-up costs of boilers and other units Validated on a real-life case study. Up to 20 % cost savings are possible 1

28 Table 1. Possible cobinations for the binary variables ycurrent y NEW ychange

29 Table 2. Stea deands for both Scenarios (t/h) Current deand New deand HP stea deands 0 0 MP stea deands LP stea deands Scenario 1: Stea inlet fro the Incineration Plant: 20 t/h Scenario 2: Stea inlet fro the Incineration Plant: 0 t/h 2

30 Table 3. Optiisation results for Scenario 1 Initial Optiisation Start-up Optiisation Start-up Device state (new deand: 5 h) cost ( ) (new deand: 0.5 h) cost ( ) Boiler 2 OFF ON OFF 0 Boiler 1 OFF OFF 0 OFF 0 Stea turbine 1 OFF ON ON Stea turbine 2 ON OFF 0 OFF 0 Turbocopressor OFF OFF 0 OFF 0 Turbopup 1 OFF ON OFF 0 Turbopup 2 OFF OFF 0 OFF 0 Turbopup 3 OFF ON OFF 0 Cost 1183 /h ( /h) (2830 /h)

31 Table 4. Optiisation results for Scenario 2 Initial Optiisation Start up Device state (new deand: 5 h) cost ( ) Optiisation (new deand: 0.5 h) Start up Boiler 2 ON ON 0 ON 0 cost ( ) Boiler 1 OFF OFF 0 OFF 0 Stea turbine 1 OFF ON ON Stea turbine 2 ON OFF 0 OFF 0 Turbocopressor OFF OFF 0 OFF 0 Turbopup 1 OFF ON OFF 0 Turbopup 2 ON OFF 0 ON 1.88 Turbopup 3 OFF OFF 0 OFF 0 Cost /h (2328 /h) ( /h) 4

32 Table B1. General paraeters Paraeter Unit Value T BFW C T C HP P bar HP P bar MP P bar 4.00 LP P bar 1.30 DA CRR NHV NG MWh/t NHV RG MWh/t Pr ice MKW /t 0.40 Pr ice NG /t Pr ice RG /t Pr ice EXHAUST /MWh Pr ice ST WIP /t Pr ice W WIP /t 0.00 Pr ice POWER /MWh

33 TableB2. Boiler paraeters Paraeter Unit Value η BL BL BFW IN MIN (BL1) t/h BL BFW IN MAX (BL1) t/h BL BFW IN MIN (BL2) t/h BL BFW IN MAX (BL2) t/h

34 Table B3. Specialised Heat Exchanger paraeters Paraeter Unit Value η SH SH ST IN MIN t/h SH ST IN MAX t/h SH W IN MIN t/h SH W IN MAX t/h

35 Table B4. GT and HRSG paraeters Paraeter Unit Value W GT MW 40 T in C t/h FG T C FG IN HRSG ST WIP MIN t/h 0.00 HRSG ST WIP MAX t/h HRSG ST HP MIN t/h HRSG ST HP MAX t/h HRSG BFW HP MIN t/h 0.00 HRSG BFW HP MAX t/h

36 Table B5. Stea turbine paraeters Paraeter Unit Value η M η M 2 ST IN MIN (ST1) t/h ST IN MAX (ST1) t/h ST OUT1 MIN (ST1) t/h 0.00 ST OUT1 MAX (ST1) t/h ST OUT 2 MIN (ST1) t/h 0.00 ST OUT 2 MAX MP (ST1) t/h A (ST1) MWh/t 0.07 B (ST1) MW MP A (ST1) MWh/t 0.10 LP B (ST1) MW LP ST IN MIN (ST2) t/h ST IN MAX (ST2) t/h ST OUT1 MIN (ST2) t/h 0.00 ST OUT1 MAX (ST2) t/h ST OUT 2 MIN (ST2) t/h ST OUT 2 MAX (ST2) t/h A (ST2) MWh/t 0.07 MP B (ST2) MW MP A (ST2) MWh/t 0.10 LP B (ST2) MW LP 9

37 Table B6. Driver paraeters Paraeter Unit Value DR IN MIN _ (TC) t/h DR IN MAX _ (TC) t/h A (TC) MWh/t 0.07 B (TC) MW 0.00 DR IN MIN _ (TP1) t/h 3.00 DR IN MAX _ (TP1) t/h A (TP1) MWh/t 0.09 B (TP1) MW DR IN MIN _ (TP2) t/h 1.00 DR IN MAX _ (TP2) t/h 4.00 A (TP2) MWh/t 0.06 B (TP2) MW DR IN MIN _ (TP3) t/h 2.00 DR IN MAX _ (TP3) t/h A (TP3) MWh/t 0.09 B (TP3) MW

38 Table B7. Pup paraeters Paraeter Unit Value P PIPES bar ρ WATER / kg

39 Table 1. Possible cobinations for the binary variables ycurrent y NEW ychange

40 Table 2. Stea deands for both Scenarios (t/h) Current deand New deand HP stea deands 0 0 MP stea deands LP stea deands Scenario 1: Stea inlet fro the Incineration Plant: 20 t/h Scenario 2: Stea inlet fro the Incineration Plant: 0 t/h 2

41 Table 3. Optiisation results for Scenario 1 Device Initial Optiisation Start- Optiisation Start-up state (new deand: 5 up cost (new deand: 0.5 cost ( ) h) ( ) h) Boiler 2 OFF ON OFF 0 Boiler 1 OFF OFF 0 OFF 0 Stea turbine 1 OFF ON ON Stea turbine 2 ON OFF 0 OFF 0 Turbocopressor OFF OFF 0 OFF 0 Turbopup 1 OFF ON OFF 0 Turbopup 2 OFF OFF 0 OFF 0 Turbopup 3 OFF ON OFF 0 Cost 1183 /h ( /h) (2830 /h)

42 Table 4. Optiisation results for Scenario 2 Device Initial Optiisation Start up Optiisation Start up state (new deand: 5 h) cost ( ) (new deand: 0.5 cost ( ) h) Boiler 2 ON ON 0 ON 0 Boiler 1 OFF OFF 0 OFF 0 Stea turbine 1 OFF ON ON Stea turbine 2 ON OFF 0 OFF 0 Turbocopress OFF OFF 0 OFF 0 or Turbopup 1 OFF ON OFF 0 Turbopup 2 ON OFF 0 ON 1.88 Turbopup 3 OFF OFF 0 OFF 0 Cost /h (2328 /h) ( /h) 4

43 Table B1. General paraeters Paraeter Unit Value T BFW C T C HP P bar HP P bar MP P bar 4.00 LP P bar 1.30 DA CRR NHV NG MWh/t NHV RG MWh/t Pr ice MKW /t 0.40 Pr ice NG /t Pr ice RG /t Pr ice EXHAUST /MWh Pr ice ST WIP /t Pr ice W WIP /t 0.00 Pr ice POWER /MWh

44 TableB2. Boiler paraeters Paraeter Unit Value η BL BL BFW IN MIN (BL1) t/h BL BFW IN MAX (BL1) t/h BL BFW IN MIN (BL2) t/h BL BFW IN MAX (BL2) t/h

45 Table B3. Specialised Heat Exchanger paraeters Paraeter Unit Value η SH SH ST IN MIN t/h SH ST IN MAX t/h SH W IN MIN t/h SH W IN MAX t/h

46 Table B4. GT and HRSG paraeters Paraeter Unit Value W GT MW 40 T in C t/h FG T C FG IN HRSG ST WIP MIN t/h 0.00 HRSG ST WIP MAX t/h HRSG ST HP MIN t/h HRSG ST HP MAX t/h HRSG BFW HP MIN t/h 0.00 HRSG BFW HP MAX t/h

47 Table B5. Stea turbine paraeters Paraeter Unit Value η M η M 2 ST IN MIN (ST1) t/h ST IN MAX (ST1) t/h ST OUT1 MIN (ST1) t/h 0.00 ST OUT1 MAX (ST1) t/h ST OUT 2 MIN (ST1) t/h 0.00 ST OUT 2 MAX MP (ST1) t/h A (ST1) MWh/t 0.07 B (ST1) MW MP A (ST1) MWh/t 0.10 LP B (ST1) MW LP ST IN MIN (ST2) t/h ST IN MAX (ST2) t/h ST OUT1 MIN (ST2) t/h 0.00 ST OUT1 MAX (ST2) t/h ST OUT 2 MIN (ST2) t/h ST OUT 2 MAX (ST2) t/h A (ST2) MWh/t 0.07 MP B (ST2) MW MP A (ST2) MWh/t 0.10 LP B (ST2) MW LP 9

48 Table B6. Driver paraeters Paraeter Unit Value DR IN MIN _ (TC) t/h DR IN MAX _ (TC) t/h A (TC) MWh/t 0.07 B (TC) MW 0.00 DR IN MIN _ (TP1) t/h 3.00 DR IN MAX _ (TP1) t/h A (TP1) MWh/t 0.09 B (TP1) MW DR IN MIN _ (TP2) t/h 1.00 DR IN MAX _ (TP2) t/h 4.00 A (TP2) MWh/t 0.06 B (TP2) MW DR IN MIN _ (TP3) t/h 2.00 DR IN MAX _ (TP3) t/h A (TP3) MWh/t 0.09 B (TP3) MW

49 Table B7. Pup paraeters Paraeter Unit Value P PIPES bar ρ WATER / kg

50 Figure 1. Utility syste flowsheet 1

51 Figure 2. Initial State Scenario 1 1

52 Figure 3. Initial State Scenario 2 1

53 Figure 4. Optial operation Scenario 1 (5 h) 1

54 Figure 5. Optial operation for Scenario 1 (30 in) 1

55 Figure 6. Optial configuration Scenario 2 (5 h) 1

56 Figure 7. Optial configuration Scenario 2 (30 in) 1