Techniques for quasi-static operating optimization of CHP plants. Mario Nervi Università di Genova (Italy)

Transcription

1 Techniques for quasi-static operating optimization of CHP plants Mario Nervi Università di Genova (Italy) PolyCity Workshop, 25 th of May 2007, Turin, ITALY 1

2 Mario Nervi Associate professor of Fundamentals of Electrical Engineering at the University of Genova, Italy. Author of many scientific papers on electromagnetic field computation, optimization methods, energy applications PolyCity Workshop, 25 th of May 2007, Turin, ITALY 2

3 Techniques for Quasi Static Operational Optimization of CHP Plants P. Girdinio, S. Moccia, G. Molinari, M. Nervi Università di Genova Dipart. di Ingegneria Elettrica (DIE), Genova, Italy A. Pini Prato Università di Genova Dipart. di Macchine, Sistemi Energetici e Trasporti (DIMSET), Genova, Italy PolyCity Workshop, 25 th of May 2007, Turin, ITALY 3

4 What a CHP is? A CHP is a machine for the Combined production of Heat and electric Power It is useful as the combined generation allows to significantly improve the energetic efficiency The cold source heat is not a waste, but a recycled resource In principle CHPs can have any size; usually, for a better exploitation of the thermal energy, they are medium-small size (some tenths of kw to some hundreds of kw) The term of micro cogeneration is widely used then PolyCity Workshop, 25 th of May 2007, Turin, ITALY 4

5 Operational considerations A CHP must produce electrical and thermal power, according to the variable requests of the loads There are constraints on the switching (a CHP cannot be switched on and off every minute, due to technical reasons) The cost of fuel is variable within the year The costs of electrical power bought from and sold to the grid are variable between night and day (there is a 4 factor from the top day price and the night price) PolyCity Workshop, 25 th of May 2007, Turin, ITALY 5

6 Operational considerations (cont d) An even more important constraint is about the energy saving: the F.E.S.R. (Fuel Energy Savings Ratio), which is defined as: Ec 1 η E es e p must be greater than 0.1. This means that the fuel saving, compared to that used for the separate production of the same electrical and thermal power, is at least 10%. + E η This constraint is evaluated at the END of the year!!! t ts PolyCity Workshop, 25 th of May 2007, Turin, ITALY 6

7 Objective and Optimization The correct management is a key issue to have a competivitive behaviour of plants: The objective is to get the maximum cash-flow Energy balance Technical operational constraints Tax and regulatory contraints (by the end of the year) Operational strategies (maintenance planning, etc.) PolyCity Workshop, 25 th of May 2007, Turin, ITALY 7

8 The problem The problem is: how to maximize the differential cash flow, fulfilling all the constraints, especially the limit on F.E.S.R.??? The latter is very critical, as a non-fulfillment (discovered only at the end of the year) leads to loose significant tax discounts, that in most cases make the difference in terms of the commercial appeal of the CHP Of course the correct management of a CHP cannot be left to the operator, as the degrees of freedom are too many to be chosen by human intervention PolyCity Workshop, 25 th of May 2007, Turin, ITALY 8

9 Problem coding objective - constraints The objective is: + δq u { MAX cash_ y Q F flow= W def y s F _ def y ( ) Ws + WLoad Wp ywp + ( F F ) y } NOTdef b F _ NOTdef Subject to the previously defined constraints: Physical congruence (energy balance) Technical congruence (due to machine characteristics) Normative/tax congruence (to achieve tax discounts) Management constraints (for ex. maintenance sched.) PolyCity Workshop, 25 th of May 2007, Turin, ITALY 9

10 Types of optimization Quasi Stationary A choice between two main options is necessary CHP Optimization Global Optimization Quasi Stationary Opt. PolyCity Workshop, 25 th of May 2007, Turin, ITALY 10

11 Types of optimization Q. S. (cont d) Global Optimization: the degrees of freedom are all the values of electrical and thermal power, for each time step, within the optimized period (past, present, and future) Quasi Stationary Optimization: the degrees of freedom are all the values of electrical and thermal power, at ONE time step, within the optimized period (the past is known, the present is optimized, and the future is estimated) This is the only practical choice (time steps are one hour long), therefore, for a Glob. Opt. over one year the total number of d.o.f. should be multiplied by 8760, leading to an unpractical problem PolyCity Workshop, 25 th of May 2007, Turin, ITALY 11

12 F.E.S.R. Constraint management The management of F.E.S.R. constraint is intrinsically complex: with a Global Optimization approach, it could be naturally enforced Using a Quasi Stationary approach, it has to be transformed into a local problem even though it remains an integral problem The problem is how to correctly enforce the constraint, without overconstraining the problem Case A is the result without overconstraining Case B is the result where the constraint (.GE. 10%) is imposed at every iteration PolyCity Workshop, 25 th of May 2007, Turin, ITALY 12

13 S/W Implementation The Quasi Stationary has been implemented using a two stage procedure: an external procedure, written in high level language (Fortran), that reads all the relevant data, and manages a loop over the time steps. Then it calls... an internal commercial state-of-the-art optimizer, able to solve the resulting MILP, then... at the end of the MILP solution the results are passed back to the external driver, checked, and the integral constraints are estimated; then the next step is executed, until the end of the period PolyCity Workshop, 25 th of May 2007, Turin, ITALY 13

14 S/W Implementation (cont d) To be mentioned that anyway TWO optimizations must be always run: the first is F.E.S.R. unconstrained, and it is only needed to have a rough estimate of the fuel consumption; the second is F.E.S.R. constrained, and it uses the fuel consumption estimates coming from the previous run PolyCity Workshop, 25 th of May 2007, Turin, ITALY 14

15 Main features of the developed software At the moment the SW allows to simulate natural gad fed micro CHP plants Input data needed: Monthly/yearly gas consumption of site Monthly/yearly electric power consumption of site Location (province) and height above sea level of site AEEG regulations about gas and electrical power prices Output data provided: Production of thermal/electrical power Switching times (on and off) of CHPs; time of production Performances (FESR, fuel savings, cash flow) Financial analyses PolyCity Workshop, 25 th of May 2007, Turin, ITALY 15

16 Typical plant layout PolyCity Workshop, 25 th of May 2007, Turin, ITALY 16

17 Library of available CHPs At the moment the following CHPs are included in the library: Capstone C30, 30 kwe Capstone C60, 60 kwe Turbec T100, 105 kwe It has been designed an extendable machine library, that needs in input a matrix of points representing the characteristic of a machine, and builds up the data neede by the optimizer through an ad hoc interpolation. This gives to the SW a remarkable use flexibility PolyCity Workshop, 25 th of May 2007, Turin, ITALY 17

18 Characteristics of Capstone C30 PolyCity Workshop, 25 th of May 2007, Turin, ITALY 18

19 Characteristics of Capstone C60 PolyCity Workshop, 25 th of May 2007, Turin, ITALY 19

20 Characteristics of Turbec T100 PolyCity Workshop, 25 th of May 2007, Turin, ITALY 20

21 The software is split in three indipendent modules: Pre Processor Pre processor Pre processor tasks are: Input AEEG regulations about electricity and gas prices, reference efficiency used to compute the FESR, etc. Number and type of (possibly) present boilers Previous consumption of electrical and thermal power, and, if available, the records of one week of the electrical loads Location (province) of installation site, and height above sea level (at the moment only the 4 ligurian provinces are implemented) Output Hourly behaviour of electrical and thermal loads, that can possibly be updated during the analysis Hourly behaviour of site temperature, that can possibly be updated during the analysis Supervisor Post processor PolyCity Workshop, 25 th of May 2007, Turin, ITALY 21

22 Supervisor and Optimizer Formed by two modules: Supervisor, written in Fortran; its purpose are: 1) to manage and update input data to submit to the optimizer, and 2) to record optimizer output data Optimizer, is the commercial code CPLEX, able to solve the resultimg MILP problem; its output allows the supervisor to update the integral parameter estimates Input Hourly requests of thermal and electrical loads, possibly updated during the analysis Number and type of available CHPs Hourly behaviour of site temperature, possibly updated during the analysis Additional consumption during transients, switch ons and shutdowns Operational parameters: minimum time between ON and OFF, etc. Month of beginning of simulation Choice of objective (max cash flow, minimum emissions, etc.) Output Complete estimate of the plant for each hour of simulated period Pre processor Supervisor Post processor PolyCity Workshop, 25 th of May 2007, Turin, ITALY 22

23 Post Processor Allows to display and to save both as picture and as text data the behaviour of all integral parameters and all local quantities Pre processor Allows to perform financial analyses, such as NPV, ROI, IRR Input Complete state of plant operational constraints Output Textual and graphic representation of plant behaviour Supervisor Post processor PolyCity Workshop, 25 th of May 2007, Turin, ITALY 23

24 Test case The optimized problem is based on: 2 CHP units, rated electrical power 80kW each, variable regeneration (the machines have a by-pass valve) One auxiliary boiler, rated thermal power 500kW The price structure of electrical power and fuel are obtained from the sites of Italian Regulatory Board for Electrical Energy and Gas The actual prices of electrical power are obtained from the Italian Electrical Market Manager PolyCity Workshop, 25 th of May 2007, Turin, ITALY 24

25 Results Fuel Energy Savings Ratio (F.E.S.R.) 22,00% 20,00% 18,00% 16,00% 14,00% Case A Case B 12,00% 10,00% 8,00% hours [h] PolyCity Workshop, 25 th of May 2007, Turin, ITALY 25

26 Results (cont d) Differential Cash Flow [ ] Case A Case B hours [h] PolyCity Workshop, 25 th of May 2007, Turin, ITALY 26

27 Machines' productions Results (cont d) kw Electrical Pwr Machine 1 Electrical Pwr Machine hours [h] PolyCity Workshop, 25 th of May 2007, Turin, ITALY 27

28 General operational parameters comparison Results (cont d) Case B 15,45% 13,37% 50,13% 36,39% 45,70% 41,04% 63,24% 84,55% Case A 10,60% 26,48% 43,15% 45,61% 44,38% 53,16% 73,31% 89,40% 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00% 100,00% Total thermal energy produced by the auxiliary boiler (compared to user's request) Total thermal energy produced by the plant (compared to user's request) Total electrical energy sold to the grid (compared to user's request) Total electrical energy purchased from the grid (compared to user's request) Total electircal energy produced by the plant (compared to user's request) Time utilization coefficient of the machine 2 Time utilization coefficient of the machine 1 Time utilization coefficient of the plant PolyCity Workshop, 25 th of May 2007, Turin, ITALY 28

29 Discussion It is apparent (even though paradoxical) that case A option leads to better results than case B This can be explained observing that using the Q.S. Opt. approach, we loose the complete control of the system If we used Glob. Opt., every d.o.f. (in past, present, and future) could potentially be modified in a single optimization, and all the integral constraints could be exactly evaluated With Q.S. Opt., this is not possible (and unrealistic, as we cannot change the past, and the future is only estimated) PolyCity Workshop, 25 th of May 2007, Turin, ITALY 29

30 Discussion (cont d) Therefore, with the Q.S. Approach, becomes of the utmost importance how to impose integral constraints If their enforcing is too hard, it can lead to different choices, perfectly logical in the Q.S. logic, but leading to worse results if considered over the period Several tests confirmed this fact, leading us to search for a different and less critical technique... PolyCity Workshop, 25 th of May 2007, Turin, ITALY 30

31 Case study: comparison between three solutions The site is a National Health Service hospital, characterized by the following parameters: Max thermal consumption : 460 kwt Max electrical consumption : 250 kwe Simulations of different possible solutions were run: Capstone C30 Capstone C60 Turbec T100 Case Case Case PolyCity Workshop, 25 th of May 2007, Turin, ITALY 31

32 Simulation parameters Max number of CHPs: 4 Minimum time between CHP switch: 6h For each type of CHP the following additional consumption were chosen: during transients: 0.5 % of the difference of required electrical power during switch ons: 25 % of the rated consumption during switch offs: 8 % of the rated consumption PolyCity Workshop, 25 th of May 2007, Turin, ITALY 32

33 Case study site characterization Hourly behaviour of: 1. Yearly thermal load [kwh]; 2. Yearly temperature [ C]; 3. Electrical load over a forthnight [kwh]. PolyCity Workshop, 25 th of May 2007, Turin, ITALY 33

34 Case 1 Integral operational variables Capstone C30 Capstone C60 Turbec T100 Caso Caso Caso PolyCity Workshop, 25 th of May 2007, Turin, ITALY 34

35 Case 1 Integral performance indicators IRE : 30.8 % CASH FLOW: BURNT FUEL IN BOILER: 743 MWh BURNT FUEL IN BOILER IN CHP: 1493 MWh FUEL SAVING: 665 MWh PolyCity Workshop, 25 th of May 2007, Turin, ITALY 35

36 Case 2 Integral operational variables Capstone C30 Capstone C60 Turbec T100 Caso Caso Caso PolyCity Workshop, 25 th of May 2007, Turin, ITALY 36

37 Case 2 Integral performance indicators IRE : 21.9 % CASH FLOW: BURNT FUEL IN BOILER: 311 MWh BURNT FUEL IN BOILER IN CHP: 1656 MWh FUEL SAVING: 695 MWh PolyCity Workshop, 25 th of May 2007, Turin, ITALY 37

38 Case 3 Integral operational variables Capstone C30 Capstone C60 Turbec T100 Caso Caso Caso PolyCity Workshop, 25 th of May 2007, Turin, ITALY 38

39 Case 3 Integral performance indicators IRE : 26.8 % CASH FLOW: BURNT FUEL IN BOILER: 345 MWh BURNT FUEL IN BOILER IN CHP: 2450 MWh FUEL SAVING: 899 MWh PolyCity Workshop, 25 th of May 2007, Turin, ITALY 39

40 Future developments An interesting development is about the setup of a different way to enforce the F.E.S.R. constraint This must be done in order to have a good accuracy, but avoiding unphysical abort of optimization, and to avoid solutions so constrained to be economically meaningless Basically there are two ways to impose the F.E.S.R., and the control logic can switch from one to the other, according to the behaviour of optimization The first results are encouraging, but they are not mature enough, yet PolyCity Workshop, 25 th of May 2007, Turin, ITALY 40

41 Future developments (cont d) It is worth mentioning that the Q.S. approach, due to its inherently low requirements, is excellent to build an online optimizer, and it is the logical choice to optimize a long operational period (for example, to assess the economic appeal of a particular co-generating plant) To have a more reliable optimizer, in shorter periods (like one day, up to a fortnight) the Glob. Opt. should lead to better results, as in the case of online optimizers Anyway it is of critical importance the quality of the estimates of fuel/electrical power/thermal power requested PolyCity Workshop, 25 th of May 2007, Turin, ITALY 41

42 Future developments (cont d) We are working on the modelling of Internal Combustion Engines for CHP applications; therefore it is appearing a distinction between the recovery of thermal energy with HIGH and LOW energy content We are also working to setup a defintion of FESR simpler to use (less constraining), but anyway fulfilling all operatioal constraints PolyCity Workshop, 25 th of May 2007, Turin, ITALY 42