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Energy and Economy Energy Modelling Lab. Department of Energy Studies, Energy Systems Division, Ajou University Prof. Suduk Kim

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Presentation on theme: "Energy and Economy Energy Modelling Lab. Department of Energy Studies, Energy Systems Division, Ajou University Prof. Suduk Kim"— Presentation transcript:

1 Energy and Economy Energy Modelling Lab. Department of Energy Studies, Energy Systems Division, Ajou University Prof. Suduk Kim


3 3 Model Review RETScreen CHP module Thermoflex –  Neither of the above model utilizes or attempts to estimate the hourly demand pattern considering the conditions such as regional, sectoral difference of categories in detail.  Even when the research is done on hourly basis, the analysis tends to discuss TLP(Typical Load Profile) instead of considering all the related variables affecting the hourly demand of heat and power.  We will review the above existing models as examples and will try to discuss the importance of modelling CHP with hourly demand pattern with proper selection of operational strategy in its economic feasibility test.

4 4 RETScreen: Combined Heat & Power Project Model

5 5 RETScreen: Model Flow

6 6 RETScreen: Monthly Load Curve Estimation

7 7 RETScreen: Equipment Database

8 8 RETScreen: Financial Summary

9 Thermoflow, Inc. GT PRO is a highly automated system design tool – a heat balance program specifically intended for design of gas turbine combined cycle power plants and cogeneration systems.  Use GT PRO to explore and design combined cycles, cogeneration systems, and simple cycle gas turbine power plants.  In combination with PEACE (Plant Engineering and Cost Estimator), GT PRO provides engineering details and cost estimation.  GT PRO performs design-point calculations only – use GT MASTER for simulations of part loads or other off-design conditions. WHAT IS GT PRO?

10 10 The Navigator column displays the major input topics. Topics in GT PRO are arranged vertically, and follow a hierarchy, progressing from top to bottom. Selections made in an upper (earlier) topic will condition the selections available in lower (later) topics. Tabs are used as necessary to subdivide topics; green tabs include PEACE inputs The Guidance window includes a description of the presently selected input Inputs – General Arrangement Every entry box has been initialized with a reasonable value. You never have to fill an empty box. Instead you edit just those that are of concern to you, trusting the others to have been logically selected.

11 11 PEACE Schematics Select from the list to see dimensioned layouts of the site and of plant equipment.

12 12 PEACE Equipment Data For each of the equipment categories, selectable by the tabs visible here, these tables indicate size, weight, and nameplate specifications.

13 13 PEACE Cost Report Project Summary is shown. Tabs give individual breakdown of costs. Note the two columns: Reference and Estimated Cost. Click on Cash Flow to see a pro forma financial projection.

14 14 PEACE Cash Flow This Financial Summary table displays the overall results of the pro forma Cash Flow projection detailed in the subsequent tab. In very little time, you can use these results to explore the influence of design decisions on plant economic performance.


16 16 Flow Diagram of Analysis CES or SCG Hourly Load Forecast Load Analysis & Estimation Power, Heat, Cool Usage, Region (Temperature and Humidity) and other information such as Area width, etc. Report on Hourly Load Forecast Peak load of Power, Heat will be estimated ! Equipment Selection Economic Feasibility Analysis Information on fixed and variable costs provided! Since there are many commercial software packages for this, we allow this be determined outside of the program! Final Report Operational Strategies Load Following Strategies based on hourly NB

17 17 Modularizing of System Analysis CategoryContents Individual Module Development Hourly Load Pattern Analysis/ Load Forecast Module Power/Heat/Cool/Load Analysis and ForecastRelated information should be easily manageable. CES/SCG system Module CHP equipments, Heat Container, HOB, Pattern of Heat Import from outside of System, etc Economic Feasibility Test Module Construction related costs and other O&M cost information Input Revenue, Cashflow Calculation, Calculation of NPV, IRR DB Module Development DB ModuleDesign the Current Requirement of System Additional Consideration for Expandability


19 19 Previous Load Forecast Methods For the calculation of each load profile, use the area space, usage category information, related Energy use pattern to find the peak load (Heat, Cool, Power) Example: Heat Load Calculation 1.Heating Space Calculation = Space(m 2 ) x Heat Demand Dev. Rate(%) x Heat area ratio(%) 2.Total Peak Load of Heat Calculated = Heating Space(m 2 ) x Unit Heat Requrement 3.Final Peak Load of Heat expected = Total Peak Load of Heat x prob. Concurrent Load /(1-loss(%))

20 20 Hourly Load Curve Forecast 1.Forecast should be made for Hourly basis (8760 Hour data to be generated) 2.TLP of Heat and Power – Region(Temperature, Humidity), Usage(23 diff. Categories), Size(eg. Heating Area) 3.7 different weekdays, holidays, Special Holidays such as New years day, Thanks giving Holidays, Seasonal Patterns should be considered. Heat Load Data:  Anyang Area (Household: Nov.1, 2003-Sept.30, 2005) -156 points  Pucheon (Household: Nov. 30, 2003-Sept. 30, 2005) – 150 points  Data with missing information should have been dropped out of analysis Power Load Data:  Data Managed by KDN (Korea Electric Power Data Network) : Typical Load profile for given usage, region is estimated using 15min. Frequency data  Current form of data – 4 separate files for each month. Read 1st file to find a company’s data and do this for other files consecutively  Data matching problems : some missing, Some of arbitrary samples were chosen for analysis

21 21 Hourly Heat Load Curve Forecast Dependent Variable : Hourly Heat Load data gathered from 306 different points of machine rooms – Conversion to Typical Household Heat Demand needs special consideration. Explanatory Variables ∘ Temperature(C 0 ): -34.7 ~ 35.9 ∘ Humidity(%): 0 ~ 93 ∘ 12 Months x 24 Hour Pattern ∘ Weekdays with its coincidence of other holidays  Holidays are categorized differently  Holiday during weekdays, New Years day, Thanks giving Holidays, etc. ∘ Heating Space

22 22 Hourly Load Curve Forecast: Model The Multi-level Model or the Hierarchical Model [the first level] : the level-1 fixed parameters : the level-1 explanatory variables associated with fixed parameters : the level-1 random parameters (random variation over the households is more important) : the level-1 explanatory variables associated with random parameters : the level-1 random error terms [the second level] : the level-2 fixed parameters : the level-2 explanatory variables : the (subject-specific) level-2 random error terms Source: C.G.Moon(2006)

23 23 Hourly Load Curve Forecast: Model the linear mixed-effects model

24 24 Estimation : GLS (MLE) Theoretical Estimation Method

25 25 Problems Encountered in Estimation Process For KDN data analysis, the data has 15 min. frequencies. For the analysis of such panel data, we need a workstation size computer system. We used a computer equipped with Windows XP Home Edition (32 bit) with 4Gb RAM. Additional Hard Drive of 1 terra byte has been provided for data matching. For our econometric analysis, we use GAUSS. Gauss Engine is also used for Foreign language interface with C ++. GAUSS (Windows version) used here cannot utilize virtual memory. It only utilized extended or expanded memory which is only a part of physical RAM. (Before 1999, GAUSS could use virtual memory and it was possible to analyze big data set.) Due to the system limitation, we had to select some samples arbitrarily, therefore, the result could not be argued to have a characteristics representative for all such cases.

26 26 Hourly Heat Load Curve Estimated

27 27 Load Forecast Window

28 28 Choice of System after Load Forecast Cogeneration Types, C1: Engine, C2: Steam TBN, C3: Gas TBN


30 30 System Application Types Source: Y.C.Hong (2006) CES SCG Power Plants G.T. Combined Steam Turbine Gas Engine Gas Turbine Hospital, School Individual Industry: Car, Paper, Chemical, Food. Power Plants Housing Complex Industry Complex

31 31 Example of Equipment Selection: Gas Engine CES Information on Load and Related Power and Heat Production Information of Unit, Flag to show usage, Capacity, Cost for Each of GE, HOB, HC, Heat Import Source such as incinerator, etc. Information on the schedule of Investment which will be used for Load & Financial Analysis

32 32 Example: Sales Price Schedule of Power to Grid by CES Time Table for Power Pricing Next Page! Household Commercial Educational Road Light Spec. Contract Power Price CES Power Sales Price Heat Sales Price

33 33 Time Table Adjustment for Power Price Base load Intermediate load Peak load


35 35 Power Load Following Strategy CHP is being operated to meet the power demand after considering the input conditions such as power & heat import pattern. Compare the heat produced in this process, determine how much more heat is required to be produced using Heat Only Boiler even after Heat Container is considered. Cost of Heat & Power import, Fuel cost of CHP, HOB with Benefits from Power & Heat Sales will be calculated to get Hourly Net Benefit.

36 36 Heat Load Following Strategy CHP is being operated to meet the Heat demand after considering the input conditions such as heat import pattern & Heat Container. Compare the power produced in this process, determine how much more power is required to be imported additionally. Additional consideration on the possibility of power sales via grid is also examined. Cost for Heat & Power import, Fuel cost for CHP, HOB with Benefits from Power & Heat Sales will be calculated to get Hourly Net Benefit.

37 37 Combined Strategy Utilizing Hourly Net Benefit obtained from Power & Heat Load Following strategies, we can determine our hourly operation strategies. Net Benefit will be maximized by optimizing hourly strategies.


39 39 Economic Feasibility Analysis Utilizing the above equation, we can find a way to calculate competitive market price for Heat, Power, etc, since with all the other information given. Various regulations on the Heat, Power transaction could be reviewed using our hourly base operation model. With more detailed information on expected CES/SCG, we would be able to predict or forecast possible future market size. - i th Period Benefit : Usually revenue from energy services - i th Period Green House Gas Reduction Credit - Initial Fixed Cost for energy service projects - Operation and Management Costs - Discount Rate - Project Life

40 40 Inputs for Financial Analysis (1) 1 2 34 1: Basic Intput Condition, 2: Efficiency, Price Info., 3: O&M, etc., 4: Equipment Related Info.

41 41 Inputs for Financial Analysis (2) 1 2 4 6 3 5 1: General Intput Condition, 2: Initial Costs, 3: Periodic Costs, 4: Price Increase Rate, 5: Indirect Costs, 6: Financing Debt

42 42 Calculation Process for Financial Analysis


44 44 Financial Statement Auxiliary calculation is done outside of the main page. But ‘Financial Summary’ takes the input values from separately prepared ‘Input conditions’ worksheet. When a person wants to have some additional analysis on financial feasibility, input values may be changed so that its impact on financial statements can be reflected in Excel file. Especially for SCG model, economic feasibility test is done by comparing the existing system and newly implemented one.


46 46 Power Load Forecast & CHP Power Production

47 47 Monthly Heat Load Pattern

48 48 Monthly Power Load Pattern

49 49 Heat Load Duration Curve

50 50 Expected Applications of the Model Major energy saving in primary energy sector can be achieved through the recycled heat energy from industrial sector and waste incineration. Energy Efficiency Improvement on national level can be achieved through identifying the potential of un-used energy and potential energy savings. Economic feasibility, energy saving, environmental improvement effect can be obtained by localized distributed community energy service  It is estimated that 4.5% of electricity transmission loss can be avoided by localized power supply  Active response to Kyoto protocol  Large scale energy service complex which needs social concession can be avoided  Many advantages obtainable through the promotion of community energy services such as the prevention of large scale power black out, etc. Lessen the grid burden of national power supply via localized cool energy and power supply  Improvement of power reserve rate through taking care of cool load via CES  Efficient energy utilization in energy supply Various regulatory problems can be identified through the selection of operation strategies.  Power import and sales back to grid and related problems  Heat import and relevant regulations required

51 51 References Frees, Edward W., 2004, “Longitudinal and Panel Data”, Cambridge: Cambridge University Press Hox, Joop, 1995, “Applied Multilevel Analysis, Amsterdam: TT-Publikaties” Hox, Joop, 2002, “Multilevel Analysis: Techniques and Applications, Mahwah”, New Jersey: Lawrence Erlbaum Associates Publishers Kang, J.S., 2006, “On the Recent Issues of Community Energy Services”, Energy Focus KEMCO Homepage, “” Lee, K.D. et al., 2005, A Research CES on Master Plan, KEEI MOCIE, 2004, Cogeneration and Policy Implementation MOCIE, 2002, CES Energy Supply Plan Natural Resources Canada, Combined Heat & Power Project Model, RETScreen Software Online User Manual, “” Petrill, E., 2004, “A Framework for Developing Collaborative DER Programs: Working Tools for Stakeholders” Rawson, M., 2004, “Distributed generation costs and benefits issue paper“, Staff Paper Shim, S.R., 2002, Restructuring of Energy Industry and Economic Feasibility of Cogeneration, KEEI Verbeke, Geert and Geert Molenberghs, 2000, “Linear Mixed Models for Longitudinal Data”, New York: Springer Weber, C., 2005, “Decentralized energy production and electricity market structures“ Yoo, S.J. et al., 2005, A Research on Energy Supply by Cogeneration, KEEI Yun, H.C., CES and Cogeneration, Seminar at Ajou Univ., 2006.4

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