Modelling CHP within a national power system

CHP SERIES

Modelling CHP within a national power system Poul Erik Grohnheit

Modelfing of a power system with combined heat and power (CHP) is analysed from the point of view of a utility or public authority with responsibility for a large regional or national power grid, or an international organization with energy policy responsibilities. The examples of existing models that are most used and consolidated are short-period models used by utilities for weekly operation and long-term capacity planning. There are several examples of models at regional or national level that are used by national authorities or international organizations; these are typically annual models using a load duration concept to describe load variations. There are no examples of models in operation that integrate the urban infrastructure of hot water transmission mains and distribution grids and power system models. Keywords: Combined heat power; National power systems; Optimization models

Models for power generating systems producing electricity only are well established for a number of purposes, from short-term planning of daily operation of load dispatch, to planning of new generation capacity, and national or international energy system models for analysing energy or environmental policy issues. The modelling task becomes more complicated when the power generation system also includes a significant amount of combined heat and power. In addition to simulating or optimizing the production of one product for one grid by a number of different generating units, another product is added which may feed several urban grids covering much more The author is with the Systems Analysis Department, Ris0 National Laboratory, DK-4000 Roskilde, Denmark.

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limited geographical areas than the power grid, or in the case of industrial CHP - is used as an energy carrier for a wide range of processes. In this article modelling of a power system with CHP is analysed from the point of view of a utility or public authority with responsibility for a large regional or national power grid, or an international organization with energy policy responsibilities. Although most countries in North-west Europe have space heating demands and an urban structure that would justify a significant penetration of CHP, there are only a small number of power systems where more than a few per cent of the demand is supplied from cogeneration. Notable examples for regional power systems are the two Danish utilities, Elsam and EIkraft, where about one-third of the total demand is supplied by power generated in combination with heat for district heating. Looking at the power systems of metropolitan areas or cities, there are larger shares of power supply from cogeneration. The most notable example is Helsinki, Finland, where most of the power supply is generated in combination with heat, covering about threequarters of the total city heat market, and more than 80% of the district heating supply.1 Unfortunately, in most cities in Western Europe natural gas has already penetrated the urban areas that are best suited for district heating. In Poland, Czechoslovakia, the states of the former USSR and the new federal states of Germany, CHP for district heating is a very significant part of the power supply. However, these systems - including the district heating grids and house installations need enormous improvement. Industrial CHP is used in many countries. As with CHP for district heating the demand for the heat or steam is essential. The decision process is easier; it does not require city-wide infrastructure development and urban planning activities involving many decision makers and public acceptance. Although 0301-4215/93/040418-12 (~ 1993 Butterworth-HeinemannLtd

C H P series - m o d e l l i n g C H P within a national p o w e r s y s t e m

is electricity, part of which is converted to heat following the isofuel line.

Power

03 c"

~3 E o

Heat

Figure 1. The operating area for a combined heat and power unit (condensing unit with heat extraction).

the actual technologies may be different, and parameters for heat load factors and heat demand patterns certainly are different for the two types of CHP, the basic modelling problem is the same. Is the cogenerated power competitive with other generating technologies, and does the benefit outweigh the investment?

Modelling basis for CHP operation The power or heat grids are most often fed from several generating units. The basis for scheduling these units for production in each time period will be their merit order, which is based on the short-term operating cost. There are three major types of steam turbines in a thermal power system with cogeneration:

Using the back pressure method, the merit order for either product will be based on the short-term operating costs of the product minus the value of the other. Using the extraction method, the total amount of fuel is used for electricity generation by condensing production according to the merit order of the power generating units. The heat demand in each heat region is satisfied in a merit order, for which the cost of the heat supply from extraction units is equal to the cost of the additional electricity generation necessary to replace the electricity lost by the extraction of heat. In an interconnected power system with many units the pattern of the operating areas of the extraction condensing units may be quite different. Together with the non-linearity of the isofuel and back pressure lines this will require a large amount of data and advanced mathematical methods for optimization. However, more simplified models with linear assumptions may give viable results for many modelling purposes. The production possibility set of power and heat, shown in Figure 2 for a single extraction condensing unit, is also valid for an interconnected generating system for which the instant marginal production is dominated by a single fuel type eg internationally traded coal. This power generating system is described by the following parameters: qp

P

max

• condensing units without cogeneration; • back pressure units producing power and heat in a nearly fixed relationship imposed by the technical layout of the turbine; and • extraction condensing units, which allow both condensing and back pressure mode production in a flexible combination of power and heat. The operating area for the latter units is illustrated in Figure 1; and in Figure 2 some parameters used for models are indicated. The figures illustrate two ways of modelling cogeneration: • The back pressure method: the output of the system is a combination of power and heat following the back pressure line. • The extraction method: the output of the system

ENERGY POLICY April 1993

the efficiency of condensing production ie power per unit fuel (typical value 0.40)

h

~

1 Pmax-Cv hma x

max

cm

Pmin

I h

.

mln

h

max

Figure2. Linear representation of the operating area for a combined heat and power unit.

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C H P series - modelling C H P within a national p o w e r system

Tim

the efficiency of back pressure production ie power plus heat per unit fuel (typical value 0.85) Cm power ratio ie power per unit heat at back pressure production, ranging from 0.4 for standard gas turbines to 0.7 for modern extraction condensing units cv power loss ratio ie power lost per unit of extracted heat (range 0.15-0.25) ~lh the efficiency of heat only boilers ie heat per unit fuel (typical value 0.85) i~pCHP instant marginal efficiency for cogenerated power (typical value 0.85) = ~l,n when Tim = rib l~hCHP instant marginal efficiency for cogenerated heat (typical value 2.0) = vlp/cv (eg 0.4/0.2)

The instant marginal efficiencies are valid for given reference technologies (eg district heating boilers or condensing power stations) and they must not be used both at the same time. These parameters and the power loss ratio can be derived from the others. The lead time for new power generating capacity is several years for large-scale technologies. Therefore, the cost of new capacity - either in the form of investment costs during the construction period or interest and depreciation of capital expenditure - is relevant only for long-term models. 2

Short-term modelling The production of power and heat must follow the load variations at any timescale, because storage possibilities are limited, unless the power system contains a considerable amount of hydro capacity with lake reservoirs. The economic dispatch of the available units connected to the system is therefore an essential task for the administrators of power and heat grids. The basic principle is the scheduling the available units in merit order taking into account the extra costs of start up and close down of the various units and their suitability for load regulation. With a few modifications the same description is valid for the district heating grids. The combined heat and power production system to be modelled consists of one power network and several heat networks. Some units produce both heat and power simultaneously while others produce either heat or power. Each heat producing unit can only be connected to one heat network. The optimization problem for the system operation for a few days or one week is to find the cheapest unit commitment and load dispatch satis-

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fying given heat, power and reserve demands using given units. For each network the heat and power demands should be given for one-hour time steps. The reserve demand is stated as a certain amount of power plus a certain percentage of the power demand. The given units are those available. A unit can only produce between some technical minimum or maximum (or not at all) as shown in Figure 1. In addition to the technical limitations the operation of a unit may be further constrained to reflect conditions that are not taken into account by the optimization. Imports or exports to neighbouring power systems are treated as special units or consumers. The operating costs to be minimized by mathematical modelling are production costs and start up costs. Production costs are mainly fuel costs, but also other variable costs such as manpower and depreciation costs that depend upon production. Different versions of this type of optimization are used for weekly operational planning and for longterm capacity planning. In the latter case it is necessary to compose a model year of a number of representative periods.

Weekly operational planning A simulation tool of this type has been used for weekly operation planning by the Danish utility Elkraft since 1989. It is part of a comprehensive and integrated computer system named EPOS, which is based on a extensive database and also offers many other functions. 3 The optimization method is based on the mathematical concepts and techniques of Lagrangian relaxation, decomposition and dynamic programming. 4 The solution method is an iteration procedure seeking to determine the shadow prices that provide for the satisfaction of the demands. The optimal unit commitments and productions of heat, power and capacity corresponding to a given set of shadow prices are determined when the total operating costs are minimized. The iteration procedure has two loops. The inner loop determines the shadow prices for heat production corresponding to a given set of shadow prices for power production and power capacity. This loop is performed separately for each heat network. The outer loop determines the shadow prices for power production and power capacity in accordance with the solution for the heat side. This loop concerns both CHP units and condensing units. Each loop covers all the time steps in the calculation period. The model assumptions are moderately simplified

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C H P series - modelling C H P within a national p o w e r system

compared to reality. In practice, only the large extraction condensing units feeding the large C H P network of the Copenhagen region are included in the optimization as true CHP units, which are allowed to operate within an area as illustrated in Figure 1. The other extraction or back pressure units are treated as condensing units with restrictions on the power production. The same simplification is used for two smaller CHP areas that are supplied from major power stations, where limited extraction facilities are added to some of the major coal condensing units. After some time of practical operation the simulation tool is considered as viable but complicated: it produces reasonable results but can never be more than a tool to which the dispatcher must add his experience and practical considerations. The calculating time for 10 days was approximately 45 minutes on a MicroVAX 3800 with VMS and the O R A C L E database system. This includes, however, two simulations, one with and one without trade with neighbouring utilities in order to calculate the accounting price for the interchange of power. The simulation tool is also used to calculate the price of heat to be offered at an exchange of heat for district heating. If the district heating company does not accept this price offer, because the heat demand is met by a cheaper source eg waste incineration, the simulation is repeated with an adjusted demand for heat. This simulation tool represents the state of the art of optimizing power systems with CHP. The computer requirements of the tool are not overwhelming, and a lot of hardware can run even larger problems more quickly. However, the limiting resource in the development and implementation of a tool for the optimal operation of a complicated system is the human understanding and experience of the operation of the particular system, and for this there is no substitute.

Short-term modelling for new capacity planning For the day to day load dispatch scheduling and unit commitment the state of the art optimization model is hardly able to compete with the experienced dispatcher, who knows all the peculiarities of the real system. For future situations with a different system structure and environment, however, model analyses are the only way to obtain a deeper understanding of the system. The basis for this analysis are short-term models of the same type as those used for day to day

ENERGY POLICY April 1993

operation. The heat and power production for a year is made up of a number of simulations for few-day periods with different demand and unit availability patterns. The demand submodel is based on the total annual energy consumption. The weekly consumption is calculated by annual distribution, and each week is categorized by week type. The daily consumption is calculated in a corresponding way. Finally, the hourly energy consumption is converted into an average power load. This demand model gives a great flexibility. The quality of the demand model depends on the selection of week and day types. The unit availability submodel must include a plan for unavailability of power due to scheduled maintenance and the results of a stochastic process that generates the forced withdrawal of supply. The task of the short-term simulation is to find feasible and optimal solutions for unit commitment and load dispatch for a set of typical demand and availability patterns that compose a year. The model described here is the most recent development within a long tradition, mainly carried out by the Danish utility Elsam (west of the Great Belt). The new optimization model for capacity planning is called SIVA,EL; 4 the database and model organization and the mathematical tool for optimization is similar to the EPOS system described above. However, different scopes of model use have led to different simplifications in model formulation, which is necessary to reduce computing time and model complexity. To secure portability the code of SIVA.EL is compiled in F O R T R A N 77, and the database is compiled in O R A C L E . The SIVA,EL database is placed on a PC, and the simulation model on a DEC-station 5000. The CPU-time is around 11-12 minutes for a simulation of one year (52 weeks, each consisting of 168 timesteps plus the first 8 steps of the next week) for a system consisting of 36 generating units, 3 neighbouring systems and 9 district heating areas. The difference between the scope of the two modelling systems should be explained not only from different corporate traditions, but also their different geographical structures. While Elkraft s e r v e s o n e large d i s t r i c t h e a t i n g a r e a ( t h e Copenhagen region) and a few small ones, Elsam serve five city regions from large extraction condensing units and several smaller towns.

Company tools and power systems analyses The amount of data used for the detailed simulations make these model systems very dependent on inter-

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C H P series - m o d e l l i n g C H P within a national p o w e r s y s t e m

Table 1. Simulachron model simulation for a three-day period of Danish CHP units in various future market environments, a

Electricity capacity Nuclear Condensing Extraction Back pressure Heat demand (GWh) Extraction units Back pressure units Boilers Electricity demand (GWh) Nuclear Coal, back pressure Coal, condensing Peak load units External sales Costs, 106 DKK-1984 Index (0 nuclear= 100)

Winter (March equinox) 25 TWh/pa 0 2 nuclear nuclear units units

40 TWh/pa 0 2 nuclear nuclear units units

Summer (September equinox) 25 TWh/pa 40 TWh/pa 0 2 0 2 nuclear nuclear nuclear nuclear units units units units

5950 0 2400 3150 400 250.2 213.3 36.9 0.0 190.8 0.0 152.8 40.7 0.0 2.7 83.8 100.0

9550 0 6000 3150 400 250.2 213.3 36.9 0.0 305.3 0.0 152.8 152.5 0.0 0.0 123.4 100.0

5950 0 2400 3150 400 140.6 119.9 20.7 0.0 179.0 0.0 85.9 93.2 0.0 0.0 72.5 100.0

5950 1800 600 3150 400 250.2 179.4 36.9 33.9 190.8 55.3 131.5 7.0 0.0 3.0 76.9 91.8

9550 i800 4200 3150 400 250.2 213.3 36.9 0.0 305.3 110.4 152.8 42.1 0.0 0.0 98.8 80,1

5950 1800 600 3150 400 140.6 119.9 20.7 0.0 179.0 87.8 85.9 5.4 0.0 0.0 54.0 74.5

9550 0 6000 3150 400 140.6 119.9 20.7 0.0 286.5 0.0 85.9 200.6 0.0 0.0 110.2 100.0

10150 1800 4800 3150 400 140.6 108.1 20.7 11.8 286.5 127.6 78.5 80.4 0.0 0.0 82.0 74.4

N o t e : a Electricity/heat rate (cm): extraction condensing units 0.63; back pressure units 0.50; fuel price assumptions: coal DKK 1984

33.9/GJ (US$95/t) nuclear: DKK 1984 0.10/kWh (US mills 10.7/kWh).

nal company data, although either of them can be used with data for other companies. However, simplified versions of these tools or their predecessors are useful for more general purposes. In particular, results from selected studies using company specific models should be analysed for conclusions of general value in improving the understanding of the system. Modelling these complicated systems requires long lead times, not only for modelling development, but - even more important - for system understanding and perception of model results by a few experienced operators, and for the dissemination of results and conclusions outside this narrow group of experts. This type of model is often successfully used within a given organizational framework eg a utility. It is well understood from experience by the operators - often aided by very detailed computer models - how the system will react to a wide range of changing conditions. However, these changing conditions must be within certain limits, which may not be well understood, because experience cannot be available. Only if boundary conditions change slowly will the system and the operators be able to adapt. To communicate the experience of a few experts within an organization to outsiders will require a more academic approach. This is necessary not only for outside decision makers such as public authorities, international organizations, financial sources, potential merger partners for the utility companies,

422

and the top management of these companies. The academic approach is also needed when looking at the system several years ahead. Too many changes will occur to allow the system to be adapted quickly even by experienced operators. Even at a shorter timescale such adaptation is difficult. Sudden changes in fuel prices and new market conditions, for example, may lead to changes in system optimization to which the system can adjust only slowly. C l I P regions in various future market environments Table 1 shows selected results from a model study, in which a power system containing a given structure of CHP regions and heat demand is optimized under different assumptions concerning other features of the power system. The model is a predecessor for those used by the Danish utilities, Simulachron. 6 It was developed to run with two-hour timesteps over two four-day periods representing sequences of different types of days in a winter and a summer week. For the selected period of a few days this model describes a regional or national CHP production system consisting of various types of power plants, district heating boilers and day to day heat storage facilities. The model simulates the unit commitment and load dispatch among these facilities in order to minimize fuel and operating cost. The mathematical method for the solution is integer programming

ENERGY POLICY April 1993

C H P series - modelling C H P within a national p o w e r system

Winter(Marchequinox) 7 ---"--Back-pressure

Summer(Septemberequinox) ---- Back-pressure Electricity total

tO 12~,'f I" Sunday

~"~"fl

~'J ~"~"f[ ~ Monday

~1

Tuesday

Sunday

I

Monday

I

Tuesday

Figure 3. Electricity and heat load variations.

using a branch and bound method. There is no built in iteration procedure; the time dependent marginal price of electricity from the national power grid must be assumed before the first run of the model, and a new set of marginal prices results, which can then be used for a next iteration. In the study described here, a three-day period representing two weekdays and one week end day or holiday - was chosen as the element to compose a year. The most simple model year that takes the seasonal variations of power and heat demand into account will consist of 61 winter and 61 summer periods. It was shown that the load curves for the periods around the equinoxes could reasonably describe the whole year, especially for a study of power system into the distant future. In Figure 3 the load variation patterns for the two equinoxes, in March and September, are shown. The absolute values are illustrated using a forecast made in 1984 for the Danish power system for the year 2010. The heat loads are shown as the equivalent back pressure power loads using an average power/heat relation (the Cm parameter). The idea behind this study was that by 2010 the electricity generating system would consist of units for which planning decisions have been or will be taken between 1970 and 2000. Either a substantial part of these units already existed in 1985 or they were under construction or planning, in particular the expansion of large-scale CHP. The question that was studied by the model was whether new condensing capacity that would be required to meet the electricity demand forecast should be nuclear or coal fired, given the planned structure of the CHP system. A low and a high

ENERGY POLICY April 1993

demand scenario were studied, represented by a total demand in Denmark by 2010 at 25 and 40 TWh; 10% of the demand was assumed to be covered by wind turbines, assuming constant load, and export of electricity was treated as a low-revenue market for surplus electricity. The fuel price assumptions were those of the mid-1980s ie coal prices as high as US$95 per ton by 2010 and no substantial increase in nuclear fuel costs. For each of the demand scenarios a few options for nuclear capacity were studied. 7 The results of this study may seem uninteresting today, when a parliamentary decision in 1985 excluded nuclear power in Denmark, and the fall in fuel prices in 1986 made studies based on high fossil fuel prices unfashionable. In addition, the most recent development towards an European market for electricity has made planning studies for national power systems obsolete. However, the Danish utilities are very active in the electricity trade because of the geographical proximity between Scandinavian hydro based power systems and the densely populated areas of Germany. The variation in annual hydro production is larger than the total Danish power demand; in 1989 and 1990 more than one-third of the Danish electricity demand was imported from Norway and Sweden, and exports to Germany were significant. The limiting factor in these years was transmission capacity. The modelling problem is no longer the optimization of a closed system. The CHP units that serve given heat markets belong to a power system which is influenced by a number of power generating units without any fixed geographical limits. To understand the market situation of these units and assess their viability, a number of model studies containing an

423

CHP series - modelling CHP within a national power system March equinox Sunday

Monday

September equinox Tuesday

Sunday

Tuesday

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Gas t u r b i n e

Coal Coal Coal Coal Coal Coal Coal

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Nuclear I s00 Mw Nuclear 2

ooo ...............I INN.. 1

I 2 600 MW 3 4 600 000 MW w 5 600 MW

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~iii~ ~iiiiiiii!iii for two

three-day

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• No power generation: p = 0 : M i n i m u m p o w e r g e n e r a t i o n : p = m a x {Pmi. - c~*h, c.,*h} :::: W i t h i n l i m i t s : m a x { P m i n - - cv*h, cm*h} < p < Pmax -- cv*h • A t m a x i m u m : p = P ~ a x - cv*h

appropriate set of market environments will be necessary. The key feature of the model is the marginal generating capacity that influences the market. In southern Scandinavia Swedish nuclear capacity is marginal for the market environment of Danish CHP stations in situations with abundant hydro power, because transmission capacity is limited. With more transmission capacity this situation becomes less important, because the larger European market will tend to make coal or gas fired units the marginal generating units. The production structure for power and heat that is shown in Table 1 may thus represent the Danish CHP system from the mid-1990s onwards in some of these market environments. It is selected from the results of a series of iterative model runs. The assumptions concerning the generating units and the structure of CHP regions is a simplified description of the target for the Danish heat planning, which was passed ~y Parliament in 1979 and largely implemented during the 1980s. A general conclusion from the model results is that coal fired CHP is fairly competitive with nuclear, even assuming a high coal price, because the output of the various model 424

iterations show great variations in the optimal production structure but little variation in fuel and operation costs.

The unit commitment matrix and marginal generating units The iteration process was started assuming a value for the coproduced electricity equal to the operating cost at a typical coal fired condensing unit. Figure 4 illustrates the model results as a unit commitment matrix which also shows the production pattern for each condensing and extraction condensing unit over the three-day period in two-hour timesteps. The nuclear stations produce at maximum in only some of the timesteps, while all the extraction condensing units produce at their minimum at the timesteps where the nuclear units are marginal. In addition to the back pressure production to meet the heat demand of their CHP regions, the extraction condensing units are used as a spinning reserve to meet variations in power demand. The condensing units are used only in a few timesteps or not at all, and in some timesteps the model prefers gas turbines to coal condensing units for a few hours. The marginal ENERGY

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C H P series - m o d e l l i n g C H P within a national p o w e r s y s t e m

Electricity load

the model result discloses that several of the 600 MW coal fired condensing units will not produce at all in typical three-day periods. It indicates that the assumed structure of generating units tends to be uneconomic and that some of the baseload units could be replaced by simplified steam turbine or gas turbine peak load units, s

Summer

Winter Oil

Oil

5

condensing

Annual models for power system operation Coal back pressure Wind

I

Wind Time

Time

Figure 5. Load duration curves and production structure for a power system with CHP (total power demand 40 TWh). unit gives the marginal electricity generating costs for each timestep, which is used in the next iteration. In the next iteration the nuclear units may replace the back pressure production in the timesteps when nuclear is marginal. However, the back pressure production at the technical minimum may be retained because the difference in generation cost does not necessarily outweigh the start up costs. When nuclear is marginal in a significant number of timesteps, this indicates that the market environment should be modified. New transmission lines or new legal requirements - may open new markets for hydro and nuclear power that would otherwise compete with CHP. However, underseas high voltage direct current ( H V D C ) cables and back to back connections between power grids that are not synchronized are expensive. The cost of the DC/AC connections alone, which is the largest cost for DC under sea cable under some 100 km, is 2-300 000 ECU/MW, while the cost of new generating capacity is some 1000 000 E C U / M W for large coal fired units. Heat storage will improve the competitiveness of the combined production. Combined production will take place during peak hours, while the stored heat will feed the district heating grid during off peak hours for power demand. This production strategy is used for the smaller CHP regions with back pressure units. A widely a c c e p t e d , but simplistic, planning assumption for new generating capacity is that new baseload - coal fired or nuclear - capacity should replace scrapped units and meet the increased capacity demand, which is derived from a demand forecast and an experienced load factor plus 20% reserve. This planning assumption was also used here, but

ENERGY POLICY April 1993

The annual fuel requirement for a generating system with load variations is calculated using a load duration curve showing the capacity requirements for an appropriate number of timesteps sorted in descending order. Then the load dispatch is calculated by scheduling the available units in merit order. The method does not take care of start up costs, and scheduled and forced unavailability of power may not be represented satisfactorily, but the method can give a reasonably good picture of the fuel requirements. Two types of modification are needed to include cogeneration. The variation costs of power generation in back pressure mode production must be determined as the additional costs necessary for power generation given the heat generation, and the load duration curve must be divided into two or more seasons to take account of heat demand variations. Figure 5 shows this representation of the C H P system described above. The efficiency of a modern condensing unit is some 40% electric output per unit fuel. The total efficiency of a back pressure unit is some 85% power and useful heat output per unit fuel, which is similar to the efficiency of a heat only boiler. The production from extraction condensing units may be divided into a condensing and a back pressure part, with these efficiencies as a basis for setting up the merit order. The simulation of the electricity and heat supply from thermal power stations requires the following technical and economic data for the national power system: • Each generating unit or type of unit is described by simplified data: maximum electricity and heat output, fuel type, efficiency, availability, operation and maintenance costs. • The variation in annual electricity demand is described by load duration curves for winter and summer.

• The heat demand in each C H P region is specified for winter and summer. The results of the simulation are the annual electric-

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C H P series - m o d e l l i n g C l I P within a national p o w e r system

ity and heat output for each generating unit or type of unit, and the fuel and operating costs of these units. The annual production from hydro and wind turbines as well as imports of electricity will reduce the demand for power from thermal stations and the load duration curve should be modified. The model framework for the CHP simulation requires a set of forecasts for the demand for useful energy and a development plan for the capacities of of the conversion and distribution system. Named the Danish Energy System (DES) Model, this type of modelling has been used extensively by the Danish Ministry of Energy for a range of planning documents since the late 1970s; it was the most comprehensive model available for national energy planning and a number of partial studies. 9 The model was used for a period of up to 40 years, covering the expected lifetime of large technical equipment. With the energy research programme of the Commission of the European Communities, the model approach was also implemented for Germany and Italy as the Detailed Energy System Simulation (DESS) Model. 10 Today, all the features of this type of modelling can be implemented using a wide range of commercial spreadsheet and database software, possibly with some features written in a general programming language (eg FORTRAN) taken from older, well consolidated models. These tools are used instead of models referred to by special acronyms.

Modelling a future energy balance A more general framework consists of three major elements: • an initial energy balance broken down into identifiable technologies; • an assumed set of forecasts for demands and generating capacities; and • a future energy balance calculated with a set of consequences. The key assumptions for the merit order in a model of this type are the efficiencies for condensing and back pressure mode power generation. If the power system is based on a single fuel type, eg coal, the back pressure mode production will always be given the highest priority. The most important task for the forecasting of CHP production will be assessing the demand for heat that may be supplied from CHP, focusing on assessments of the potential in each heat region. When the heat demand is given, the modelling of

426

primary fuel requirements, generating capacities, costs and emissions may be made properly using a few parameters, namely the efficiency for back pressure mode production, the power and heat load factors, and the electricity/heat ratio (cm parameter) or electricity loss ratio (c,, parameter) for each type of CHP unit. The assessment of the potential heat demand, however, will require detailed studies of the urban topography and detailed cost assessments of investment in urban grid and house installations. Similar studies are needed for forecasts of the industrial CHP potential. This type of forecast may require a systematic use of models for local CHP systems.

Annual models for capacity development The model described so far simulates the operation of the power system with a given structure of generating units. Whether the model uses sophisticated mathematical methods or back of the envelope calculations, the only way to analyse the viability of the technologies that are included in the structure is by analysing or comparing scenarios. The models themselves do not choose between technology options; this task requires a modelling framework developed for a much wider set of technologies, which belongs to the bottom up approach to the technoeconomic modelling of the development of the energy system; the complementary top down approach is within the tradition of econometric models. The example to be described in this paper is the Energy Flow Optimization Model, EFOM, which belongs to the supply part of the energy model complex of the Commission of the European Communities. 1l The energy system is described as a network combining the extraction of primary fuels through a number of conversion and transport technologies to the demand for energy services or large energy consuming materials. The overall structure of the EFOM model is shown in Figure 6. Each of the subsystems may consist of a large number of links. Obviously the power generating system is a central part of the model, and modelling of CHP is the main purpose of two of the subsystems: industrial CHP, and decentralized electricity and CHP. The model also allows the modelling of technology substitution in the demand sectors, which may lead to different demands for electricity and heat. Figure 7 shows the principle for the links that are used to describe CHP. Each link refers to a database that contains the network information (upstream

ENERGY POLICY April 1993

C H P series - modelling C H P within a national pow e r system

Supply subsystems

Demand subsystems Intermediate subsystems

Coal

I

Iron and

steel

v

Cement

Gas

Electricity Central electricity and CHP

Heat

v

I

Oil Electricity Industrial CHP

Other industrial sectors

Steam

Biomass

Transport Decentral electricity and CHP

Electricity Heat y

Tertiary and domestic

Figure 6, Modular structure of the EFOM-ENV model.

and downstream nodes) and a number of parameter values. The output of the back pressure units is a mixture of electricity and heat, which is split between the two products by using product allocations based on the parameter Cm. The back pressure method is used in the standard version of the model for a number of technologies - including extraction condensing units - in the subsystems for industrial or district heating CHP. In the Danish version of the model the large extraction condensing units are treated as condensing units that produce electricity, part of which is converted to heat using the conver-

~

Coal

Condensing units ('~ Electricity ~

~

Gas

~

Coal Back pressure units Output mix

~

sion factor 1/c,,, as illustrated by the isofuel line of Figure 1. Electricity and heat load variations are modelled using two-step load duration curves for two seasons, which are used to divide annual demand flows into four, representing base- and peak loads for summer and winter. This four-stream decomposition is also used to model diurnal or seasonal storages. The parameters that are associated with the various links contain a wide range of information for the technologies eg capacities, conversion or transport losses, flow or capacity costs, emissions of

Transmission (~ Electricity y

Distribution ~ Electricity

Distribution Heat

Gas

Figure 7. EFOM model structure for CHP systems.

ENERGY POLICY April 1993

427

C H P series - modelling C l I P within a national p o w e r system

pollutants, and availability or demand variations over time. All this information is converted into a standard linear programming matrix, which is optimized using a commercial linear programming solver. Scenarios for a period of 20-40 years are defined by demand forecasts, energy prices, infrastructure constraints, emission regulations, capacity development plans etc. The optimum for each scenario is found by minimizing the discounted costs over the period. The EFOM model has been used by the Commission of the European Communities since the 1970s. It was used for the Commission's Energy 2000 study in the mid-1980s for a reference projection of the energy systems in the member countries, and scenario assumptions concerning economic growth levels, oil import prices, and the role of solid fuels and nuclear power were studied.lZ An extension of the model that includes emissions of pollutants and emission abatement techniques has been implemented for all EC countries for a study on SO2 and NOx emissions reduction strategies as a part of the assessment of the Council's Directive on Large Combustion Installations. ~3Most recently it has been used for a study of a CO2 constrained policy. ~4 None of the studies, however, contains a more detailed assessment of CHP potentials and technologies. A similar modelling community has developed around the M A R K A L model of the International Energy Agency. This type of model is best suited for describing identifiable technologies producing one or few commodities, and covering a reasonable part of the energy system. These are either large-scale technologies eg power plants, or small-scale technologies that are used in large quantities eg individual heating systems or electric appliances.

trict heating grids to heat centres and power stations. Transmission lines that connect power stations to district heating grids or merge smaller grids into larger ones, can be assessed by a scenario model framework, if the transmission lines have individual investment cost estimates. The same can be done for projects for urban grids at specific locations. It is far more difficult to assess the cost of the detailed urban grids at a national level because the costs are dependent on the existing infrastructure and actual topographic conditions. Assessments of a regional or national potential for CHP will require a total evaluation of all urban areas, or a set of representative urban patterns where a grid may be developed at some estimated costs. Both strategies were used for the Danish heat planning in the early 1980s, involving a large amount of manpower both at national and local level. 15 On the basis of these detailed analyses a set of representative smaller towns and villages were used to assess the potential for decentralized CHP on a national level. 16 To use this strategy on a multinational level will require a representative set of urban structure patterns that may be aggregated into metropolitan areas and regions. There is no large-scale example of this. An interesting example of a set of urban structural patterns for dwellings and an analysis of options for heating systems was presented at a conference on energy and human settlement in 1980 on the basis of several studies in Germany. 17 The method was, however, used to draw more general conclusions concerning optimal insulation levels for a given heating system. Models of urban infrastructure and power systems will require contributions from two different academic cultures and traditions: urban planning and power engineering.

Conclusions Urban structures and CHP potential markets A key question to be answered by modelling CHP systems is, however, whether the benefits of CHP as assessed by power system modelling are sufficient to outweigh the infrastructural investment in transmission lines or an urban district heating grid. This is the main topic to be resolved by the planning models on the local or regional level that have been used in several countries. In the very near future this type of modelling will be an important tool in Central and Eastern Europe for the analysis of strategies for renewal of the various parts of the system, from radiator system and building insulation, urban dis-

428

The understanding of the relation between the CHP systems and the national power grid which can be derived from modelling of the systems using different time horizons is necessary not only for the development of CHP schemes, but also for a wide range of investments in baseioad generating capacity and transmission lines - particular underseas HVDC lines or back to back connections between systems that are not synchronized. It is also essential for the competitiveness and f u r t h e r p e n e t r a t i o n of CHP when the legal framework of the European internal market is being developed. The transit directives for electricity and natural gas from 1991, and the dynamics in the

ENERGY POLICY April 1993

ClIP series - modelling CHP within a national power system

markets towards third party access to the power and natural gas grids, may have both positive and negative impacts on the penetration and economic viability of CHP. There are several model systems available for power utilities that also supply heat for district heating or industry, which are used by the utilities for both the optimal daily operation and investment planning. On the other hand, modelling involving power systems on a national level used by national authorities seldom includes a satisfactory representation of CHP. In the past, CHP has been virtually neglected as a significant strategy for rational use of energy and emissions abatement in the mainstream of international energy system studies, because the technology is considered as somewhat exotic and important only for peripheral countries. The integration of Central and Eastern Europe into the European energy market will make CHP a far more important technology for model analyses of these strategies, because of the penetration of the technology and the poor standard of the existing infrastructure in these countries. ~World Energy Council, District Heating~Combined Heat and Power: Decisive Factors for a Successful Use as Learnt from Experience, Report, June 1991, pp A-17-20. -~P.E. Grohnheit and A. Verbruggen, ~Cogeneration and the internal market for energy', Thirteenth Annual International Conference of the International Association for Energy Economics: Integrated Energy Markets and Energy Systems - Lessons and Perspectives, Copenhagen, Denmark, 19-21 June 1990. 3Ole Jan Olesen, 'Optimization of combined heat and power production in operational planning', International Conference on Applications of Power Production Simulation, Washington DC, 11-13 June 1990 (developer's address: Elkraft Power Company Ltd, Lautruphcj 5, DK-2750 Ballerup, Demark). 4Mogens Pedersen and Hans F. Ravn, Optimal Electrical Dis'patch and Unit-commitment with Nonconvex Costs, The Institute of Mathematical Statistics and Operations Research, The Technical University of Denmark, Research Report 0711985. ~Jens Pedersen, 'SIVAEL: simulation program for combined heat and power production', International Conference on Applications of Power Production Simulation, Washington DC, 11-13 June

ENERGY POLICY April 1993

1990 (developer's address: Elsam, Fjordvejen 1-11, DK-7000 Fredericia, Denmark). 6Helge V. Larsen, Simulachron: A Simulation Model ]or a Combined Heat and Power Production System, Ris~-R-508, Ris¢~ National Laboratory, Roskilde, 1984. 7Energiministeriet, Kul-Kernekraft. Forhold af betydning for electricitetsproduktion p~ basis af kul og uran, Copenhagen, 1984. 8P.E. Grohnheit and P. Laut, "Nuclear power and coal-fired CHP', Energy Economics, Vol 9, No 2, 1987, pp 82-92. "The DES Model was used for scenario studies for the Danish national energy plan 1981: see Energiministeriet, Energiplan 81, Copenhagen 1981. Partial studies include coal/nuclear assessments, 'Kraftv~erksC~konomiske analyser, ibid. Bilag 1 (Appendix)' and note 7, and a study of SO2 emissions; several of these are referred in Poul Erik Grohnheit, The DES Model and Itr Applications, Ris~-R-519, Rise, National Laboratory, Roskilde, 1986. l°Ulf Hansen and Harry'Pospischill, Energimodelle fiir Analyse und Planing der Versorgung, Essener Universit~itsberichte No 3, 1988, Universit~it-Gesamthochschule Essen, pp 31-37; Kirsten Halsna~s and Henrik S¢~rensen, Simulation of the Italian Energy System with the DESS, Risq~-M-2798, Riso National Laboratory, Roskilde, 1989. ~lE. van der Voort, E. Donni, C. Thonet, E. Bois d'Enghien, C. Dechamps a'nd J.F. Guilmot, Energy Supply Modelling Package EFOM 12 C Mark 1 - Mathematical Description, CABAY, Louvain-la-Neuve, for the Commission of the European Communities, 1984. 12J.-F. Guilmot, D. McGlue, P. Valette and C. Waeterloos, Energy 2000, Cambridge, 1986. 13Energy and Environment - Methodology for the Assessment of Acid Pollution in Europe, JOULE Programme, Commission of the European Communities, Brussels 1990, O. Rentz, H.-D. Haasis, A. Voss, M. Meyer and G. Schmid, ~Energy and environment: optimal control strategies for reducing emissions from energy conservation and energy use', in Proceedings of Riso International Conference on Environmental Models': Emissions and Consequences, 22-25 May 1989, Elsevier, Amsterdam, 1990, pp 237-256; and P.E. Grohnheit, "Economic interpretation of the EFOM model', Energy Economicw, Vol 13. No 2, 1991, pp 14,3152. 14Cost-Effectiveness Analysis of C02 Reduction Options. Synthesis Report, Report for the Commission of the European Communities, DG XII, JOULE Programme, Models for Energy & Environment, May 1991. 15Energy in Denmark: A Report on Energy Planning 1984, Ministry of Energy, Copenhagen, 1984. 16Energy 2000: A Plan of Action for a Sustainable Development, Danish Ministry of Energy, Copenhagen, 1990. ~7Ueli Roth, 'Interaction between heating systems, settlement structure and urban planning at the local level', in Research into Energy and Human Settlement Planning. A Report J?om a Colloquium in HOrsholm, Denmark, March 1980.

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