Olukoye, Babatunde Olusegun
MASTER’S THESIS FOR 15 HP
Study of District Heating Supply
Temperature with Electricity Production
in CHP Plant
“
A Case Study of Enkoping Energy (ENAE)”
Master’s Thesis in Energy Engineering
School of Sustainable Development of Society and Technology Malardalen University, Vasteras, Sweden
In Collaboration with Enkoping Energy (ENAE)
Olukoye, Babatunde Olusegun (Student / Researcher) Fredrik Starfelt (Supervisor)
Camilla Ahlund (ENAE Supervisor) Eva Thorin (Examiner)
Olukoye, Babatunde Olusegun
Abstract
Existence of integrated approach in supplying a local community with its energy requirements from renewable energy or high-efficient co-generation energy sources has come to stay in Sweden. However, in response to the goal of maximizing profit on heat and electricity production with such approach, it then becomes important for combined heat and power (CHP) plants operators to fine-tune their production to adequately fulfil this goal. ENA Energi AB initiated this study on district heating (DH) system as a step in achieving this huge aim.
This master’s thesis focussed basically on relationship and variations of the district heating supply temperature with other variables that affects electricity and heat production in the plant; validation attempt was also carried out on the plant’s model based on some data simulation on the model.
In view of these, minimizing the district heating supply temperature with increasing heat flow was observed as a vital step towards maximizing heat and electricity production for this plant in order to meet the customer’s demand. More so, the plant model was confirmed valid for a certain temperature limit, therefore, based on the findings, the relationship and the model could be used in decision making for the daily operations for the plant.
Keywords: District heating, Electricity, Supply and Return temperature, Heat load and Accumulator
Olukoye, Babatunde Olusegun
Preface
As a summary to my Master’s study on energy engineering, this thesis was carried out at the School of Sustainable Development of Society and Technology at Malardalen University, Vasteras in collaboration with Enkoping Energi AB (ENAE) Sweden from late November 2007 to April 2008.
My appreciation goes to Eddie Johansson for his support and provision of this wonderful opportunity to conduct this research at ENA Energi AB. Special thanks also goes to Urban Eklund and Erik Holmen for sharing their wealth of experience with me on district heating systems for this study. My sincere appreciation also goes to my supervisors; Fredrik Starfelt (MDH) and Camilla Ahlund for their immense support and contributions towards the completion and success of this work. I would ever live to remember the hospitality treatment given to me by the entire staff and personnel of ENAE both the office and the control area, especially the “fika” sessions.
I am also grateful to my examiner (Eva Thorin), for her support on this study. I would also mention Prof. Erik Dahlquist as one of my advisers on this study. I equally appreciate the support from my friends and the entire staff of School of Sustainable Development of Society and Technology, Malardalen University.
Finally, I would like to thank almighty God for giving me the grace and inspiration to complete this study despite all odds. I would also like to thank my family, especially my mother, sisters and relatives for their moral and financial support for my education in Sweden. I dedicate this work to my daughter (Julia Louise Olukoye-Bergstrom); I pray God keeps you for me as I would always live to love you.
Olukoye, Babatunde Olusegun
Table of Content
1 Introduction 1
1.1 Background 1
2 Case Study 2
2.1 Enkoping Energy (ENAE) and Its District Heating System 2
2.2 How It Works 3
3 Methodology 5
3.1 Statement of Problem 5
3.2 Objective of Study 6
3.3 Method of Tackle 6
4 Data Collection and Simulation Process 6
4.1 Power Generation Information Manager (PGIM) 6
4.1.1 PGIM System Structure 7
4.1.2 PGIM Server 7
4.1.3 Signal Explorer 8
4.1.4 Microsoft Office Integration 8
4.2 IPSEpro Process Simulators 8
4.3 PSE (Process Simulation Environment) 9
4.4 District Heating and Electricity Effects in a CHP Plant 9
4.4.1 District Heating System 9
4.4.2 District Heating Network 10
4.4.3 Temperatures in the District Heating Network 10
4.4.4 Advantages and Disadvantages of District Heating 11
4.4.5 Electricity Production and Alpha Value 11
4.4.5 Accumulator 12
5 Results and Validation of Model 13
5.1 DH Supply Temperature effects on Electricity Production, Power-to-Heat Ratio and Heat
Storage Accumulator 13
5.2 Model Validation 19
6 Conclusions / Discussions 21
7 References 22
APPENDIX 24
Appendix A: ENAE Plant Model 24
Appendix B: Data Signals from ENAE Database 25
LIST OF FIGURES 26
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1 Introduction
1.1 Background
In the energy industry today, the demand for environmentally sound energy production technologies is becoming more famous. Combined heat and power (CHP) happens to be a leading technology responding to this market and environmental demands (Rong Y. and Lahdelma R., 2007). CHP production means the simultaneous production of useful heat and electric power. In electricity production, heat can be produced as a by-product for an industrial plant or a residential area. This combined method of production is also known as cogeneration, which is highly recognised by the EU, which consequently led to the issuance of a directive to promote high efficiency cogeneration of heat and electricity.
Recently, the European Commission also set a goal of doubling the share of electricity produced with cogeneration of heat and power by the year 2010. More importantly, biomass based cogeneration has been seen as a good option for the future by the EU, and the use of biomass as an energy source in general is encouraged (Keppo I.and Savola T., 2007).
The sole purpose of a district heating system is to supply adequate heat to its customers. Consumer uses the heat supplied to maintain indoor temperature at a reasonably constant level and counter for building heat loss to the surroundings (Yildirim et al, 2006). Basically, district-heating supply is based on the possibility of obtaining higher efficiency, and consequently lowering the heating and electricity cost, when producing the heat in a CHP plant (Benonysson et al, 1995).
According to Eriksson et al 2007, district heating is available in approximately 200 larger and 300 smaller built-up areas in Sweden. About 75% of all Swedish blocks of flats and approximately 140,000 detached houses are currently heated by district heating (DH), hence, concluding that approximately 50% of all Swedish space heating is supplied by DH. Since heat and electricity demand has huge relationship with the outdoor climatic condition, it is hence important for CHP plants to find optimum operating values for the heat and electricity production for cost and other benefits.
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Consequently, this study is focussed on researching the effects of supply temperatures and electricity production in CHP plants, and also validating a model that may not only be useful in daily operations of the plant but also in further study on improvements and optimization purposes.
2 Case Study
2.1 Enkoping Energy (ENAE) and Its District
Heating System
Enkoping is located in central Sweden, around 70 km from the state capital (Stockholm) with a population of about 20,000. The municipal authority of Enkoping owns this local utility (ENA Energi AB) that is responsible for its electricity and district heating system. 1994 was the start date in which ENAE commenced the CHP plant operation with biofuel. So far, the company have supplied the whole city of Enkoping with almost 100% renewable heat and also produced 50% of the electricity demand, even though a small proportion of oil is usually used as a backup.
The CHP plant depends on fuel mix from forest industry waste materials and short rotation crops called Salix. The fuel consists mainly of tops and branches from trees, saw dust and bark from wood processing industry. All the biofuel passes through an 8,000m3 fuel storage, where various deliveries are mixed to yield a homogeneous fuel. Variations in the moisture content are small in the mix of fuels with an average moisture content of about 45%.
2005 report from the company shows that about 137.1 MW effective heat power is supplied by the district heating system to Enkoping city, which has a total length of about 84 km. The area covered is shown with the red colour in figure 1 below. Heat and electricity production in a normal year approximately amounts to 250 GWh and 100 GWh respectively with an approximate use of 400 GWh bio fuel. This company with collaboration with Fredrik Starfelt (Starfelt F. 2006) developed a simulator-based model for the plant as an attempt to investigate how the boiler at ENA Energy would react to changes in the plant.
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Figure 1 District Heating Network of Enkoping City (www.enae.se) and permission from ENAE
2.2 How It Works
The farthest houses on the district-heating network are of high importance in obtaining the pressure level difference between the outgoing temperature flow and the return. On this network or system, a valve and pump are connected to the condenser, which in turn has a connection with the low-pressure turbine. From the condenser, a valve regulating the outgoing flow is connected to the accumulator (Energy Storage medium), and the other piping systems for loading and unloading of high energy water in and out of the accumulator. Also, fuel supply unit to the boiler is connected with valves to the turbine. See figure 2 and 3 for clarity.
The pressure level on the pressure gauge is sensitive to the consumer’s demand of heat from the district heating; this in turn have effects on the pump that is connected to the condenser. If the demand for heat from the consumers is low, the pressure difference is high; the pump senses this, and thus reducing the pumping effect of the pump connected to the condenser. Simultaneously, the valves regulating the steam flow to the low-pressure turbine closes, hence, reducing the fuel input to the boiler. This results into reduction effects on electricity production. On the other
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hand, if the demand is high, the pressure difference is low and more fuel is supplied to the boiler, resulting in more electricity production.
5 Figure 3 District Heating with an Accumulator
3 Methodology
3.1 Statement of Problem
Maximizing profit is the drive for CHP plants operators. Since the cost of electricity on the stock market is considerably increasing, this is a wake up signals for all CHP plant operators to maximize their profit on electricity. However, CHP plants is also responsible for heat production that is used in DH, it is therefore a challenge to keep the two products in balance.
When it comes to the daily operation of a DH system, the structure of the problem is somewhat different. In this case, the task is to find optimum supply or outgoing temperatures of the DH system that would produce maximum electricity, bearing in mind instability of the heat demands by the consumers which is based on the outdoor weather conditions, and also the storage nature of the heat storage vessel (accumulator).
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3.2 Objective of Study
The purpose of this study is to investigate the daily possibilities of achieving maximum electricity production from a CHP plant with various supply or outgoing temperatures of a DH system, and its implications on the heat accumulator vessel.
For clarity and simplicity, this study shall be structured on some basic research questions which would be helpful to fulfill the objective of this study. The questions are as follows;
• How does the supply temperature on the DH system affect the electricity production, power-to heat ratio and heat storage in the accumulator tank?
• Is the developed simulation model accurate in the simulation of different supply temperatures?
• How could operation optimization be developed from this information and implemented in the daily operation of the plant?
3.3 Method of Tackle
This work shall be partly structured on few literature studies on some concepts of electricity and heat production with reference to district heating systems in a CHP plants using ENAE as a case study. Information/data shall be retrieved from the web manager (PGIM) for ENAE’s operation data. These data shall be analysed and validated with a developed simulation model based on IPSEpro provided by MDH, and then followed with discussions.
4 Data Collection and Simulation Process
4.1 Power Generation Information Manager
(PGIM)
Access to real-time information is a good step to achieving great optimization improvement in production processes. PGIM used at ENAE is a package supplied from ABB; it operates on distributed open client/server architecture. The various elements of the system can run on Microsoft windows operating system.
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4.1.1 PGIM System Structure
PGIM scanners collect process data from lower level control systems, PLC’s, or other recording systems. The PGIM server accepts the data, and then stores;
• Signal descriptions
• Current (Real-time data) and historical values (Long-term storage) • Messages with detailed status information.
This data is stored for periods up to several years in the process database.
PGIM installed at ENAE offers a wide range system architecture that performs the following functions;
• Collecting, archiving and consolidating data from production facilities, control systems and commercial systems
• Conducting remote diagnostics on numerous plants components
• Visualizing and analyzing process parameters with a convenient user interface
• Making data available to other evaluating applications (e.g. for performance calculations, operation schedule optimization, EXCEL reporting)
• Delivering process parameters, status variables and counter readings to maintenance and financial systems
• Archiving data over long periods of time
4.1.2 PGIM Server
The core of PGIM is the server. All relevant data is stored here; • Signal descriptions
• Current process data (Real-time data) • Historical process data (Long-term storage)
8 • Messages (Events)
The PGIM server can sometimes be configured as a redundant database. All process data is stored with the acquisition time (millisecond resolution), the physical value and detailed status information. The PGIM database also allows future values that are suitable for forecasting.
4.1.3 Signal Explorer
The signal Explorer is a centralized utility for fast and efficient configuration. A clear user interface enables the configuration of trends, graphic displays, logs and calculations. It also presents all configured process data from all reachable servers and scanners. Signals can be filtered and sorted according to codes.
4.1.4 Microsoft Office Integration
Microsoft office integration feature of PGIM makes it possible to generate ad hoc, hourly, daily, monthly, and yearly reports production of balances and maintenance logs.
4.2 IPSEpro Process Simulators
IPSEpro can be described as a highly flexible and comprehensive environment for modelling and analyzing processes in energy engineering, chemical engineering and other numerous related areas. This package is designed to solve problems that are represented by a network of discrete components and their connections. IPSEpro allows users to create models of arbitrary process schemes using components from a standard library, or using component models that were created by the user already (IPSEpro, 2003).
IPSEpro offers unlimited flexibility in defining the characteristics of the component models that are used for modelling process. This gives the users opportunity to build component model libraries that exactly match their application requirements. The package also allows total freedom in arranging the available components in order to represent a process scheme. In setting-up a process model with IPSEpro’s process simulation environment (PSE), the user chooses component icons from a library menu, place them in the project window and connect them appropriately. Numerical data and results of process calculations are entered and displayed
9 directly in the project window.
IPSEpro’s structure is good in simulating the behaviour of single elements of processes, of parts of a process and of models of complete plants. It also provides efficient data management and users robust algorithms that extremely short calculation time.
4.3 PSE (Process Simulation Environment)
PSE is IPSEpro’s process simulation environment. PSE allows users to create a process model based on components from a library. It also provides user-friendly flow sheet editor, where it is possible to build process models by selecting the components from a menu. The component is placed in the project window by users specifying the data interactively, and then connecting the components according to their requirements.
The provision of a strong mathematical method by PSE guarantees fast and accurate calculations. PSE is structured in a manner that all data related to the process model is entered directly in the flow sheet. There is possibility of results being displayed in the flow sheet or in separate data tables which can be individually configured.
4.4 District Heating and Electricity
Effects in a CHP Plant
4.4.1 District Heating System
District heating systems basically consist of three (3) parts; production plant, network or distribution pipes and the connected buildings as shown in figure 4. In this case, the production plant is a combined heat and power plant and it is also connected to a heat accumulator as storage for excess produced heat and additional heat production medium.
10 Figure 4 District Heating System
4.4.2 District Heating Network
This is the main link between the production unit and the consumers. This network is made of steel pipe materials that are laid in pairs and also insulated for low temperature losses. A pipe is used for supplying the buildings with hot water and the other for returning the cooled water to the production units. The length and volume of the pipes in a network are usually very large. The medium for heat distribution at ENA Energy is water, but steam can be used also. The advantage of steam is that its addition to heating purposes it can be used in industrial processes due to its higher temperature. The disadvantage of steam is a higher loss due to the high temperature. More so, the thermal efficiency of CHP plant is significantly lower if the cooling medium is higher temperature steam, resulting into lower electric power generation.
4.4.3 Temperatures in the District Heating Network
Maintaining low temperatures in the pipes is important since the heat loss increases with the temperature in the pipes. However, lower temperatures means higher flow rates for a given heat demand. The return temperature is always subject to the plant; consequently, this should be designed to give lowest possible return temperature. The effect of this is that lower return temperature decreases the heat loss in the return pipes but also decreases the necessary flow rate11
for a given power and supply temperature. Furthermore, a lower return temperature thus gives the opportunity of lowering the supply temperature.
Supply temperature is also an important parameter always considered in controlling the network. The supply temperature is increased to such an extent that the flow rate, necessary for the consumers, can be handled by the pumps. However, flow rate is a parameter not controlled by the district heating company, but keeping of an appropriate pressure in the pipes so as to enable all connected customers obtaining their energy demand is achieved by varying the flow rates. Obviously, the flow rate is higher in the winter period with high demand and low during the summer.
4.4.4 Advantages and Disadvantages of District
Heating
District heating has various advantages over individual heating systems; usually DH is more energy efficient due to simultaneous production of heat and electricity in CHP plants. The larger combustors have a more advanced flue gas cleaning than single boiler system. In the case of surplus heat from industries, DH systems do not use additional fuel because they use heat (Heat recovery) which would be disbursed to the environment.
DH is a long-term commitment that fits poorly with a focus on short-term returns on investment. Benefits of the community include avoided cost of energy, through the use of surplus and wasted heat energy, and reduction in individual household or building heating equipment. District heating network, heat –only boiler stations and co-generation plants requires high initial capital expenditure and financing. If considered as a long-term investment, it may results into profitable operations for the owner of district heating systems; or combined heat and power plant operators. DH is less attractive for areas with low population densities, as the investment per household is considerably higher.
4.4.5 Electricity Production and Alpha Value
Electricity can be produced with a steam circuit including one or several boilers, turbines and condensers. In the case of ENA Energy CHP plant, the boiler generates steam under high pressure to drive the turbines (540 C @ 100 Bars). After the turbines comes the steam with low
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pressure and temperature, however no condensing is allowed in the turbine, since any water droplets would damage the turbine. The turbines are connected to a generator with a condenser.
Since electricity production is largely dependent on the magnitude of the pressure in the condenser, which is a function of the return temperature from the district heating, hence low return temperature gives low pressure in the condenser and thus yields higher electricity production in the plant. Lower return temperatures also decreases heat loss in the pipes and also flow rates for a given power and supply temperature. Also, an increase in demand of energy is met with an increased supply temperature by the network operators. However, the supply temperature can be increased to such an extent that the flow rate necessary for the consumers can be handled by the pumps. Alpha (power-to-heat ratio) value is an important parameter in the process of heat and electricity production process. A typical alpha value is 0.3 for a back-pressure unit where the heat is utilized in a district heating network. Alpha value for ENA Energi is observed to be between 0.07- 0.49, which is a reflection of the plant’s operations for year 2007. The alpha value can be controlled to some extent; this is because of its dependence on the condensing temperature.
4.4.6 Accumulator
Since electricity must be generated whenever the consumer wants it- electrical energy cannot be stored. So the CHP plant produces hot water even when it is not required, this however necessitate the need of a heat water storage medium called “Accumulator”. The accumulator tank at ENA energy is about 7000 m3 capacity. It is usually loaded with hot water with an approximate temperature of about 75- 100 degree in respect to the heat demand that is dependent of the outdoor temperature.
13 Figure 5 Accumulator Storage Tank
This medium is useful when electricity production is needed during the peak hours at a high market electricity prices. Excess heat from this production is stored in this medium and it can of course discharge during hours when the price is low, see figure 5 above. It can also be used to avoid starting a more expensive plant during peak hours of heat demands.
5 Results and Validation of Model
5.1 DH Supply Temperature effects on Electricity
Production, Power-to-Heat Ratio and Heat
Storage Accumulator
In an attempt to answer the afore mentioned research questions, operation values were taken from ENA Energy data base from January 1st 2007 to December 31st 2007 on hourly bases. The plant components are the boiler, turbines, generator, condenser with the district heating system and the feed water tank. The data signals values used for the analysis were obtained from steam
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flow to the turbines, electric power generated, condenser pressures, district heating supply and return temperatures, heat demand, energy storage in the accumulator, cooler, outdoor temperatures and calculated power-to-heat ratio as shown in appendix B at the end of this report. Supply temperatures ranging from 89-98 degree(c) and its corresponding variable values were made choice of focus for this study because it tends to give a clearer picture and idea of the happenings around these operating conditions. These values at constant flow at each particular operating temperature were plotted on graphs and subsequently discussed.
Figure 6 Electric Power Production as a function of the supply temperature from ENA Energy database
The figure 6 above clearly shows and confirms a linear relationship at a constant flow between the DH Supply temperature and the electrical power output based on the data from ENA Energy. This also confirms that at higher supply temperatures more power is generated in the plant based on constant flow at each temperature range. However, the figure above did not follow a smooth curve pattern due to the fact that many operation temperatures were plotted against their corresponding power output. It also follows that between 90 to 91 and 96 to 97 degree supply temperatures; an approximate corresponding 1MW power was noticed.
• This may be explained by a substantial increase in the flow at this particular supply temperature which could yield an increase in the electrical output based on the gathered data.
• This may also happen due to sensitivity of the PGIM recording system.
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• About 3 to 4 hours delays in consumers respond could be a reason for this as well.
This means that the return temperature changes after 3-4 hours after the supply temperature has been changed and thus affects the increase in electricity production.
It is also observed that at 97 degree the curve tends to behave in a constant manner for the electrical power production, this seems to be the maximum temperature set when operating at the maximum heat flow.
Fig 7 Electrical Power production as a function of the DH supply and return temperatures from the ENA Energy databases
For the range of data plotted in figure 7 above, the range of temperature difference between the supply temperatures and the return temperature appears to be parallel for the corresponding electrical power generated which is basically dependant on the heat flow. It shows that the return temperatures for the plant operation for 2007 never gone beyond 60 degree. It can also be observed from the same figure above that, electrical power generated between 20.5 and 22 MW has some inconsistencies in the flow at supply temperature range of 85 to 97 degree. One of the reasons for these inconsistencies may be due to instability in the choice of heat flow condition. Though, (Bennonysson A., 1991) confirms the relationships between DH supply and return temperatures with heat loadings as primary. However, the result also confirms the variations between DH supply and return temperatures with respect to electrical power output.
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the return temperature and a “set” supply temperature when estimating desired electrical output in a CHP plant, bearing in mind the need to keep the return temperature as low as possible.
Figure 8 Alpha Value as a function of the Supply temperature from ENA Energy database.
Figure 9 Alpha Value as a function of the Return temperature from ENA Energy database.
Since alpha value can to some extent be controlled based on its dependents on the condensing temperature, therefore, the existence of a condenser in a CHP plant makes the alpha value dependent of both the district heating (DH) network supply temperature and return temperature. This can be explained by the relationship between alpha values and the DH Supply and return temperature of ENA Energy 2007 operation data.
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Though, alpha values increases indirectly with DH supply temperature but directly with heat load. But figure 8 above shows how the DH supply temperature increases with alpha value and figure 9 with alpha value increases with lower return temperatures, this could be based on the fact that the heat loads at the various temperatures were varied.
Figure 10 Outdoor temperatures as a function of the DH Supply temperature from ENA Energy database
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Figure 12 Heat flow rate as a function of the Outdoor temperature from ENA Energy database
The control of the supply temperature at ENA Energy DH system is set as a function of the outdoor temperature. The figure 10 above confirms the relationship between the low outdoor temperatures with set high DH supply temperatures. The pattern of the curve can be explained by the use of the main boiler for production up to a temperature of about -5 before engaging the accumulator for other part of the heat supply. The inconsistencies in figure 10 curve could be due to variation in flow rate at this various supply temperatures from 0 to -5 degree. It can also observed from both figure 11 and 12 that the plant has been operating under full load and flow rate for the temperature range of 10 to -10 degree, hence, high electrical power output. Though, this actual control of the supply temperature is set manually at ENA energy, but it does so far meets with the heat demands of the consumers as well as the electrical output.
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Figure 13 Energy in the Accumulator with corresponding DH Supply temperature from ENAE database
ENA Energy’s accumulator is confirmed not to be a pressurized storage one. This however limits the maximum storage temperature to be somewhat below 100 o C. So far in 2007, maximum storage of about 2900MWh was achieved at temperatures not exceeding 100 o C. Even though, the sensitivity between the DH supply and return temperature plays a vital role in controlling the heat storage system, however it can also be inferred that the outdoor temperature is also a vital parameter to consider.
5.2 Model Validation
In energy engineering, models could be developed for optimization, improvement and other reasons. ENA Energy plant boiler, turbine, flue gas condenser and the district heating system were modelled using IPSEpro (Starfelt F. 2006). In reference to Appendix 1, the plant’s model consists of the boiler, turbines, generator and the condenser with district heating system connection. The steam data is set to 540 (c), with input data on the district heating return temperature being estimated while the supply temperature is set before running the simulation exercise. The output electricity generation value is then recorded. The purpose of the plant model is to be used for off-design calculations and optimization purposes. This model may also be used as a tool to predict the accuracy of the heat loadings and electrical output for the plant, when minimizing the supply temperatures against the electrical power output in the district heating system, hence the need to validate the model.
Mean values of the DH Supply temperatures for ENA Energy operations data were collected and estimated at the range of 77-97 (c), with their corresponding values for the DH Return temperatures, Heat flow and the Electricity power production. Input data for simulation are shown in Table 1.
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Table 1 Simulated Value of Electricity (MW) from the Model.
DHSupply(c) DHReturn(c) Flow (kg/s) El. Power(MW) El.
Power(MW)Simulated % Error 82 48.9 12.5 9.55 9.27 0.03 84 51.7 14.5 11.98 11.66 0.027 85 51.8 16.5 13.58 13.32 0.0195 89 52.1 19 15.42 15.21 0.0138 90 53.2 21.3 17.38 17.23 0.0087 92 53.8 23.6 19.4 19.28 0.00622 94 54.3 25.5 20.22 19.98 0.012 96 54.5 26.5 20.88 20.78 0.00048 97 54.64 28.3 21.55 20.85 0.0336
Figure 14 Graphical Relationship of the simulated and the real values
The simulation result and the followed graphical representation clearly show close relationship between the actual ENA Energy data and the simulated ones. It somewhat indicates that for optimization, improvements and further operational integrations purposes, the ENA Energy model may be considered reliable with minimal degree of error within the above temperature range.
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6 Conclusions / Discussions
How does the supply temperature on the DH system affect the electricity production, power-to heat ratio and heat storage in the accumulator tank?
Following the discussion of the result above, this study has been able to present an established relationship between the district heating (DH) supply temperature and other vital variables needed to be considered for optimum electricity production in a CHP plant, using values from ENA Energy database. Since lower temperatures means higher flow rates for a given power output, therefore, increasing the flow rate means higher pressure in the pipes, but this pressure should be within a limit that can be handled by the pumps. Consequently, it can be inferred from the result above that lower supply temperature could leads to lower return temperature therefore, this linear relationship (fig. 14) may be useful in the control section of a CHP plant when choosing supply temperature for heat and electrical power production and their corresponding effects on other variables in a DH system. Though, this study confirms the importance and the usefulness of the accumulator used at ENA Energy, however, the unpressurized nature of the accumulator tank in plant, has placed a restriction on the highest value of the supply temperature that could be set for the DH system. However, in order to minimize the DH supply temperature and to maximize the use of the unpressurized accumulator, this study suggests that higher flow rates pumps should be used such that the flow rate increases instead of the supply temperature.
Is the developed simulation model accurate in the simulation of different supply temperatures and how could operation optimization be developed from this information and implemented in the daily operation of the plant?
With regards to the validation attempt made by this study on ENA Energy plant model, it can be considerably said that the simulated values were in great agreement with the values taken from ENA Energy database for 2007 operation; hence making this model valid. This model would be of use for optimization of the plant by maximizing electrical output, efficiency or overall cost. Quick simulation of data with the model for decisions in the daily production is another possibility for this plant. Conclusively, the result of this study has shown the possibilities of achieving maximum electrical output at various DH supply temperatures while maximum the use of the heat accumulator in a CHP plant like ENAE.
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7 References
Arvastson Lars, 2001. “Stochastic Modelling and Operational Optimization in District Heating Systems”: Doctoral Thesis in Mathematical Sciences, Centre for Mathematical Sciences,
Mathematical Statistics, Lund University.
Benonysson Atli, Bohm Benny and Ravn F. Hans, 1995. “Operational Optimization in a District Heating System”: Energy Conversion and Management, Vol. 36, Elsevier.
Benonysson Atli, 1991. “Dynamic Modelling and Operational Optimization of District Heating Systems”. Laboratory of Heating and Air-Conditioning, Technical University of Denmark, DK 2800 Lyngby.
Eriksson Ola, Finnveden Goran, Ekvall Tomas and Bjorklund Anna, 2007. “Life-cycle Assessment of Fuels for District Heating: A Comparism of Waste Incineration, Biomass and Natural Gas Combustion”. Energy Policy, Vol. 35, Elservier.
Keppo Ilkka and Tuula Savola, 2007. “Economic Appraisal of Small Biofuel Fired CHP Plants”. Energy Conversion and Management, Vol. 48, Elservier.
Rong Aiying and Lahdelma Risto, 2007. “Efficient Algorithms for CHP Production Planning Under the Deregulated Electricity Market”. European Journal of Operational Research, Vol. 176, Elservier.
Simtech Simulation Technology, IPSEpro User Library, 2003.
Starfelt Fredrik, 2006. “ Biobranslleeldat Kombikraftverk Inforande av Trapulvereldad Gasturbine I Varmeverket Enkopings Befintliga Kraftvarmeverk” Master Thesis in Energy Engineering, Department of Public Technology, Malardalen University, Vasateras, Sweden.
Usman Musibau, 2007. “Simulation Model of Flue Gas Condensation Unit and Complete Process Plant Simulation; Case Study of ENA Energi”. Master Thesis in Energy Engineering, Department of Public Technology, Malardalen University, Vasateras, Sweden.
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Yildirim Nurdan, Toksoy Macit and Gokcen Gulden, 2006. “District Heating System Design for A University Campus”. Energy and Buildings, Elservier.
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APPENDIX
Appendix A: Showing ENAE Plant Model
developed from IPSEpro
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Appendix B: Data Signals for the variables used
for the analysis from ENAE Database
Data
Signal Description
Unit
Electrical Power Net
1BAT10CE201-XK01 MW DH Supply Temperature 1NDA1OCT203 C Accumulator Energy QI847-XU01 MWh Heat Demand 1NDY10CU201-XP01 MW DH Return Temperature TI702-XJ01 C DH Power 1DY00CU201-XK01 MW Steam Flow 1LBA10CF201-XU01 Kg/s Cooler 1NDA22CT201 C
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LIST OF FIGURES
Figure 1 District Heating Network of Enkoping City
Figure 2 Picture of How It Works at ENAE
Figure 3 District Heating With an Accumulator
Figure 4 District Heating System
Figure 5 Accumulator Storage Tank
Figure 6 Electric Power production as a Function of the Supply temperature from ENAE database
Figure 7 Electric Power production as a Function of the DH Supply and Return temperatures from ENAE database
Figure 8 Alpha Value as a function of the Supply temperature from ENAE database
Figure 9 Alpha Value as a function of the Return temperature from ENAE database
Figure 10 Outdoor Temperatures as a Function of the DH Supply temperatures from ENAE Database
Figure 11 Heat Load as a function of the Outdoor temperature from ENAE database
Figure 12 Heat flow rate as a function of the Outdoor temperature from ENAE database
Figure 13 Energy in the Accumulator with Corresponding DH Supply temperature
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