 ORIGINAL ARTICLE
 Open Access
Upscaling a district heating system based on biogas cogeneration and heat pumps
 Richard P van Leeuwen^{1, 2}Email author,
 Jirka Fink^{2},
 Jan B de Wit^{1} and
 Gerard JM Smit^{2}
https://doi.org/10.1186/s137050150044x
© van Leeuwen et al.; licensee Springer. 2015
 Received: 23 February 2015
 Accepted: 25 April 2015
 Published: 16 May 2015
Abstract
Background
The energy supply of the Meppel district Nieuwveense landen is based on biogas cogeneration, district heating, and ground source heat pumps. A centrally located combined heat and power engine (CHP) converts biogas from the municipal wastewater treatment facility into electricity for heat pumps and heat for district heating purposes. Development of the urban district is influenced by the current economic and building decline. For the district heating energy concept, a migration strategy for the required infrastructure is required. The migration spans the district’s smallscale starting phase involving 40 houses up to a scale of 176 houses.
Methods
An optimization model which maximizes profitability is developed which includes data from district heating and cooling demand patterns.
Results
With the optimization model, optimal CHP size, boiler size, and operational hours are determined for various scenarios.
Conclusions
From the scenario analysis, a migration strategy is developed which starts with a simple system concept supported by boilers to a larger system which includes a CHP. Sustainability in terms of CO _{2} emission savings of the energy concept is compared with other possible energy concepts.
Keywords
 Cogeneration
 Biogas
 District heating
 Heat pumps
 Renewable energy
 Lowenergy building
 System migration
Background
In the Netherlands, at least 40% of the national carbon dioxide emissions are related to the heating demand of buildings. Due to the natural gas reserves, conversion by natural gas boilers dominates the heating supply for space heating and hot tap water. But natural gas reserves are gradually depleting and largescale carbon dioxide emissions cause climate change. Hence, Dutch policies are aimed to gradually diminish the consumption of fossil fuels in general and increase the share of renewable energy. Recently, local governments take up their responsibility and create favorable policies to stimulate reduction of energy consumption and increase the share of renewable energy on a local scale. One of these initiatives is the smart grid demonstration project MeppelEnergie, which is funded by the Dutch program Switch2SmartGrids. For the project’s energy system, we develop a smart grid control. The Meppel energy concept consists of a biogas combined heat and power engine (CHP), backup boilers, high temperature (HT) water storage, heat pumps, and aquifer underground thermal storage. The CHP generates electric and thermal energy. The thermal energy of the CHP is used for district heating, the electricity is used to supply heat pumps placed at houses with no connection to the district heating, or is sold through the external grid.
Cooling energy for the houses is provided by an underground aquifer consisting of a warm and cold well. During the heating season, the warm well provides low temperature (LT) heat for the heat pumps. Cooling energy of the houses also provides part of the required regeneration energy to maintain temperature balance within the underground thermal storage. Another part of the regeneration is provided either by a dry cooler or an effluent stream from the municipal wastewater treatment plant (MTP).
The first goal of this paper is to determine the required heating and cooling generator capacity rates for a district heating and cooling system. The second goal is to determine the most economic heat converter (boiler or CHP) in relation to the number of houses. The third goal is to compare the sustainability performance of the starting energy concept with other options. Contributions of this paper are twofold, first we develop a simplified methodology to estimate heating and cooling demand data for a district heating network and a method to determine the operating time of a heating or cooling device based on such data. Second, we demonstrate the application of such methods for an integrative study on financial and sustainability aspects of a complex energy system.
The paper is structured as follows: the ‘Background’ section introduces the energy concept and problem statement. The ‘Related work’ subsection outlines work related to our investigation. The ‘Methods’ section defines the energy model, equations and case related parameters. The ‘Results and discussion’ section includes results, and finally, in the ‘Conclusions’ section, the conclusions are drawn.
Related work
Experience with district heating in the Netherlands is traditionally related to largescale steam power plants (e.g., Amsterdam and Almere city heating network and many smaller city projects). More recently, decentral projects for new urban districts apply either biomass (wood) thermal conversion in boilers (e.g., muziekwijk Zwolle) or biogas cogeneration (e.g., Apeldoorn, Zeewolde, and Leeuwarden) [1]. An overview of Dutch district heating projects and profitability investigation is presented in [2].
Vallios et al. [3] develop a modeling approach for sizing biomassfueled district heating systems. The district heating (DH) system only includes biomass boilers. Optimal size of a residential micro CHP and storage in Japan is investigated by Ren et al. [4] who apply a mixed integer nonlinear programming model. They determine optimal CHP size in relation to varying electricity prices. In a similar study, Cho et al. [5] determine with a linear programming model, for a micro CHP an optimal control scheme to reach economic optimal operation. Chinese et al. [6] develop two optimization models to investigate the design of biomass based district heating networks in Italy in relation to profit maximization and greenhouse gas minimization. The optimization is applied to the design of a new industrial district heating network. Curti et al. [7] investigate configurations for district heating heat pumps which draw low temperature heat from Swiss lakes, supported by cogeneration. They use genetic algorithms to find optimal system sizes. Hlebnikov et al. [8] investigate different scenarios to renovate an existing Estonian district heating network and they apply EnergyPLAN and RETScreen softwares. Lund et al. [9] investigate the role of district heating for the future energy system of Denmark, developing a model of the Danish energy system in EnergyPLAN. Pirouti [10] develops a nonlinear programming model to design district heating networks which are minimized on total energy and exergy consumption and operational costs. Finally, Østergaard [11] reports on the regional energy system of the Danish city Frederikshavn in which an EnergyPLAN model of the region’s energy production and consumption is used to predict the outcome of geothermal energy application.
Most authors develop specific models for optimization of case specific energy systems, while others apply modeling capabilities of specific software like EnergyPLAN or RETScreen. Both approaches require specific input generation, e.g., home heating and cooling demand, energy price, and tax schemes. Besides that, constraints of the energy supply system are required, e.g., amount of available biogas, supplying order between the CHP and boilers, and system efficiencies. We consider using EnergyPLAN at a later stage of our research. Due to the complexity involved, we develop our own optimization model within this paper which at a later stage serves as validation tool for results generated by EnergyPLAN.
An important difference between the optimization carried out in, e.g., the papers of Vallios et al., Ren et al., Chinese et al., and Curti et al. and our optimization is that we have limited the amount of variables by developing an additional constraint which relates size of each generator (CHP, boiler) to its operational time. Another difference is that within the present paper, our focus is not limited to technical size but also to compare energy concepts on profit and sustainability performance.
Methods
District heating system energy model
Nomenclature energy concept
Term  Signification 

η _{chp,e }, η _{chp,t }  CHP electric and thermal efficiency 
η _{ b,t }  Boiler thermal efficiency 
η _{sto,ht}  High temperature (HT) thermal storage average efficiency 
η _{sto,lt}  Low temperature (LT) thermal storage average efficiency 
η _{distr}  DH distribution network average efficiency 
COP_{hp}  Heat pump (HP) coefficient of performance (COP) 
COP_{ c }  Refrigeration chiller COP 
f _{netw,p }  Fraction of required electric pump energy for network pumps related to transmitted thermal energy of the network 
ϕ _{biogas}, ϕ _{ngas}  Biogas supply to CHP, natural gas supply to boilers (m ^{3}/h) 
LHV  Lower heating value (MJ/m ^{3}) 
\(\dot {Q}_{\text {biogas}}\) or \(\dot {Q}_{\text {ngas}}\)  Fuelrelated thermal energy input (kW) 
Q _{dh,demand,h }  Total DH heating demand of (n) connected houses (GJ) 
Q _{hp,demand,h }  Total heating energy demand of (m) houses with a heat pump (GJ) 
Q _{hp,source}  Total low temperature source heat for the heat pumps (GJ) 
Q _{dhw}, Q _{sh}  Domestic hot water demand and space heating demand (GJ) 
Q _{demand,c }, Q _{dh,c }, Q _{hp,c }  Cooling energy demand, for DH network houses, for houses with heat pumps (GJ) 
\(\dot {E}_{\text {chp}}\)  CHP electric rated power (kW) 
\(\dot {Q}_{\text {chp}}\)  CHP thermal rated power (kW) 
\(\dot {Q}_{b}\)  Boiler thermal rated power (kW) 
t _{op,chp} and t _{op,b }  Full load operational hours of the CHP and boiler (h/year) 
\(\dot {E}_{c}\), \(\dot {Q}_{c}\)  Refrigeration chiller electric rated power and cooling rate (kW) 
Efficiency of the thermal storage is determined by the size, the wall insulation, and the average temperature difference between water within the storage and outside air. For the model, we assume that the size of the storages is sufficient to store and supply thermal energy within a daily cycle. As a rule of thumb, 125 L/household storage capacity is estimated for a DH system with maximum storage temperature of 90°C. With this, we determine an average storage efficiency. Hence, capacity or size of the storage is not a variable within the model.
The DH system is conceived as a utility which buys energy (biogas, natural gas) from the upstream market, converts it to electricity and heat, and sells heat to the downstream market, i.e., the households. The electricity is used by circulation pumps and by heat pumps. Surplus electricity produced by the CHP and not used by the local energy system is sold to the grid. Our first emphasis is on the primary choice between either a DH system with only natural gas boilers or with a biogas CHP and some supporting natural gas boilers.
Besides differences in capital and operational costs of the generators, we have to take cost differences of the downstream system (i.e., storage and network assets) into account caused by the type of converter being applied. The network assets are not influenced but we expect a larger HT storage in case of a CHP. When only boilers are applied, it is easy to adjust the generated thermal power instantly within a large range due to cascading and power modulation. With a CHP the thermal modulation range is not as large and partload conditions lead to a loss of electrical generation efficiency. Besides that, for best efficiency and lifetime performance, a CHP should run more or less continuous when it runs and should not constantly follow the actual demand of the network. Therefore, increased HT storage capital costs are calculated for the CHP case.
Based on average climatic data, the model determines the base generator sizes for heating (CHP, boilers). How this is done is explained in the ‘Relation between CHP size and operational time’ subsection. In practice, an additional peak boiler is added to provide sufficient heating capacity during extremely cold days which occur infrequently. The peak boiler capacity is calculated from the coldest day during the last 5 years. For cooling, we determine the generator size directly from the warmest day during the last 5 years.
We assume energy tariffs (natural gas, electricity) are only related to total yearly amounts, although on the electricity spot market and day ahead market, prices vary hourly. But these variations are small and often fixed electricity prices are contracted for longer periods. In this way, the complexity and the number of required iterations for the optimization problem to find the number of houses for break even profit is significantly reduced.
When the CHP and the boilers together generate the required heat, the CHP has priority and the boilers have a supportive function. Hence, an additional expression is required which relates the operational hours of the CHP to the size of the CHP, which is developed in the ‘Relation between CHP size and operational time’ subsection.
f _{netw,p } is an estimated average percentage which discounts the required electric pump energy for network pumps as a percentage of the thermal energy transmitted through the piping network. Real pump energies are to be estimated from network fluid friction calculations and frequency controlled pump characteristics. A value of 5% is derived from practical calculations.
Optimization model equations
Nomenclature profit function
Term  Signification (unit: €/year) 

P _{ E,grid,sell}  Profit made on electricity sold to the grid 
P _{ Q,dh,h }  Profit made on heat sold through the DH network 
P _{ Q,hp,h }  Profit made on heat sold to houses fitted with a heat pump 
P _{ Q,dh,c }  Profit made on cooling sold through the DH network 
P _{ Q,hp,c }  Profit made on cooling sold to houses fitted with a heat pump 
C _{ E,grid,buy}  Costs of electricity bought from the grid 
C _{biogas}  Costs of biogas supply to the DH system 
C _{ngas}  Costs of natural gas supply to the DH system 
C _{cap,j }  Capital costs of investments and required future reinvestments into specific equipment type j 
C _{op,j }  Operational costs (operator, maintenance, insurance, etc) of specific equipment type j 
chp, b, hp, sto_{chp},sto_{hp}  Specific equipment: CHP, boiler, heat pump, storage related to the CHP, storage related to the heat pumps 
Electricity generated by the CHP is partly consumed by the electric equipment of the DH network (pumps, heat pumps, chiller). One of the objectives of the smart grid control is to balance power generation and consumption as much as possible. The objective is to match the daily CHP operating times with the operating times of electric and thermal consumers. The smaller the CHP, the longer it will run daily and the higher the chance of matching its generation power with consumption. Therefore, we have implemented a simple algorithm which calculates the fraction of CHP operating time matching operational times of electrical demand by the heat pumps, chiller, and network pumps. This is combined with an algorithm that determines the electricity costs and profit for buying energy from the grid and feeding into the grid.
Investment capital rate I and investments (INV) (unit: €) for the CHP, boiler, heat pumps, and storage are related to size or capacity and are described with power functions. Suitable power function coefficients are obtained from cost engineering handbooks, field experts, and company quotations.
Operational costs for each equipment are assumed as a fixed percentage of the initial investment.
Thermal demand specification
Approach and assumptions
Thermal demand includes demand for domestic hot water and space heating. As the district does not yet exist, we develop a straightforward design calculation method in subsequent sections to generate an average demand profile from average daily ambient temperature data for the district.
Domestic hot water demand
In which n the number of households, p the fraction of households not on holiday during the holiday season, and day_shift the number of days that the minimum of the cosine function (peak day of the holiday season with the least domestic hot water demand) is shifted forward from the first of July. In reality, demand profiles are not smooth functions, hence a random daily variation on the profile is generated between 80% and 120%. This is shown in the case application in the subsequent subsection.
Equations for space heating demand

Heat loss to the environment. In general, the method we apply to construct a daily demand profile is to relate space heating demand to the temperature difference between a constant base temperature (T _{base}) and the average daily ambient temperature (\(\bar {T}_{a,i}\)). The base temperature is defined as the average daily ambient temperature for which no space heating is required. Due to solar and internal thermal gains within the interior of a house, the base temperature is lower than the heating setpoint temperature and can be found from experience (i.e., observed number of heating days) or dynamic simulation. The following constraints signifying the space heating demand limit apply:$$ \ \Delta T_{\text{heat},i}= \left\{ \begin{array}{ll} T_{\text{base}}\bar{T}_{a,i} & ~~\text{if}~~ (T_{\text{base}}\bar{T}_{a,i}>0\\ 0 & ~~\text{if}~~ (T_{\text{base}}\bar{T}_{a,i}\leq 0 \end{array} \right. $$(11)

For the daily sum of the space heating demand, Q _{sh,i } (unit MJ/day), a simple prediction profile for a single household is constructed from the following relation:$$ Q_{\text{sh},i}=U \ \Delta T_{\text{heat},i} $$(12)In which parameter U (unit MJ/K) is a constant total heat transfer coefficient, signifying all heat loss to the ambient due to envelope conduction, air ventilation and infiltration, and gains by solar radiation, residents, and appliances. U is determined from a known yearly sum of space heating demand as follows:$$ U=\frac{\sum_{i} Q_{\text{sh},i}}{\sum_{i} \Delta T_{\text{heat},i}} $$(13)

The yearly sum Q _{sh} (unit MJ/y) indicated in Equation 13 is determined either by simulation, from literature or energy performance coefficient (EPC) calculation tools which are developed specifically for Dutch houses. We used the EPC verification tool EPG & Kosten [13] which is distributed by the Dutch government free of charge. Within this tool, various house types (AgentschapNL reference houses, [14]) with reference dimensions based on comparative studies are defined. Building and installation details can be selected and the program calculates the relevant yearly energy demands, based on EPC reference calculations, involving the reference Dutch climate year. The EPC value is the most important parameter for determining the yearly space heating demand. According to current building regulations, an EPC value of 0.4 will be effective from 1 January 2015 for all newly built houses within the Netherlands. Houses within a district heating system which is based on renewable energy receive an EPC reduction, according to [15]. An equivalent generation efficiency of the DH system is calculated with a calculation tool [16], also distributed by the Dutch government.
Case related thermal demand specification

Domestic hot water demand. Blokker and Poorteman [17] investigate hot water patterns of an urban district built in 2002 which consists of 3,000 houses. The population composition (age, type of households) compares with the target population of the Meppel case. On average, 2.6 persons per household are calculated. According to [17], [18], and [19] the daily hot water demand per person is 60 L/day of 45°C. With an average cold water supply temperature of 12°C, this amounts to 8.3 MJ/day · person and for an average household of 2.6 persons, this yields Q _{dhw,hh}=21.6 MJ/day and 7.88 GJ/year.

Space heating and cooling demand. Ambient temperature data for Meppel is taken from weather station in Hoogeveen which is close to Meppel. We determine the average daily temperature for the preceding 5 years. By comparing with simulations, we calculate T _{base}=14°C. The number and type of houses is estimated from the current progress of the Meppel house building project. The target EPC value is estimated at 0.4. A mixed DH system based on biogas CHP and natural gas boilers yields an equivalent generation efficiency of 1.5 and results in an EPC reduction of 0.15. Hence, we estimate that the real heat demand of houses is designed for an EPC value of 0.55. With this, the heating and cooling demand values presented in Table 3 are calculated. The resulting district heating demand profile per average household is shown in Figure 2. The profile is constructed by applying Equations 12 and 10 which are added together. The cooling demand profile is shown in Figure 3.Table 3
District household space heating and cooling parameters
House type
% of houses
Q _{ sh,year } (GJ)
U _{ sh } (MJ/K)
Q _{ c ,year } (GJ)
U _{ c } (MJ/K)
Apartment
14
12.2
6.38
10.8
−41.4
Terraced house
57
15.3
8.00
4.6
−17.7
Corner house
29
20.3
10.62
9.2
−35.3
Average DH house
16.3
6.8
Average HP house
21.1
6.0
Relation between CHP size and operational time
Case related energy cost parameters
Profit and cost rate of electricity
In the Meppel case, the utility company Meppel Energie buys electric energy from the grid and in case of a CHP it will also feed in surplus electricity into the grid. Contractual buying and selling prices of energy are usually based on the expected amount of energy drawn from and fed into the grid within a certain time period. Following Dutch CHP practice, we assume the buying and selling prices are equal for equal amounts of energy transported to and from the grid over the period of 1 year.
In general, the price of energy drops when the amount of energy being bought increases. For very large amounts (i.e., above 500.000 kWh/year) electricity prices are close to the spot market or day ahead market price of electricity, which is currently around 5.5 €ct/kWh. In case of CHP generation with various electricity consumers, if the amount of sold (surplus) energy exceeds the amount of bought energy, network utilities are often reluctant to pay any compensation, or spot market prices at most. Specific energy price equations are developed for the Dutch situation and implemented into the model.
Profit rate for selling heat
As we explain in the ‘Optimization model equations’ subsection, infrastructural investments are payed off by the residents with a onetime connection fee. This fee is usually part of the building price of a house. Besides the connection fee, residents are charged for the consumed heat according to the following equation, i.e., the Dutch heat law, effective from January 2014 which defines the maximum possible tariff: P _{ Q,h }=209,92+19.86·Q _{demand,h } (excluding VAT).

In the case of houses connected to the DH system, we estimate the yearly costs of reservations for infrastructural reinvestments at approximately: €146.00 /(year · household). This leaves as income for the profit model, refer to Equation 8: P _{ Q,dh,h }=63.00+19.86·Q _{demand,dh,h }

In case of houses with a heat pump, we estimate lower reservations for infrastructural reinvestments due to the use of noninsulated pipes and a generation building is not required for heat pumps, i.e., €65.00 /(year ·household). This leaves as income: P _{ Q,hp,h }=145.00+19.86·Q _{demand,hp,h }.
Profit rate of cooling
Originally, the Meppel project was planned for a larger starting scale, including the use of the aquifer for cooling energy and source energy for the heat pumps. Cooling of the houses provides part of the required energy to balance temperature of the aquifers and is therefore offered for free to the residents. Meppel energy wants to keep its promise of free cooling to the residents and therefore the profit rate for cooling is 0, P _{ Q,c }=0.
Cost rate of biogas for CHP and natural gas for boilers
Cost relations for natural gas from the national grid, specific relations for biogas, and applicable energy taxes are developed and implemented as algorithms into the optimization model.
Results and discussion
Generator and storage capacities
Calculated generator capacities
Heat generation  Peak boiler  Cooling  

\({\dot {Q}_{\text {max}}}\) (kW)  \({\dot {Q}_{b,p}}\) (kW)  \({\dot {Q}_{c}}\) (kW)  
Phase 1  87  87  74 
Phase 2  373  373  322 
Profit generation, optimal CHP size, and operating times
Optimal CHP size for building phases 1 and 2
( n , m )  Profit without  Optimal CHP  t _{ op,chp }  Boiler size  t _{ op, b }  Profit with 

CHP (€/year)  size \({\dot {E}_{\textit {chp}}}\) (kW)  (h/year)  \({\dot {Q}_{b}}\) (kW)  (h/year)  CHP (€/year)  
(40, 0)  −6,911  6  8,659  78  3,077  −2,571 
(160, 16)  −20,807  100  6,140  228  2,033  −12,380 
Table 5 shows a negative profit for both building phases. Care should be taken in the interpretation of the profit values as they are influenced by assumptions explained in the ‘Profit rate for selling heat’ subsection. We may have exaggerated the reinvestment costs, lacking detailed insight in the Meppel Energie business case. On the other hand, practical experience indicates problematic profitability of smallscale DH systems, which support our results. In the Meppel case, there is more pressure on profitability because the houses have a lower heating demand (and thus lower revenue per household) than usual, and there are generation costs involved for cooling while on the other hand, cooling is offered free of charge. If we calculate a fee for cooling based on break even operation, this yields €64/year and €70/year per household for phases 1 and 2, respectively. To place this in perspective of what households would normally pay if they generate the required cooling with homebased air conditioning equipment, the average cooling demand of 6 GJ/year requires 555 kWh per household electric energy which would costs each household €127/year only for electricity. Hence, the calculated fees are reasonable propositions to the residents.
What is the influence of houses with heat pumps on the profit? By maximizing the net profit Equation 8 with \(\dot {Q}_{\text {chp}}\) and m as variables, we find that for various numbers of n houses, m=0. So in all cases, houses with heat pumps have a negative influence on profit. There are two reasons for this. First, in the starting energy concept, the source heat for the heat pumps is generated by the CHP and boilers, with fuel costs as a consequence. Second, individual heat pumps for each house are relatively expensive in comparison with DH delivery sets as HP’s require more investments and maintenance and also have a shorter life time. Clearly, the compensation offered by a higher tariff (‘Profit rate for selling heat’ subsection) is not sufficient for an equally profitable operation of heat pumps compared to houses connected to the DH system.
If the number of houses connected to the DH system expands, when will operation become profitable? This depends mostly on (a) the applied interest rate r and (b) the household heating demand. If r=4% then operation breaks even at n=490 houses. The corresponding optimal generation sizes and operating times are as follows: \(\dot {E}_{\text {chp}}=426\) kW, t _{op,chp}=5,293 h/year, \(\dot {Q}_{b}=448\) kW, and t _{op,b }=1,340 h/year. For r=2% the break even point is n=277 houses.
CHP migration steps
For each line in the Figure, the value for (n,m) is indicated. Around the optimum of each line, the relation between profit and CHP size is rather flat. This gives flexibility to choose the CHP size according to logical migration steps for the district. An attractive migration route is to start with a 30kWe CHP for a small district with 40 houses and to install a second, larger CHP, e.g., 140 kWe when 160 houses are connected.
Comparison on sustainability
 1.Case 1: starting energy concept with optimal sized CHP on biogas supported by a boiler on natural gas. The Sankey or energy flow diagram for this case is shown in Figure 7. Total natural gas input: 1,648 GJ/year. Total biogas input: 6,177 GJ/year. If the CHP runs on natural gas, the total natural gas input is: 7,825 GJ/year.
 2.
Case 2: starting energy concept without a CHP. In this case, the boiler delivers 4,860 GJ/year with an equal natural gas input. The required electricity for heat pumps, cooling and network pumps, and energy delivered to the region total 2,222 GJ/year. This requires 4,938 GJ/year of natural gas input at a gridbased electrical power plant, taking Dutch national fuel efficiency of 45% into account. Hence, total natural gas input: 9,798 GJ/year.
 3.
Case 3: Dutch conventional house heating by individual homefitted natural gas boilers. In this case for heating, 4,318 GJ/year natural gas is required. The required electricity for cooling and comparable electricity supplied to the region total 1,816 GJ/year. If a gridbased electrical power plant is used, 4,036 GJ/year natural gas input is required. In that case, the natural gas input totals: 8,354 GJ/year.
 4.
Case 4: as an alternative for case 3, the 1,816 GJ/year electricity is generated by home installed solar PV. For 176 houses, this is 2,866 kWh/year per household. Under Dutch circumstances, this is feasible. Total natural gas input: 4,318 GJ/year only for heating is required.
Case 4 with home PV installations is interesting as improved sustainability is combined with less infrastructural investments than case 1 and is hence easier to implement in an unsure house building market. Case 4 also leads to another objective, i.e., to invest into more energy efficient houses, in order to further reduce natural gas consumption. We recalculated case 4 with passive house (PH) space heating requirements, and this results in the fifth column in Figure 8. This case can be taken further, replacing natural gas boilers with heat pumps. The district then contains passive houses, each with a solar PV roof and a heat pump connected to a ground source or aquifer. Natural gas input is then eliminated totally. The drawback of such a concept however is the imbalance between periods of major PV electricity generation and periods with the highest heating demand. Further comparisons of these concepts including financial aspects and loads on the electricity network are left for future work.
The use of biogas instead of natural gas reduces carbon dioxide (CO _{2}) emissions, while biogas is formed with CO _{2} from the earth’s atmosphere and the CO _{2} is released when the biogas is burned. For natural gas, a conversion factor of 52 kg CO _{2}/GJ is derived. Case 1 with biogas results in 86 tons CO _{2}/year and case 2 in 509 tons CO _{2}/year. Hence, case 1 results in a CO _{2} reduction of 423 tons/year.
The last question of interest for the Meppel case is how many houses and what size of the CHP are appropriate if all the available biogas of the municipality (i.e., 9,200 GJ/year) is applied? As optimum, we find n=232 houses and \(\dot {E}_{\text {chp}}=159\) kW. The natural gas input required for boilers is then 1,775 GJ/year, resulting in net CO _{2} emissions of 92 tons/year. The net profit (or loss in this case) is calculated at −€5,711/year. When a cooling fee of €50/year is asked from each household, the net profit is positive, i.e., €5,899/year.
Conclusions
In this paper, optimal heat and power generation sizes are determined for the new urban district Nieuwveenslanden situated in Meppel for two building phases starting at 40 houses and expanding towards 176 houses. Besides optimal CHP and boiler sizes, we determine corresponding profits and sustainability of the network on fossil fuel requirements. Profits appear to be negative, however more than 50% improvement is possible if a CHP is used for heat and power generation compared to operation with only boilers. With an interest rate of 4% on invested capital, profits are positive when more than 490 houses are connected, with 2% this is reduced to 277 houses. If households are charged a modest fee for cooling, the energy concept with a CHP is profitable from the start. Based on maximizing profit, a migration scenario for the CHP is developed starting with a single 30kWe CHP for 40 houses connected to the DH system and adding a 140kWe CHP when 160 houses are connected.
Sustainability of the energy concept is compared with the Dutch reference, i.e., houses heated by individual natural gas boilers. If a CHP on biogas is applied, the Meppel energy concept reduces 351 tons CO _{2}/year or 80% reduction. However, if the CHP runs on natural gas, the CO _{2} reduction is only 6%. It has to be considered that a scenario based on the Dutch reference combined with home solar PV is another possible route towards improved sustainability but involves less infrastructural investments. However, the Meppel DH system concept with a CHP on biogas still performs better on sustainability than this alternative route.
For the Meppel DH system, houses with a heat pump have a negative influence on the profitability. This is because individual household heat pumps are more expensive to operate than the DH system. It is interesting to compare this also for the final energy concept in which source heat and cooling are generated by an underground aquifer. In that case, we expect to reach better profitability. Investigating the final energy concept is left for future work. Also a more comprehensive comparison between the Meppel energy concept and more individual energy generation solutions requiring less infrastructure is left for future work.
Declarations
Acknowledgements
The authors would like to thank the Dutch national program TKISwitch2SmartGrids for supporting the project MeppelEnergie and the STW organization for supporting the projects iCare and Dream that resulted in this work.
Authors’ Affiliations
References
 AgentschapNL (2014) Biogaswarmtekrachtkoppeling (WKK) voor Een Wijk. http://www.rvo.nl/onderwerpen/duurzaamondernemen/gebouwen/energieprestatienieuwbouwepn/ontwerpen. referentiewoningennieuwbouw. visited January 2014.
 Schepers BL, Valkengoed MPJv (2009) Warmtenetten in nederland  overzicht van grootschalige en kleinschalige warmtenetten in nederland. Technical Report 09.3031.45, CEDelft http://www.ce.nl/publicatie/warmtenetten_in_nederland/976. visited May 2015.
 Vallios I, Tsoutsos T, Papadakis G (2009) Design of biomass district heating systems. Biomass and bioenergy 33(4): 659–678.View ArticleGoogle Scholar
 Ren H, Gao W, Ruan Y (2008) Optimal sizing for residential CHP system. Appl Therm Eng 28(5): 514–523.View ArticleGoogle Scholar
 Cho H, Luck R, Eksioglu SD, Chamra LM (2009) Costoptimized realtime operation of CHP systems. Energy and Buildings 41(4): 445–451.View ArticleGoogle Scholar
 Chinese D, Meneghetti A (2005) Optimisation models for decision support in the development of biomassbased industrial districtheating networks in Italy. Appl energy 82(3): 228–254.View ArticleGoogle Scholar
 Curti V, Favrat D, von Spakovsky MR (2000) An environomic approach for the modeling and optimization of a district heating network based on centralized and decentralized heat pumps, cogeneration and/or gas furnace. Part II: application. Int J Ther Sci 39(7): 731–741.View ArticleGoogle Scholar
 Hlebnikov A, Dementjeva N, Siirde A (2009) Optimization of Narva district heating network and analysis of competitiveness of oil shale CHP building in Narva. Oil Shale 26: 269–282.View ArticleGoogle Scholar
 Lund H, Möller B, Mathiesen BV, Dyrelund A (2010) The role of district heating in future renewable energy systems. Energy 35(3): 1381–1390.View ArticleGoogle Scholar
 Pirouti M (2013) Modelling and analysis of a district heating network.Google Scholar
 Østergaard PA, Lund H (2011) A renewable energy system in Frederikshavn using lowtemperature geothermal energy for district heating. Appl Energy 88(2): 479–487.View ArticleGoogle Scholar
 Jordan U, Vajen K (2001) Realistic domestic hotwater profiles in different time scales. Report for IEASHC Task 26. http://sel.me.wisc.edu/trnsys/trnlib/ieashctask26/ieashctask26loadprofilesdescriptionjordan.pdf, visited May 2015.
 RVO (2014) EPG Rekentool. https://www.rvo.nl/onderwerpen/duurzaamondernemen/gebouwen/energieprestatienieuwbouwepn/epcberekenen/hulpmiddelenepcberekening?wss%l=1, visited January 2014.
 AgentschapNL (2013) Referentiewoningen Nieuwbouw 2013. Agentschap NL. http://www.rvo.nl/onderwerpen/duurzaamondernemen/gebouwen/energieprestatienieuwbouwepn/ontwerpen/referentiewoningennieuwbouw visited January 2014.
 AgentschapNL (2014). http://www.rvo.nl/sites/default/files/Gebiedsmaatregelen\%20voor\%20het\%20eerst\%20gewaardeerd\%20in\%20de. %20EPC_bepaling_0.pdf, visited January 2014.
 RVO (2014) EMG Rekentool. https://www.nen.nl/NENShop/Norm/Updaterekentoolenergieprestatiegebiedbeschikbaar.htm, visited January 2014.
 Blokker EJM, Poortema K (2007) Effecten levering warm tapwater door derden. Technical report, sKIWA.Google Scholar
 Geudens PJJG (2012) Drinkwaterstatistieken 2012. VEWIN 41(4): 445–451.Google Scholar
 Blokker EJM (2006) Modelleren van afnamepatronen; beschrijving en validatie van het simulatiemodel simdeum. Technical report, KIWA. http://www.kwrwater.nl/WorkArea/DownloadAsset.aspx?id=8787. visited May 2015.
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