Opportunities and Challenges of Future District Heating Portfolios of an Austrian Utility

In this paper, opportunities and challenges of concrete portfolio options of an Austrian district heating (DH) supplier are assessed against the background of current challenges of the DH sector. The following steps are performed: (1) analysis of status quo; (2) analysis of current and possible future economic framework conditions; (3) definition of four concrete future portfolio options for investment planning until the year 2030; (4) modeling of status quo and future portfolios together with the respective framework conditions in a linear dispatch optimization model; and (5) perform techno-economic analysis for each portfolio under the different possible future framework conditions. The expected increase in renewable power generation capacity is likely to increase volatility in future electricity prices with hours of both very low and very high prices. This higher volatility results in higher technical flexibility requirements for the heat generation plants and a need for heat generation portfolios to respond to both high and low electricity prices. The results indicate that the combination of heat pumps and combined heat and power (CHP) plants is well suited to cope with these challenges from a microeconomic point of view. At the same time, we show that a shift to a high share of renewables of more than 60%, implying a complete exit of gas fired CHPs, is also feasible with costs in a very similar range as the current DH generation portfolio.


Introduction
In urban and densely populated areas, district heating (DH) is seen as an important decarbonization option, where it is often the only possible option to integrate large shares of renewable and excess heat into the heating sector. This was found already in various studies, e.g., in [1] or during the second phase of the Heat Roadmap Europe Project [2]. However, the district heating sector is currently in transition and challenged in many ways. One major challenge is the uncertainty in future framework conditions and how these will affect the feasibility of the suppliers' portfolio: e.g., as one important policy framework, the revised directive 2018/2001 [3] on the promotion of the use of energy from renewable sources, for the first time sets concrete targets until 2030 for the annual increase of the share of renewable heat in general and renewable energies in the district heating sector in particular. For many district heating utilities, it is still unclear how to develop the current portfolio to reach this target. Furthermore, a significant increase in costs of European CO 2 emission allowances from an average of 5 EUR/t CO2 to around 20-25 EUR/t CO2 has taken place in 2018, and additionally fossil energy prices are subject to high fluctuations and affect electricity and heat generation costs. So far, only a few assessments have been published on political framework conditions and its influence on the feasibility of district heating systems: e.g., the assessment in [4] evaluates the impact of different policy frameworks on the future of district heating in a case study city in Romania and shows that Energies 2020, 13, 2457; doi:10.3390/en13102457 www.mdpi.com/journal/energies Energies 2020, 13, 2457 2 of 20 energy and climate policy objectives have a big impact on the future of district heating. The work in [5] studies the current and former framework conditions in Nordic DH to project the likely future development of Nordic DH and also finds that fuel prices, tax, and subsidy schemes affect the fuel composition, and the future of Nordic DH relies on CO 2 prices, electricity prices, and energy policy. Other big challenges for the district heating sector are the integration with the electricity sector, which is rapidly taking place, and the expected heat demand reduction due to thermal building renovation measures and global warming, which will impact the techno-economic parameters of district heating according to the analysis in [6]. Generally, the concept of fourth generation district heating tackles several of the future challenges, and many researches related to this concept have taken place within the last years and have provided various additional insights. For example, the recent status analysis of fourth generation district heating in [7] provides a review of this topic in scientific papers and especially highlights the significant benefits of systems integration and mentions that smart energy systems require innovative planning practices, support tools, and policies facilitating the transformation, including strategic and innovative energy planning considering legal perspectives. One such integrated strategic energy planning process to assess the possible future role of district heating but also the interactions with heat savings and other heat supply options is described in [8].
Regarding current challenges of district heating, [9] provides a perspective on the development of future district heating systems and technologies and their role in future smart energy systems. However, it concludes that the status of the scientific contributions demonstrates a high level of understanding of how to deal with the technical aspects but that the primary current challenge seems to be the understanding of the implementation of these. Furthermore, in [10] it was found that more efforts are required for the identification, assessment, and implementation with the aim to harvest the global benefits of district heating and cooling.
Therefore, this paper adds exactly to this literature gap and focusses on the challenges of transforming the district heating supply from a specific provider's point of view. This viewpoint usually differs from an overall system view, which typically is applied for analysis of the district heating sector. As already pointed out, district heat producers have to deal with uncertainties of future framework conditions and their effect on different concrete investment options they have. District heating suppliers ask themselves how their concrete portfolio options perform under expected bandwidths of these future conditions and how the addition of heat pumps, excess heat, or a biomass combined heat and power plant (CHP) influence the performance of their current supply portfolio. Smaller district heating suppliers usually do not have the know-how and tools to perform assessments like these and-if performed-concrete analyses usually are kept confidential and scientific publications of this kind are hardly available. This paper identifies, describes, and evaluates certain concrete heat supply portfolio options of an Austrian district heating supplier under current and a bandwidth of expected future framework conditions by simulating the status quo and the portfolio options in a district heating dispatch optimization model. Most of the assessed concrete portfolio options are based on prefeasibility studies commissioned by the DH utility. The used values (technical as well as financial) were set in accordance with the utility and may better reflect the effective framework conditions relevant to a DH supplier than other literature values.

Materials and Methods
The overall methodology of this work is to first create different consistent sets of input and framework conditions for a dispatch optimization model, which then is used to assess various future portfolio options of an existing Austrian district heating utility in the time horizon to 2030. This includes a holistic view on the main factors affecting the assessed district heating system and their possible future developments by performing the following steps: (1) Analysis of status quo of the district heating system; in this step the current demand for district heating and the technical parameters of the district heating supply portfolio were analyzed. (2) Analysis of current and possible future economic framework conditions; in this step one consistent set of current (2017) and two sets of future (2030) Energies 2020, 13, 2457 3 of 20 framework conditions (one representing an upper limit and one representing a lower limit within a range of expected future framework conditions) were generated, including energy and carbon prices and charges, taxes, and fees for the different technologies. Each set includes a scenario of hourly electricity prices calculated with a fundamental electricity market model for Germany and Austria fed with the defined framework conditions taking into account expected expansion of renewable electricity generation in these two countries.
(3) Definition of realistic future portfolio options; in this step, four concrete portfolio options were developed together with the district heating utility each to enhance the current status quo supply portfolio. Most of these portfolio options are based on prefeasibility studies commissioned by the utility taking into account local actual available potentials and local conditions. (4) Modeling; in this step the status quo and the future portfolios together with the respective framework conditions were implemented in a linear dispatch optimization model, which is described in Section 2.1. (5) Perform techno-economic analysis; in this last step, the dispatch optimization was performed for the status quo and the four portfolio options each for the current and the two sets of possible future framework conditions. For all units, the generated heat and the full load hours of each technology were calculated and indicators like total CO 2 emissions, total share of renewable heat, and resulting levelized cost of heat were compared for the different portfolios and for sensitivities of the most important factors. Figure 1 gives a schematic overview on the methodological framework.
Energies 2020, 13, x FOR PEER REVIEW 3 of 21 of future (2030) framework conditions (one representing an upper limit and one representing a lower limit within a range of expected future framework conditions) were generated, including energy and carbon prices and charges, taxes, and fees for the different technologies. Each set includes a scenario of hourly electricity prices calculated with a fundamental electricity market model for Germany and Austria fed with the defined framework conditions taking into account expected expansion of renewable electricity generation in these two countries.
(3) Definition of realistic future portfolio options; in this step, four concrete portfolio options were developed together with the district heating utility each to enhance the current status quo supply portfolio. Most of these portfolio options are based on prefeasibility studies commissioned by the utility taking into account local actual available potentials and local conditions. (4) Modeling; in this step the status quo and the future portfolios together with the respective framework conditions were implemented in a linear dispatch optimization model, which is described in Section 2.1. (5) Perform techno-economic analysis; in this last step, the dispatch optimization was performed for the status quo and the four portfolio options each for the current and the two sets of possible future framework conditions. For all units, the generated heat and the full load hours of each technology were calculated and indicators like total CO2 emissions, total share of renewable heat, and resulting levelized cost of heat were compared for the different portfolios and for sensitivities of the most important factors. Figure 1 gives a schematic overview on the methodological framework.

Dispatch Optimization Model
For the assessment of the different district heating portfolios, a linear dispatch optimization model was used, which was mainly developed within the Hotmaps project [11]. The model is implemented in Python programming language and depicts the essential characteristics and restrictions of the different technologies but does not contain a detailed modeling of the start-up and ramp constraints, the necessary downtime or partial load efficiencies. The model therefore is not suited for very detailed modeling of single technologies but is designed to be able to run several scenarios within reasonable solver time.
The main model inputs are hourly profiles for district heating demand, electricity prices, solar irradiation and outdoor temperature, and the installed heat generation capacities with respective thermal and electrical efficiencies. Each modeled technology is described by its (nominal) thermal power generation and its (nominal) thermal and electrical efficiencies. By means of linear equations,

Dispatch Optimization Model
For the assessment of the different district heating portfolios, a linear dispatch optimization model was used, which was mainly developed within the Hotmaps project [11]. The model is implemented in Python programming language and depicts the essential characteristics and restrictions of the different technologies but does not contain a detailed modeling of the start-up and ramp constraints, the necessary downtime or partial load efficiencies. The model therefore is not suited for very detailed modeling of single technologies but is designed to be able to run several scenarios within reasonable solver time.
The main model inputs are hourly profiles for district heating demand, electricity prices, solar irradiation and outdoor temperature, and the installed heat generation capacities with respective thermal and electrical efficiencies. Each modeled technology is described by its (nominal) thermal Energies 2020, 13, 2457 4 of 20 power generation and its (nominal) thermal and electrical efficiencies. By means of linear equations, the required (fuel) input and the generated electricity can be calculated for each heat generation operating point at every hour.
The objective function in Equation (1) minimizes the difference from the total costs for heat generation and the revenues from electricity production.
Under the constraint that the sum of heat generation of all units and the sum of loading and unloading of the heat storages in all hours has to be equal to the heat demand in each hour according to Equation (2).
The total costs of heat generation are calculated according to Equation (3) and consist of the capital costs, the fixed costs, the variable costs, and include for CHP and waste incineration plants the ramp costs.
With the capital costs calculated according to Equation (4) via the specific investment costs, the installed capacity and the annuity factor from Equation (5) for each heat generation and storage unit can be calculated. The annuity factor is used to calculate the annually equal payments that equal the overall amount considering the lifetime and the interest rate of an investment. CC = j Cap j ·i j ·α j + hs sCap hs ·i hs ·α hs (4) The fixed costs are calculated according to Equation (6) via the sum of fixed specific opex costs multiplied by the installed capacity of the respective heat generator or heat storage.
The variable costs are calculated according to Equation (7) for each produced unit of heat in each hour, including the variable opex plus the price of the respective energy carrier and the emission costs considering the efficiency of the technology: For CHP plants and waste incineration plants, ramp costs are included according to Equation (8).
The total revenues from electricity production are calculated via Equation (9) summing the electricity production of each unit and each hour multiplied by the respective electricity price. rev total = j,t x el jt ·p s.el j,t Energies 2020, 13, 2457 5 of 20 The electricity generation of each unit and each hour is calculated via Equation (10) using the efficiencies of each production unit.
x el j,t = x th j,t · η el j η th j,t As main outputs, the model calculates the levelized heat generation costs, the investment, operation and fuel costs, the heat generation mix per heat generator, CO 2 emissions, full load hours (FLH), etc.

Electricity Market Model
To generate scenarios of hourly electricity prices as an input for the dispatch model, a fundamental electricity market model of the Austrian and German electricity system is used. A detailed description and formulation of the model can be found in [12]. The model is a linear optimization model that uses hourly resolution to optimize the use of power generation technologies for a given installed capacity of controllable power plants (lignite, coal, nuclear, gas, and oil) and given installed volatility (sun, wind, and running water) in combination with historical feed profiles. Storage power plants are modeled in aggregated form. The used model is a linear dispatch model that minimizes variable generation costs of thermal power plants considering energy demand constraints, feed in of renewables, and installed capacities of hydro storage systems in Austria and Germany. The hourly electricity prices are derived from the shadow price of the demand equation, which demands that the generation in each hour has to exceed the demand.

Input Data: Framework Conditions and Portfolio Options
In this section, the used input data and parameter assumptions as well as the portfolio options considered for the district heating system are described in detail. Three different sets of framework conditions (one set of "current" (2017) framework conditions and two sets of "future" framework conditions (2030), which all are described in Section 3.1.) were defined to assess five different portfolio options (status quo plus four alternative portfolios, which are all described in Section 3.2.).

Framework Conditions
Because there is a high interdependency of different input parameters, sets of consistent framework conditions were created. Each set consists of the assumed energy and CO 2 certificate price and the resulting hourly electricity price determined by the electricity market model described in Section 2.2, as well as the applicable network charges, taxes, and subsidies for each energy carrier and each portfolio. The naming of the three main sets of framework conditions is according to the represented scenario year, the assumed price level of fossil energy carriers ("Hi" . . . high, "Lo" . . . low), and the respective CO 2 price (30 EUR/t CO2 , 60 EUR/t CO2 ). They are named as follows:

.1. Energy and CO 2 Prices
Wholesale primary energy carrier prices and CO 2 prices based on nationally or internationally recognized studies are used which are given in the Appendix A in Table A2.  [14]. The logic behind the combination of fossil energy prices and CO 2 prices within the IEA's World Energy Outlook is that a higher CO 2 certificate price leads to less demand and therefore a lower price for fossil fuels. For the locally available energy carrier biomass, excess heat and municipal solid waste prices are defined according to national studies [15] and were fixed in accordance with the district heating utility. The price for waste is assumed to be 0 EUR/MWh in the model calculations to represent on the one hand a must run situation for waste treatment and on the other hand not influence the levelized cost of heat of the remaining supply portfolio. For high temperature, excess heat above 100 • C, which can be used directly for the district heating network, an energy price of 15 EUR/MWh, and for low temperature, excess heat at a temperature level of 50 • C an energy price of 5 EUR/MWh is assumed. These values are kept constant for all three price scenarios.

Electricity Prices
Each of the three sets of framework conditions includes a respective scenario of hourly electricity prices, which is generated using the electricity market model described in Section 2.2, which was fed with the energy prices from Section 3.1.1. The marginal costs of the power plants are based on the fossil fuel and CO 2 prices and therefore are different for each scenario. For the calculations, the controllable power plant portfolio of Austria and Germany is represented with the status of the year 2014. Only the capacities of the nuclear power plants were replaced with simple gas turbines, which then operate as back-up capacities at the end of the merit order. The installed renewable capacities were set according to the data in [15] and are listed in Table 1.  Figure 2 shows the resulting hourly electricity prices sorted by size and the average annual price for the three scenarios as an outcome of the electricity market model. It can be seen that the assumed price developments for the year 2030 lead to significantly higher average electricity prices in both 2030 scenarios compared to the starting year 2017. The results clearly show that the main drivers for these higher electricity prices are the high CO 2 prices as the scenario with the low fossil prices still has the highest average electricity prices (77.6 EUR/MWh) due to higher CO 2 prices. In addition to a rise in the average price level, there is also a clear increase in price volatility. Not only do the shares of hours with prices of 0 EUR/MWh increase (form 400 h in 2017 to 1600 h in the 2030 scenario) but also peaks with high electricity price occur more frequently.
This is a result of the missing nuclear power plants, which allows other power plants with higher marginal costs than in the starting year 2017 come into the merit order at times of high residual loads.
Another reason is that no additional flexibility options have been assumed for the 2030 scenarios in the market model. A strong increase in flexibility in the market would reduce the number of hours with low prices and at the same time reduce price peaks and especially the occurrence of higher price spreads provides incentives for additional flexibility. However, the trend towards increased numbers with lower prices due to strong feed-in peaks from renewable energies would remain. This is a result of the missing nuclear power plants, which allows other power plants with higher marginal costs than in the starting year 2017 come into the merit order at times of high residual loads.
Another reason is that no additional flexibility options have been assumed for the 2030 scenarios in the market model. A strong increase in flexibility in the market would reduce the number of hours with low prices and at the same time reduce price peaks and especially the occurrence of higher price spreads provides incentives for additional flexibility. However, the trend towards increased numbers with lower prices due to strong feed-in peaks from renewable energies would remain.

Network and Transportation Costs
The considered network and transport costs are derived from data from the Austrian network regulator e-control (https://www.e-control.at) and are shown in detail in the Appendix A in Table A3. To feed produced electricity into the public grid, an electricity system charge depending on the network (voltage) level must be paid, which consists of a network service charge, the charge for primary regulation, and the network loss charge of the respective network level. For the assessed district heating utility, there are plants feeding into the grid level 5 and grid level 3 depending on their power generation capacity. The consumption of electricity via public network is also subject to an electricity system charge consisting of network usage and network loss charges at the respective network level. This applies to portfolio options like heat pumps or power-to-heat systems. For rated electrical power consumption below 20 MW el , network level 5 applies and for rated power consumption above 20 MW el network level 4. The natural gas fired power plants are connected to the high-pressure natural gas network (>30 bar), which causes a respective network utilization charge of 0.65 EUR/MWh for customer with annual consumptions over 200 GWh. For the transport of biomass, average transport costs for truck delivery within a radius of 100 km around the power plant are calculated. Assuming uniformly distributed transportation distances within this area resulting in average transport distance of 66 km, fixed costs of 1.1 EUR/MWh and variable costs of 2.49 EUR/MWh occur according to [16]. For the remaining energy carriers (municipal solid waste and industrial excess heat), no direct transportation costs are assumed.

Taxes, Surcharges, and Subsidies
The considered taxes and surcharges are shown in detail in the Appendix A in Table A4. For the consumption of electricity via the public electricity network, e.g., for heat pumps, an electricity consumption tax of 15 EUR/MWh and a network level dependent green electricity support payment applies. Furthermore, the consumption of natural gas via heat only boiler is charged with an energy consumption tax. For natural gas used in CHP plants this tax does not apply. For power plants taking part in the European Emission Allowance Trading Scheme (ETS), a CO 2 certificate price per emitted ton of CO 2 applies. For the use of biomass, municipal waste, or industrial excess heat no tax or surcharge applies. For the generation of green electricity via Biomass CHP plants, a feed-in tariff of currently 103 EUR/MWh el is granted, which is considered in the 2017_current scenario. For the 2030 scenarios, the possible future policy schemes of a fixed market premium of 70 EUR/MWh el in addition to the electricity market price is implemented in the three 2030 scenarios.

Status Quo of Heat Generation and Assessed Portfolios
In this chapter, each modeled plant of the current supply portfolio and of all assessed future portfolio options are described briefly in Table 2. The respective detailed model parameters, including technical and financial values, are given in the Appendix A in Table A5. All portfolio options and model settings were derived in close cooperation with the corresponding district heating company in order to reflect the actual availability and limitation of resources as well as authentic investment assumptions to support the strategic decision-making process of the DH utility. "Portfolio P0 -Status Quo" represents the current heat supply portfolio of the assessed district heating system and consists of the heat generation and heat storage units described in the upper part of Table 2. The current heat generation capacities sum up to 565 MW plus 60 MW heat storage loading/unloading capacity and feed around 1100 GWh into the district heating grid. For some of the existing plant capacities, reinvestment costs of altogether 120 Mio EUR until 2030 are expected and considered in the calculation. These costs are based on actual estimations of the utility due to age and status of the different plants and necessary revisions.

Portfolio Options and Assessed Portfolios
The different portfolio options identified together with the district heating utility are described in the lower part of Table 2. Many of the identified options are based on technical prefeasibility studies performed by the district heating utility and therefore reflect actual availability and limitation of resources and are based on authentic expected investment costs. The future supplied heat demand is assumed to be constant until 2030 because it is expected that expansion of the network and decrease of heat demand due to building refurbishment level out. The different portfolio options are combined to following four possible future portfolios: • "Portfolio P1-Heat Pump Portfolio": In this possible future portfolio, investments into several heat pump options plus a second daily heat storage are made additionally to the existing status quo capacities as shown in Table 2. For all the heat pump configurations, the temperature rise of the heat sink is assumed to be constant from an average return temperature of 55 • C to a flow temperature of 85 • C without adaptation of the flow temperature according to outside temperature. The resulting efficiencies (coefficient of performance-COP) of the heat pumps result from simulations of different heat pump technologies performed in the Austrian project "Power to heat Potentials" [17]. The expected investments into the additional capacities of 55 MW sum up to 38 Mio EUR. • "Portfolio P2-Excess heat integration": In this portfolio nearby high-and low-temperature excess heat is integrated in the current system directly and via a heat pump respectively. The expected investments into these 60 MW of capacities sum up to 21 Mio EUR.

Sensitivities
To investigate the sensitivities of input sets, the following variations of the described scenario sets are assessed: • Low CO 2 price: in this sensitivity, it is assumed that no increase in the future CO 2 price occurs and that the corresponding CO 2 prices for all four energy price scenarios is 10 EUR/t CO2 instead. For each of these sensitivities, again an hourly electricity price is generated using the electricity market model (see Section 2.2). • Natural gas tax for CHP: in this sensitivity scenario, natural gas used in CHP plants is taxed the same as natural gas use in heat only boiler.

Results
In this section, first the results of the effects of the different sets of frameworks on each portfolio are described and then a comparison across all portfolio options is given. In Table A6 in the Appendix A, the detailed results of the analysis can be found.

Portfolio P0: Status Quo
For the generation capacities of the Status Quo Portfolio, the effects of the different sets of framework conditions can be summarized as follows: • For the Waste-to-Energy CHP plant, there is almost no difference in the Status Quo Portfolio between the current framework conditions and the different sets of future framework conditions. It supplies around 34% of the heat demand in the current and in all three future scenarios. This is due to the assumed price of 0 EUR/MWh of municipal waste, which leads to very low costs of heat generation fulfilling the requirement that the waste incineration plant operates throughout the year as its primary purpose is the treatment of waste and not the generation of energy.

•
The existing Biomass CHP, which in Portfolio P0 is subsidized in all three scenario sets with 103 EUR/MWh el , achieves around 5600 full load hours (FLH), which drops to between 3500 FLH and 1800 FLH in the 2030 scenarios, which means a reduction of the supplied heat demand from 12% to between 8% and 4%. This is mainly because the higher average electricity prices in the future scenarios lead to increased economic viability of fossil cogeneration, which reduces the economic viability of the biomass CHP.

•
The different fossil CHP plants are operated between 400 FLH and 2200 FLH in the 2017 scenario, depending on the system design with respect to the power to heat ratio of the plant. In the 2030 price scenarios in particular, the plants with high el. efficiency increase their operation to between 3000 to 4000 FLH. On the other hand, the FLH of the CHP plants with lower el. efficiency but higher thermal efficiency drops to between 300 to 700 FLH. In total, this results in an increased economic viability of the fossil CHP plants in the 2030 scenarios compared to the 2017 scenario due to the higher average and higher peak electricity prices, which means higher revenues for electricity production.

•
The use of the daily storage increases strongly in all 2030 scenarios compared to the 2017 scenario in order to temporarily store the heat from CHP production in hours of high electricity prices.

Portfolio P1: Heat Pumps
• For the Waste-to-Energy CHP plant also in this portfolio, the results are very similar as in the Status Quo Portfolio due to the very low costs of heat generation fulfilling the "must run" requirement.

•
The Biomass CHP, which is not subsidized in this portfolio, is not economically viable in any of the scenarios and does not generate heat at all.

•
Compared to the P0 Portfolio, the natural gas CHP plants are now operated between 500 FLH and 2200 FLH in the 2017 scenario and in the 2030 scenarios between 2800 and 4000 FLH for plants with high el. efficiency and between 500 and 700 FLH with lower el. efficiency but higher thermal efficiency.

•
For the different heat pump technologies, the 2017 price scenario allows economic viable use of heat pumps between 2400 and 4700 FLH, producing around 19% of the heat demand. In the 2030 price scenarios, the FLH, in particular of the heat pumps with higher COP, drops compared to the 2017 scenario, but the minimum FLH of all heat pump options in all scenarios are limited to the number of hours with electricity prices close to zero • The use of the enlarged daily storage increases strongly in all 2030 scenarios compared to the 2017 scenario in order to temporarily store the heat from CHP production in hours of high electricity prices but also the heat generated by heat pumps during hours of low electricity prices.

Portfolio P2: Excess Heat Integration
• For the Waste-to-Energy CHP plant also in this portfolio, the results are very similar as in the Status Quo Portfolio due to the very low costs of heat generation fulfilling the "must run" requirement. • Additionally, in Portfolio P2, the Biomass CHP, which is not subsidized in this portfolio, is not economically viable in any of the scenarios and does not generate heat.

•
In this portfolio, the different natural gas CHP plants are operated between 400 FLH and 2100 FLH in the 2017 scenario and in the 2030 scenarios between 2700 and 3800 FLH for plants with high el. efficiency and between 200 and 500 FLH with lower el. efficiency but higher thermal efficiency. Overall, this still means an increased economic viability of the fossil CHP plants in the 2030 scenarios compared to the 2017 scenario but lower than in the P0 and P1 portfolio.

•
The price scenario 2017 allows integration of direct excess heat with around 5300 FLH supplying 14% of the heat demand. In the 2030 price scenarios, the economic viability of direct waste heat drops to between 2200 FLH and 4600 FLH, supplying between 6% and 12% of the heat demand. This decrease is a result of the increased economic viability of the natural gas CHPs in the 2030 scenarios due to the higher average electricity prices, which reduces the integration of excess heat.

•
Additionally, in this portfolio, the use of the daily storage increases strongly in all 2030 scenarios compared to the 2017 scenario in order to temporarily store the heat from CHP production in hours of high electricity prices.

Portfolio P3: Biomass
• For the Waste-to-Energy CHP plant also in this portfolio, the results are very similar as in the Status Quo Portfolio due to the very low costs of heat generation fulfilling the "must run" requirement. The new Biomass CHP 2, which in this portfolio is subsidized with a market premium of 70 EUR/MWh el in addition to the electricity price, is economically viable at around 5500 FLH in the 2017 scenario and produces 14.5% of the heat demand. In the 2030 scenarios, the economically viable operation of Biomass CHP 2 decreases to between 4800 and 2400 FLH (13% to 6% of heat demand) because of increased use of fossil CHP due the higher average electricity prices. Although a high electricity price generally benefits the economic viability of Biomass CHPs, a higher electricity price favors fossil CHP more than biomass CHP and therefore reduces the use of biomass CHP in portfolios with fossil CHP.

Portfolio P4: Renewable
• For the Waste-to-Energy CHP plant also in this portfolio, the results are very similar as in the Status Quo Portfolio due to the very low costs of heat generation fulfilling the "must run" requirement.

•
In the renewable portfolio, the unsubsidized Biomass CHP cannot be operated economically in the 2017 scenario but between 2100 and 3100 FLH in the 2030 scenarios supplying 5% to 7% of the heat demand. The absence of fossil CHPs in this portfolio allows the economically viable operation of biomass CHP without subsidies at an electricity price level of the 2030 scenarios. • Direct excess heat can be integrated in an economically viable manner at around 5000 FLH in the 2017 scenario and to between 5200 and 5300 FLH in the 2030 price scenarios supplying 13% to 14% of the heat demand. The higher electricity prices reduce the economically viable use of heat pumps and thus lead to increased excess heat integration.

•
For the different heat pump technologies, the 2017 price scenario allows economical use of heat pumps in between 1700 and 5300 FLH depending on the COP supplying altogether around 39% of the heat demand. In the 2030 price scenarios with higher electricity prices, the heat pumps still can be operated economically between 1400 and 4700 FLH depending on the COP supplying 33% to 35% of the heat demand. The minimum FLH of the heat pumps with lowest COPs are limited to those hours with electricity prices close to zero.

Portfolio Comparison
In this section, annual heat generation, levelized costs of heat, and share of renewable generation are compared for the different scenarios across the portfolios. Figure 3 shows the comparison of annual heat generation of the different generation units for each portfolio and for the three assessed price scenarios. In all scenarios, 1100 GWh have to be supplied to the district heating network. The slight overproduction in the different scenarios is due to the losses of the heat storage. Especially in the scenarios with high electricity prices and bigger storage capacities, the losses are slightly higher due to heavy use of the storage by the CHP units and the heat pumps to maximize the revenues from the electricity production. In Figure 4, the levelized costs of heat for each portfolio and all scenarios are shown relative to the scenario "2017_Current" of the Portfolio "P0 Status Quo" (=100%). Additionally, the share of renewables is also shown for each portfolio and for all scenarios. The results show that in the scenario "2017_Current", the "Portfolio P4-Renewable" achieves the by far lowest levelized costs of heat together with the by far highest share of renewables. The second lowest costs of heat can be achieved by the status quo portfolio but at the same time it also has the lowest share of renewables. In the price scenario "2030_FossilHi_CO2-30", still the "Portfolio P4-Renewables" has the lowest cost of heat, together with by far the highest share of renewables. The three portfolios "P0 Status Quo", "P1 Heat Pump", and "P2 Waste Heat Integration" have similar costs of heat with the highest share of renewables in the "P2 Waste Heat Integration" portfolio. If electricity led operation of the CHP plants would be allowed in these model runs, the revenues from produced electricity would lead to a further reduction in levelized costs of heat. In the price scenario 2030_FossilLow_CO2-60, which has the highest electricity prices, the three portfolios "Status Quo", "Heat Pump", and "Waste Heat Integration" achieve the lowest levelized costs of heat. In these portfolios, the levelized costs of heat are mainly determined by the revenues of CHP on the electricity market. Again, if electricity led operation of the CHP plants would be allowed in these model runs, the revenues from produced electricity would lead to a further reduction in levelized costs of heat.   In Figure 4, the levelized costs of heat for each portfolio and all scenarios are shown relative to the scenario "2017_Current" of the Portfolio "P0 Status Quo" (=100%). Additionally, the share of renewables is also shown for each portfolio and for all scenarios. The results show that in the scenario "2017_Current", the "Portfolio P4-Renewable" achieves the by far lowest levelized costs of heat together with the by far highest share of renewables. The second lowest costs of heat can be achieved by the status quo portfolio but at the same time it also has the lowest share of renewables. In the price scenario "2030_FossilHi_CO2-30", still the "Portfolio P4-Renewables" has the lowest cost of heat, together with by far the highest share of renewables. The three portfolios "P0 Status Quo", "P1 Heat Pump", and "P2 Waste Heat Integration" have similar costs of heat with the highest share of renewables in the "P2 Waste Heat Integration" portfolio. If electricity led operation of the CHP plants would be allowed in these model runs, the revenues from produced electricity would lead to a further reduction in levelized costs of heat. In the price scenario 2030_FossilLow_CO2-60, which has the highest electricity prices, the three portfolios "Status Quo", "Heat Pump", and "Waste Heat Integration" achieve the lowest levelized costs of heat. In these portfolios, the levelized costs of heat are mainly determined by the revenues of CHP on the electricity market. Again, if electricity led operation of the CHP plants would be allowed in these model runs, the revenues from produced electricity would lead to a further reduction in levelized costs of heat.

Sensitivities
The sensitivities investigated show the following results: • Low CO2 price: The assumption of a low CO2 certificate price of 10 EUR/t has a big impact on the expected electricity prices and thus on the use of the generation technologies influenced by the electricity price. In this scenario, fossil CHPs are only economical in hours of high electricity prices up to 2500 FLH. However, for heat pump technologies with COPs between 3.5 and 4, in this scenario, an economic operation in more than 4500 FLH per year is possible. • Natural gas tax for CHP: The introduction of energy taxation, even for natural gas used in CHP plants, leads to a much lower cost-effectiveness of gas-fired cogeneration. The heat generation would then shift towards heat only plants but also to the biomass CHP. In the 2030 scenario with highest electricity prices however, still an economical operation of the gas-fired CHP in up to 3700 h would be possible.

Discussion and Conclusions
The results of the electricity market model (see Chapter 2.2) showed that increased renewable electricity generation capacity is very likely to result in higher volatility of future electricity prices with hours of both very low and very high electricity prices. A crucial question in this context is the number of hours with very high or very low prices and which plants of the merit order will be pricesetting in the mid-range. If the current expectations of a strong increase of the future CO2 price come true, this probably will be the most important factor regarding the future electricity price, which makes future developments very dependent on political decisions and framework conditions. This higher volatility of electricity prices will also result in higher flexibility requirements for the generation plants per se (fast and frequent start-up and shut-down, less nominal operation) but also there is a need for heat generation portfolios to respond to both high and low electricity prices.
The results of the portfolio assessment show that under the assumptions made in this paper, from a microeconomic point of view until the year 2030, the combination of large heat pumps with efficient CHP systems seems to be an effective strategy in order to hedge against different electricity

Sensitivities
The sensitivities investigated show the following results: • Low CO 2 price: The assumption of a low CO 2 certificate price of 10 EUR/t has a big impact on the expected electricity prices and thus on the use of the generation technologies influenced by the electricity price. In this scenario, fossil CHPs are only economical in hours of high electricity prices up to 2500 FLH. However, for heat pump technologies with COPs between 3.5 and 4, in this scenario, an economic operation in more than 4500 FLH per year is possible. • Natural gas tax for CHP: The introduction of energy taxation, even for natural gas used in CHP plants, leads to a much lower cost-effectiveness of gas-fired cogeneration. The heat generation would then shift towards heat only plants but also to the biomass CHP. In the 2030 scenario with highest electricity prices however, still an economical operation of the gas-fired CHP in up to 3700 h would be possible.

Discussion and Conclusions
The results of the electricity market model (see Section 2.2) showed that increased renewable electricity generation capacity is very likely to result in higher volatility of future electricity prices with hours of both very low and very high electricity prices. A crucial question in this context is the number of hours with very high or very low prices and which plants of the merit order will be price-setting in the mid-range. If the current expectations of a strong increase of the future CO 2 price come true, this probably will be the most important factor regarding the future electricity price, which makes future developments very dependent on political decisions and framework conditions. This higher volatility of electricity prices will also result in higher flexibility requirements for the generation plants per se Energies 2020, 13, 2457 14 of 20 (fast and frequent start-up and shut-down, less nominal operation) but also there is a need for heat generation portfolios to respond to both high and low electricity prices.
The results of the portfolio assessment show that under the assumptions made in this paper, from a microeconomic point of view until the year 2030, the combination of large heat pumps with efficient CHP systems seems to be an effective strategy in order to hedge against different electricity price developments. In this respect, heat pumps are well suited to use periods of low electricity prices and CHP plants for periods of high electricity prices. However, in hours with high electricity prices, the units with highest electricity output are operated, which then even may replace other renewable generation units in the portfolio like biomass CHP or excess heat. Therefore, from a microeconomic perspective (the viewpoint of the district heating utility), and as long as no stringent regulations for an increase in renewable heat generation are in place, a complete withdrawal from the fossil CHP is not the optimal solution according to the model results until 2030, as otherwise periods of high electricity prices and thus potential revenues from the electricity market could not be exploited. Nevertheless, the assessment also showed that by combining heat pumps, integrating waste heat, and generating heat from biomass, an almost CO 2 -free district heating supply of the assessed district heating network seems to be possible at reasonable costs in the medium to long term. This scenario of high renewable district heating generation turns out to be most insensitive and resilient subject to the future development of energy and CO 2 prices. In any case, the increase of heat storage capacity turns out to be a no-regret strategy: it is beneficial in all scenarios by allowing the decoupling of electricity generation and the heat demand to optimize the plant dispatch according to electricity prices.
The most recent developments in the context of the Corona crisis and related impacts on global energy demand and prices even more illustrate that the future framework conditions of such analyses are still associated with high uncertainties. This refers in particular to the available fluctuating renewable electricity capacities, future CO 2 price development, the availability of flexibility options, and the future role of conventional electricity generation including from CHP plants. Moreover, the national implementation of EU legislation in Austria (in particular regarding the renewable energy directive [3] and the upcoming further requirements in course of the European Green Deal [18]) is still uncertain and will affect the profitability of investment decisions to be taken now. Overall, it will be essential to take decisions for a heat supply portfolio, which is able to fulfil two requirements: (1) take advantage of different market and price constellations and (2) fulfil the upcoming more and more stringent decarbonization targets. Under the assumptions and portfolio settings considered in this paper, a combination of CHP and heat pumps fulfils the first requirement but most probably not the second one. The presented portfolio with a high share of renewables fulfils the second requirement and seems sufficiently acceptable regarding the first requirement, even though it is less favorable as the portfolio including fossil CHPs.
The analysis shows that the district heating company is able to respond to the different upcoming challenges-as far as they could be considered in our study-by gradually transforming towards a renewable district heat portfolio including heat storages until the year 2030. However, this requires the corresponding investments to be taken within the next few years.