UCB-SEnMod: A Model for Analyzing Future Energy Systems with 100% Renewable Energy Technologies—Methodology

Abstract

While the contribution of renewable energy technologies to the energy system is increasing, so is its level of complexity. In addition to new types of consumer systems, the future system will be characterized by volatile generation plants that will require storage technologies. Furthermore, a solid interconnected system that enables the transit of electrical energy can reduce the need for generation and storage systems. Therefore, appropriate methods are needed to analyze energy production and consumption interactions within different system constellations. Energy system models can help to understand and build these future energy systems. However, although various energy models already exist, none of them can cover all issues related to integrating renewable energy systems. The existing research gap is also reflected in the fact that current models cannot model the entire energy system for very high shares of renewable energies with high temporal resolution (15 min or 1-h steps) and high spatial resolution. Additionally, the low availability of open-source energy models leads to a lack of transparency about exactly how they work. To close this gap, the sector-coupled energy model (UCB-SEnMod) was developed. Its unique features are the modular structure, high flexibility, and applicability, enabling it to model any system constellation and can be easily extended with new functions due to its software design. Due to the software architecture, it is possible to map individual buildings or companies and regions, or even countries. In addition, we plan to make the energy model UCB-SEnMod available as an open-source framework to enable users to understand the functionality and configuration options more easily. This paper presents the methodology of the UCB-SEnMod model. The main components of the model are described in detail, i.e., the energy generation systems, the consumption components in the electricity, heat, and transport sectors, and the possibilities of load balancing.

1. Introduction

The feasibility of supplying energy to all sectors through 100 percent renewable energy sources has received much attention [1,2,3,4,5]. The current energy supply is based mainly on fossil fuels, which through their combustion inevitably emit carbon dioxide (CO₂). Due to the progressive increase in the CO₂ concentration in the atmosphere, the global population is confronted with irreversible climatic changes that endanger life as we know it [6]. The substitution of fossil fuels with renewable energy and the associated decoupling of energy production and resource consumption ensures a reliable, economic, and environmentally compatible energy supply. Additionally, decarbonizing the energy sector achieves independence from gas and coal exports. The energy system’s transformation can be achieved by using solar energy directly by photovoltaic systems or indirectly as wind energy by wind turbines. The change from a centralized, unidirectional energy system to a decentralized, bidirectional system can be accomplished with the supporting pillars of photovoltaics and wind power. Due to the temporary large overproduction of electricity by fluctuating power generation plants, the need for storage systems increases but can be reduced to a minimum by energy demand reduction measures, such as general technical efficiency improvements and changes in consumer behavior, as well as through load shifting and the expansion of domestic and cross-border transmission grid capacities. In this context, sector coupling combines the electricity, heat, and transport sectors into a holistic system. The integration of renewable energy at all levels of the energy system will significantly increase its complexity. Therefore, appropriate power grid and energy models must be developed to meet these challenges. Power grid models focus on power flow in different grid topologies and voltage levels in high-resolution time steps (focusing on frequency and voltage stability, reactive power compensation and control, and other relevant system services). In contrast to power system models, energy models do not aim to simulate the behavior of electric transmission networks and their relevant ancillary services; instead, energy models are used to balance and analyze the system’s energy production and consumption.

Various energy models that simulate systems based on 100 percent renewable energy exist. However, those models differ significantly in their considered spatial resolution: some models represent off-grid systems or small regions, and others national, multinational, continental, or global systems. Moreover, the models vary from approaches that neglect regions’ internal structure to models with a high spatial resolution of a few square kilometers and a detailed grid. Another key difference between the models is which sectors are considered. Much research focuses exclusively on the electricity sector and does not adequately map the consumption sectors. Breyer et al. conclude that a research gap remains in modeling the entire energy system for very high sustainability shares in full hourly resolution and high spatial resolution [7,8]. However, progress can be seen lately: a study conducted by LUT University (LUT) and the Energy Watch Group (EWG) models a global energy system transition toward 100 % renewable energies by 2050 with a high temporal resolution in the electricity, heat, transportation, and desalination sectors [9]. Connolly et al. [10] have analyzed 37 computer-based models that can be used for integration analysis of renewable energy systems. From the study, none of the models covers all issues related to renewable energy integration, but that the ideal energy tool depends heavily on the specific modeling aims. These results are consistent with Hall and Buckley [11], who identify the prevailing energy system models and instruments in the United Kingdom. For instance, the GENESYS energy model used in a publication by Bussar et al. [12] or the DESSTinEE model by Staffell et al. [13,14] focus on the electricity sector but does not sufficiently address the heat and transport sectors, thus neglecting sector coupling. Another example is the energy system model elesplan-m developed for the European power sector, which was used in a work by Pleßmann and Blechinger [15]. In contrast, EnergyPlan [16], a multisectoral energy system model, can cover all relevant sectors but has limitations in the adaptability of the system constellation and the pursuit of different optimization strategies. However, it can be noted that the functionality of open-source energy models has been increasing recently. Oberle and Elsland [17] describe the four models OSeMOSYS, GCAM, renpassG!S, and REMIND cover all sectors and have high geographical coverage. Nevertheless, these models differ significantly in their optimization strategies. GCAM [18,19] considers the dynamics between the energy system and the environment and the impact on our global climate. renpassG!S [20], on the other hand, is used for minimum cost dispatch analysis of power systems. REMIND [21,22] considers the developments of the world economy with a special focus on the energy sector and climate impacts. Whereas OSeMOSYS [23,24] is a tool for long-run integrated assessment and energy planning. With a suitable software design, the described limitations of most models can be avoided, and both flexibility and applicability can be optimized. As Candas et al. [25] note, the low availability of open-source frameworks for modeling renewable energy systems often leads to a lack of transparency about how energy system models work. By releasing the UCB-SEnMod framework, we are making a valuable contribution to improving the interpretability of the functionality and configuration options of the energy model’s system components.

Our work aims at creating a tool for the analysis of energy solutions for future energy systems. In particular, our software addresses open questions for detailed modeling of energy consumption in the power and heat sector and maximum flexibility in application to decarbonization issues of energy systems of different sizes (countries, regions, energy districts, and individual industrial enterprises). In addition to the detailed consumption modeling, a unique feature of the model is the modular structure consisting of clusters, which can be used in any system constellation due to its highly flexible application possibilities and can continuously be expanded due to its modularity. Thus, our model closes the gap in existing models, which often can only cover particular issues related to integrating renewable energy. Our publication presents the methodology of a complete model of all energy sectors, in which the minutely or hourly interaction of energy supply and energy consumption can be analyzed [26].

In Section 2, the methodology is presented for synthesizing the components’ profiles that generate electrical energy. In Section 3, the consumption sectors (electricity, heat, and transport) are discussed. After that, the load profiles, which can be synthesized with the methods presented in Section 3, are compared with the generation profiles to determine the demand for balancing power. Since the control power at each time step is usually not Zero, Section 4 discusses approaches that can be used to balance the generation and consumption of electrical energy. The conclusion, presented in the last section, serves as a recapitulation of this study’s central outcome and impact.

2. Energy Supply

An integral part of an energy system is its energy generation system. These generation components are described mathematically to create profiles of the energy supply over a period of time. The system units shown as black blocks in the example constellation of the UCB-SEnMod energy model in Figure 1 represent those units that generate electrical energy. In addition to photovoltaics and wind power (on- and offshore), these also include run-of-river hydropower and systems that burn biomass or synthetically/biogenically generated gas to produce electricity and heat, the so-called combined heat and power plants (CHP). At the end of this section, the heat storage module is being discussed, which can be linked to any heat generating technology, reducing short supply and demand mismatches in the low-temperature segment.

2.1. Photovoltaic Systems

A tool developed by Sandia National Laboratories is used to simulate the performance of photovoltaic power systems [27,28,29,30,31]. In the model, a photovoltaic system can be configured for power simulation, whose electrical characteristics are defined by its module and inverter parameters and module interconnection. In addition, the geographical location of the system is defined by latitude, longitude, time zone, and altitude. The orientation of the photovoltaic system corresponds to the azimuth and tilt angle of the system surface. The time- and site-specific radiation, global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI) required for the simulation are obtained from the Copernicus Atmospheric Monitoring Service (CAMS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Furthermore, weather-related data, such as ambient temperatures and wind speeds, are provided by the web application Renewables.ninja [32].

For the yield estimation in a region, not all photovoltaic systems of the considered region are simulated singularly, but the region is divided into zones distinguished by their climatic conditions. A representative photovoltaic system is defined for each zone whose plant capacity would correspond to all plants in this region. The yield profiles of all zones generated in this way are summarized additively to create the regions’ overall yield profile.

To determine the orientation of the fictitious photovoltaic plant positioned in the center of the considered zone, the frequency distribution of azimuth and elevation angles (presented in [33,34]) of more than 30,000 photovoltaic systems in Europe are used. A suitable orientation is determined numerically using this data and the solar radiation at the reference site. For instance, the azimuth and inclination angle of a photovoltaic system, positioned in the center of Germany, corresponds to about ${137}^{\circ }$ orientation and ${27}^{\circ }$ inclination. This procedure is repeated for all zones of the regions and countries considered, followed by the calculations shown schematically in Figure 2.

The Python implemented NREL SPA algorithm is used to determine the elevation and azimuth angle of the sun position. This algorithm is described in more detail by Reda and Andreas [36]. The model of Kasten and Young [37] is used to estimate the relative and absolute air mass at the system’s absolute altitude (10 m above sea level). The Ineichen/Perez model determines the clear sky’s GHI, DNI, and DHI. The procedure is described in more detail in the publications by Perez and Ineichen [38,39]. A pioneering study by Reindl et al. [40,41,42] demonstrated an approach predicting global radiation’s diffuse fraction on a tilted plane. The inclination and azimuth angle of the surface, the radiation intensities DHI, DNI, and GHI, the extraterrestrial radiation intensity, and the solar zenith and azimuth angle are used for that calculation. Subsequently, a one-diode model is used to determine the current-voltage characteristic of the system using the technical and physical parameters of the module and module interconnection. Finally, to calculate the energy yield of the photovoltaic system over the period considered, Sandia’s grid-connected inverter model [43] is used to convert the previously calculated direct current into alternating current.

The cell temperature of a photovoltaic module depends on weather conditions, the type of module, and its construction. The reduction in the system’s energy yield due to increasing cell temperature and the associated voltage drop must be considered in the simulation. Additional yield reductions can occur due to shading, soiling, snow cover, and other environmental influences. Electrical wiring and system age also affect yield. These losses $L_{\mathrm{i}}$ can be summarized and described in percent by Equation (1)

$$\begin{array}{c}\hfill L_{\mathrm{total}}=100·\left(1-\prod _i·\left(1-\frac{L_{\mathrm{i}}}{100}\right)\right).\end{array}$$

(1)

Figure 3 shows the validation of the photovoltaic yield simulations using the example for Germany in 2018 with the data from the German Federal Network Agency. Yield data are trend adjusted to compensate for the increase in plant capacity during the year. Only one representative location was chosen for the example shown, namely a site in the center of Germany. The simulated electricity yield over one year is about 40 TWh, similar to that of the Federal Network Agency. However, significant differences in the daily totals can be seen. The root-mean-square error (RMSE) of both data sets $X_1=\{x_{1,1},x_{1,2},\cdots ,x_{1,8760}\}$ and $X_2=\{x_{2,1},x_{2,2},\cdots ,x_{2,8760}\}$ is described by

$$\begin{array}{c}\hfill \mathrm{RMSE}=\sqrt{\frac{{\sum }_{t=1}^{8760}{(x_{1,t}-x_{2,t})}^2}{8760}}\end{array}$$

(2)

and is calculated to be 4.9 GWh. A fairer geographical resolution of zones with different climatic conditions per region or country can further reduce this difference in hourly and daily totals, and thus the RSME.

For the yield simulation for any chosen future year (e.g., 2030 or 2050), a test reference year is calculated for each climatic zone using statistical methods representing the typical radiation and weather pattern of a whole year with hourly accuracy [44]. This calculation is based on meteorological data over the last decades. It also assumes that the plants installed in 2018 are distributed in all geographic regions, which may differ in their climatic conditions. Therefore, the region-specific yield profile of the test reference year correlates perfectly with the projected energy generated in the chosen future year [45]. This allows the yield profile to scale proportionally to the installed system capacity in present (e.g, in 2018) with the assumed system capacity in the future. This approach is also used for wind and hydropower simulations.

2.2. Onshore and Offshore Wind Turbines

For the simulation of the net electricity yield of wind energy, typical wind turbines are selected for the climate zones of the individual regions. Turbine characteristics of the individual turbines can then be used to interpolate the wind speed at hub height at the reference site to determine the yield profile. Those turbine characteristics specify the relation between turbine output power and corresponding wind speed. Its function values denote the percentage of power taken by the turbine at a given wind speed. In addition to wind turbines’ turbine-specific and turbine control-related properties, the proportionality $P\propto {\overline{v}}^3$, which is described by

$$\begin{array}{c}\hfill P\left(t\right)=\frac{1}{2}·\rho ·A·c_P\left(\overline{v}\right)·{\overline{v}}^3\left(t\right),\end{array}$$

(3)

determines the shape of the turbine characteristic curve. Equation (3) simplifies the power $P\left(t\right)$ that can be taken from the wind by the turbine, $\rho$ is the density of the atmosphere, A describes the spanned rotation area by the turbine blades, $c_P\left(\overline{v}\right)$ is the power factor of the turbine, and $\overline{v}$ the mean wind speed at hub height. Since the air density is nearly constant at hub height, the plant’s power depends crucially on the power factor and the wind speed [46].

Once the wind turbine yield profile has been determined, the reference turbine power can be scaled based on the total installed capacity in the corresponding zone. This approach is repeated for offshore wind energy in regions with the necessary coastal topology [47]. If the site-specific wind speed is not provided at hub height, statistical and mathematical methods can transform wind speed from measurement height to hub height. An approximation to the mean wind speed $\overline{v}$ at height h and time t given a measured wind speed $v_{\mathrm{ref}}\left(t\right)$ at height $h_{\mathrm{ref}}$ can be obtained using Hellmann’s power law [48], shown in Equation (4)

$$\begin{array}{c}\hfill \overline{v}\left(t\right)=v_{\mathrm{ref}}\left(t\right)·{\left(\frac{h}{h_{\mathrm{ref}}}\right)}^{\alpha },with\alpha \approx 0.14.\end{array}$$

(4)

When extrapolating the wind speed at altitudes above 100 m, it should be noted that an exponential and logarithmic description of the height profile no longer represents the wind conditions with sufficient accuracy. Studies on the determination of profile laws valid for most atmospheric boundary layers can be found in [49,50].

The upper and lower part of Figure 4 shows the validation of the wind power yield simulations for onshore and offshore plants for Germany in 2018 with the German Federal Network Agency data. As with the photovoltaic yield profile, the area of the yield profile shown integrated over the entire year 2018 corresponds to that of the German Federal Network Agency. However, significant differences in the daily sums can also be seen here for already described reasons. The yield profiles are trend-adjusted to compensate for the increase in turbine capacity within the year without changing the average.

Once the model has been validated, the simulation can be repeated to determine the yield profile for the future year based on assumptions about plant distribution and dimensioning and the meteorological test reference years.

2.3. Run-of-River Hydro Plants

The yield profile of run-of-river-hydro plants is derived from a standard load profile synthesized from the real generated power of German plants in the last years. This approach is also used in a work by Henning and Palzer [2]. The profile is normalized, aggregated to hourly values, and converted to coordinated universal time (UTC). Then, under the simplifying assumption that hydropower generation in all regions follows the same pattern, the profile is scaled as a function of region-specific installed nominal capacity. Pumped storage power plants are discussed in a later section.

2.4. Biomass Plants

In a future power system where volatile generation plants generate the most electrical power; it makes sense to utilize biomass plants’ flexibility and planning abilities. This model assumes that up to 50 TWh of biomass can regulate the power balance. The waste heat from power generation is fed into a heating network as low-temperature heat for heating purposes. The remainder of the plant’s capacity produces biogenic fuels for transportation and industrial heat processes.

2.5. Solar Thermal Energy Systems

Solar thermal systems convert solar radiation energy into thermal energy. The thermal energy is stored in a heat storage tank and is provided as low-temperature heat. To determine the irradiation on the inclined surface, azimuth, and tilt angle of solar thermal modules, the approach already described in Section 2.1 is used. The meteorological data required for this are obtained from the Copernicus Atmospheric Monitoring Service (CAMS) from the European Centre for Medium-Range Weather Forecasts (ECMWF), and the web application Renewables.ninja, as mentioned above, are averaged per reference site over the last decades without losing the natural variation.

The method for calculating the thermal energy fed into the thermal storage $Q_{\mathrm{use}}\left(t\right)$ at time t, depending on the plane of array (POA) irradiance $G\left(t\right)$ and the solar thermal collectors’ area $A_{\mathrm{coll}}$, is described in [2] and calculated as

$$\begin{array}{c}\hfill Q_{\mathrm{use}}\left(t\right)={\eta }_{\mathrm{coll}}\left(t\right)·A_{\mathrm{coll}}·G\left(t\right).\end{array}$$

(5)

The feed-in heat is strongly influenced by the efficiency ${\eta }_{\mathrm{coll}}\left(t\right)$. This depends on both the ambient temperature and the temperature of the storage medium, and the module characteristics. The efficiency ${\eta }_{\mathrm{coll}}\left(t\right)$ of the solar collector is given by

$$\begin{array}{c}\hfill {\eta }_{\mathrm{coll}}\left(t\right)=\max \left\{c_0-c_1·\left(\frac{T_{\mathrm{stor}}\left(t\right)-T_{\mathrm{amb}}\left(t\right)}{G\left(t\right)}\right),0\right\}\end{array}$$

(6)

with the temperature of the storage tank $T_{\mathrm{stor}}\left(t\right)$, the ambient temperature $T_{\mathrm{amb}}\left(t\right)$ and the irradiance on the module plane $G\left(t\right)$ in $\mathrm{J}/{\mathrm{m}}^2$ at a given time t. Here, the optical efficiency $c_0$ of the collector is assumed to be 0.8, and the heat loss coefficient $c_1$ is assumed to be $3\mathrm{W}/\left({\mathrm{m}}^2\mathrm{K}\right)$ [2].

2.6. Combined Heat and Power Plants

In contrast to combined cycle power plants, combined heat and power plants (CHP) serve simultaneous electricity and heat supply and are designed decentralized and monovalent in this model. The fuel used is gas, which is produced synthetically with renewable energy. The heat generated by the combined heat and power process is used as low-temperature heat for space heating and hot water preparation and can be stored in heat storage in case of surplus.

The basis for the combined heat and power plants’ design is the heat load profile of households minus the heat energy used by solar thermal energy. The design point on the load duration curve is set at 6000 h. The overall efficiency with

$$\begin{array}{c}\hfill {\eta }_{\mathrm{total}}=\frac{P_{\mathrm{el}}+{\dot{Q}}_{\mathrm{th}}}{{\dot{B}}_{\mathrm{gas}}}={\eta }_{\mathrm{el}}+{\eta }_{\mathrm{th}}\end{array}$$

(7)

is assumed to be 0.95 and for the electrical efficiency 0.35 is considered [51]. $P_{\mathrm{el}}$ and ${\dot{Q}}_{\mathrm{th}}$ describe the electrical and thermal power provided by the plant—when the fuel ${\dot{B}}_{\mathrm{gas}}$ is supplied. Here, the relationship in Equation (8) applies to the electrical and thermal efficiency

$$\begin{array}{c}\hfill {\eta }_{\mathrm{el}}=\frac{P_{\mathrm{el}}}{{\dot{B}}_{\mathrm{gas}}}\mathrm{and}{\eta }_{\mathrm{th}}=\frac{{\dot{Q}}_{\mathrm{th}}}{{\dot{B}}_{\mathrm{gas}}}.\end{array}$$

(8)

The fuel is the gas produced synthetically with electrical energy from renewable generation plants or biomass.

2.7. Heat Storage

To reduce short- and long-term up to seasonal mismatches of supply and demand in the low-temperature heat sector, the heat generated by solar thermal collectors and combined heat and power plants is stored in heat storage systems. Water is used as a storage medium. The single-node model used to represent the heat storage’s energy balance is described by the following differential equation [2]

$$\begin{array}{c}\hfill C_{\mathrm{stor}}\frac{\mathrm{d}{T}_{\mathrm{stor}}\left(t\right)}{\mathrm{d}t}={\eta }_{\mathrm{charge}}·P_{\mathrm{charge}}\left(t\right)-P_{\mathrm{discharge}}\left(t\right)-P_{\mathrm{loss}}\left(t\right).\end{array}$$

(9)

Here, $C_{\mathrm{stor}}$ is the total heat capacity of the storage tank, which is the product of the specific heat capacity and the total mass of the storage medium (i.e., water). $T_{\mathrm{stor}}\left(t\right)$ is the temperature of the storage medium, $P_{\mathrm{charge}}\left(t\right)$ and $P_{\mathrm{discharge}}\left(t\right)$ are the thermal power fed to and taken from the storage tank, $P_{\mathrm{loss}}\left(t\right)$ is the power loss of the storage tank, and ${\eta }_{\mathrm{charge}}$ is the storage’s charging efficiency. The thermal stratification in the storage medium, which positively affects the heat transfer from heat transfer fluid to the storage medium, is neglected. Equation (10) describes the power loss of the storage tank due to self-discharge

$$\begin{array}{cc}\hfill P_{\mathrm{loss}}\left(t\right)& =U_{\mathrm{A},\mathrm{stor}}·\left(T_{\mathrm{stor}}\left(t\right)-T_{\mathrm{amb},\mathrm{stor}}\left(t\right)\right)=\frac{C_{\mathrm{stor}}}{{\tau }_{\mathrm{stor}}}·\left(T_{\mathrm{stor}}\left(t\right)-T_{\mathrm{amb},\mathrm{stor}}\left(t\right)\right).\hfill \end{array}$$

(10)

$U_{\mathrm{A},\mathrm{stor}}$ describes the heat loss coefficient, $T_{\mathrm{amb},\mathrm{stor}}\left(t\right)$ the ambient temperature of the storage tank and ${\tau }_{\mathrm{stor}}$ the time constant of self-discharge.

Table 1. Parameters used for the heat storage simulation.

Parameter	Value
$T_{\min }$	55 °C
$T_{\max }$	95 °C
${\eta }_{\mathrm{charge}}$	0.9
$c_{\mathrm{water}}$	4.184 J/(kg K)
${\tau }_{\mathrm{stor}}$	72 h

shows examples of technical and physical parameters for the heat storage simulation. The storage volume is designed for 80 L per installed square meter of solar thermal area. It is assumed that the temperature does not fall below a minimum value.

3. Energy Demand

In addition to the components presented in the previous section, the energy model also includes features for energy demand. These are subdivided into electricity, heat, and transportation and are shown below. As the generated profiles describe the final energy demanded by consumers, the profiles are highly dependent on the technologies powered by the final energy. That applies in particular to the technologies used for heat supply. Therefore, the following section provides an overview of the technologies used and discusses various considerations for modeling load profiles for future scenarios.

3.1. Electricity

Standard load profiles are used to approximate households, companies, or regions’ electricity load profiles. When simulating countries, data by the European Network of Transmission System Operators for Electricity (ENTSO-E) can be taken. ENTSO-E provides the countries’ electricity consumption and refers to 2015 [52]. Those European countries that are not available in the database can be estimated using countries with comparable locations and structures. Electricity demand data already includes electrical energy used to provide heat. Growth rates for electricity consumption in European countries vary from one to two percent per year, consistent with assumed average growth projections in other publications [53,54].

3.2. Heat

The heat sector’s energy demand includes the supply of space heating and hot water and industrial process heat of different temperature segments. Figure 5 schematically shows the systems used to provide heat. The supply of space heating and hot water is supplied by solar thermal systems, heat pumps, and combined heat and power plants. Only heat pumps provide process heat in the lower temperature segment. Due to the high-temperature requirements of many industrial processes, technologies used to provide low-temperature ranges cannot be used. Electric boilers and heating furnaces provide heat for medium and high-temperature process heat.

3.2.1. Space Heating and Water Heating

The space and water heating demand profiles are calculated using an approach described by the German Association of Energy and Water Management [55]. This approach is described based on standard load profiles by Equations (11) and (12), which map the correlation between ambient temperature and heat demand

$$\begin{array}{c}\hfill Q\left(t\right)=F_{\mathrm{KW}}·F_{\mathrm{WT}}·F_{\mathrm{SF}}·f\left({\vartheta }_{\mathrm{amb},\mathrm{d}}\right)\end{array}$$

(11)

with

$$\begin{array}{c}\hfill f\left({\vartheta }_{\mathrm{amb},\mathrm{d}}\right)=\frac{A}{1+{\left(\frac{B}{{\vartheta }_{\mathrm{amb},\mathrm{d}}-{\vartheta }_0}\right)}^C}+D+\max \left\{\begin{array}{c}{m}_{\mathrm{H}}·{\vartheta }_{\mathrm{amb},\mathrm{d}}+b_{\mathrm{H}}\hfill \\ m_{\mathrm{W}}·{\vartheta }_{\mathrm{amb},\mathrm{d}}+b_{\mathrm{W}}.\hfill \end{array}\right.\end{array}$$

(12)

Here, $Q\left(t\right)$ describes the energy demand at a given time t depending on both the day of the week and the daily average temperature ${\vartheta }_{\mathrm{amb},\mathrm{d}}$, A to D the load profile characterizing sigmoid parameters, ${\vartheta }_0$ the reference temperature (40 °C), $m_{\mathrm{H}}$ and $m_{\mathrm{W}}$ the slope coefficients of the heating and hot water ranges, $b_{\mathrm{H}}$ and $b_{\mathrm{W}}$ the shift constants of the heating and hot water ranges, $F_{\mathrm{KW}}$ the individual customer value, $F_{\mathrm{SF}}$ the temperature-dependent hourly factor, and $F_{\mathrm{WT}}$ the industry-dependent weekday factor.

By adding a linear component to the sigmoid function, the course of the load profile can be further improved [55]. For simplification, the total heat demand of households is calculated using the standard load profile HMF03, the total heat demand of trade, commerce, and services (GHD), and the industry’s hot water and hot water consumption using the standard load profile GHD03. Figure 6 shows the simulated heat load profile of space and water heating and the ambient temperature profile for households, GHD, and industry using Germany as an example for 2018. The potential for saving thermal energy in the building sector is enormous. For instance, the energy demand for space heating can be reduced by up to 60 percent by 2050 [56,57]. This also corresponds to the assumption made in a scenario by Palzer and Henning [3] simulated with the energy model REMod developed by Fraunhofer ISE.

3.2.2. Process Heat

In addition to the energy supply for space heating and hot water, process heat consumption, which accounts for the most significant industry share, represents the third area in the heating sector [58,59]. According to a European Commission report, 74 percent of final industrial energy consumption in Europe was spent on heating, hot water, and air-conditioning cooling in 2012. With a share of 60 percent of final energy consumption, processes for providing process heat of different temperature ranges took the largest share. The remaining 14 percent was distributed among space heating, hot water, and air-conditioning cooling [60]. About 26 percent of final energy consumption in the industry that was not used for heat-related processes was used for lighting, information and communication technologies, and mechanical energy. These industrial consumers’ shares are roughly consistent with [61,62] for other years. The mean distribution of final energy consumption shares of different temperature levels for the provision of process heat at the European level for 2012 was examined in a publication by Naegler [63]. Low-temperature processes take about 8 percent of industries’ final energy demand, medium-temperature operations about 19 percent, and the highest temperature segment processes approximately 33 percent. The respective percentage share is multiplied by the industry’s annual final energy consumption in the region to determine the heat demand of low-temperature, medium-temperature, and high-temperature. It is assumed that the distribution of process heat corresponds to that of the electricity load profile presented in Section 3.1. For this purpose, the regions’ electricity load profiles are normalized and scaled by the respective amount of process heat. As mentioned above, only heat pumps provide low-temperature process heat. Medium- and high-temperature processes are supplied 80 percent by electric boilers and 20 percent by biogenic fuel furnaces. Furthermore, it is assumed that efficiency measures and process changes will reduce the final energy demand for process heat by 30 percent by 2050 [59]. Studies [64,65] assume potential end-use energy savings of 34 to 50 percent by 2050.

3.3. Transportation

There are different measures to reduce final energy consumption in the transport sector. This publication divides these measures into three types, which are now briefly presented.

• Type A: The amount of transportation is directly reflected in the final energy consumption. The change in kilometers traveled relative to a reference year is covered by Type C measures.
• Type B: By changing the propulsion concepts, such as, e.g., the electrification of a previously combustion-based means of transportation, are grouped under Type B. It is assumed that in the next decades the average efficiencies of the propulsion systems will be equivalent to those of today’s state-of-the-art propulsion systems of new vehicles of the same type [58,66]. This would correspond to no efficiency improvements in today’s powertrain designs. This assumption can be considered as a worst-case estimate.
• Type C: These measures include vehicle and infrastructure improvements that help reduce final energy consumption. Measures considered for this purpose include board energy management improvements, improvements in vehicle aerodynamics, weight and rolling resistance reduction, or changes in travel conditions, such as speed or traffic flow.

Figure 7 shows the exponential progression of the final energy consumption change assumed in a scenario that could be made for European private motorized transport, road freight transport, air transport, rail transport, and inland waterway transport up to 2050 compared to 2018. A constant percentage change in the respective final energy consumption of the specific transport sectors from 2018 is assumed for all European countries considered through measures A, B, and C.

The country-specific development of final energy demand up to the chosen future year is calculated based on the final energy demand of the reference year (e.g., for 2018) and modeled differentiated by energy carriers. It is assumed that 70 percent of the final energy consumption of road transport is attributable to private motorized vehicles and 30 percent to road-based freight transport [67]. Different methods are chosen to generate the load profiles depending on the mode of transportation. The standard load profiles developed by the Karlsruhe Institute of Technology (KIT) based on long-term studies of the German Mobility Panel (MOP) [68] are used to calculate the load distribution by battery electric vehicles. The standard load profiles are differentiated by weekdays and weekends and by household types and charging strategies, which depend on the availability of charging infrastructure. The load profile of road-bound freight traffic is the data on mileage on German autobahns and federal roads from the Federal Office for Freight Transport. This load profile is subsequently corrected with the actual driving bans and the driving performance in the European countries. To generate the load profile of air transport, rail transport, and inland waterway transport, the required final energy of the selected fuel is distributed evenly over all hours of the year.

4. Regulation of the Current Account Balance

In general, the difference between electricity production G(t) and electricity consumption L(t) at a given time t in, i.e., $t:\left[t,t+1\mathrm{h}\right[$ is not Zero. This difference ${\delta }_{\mathrm{n}}\left(t\right)$ of region n is defined as

$$\begin{array}{c}\hfill {\delta }_{\mathrm{n}}\left(t\right)=G_{\mathrm{n}}^{\mathrm{W}}\left(t\right)+G_{\mathrm{n}}^{\mathrm{P}}\left(t\right)+G_{\mathrm{n}}^{\mathrm{H}}\left(t\right)+G_{\mathrm{n}}^{\mathrm{K}}\left(t\right)-L_{\mathrm{n}}\left(t\right)\end{array}$$

(13)

and describes the difference between non-shiftable power generation and non-shiftable power load. In this context, electricity generation is provided by wind power systems W, photovoltaic systems P, hydropower systems W, and CHP systems K. It can be balanced by storage processes and cross-border transit of electrical energy. Figure 8 schematically shows the hierarchical flow of the energy balancing process.

4.1. Electrical Energy Storage

Storage capacities must increase with a high share of fluctuating power generation units. Assuming that the electricity generation and consumption pattern repeats annually, the storage volume is dimensioned. It is also determined that the state of charge at the beginning of the year should correspond to the state at the end of the year to obtain a periodical state of charge. The minimum storage volume required equals the maximum capacity at which the storage tank is full for at least one hour of the year. The systems available for electric energy storage are battery storage (s = 1), pumped storage (s = 2), and gas storage (s = 3), which are charged and discharged in the mentioned order. The simulated storage processes are characterized by charging and discharging efficiencies and maximum storage currents.

4.1.1. Battery Storage

Batteries store electrical energy highly efficiently in electrochemical processes and convert it into electrical charge when needed. Due to the high round-trip efficiency of batteries, they are used in the simulation as short-term storage devices. As can be seen in the upper part of Figure 9, this is evident in a high number of charge cycles over the period of time being considered. Compared to pumped storage, the locations of stationary battery storage are not limited by special geological conditions, but the storage capacity is kept to a minimum for monetary reasons.

4.1.2. Pumped-Storage Hydroelectricity

Pumped-storage hydropower can contribute significantly to grid stabilization by taking power from the grid when there is a power surplus (storing potential energy) and feeding power back into the grid when there is a power deficit. Pumped storage hydropower plants can switch between these two operating states within minutes. A report commissioned by the European Commission estimated the European potential for energy storage in pumped storage power plants [69]. The maximum total capacity of pumped storage to be developed was investigated under specific topologies and scenarios. The report focuses on two topologies: on the one hand, existing pairs of reservoirs with suitable elevation differences were considered, whose distance would allow them to be connected by a penstock and appropriate electrical equipment. Secondly, individual reservoirs were studied whose topologies will enable the installation of a second artificial basin. These topologies were then considered differentiated and divided into different scenarios. The results show that the latter topology has about twice the potential of the former topology. Therefore, the maximum available potential capacity is a maximum upper bound when simulating countries.

4.1.3. Gas Storage

The electricity sector can be bidirectionally coupled with the heat, transport, and industry sectors via the gas infrastructure by storing electrical energy by synthesizing gases from excess electricity. By using gas storage as long-term storage (see Figure 9), seasonal fluctuations such as more extended dark periods and persistent wind lulls can be compensated. Long-term storage characteristics include high storage capacities, low self-discharge power losses, and a low number of charge cycles and efficiency [70]. The coupling between the electricity and transport sectors occurs directly via electromobility or indirectly via the further processing of gas into synthetic fuels. Regenerative power generation is performed via highly efficient gas-fired power plants. Due to the plant’s high power and conversion efficiency, it is difficult to use the heat generated during the conversion process [2]. The model operates with an efficiency typical for converting electricity to methane, but this efficiency highly depends on the carbon dioxide source used [71]. Other conversion losses, such as those due to compression or liquefaction of the gases, are not considered—hydrogen and methane are considered gases throughout the system.

Positive Energy Balance

The electric energy ${\delta }_{\mathrm{n}}^{+}\left(t\right)$ at a given time t, defined as

$$\begin{array}{cc}\hfill {\delta }_{\mathrm{n}}^{+}\left(t\right)& =\max \left\{{\delta }_{\mathrm{n}}\left(t\right),0\right\},\hfill \end{array}$$

(14)

Which is greater than Zero, can be supplied to a storage facility. However, technical and physical parameters of the storage system limit the energy that can be fed into the storage. This total amount of energy used at time t is the sum of the subsets $C_{\mathrm{n},\mathrm{s}}\left(t\right)\forall s\in \left\{1,2,3\right\}$ with

$$\begin{array}{c}\hfill {\delta }_{\mathrm{n}}\left(t\right)>0⟶0\le \sum _{s=1}^3{C}_{\mathrm{n},\mathrm{s}}\le {\delta }_{\mathrm{n}}\left(t\right)\end{array}$$

(15)

and

$$\begin{array}{c}\hfill C_{\mathrm{n},\mathrm{s}}\left(t\right)=\min \left\{\begin{array}{c}{\delta }_{\mathrm{n}}\left(t\right)-\sum C_{\mathrm{n}}^{\prime }\left(t\right)\hfill \\ \frac{{\Psi }_{\mathrm{n},\mathrm{s}}-{\Psi }_{\mathrm{SOC},\mathrm{n},\mathrm{s}}}{{\eta }_{C,s}}\hfill \\ \frac{{\psi }_{\max ,\mathrm{C},\mathrm{s}}}{{\eta }_{\mathrm{C},\mathrm{s}}}.\hfill \end{array}\right.\end{array}$$

(16)

Here, $C_{\mathrm{n}}^{\prime }\left(t\right)$ denotes the energy already stored in an upstream storage process, ${\Psi }_{\mathrm{n},\mathrm{s}}$ is the storage capacity, ${\psi }_{\max ,\mathrm{C},\mathrm{s}}$ is the maximum charging power, and ${\eta }_{C,s}$ is the efficiency of the charging process. Finally, the state of charge of the storage s at time t of region n is given by

$$\begin{array}{c}\hfill {\Psi }_{\mathrm{SOC},\mathrm{n},\mathrm{s}}\left(t\right)={\Psi }_{\mathrm{SOC},\mathrm{n},\mathrm{s}}(t-1)+C_{\mathrm{n},\mathrm{s}}\left(t\right)·{\eta }_{\mathrm{C},\mathrm{s}}.\end{array}$$

(17)

where ${\Psi }_{\mathrm{SOC}},\mathrm{n},\mathrm{s}\left(t\right)$ is the state of charge of the storage at a given time t. If the excess energy ${\Gamma }_{\mathrm{n}}^{+}\left(t\right)$ after the storage processing is not Zero, it has to be exported to a neighboring region. That amount is defined as

$$\begin{array}{c}\hfill {\Gamma }_{\mathrm{n}}^{+}\left(t\right)={\delta }_{\mathrm{n}}\left(t\right)-\sum _{s=1}^3{C}_{\mathrm{n},\mathrm{s}}.\end{array}$$

(18)

Negative Energy Balance

In the case of a negative difference ${\delta }_{\mathrm{n}}^{-}\left(t\right)$ from electricity production and demand at time t with

$$\begin{array}{cc}\hfill {\delta }_{\mathrm{n}}^{-}\left(t\right)& =\max \left\{-{\delta }_{\mathrm{n}}\left(t\right),0\right\}\hfill \end{array}$$

(19)

The storage systems are unloaded in the order already mentioned, if possible. This is performed in an analogous way to the loading process. The sum of the subsets $D_{n,s}\left(t\right)\forall s\in \left\{1,2,3\right\}$ at time t results with

$$\begin{array}{c}\hfill {\delta }_{\mathrm{n}}\left(t\right)<0⟶0\le \sum _{s=1}^3{D}_{\mathrm{n},\mathrm{s}}\le \left|{\delta }_{\mathrm{n}}\left(t\right)\right|\end{array}$$

(20)

The total amount of energy discharged

$$\begin{array}{c}\hfill D_{\mathrm{n},\mathrm{s}}\left(t\right)=\min \left\{\begin{array}{c}|{\delta }_{\mathrm{n}}\left(t\right)+\sum D_{\mathrm{n}}^{\prime }\left(t\right)|\hfill \\ {\Psi }_{\mathrm{SOC},\mathrm{n},\mathrm{s}}·{\eta }_{\mathrm{D},\mathrm{s}}\hfill \\ {\psi }_{\max ,\mathrm{D},\mathrm{s}}·{\eta }_{\mathrm{D},\mathrm{s}}.\hfill \end{array}\right.\end{array}$$

(21)

The state of charge of the storage system s at time t must be corrected by

$$\begin{array}{c}\hfill {\Psi }_{\mathrm{SOC},\mathrm{n},\mathrm{s}}\left(t\right)={\Psi }_{\mathrm{SOC},\mathrm{n},\mathrm{s}}(t-1)-\frac{D_{\mathrm{n},\mathrm{s}}\left(t\right)}{{\eta }_{\mathrm{D},\mathrm{s}}},\end{array}$$

(22)

considering its discharging efficiency after the discharging process.

4.2. International Exchange of Electric Energy

If the amount of energy $|{\Gamma }_{\mathrm{n}}\left(t\right)|$ remaining in the network after the storage processes is greater than Zero, it is further reduced by electricity import or export within the European interconnected system. The index n used in the following represents the region currently being considered, and the index m represents its neighboring regions. Neighboring regions are those that are electrically connected to the considered region n. To simulate the energy flow from the region n to a region m, the interconnected network consisting of nodes connected by lines is assumed to behave according to Kirchhoff’s laws with

$$\begin{array}{c}\hfill \sum _{\mathrm{n}=1}^N{\Gamma }_{\mathrm{n}}\left(t\right)=0.\end{array}$$

(23)

Under the necessary condition

$$\begin{array}{c}\hfill {\Gamma }_{\mathrm{n}}^{+}\left(t\right)>0\cap {\Gamma }_{\mathrm{m}}^{-}\left(t\right)<0,\forall \left(\mathrm{n},\mathrm{m}\right)\in \left\{0,1,2,\cdots \right\}\end{array}$$

(24)

The energy $T_{\mathrm{n}\to \mathrm{m}}\left(t\right)$ with

$$\begin{array}{c}\hfill T_{\mathrm{n}\to \mathrm{m}}\left(t\right)=\min \left\{\begin{array}{c}{\Gamma }_{\mathrm{n}}^{+}\left(t\right)\hfill \\ |{\Gamma }_{\mathrm{m}}^{-}\left(t\right)|\hfill \end{array}\right.\end{array}$$

(25)

is transferred from region n to region m. As a result, the export demand ${\Gamma }_{\mathrm{n}}^{+}\left(t\right)$ of region n and the import demand ${\Gamma }_{\mathrm{m}}^{-}\left(t\right)$ of region m decrease by the transferred energy $T_{\mathrm{n}}\to \mathrm{m}\left(t\right)$.

$$\begin{array}{c}\hfill {\Gamma }_{\mathrm{m}}^{-}\left(t\right)={\Gamma }_{\mathrm{m}}^{-}\left(t\right)+T_{\mathrm{n}\to \mathrm{m}}\end{array}$$

(26)

$$\begin{array}{c}\hfill {\Gamma }_{\mathrm{n}}^{+}\left(t\right)={\Gamma }_{\mathrm{n}}^{+}\left(t\right)-T_{\mathrm{n}\to \mathrm{m}}\end{array}$$

(27)

This is repeated for all regions n and its neighboring regions m under the condition shown in (24).

In the case that the electricity balance ${\Gamma }_{\mathrm{n}}^{+}\left(t\right)$ in any hour t of a region n is still less than Zero, in a subsequent process, the storage systems of the neighboring regions, provided that they do not fall below a minimum storage level, can be discharged and provide energy under the technical conditions mentioned in Section 4.1. The goal is to balance the power balance ${\Gamma }_{\mathrm{n}}\left(t\right)$ at every hour t. If the power balance cannot be balanced at every hour, the installed capacity of the generating units and, if necessary, the installed storage volume must be corrected accordingly. In real power grid systems, this would impact grid stability. To avoid overvoltage damage and emergency shutdowns of loads, systems for power destruction or curtailment would have to be created.

5. Conclusions

This publication introduced the methodology of the sector-coupled energy model UCB-SEnMod. Due to the novel software design, the model can represent off-grid systems or small regions and national, multinational, continental, or global systems, which can differ significantly in their spatial resolution. The possible temporal resolutions of the systems can range from minutely or hourly resolution to an observation of typical days, weeks, or months. Therefore, our model differs from conventional energy simulation tools by providing higher temporal and spatial resolution. That allows more precise modeling of energy flows of all sectors and layers of countries or municipalities down to a building-specific view. Another notable feature of UCB-SEnMod is the detailed consumption modeling of the demand sectors, especially the heat and transport sector. Furthermore, due to the model’s modular design, any system constellation can be represented, any optimization task can be aimed at, and the model can always be extended with additional functions. This simulation depth ensures a realistic consideration of scenarios, especially with regard to the integration of decentralized storage (electrical and thermal) and sensible flexibilization options for decarbonizing energy clusters. However, similar simulation software tools for energy systems often do not offer the possibility to synthesize electricity, heat, and transport load profiles, especially for companies (e.g., in the energy-intensive sector), smaller energy clusters, or energy hubs (e.g., a city with surrounding area), so that a realistic simulation of smaller clusters has a limited transferability.

As the energy models GENESYS, DESSTinEE, and elesplan-m mentioned at the beginning, there are other various models which are limited to individual sectors (usually to the electricity sector with a coupling to space heating/drinking water) or focus only on national structures since corresponding consumption, and generation profiles in high temporal resolution are only available for huge energy clusters. Limitations adaptability or variation of energy systems’ components are further limitations observed, i.e., in Aalborg University’s EnergyPlan model. Thus, the UCB-SEnMod energy model closes the gap in existing models, which (in addition to the limitations already mentioned) often can only cover specific issues related to integrating renewable energy. Using the simulation tool UCB-SEnMod, various scenarios for future energy generation and consumption can be analyzed. The energy system modeling is intended to help decision-makers in policy and industry identify realistic scenarios and derive recommendations for action. For instance, industrial companies can use the simulation environment to analyze their energy and climate balance to derive measures to increase energy and resource efficiency as a building block of a circular economy.

After providing the UCB-SEnMod software as open-source, it is possible for users to couple all energy sectors, flexibly link the program modules to simulate different energy scenarios, and model the load profiles of all energy sectors (electricity, space heating, process heating, transportation (especially charging profiles)).

We are currently preparing further publications on the application of UCB-SEnMod to different scenarios. This publication aimed to present the basic methods of our energy system software, place them in the scientific context, and distinguish them from other software tools. We showed that UCB-SEnMod brings new aspects to the modeling of energy systems of different sizes (countries, regions, energy hubs, and industrial companies), and thus contributes to scenario analysis for the decarbonization of energy supply.