Scenario framework
The scenario framework for an almost climate neutral energy system for 2050 in the research project ZellNetz2050 is based on the electrification scenario with a 95 % greenhouse gas reduction target from the dena-study "Integrated Energy Transition" from 2018. In addition, several studies regarding the probable developments of different technology options are used to complete the parametrisation of the simulation models.
The overall scenario of generation, new loads, storages, and sector couplings is composed as follows1:
- Photovoltaics: 159.2 GW
- Wind onshore: 167.3 GW
- Wind offshore: 27.6 GW
- Bio-energy: 9.7 GW
- Run-of-river power plants: 3.6 GW
- Pumped storage hydro: 7.1 GW and 38.6 GWh
- Gas power plants: 98 GW
- CHP (gas fired): 49.3 GW
- Battery storage: 58.4 GW and 58.4 GWh
- Battery electric vehicles: 35.4 million
- Heating systems: 16.8 million heat pumps, 1.5 million district heating systems, 1.2 million gas heating systems
- Power-to-gas (hydrogen): 52.9 GW
- Power-to-heat: 4.3 GW
This nation-wide scenario for Germany was then regionalised on the basis on statistical data on population, historic data, technology potentials, land use, wealth, income, industrial hydrogen demand et cetera to the nodes of the extra high, high, medium, and low voltage grids. Large-scale power plants and offshore wind farms were places manually during the grid planning process.
The time series for conventional loads were created using a stochastic tool for generating household load profiles, standard load profiles for commercial and industrial consumers, and measurement data from ENTSO-E. For weather-dependent generators and loads, weather data from the German Weather Service for the test reference year 2045 is used. The generation profiles for bio-energy and run-of-river power plants is based on the measurement data from ENTSO-E for the year 2020. To represent the behaviour of BEV usage, statistical data from real-world mobility investigations as well as a pseudo-stochastic model for individual vehicles were used.
1) Deviations from the values given in the dena-study "Integrated Energy Transition" are due to simplifications in the process of the regionalisation as well as adaptions during the grid planning process.
Grid models
As described before, the focus of the investigations in ZellNetz2050 lies on the electrical system. Therefore, the electrical energy supply system is represented extensively, in great detail and completely georeferenced using the actual grid configuration from public maps wherever possible. The grid model includes the whole extra high voltage grid, comprised of the voltage levels 220 kV and 380 kV as well as all currently planned HVDC connections as per the German grid development plan and the interconnectors to the European neighbours. It is accompanied by five high voltage grids representative of the German high voltage level based on publicly available maps and standard parameters for the operational equipment.
| Region | Charakterisierung |
|---|---|
| Schleswig-Holsein |
|
| Gelsenkirchen |
|
| Dresden |
|
| Palatinate (Pfalz) |
|
| Allgäu |
|
Within the five representative regions, each 5-6 representative medium voltage and 5-6 representative low voltage grids are added to the high voltage grids, so that in approximately 30 cases the electrical system is modelled down to the individual building connections. This is of great importance, because the role of the distribution systems will become more and more significant, for example in the context of the decentralised provision of renewable generation and flexibilities to support the overall system. The grid topology and parameters of the operational equipment is oriented at the synthetic medium and low voltage grid developed at TUK, while the geographical location and configuration is once again based on public map data.
The grid models, created using today's generation and load scenario were then updated in PSS Sincal for 2050 using the regionalised scenario framework and reasonable assumptions for simultaneity factors. Three scenarios were considered:
- One scenario with very high load and very low renewable generation at the same time, representative of a cold winter morning
- One scenario with low load and very high renewable generation at the same time, representative of a summer's holiday
- One scenario with a north-south-gradient in residual generation, representative of a day with high generation in the north of Germany and high load in the south
In addition to the electrical system model, a simplified, linear model of the German gas transmission grid was created from the SciGrid-dataset released in 2021 and additional public sources in order to account for quantity limitations within the gas grid when supplying gas-fired backup power plants within the optimisation. Since the gas demand in Germany is expected to decline to approximately 500 TWh/y, today's gas grid remains unchanged as it will be capable of supplying all consumers in the future. In concurrence with the dena-study "Integrated Energy Transition", no nationwide public hydrogen grid is modelled, however, the option to supply the conventional thermal power plant with hydrogen instead of mehtane is investigated. Heat grids are modelled only as "heat lakes" in analogy to the copper-plate-model for the electrical system, i.e., as aggregated consumers and heat generators without consideration of the pipe infrastructure.
Technology models
The characteristic element of the technology models consists of the representation of flexibility, i.e, the ability to adjust the active power exchange within the proposed intra-day market. Flexibility can be provided in two different ways in the simulation models:
- Utilisation of an existing storage, e.g. a combination of heat pumps and heat storages
- Adjustment of the active power exchange within a defined bandwidth, e.g. processes with high thermal time constant or curtailment of renewables
Flexibility around an operational point is limited by several technological parameters, for example by the minimal admissible power of thermal power plants or the maximum interruptible energy of a load. The following table gives an overview of the technologies used in ZellNetz2050, their connection to the energy system, and possible restrictions regarding their flexibility. Further differentiation is made between the actual sources of energy (e.g. direct current produced by photovoltaics) and the subsequent conversion technologies (e.g. inverters).
| Technology | Energy Form | Restrictions for Flexibilities | Comment | ||||
|---|---|---|---|---|---|---|---|
| PDC | PAC | QAC | Ptherm | Pchem | |||
| Photovoltaic | X | ||||||
| Wind turbine | X | X | X | PDC: Converter-connected units, PAC/QAC: Directly connected units | |||
| Bio-energy generation | X | ||||||
| Run-of-river hydro | X | F | Rated power | Run-of-river hydro power plants usually do not offer flexibililty for reasons of hydrological balancing | |||
| Import | F | F | F | F | Rated power of interconnectors | ||
| Inverter | X | X | F | Rated power | |||
| CHP plants | F | F | F/X | X | Rated power, minimal power, thermal demand | Thermal demand flexible with extraction condensing turbines, fixed with backpressure turbines | |
| Gas turbine | F | F | X | Rated power, minimal power | |||
| Heat plant | F | X | Thermal demand | ||||
| Heat pump | F | F | F | F | Rated power, thermal demand | Coupled either directly or through a converter | |
| Electrolysis | F | X | Rated power | ||||
| Battery storage | F | Rated power, rated capacity, state of charge | |||||
| Pumped-storage hydro | F | F | Rated power, maximum storage basin level, current storage basin level | ||||
| Hot water storage | F | Rated power, maximum storage capacity, current storage capacity | |||||
| Gas storage | F | Rated power, maximum storage capacity, current storage capacity | |||||
| Battery-electric vehicles | F | Rated charing power, maximum battery capacity, minimal state-of-charge | For BEVs, a minimal admissible state-of-charge can be defined that has to be available at a specific point in time | ||||
| Controllable load | F | F | F | F | Rated power, maximum power, minimal power, interruptible energy | ||
| Load profile | X | X | X | X | |||
Offline simulation
The offline simulation model is developed to provide a proof-of-concept for the proposed system design. It features the scenario framework, the grid and technology models described above, and the market concept with its nodal pricing regime. The electricity, gas, and heat systems are interdependent in the optimization problem, however, they follow different market principles (see here).
Following the overall concept, a rolling horizon is implemented for the operational planning process ('rolling planning', see figure), reflecting the limited availability and accuracy of forecasts. The individual planning windows overlap, creating continuity and feed-back for each iteration. This approach, however, is not eniterly adequate for long-term or seasonal storage options which require a much longer planning horizon. Therefore, a governing structure was implemented for long-term storage, e.g., hydrogen, determining adequate storage levels on a longer time base.
The objective of the optimization is to find the most economic way to supply loads in each timestep, taking into account temporal flexibility across adjacent timesteps and the cost of energy transmission. Therefore, the objective function as such is to minimize the variable costs of conventional, biogenous, and CHP power generation. Other renewable generation has no variable cost and is therefore not part of the objective function. The most important constraint is the power balance at each node at each point in time: the sum of all generation, demand, storage in- or output, sector-coupling consumption, and power exchange must be equal to zero. For all these, further technology-specific constraints apply, e.g., line ratings, power ramping limitations, minium power outputs etc.
The same principles apply to the gas and heat systems, albeit the model behavior is much simpler than in the electrical system.
Flexibilities add additional constraints as they are often subject to energy constraints (e.g., battery storages), timestep-coupling characteristics (e.g., electric vehicles), intermittency (e.g., PV plants), and secondary demand restrictions (e.g., heat pumps during cold times).
Online simulation
The online simulation complements the previously described offline simulation with an operational model in a realistic control centre environment. Basis of the online simulation is the Power System Handler (PSH) by Dutrain from the German city of Duisburg, intended and developed as a training simulator for control centre personnel. In addition to the visualisation and the user interface, the temporal resolution of the power flow calculations and frequency responses is based on usual values in control centre applications. The results of the frequency calculations on the single-mass model are displayed in 100 millisecond steps, the results of the power flow calculation in 10 second steps. The simulation environment of the PSH is therefore not comparable to a fully dynamic simulation of the electrical system, even if the graphical representation may indicate otherwise.
The goal of the online simulation is the analysis of the manageability of the proposed system concept from the perspective of the control centre operators. One of the first objects of investigation is the visualisation of the processes of the optimisation model and the resulting events in the electrical grid to enable the operators to understand the behaviour of the energy system at all times. Ensuing, disturbances are inserted into the otherwise ideal offline simulation in order to initiate different tasks of the control centre. This includes deviations from weather and load prognoses or outages of operational equipment or level A cells. In all cases, different measures according to the operational and automation concept are available to manage the active power imbalance. Because the PSH is based on a AC power flow model, problems with reactive power or voltage stability are also in the scope of the investigations. Finally, basic investigations into power system restoration after large disturbances are conducted, applying established methods of power system restoration. In all cases, not only the observability and manageability of the electrical system for the control centre operators is in focus, but also the specific information and its processing needed to fulfil the control centre's responsibilities is in focus.
Because the strategic target of the online simulation is the analysis of the human interaction with the energy system, the scale of the simulation model is reduced to the necessary level of detail compared to the offline simulation model. Therefore, all low voltage grids and the medium and high voltage grids in Dresden, Palatinate and Allgäu are aggregated and represented by equivalent models. This leaves the distribution systems in Schleswig-Holstein and Gelsenkirchen, representative of the extreme configurations. All values for the online simulation are taken from the offline simulation results and adjusted to suit the PSH data model and according to the individual investigative scenarios. Since the initialisation time of the offline simulation is significant, no real-time coupling of the offline and online simulation is possible.