Sammendrag
The project partners have agreed on the need to define long-term storylines for external variables influencing the modeling activities. A Storyline workshop for all industrial and research partners was held in January 2020. The four identified Storylines are described in Chapter 2 and are named:
• Energy nation Norway
• Petroleum nation Norway
• Nature nation Norway
• Climate panic nation
The definition of quantitative data that is consistent with the qualitative long-term storylines is a task that has been performed for each model and is described in the following chapters. The extent to which this has been feasible during the first year of the project varies between the models. For example, in TIMES datasets and boundary conditions have been defined for three out of four storylines; in EMPIRE the four storylines have been grouped into two, identifying the introduction of CCS technology in Europe as the biggest discriminant, while BUTLER and FanSi have not yet implemented storylines differentiations. This work will continue and be refined in the following years to ensure a coherent implementation of the storylines in all models. FlexBuild uses a set of models to provide insights on the future role and value of end-use flexibility available in buildings from a Norwegian energy system perspective. In FlexBuild, we use three sectorspecific models to provide details of specific parts of the energy system, whereas the energy system model is used to cover the connections between the different parts of the energy system (see Figure below). The models are: • EMPIRE, for the power system interconnections outside Norway, with the rest of Europe:
• BUTLER, for the building sector in Norway;
• TIMES-Norway, for the energy system in Norway
• FanSi, for a zoom-in on the hydropower sector in Norway
This approach allows us to exploit the strength of each model, but the challenge of using numerous models is that these models need to be harmonized and linked in an adequate manner to provide reasonable project insight. The methodology for linking the different models is described in Chapter 3.
The aim of the linking methodology is to achieve a bi-directional linking strategy with clearly defined convergence criteria, between TIMES-Norway and the other sectorial models. However, the linking between the models is an ongoing development, and so the extent to which results from different models can be compared is somewhat limited for the time being. In particular, this has been achieved by now in the linking between TIMES-Norway and the sectorial models:
• EMPIRE: the expected electricity prices for countries outside Norway are a result of EMPIRE that is used in input to TIMES-Norway;
• BUTLER: harmonization of the technical data, demand profiles (heat, hot water, and electricity specific) and solar generation profiles. The resulting electricity prices and district heat prices from TIMES-Norway is used as an input to BUTLER;
• FanSi: Linking to TIMES was implemented in an earlier project with a similar power market model, which needs to be further improved. The input weather scenarios in FanSi require a
higher level of detail than the other models. Existing future analyses are compared with TIMESNorway to identify proper sets of input for translating the storyline in FanSi.
Chapters 4 to 7 describe in detail the developments and main results for each model during this first year of the project, for which a summary is given here.
EMPIRE – Power system Europe:
The main result is the simulation of two scenarios, respectively, with and without CCS technology in Europe. The two scenarios lead to different energy production mixes, different transmission capacity expansion for Norway, and different prices. In particular, the scenario without CCS shows significantly higher price variability than the scenario with CCS.
BUTLER – Building sector Norway:
The main result is the simulation of the effect of different grid tariffs on single buildings. The tariff schemes considered are those proposed by NVE in its proposal's hearing of 2019, Daily peak power, Power subscription, Fuse differentiated, plus the current tariffs energy pricing (small customers) and Monthly peak power (large customers). Although different heating technologies are affected somewhat differently by the grid tariff, the daily peak power tariff scheme appears to be most promising to reduce peak power during the coldest days. The reduction applies to both regular and efficient buildings (in terms of goodness of the building envelope), although the difference in peak power is significantly
lower in the efficient type, to begin with.
Most of the work has been concentrated on enhancing the models for space heating technology, and to introduce an EV model. An effort has also been put in harmonizing technology data and weather data with TIMES-Norway, and to aggregate the result for a geographical area. The Figure below shows that the subscription tariff (light colors) keeps the load below the subscribed limit (here: 8 kW for REF, 6 kW for rglASHP, and 4 kW for effASHP), as long as possible, but once it is necessary to go above this limit, the model seems to be indifferent to how much the limit is violated. Hence, the peak load is only reduced by 1%, 1%, and 8% relative to the peak load with current energy pricing (solid dark line) for each of the three-building cases REF, rglASHP and effASHP, respectively Duration curve of the net electric load profile with the alternative power tariffs: current energy pricing (solid dark
line), power subscription (solid light line), and daily measured peak (dashed line). The boxes to the right show the reduction of the maximum peak load relative to the reference case.
Results for the daily measured peak tariff are shown with dashed lines. With the daily measured peak tariff, there is a stronger incentive to keep the peak load as low as possible in all hours, and the peak load is reduced by respectively 14%, 17% and 18% for REF, rglASHP, and effASHP compared to the peak load with current energy pricing for each of the cases. Why do these peak load reductions occur? The answer lies in the flexibility of the heat storage that the waterborne heat distribution system offers, in addition to the domestic hot water tank.
TIMES-Norway – Energy system Norway
Three out of the four storylines have been quantified as different input datasets and analyzed by the Norwegian energy system model, TIMES-Norway. The corresponding results provide cost-optimal investment and operational decisions of the Norwegian energy system from 2020 to 2050 for the five Norwegian spot price regions. The results show that investments in renewables, electricity consumption by sector, electricity trade to Europe and electricity prices varies significantly between the storylines. More specific for the building sector, the results demonstrate the cost-optimal building-integrated PV generation contributes to between 6% and 8% of the Norwegian electricity supply in 2050, the energy
use and the peak electricity demand highly depends on the future evolvement of the energy system; the energy efficiency measures and a more centralized settlement pattern has a significant impact on peak electricity and total energy demand in buildings. Another finding is that flexible EV charging influences the integration of renewables and that a flexible operation of hot water tanks will lower the peak electricity demand, but to a limited extent. The figure below demonstrates the supply and demand of commercial buildings in 2050 (in power capacity) for two storylines for NO1.
Demand (top) and supply (bottom) for commercial buildings in the two storylines Oil Nation (left) and Energy Nation (right) for the spot region NO1 in 2050.
In total, the demand from the grid is lowered by 3.2 TWh in the Oil nation, 5.3 TWh in Energy nation and 4.7 TWh in Nature nation due to PV on commercial buildings.
FanSi - Power system Norway
The purpose with FanSi – which will not be further developed in this project – is to assess the effect of the Norwegian hydropower system for the profitability of flexibility, by linking with the results from TIMES-Norway. Since the linking is still under development, a set of previously developed lowemission scenarios modeled in FanSi have been compared with the Storyline results from TIMESNorway. A major outcome is that the main impact of different scenarios is on power price variability (e.g. number of hours with high price) rather than price levels. Hydropower has the ability to exploit price variability in order to achieve higher prices than average by delivering flexibility. Since the linking between the models is an ongoing development, the extent to which results from different models can be compared is somewhat limited for the time being. The most essential linking is between BUTLER and TIMES-Norway, both because the end-use flexibility (from the building sector) impact on the energy system is the central focus of this project – while influence from the European power market and impact on the Norwegian hydropower system are boundary conditions – and because the results of these two models are mostly interdependent. A comparison of the results from the two models on the aggregated level of market area NO1 (South-East region) shows that there is substantial agreement between the two models. This is encouraging because it shows that once the two models are fed with harmonized input, one obtains harmonized outputs despite the inner technical differences. On the other hand, it looks promising to aim at the convergence of two models (with a limited number of iterations) with a bi-directional linking when the results are already substantially similar, even when
there is a simple uni-directional link.
The following points are the summary of the future work proposed by the research partners:
• Storylines: continue with the definition of datasets and assumptions that quantify the storyline description in a consistent and harmonized way across the models;
• Linking methodology: the next step of the linking between TIMES-Norway and the sectorial models is to develop a bi-directional linking strategy with a clearly defined convergence
criterion;
• EMPIRE developments: this will be defined within the Ph.D. plan with the candidate
• BUTLER developments:
o continue with heating technologies improvements, especially heat pumps.
o Improve the modeling of storage technologies: hot water tank, battery and EV.
o Implementation of the thermal mass dynamics of buildings;
• TIMES developments:
o include stochastic modeling of short-term uncertainty related to PV generation, wind power, and heat demand.
o Include modeling of end-use (storage) flexibility measures: hot water tank and EV flexible charging, storage in district heating, and (comfort) flexibility: thermal storage
in buildings.
• FanSi developments:
o define datasets and other boundary conditions that are coherent/compatible with the Storylines, with the necessary level of detail to account for the short-term uncertainties
for inflow, temperature, wind- and solar power generation.
o Assess the power price structure and profitability of hydropower and potential other flexibility options within the power system for the developed storylines.
Finally, the final priorities for future work in the next year(s) of the project will be defined considering the feedback from the industrial partners, included in their Annual Memo.