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Gorm Bruun Andresen

Publications and source records attributed to Gorm Bruun Andresen.

15 recordsLinked to original sources

Designing a sector-coupled European energy system robust to 60 years of historical weather data

As energy systems transform to rely on renewable energy and electrification, they encounter stronger year-to-year variability in energy supply and demand. However, most infrastructure planning is based on a single weather year, resulting in a lack of robustness. In this paper, we optimize energy infrastructure for a European energy system designed for net-zero CO$_2$ emissions in 62 different weather years. Subsequently, we fix the capacity layouts and simulate their operation in every weather year, to evaluate resource adequacy and CO$_2$ emissions abatement. We show that interannual weather variability causes variation of $\pm$10\% in total system cost. The most expensive capacity layout obtains the lowest net CO$_2$ emissions but not the highest resource adequacy. Instead, capacity layouts designed with years including compound weather events result in a more robust and cost-effective design. Deploying CO$_2$-emitting backup generation is a cost-effective robustness measure, which only increase CO$_2$ emissions marginally as the average CO$_2$ emissions remain less than 1\% of 1990 levels. Our findings highlight how extreme weather years drive investments in robustness measures, making them compatible with all weather conditions within six decades of historical weather data.

physics.soc-ph↗

Cost and CO2 emissions co-optimisation of green hydrogen production in a grid-connected renewable energy system

Green hydrogen is essential for producing renewable fuels that are needed in sectors that are hard to electrify directly. Hydrogen production in a grid-connected hybrid renewable energy plant necessitates smart planning to meet long-term hydrogen trading agreements while minimising costs and emissions. Previous research analysed economic and environmental impact of hydrogen production based on full foresight of renewable energy availabilty, electricity price, and CO2 intensity in the electricity grid. However, the full foresight assumption is impractical in day-to-day operation, often leading to underestimations of both the cost and CO2 emissions associated with hydrogen production. Therefore, this research introduces a novel long-term planner that uses historical data and short-term forecasts to plan hydrogen production in the day-to-day operation of a grid-connected hybrid renewable energy plant. The long-term planner co-minimises cost and CO2 emissions to determine the hydrogen production for the next day taking into account the remaining hydrogen production and the time remaining until the end of the delivery period, which can be a week, a month, or a year. Extended delivery periods provide operation flexibility, enabling cost and CO2 emissions reductions. Significant reductions in CO2 emissions can be achieved with relatively small increases in the levelised cost. Under day-to-day operation, the levelised cost of hydrogen is marginally higher than that of the full foresight; the CO2 emissions can be up to 60% higher. Despite a significant portion of the produced hydrogen not meeting the criteria for green hydrogen designation under current rules, CO2 emissions are lower than those from existing alternative hydrogen production methods. These results underscore the importance of balancing cost considerations with environmental impacts in operational decision-making.

eess.SY↗

Direct and Indirect Hydrogen Storage: Dynamics and Interactions in the Transition to a Renewable Energy Based System for Europe

To move towards a low-carbon society by 2050, understanding the intricate dynamics of energy systems is critical. Our study examines these interactions through the lens of hydrogen storage, dividing it into 'direct' and 'indirect' hydrogen storage. Direct hydrogen storage involves electrolysis-produced hydrogen being stored before use, while indirect storage first transforms hydrogen into gas via the Sabatier process for later energy distribution. Firstly, we utilize the PyPSA-Eur-Sec-30-path model to capture the interactions within the energy system. The model is an hour-level, one node per country system that encompasses a range of energy transformation technologies, outlining a pathway for Europe to reduce carbon emissions by 95 percent by 2050 compared to 1990, with updates every 5 years. Subsequently, we employ both quantitative and qualitative approaches to thoroughly analyze these complex relationships. Our research indicates that during the European green transition, cross-country flow of electricity will play an important role in Europe's rapid decarbonization stage before the large-scale introduction of energy storage. Under the paper cost assumptions, fuel cells are not considered a viable option. This research further identifies the significant impact of natural resource variability on the local energy mix, highlighting indirect hydrogen storage as a common solution due to the better economic performance and actively fluctuation pattern. Specifically, indirect hydrogen storage will contribute at least 60 percent of hydrogen storage benefits, reaching 100 percent in Italy. Moreover, its fluctuation pattern will change with the local energy structure, which is a distinct difference with the unchanged pattern of direct hydrogen storage and battery storage.

eess.SY↗

Investment-based optimisation of energy storage design parameters in a grid-connected hybrid renewable energy system

Grid-connected hybrid renewable power systems with energy storage can reduce the intermittency of renewable power supply. However, emerging energy storage technologies need improvement to compete with lithium-ion batteries and reduce the cost of energy. Identifying and optimizing the the most valuable improvement path of these technologies is challenging due to the non-linearity of the energy system model when considering parameters as independent variables. To overcome this, a novel investment-based optimization method is proposed. The method involves linear optimization of the hybrid renewable energy system and subsequent investment optimization, accounting for diminishing improvements per investment. Applied to thermal energy, pumped thermal energy, molten salt, and adiabatic compressed air energy storage technologies, the results show that enhancing discharge efficiency is most valuable for all technologies. Reducing discharge capacity costs and energy storage capacity cost can also become important. Charge capacity cost and charge efficiency are found to be of lesser significance. The study provides detailed improvement pathways for each technology under various operational conditions, assisting developers in resource allocation. Overall, the investment-based optimization method and findings contribute to enhancing the competitiveness of emerging energy storage technologies and reducing reliance on batteries in renewable energy systems.

eess.SY↗

Cost and efficiency requirements for a successful electricity storage in a highly renewable European energy system

Future highly renewable energy systems might require substantial storage deployment. At the current stage, the technology portfolio of dominant storage options is limited to pumped-hydro storage and Li-Ion batteries. It is uncertain which storage design will be able to compete with these options. Considering Europe as a case study, we derive the cost and efficiency requirements of a generic storage technology, which we refer to as storage-X, to be deployed in the cost-optimal system. This is performed while including existing pumped-hydro facilities and accounting for the competition from stationary Li-ion batteries, flexible generation technology, and flexible demand in a highly renewable sector-coupled energy system. Based on a sample space of 724 storage configurations, we show that energy capacity cost and discharge efficiency largely determine the optimal storage deployment, in agreement with previous studies. Here, we show that charge capacity cost is also important due to its impact on renewable curtailment. A significant deployment of storage-X in a cost-optimal system requires (a) discharge efficiency of at least 95%, (b) discharge efficiency of at least 50% together with low energy capacity cost (10EUR/kWh), or (c) discharge efficiency of at least 25% with very low energy capacity cost (2EUR/kWh). Comparing our findings with seven emerging technologies reveals that none of them fulfill these requirements. Thermal Energy Storage (TES) is, however, on the verge of qualifying due to its low energy capacity cost and concurrent low charge capacity cost. Exploring the space of storage designs reveals that system cost reduction from storage-X deployment can reach 9% at its best, but this requires high round-trip efficiency (90%) and low charge capacity cost (35EUR/kW).

physics.soc-ph↗

Optimal Scheduling of Flexible Power-to-X Technologies in the Day-ahead Electricity Market

The ambitious CO2 emission targets of the Paris agreements are achievable only with renewable energy, CO2-free power generation, new policies, and planning. The main motivation of this paper is that future green fuels from power-to-X assets should be produced from power with the lowest possible emissions while still keeping the cost of electricity low. To this end we propose a power-to-X scheduling framework that is capable of co-optimizing CO2 emission intensity and electricity prices in the day-ahead electricity market scheduling. Three realistic models for local production units are developed for flexible dispatch and the impact on electricity market scheduling is examined. Furthermore, the possible benefits of using CO2 emission intensity and electricity prices trade-off in scheduling are discussed. We find that there is a non-linear trade-off between CO2 emission intensity and cost, favoring a weighted optimization between the two objectives.

eess.SY↗

Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling

The world is facing major challenges related to global warming and emissions of greenhouse gases is a major causing factor. In 2017, energy industries accounted for 46% of all CO2 emissions globally, which shows a large potential for reduction. This paper proposes a novel short-term CO2 emissions forecast to enable intelligent scheduling of flexible electricity consumption to minimize the resulting CO2 emissions. Two proposed time series decomposition methods are developed for short-term forecasting of the CO2 emissions of electricity. These are in turn bench-marked against a set of state-of-the-art models. The result is a new forecasting method with a 48-hour horizon targeted the day-ahead electricity market. Forecasting benchmarks for France show that the new method has a mean absolute percentage error that is 25% lower than the best performing state-of-the-art model. Further, application of the forecast for scheduling flexible electricity consumption is studied for five European countries. Scheduling a flexible block of 4 hours of electricity consumption in a 24 hour interval can on average reduce the resulting CO2 emissions by 25% in France, 17% in Germany, 69% in Norway, 20% in Denmark, and just 3% in Poland when compared to consuming at random intervals during the day.

stat.AP↗

A graphical approach to carbon-efficient spot market scheduling for Power-to-X applications

In the Paris agreement of 2015, it was decided to reduce the CO2 emissions of the energy sector to zero by 2050 and to restrict the global mean temperature increase to 1.5 degree Celcius above the pre-industrial level. Such commitments are possible only with practically CO2-free power generation based on variable renewable technologies. Historically, the main point of criticism regarding renewable power is the variability driven by weather dependence. Power-to-X systems, which convert excess power to other stores of energy for later use, can play an important role in offsetting the variability of renewable power production. In order to do so, however, these systems have to be scheduled properly to ensure they are being powered by low-carbon technologies. In this paper, we introduce a graphical approach for scheduling power-to-X plants in the day-ahead market by minimizing carbon emissions and electricity costs. This graphical approach is simple to implement and intuitively explain to stakeholders. In a simulation study using historical prices and CO2 intensity for four different countries, we find that the price and CO2 intensity tends to decrease with increasing scheduling horizon. The effect diminishes when requiring an increasing amount of full load hours per year. Additionally, investigating the trade-off between optimizing for price or CO2 intensity shows that it is indeed a trade-off: it is not possible to obtain the lowest price and CO2 intensity at the same time.

econ.GN↗

Real-Time Carbon Accounting Method for the European Electricity Markets

Electricity accounts for 25% of global greenhouse gas emissions. Reducing emissions related to electricity consumption requires accurate measurements readily available to consumers, regulators and investors. In this case study, we propose a new real-time consumption-based accounting approach based on flow tracing. This method traces power flows from producer to consumer thereby representing the underlying physics of the electricity system, in contrast to the traditional input-output models of carbon accounting. With this method we explore the hourly structure of electricity trade across Europe in 2017, and find substantial differences between production and consumption intensities. This emphasizes the importance of considering cross-border flows for increased transparency regarding carbon emission accounting of electricity.

physics.soc-ph↗

Estimating country-specific space heating threshold temperatures from national consumption data

Space heating in buildings is becoming a key element of sector-coupled energy system research. Data availability limits efforts to model the buildings sector, because heat consumption is not directly metered in most countries. Space heating is often related to weather through the proxy of heating degree-days using a specific heating threshold temperature, but methods vary between studies. This study estimates country-specific heating threshold temperatures using widely and publicly available consumption and weather data. This allows for national climate and culture-specific human behaviour to be captured in energy systems modelling. National electricity and gas consumption data are related to degree-days through linear models, and Akaike's Information Criteria is used to define the summer season in each country, when space heating is not required. We find that the heating threshold temperatures computed using daily, weekly and monthly aggregated consumption data are statistically indifferent. In general, threshold temperatures for gas heating centre around 15.0 +/- 1.7 degree C (daily averaged temperature), while heating by electricity averages to 13.4 +/- 2.4 degree C. We find no evidence of space heating during June, July and August, even if heating degree-days are present.

physics.soc-ph↗

The role of hydro power, storage and transmission in the decarbonization of the Chinese power system

Deep decarbonization of the electricity sector can be provided by a high penetration of renewable sources such as wind, solar PV and hydro power. Flexibility from hydro and storage complements the high temporal variability of wind and solar, and transmission infrastructure helps the power balancing by moving electricity in the spatial dimension. We study cost-optimal highly-renewable Chinese power systems under ambitious CO$ _2 $ emission reduction targets, by deploying a 31-node hourly-resolved techno-economic optimization model supported by a validated weather-converted 38-year-long renewable power generation and electricity demand dataset. With a new realistic reservoir hydro model, we find that if CO$_2$ emission reduction goes beyond 70\%, storage facilities such as hydro, battery and hydrogen become necessary for a moderate system cost. Numerical results show that these flexibility components can lower renewable curtailment by two thirds, allow higher solar PV share by a factor of two and contribute to covering summer cooling demand. We show that expanding unidirectional high-voltage DC lines on top of the regional inter-connections is technically sufficient and more economical than ultra-high-voltage-AC-connected "One-Net" grid. Finally, constraining transmission volume from the optimum by up to 25\% does not push total costs much higher, while the significant need for battery storage remains even with abundant interconnectivity.

physics.soc-ph↗

A validated high-resolution hydro power time-series model for energy systems analysis

We expand the renewable technology model palette and present a validated high resolution hydro power time series model for energy systems analysis. Among the popular renewables, hydroelectricity shows unique storage-like flexibility, which is particularly important given the high variability of wind and solar power. Often limited by data availability or computational performance, a high resolution, globally applicable and validated hydro power time series model has not been available. For a demonstration, we focus on 41 Chinese reservoir-based hydro stations as a demo, determine their upstream basin areas, estimate their inflow based on gridded surface runoff data and validate their daily inflow time series in terms of both flow volume and potential power generation. Furthermore, we showcase an application of these time series with hydro cascades in energy system long term investment planning. Our method's novelty lies in:it is based on highly resolved spatial-temporal datasets; both data and algorithms used here are globally applicable; it includes a hydro cascade model that can be integrated into energy system simulations.

physics.soc-ph↗

Climate change impacts on large-scale electricity system design decisions for the 21st Century

Efforts to reduce climate change, but also falling prices and significant technology developments currently drive an increased weather-dependent electricity production from renewable electricity sources. In light of the changing climate, it is highly relevant to investigate the extent of weather changes that directly impacts the best system design decisions for these weather-dependent technologies. Here, we use three IPCC representative CO2 concentrations pathways for the period 2006--2100 with six high-resolution climate experiments for the European domain. Climate elements are used to calculate bias adjusted 3-hourly time series of wind and solar generation, and temperature corrected demand time series for 30 European countries using state-of-the-art methodology. Weather-driven electricity system analysis methodology is then applied to compare four key metrics of highly renewable electricity systems. We find that climate change does not have a discernible impact on the key metrics of the combined electricity system dynamics, and conclude that the effect on important system design parameters can likely be ignored.

eess.SP↗

Cost-optimal design of a simplified highly renewable Chinese electricity network

Rapid economic growth in China has lead to an increasing energy demand in the country. In combination with China's emission control and clean air initiatives, it has resulted in large-scale expansion of the leading renewable energy technologies, wind and solar power. Their intermittent nature and uneven geographic distribution, however, raises the question of how to best exploit them in a future sustainable electricity system, where their combined production may very well exceed that of all other technologies. It is well known that interconnecting distant regions provides more favorable production patterns from wind and solar. On the other hand, long-distance connections challenge traditional local energy autonomy. In this paper, the advantage of interconnecting the contiguous provinces of China is quantified. To this end, two different methodologies are introduced. The first aims at gradually increasing heterogeneity, that is non-local wind and solar power production, to minimize production costs without regard to the match between production and demand. The second method optimizes the trade-off between low cost production and high utility value of the energy. In both cases, the study of a 100% renewable Chinese electricity network is based on 8 years of high-resolution hourly time series of wind and solar power generation and electricity demand for each of the provinces. From the study we conclude that compared to a baseline design of homogeneously distributed renewable capacities, a heterogeneous network not only lowers capital investments but also reduces backup dispatches from thermal units. Installing more capacity in provinces like Inner Mongolia, Jiangsu, Hainan and north-western regions, heterogeneous layouts may lower the levelized cost of electricity (LCOE) by up to 27%, and reduce backup needs by up to 64%.

physics.soc-ph↗

Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis

We present a new high-resolution global renewable energy atlas ({REatlas}) that can be used to calculate customised hourly time series of wind and solar PV power generation. In this paper, the atlas is applied to produce 32-year-long hourly model wind power time series for Denmark for each historical and future year between 1980 and 2035. These are calibrated and validated against real production data from the period 2000 to 2010. The high number of years allows us to discuss how the characteristics of Danish wind power generation varies between individual weather years. As an example, the annual energy production is found to vary by $\pm10\%$ from the average. Furthermore, we show how the production pattern change as small onshore turbines are gradually replaced by large onshore and offshore turbines. Finally, we compare our wind power time series for 2020 to corresponding data from a handful of Danish energy system models. The aim is to illustrate how current differences in model wind may result in significant differences in technical and economical model predictions. These include up to $15\%$ differences in installed capacity and $40\%$ differences in system reserve requirements.

physics.data-an↗