Long-term Energy System Model Research Articles

Seasonal large-scale thermal energy storage in an evolving district heating system – Long-term modeling of interconnected supply and demand

Given the strong seasonal nature of heating demands, peak heat is important during colder seasons. Instead of peak heat plants, seasonal large-scale thermal energy storage (TES) could be utilized. These can be charged during warmer seasons and discharged when required, decreasing the need for peak heat plants. Systems modelling studies on seasonal TES are lacking. Thus, a long-term local energy system model is applied under different scenarios to investigate the potential roles of seasonal TES in an evolving heating system. The results show that seasonal TES is economically viable for: all future electricity price cases for low TES construction costs, corresponding to repurposing of underground oil storages, and for most electricity price cases for mid- and high construction costs, corresponding to new underground excavations. Seasonal TES mainly decrease the investments in and usage of electric boilers or biogas boilers, while increase the utilization of heat pumps. Other technologies may be affected depending on the future trajectory of electricity price developments. The size of the TES is between 3 and 7% of the annual district heating heat demand, depending on construction cost and electricity price development. The expansion of district heating into new housing is mostly unaffected by the availability of TES.

System flexibility in the context of transition towards a net-zero sector-coupled renewable energy system—case study of Germany

To integrate variable renewable energy sources into the energy system and achieve net-zero emissions, the flexible operation of the power system is essential. Options that provide flexibility include electrolysis, demand side management, import and export of electricity, and flexible power plants. However, the interplay of these flexibility options in a renewable energy system with highly interacting energy and end-use sectors (known as sector coupling) is not yet fully understood. The aim of this paper is to improve the understanding of energy flexibility from a system perspective by explaining which flexibility options can provide how much flexibility and when are they operated. The analysis of the hourly results of the sector-coupled, long-term energy system model REMod shows that in times with high renewable electricity production, sector coupling technologies, specifically electrolysis and power-to-heat, dominate the annual flexibility shares. On the other hand, in times with low renewable production and high non-flexible demand, combined and open cycle gas turbines and electricity imports dominate in winter, while discharging electricity storage technologies dominate in summer. The operation of short-term electricity storage aligns in particular with photovoltaic production, while the operation of electrolysis is especially aligned to wind production. Non-flexible demand variations are driving the operation of combined and open cycle gas turbines and electricity imports. The results emphasize the pivotal role of flexibility, highlighting the need for efficient surplus electricity utilization and sector coupling. The results further suggest that it is crucial to establish market conditions that facilitate the flexible operation of various technologies in order to achieve economic efficiency.

Review of multi-objective optimization in long-term energy system models

Modeling and optimizing long-term energy systems can provide solutions to various energy and environmental policies involving public-interest issues. The conventional optimization of long-term energy system models focuses on a single economic goal. However, the increasingly complex demands of energy systems necessitate the comprehensive consideration of multiple dimensional objectives, such as environmental, social, and energy security. Therefore, a multi- objective optimization of long-term energy system models has been developed. Herein, studies pertaining to the multi- objective optimization of long-term energy system models are summarized; the optimization objectives of long-term energy system models are classified into economic, environmental, social, and energy security aspects; and the multi-objective optimization methods are classified and explained based on the preferential expression of decision makers. Finally, the key development direction of the multi-objective optimization of energy system models is discussed.

System-friendly process design: Optimizing blue hydrogen production for future energy systems

While the effects of ongoing cost reductions in renewables, batteries, and electrolyzers on future energy systems have been extensively investigated, the effects of significant advances in CO2 capture and storage (CCS) technologies have received much less attention. This research gap is addressed via a long-term (2050) energy system model loosely based on Germany, yielding four main findings. First, CCS-enabled pathways offer the greatest benefits in the hydrogen sector, where hydrogen prices can be reduced by two-thirds relative to a scenario without CCS. Second, advanced blue hydrogen technologies can reduce total system costs by 12% and enable negative CO2 emissions due to higher efficiencies and CO2 capture ratios. Third, co-gasification of coal and biomass emerged as an important enabler of these promising results, allowing efficient exploitation of limited biomass resources to achieve negative emissions and limit the dependence on imported natural gas. Finally, CCS decarbonization pathways can practically and economically incorporate substantial shares of renewable energy to reduce fossil fuel dependence. Such diversification of primary energy inputs increases system resilience to the broad range of socio-techno-economic challenges facing the energy transition. In conclusion, balanced blue-green pathways offer many benefits and deserve serious consideration in the global decarbonization effort.

Modelling Heat Pump Systems in Low-Carbon Energy Systems With Significant Cross-Sectoral Integration

Pursuing long-term climate stabilisation scenarios subjects power, heat, industry, and transport sectors to undergo fundamental transitions towards fully renewable energy systems. Cost-efficient strategies involve the integration of energy vectors, introducing additional cross-sectoral electricity demand and flexibility for traditional power sectors. When performing cross-sectoral capacity expansion planning (CEP) analyses of low-carbon energy systems for decision making support, the underlying mathematical optimisation problems easily become intractable. To overcome the computational challenges of optimising real-world instances of future energy systems with adequate representations of temporal, spatial, and technological detail, this work investigates different modelling approaches for flexible heat pump systems at the power and heat sector interface of the long-term energy system model SCOPE SD. A comparative case study carried out for a net-neutral decarbonisation scenario in Europe and Germany highlights the implications of using model aggregation techniques. It is shown that the proposed modelling approaches can present computational benefits at only limited cost of result accuracy. However, poorly implemented grouping strategies can lead to high result inaccuracies and even a lack of performance improvements.

A multi-model method to assess the value of power-to-gas using excess renewable

Power-to-Gas (P2G) is a process that produces a gas from electricity, which is most commonly hydrogen via electrolysis. While some studies have considered hydrogen as a power-to-power storage vector, it could also be used as a fuel across the energy system, for example for transport or heat generation. Here, two energy models are used to explore the potential contribution of P2G as a cost-effective source of hydrogen, particularly for future energy systems with high variable renewable energy (VRE) in which there are occasional periods when electricity supply exceeds demand. A detailed electricity system model is iterated with a multi-vector energy system model using a soft-linking approach. This iterative approach addresses shortcomings in each model to better understand the optimal capacity of P2G and the potential economic capture rate of excess VRE. The modelling method is applied to Great Britain in 2050 as a case study. A substantial proportion of excess VRE in 2050 can be captured by P2G, and it is economically competitive compared with alternative sources. Moreover, the effectiveness and economic viability of P2G for reducing excess renewable is robust at even very high levels of renewable penetration. • We soft-linked long-term energy system model (UK TIMES) with power system model to examine value of Power-to-Hydrogen(P2G). • The variability of VRE and the potential curtailment are investigated at spatially and temporally explicit details. • P2G using excess renewable could be an important hydrogen supply and also help integrate high level of VRE. • P2G using excess renewable is found as the most cost-effective technology for generating hydrogen for 2050 in GB case study. • P2G would accounts for 87%-100% of total excess renewable in different scenarios, with the lowest production cost.

Stochastic modelling of variable renewables in long-term energy models: Dataset, scenario generation & quality of results

Variable electricity generation from wind and solar influences the design of a cost-efficient and reliable energy system. This paper presents a method that uses stochastic programming to represent variable renewable electricity generation in long-term energy system models, and demonstrates this on a Norwegian TIMES model. First, we derive hourly PV- and wind-generation data by modifying satellite-based data, based on a comparison with historical generation data. Second, the satellite-based dataset is transformed into a manageable set of scenarios that is used as an input to the stochastic energy-system model. This is done using six different scenario generation methods. Third, we solve the energy-system model with three of the scenario-generation methods and evaluate the quality of the corresponding model value by stability tests. We demonstrate that scenarios generated from the six methods have significantly different moment-based and Wasserstein distance error relative to the dataset. Further, the energy system model results show that the number of scenarios needed to achieve stability differs between the three used scenario generation methods.

Short-term solar and wind variability in long-term energy system models - A European case study

Integration of variable renewables such as solar and wind has grown at an unprecedented pace in Europe over the past two decades. As the share of solar and wind rises, it becomes increasingly important for long-term energy system models to adequately represent their short-term variability. This paper uses a long-term TIMES model of the European power and district heat sectors towards 2050 to explore how stochastic modelling of short-term solar and wind variability as well as different temporal resolutions influence the model performance. Using a stochastic model with 48 time-slices as benchmark, the results show that deterministic models with low temporal resolution give a 15–20% underestimation of annual costs, an overestimation of the contribution of variable renewables (13–15% of total electricity generation) and a lack of system flexibility. The results of the deterministic models converge towards the stochastic solution when the temporal resolution is increased, but even with 2016 time-slices, the need for flexibility is underestimated. In addition, the deterministic model with 2016 time-slices takes 30 times longer to solve than the stochastic model with 48 time-slices. Based on these findings, a stochastic approach is recommended for long-term studies of energy systems with large shares of variable renewable energy sources.

Achieving the Paris Agreement’s 2 degree target in Nepal: the potential role of a carbon tax

ABSTRACT The 2015 Paris Agreement aims to limit global temperature rise in this century to well below 2°C above the pre-industrial level, and pursue efforts to limit further the temperature rise below 1.5°C. All parties ratifying the Paris Agreement have submitted Nationally Determined Contributions (NDCs), many stating emission reduction targets. The international climate research community has designed five different Shared Socioeconomic pathways (SSPs) to characterize various possibilities in demographic and economic changes over the next century. These SSPs were implemented by six integrated assessment model (IAM) teams, which also determined the carbon price trajectory required to limit the temperature rise below 2°C in respective SSPs. In SSP5, also termed the fossil-fueled development scenario, only three IAMs identified that the 2°C target would be feasible, with carbon prices ranging from 220 to 518 US$/tCO2e in 2050. This study aims to analyse the effects of these carbon prices on energy and emissions during 2015–2050 in Nepal. It does so using a long-term energy system model using the framework of the Asia-Pacific Integrated Model/Enduse (AIM/Enduse) modelling tool. The base case scenario and three carbon price scenarios are developed. Primary energy supply, energy security, energy technology-mix (especially renewable energy usage and hydropower development), emissions of greenhouse gases (GHGs) and local pollutants, and local/regional environmental co-benefits are compared between the base case scenario and carbon price scenarios. The study finds that the implementation of a carbon tax would promote domestic hydropower, improve energy-efficiency and reduce imports of fossil fuels when compared to the base case. Hydropower-based electricity would have a major role in reducing emissions. Key policy insights The carbon prices in SSP5 determined by IAMs to achieve the 2°C target would be sufficient to achieve Nepal’s targets under its NDC in the energy sector. The industry and transport sectors would offer the highest GHG emission reduction. Hydropower and biomass would have major roles in decarbonizing the energy system.

Bidirectional linkage between a long-term energy system and a short-term power market model

This paper proposes a novel bidirectional linkage between a long-term energy system model and an operational power market model. This combined modelling framework provides long-term energy system investment strategies that explicitly consider the operational complexity of the power sector. The linkage is demonstrated using an energy system model of Norway and a European power market model with a specific high detail level of hydropower operation. For Norway, with its hydropower-dominated electricity sector, the linkage is designed to improve the modelling of hydropower generation and external electricity markets in the energy system model, and to provide consistent assumptions concerning Norwegian electricity demand and capacity in the power market model. The difference in income of hydropower producers, which is endogenous in both models, is used as a convergence criterion. The linkage is tested for various future developments of the European power market and is successful for three of the four analysed instances. However, when the linkage is evaluated in a system with a very high share of intermittent electricity generation and large variations in electricity price, it fails to converge on hydropower income. This is because the simulated Norwegian electricity prices differ significantly between the two models in this situation.

Sample average approximation and stability tests applied to energy system design

This paper uses confidence intervals from sample average approximation (SAA) and stability tests to evaluate the quality of the solution of a long-term energy system model with stochastic wind power production. Using poorly designed scenarios can give stochastic model results that depend on the scenario representation rather than the actual underlying uncertainty. Nevertheless, there is little focus on the quality of the solutions of stochastic energy models in the applied literature. Our results demonstrate how too small a sample size can give a poor energy system design and misrepresent the value of the stochastic solution (VSS). We demonstrate how to evaluate the number of scenarios needed to ensure in-sample and out-of-sample stability. We also show how replication and testing of many candidate solutions using SAA iterations can provide a solution with a satisfactory confidence interval, including when the samples contain fewer scenarios than required for stability. An important observation, though, is that if SAA repeatedly solves the model with a sample size that satisfies in-sample and out-of-sample stability, the confidence interval is narrow, and the solutions are of high quality in terms of providing a tight bound for the optimal solution.

Representation of Balancing Options for Variable Renewables in Long-Term Energy System Models: An Application to OSeMOSYS

The growing complexity and the many challenges related to fast-changing and highly de-carbonised electricity systems require reliable and robust open source energy modelling frameworks. Their reliability may be tested on a series of well-posed benchmarks that can be used and shared by the modelling community. This paper describes and integrates stand-alone, independent modules to compute the costs and benefits of flexible generation options in the open source energy investment modelling framework OSeMOSYS. The modules are applied to a case study that may work as a benchmark. The whole documentation of the modules and the test case study are retrievable, reproducible, reusable, interoperable, and auditable. They create a case to help establish a FAIR-compliant, user-friendly, and low-threshold model and data standards in modelling practices. As is well known, one of the options for balancing high shares of variable renewables is flexible power generation by dispatchable units. The associated costs need to be considered for short-term operational analyses and for long-term investment plans. The added modules contribute to extending the modelling capacity by introducing (a) costs of ramping, (b) non-linear decrease of efficiency at partial load operation, and (c) refurbishment of existing units in the cost minimisation objective function of OSeMOSYS. From application to the test case study, two main insights are drawn: costs of ramping and decreased partial load efficiency may influence the competitiveness of generation technologies in the provision of reserve capacity; and refurbishment of existing units may represent attractive investment options for increasing flexibility. Both effects are also seen in the long-term and may impact infrastructure investment decisions to meet decarbonisation targets. These effects would not be captured without the introduction of the modules.

Implications of biogas and electric cooking technologies in residential sector in Nepal – A long term perspective using AIM/Enduse model

This paper aims to analyze the effects of biogas and electricity based cooking on energy use and greenhouse gas (GHG) as well as local air pollutant emissions during 2010–2050 in the case of Nepal, which is highly dependent on traditional biomass (mainly fuelwood) for cooking. The country is rich in hydropower resources. A long-term bottom-up energy system model has been developed using Asia-Pacific Integrated Model/Enduse (AIM/Enduse) model for the analysis. The study developed a business as usual (BAU) scenario and three alternative cooking scenarios. Three alternative scenarios, named as “CL”, “CM” and “CH” scenarios; consider low, medium and high level of penetrations of electric- and biogas-based cooking options, respectively. The changes in energy use and electricity generation in the BAU and alternative scenarios have been compared. Fuelwood consumption in the residential sector in 2050 when compared to the BAU would decrease by 12.5% in CL, 19.0% in CM and 24.2% in CH scenarios; and liquefied petroleum gas (LPG) consumption would decrease by 12.8% in CL, 16.3% in CM and 19.6% in CH scenarios. The electricity generation requirement in 2050 would increase by 9.4% in CL, 13.9% in CM and 17.0% in CH scenarios. Finally, the assessment of GHG and local pollutant emissions shows the decrease in all gases in CL, CM and CH scenarios when compared to the BAU.

Designing low-carbon power systems for Great Britain in 2050 that are robust to the spatiotemporal and inter-annual variability of weather

The design of cost-effective power systems with high shares of variable renewable energy (VRE) technologies requires a modelling approach that simultaneously represents the whole energy system combined with the spatiotemporal and inter-annual variability of VRE. Here, we soft-link a long-term energy system model, which explores new energy system configurations from years to decades, with a high spatial and temporal resolution power system model that captures VRE variability from hours to years. Applying this methodology to Great Britain for 2050, we find that VRE-focused power system design is highly sensitive to the inter-annual variability of weather and that planning based on a single year can lead to operational inadequacy and failure to meet long-term decarbonization objectives. However, some insights do emerge that are relatively stable to weather-year. Reinforcement of the transmission system consistently leads to a decrease in system costs while electricity storage and flexible generation, needed to integrate VRE into the system, are generally deployed close to demand centres. The rising share of variable renewable energy sources in the grid makes planning future power systems more complex. Zeyringer et al. present an approach that uses multiple weather-years of data and highlights the need to incorporate inter-annual weather variability into planning decisions.

Electric and Biogas Stoves as Options for Cooking in Nepal and Thailand

Solid biomass accounts for eighty percent cooking share in Nepal whereas in Thailand it accounts for nearly 40%. With the increase in income of the people in urban as well as rural areas, fuel switching from biomass to LPG for cooking has been the most prevalent. Domestic hydropower resources in Nepal have remained unutilized. Electricity based cooking is one of the option to reduce fossil fuel consumption in Nepal. Likewise, the use of biogas in rural areas can be another option to reduce LPG consumption as well as fuelwood consumption. In the case of Thailand, nearly 60 percent cooking is attributed to LPG based cooking. Shift from LPG to electric and biogas based cooking can reduce its dependency on LPG as well as dependence on biomass resources. This study aims to develop a business as usual (BAU) scenario and various levels of electric and biogas based cooking scenarios to analyze its implication on primary energy use, energy mix, electricity generation requirement and GHG emissions during 2010-2050 in the case of Nepal and Thailand. The study uses Asia-Pacific Integrated Model (AIM)/Enduse model, a long-term bottom-up energy system model as an analytical tool. In Nepal, fuelwood would remain the dominant source of energy during 2010-2050 in the residential sector in BAU. The consumption of imported fossil fuels would decrease with the use of electricity and biogas for cooking while that of domestic hydropower would increase. In the case of Thailand, the consumption of LPG would decrease while that of coal and natural gas would increase due to additional power generation. The national GHG emission level would decrease in the case of Nepal whereas it would increase in the case of Thailand.

Access to electricity in the World Energy Council's global energy scenarios: An outlook for developing regions until 2030

Approximately 2 out of 10 people in the world still live without access to electricity. The UN “Sustainable Energy to All – SE4All” initiative aims at eradicating this electricity access deficit by 2030. To estimate the financial effort required to achieve this target we analyse two long-term scenarios developed by the Paul Scherrer Institute and the World Energy Council, which describe two alternative economic and energy system developments. We focus on developing Asia, Latin America and Sub-Saharan Africa, which currently have the largest percentage of population without access to electricity. We couple a long-term energy system model with regional econometric models that forecast population electrification rates. We find that establishing universal electricity access by 2030 requires significant, but attainable investments in power generation infrastructure, and results in low impacts on primary energy demand and CO2 emissions.

Short-term uncertainty in long-term energy system models — A case study of wind power in Denmark

When wind power constitutes a larger share of the electricity production mix, credible and reliable modelling of its operation in long-term investment models becomes increasingly important. In this paper the intermittent characteristics of wind power are modelled as a stochastic parameter in a long-term TIMES model of the Danish heat and electricity sector. To our knowledge, this is not a common approach in long-term investment models, and has not been done previously in TIMES, where the short-term uncertainty of wind power is normally taken into account by a deterministic constraint that ensures excess back-up capacity. In our model, the stochasticity gives lower total energy system costs, significant lower investments in wind power, less expected electricity export and higher expected biomass consumption compared to using the traditional deterministic approach. Also, the deterministic investment strategy can be insufficient in periods with poor wind conditions. Based on our findings, we recommend using a stochastic representation of intermittent renewables in long-term investment models to provide more solid results for decision makers.

Incorporating flexibility requirements into long-term energy system models – A case study on high levels of renewable electricity penetration in Ireland

Efforts to meet climate change mitigation and energy security targets spur investments in variable renewable energy sources. Their implications for the operation of power plants are frequently investigated drawing on unit commitment and dispatch models. However, the temporal granularity and operational detail these models consider is commonly omitted in the broader family of long-term energy system models. To compensate this short-coming, these two types of tools have sometimes been ‘soft-linked’ and harmonised for limited simulation years. This paper assesses an alternative approach. We examine an extended version of an open source energy system model (OSeMOSYS), which is able to capture operating reserve and related investment requirements within a single tool. The implications of these model extensions are quantified through comparison with an Irish case study. That case study examined the effects of linking a long-term energy system model (TIMES) with a unit commitment and dispatch model (PLEXOS). It analysed the year 2020 in detail, applying a yearly temporal resolution that is over 700 times higher than in OSeMOSYS. Without increasing temporal resolution (and computational burden) we show that results of the enhanced OSeMOSYS model converge to results of TIMES and PLEXOS: Investment mismatches decrease from 21.4% to 5.0%. The OSeMOSYS analysis was then extended to 2050 to assess the implications of short-term variability on future capacity investment decisions. When variability was ignored, power system investments in 2050 were found to be 14.3% lower. This might imply that energy policies derived from such long-term models – of which there are many – may underestimate the costs of introducing variable renewables and thus meeting climate change or energy security targets.

Supporting security and adequacy in future energy systems: The need to enhance long-term energy system models to better treat issues related to variability

As the shares of variable renewable generation in power systems increase, so does the need for, inter alia, flexible balancing mechanisms. These mechanisms help ensure the reliable operation of the electricity system by compensating for fluctuations in supply or demand. However, a focus on short-term balancing is sometimes neglected when assessing future capacity expansions with long-term energy system models. Developing heuristics that can simulate short-term system issues is one way of augmenting the functionality of such models. To this end, we present an extended functionality to the Open Source Energy Modelling System (OSeMOSYS), which captures the impacts of short-term variability of supply and demand on system adequacy and security. Specifically, we modelled the system adequacy as the share of wind energy is increased. Further, we enable the modelling of operating reserve capacities required for balancing services. The dynamics introduced through these model enhancements are presented in an application case study. This application indicates that introducing short-term constraints in long-term energy models may considerably influence the dispatch of power plants, capacity investments, and, ultimately, the policy recommendations derived from such models.

Network constraints in techno-economic energy system models: towards more accurate modeling of power flows in long-term energy system models

Power systems are subject to extensive structural changes as a result of the fact that the share of renewable energies in power supply will increase significantly within the next decades. This requires the transport of large amounts of electricity, e.g. from the North Sea to the large load centres. Moreover, the decentralized installations for the generation of electricity (e.g. PV) need to be integrated in the lower voltage power grids without violating net-safety constraints. As a consequence, the grid load in the system will rise to an extent that is hardly manageable with existing power grid capacities. Therefore, while mostly neglected to date, the importance of considering the power grid in energy system models increases significantly. Within this paper, different examples will be given how network constraints can be considered in techno-economic energy system models with a focus on capacity expansion planning and a long-term time horizon. Firstly, a multi-period linear optimization model will be presented, which comprises the system equations for power generation and transmission. The latter is analyzed with the help of a DC power flow model. Secondly, the usage of an AC power flow modeling tool for a detailed representation of the medium and low voltage power grid will be described. Finally, we will present an illustrative example application of a new mathematical approach for grid modeling in techno-economic energy system models.