Energy System Optimization Model Research Articles

Integration of groundwater heat pumps into energy system optimization models

Heat pumps are one of the key technologies for the mitigation of carbon emissions in the heating sector. Therefore, they are an essential modelling component when analyzing future energy systems. The focus of this work are groundwater heat pumps and their integration into energy system optimization models. Three different integration approaches are introduced and compared to each other. The approaches differ in the representation of heat pumps efficiency: constant, time dependent and both time and spatially dependent. The comparison of the proposed approaches is conducted using an energy system model of the residential heating sector in Munich and two optimization scenarios: 70 % and 95 % emission reduction compared to 2014. Assuming constant efficiency of heat pumps throughout the year produces incorrect optimization results, whereas adding spatial component to the temporal one does not change cumulative results significantly. Thus, the second approach is the most suitable one due to its lower complexity compared to the last approach. However, if spatially distributed results are required, then the last approach is necessary. Finally, these integration approaches are applicable to all types of heat pumps.

Long term storage in generation expansion planning models with a reduced temporal scope

To reduce the computation time of Energy System Optimization Models and Generation Expansion Planning Models operational detail is typically limited to several hours, days, or weeks in a year selected using Time Series Aggregation methods. We compare time series aggregation methods and generation expansion planning models which aim to capture the value of long-term storage for the first time in the literature. Time Series Aggregations methods were compared by varying the number of representative periods and then running a full year generation expansion planning model on novel synthetic time series. Generation Expansion Planning Models were run on selections and ordering of representative periods in order to compare them. Our results suggest that approximating the full-year time series does not necessarily translate to approximating the full-year generation expansion planning solution and that selecting hours or days is a greater determinant of performance than the time series aggregation method itself. Two of the generation expansion planning models considered, Enhanced Representative Days and Chronological Time Period Clustering, could capture the value of long-term storage, though over or underinvestment in long-term storage by more than a factor of 2 was also possible and the latter formulation exhibited a clear bias towards long-term storage. Based on these results we formulate recommendations for modelers seeking to include long-term storage in generation expansion planning models.

Environmental and techno-economic assessment of power system expansion for projected demand levels in Kenya using TIMES modeling framework

This study develops a new national-scale bottom-up energy system optimization model called Kenya-TIMES. The model evaluates the implication of greenhouse gas emission's reduction on the techno-economic and environmental evolution of Kenya's power system under three government-projected electricity demand levels, which covers the 2020–2045 period. To assess the implications of greenhouse gas emission reduction measures, a business as usual and a carbon emission cap scenarios were developed. The model shows that energy security can be achieved under the two scenarios, and for all the three demand levels. The generation mix suggested by the model is dominated by renewable sources under the carbon emission cap scenario compared to the business as usual scenario. The higher share of renewable technologies under the carbon emission cap scenario results in lower emission but increased electricity cost. Consequently, to meet its emission reduction targets, the Kenyan government need to enact and implement policies that will enhance deployment of renewable energy technologies. The findings indicate that the Kenyan government should prioritize developing geothermal and hydropower resources in the short- to medium-term, which can provide affordable and secure energy while limiting GHG emissions.

The feasibility of energy autonomy for municipalities: local energy system optimisation and upscaling with cluster and regression analyses

The sheer number of alternative technologies and measures make the optimal planning of energy system transformations highly complex, requiring decision support from mathematical optimisation models. Due to the high computational expenses of these models, only individual case studies are usually examined. In this article, the approach from the author’s PhD thesis to transfer the optimisation results from individual case studies to many other energy systems is summarised. In the first step, a typology of the energy systems to be investigated was created. Based on this typology, representative energy systems were selected and analysed in an energy system optimisation model. In the third step, the results of the representative case studies were transferred to all other energy systems. This approach was applied to a case study for determining the minimum costs of energy system transformation for all 11,131 German municipalities from 2015 to 2035 in the completely energy autonomous case. While a technical potential to achieve energy autonomy is present in 56% of the German municipalities, energy autonomy shows only low economic potential under current energy-political conditions. However, energy system costs in the autonomous case can be greatly reduced by the installation and operation of base-load technologies like deep-geothermal plants combined with district heating networks. The developed approach can be applied to any type of energy system and should help decision makers, policy makers and researchers to estimate optimal results for a variety of energy systems using practical computational expenses.

Recoupling Climate Change and Air Quality: Exploring Low-Emission Options in Urban Transportation Using the TIMES-City Model

Fossil fuels in transportation are a significant source of local emissions in and around cities; thus, decarbonising transportation can reduce both greenhouse gases (GHGs) and air pollutants (APs). However, the degree of these reductions depends on what replaces fossil fuels. Today, GHG and AP mitigation strategies are typically ‘decoupled’ as they have different motivations and responsibilities. This study investigates the ancillary benefits on (a) APs if the transport sector is decarbonised, and (b) GHGs if APs are drastically cut and (c) the possible co-benefits from targeting APs and GHGs in parallel, using an energy-system optimisation model with a detailed and consistent representation of technology and fuel choices. While biofuels are the most cost-efficient option for meeting ambitious climate-change-mitigation targets, they have a very limited effect on reducing APs. Single-handed deep cuts in APs require a shift to zero-emission battery electric and hydrogen fuel cell vehicles (BEVs, HFCVs), which can result in significant upstream GHG emissions from electricity and hydrogen production. BEVs powered by ‘green’ electricity are identified as the most cost-efficient option for substantially cutting both GHGs and APs. A firm understanding of these empirical relationships is needed to support comprehensive mitigation strategies that tackle the range of sustainability challenges facing cities.

Incorporating consumer choice into an optimization model for the German heat sector: Effects on projected bioenergy use

The energy transition requires policy makers to adopt a holistic view that also considers non-economic factors when developing cleaner technology deployment schemes. In particular, a broad knowledge base is required to ensure an efficient energetic use of the limited biomass potential. Energy system optimization models are widely used to inform decision makers about energy transition strategies. The heterogeneity of consumers, especially in the heat sector, is rarely considered in these models and therefore these models lack of completion to contribute to this holistic approach. In this study, a literature review was conducted to find empirical data on consumer behavior regarding the adoption of residential heating systems. This data was integrated into an optimization model for the German heat sector, combining established methods for integrating consumer heterogeneity with a novel approach for calculating indirect costs representing behavioral factors. The incorporation of consumer choice leads to a broader distribution of market shares of different technologies in both a “business-as-usual” scenario and an “ambitious measures” scenario. In particular, the future role of log wood technologies in the private household sector may have been underestimated in previous studies and should be discussed, when designing policies. With this study, the knowledge base for decision makers was extended to discuss the future efficient use of biomass within the German heat sector.

Capacity credit of storage in long-term planning models and capacity markets

Capacity credits, i.e., metrics that describe the contribution of different technologies in meeting the load during peak periods, are widely used in the context of long-term energy-system optimization models to ensure a pre-defined level of firm capacity. In the same vain, such capacity credits may be used in capacity markets to reflect the availability of an asset during periods of peak load. For storage technologies it seems that there is a discrepancy between the capacity credit that correctly captures the capacity contribution to the capacity target, and the capacity credit that correctly values the storage capacity. This is illustrated in a case study, which shows the differences in planning model outcomes when different capacity credit interpretations are used. Our results indicate that defining the capacity credit according to the contribution to the capacity target overvalues storage technologies, causing overinvestments. On the contrary, defining the capacity credit to reflect the value of the storage capacity, triggers the correct amount of storage investments, but underestimates the true peak reduction potential, which results in overinvestments in other firm capacity providers. In this regard, a novel iterative approach is introduced that leverages both capacity credit interpretations simultaneously to remove any bias in the solution.

To Prevent or Promote Grid Expansion? Analyzing the Future Role of Power Transmission in the European Energy System

Future energy supply systems must become more flexible than they are today to accommodate the significant contributions expected from intermittent renewable power sources. Although numerous studies on planning flexibility options have emerged over the last few years, the uncertainties related to model-based studies have left the literature lacking a proper understanding of the investment strategy needed to ensure robust power grid expansion. To address this issue, we focus herein on two important aspects of these uncertainties: the first is the relevance of various social preferences for the use of certain technologies, and the second is how the available approaches affect the flexibility options for power transmission in energy system models. To address these uncertainties, we analyze a host of scenarios. We use an energy system optimization model to plan the transition of Europe’s energy system. In addition to interacting with the heating and transport sectors, the model integrates power flows in three different ways: as a transport model, as a direct current power flow model, and as a linearized alternating current power flow model based on profiles of power transfer distribution factors. The results show that deploying transmission systems contribute significantly to system adequacy. If investments in new power transmission infrastructure are restricted—for example, because of social opposition—additional power generation and storage technologies are an alternative option to reach the necessary level of adequacy at 2% greater system costs. The share of power transmission in total system costs remains widely stable around 1.5%, even if cost assumptions or the approaches for modeling power flows are varied. Thus, the results indicate the importance of promoting investments in infrastructure projects that support pan-European power transmission. However, a wide range of possibilities exists to put this strategy into practice.

Effect of the Foresight Horizon on Computation Time and Results Using a Regional Energy Systems Optimization Model

Due to the high complexity of detailed sector-coupling models, a perfect foresight optimization approach reaches complexity levels that either requires a reduction of covered time-steps or very long run-times. To mitigate these issues, a myopic approach with limited foresight can be used. This paper examines the influence of the foresight horizon on local energy systems using the model DISTRICT. DISTRICT is characterized by its intersectoral approach to a regionally bound energy system with a connection to the superior electricity grid level. It is shown that with the advantage of a significantly reduced run-time, a limited foresight yields fairly similar results when the input parameters show a stable development. With unexpected, shock-like events, limited foresight shows more realistic results since it cannot foresee the sudden parameter changes. In general, the limited foresight approach tends to invest into generation technologies with low variable cost and avoids investing into demand reduction or efficiency with high upfront costs as it cannot compute the benefits over the time span necessary for full cost recovery. These aspects should be considered when choosing the foresight horizon.

OPERA: a New High-Resolution Energy System Model for Sector Integration Research

This article introduces and describes OPERA, a new technology-rich bottom-up energy system optimization model for the Netherlands. We give a detailed specification of OPERA’s underlying methodology and approach, as well as a description of its multiple applications. The model has been used extensively to formulate strategic policy advice on energy decarbonization and climate change mitigation for the Dutch government, and to perform exploratory studies on the role of specific low-carbon energy technologies in the energy transition of the Netherlands. Based on a reference scenario established through extensive consultation with industry and the public sector, OPERA allows for examining the impact of autonomous technology diffusion and energy efficiency improvement processes, and investigating a broad range of policy interventions that target greenhouse gas emission abatement or air pollution reduction, amongst others. Two characteristics that render OPERA particularly useful are the fact that (1) it covers the entire energy system and all greenhouse gas emissions of the Netherlands, and (2) it possesses a high temporal resolution, including a module for flexibly handling individual time units. The simulation of the complete Dutch energy system and the ability to represent energy supply and demand on an hourly basis allow for making balanced judgements on how to best accommodate large amounts of variable renewable energy production. OPERA is therefore particularly suited for analyzing subjects in the field of system integration, which makes it an ideal tool for assessing the implementation of the energy transition and the establishment of a low-carbon economy. We outline several near-term developments as well as opportunities for future improvements and refinements of OPERA. One of OPERA’s attractive features is that its structure can readily be applied to other countries, notably in Europe, and could thus relatively easily be extended to cover the entire European Union

Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico

Energy system optimization models often incorporate climate change impacts to examine different energy futures and draw insights that inform policy. However, increased risk of extreme weather events from climate change has proven more difficult to model. Here, we present an energy system optimization model that incorporates hurricane risks by combining storm probabilities with infrastructure fragility curves, and demonstrate its utility in the context of Puerto Rico, an island territory of the United States that had its energy system severely damaged by Hurricane Maria in 2017. The model assesses the potential to change grid architecture, fuel mix and grid hardening measures considering hurricane impacts as well as climate mitigation policies. When hurricane trends are included, 2040 electricity cost projections increase by 32% based on historical hurricane frequencies and by 82% for increased hurricane frequencies. Transitioning to renewables and natural gas reduces costs and emissions independent of climate mitigation policies. In order to assess the impact of climate change on energy systems, models need to incorporate the increased risk of extreme weather events. Here, Bennett et al. provide a framework to integrate increasing extreme event risk in grid expansion planning models and apply the method to hurricane risks in Puerto Rico.

Energy system optimization model for tissue papermaking process

The drying process accounts for the largest proportion of energy consumption in paper mills. Energy system optimization has a great significance for reducing the energy consumption of the paper drying process. The drying process is a complex system that consists of several subsystems, such as cylinder and air hood systems. Previous optimization models for energy systems usually focused on these subsystems. A global optimization methodology for the entire drying process is lacking, and no existing models can be applied in practice. In this work, an energy system optimization model for the tissue paper drying process is proposed based on a process simulation model. The modeling process integrates the various subsystems and fully considers the coupling of the paper drying process, which greatly enhances the industrial application value of the model. Industrial operating data are used to test the simulation model, and the results show that the simulation error of each key variable is within 5%, which meets the real-world production requirements and lays the foundation for an energy efficiency analysis of each subsystem. Applying the optimization model to a tissue paper mill, the results show that it can reduce drying costs by 8.71%.

Transportation emissions scenarios for New York City under different carbon intensities of electricity and electric vehicle adoption rates.

Like many cities around the world, New York City is establishing policies to reduce CO2 emissions from all energy sectors by 2050. Understanding the impact of varying degrees of electric vehicle adoption and CO2 intensities on emissions reduction in the city is critical. Here, using a technology-rich, bottom-up, energy system optimization model, we analyse the cost and air emissions impacts of New York City's proposed CO2 reduction policies for the transportation sector through a scenario framework. Our analysis reveals that the electrification of light-duty vehicles at earlier periods is essential for deeper reductions in air emissions. When further combined with energy efficiency improvements, these actions contribute to CO2 reductions under the scenarios of more CO2-intense electricity. Substantial reliance on fossil fuels and a need for structural change pose challenges to cost-effective CO2 reductions in the transportation sector. Here we find that uncertainties associated with decarbonization of the electric grid have a minimum influence on the cost-effectiveness of CO2 reduction pathways for the transportation sector.

The Importance of Renewable Gas in Reaching Carbon-Neutrality: Insights from an Energy System Optimization Model

The Importance of Renewable Gas in Reaching Carbon-Neutrality: Insights from an Energy System Optimization Model

Systematic evaluation for hydropower exploitation rationality in hydro-dominant area: A case study of Sichuan Province, China

Despite the considerable contribution of hydropower for meeting electricity demand and meanwhile achieving free carbon emission, the rationality of hydropower exploitation needs to be reassessed in the context of hydropower curtailment being severe in some hydro-dominant areas. In this paper, a two-stage fuzzy-stochastic chance-constrained interval (TFCI) regional energy system optimization model under multiple complexities is developed for systematically assessing the rationality of existing hydropower exploitation plan, and reflecting the tradeoff between system cost and reliability for Sichuan territory where the exploitable hydropower resources rank first in China. Results showed that the degree of hydropower overcapacity would become far more severe under the real existing energy structure planning. Instead of continuously boosting hydropower exploitation, proper limitation on hydropower installed capacity expansion to make other renewable energy obtain deserved promotion can effectively reduce system cost and improve the utilization efficiency of all the renewable technologies. The highest utilization efficiency of hydropower, wind power and solar power could achieve [90%, 100%], [100%, 100%], and [100%, 100%], respectively. The obtained results also indicated that greater uncertainty about system cost may have to be faced if policymakers aim to lower risk and enhance system reliability. Suggestions are provided for eliminating the irrationality of hydropower in Sichuan and other similar hydro-dominant regions based on above mentioned conclusions.

System analysis of the bio‐based economy in Colombia: A bottom‐up energy system model and scenario analysis

AbstractThe transition to a sustainable bio‐based economy is perceived as a valid path towards low‐carbon development for emerging economies that have rich biomass resources. In the case of Colombia, the role of biomass has been tackled through qualitative roadmaps and regional climate policy assessments. However, neither of these approaches has addressed the complexity of the bio‐based economy systematically in the wider context of emission mitigation and energy and chemicals supply. In response to this limitation, we extended a bottom‐up energy system optimization model by adding a comprehensive database of novel bio‐based value chains. We included advanced road and aviation biofuels, (bio)chemicals, bioenergy with carbon capture and storage (BECCS), and integrated biorefinery configurations. A scenario analysis was conducted for the period 2015–2050, which reflected uncertainties in the capacity for technological learning, climate policy ambitions, and land availability for energy crops. Our results indicate that biomass can play an important, even if variable, role in supplying 315–760 PJ/y of modern bio‐based products. In pursuit of a deep decarbonization trajectory, the large‐scale mobilization of biomass resources can reduce the cost of the energy system by up to 11 billion $/year, the marginal abatement cost by 62%, and the potential reliance on imports of oil and chemicals in the future. The mitigation potential of BECCS can reach 24–29% of the cumulative avoided emissions between 2015 and 2050. The proposed system analysis framework can provide detailed quantitative information on the role of biomass in low carbon development of emerging economies. © 2020 The Authors. Biofuels, Bioproducts, and Biorefining published by Society of Chemical Industry and John Wiley & Sons, Ltd

Identification of Potential Off-Grid Municipalities With 100% Renewable Energy Supply for Future Design of Power Grids

An increasing number of municipalities are striving for energy autonomy. This study determines in which municipalities and at what additional cost energy autonomy is feasible for a case study of Germany. An existing municipal energy system optimization model is extended to include the industrial, commercial and personal transport sectors. Multiple regression methods are benchmarked in order to identify the model best suited for the transfer of individual optimization results to a large proportion of German municipalities. The resulting levelized cost of energy (LCOE) from the optimization of representative case study municipalities are transferred using energy-relevant indicators. The study demonstrates that energy autonomy is technically feasible in 6,314 (56%) municipalities. Thereby, the LCOEs increase in the autonomous case on average by 0.41 €/kWh compared to the minimum cost scenario. Apart from energy demand, base-load-capable bioenergy and deep geothermal energy have the greatest influence on the LCOEs. Overall, it appears that municipal energy autonomy is not economically viable under current framework conditions. This study represents a starting point for defining possible scenarios in studies of future national energy system or transmission grid expansion planning, which for the first time consider completely energy autonomous municipalities.

Minimising the effects of spatial scale reduction on power system models

The spatial resolution of energy system optimisation models (ESOMs) is often compromised for computational performance. Reducing the spatial resolution impacts the least-cost solutions when optimising the generation capacity and transmission capacity of the ESOMs with high penetration of variable renewable energy sources. Previous studies show that the two main effects of spatial resolution reduction are the increase in solar and wind expansion capacity and the decrease in transmission capacity expansion. This paper introduces a targeted method of defining regions during spatial scale reduction by using the max-p-regions clustering algorithm to aggregate similar areas into regions. The attributes used to determine the similarity between areas in the max-p-regions method are population; wind and solar resource potential; and pumped-hydro storage capacity. Two alternative spatial resolution reduction methods were used to compare how the effects of spatial resolution impacted the optimisation results of the ESOMs. Evaluation results for three country groups showed that using the max-p-regions method to define regions caused the two effects of spatial resolution reduction to be generally lower than using national jurisdictions. For the case studies Germany and Spain, the results showed that the max-p-regions method identified sets of regions, which were less impacted by the two effects of spatial resolution reduction.

An Air Pollutant Emission Reduction Path of China’s Power Industry

In China, as the major source of energy consumption and air pollutant emissions, the power industry is not only the principal force that bears the responsibility of national emission reduction targets but also a breakthrough that reflects the effectiveness of emission reduction. In this study, based on the integrated MARKAL-EFOM system (TIMES) model and scenario analysis method, a bottom-up energy system optimization model for the power industry was established, and four scenarios with different constraints were set up to predict and analyze the power demand and the energy consumption structure. Emission characteristics, emission reduction characteristics, and emission reduction cost of sulfur dioxide (SO2), nitrogen oxide (NOX), particulate matter 2.5 (PM2.5), and mercury (Hg) were quantitatively studied. Finally, for the environmentally friendly development and optimal adjustment of power production systems in China, the control path in the power industry that is conducive to the emission reduction of air pollutants was obtained, which is of great significance for the ultimate realization of climate friendliness. The results demonstrate that from 2020 to 2050, the power demand of the terminal departments will increase, with the composition significantly changed. The focus of power demand will change from industry to the service industry gradually. If no additional targeted emission reduction or adjustment policies are added in the power industry, the primary energy and air pollutant emissions will increase significantly, putting great pressure on resources and the environment. For the emission reduction of air pollutants, the promotion effect of emission reduction measures, such as the implementation and promotion of non-fossil fuels, is restricted. The power industry can introduce and maximize the best available technologies while optimizing the structure of energy consumption to realize efficient emission reduction of air pollutants and energy conservation. In 2030, emissions will reach peak values with reasonable emission reduction cost. This has the additional effect of abating energy consumption and preventing deterioration of the ecological environment, which is of profound significance for the ultimate realization of climate friendliness.

Low-carbon development path research on China’s power industry based on synergistic emission reduction between CO2 and air pollutants

The compression of the industrialization process has forced China to confront the double pressure of greenhouse gas emissions and air pollution. This paper constructs an energy system optimization model for China’s power industry; establishes four energy consumption scenarios with different constraints; and forecasts and analyses the energy consumption structure, power consumption demand and production composition of China’s power industry from 2020 to 2050. Furthermore, based on synergistic effects, the emission characteristics, emission reduction potential and costs of CO2 and air pollutants are quantitatively analysed, and the obtained synergistic emission reduction effect and influencing factors are decomposed based on technical effect and structural effect. Finally, a low-carbon emission reduction path that can realize the synergistic control of CO2 and traditional air pollutants in China’s power industry is obtained. The results show that in the future, China’s power industry will continue to grow at a greater rate than primary energy consumption and the focus of power demand will gradually shift from industry to transportation and construction. The power industry can introduce and maximize the application of optimal control technologies while optimizing the energy consumption structure in order to realize synergistic emission reduction for CO2 and traditional air pollutants in China’s power industry. While saving energy, the corresponding cost of emission reduction will remain relatively low. After CO2 emissions peak in the power industry, the main way to reduce CO2 emissions will be to optimize the structure and upgrade the technology for CO2 self-governance. For the reduction of air pollutants, the promotion effect is limited only by the implementation and promotion of structural emission reduction measures focused on non-fossil energy.