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Trends in tools and approaches for modelling the energy transition
Abstract
Energy system models are crucial to plan energy transition pathways and understand their impacts. A vast range of energy system modelling tools is available, providing modelling practitioners, planners, and decision-makers with multiple alternatives to represent the energy system according to different technical and methodological considerations. To better understand this landscape, here we identify current trends in the field of energy system modelling. First, we survey previous review studies, identifying their distinct focus areas and review methodologies. Second, we gather information about 54 energy system modelling tools directly from model developers and users. Unlike previous questionnaire-based studies solely focusing on technical descriptions, we include application aspects of the modelling tools, such as perceived policy-relevance, user accessibility, and model linkages. We find that, to assess the possible applications and to build a common understanding of the capabilities of these modelling tools, it is necessary to engage in dialogue with developers and users. We identify three main trends of increasing modelling of cross-sectoral synergies, growing focus on open access, and improved temporal detail to deal with planning future scenarios with high levels of variable renewable energy sources. However, key challenges remain in terms of representing high resolution energy demand in all sectors, understanding how tools are coupled together, openness and accessibility, and the level of engagement between tool developers and policy/decision-makers.
... Furthermore, they examined the need to support governmental processes for greenhouse gas reduction. In recent research, Chang et al (Chang et al., 2021). addressed the importance of interacting with developers and users to assess applications and capabilities. ...
... Addressing the complexities related to the accessibility, quality, and availability of data for modeling energy systems involves overcoming obstacles in data collection, processing, and effective utilization (Lopion et al., 2018;Chang et al., 2021;Klemm and Vennemann, 2021). ...
... While prior research has pinpointed challenges in integrating renewable energy, especially within the electrical sector (Kitamura et al., 2022), recent studies underscore the importance of incorporating sectors like transportation to achieve a more effective transition, providing additional flexibility to the energy .A. Camargo-Bertel et al. Energy Reports 12 (2024) 3075-3095 system (Chang et al., 2021). In the Latin American context, research endeavors have examined the potential for integrating various sectors, including electricity, heat, transportation, and industry. ...
... Approaches to analyzing energy systems can be categorized into optimization models, simulation models [16], and equilibrium models [17]. Optimization models aim to identify the optimal energy solution. ...
... For example, the effects of population, economics, and energy variables on carbon emission were explored using the LMDI technique in China, Turkey, Malaysia, and the European Union [4][5][6][7]. In other studies, the ARDL model and Granger causality test have been widely used to examine the link between emission, energy consumption, and economic growth in Indonesia, Myanmar, and Malaysia [8], and multi-periods not had significant policy contributions due to their recent development or limited usage [17]. ...
... Furthermore, even in studies predicting future scenarios, the consideration of dynamic feedback mechanisms between social, economic, energy, and emission factors is limited. Chang, Thellufsen [17] found that widely considered tools with policy applications should include the ability to provide long-term outcomes and represent multiple energy sectors. ...
Vietnam, a rapidly growing economy with high energy demand, aims for net-zero emissions by 2050. This study employs a system dynamics model to analyze the complex dynamics of energy-related carbon emissions at the national level. This study utilized historical data from 1990 to 2020 and projections up to 2050 from five shared socioeconomic pathway (SSP) scenarios from the International Institute for Applied Systems Analysis (IIASA). Sensitivity analysis identifies gross domestic product growth rate, energy intensity, and energy structure as crucial drivers of energy consumption and carbon emissions. Predictions show that energy consumption and emissions peak in the SSP5 scenario, followed by SSP1, SSP2, SSP4, and SSP3. The projected energy consumption and carbon emissions for Vietnam in 2050 are highest under SSP5, reaching 16,536 PJ and 1,001 Mt CO2, respectively. While all scenarios meet the 2030 emission targets, they fail to meet the 2050 targets, with SSP5 requiring the most significant emission reductions. With robust policy interventions, Vietnam may achieve its net-zero emission goal, emphasizing the need to promote energy-efficient sectors and transition to renewable energy sources. Efforts for Vietnam’s energy system to meet the 2050 carbon emission target require increasing the renewable energy share by 20%-28% and reducing the energy intensity of the residential sector by 21–65% and the industrial sector by 21%-50%, depending on the scenarios
... This would help build a trusting relationship with the modelers. Active engagement with the participants after the tool is deployed can also help (Chang et al. 2021). ...
... Out of 54 energy systems models surveyed by Chang et al. (2021), only six had online GUIs. Half of those six models covered only electricity generation or thermal demand. ...
... One example of the six models is the Energy Transition Model (Quintel 2024). It is the only one surveyed by Chang et al. (2021) that entails an online interface, spans multiple sectors, and is formulated over multiple time periods. Two additional examples are My2050 and UBEM.io. ...
KAPSARC has developed a long-term planning model for the refined oil fuels distribution network. It also designed an online user interface that makes using the model easy for non-modelers. The product is called the Fuel Distribution Network Optimizer.
... 12 These models offer modeling practitioners, planners, and decision-makers different alternatives for representing the systems based on various considerations. 13 This work combines key features from these models to propose an integration framework for power generation and storage systems with different CO 2 abatement pathways to optimize the integrated system's performance under different complexities arising from the dynamic operation and process interactions. ...
... The volatility of power generation with the increasing share of variable renewable energy sources requires the consideration of high temporal, spatial, and technical resolutions when designing low-emissions energy systems. 14 Chang et al. 13 reviewed 54 different energy system modeling tools, and one of the main trends identified was the increased focus on improving the temporal detail to deal with planning future scenarios with high variability in renewable energy sources. Accounting for the temporal variations allows the consideration of properties with dynamic behavior, such as energy storage levels, which is a critical measure for overcoming renewable energy intermittency. ...
... Another complex aspect that should be accounted for is the interactions across different energy production and storage systems. This has been the focus of recent studies that focused on coupling main energy sectors to benefit from their potential synergies 13 and to incorporate advanced flexibility measures. 21 Considering different sectors in energy system models has applications in understanding the impact of electric vehicles on power distribution networks, 22 integrating renewable energy into district heating, 23 and developing smart energy systems. ...
Reducing emissions requires transitioning towards decarbonized systems through avoiding, processing, or offsetting. Decisions on system design are associated with high costs which can be reduced at the planning stage through optimization. The temporal variations in power demand and renewable energy supply significantly impact the design of a low‐emissions energy system. Effective decision‐making must consider such impact in a comprehensive framework that accounts for the potential synergies between different options. This work presents a mixed integer linear programming model that considers the impacts of energy supply and demand dynamics to optimize the design and operation of an integrated energy system while adhering to a set emissions limit. The model integrates renewable power with CO2 capture, utilization, and sequestration by considering H2 production and storage. The case study showed including negative emissions technologies and CO2 capture and processing with renewable energy allows achieving net zero emissions power.
... The economic and financial dimensions of modelling industrial process heat systems are critical, particularly for small and mediumsized industries where the economic viability of transitioning to sustainable technologies is a deciding factor but challenging due to limited data and complex long-term planning [5]. A modelling tool that can implement analyse the economic impact of policy changes is indispensable [102]. Table 5 compares the selected modelling tools based on their economic and financial analysis capabilities, highlighting their strengths and suitability for industrial process heat system modelling. ...
... Key factors include accessibility, cost, support, academic or commercial origins, user-friendliness, and expertise development requirements. Accessibility encompasses how readily users can obtain and start using the tool, whether it's freely available or requires purchase [102]. Cost includes initial and ongoing expenses, with commercial tools often being more expensive, impacting affordability [107]. ...
Industrial Process Heat systems are critical to various industrial processes, representing a significant share of global energy use and emissions. Effective modelling of these systems is essential for evaluating long-term economic and environmental impacts of different technologies. This modelling approach must integrate internal process-specific parameters, such as heat demand dynamics and technological metrics, alongside broader factors like energy costs, emissions policies, and resource availability. This research introduces a comprehensive framework for selecting tools to model industrial process heat systems, focusing on technological, economic, and environmental performance. An initial evaluation of twenty-five tools led to the shortlisting of five based on criteria such as modelling accuracy, scalability, data handling, compatibility with industrial systems, and environmental impacts. Using software engineering principles, a systematic selection process was developed to categorise tools based on essential and desirable capabilities. This framework was validated through an example application, incorporating both technical and practical considerations. The findings highlight the importance of integrating dynamic simulation capabilities with real-time data analysis to improve evaluation accuracy and emphasise user-friendly interfaces to broader industry adoption. The study discusses the framework's applicability, provides key insights, and identifies existing gaps, emphasising the need for adaptable modelling tools to meet evolving industrial requirements. The future applicability of the selection process is discussed, highlighting findings from the capability categorisation, gaps to be addressed, and future trends in modelling these systems. This research contributes to sustainable industrial operations by offering a robust tool selection framework, supporting informed decision-making to reduce emissions and advance industrial sustainability.
... Restrictive policies were different in each country, and their implementation led to a reduction in activities in general, changes in social habits, and negative effects on the economy of the countries. In the case of Mexico, on 19 March 2020, the epidemic of the COVID-19 virus was recognized as a serious disease warranting priority attention; measures were established for prevention and control to reduce the number of infections, and, therefore, prevent its spread, and activities in the public and private sector were suspended until further notice by the health authorities [2]. ...
... Today, modeling tools are a key part of best practices in crafting and implementing a decarbonization plan in any organization, city, or country [18]. A wide range of energy system modeling tools is available, providing modeling professionals, planners, and decision makers with multiple alternatives to represent the energy system according to different technical and methodological considerations in response to emerging challenges and new technological advancements [19]. ...
Electricity is fundamental to modern societies and will become even more so as its use expands through different technologies and population growth. Power generation is currently the largest source of carbon-dioxide (CO2) emissions globally, but it is also the sector that is leading the transition to net zero emissions through the rapid rise of renewables. The impacts of COVID-19 on the electricity sector led to a reduction in the demand for electricity, while at the same time, the current global energy crisis has placed the security and affordability of electricity at the top of the political agenda in many countries. In this way, the decrease in the demand for electricity, as well as its gradual recovery, makes it necessary to carry out energy planning that considers the adverse effects caused by global events with a high socioeconomic impact. In this article, the Low Emission Analysis Platform (LEAP) 2020 software has been used to determine the distribution of energy sources to 2050 for Mexico. The variables that lead to the possible profiles for 2050 are social, economic, and technological. The results correspond to a possible future based on official data from the National Electric System (SEN) of Mexico. The forecast for 2050 indicates that the electricity sector will have almost double the current installed capacity; however, emissions do not correspond to twice as much: they are practically 50% higher.
... Developing a comprehensive dataset encompassing key variables essential for modelling and understanding the pathways for energy transition in the country is vital to analysing transportation demand and energy consumption, making long-term projections, and designing transformative policies. 112 India's Ministry of Statistics and Programme Implementation (MoSPI) must establish a data collection framework dedicated to the transport sector, involving diverse stakeholders from government and civil society, academia, transport engineers, think tanks and modelling teams. The framework must facilitate systematic data collection and analysis of shortand long-distance travel behaviour, energy consumption by vehicle type, the number of vehicles categorised by type and emission standard, load factors, and service demand for passenger vehicles and freight transport. ...
The increasing demand for mobility and the rising rates of motorisation in India have substantially increased energy consumption and carbon dioxide (CO₂) emissions from the road transport sector. As of 2021, road transport was responsible for 14 percent of the nation's total energy consumption, 92 percent of transport-related energy demand, and 94 percent of transport-related CO₂ emissions. In 2021, India committed to net-zero carbon emissions by 2070, making the need to decarbonise the transport sector more urgent. This study utilises a review of literature on the trajectories of decarbonisation and energy transition in India's transport sector. It analyses projections of current and future energy requirements and CO₂ emissions; identifies social, infrastructural, institutional, financial, and policy barriers to a sustainable energy transition; and recommends policy interventions.
... System-deployment pathways of community energy systems are increasingly important for stimulating community participation because the decentralized nature of these systems provides community households, as critical stakeholders, with more avenues for engagement in future energy communities 30 . However, conventional energy models fail to consider the impacts of varying deployment pathways on diverse communities, risking inaccurate estimates of community participation scales 31 . In this work, we built a bottom-up energy model that established links between energy systems and local urban communities, incorporating necessary technological details related to climate, human behavior, building archetypes and energy system characteristics. ...
Community green hydrogen systems, typically consisting of rooftop photovoltaic panels paired with hybrid hydrogen-battery storage, offer urban environments with improved access to clean, on-site energy. However, economically viable pathways for deploying hydrogen storage within urban communities remain unclear. Here we develop a bottom-up energy model linking climate, human behavior and community characteristics to assess the impacts of pathways for deploying community green hydrogen systems in North America from 2030 to 2050. We show that for the same community conditions, the cost difference between the best and worst pathways can be as high as 60%. In particular, the household centralized option emerges as the preferred pathway for most communities. Furthermore, enhancing energy storage demands within these deployment pathways can reduce system design costs up to fourfold. To achieve cost-effective urban decarbonization, the study underscores the critical role of selecting the right deployment pathway and prioritizing the integration of increased energy storage in pathway designs.
... Energy transition models used in the decision-arena are often techno-economic models that are not adequately equipped to incorporate social aspects such as justice (Chang et al., 2021). In the context of a global imperative for a just energy transition, this paper delves into the often overlooked yet pivotal aspect of operationalizing justice considerations within computational models. ...
It is widely considered that the energy transition should be just, yet achieving this goal is a complex socio-technical process. Models serve as valuable tools to support decision-making in navigating these complexities. However, they are not adequately equipped to address justice considerations that are becoming central to energy transition planning. They are unable to provide support in decision spaces that are rich in normative uncertainties, with stakeholders holding differing interpretations of what a just energy transition is. While the importance of integrating justice into computational models is recognized, a significant gap remains in understanding how justice is and can be defined, interpreted, and implemented within these models or, in short, how justice can be operationalized. This paper addresses the gap by examining studies that use computational models for decision-support through the lens of the three tenets of energy justice: procedural, recognition, and distributive justice. We argue that operationalizing justice in energy transition modelling can take place both in the modeling process and within model logic. This paper emphasizes that discussions of justice in relation to models cannot be separated from the design of effective participatory modelling settings that stem from a careful evaluation of the justice requirements of stakeholders in the decision space. We propose a framework that enables modellers and model users to be more explicit about their normative interpretations of justice and derive modelling processes and model requirements that represent diverse justice perceptions in the decision space. By doing this, models can refrain from propagating only dominant ideas of justice and instead actively try to incorporate otherwise neglected perceptions, to ensure that the decision-support facilitates a just energy transition.
... However, most IAMs often lack the detailed representation of energy systems with high temporal, spatial, and technological resolution that energy system models (ESMs) have [26], which typically represent the energy system in greater detail. ESMs are important tools in planning energy system transformations and assessing their impacts [27], as well as providing the basis for developing appropriate policies, including for climate mitigation, at the local, national or regional level [28]. A number of studies have included NET in energy systems decarbonisation pathways with various regional-level energy models [29][30][31][32][33][34][35]; however, most of them have neglected certain aspects that are expected to be represented in their models, such as diversifying NET options and improving alternative mitigation measures, which may result in an incomplete understanding of the role of NETs in future low-carbon energy systems. ...
A systematic review of BECCS and DACCS in low-carbon energy systems is presented. • NETs present significant contributions to climate mitigation and energy system transition. • Research at the national level shows uneven coverage in country models. • The emerging best practices for modelling NETs in ESMs are provided. A R T I C L E I N F O Keywords: NETs Energy system models BECCS DACCS Low-carbon scenarios A B S T R A C T Achieving global commitments on climate mitigation necessitates a rapid transition of the global energy system away from fossil fuels. Modelled scenarios within Integrated Assessment Models (IAMs) and Energy Systems Models (ESMs) indicate that limiting global temperature rise to safe levels will require some dependence on negative emissions technologies (NETs). However, the representation of NETs varies significantly across models, leading to differences in their roles across mitigation scenarios. Bioenergy with carbon capture and storage (BECCS) and direct air carbon capture and storage (DACCS) are two possible solutions that are directly related to the energy system. Here, we perform a systematic review of the representation of BECCS and DACCS within ESMs and IAMs, exploring their roles in decarbonisation scenarios and identifying how NETs contribute to energy system transition pathways. In terms of model structure, we examine the sensitive factors like discount rate, and limitations of modelling NETs in ESMs and provide insights for best practices. The results show that the availability of BECCS and DACCS can facilitate the transformation of energy systems towards low-or net-zero emissions and reduce the marginal abatement cost (MAC) of CO 2 for achieving ambitious climate targets in modelled scenarios. When NETs are not available, a more rapid and deep emissions reduction is required, including a larger-scale deployment of renewables and earlier phaseout of fossil fuels. More significant changes in energy demand, such as energy efficiency improvements, electrification of end-use sectors and lowering energy services demand, can reduce the dependency on NETs. In addition, although NETs deployment helps to reduce the energy system mitigation costs, sticking to technically feasible pathways also presents challenges as MACs remain high, and relying heavily on these technologies may result in risks, such as temperature overshoot and fossil fuel lock-in.
... There is an extensive body of literature on the applications of ML and MO in energy management [51,52], ranging from forecasting of variable renewable energy (VRE) production [53] to solving complex optimal power flow [54]. To understand the new possibilities that quantum computing brings to these applications, it is beneficial to examine the theoretical foundations of quantum speedup in ML and MO. ...
The pursuit of energy transition necessitates the coordination of several technologies, including more efficient and cost-effective distributed energy resources (DERs), smart grids, carbon capture, utilization, and storage (CCUS), energy-efficient technologies, Internet of Things (IoT), edge computing, artificial intellience (AI) and nuclear energy, among others. Quantum computing is an emerging paradigm for information processing at both hardware and software levels, by exploiting quantum mechanical properties to solve certain computational tasks exponentially faster than classical computers. This chapter will explore the opportunities and challenges of using quantum computing for energy management applications, enabling the more efficient and economically optimal integration of DERs such as solar PV rooftops, energy storage systems, electric vehicles (EVs), and EV charging stations into the grid
... Chang et al. (2021; H2020 SENTINEL) report trends of modelling synergies across sectors, improved temporal detail to enable the planning of future scenarios with high levels of variable renewable energy sources, and increased attention to open access data and science. The design of the different types of transitions required would benefit from further such research that maps the characteristics of the environmental, social and economic changes onto the types of climate resilient development pathways introduced in Section 1, to inform the thinking of what and how processes of change can be undertaken equitably.Miller et al. (2022) summarise findings on attitudes of European citizens towards climate change, drawing from Eurobarometer surveys (H2020 SHERPA). ...
The SHERPA process supports the collection of scientific and practice evidence, at multiple levels, regarding climate change and land use management. It provides an overview of the key themes relating to tackling climate change, and the associated roles of land use in rural areas of Europe.
The evidence shows that significant and rapid action is required to restrict the magnitude of global warming. The target of keeping global warming to below 2ºC is only possible if all conditional and unconditional pledges made before the COP26 (Glasgow, November 2021) are implemented in full and on time. Transitions in land uses, with greater investments in natural capital and associated changes in land management practices, and public attitudes and behaviours, will be essential if policy objectives of reducing the emissions of greenhouse gases (GHGs), coupled with reversing the loss of biodiversity and protecting human rights.
... The Legislative and Regulatory Situation concerns the legislative framework of the country where the sustainable energy investment project is implemented, and it could be a decisive factor for selecting the country of implementation [42]. The criterion includes the understanding of government policies, incentives, and regulations, as well as climate change policies and regulation legislation addressing climate change adaptation, mitigation, and resilience supporting such sustainable energy projects [40,45]. ...
Currently, the need for a clean transition has made the upscaling of sustainable energy investment projects imperative. This paper addresses the increasing importance of sustainable energy investment projects in the context of climate change and the urgent need for a global energy transition. Given the complexity of decision-making in this field, a multi-criteria decision-making (MCDM) approach is employed to assess the main criteria considered by project developers and financial institutions. Using the Analytic Hierarchy Process (AHP) method, eight criteria are identified and evaluated. Results highlight differing priorities between project developers and investors, emphasizing the need for adaptable approaches to accelerate sustainable energy investments. The study underscores the importance of understanding diverse stakeholder preferences and priorities in formulating effective strategies and managing associated risks to effectively promote sustainable energy projects. Future research should focus on real-life case studies and policy assessments to further enhance the understanding of sustainable energy investment dynamics.
... In the energy system, the heat demand for DH potential will be derived to the technologies powering DH systems, while the heat demand outside the identified DH areas will be derived to technologies supplying individual heating solutions. There is an array of tools for energy system analysis to choose from, depending on the capabilities and scope and the purpose intended [106], some of which already have coupled GIS systems [107]. The integration of the outputs from the ODHeatMap tool into energy system analysis is pivotal for informing the formulation of energy scenarios and enhancing the precision of analysis. ...
Building footprints are a geographical indication of the spatial distribution of built-up infrastructure, thereby reflecting energy demand patterns, including heating requirements. Heating demands spatial distribution shown in heat atlases are primordial for evaluating district heating systems feasibility, which are a key decarbonizing technology that offers more sustainable heat supply in dense urban areas. Sustainable energy planning frameworks utilize district heating potentials as metrics for the formulation of alternate system configurations aimed at decarbonizing societies and creating an understanding of heating transition pathways. However, the availability and accessibility of the data needed for assessing these potentials is highly contextual and often challenges modelling processes. Simultaneously, there is a growing potential for open data and software mechanisms that could aid in addressing these challenges and create otherwise unavailable heat mapping resources. This paper describes the development of the ODHeatMap tool, a workflow built with open data in python functions that transform building footprints into a heat atlas. Ulaanbaatar city is used as a demonstration area for the tools functionalities, with the outputs being applied in a broader study aimed at developing strategies for Mongolia's heating sector. The tool is accessible through a fully cloud-based environment and can be used in any given geographical context.
... In this review, the 62 selected articles use this specific modeling approach and are classified according to the way they cope with different areas of complexity characterizing the energy transition process. Chang et al. (2021), based on past reviews and experts/developers' opinions, assess energy systems models focusing on tools for energy planning, with a technical and analytical orientation. The review of Hafner et al. (2020) systematically assesses non-optimization macroeconomic ecological models evaluating their capability to address features related to complexity and uncertainty of low-carbon energy transitions. ...
Green energy transition models provide frameworks for policymakers, businesses, and stakeholders to plan and implement the shift toward more sustainable energy systems. These models evolve as technologies advance, policies change, and societal priorities shift, highlighting the importance of flexibility and adaptation in energy transition planning. This study presents a systematic literature review of 115 papers to elucidate methodological approaches, variables, and assessed impacts involved in these models. The descriptive analysis of the sample reveals a burgeoning interest in this pivotal domain alongside a conspicuous dearth of consolidated model and methodological approaches for evaluating the multifaceted environmental, societal, and economic impacts of green energy transition processes. Our results show a broad spectrum of methodological approaches proposed in the literature, with Agent-Based Modeling & Simulation and Statistical Methodologies emerging as prominently featured methodologies. Through a rigorous content analysis of the article sample, we developed a taxonomy of variables that encompasses environmental, economic, societal, governance, knowledge, and technological domains. Then, to elucidate the intricate relationships between output variables and methodologies, we utilised Sankey diagrams and a conceptual map to clarify the complexity inherent in studying related processes. Lastly, a meticulously crafted research agenda is outlined, derived from critical themes identified in the articles, to guide future research endeavours and contribute to advancing knowledge in the green energy transition modelling field.
... It is clear that the energy transition is more complex than just considering generation sources. However, given the scale of the system and the fact that the analysis does not involve deep market considerations or interconnection to the grid, the proposed index can be acceptable in terms of information to users and energy performance, its interaction with the grid, and the autonomy of the implemented system (Chang et al. 2021). ...
This paper addresses the coupling between two technologies commonly used on roofs in urban ecosystems: green roofs and photovoltaic panels. The water-energy-carbon nexus is used as a reference framework to generate a multi-objective model in which the energy transition index, carbon balance, and water consumption are proposed as functions to address the nexus. The economic performance of the system is also considered. The utopia-tracking method, based on objective function normalization and sub-setting, is used to generate nexus performance metrics. Factors such as building thermal load and grid interaction are considered, as well as the use of different vegetation species. Since it is a many-objective problem, alternatives to the Pareto front representation are explored to streamline decision-making processes. The results show that, although green roofs can have significant advantages in carbon abatement environments, considering factors such as carbon sequestration and energy consumption reduction, they are not suitable in semi-arid regions with high water consumption sensitivity. On the other hand, photovoltaic panels generate thermal insulation and power. This makes them a better option for implementation in low-income housing.
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... The power sector is a significant revenue generator and politically important economic sector, serving as essential infrastructure for socioeconomic development (Pietzcker, Osorio, & Rodrigues, 2021). The long lifetime of assets in the electric power sector necessitates investment decisions based on transparent assumptions and evidence used in modeled pathways in the near term (Chang et al., 2021;Dagoumas & Koltsaklis, 2019;Hong et al., 2020). For a developing country like India, the imperative is to engage in a visioning process for a future power system through capacity expansion energy modeling. ...
This paper argues that a fit-for-purpose model and datasets are necessary to generate transition pathways for the electricity generation sector at the subnational level. We present the methodology, data, and results focusing at a sub-national level, the state of West Bengal in India. The approach can be generalized for any region with necessary customization. By utilizing high-resolution spatio-temporal input datasets, this study proposes a power sector capacity expansion model to compute three sets of transitional scenarios and one set of the current-as-usual scenario. These scenarios consider sub-national energy carrier-resource constraints and are solved to identify the most economically cost efficient future transition pathway for the electricity sector in West Bengal. Based on the least-cost solution, computations determine the optimal energy mix, operations, investments, and emissions for alternative scenarios. The results show that integrating demand-side flexibility (DSF) as a balancing option can lead to transformative outcomes. Compared to the current capacity expansion trend (ScenCA), adopting a thermal mix renewable scenario with intraday load-shifting (ScenTMDSF) could reduce 77% of CO2 emissions by 2040. This does not necessitate early retirement of existing thermal power plants, total investment increases by 13% compared to ScenCA. Without DSF as a balancing option, an additional 26% investment is required compared to the current-as-usual scenario for 2040. Transitioning to 100% renewable energy (ScenREN) requires 30% more investment, early retirement of 5.34 GW of thermal capacity, and nearly 2.7 times more storage battery capacity. These numbers help in understanding the magnitude of the financial resource and kind of technological need for the developing countries not only from the point of view of equitable climate action from burden sharing and just transition principles but also provides practical example of need for redirecting global capital for creating global good through subnational scale actions.
... Numerous modelling tools and simulation environments for power devices and power systems exist and have been subject to various reviews [3]- [5]. These tools often focus on specific topics or consider just the general power system. ...
The significance of residential prosumer households within future power systems will be elevated due to digitalisation and electrification. To estimate the impact and potential of prosumers, various simulative investigations are needed. In this paper, we present an open-source model library for power systems focused on prosumer households, which is called eELib. Its focus is on controllable prosumer devices and energy management operating strategies, including considerations of the power grid and markets. This allows for versatile simulation of prosumer and smart grid scenarios. The library can be used for studies concerning grid integration and flexibility utilisation. Exemplary results demonstrate the usability on a building level and grid level.
... In the energy sector, examples include energy stored in water systems (Kuang et al., 2020), the variability of solar and wind production and storage (Alam et al., 2023), fluctuations in electrical networks (Deng and Lv, 2020), the energy demands of agricultural systems (Iddio et al., 2020), and the variable energy demand in cities (Ang et al., 2020). Additionally, social responses can be incorporated (Chang et al., 2021). In this manner, PSE, through its multidisciplinary nature, brings together technologists and engineers from diverse disciplines to a unified focal point. ...
Established as a sub-discipline of Chemical Engineering in the 1960s by the late Professor R.W.H. Sargent at Imperial College London, Process Systems Engineering (PSE) has played a significant role in advancing the field, positioning it as a leading engineering discipline in the contemporary technological landscape. Rooted in Applied Mathematics and Computing, PSE aligns with the key components driving advancements in our modern, information-driven era. Sargent’s visionary foresight anticipated the evolution of early computational tools into fundamental elements for future technological and scientific breakthroughs, all while maintaining a central focus on Chemical Engineering. This paper aims to present concise and concrete ideas for propelling PSE into a new era of progress. The objective is twofold: to preserve PSE’s extensive and diverse knowledge base and to reposition it more prominently within modern Chemical Engineering, while also establishing robust connections with other data-driven engineering and applied science domains that play important roles in industrial and technological advancements. Rather than merely reacting to contemporary challenges, this article seeks to proactively create opportunities to lead the future of Chemical Engineering across its vital contributions in education, research, technology transfer, and business creation, fully leveraging its inherent multidisciplinarity and versatile character.
... Distributed energy systems are fundamentally characterized by locating energy production systems closer to the point of use [17], [18]. Several comparative description of modelling tools for integrated energy systems were recently made and presented in comprehensive review papers as [19], [20] and [21]. This makes it unnecessary to repeat such overview in the present paper. ...
... Distributed energy systems are fundamentally characterized by locating energy production systems closer to the point of use [17], [18]. Several comparative description of modelling tools for integrated energy systems were recently made and presented in comprehensive review papers as [19], [20] and [21]. This makes it unnecessary to repeat such overview in the present paper. ...
Reduction of transport emissions, including critical infrastructures like airports, is a consistent part of the European political goals. Introduction of local renewable energy sources into a complicated energy system as a modern airport requires dedicated optimisation tools and methods. The present paper presents results of comparing two modelling tools OSeMOSYS and Integrate applied for configuration of two case airports: Torino (Italy) and Stavanger (Norway) respectively. The results outline optimal paths for sustainable decarbonisation creating integrated energy systems, which rely on utilisations of locally available resources as PV, Wind and biomethane or deployment of a local hydrogen network, the latter a fundamental enabler for a long-term strategy based on hydrogen. The study highlights the importance of dedicated modelling tools for planning and operation phases for integrated energy systems.
... The meaning of "citizen voices" by initiatives at a municipal level is specifically highlighted by Phillips et al. (2012). However, previous projects on municipal energy transition have predominantly consid-ered individual change aspects, focusing on either technology or economy or communication (e.g., Thomas Krebs 2021;Eggers 2017;Chang et al. 2021;Akyol et al. 2022). The overall perspective that addresses equally the technical and economic challenges as well as the successful communication between the involved municipal stakeholders has been neglected. ...
The energy transition toward a sustainable, decentralized energy supply requires the active participation of local municipalities. Its success depends on acceptance and participation across all sections of the population. A role-specific communication strategy is therefore essential to meet the different communication and information habits, value systems, and needs. In this study, a corresponding transactional communication approach is proposed and evaluated, in which communication is understood as a dynamic and interactive process that is oriented toward preference-specific roles. The methodological approach transfers both Jakobson's communication model from linguistics and Freeman's matrix from stakeholder management to the context of municipal energy transition communication. In line with our newly proposed agile reference process, a contextualization-based instead of a predominantly functionally oriented energy transition communication strategy can thus be formulated. Applying Jakobson's model, roles are described primarily in terms of their communicative aspects rather than their functional meaning. The new interdisciplinary approach is demonstrated using the example of a municipality. Results are used to demonstrate a role-specific communication strategy as the base of the subsequent derivation of role-specific actions and instruments.
... The process of planning the transition towards a sustainable energy system relies on mathematical models to quantify the impacts of different energy transition pathways while capturing the complex interactions within the energy system (Prina et al. 2020;Chang et al. 2021). These modelling tools often have a technoeconomic perspective that captures the technical details and flows from supply technologies to end-use sectors. ...
This study presents an interdisciplinary approach to analyze different transition pathways towards the sustainable development of a low-carbon society, focusing on Norway as a case. The study bridges a socio-technical perspective on sustainability transitions with techno-economic energy systems and regional-economic modelling analyses. Incorporating a socio-technical perspective in the scenario design allows us to envision pathways considering causal processes of technological and socio-institutional change, and potential transition bottlenecks. The resulting scenarios are used in the techno-economic energy system analysis to show cost-optimal energy system configurations, including varying levels of new renewable capacity needed, new conversion technologies, and fuel substitutions across all sectors leading to different decarbonization pathways for the Norwegian energy system by 2050. The regional-economy analysis addresses the impacts of these pathways on general economic growth and labor. The results show that higher levels of decarbonization are possible for Norway; however, potential bottlenecks can slow down the transition, while trade-offs in economic growth and development must be balanced out with decarbonization ambitions.
... This approach operates on the premise that past price trends often repeat, empowering traders and investors to pinpoint potential opportunities. Through the scrutiny of historical price and volume data, technical analysis facilitates the recognition of recurring patterns, which can be leveraged using various tools and methodologies associated with technical analysis [3]. In energy and financial markets, technical analysis helps traders, investors, and analysts make investment decisions, find market entry and exit points, and manage risk. ...
This review paper thoroughly examines the role of technical analysis in energy and financial markets, with a primary focus on its application, effectiveness, and comparative analysis with fundamental analysis. The discussion encompasses fundamental principles, investment strategies, and emerging trends in technical analysis, underscoring their critical relevance for traders, investors, and analysts operating within these markets. Through the analysis of historical price data, technical analysis serves as a crucial tool for recognizing market trends, determining trade timing, and managing risk effectively. Given the complex nature of energy and financial markets, where many factors influence prices, the significance of technical analysis is particularly pronounced. This review aims to provide practical insights and serve as a roadmap for future research in the realm of technical analysis within energy and financial markets. This review contributes to the ongoing discourse and advancement of knowledge in this crucial field by synthesizing existing perspectives and proposing avenues for further exploration.
... Mathematical optimisation, in particular linear programming (LP), has become one of the key methods in established and emerging energy system modelling tools used for planning the energy transition and assessing energy and climate policy options around the world [5,6,31,34]. Renewable energy technologies need to be represented with a high spatio-temporal resolution and scope, to capture and account for the effect of their intermittency on system stability, to ensure that weather variability can be captured across years [37], and to exploit the balancing effect of geographically distant weather systems [12]. ...
Background
Energy system models based on linear programming have been growing in size with the increasing need to model renewables with high spatial and temporal detail. Larger models lead to high computational requirements. Furthermore, seemingly small changes in a model can lead to drastic differences in runtime. Here, we investigate measures to address this issue.
Results
We review the mathematical structure of a typical energy system model, and discuss issues of sparsity, degeneracy and large numerical range. We introduce and test a method to automatically scale models to improve numerical range. We test this method as well as tweaks to model formulation and solver preferences, finding that adjustments can have a substantial impact on runtime. In particular, the barrier method without crossover can be very fast, but affects the structure of the resulting optimal solution.
Conclusions
We conclude with a range of recommendations for energy system modellers: first, on large and difficult models, manually select the barrier method or barrier+crossover method. Second, use appropriate units that minimize the model’s numerical range or apply an automatic scaling procedure like the one we introduce here to derive them automatically. Third, be wary of model formulations with cost-free technologies and dummy costs, as those can dramatically worsen the numerical properties of the model. Finally, as a last resort, know the basic solver tolerance settings for your chosen solver and adjust them if necessary.
Comparative analysis is crucial for evidence-based decision-making for sustainable energy transitions. Identifying the strengths and weaknesses of various approaches is necessary before application. This review article presents a comparative analysis of energy system modeling approaches applied in the literature to develop decarbonization pathways for the electricity sector. The six most widely used applications are analyzed: TIMES/MARKAL, PyPSA, EnergyRT, SWITCH, OSeMOSYS, LEAP, and TIMES. SWOT analysis evaluates the strengths, weaknesses, opportunities, and threats of these models when applied toward net zero electricity systems by midcentury. PyPSA and Switch models emerge as top choices in analyzing the decarbonized future electricity sector due to adaptability, stakeholder compatibility, and policy alignment. The study aids researchers and policymakers in selecting an appropriate modeling approach by considering the strengths and limitations of each modeling technique. Furthermore, it underscores the necessity of ongoing evaluation and refinement to meet the evolving needs of the energy sector effectively, especially with the development of a new generation of open-source non-proprietary models.
Here a plausible transition management model to accelerate the decarbonization of urban district heating systems is presented. Many cities and energy utilities have struggled in identifying sustainable, socially acceptable, and cost-efficient solutions to replace fossil fuels and unsustainable biofuels. A model was developed based on a case study made for the City of Helsinki in Finland to phase out coal and to reach carbon neutrality without additional bioenergy capacity by 2035. Methods included energy system modeling, environmental and economic assessment, and transition pathway co-creation. The main technical solutions included heat pump systems, demand response, energy storage, and strengthening electricity distribution networks. Achieving cost-efficient and socially acceptable local energy transition requires decentralizing heating solutions. The city can accelerate investments and promote third-party network access through heat auctions, open energy map data, and fast-track permits. Urban transitions need to be iteratively managed based on energy system modeling to secure sufficient heat supply, cost-efficiency, and rapid decarbonization simultaneously. Adoption of a new operational, business and market models is challenging but necessary. More research and development are needed on heat auctions and local energy transition management models, which enable coordinated investments by multiple actors to reach zero-emission district heating systems.
Energy system modelling for sustainable development has advanced significantly as a critical tool for designing cost-effective energy transitions. Modellers and analysts have used these tools to support international organizations and policymakers in crafting and making decisions about energy policy. Open-source frameworks have been instrumental in this progress, enhancing stakeholder engagement, transparency, and public acceptance. Among these, the Open-Source Energy Modelling System (OSeMOSYS) stands out as a key example, widely applied in energy transition and planning studies. As the body of OSeMOSYS literature rapidly expands, it is essential to track research advancements to guide both current and future modellers. This paper presents a systematic literature review, exploring the applications, developments, and research trends related to OSeMOSYS over the last 10 years. The findings highlight a significant growth in OSeMOSYS-based research, with an annual increase of approximately 28%, and most applications occurring in Africa and Latin America, though largely conducted by European institutions. Six key application areas were identified, such as capacity expansion planning in the power sector, transport sector planning, and sector coupling opportunities. Nine categories of complementary methods commonly integrated with OSeMOSYS were also categorized, including power sector performance, stakeholder engagement, and geospatial assessments. A thorough review of code enhancements demonstrates the framework’s adaptability to various fields, such as flexibility assessment, hydropower systems, and storage modelling. Furthermore, seven key future research directions were identified: operational feasibility, uncertainty evaluation, temporal and spatial resolutions, technological detail, storage modelling, and macroeconomic impacts.
National energy system models are vital to climate policy. However, they do not assess environmental impacts beyond territorial greenhouse gas (GHG) emissions. Here, we evaluate a territorial net zero GHG emissions energy scenario for Switzerland coupled with life-cycle assessment to quantify non-domestic environmental burdens. We stress the limited insights from considering territorial GHG emissions only. Indeed, significant GHG emissions persist outside of Switzerland by 2050 (~3-5 Mtons CO 2 -eq./year) because of imports and energy related infrastructure, even though domestic emissions are reduced to net zero. Global climate policies influence the extra-territorial GHG emissions Switzerland is responsible for. Additionally, we must broaden the spectrum of environmental indicators in the context of many countries’ ambitions to achieve net zero goals. Our findings highlight the trade-offs involved, showing how environmental impacts other than those on climate change (ecosystem impacts, air pollution, natural resource use) could increase and shift from Switzerland to the rest of the world as the country electrifies its economy.
We analyze how the energy transition responds to changes in critical mineral prices. We define energy transition as the increase in the share of renewable energy in total energy production. We construct a GVAR model with 10 economies from July 2012 to December 2021. We incorporate major producers of critical minerals, such as Australia for lithium, Indonesia for cobalt and nickel, and China for rare earth elements. We found that critical mineral price shocks harm the energy transition (spillover effect), mainly in major producer countries. Potential transmission channels include financial markets and industrial production. We break down the index of critical mineral prices into individual prices (nickel, lithium, cobalt, and rare earth prices) and find that shocks from these prices have a minor impact, except in countries that are major producers. These results are robust to alternative specifications such as time-varying bilateral trade and the periods before and after Covid-19. Regarding policymaking, we show that a reduction in credit costs accelerates the energy transition: governments can promote the energy transition by providing subsidized credit to companies and individuals.
With the energy transition underway, the wider penetration of multi-energy hubs (MEHs) is inevitable as they allow for the integration of multiple energy carriers at the local level and especially at the users' side by enhancing flexibility of energy supply and renewabIes penetration. Thus, addressing their design properly is imperative. While numerous energy modeling software and tools are extensively discussed in the literature, they primarily focus on a single energy carrier, mainly dedicated to the electrical network. In this study, an optimization planning tool capable of analyzing the synergies of various energy carriers is presented. The tool aims to determine the optimal configuration of MEHs with sizes of the chosen technologies based on the modeler's preferences. The tool offers flexibility in modelling a wide range of technologies as potential options for the optimal configuration, and in considering different types of objectives as economic and environmental ones that can be assessed through a multi-objective approach. It is formulated through mixed-integer linear programming in a modular manner, facilitating the easy implementation of new emerging technologies, by enhancing scalability and applicability in real context. To prove the effectiveness of the optimization tool, it is applied for the design of a MEH for a residential building cluster located in Torino (Italy). Different scenarios are analyzed to determine the impact of high levels of renewabIes penetration on the design of the MEH while guaranteeing the economic sustainability of the solution.
The combination of different energy vectors like electrical energy, hydrogen, methane, and water is a crucial aspect to deal with in integrated local energy communities (ILECs). The ILEC stands for a set of active energy users that maximise benefits and minimise costs using optimisation procedures in producing and sharing energy. In particular, the proper management of different energy vectors is fundamental for achieving the best operating conditions of ILECs in terms of both energy and economic perspectives. To this end, different solutions have been developed, including advanced control and monitoring systems, distributed energy resources, and storage. Energy management planning software plays a pivotal role in developing ILECs in terms of performance evaluation and optimisation within a multi-carrier concept. In this paper, the state-of-the-art of ILECs is further enhanced by providing important details on the critical aspects related to the overall value chain for constituting an ILEC (e.g., conceptualisation, connecting technologies, barriers/limitations, control, and monitoring systems, and modelling tools for planning phases). By providing a clear understanding of the technical solutions and energy planning software, this paper can support the energy system transition towards cleaner systems by identifying the most suitable solutions and fostering the advancement of ILECs.
To reach the Paris Agreement goals, European governments have defined national contributions to the EU binding climate goals and have developed national climate and energy plans for 2021–2030 (NECPs). NECPs are detailed strategies in which governments can flexibly emphasize specific sectors, technologies and national energy policy choices. Every country has built its own energy modelling capacity for forecasting purposes. In most countries, this national modelling relies on optimization tools such as Markal or TIMES. For Latvia's energy sector, a system dynamics (SD) model was built to complement the TIMES model. The SD model deals with an integrated energy system, including the primary energy supply and transformation sector; energy distribution and storage system; and energy demand sectors (residential, tertiary, public, industry and transportation). Sectoral policies are presented in the Stella Architect interface. This study demonstrates how the interactive simulation tool was used in the Environmental Engineering Master level course ‘Environmental Policy and Economics’ as an experiential learning approach. The student assignment involved applying this tool to develop and analyse the energy transition policy package for one of four ideological interest groups: deep greens, bright greens, light greens and greys. Students had to assume a role and adopt its perspective by applying their border‐crossing competence and reflecting on selected policy mixes for that ideology. Various transition pathways, based on different ideologies, illustrate the possibilities for application. Results show that students were successful in fulfilling the assignment.
This paper explores the critical engineering challenges and innovative solutions for successfully integrating intelligent grids and electric vehicles (EVs), emphasizing the increasing need for a resilient and adaptive electrical grid as global EV adoption accelerates. It comprehensively examines the technological, infrastructural, and regulatory obstacles that must be addressed to ensure seamless integration, focusing on advanced energy management systems, grid stability amidst fluctuating demand, and incorporating renewable energy sources. The study delves into the infrastructural requirements, including the expansion of charging networks, upgrades to transmission and distribution systems, and the implementation of vehicle-to-grid (V2G) technologies, while also analyzing the necessary regulatory and policy frameworks, stressing the importance of clear standards, incentives, and public-private collaboration. The paper offers a forward-looking perspective on overcoming current challenges by reviewing recent advancements in innovative grid technology—such as high-capacity energy storage and artificial intelligence (AI) use for predictive maintenance and load balancing. It highlights the need for interdisciplinary collaboration among engineers, policymakers, and industry leaders to develop a cohesive strategy for future energy distribution while underscoring the role of AI in optimizing grid performance, predicting energy consumption patterns, and enhancing overall efficiency. Ultimately, the paper provides a comprehensive analysis of the current state of smart grid and EV integration, offering actionable insights for stakeholders and concluding with recommendations for future research and development priorities, with a strong emphasis on continued innovation and cooperation to achieve a sustainable and resilient energy future.
Recent rapid and unexpected cost reductions in decarbonization technologies have accelerated the cost-effective decarbonization of the US economy, with greenhouse gas (GHG) emissions falling by 20% from 2005 to 2020. The literature on US economy-wide decarbonization focuses on maximizing long-term GHG emissions reduction strategies that rely mostly on renewable energy expansion, electrification, and efficiency improvements to achieve net-zero GHG emissions by 2050. While these studies provide a valuable foundation, further research is needed to properly support decarbonization policy development and implementation. In this review, we identify key decarbonization analysis gaps and opportunities, including issues related to cross-sectoral linkages, spatial and temporal granularity, consumer behavior, emerging technologies, equity and environmental justice, and political economy. We conclude by discussing the implications of these analysis gaps for US decarbonization pathways and how they relate to challenges facing major global emitters.
Cameroon possesses a significant endowment of solar energy, granting it exceptional potential for the generation of hydrogen through environmentally friendly means. However, the continued expansion of the nation's petroleum industry presents an obstacle to the domestic utilization of green hydrogen due to its present costliness for energy purposes. Nonetheless, the prospect of exporting green hydrogen to developed nations remains an intriguing proposition. Indeed, a pact concerning hydrogen was established between Australia and Cameroon in the year 2021, thus opening avenues for the export of green hydrogen to facilitate the decarbonization of national energy supplies in Australia and other industrialized nations. Presently, there are no documented large-scale projects within Cameroon dedicated to the electrolytic production of hydrogen. This study projects the potential hydrogen demand in the electricity and transportation sectors up to 2040. Electricity demand is expected to be as high as 8675 GWh in 2040, while gasoline and diesel demand are expected to reach 1.75 and 3.26 million cubic meters, respectively. Therefore, the total amount of hydrogen needed to power both the electricity and transportation sectors is estimated at 0.532 megatonnes. Even a relatively modest allocation, merely 5 %, of Cameroon's land for the production of hydrogen via solar-powered electricity generation could yield a surplus. This resultant quantity of hydrogen, estimated at a substantial 16.68 megatonnes, would likely be more than enough to satisfy the projected domestic needs for both electrical and transportation uses by the year 2040.
Renewable energy is widely used in combined cooling, heating and power (CCHP) systems. This is important for building a low-carbon, flexible, multi-energy complementary energy system. However, coupling different renewable energy sources can have a somewhat differentiated impact on the performance of the system. In this study, an approach combining a long short-term memory (LSTM) network with multiple optimization algorithms is proposed. Comparative performance analysis of CCHP systems coupling solar and wind subsystems is conducted. Firstly, the renewable energy output is predicted by LSTM. Then, the Pareto frontiers of the coupled renewable energy CCHP system are generated by the Non-dominated Genetic Sorting Algorithm. The results are fed into the distance between superior and inferior solution methods to arrive at a decision, completing the multi-objective optimization of the system. Results show that the CCHP system coupling photovoltaic (PV) and solar collector (ST) is superior to the CCHP system coupling photovoltaic-photovoltaic-thermal integrated device. The system performance can be further improved by adding wind turbines to the integrated system coupling PV and ST.
Achieving high performance in energy systems is crucial for sustainability. Energy economy optimization (EEO) models offer transparent analysis for energy policy decision-making. However, evaluating and benchmarking these models is a complex multicriteria decision making (MCDM) problem. Challenges include multiple criteria, data variation, and the importance of diverse criteria. This study develops an integrated MCDM approach to evaluate and benchmark EEO models. The methodology involves three phases. First, 12 commonly used EEO models and five evaluation criteria (software licenses, public source code, redistribution, public source data, and commercial software) are identified to create an evaluation decision matrix. Second, the fuzzy-weighted zero-consistency method (FWZIC) is used to evaluate and assign weights to the criteria. These weights are utilized in the benchmarking phase. Third, individual and group fuzzy decision by opinion score method (FDOSM) techniques are integrated to benchmark the EEO models based on the weights acquired. The FWZIC weighting reveals that the public source code criterion has the highest weight (0.3347), while redistribution has the lowest weight (0.1021). The group FDOSM results show that the OSeMOSYS model ranks first with the highest score (0.1595), while the DNE21+, MARIA, and MESSAGE models have the lowest score (0.0646), ranking them last. Systematic ranking, sensitivity ranking, and comparative analysis verify the proposed evaluation and benchmarking framework.
About 75% of the world's energy consumption takes place in cities. Although their large energy consumption attracts a large number of research projects, only a small fraction of them deal with approaches to model energy systems of city districts. These are particularly complex due to the existence of multiple energy sectors (multi-energy systems, MES), different consumption sectors (mixed-use), and different stakeholders who have many different interests.
This contribution is a review of the characteristics of energy system models and existing modeling tools. It evaluates current studies and identifies typical characteristics of models designed to optimize MES in mixed-use districts. These models operate at a temporal resolution of at least 1 h, follow either bottom-up or hybrid analytical approaches and make use of mixed-integer programming, linear or dynamic.
These characteristics were then used to analyze minimum requirements for existing modeling tools. Thirteen of 145 tools included in the study turned out to be suitable for optimizing MES in mixed-use districts. Other tools where either created for other fields of application (12), do not include any methodology of optimization (39), are not suitable to cover city districts as a geographical domain (44), do not include enough energy or demand sectors (20), or operate at a too coarse temporal resolution (17). If additional requirements are imposed, e.g. the applicability of non-financial assessment criteria and open source availability, only two tools remain.
Overall it can be stated that there are very few modeling tools suitable for the optimization of MES in mixed-use districts.
We reviewed the literature focusing on nineteen integrated Energy System Models (ESMs) to: (i) identify the capabilities and shortcomings of current ESMs to analyze adequately the transition towards a low-carbon energy system; (ii) assess the performance of the selected models by means of the derived criteria, and (iii) discuss some potential solutions to address the ESM gaps.
This paper delivers three main outcomes. First, we identify key criteria for analyzing current ESMs and we describe seven current and future low-carbon energy system modeling challenges: the increasing need for flexibility, further electrification, emergence of new technologies, technological learning and efficiency improvements, decentralization, macroeconomic interactions, and the role of social behavior in the energy system transition. These criteria are then translated into required modeling capabilities such as the need for hourly temporal resolution, sectoral coupling technologies (e.g., P2X), technological learning, flexibility technologies, stakeholder behavior, cross border trade, and linking with macroeconomic models. Second, a Multi-Criteria Analysis (MCA) is used as a framework to identify modeling gaps while clarifying high modeling capabilities of MARKAL, TIMES, REMix, PRIMES, and METIS. Third, to bridge major energy modeling gaps, two conceptual modeling suites are suggested, based on both optimization and simulation methodologies, in which the integrated ESM is hard-linked with a regional model and an energy market model and soft-linked with a macroeconomic model.
This paper reviews different approaches to modelling the energy transition towards a zero carbon economy. It identifies a number of limitations in current approaches such as a lack of consideration of out-of-equilibrium situations (like an energy transition) and non-linear feedbacks. To tackle those issues, the new open source integrated assessment model pymedeas is introduced, which allows the exploration of the design and planning of appropriate strategies and policies for decarbonizing the energy sector at World and EU level. The main novelty of the new open-source model is that it addresses the energy transition by considering biophysical limits, availability of raw materials, and climate change impacts. This paper showcases the model capabilities through several simulation experiments to explore alternative pathways for the renewable transition. In the selected scenarios of this work, future shortage of fossil fuels is found to be the most influential factor of the simulations system evolution. Changes in efficiency and climate change damages are also important determinants influencing model outcomes.
Energy systems are becoming increasingly complex as developments such as sector coupling and decentral electricity generation increase their interconnectedness. At the same time, energy system models that are implemented to depict and predict energy systems are limited in their complexity due to computational constraints. Thus, a trade-off has to be made between high degrees of detail and model runtimes. As a first step towards efficiently managing the complexity of energy system models, we examine the relationship between the purpose of models and their complexity. Using fact sheets on 145 models, we manually cluster these models based on their purpose and underlying research questions. Further, we conduct mathematical clustering using several clustering methods to investigate the reproducibility of our results. For our study, we define the complexity of a model as the level of detail in which it represents reality. We distinguish the level of detail into the four dimensions of temporal, spatial, mathematical and modeling content complexity. The differences between the clusters found in these dimensions are verified statistically using confidence intervals. 112 out of 145 models can be allocated to one out of four major clusters possessing clearly distinguishable complexity profiles: unit commitment, electrical grids, policy assessment, and future energy systems. In each of these profiles, high complexity in one dimension or subdimension is compensated by low complexities in other dimensions. We therefore conclude that when creating a model, modelers allocate complexity in order of priority on those features and properties that are particularly important for fulfilling the model's purpose. Our results provide a necessary basis for the emerging field of complexity management in energy system modeling and are therefore of high interest for the scientific community and the interpreters of model results such as decision makers from policy and industry.
This paper reviews the classification schemes used for bottom-up energy system modelling and proposes a novel one as re-elaboration of the previous schemes. Moreover, this paper identifies that the main challenges of this research field rotate around the concept of resolution. A matrix of challenges in which four main fields are identified: resolution in time, in space, in techno-economic detail and in sector-coupling. These main fields are divided into different levels of resolution: low, medium and high. The use of a low resolution introduces errors in the modelling as demonstrated by different studies. Several existing bottom-up energy system models are reviewed in order to classify them according to the proposed approach and map them through the proposed matrix. 13 different models are analyzed in the category of bottom-up short-term and 9 as bottom-up long-term energy system models. The following mapping shows how several models reach a high level of resolution in one or more than one area. However, the ultimate challenge is the simultaneous achievement of high resolution in all these fields. The literature review has shown how this final aim is not reached by any model at the current stage and it highlights the gap and weaknesses of this branch of research and the direction versus which is important to work to improve this type of modelling.
The transition towards renewable energy will operate on different geographical scales. Many of the concrete steps will address the local level; however, these have to align with the broader energy perspective. It is therefore necessary to develop methods that enable cities to assess the compatibility of the local renewable energy strategy to the surrounding national and global energy systems. This paper presents a methodology to design Smart Energy Cities within the context of 100% renewable energy at a national level. Cities and municipalities should act locally with regard to local demands but acknowledge the national and global context when addressing resources, industry and transport. The method is applied to the case of transitioning the municipality of Aalborg to a 100% renewable smart energy system within the context of a Danish and European energy system. The case demonstrates how it is possible to transition to a Smart Energy City that fits within a 100% renewable energy context of Denmark and Europe. The suggested methodology is framed in a way that makes it applicable to other cases globally.
Research attention on decentralized autonomous energy systems has increased exponentially in the past three decades, as demonstrated by the absolute number of publications and the share of these studies in the corpus of energy system modelling literature. This paper shows the status quo and future modelling needs for research on local autonomous energy systems. A total of 359 studies are investigated, of which a subset of 123 in detail. The studies are assessed with respect to the characteristics of their methodology and applications, in order to derive common trends and insights. Most case studies apply to middle-income countries and only focus on the supply of electricity in the residential sector. Furthermore, many of the studies are comparable regarding objectives and applied methods. Local energy autonomy is associated with high costs, leading to levelized costs of electricity of 0.41 $/kWh on average. By analysing the studies, many improvements for future studies could be identified: the studies lack an analysis of the impact of autonomous energy systems on surrounding energy systems. In addition, the robust design of autonomous energy systems requires higher time resolutions and extreme conditions. Future research should also develop methodologies to consider local stakeholders and their preferences for energy systems.
This article considers academic energy modelling as a scientific practice. While models and modelling have been of considerable interest in energy social science research, few studies have brought together approaches from philosophy of science and anthropology to examine energy models both conceptually and in the applied sense. We develop a conceptual approach on epistemological ethics that distinguishes between epistemic values - such as accuracy, simplicity, and adequate representation - and non-epistemic values - such as policy relevance, methodological limitations, and learning - built into energy models. The research question is: how do modellers articulate and negotiate epistemic values and what does this imply for the status of models in scientific practice and policymaking? The empirical part of the article draws from ethnographic fieldwork and interviews amongst 40 energy modellers in university research groups in the UK from two complementary arenas: scholars preparing their PhD in modelling and scholars working in a large-scale energy modelling project. Our research uses ethnographic methods to complement themes recognised in earlier literatures on modelling, demonstrating what models and modellers know about the energy system and how they come to know it in particular ways.
A diversity of integrated assessment models (IAMs) coexists due to the different approaches developed to deal with the complex interactions, high uncertainties and knowledge gaps within the environment and human...
Global warming, air pollution, and energy insecurity are three of the greatest problems facing humanity. To address these problems, we develop Green New Deal energy roadmaps for 143 countries. The roadmaps call for a 100% transition of all-purpose business-as-usual (BAU) energy to wind-water-solar (WWS) energy, efficiency, and storage by 2050 with at least 80% by 2030. Our studies on grid stability find that the countries, grouped into 24 regions, can match demand exactly from 2050 to 2052 with 100% WWS supply and storage. We also derive new cost metrics. Worldwide, WWS energy reduces end-use energy by 57.1%, aggregate private energy costs from 6.8 trillion/year (61%), and aggregate social (private plus health plus climate) costs from 6.8 trillion/year (91%) at a present value capital cost of ∼$73 trillion. WWS energy creates 28.6 million more long-term, full-time jobs than BAU energy and needs only ∼0.17% and ∼0.48% of land for new footprint and spacing, respectively. Thus, WWS requires less energy, costs less, and creates more jobs than does BAU.
Whether and how long-term energy and climate targets can be reached depend on a range of interlinked factors: technology, economy, environment, policy, and society at large. Integrated assessment models of climate change or energy-system models have limited representations of societal transformations, such as behavior of various actors, transformation dynamics in time, and heterogeneity across and within societies. After reviewing the state of the art, we propose a research agenda to guide experiments to integrate more insights from social sciences into models: (1) map and assess societal assumptions in existing models, (2) conduct empirical research on generalizable and quantifiable patterns to be integrated into models, and (3) build and extensively validate modified or new models. Our proposed agenda offers three benefits: interdisciplinary learning between modelers and social scientists, improved models with a more complete representation of multifaceted reality, and identification of new and more effective solutions to energy and climate challenges.
In this thesis an iterative model concept is developed, which optimises the capacity additions and operation of future power plants and storage facilities in systems with an increasing share of fluctuating generation. The task is divided into sub-models; one for the extension of power plants and one for storage additions. Both are realised as linear optimisation problems. An additional model evaluates the reliability of the power system. In an iterative loop, the power plant dispatch influenced by the storage operation and the reliability of the changed system is fed back to the extension models. This novel methodology enables a gradual adaption of the power plant and storage extensions. The minimum capacity of power plants is determined by the required reliability level of the generation system.
The transition towards more sustainable, fossil-free energy systems is interlinked with a high penetration of stochastic renewables, such as wind and solar. Integrating these new energy resources and technologies will lead to profound structural changes in energy systems, such as an increasing need for storage and a radical electrification of the heating and mobility sectors. To capture the increasing complexity of such future energy systems, new flexible and open-source optimization modelling tools are needed.
This paper presents EnergyScope TD, a novel open-source model for the strategic energy planning of urban and regional energy systems. Compared to other existing energy models, which are often proprietary, computationally expensive and mostly focused on the electricity sector, EnergyScope TD optimises both the investment and operating strategy of an entire energy system (including electricity, heating and mobility). Additionally, its hourly resolution (using typical days) makes the model suitable for the integration of intermittent renewables, and its concise mathematical formulation and computational efficiency are appropriate for uncertainty applications. We present the linear programming model, detailing its formulation, and we apply it to a real-world case study to discuss advantages and disadvantages in comparison to other modelling frameworks. In particular, we model the national energy system of Switzerland to evaluate a 50% renewable energy scenario.
This paper examines the major challenges associated with evaluating energy demand in the residential building sector in an integrated energy system modelling environment. Three established modelling fields are examined to generate a framework for assessing the impact of energy policy: energy system models, building stock models and dynamic building simulation. A set of profound challenges emerge when attempting to integrate such models, due to distinct differences in their intended applications, operational scales, formulations and computational implementations.
Detailed discussions are provided on the integration of temporally refined energy demand, based on thermodynamic processes and socio-technical effects which may stem from new policy. A detailed framework is discussed for generating aggregate residential demands, in terms of space heating demand, domestic hot water demand, and lighting, appliance and consumer electronics demand. The framework incorporates a pathway for interpreting the effects of changes in household behaviour resulting from prospective policy measures. When long-term planning exercises are carried out using this framework, the cyclic effects between behavioural change and policy implementation are also considered. This work focused specifically on the United Kingdom energy system, however parallels can be drawn with other countries, in particular those with a mature privatised system, dominated by space heating concerns.
The current transition towards a low-carbon energy system requires an increasingly complex energy system framework. This is accompanied by the demand for high result reproducibility in order to provide transparency to decision-makers in terms of assumptions and methodological issues. Given this background, Open Access Models (OAMs) are increasingly entering the market that already have a high degree of diversity. This study analyses and compares the methodological framework of different OAMs to assess long-term energy scenarios. In a first step, selected OAMs are typified and characterised based on predefined criteria. In general, the analysis reveals that OAMs with a high level of accessibility appear to have a rather low level of complexity and often focus on the analysis of a single target year. In a second step, we underline our findings of the model overview by applying a well-established OAM (DESSTinEE-Demand for Energy Services, Supply and Transmission in EuropE) to a current energy scenario for Germany. Overall, we conclude that current OAMs can already be applied to a large variety of research questions. However, comparing OAMs to conventional models applied in the field of energy system analysis reveals that there is still a significant performance gap in terms of the degree of methodological sophistication.
Historically, energy system tools were predominantly proprietary and not shared with others. In recent years, there has been an increase in developing open source tools by international research and development organizations. More than half of the open energy modeling (openmod) initiative listed tools are based on the freely available scripting language Python. Previous comparisons of energy and power system modeling tools focused on comparisons such as which tool category (e.g. optimization, simulation) or energy demand (e.g. electricity, cooling, and heating) can be considered. Until now, the assessment of incorporated functions such as unit commitment (UC) or optimum power flow (OPF) has been ignored. Therefore, this work assesses 31 mostly open source tools based on 81 functions for their maturity. The result shows that available open source tools such as Switch, TEMOA, OSeMOSYS, and pyPSA are mature enough based on a function comparison with commercial or proprietary tools for serious use. Nevertheless, future commercial, as well as open source energy system analysis tools, have to consider more functions such as the impact of ambient air conditions and part-load behavior to allow better assessments of including high shares or renewable energy sources and other flexibility measures in existing and new energy systems.
The development of sustainable system strategies at the municipal level is a challenging task since various factors like the business portfolio, the technological progress, the actor base, the regulatory framework and the market status might influence the results of an assessment. Given the complexity, system interdependencies between different alternatives need to be considered. One possibility to support decision makers is to apply Energy System Optimization Models (ESOMs). This paper reviews selected ESOMs with a high level of modeling detail and thus with high spatial, temporal and contextual resolutions that can be applied to support the decision making process at municipalities. The main objective is to identify modeling approaches and future challenges to design such systems using several modeling frameworks. First, necessary system elements and interrelations are elaborated based on existing municipal system descriptions. The requirements are included in the derived definition of Integrated Multi-Modal Energy System (IMMES). Second, selected ESOMs are analyzed in terms of the requirements of the system definition. In doing so, existing fundamental approaches are demonstrated. Third, challenges for new mathematical approaches are provided. This review shows that a few mod-eling approaches are quite prevalent. Future design of models can be directly based on these practices. Additionally, most of the reviewed ESOMs only fulfill certain requirements of IMMES with a more complex approach. Concluding, future ESOMs need to address six research challenges: integrated view, business mod-eling, spatial planning, complexity level, temporal resolution and uncertainty analysis.
Quantitative modelling analysis in support of national and global decarbonisation pathways has never been more important to achieving global climate stabilisation in line with the Paris Agreement. However, established equilibrium and optimisation models tend to radically simplify their depiction of societal and political/institutional actors. This can make them difficult to use for implementing specific energy and climate policies in the near term and aligning these with long term targets. Most energy systems analysis continues to pair such techno-economic models with entirely qualitative narratives about future political and societal developments. The result is that these critical factors often fade into the background in subsequent discourse. In this paper, we utilise BLUE – a leading socio-technical energy transition (STET) model of the United Kingdom’s (UK) energy system – to capture elements of the heterogeneity, consistency and co-evolution of societal and political drivers. We focus specifically on exploring government-led and societally-led energy transitions and investigating the differences in their decarbonisation pathways and end states. Our modelling exercise finds that it is not who leads per se that is the most critical, but rather the level of the initial effort and subsequent commitment from both leader and follower actors that appears to regulate the pace at which decarbonisation pathways unfold. However, systemic inertia in all cases means that the deepest decarbonisation targets continue to appear very difficult to achieve.
In this study, we model seven scenarios for the European power system in 2050 based on 100% renewable energy sources, assuming different levels of future demand and technology availability, and compare them with a scenario which includes low-carbon non-renewable technologies. We find that a 100% renewable European power system could operate with the same level of system adequacy as today when relying on European resources alone, even in the most challenging weather year observed in the period from 1979 to 2015. However, based on our scenario results, realising such a system by 2050 would require: (i) a 90% increase in generation capacity to at least 1.9 TW (compared with 1 TW installed today), (ii) reliable cross-border transmission capacity at least 140 GW higher than current levels (60 GW), (iii) the well-managed integration of heat pumps and electric vehicles into the power system to reduce demand peaks and biogas requirements, (iv) the implementation of energy efficiency measures to avoid even larger increases in required biomass demand, generation and transmission capacity, (v) wind deployment levels of 7.5 GW y⁻¹ (currently 10.6 GW y⁻¹) to be maintained, while solar photovoltaic deployment to increase to at least 15 GW y⁻¹ (currently 10.5 GW y⁻¹), (vi) large-scale mobilisation of Europe's biomass resources, with power sector biomass consumption reaching at least 8.5 EJ in the most challenging year (compared with 1.9 EJ today), and (vii) increasing solid biomass and biogas capacity deployment to at least 4 GW y⁻¹ and 6 GW y⁻¹ respectively. We find that even when wind and solar photovoltaic capacity is installed in optimum locations, the total cost of a 100% renewable power system (∼530 €bn y⁻¹) would be approximately 30% higher than a power system which includes other low-carbon technologies such as nuclear, or carbon capture and storage (∼410 €bn y⁻¹). Furthermore, a 100% renewable system may not deliver the level of emission reductions necessary to achieve Europe's climate goals by 2050, as negative emissions from biomass with carbon capture and storage may still be required to offset an increase in indirect emissions, or to realise more ambitious decarbonisation pathways.
Giving policy advice related to climate mitigation requires insights that take both sectoral and technology effects (and their interactions) into account. This paper develops a novel soft-linking method for bridging the gap between sectoral top-down and technology rich bottom-up models. A unique feature of the approach is the explicit modelling of energy service demand in the top-down model, which creates a direct correspondence to the energy service production in the bottom-up model. This correspondence allows us, unlike previous work, to capture the macroeconomic impact of energy system investment flows. The paper illustrates the full-scale application of the method in the Danish IntERACT model, considering the unilateral introduction of coal carbon capture and storage in the Danish concrete sector. The policy leads to a reduction in the Danish concrete production, and in turn, a carbon leakage effect of 88%. Results also underscores the importance of accounting for the macroeconomic impact of energy system investment flows, as this is the source of approximately half of the policy-induced reduction in macroeconomic activity.
Climate change is one of the most serious threats to the human habitat. The required structural change to limit anthropogenic forcing is expected to fundamentally change daily social and economic life. The production of iron and steel is a special case of economic activities since it is not only associated with combustion but particularly with process emissions of greenhouse gases which have to be dealt with likewise. Traditional mitigation options of the sector like efficiency measures, substitution with less emission-intensive materials, or scrap-based production are bounded and thus insufficient for rapid decarbonization necessary for complying with long-term climate policy targets. Iron and steel products are basic materials at the core of modern socio-economic systems, additionally being essential also for other mitigation options like hydro and wind power. Therefore, a system-wide assessment of recent technological developments enabling almost complete decarbonization of the sector is substantially relevant. Deploying a recursive-dynamic multi-region multi-sector computable general equilibrium approach, we investigate switches from coke-to hydrogen-based iron and steel technologies in a scenario framework where industry decisions (technological choice and timing) and climate policies are misaligned. Overall, we find that the costs of industry transition are moderate, but still ones that may represent a barrier for implementation because the generation deciding on low-carbon technologies and bearing (macro)economic costs might not be the generation benefitting from it. Our macroeconomic assessment further indicates that anticipated bottom-up estimates of required additional domestic renewable electricity tend to be overestimated. Relative price changes in the economy induce electricity substitution effects and trigger increased electricity imports. Sectoral carbon leakage is an imminent risk and calls for aligned course of action of private and public actors.
Power-to-Methane (PtM) can provide flexibility to the electricity grid while aiding decarbonization of other sectors. This study focuses specifically on the methanation component of PtM in 2050. Scenarios with 80–95% CO2 reduction by 2050 (vs. 1990) are analyzed and barriers and drivers for methanation are identified. PtM arises for scenarios with 95% CO2 reduction, no CO2 underground storage and low CAPEX (75 €/kW only for methanation). Capacity deployed across EU is 40 GW (8% of gas demand) for these conditions, which increases to 122 GW when liquefied methane gas (LMG) is used for marine transport. The simultaneous occurrence of all positive drivers for PtM, which include limited biomass potential, low Power-to-Liquid performance, use of PtM waste heat, among others, can increase this capacity to 546 GW (75% of gas demand). Gas demand is reduced to between 3.8 and 14 EJ (compared to ∼20 EJ for 2015) with lower values corresponding to scenarios that are more restricted. Annual costs for PtM are between 2.5 and 10 bln€/year with EU28’s GDP being 15.3 trillion €/year (2017). Results indicate that direct subsidy of the technology is more effective and specific than taxing the fossil alternative (natural gas) if the objective is to promote the technology. Studies with higher spatial resolution should be done to identify specific local conditions that could make PtM more attractive compared to an EU scale.
Rural electricity plans are usually designed by relying on top-down rough and aggregated estimations of the electricity demand, which may fail to capture the real dynamics of local contexts. This study aims at soft-linking a bottom-up approach for short- and long-term forecasts of load profiles with an energy optimisation model in a more comprehensive rural energy planning procedure. The procedure is applied to a small Indian community, and it is based on three blocks: (i) a bottom-up model to project households’ electrical appliances, which adopts socio-economic indicators to make long-term projections; (ii) a stochastic load profile generator, which employs correlations and users’ habits for assessing the coincidence and load factors; (ii) an energy optimisation model based on OSeMOSYS to find the economic optimum. The simulations show that demand models based on socio-economic indicators lead to more structured and less arbitrary scenarios. The soft-link with the energy optimisation model confirms that when accounting for short- and long-term variabilities of electricity demand together, the optimal capacities and costs can vary up to 144% and 50% respectively. Integrating optimisation tools to bottom-up models based on socio-economic indicators for forecasting electricity demand is therefore pivotal to set more reliable investments plans in rural electrification.
Designing highly renewable power systems involves a number of contested decisions, such as where to locate generation and transmission capacity. Yet, it is common to use a single result from a cost-minimizing energy system model to inform planning. This neglects many more alternative results, which might, for example, avoid problematic concentrations of technology capacity in any one region. To explore such alternatives, we develop a method to generate spatially explicit, practically optimal results (SPORES). Applying SPORES to Italy, we find that only photovoltaic and storage technologies are vital components for decarbonizing the power system by 2050; other decisions, such as locating wind power, allow flexibility of choice. Most alternative configurations are insensitive to cost and demand uncertainty, while dealing with adverse weather requires excess renewable generation and storage capacities. For policymakers, the approach can provide spatially detailed power system transformation options that enable decisions that are socially and politically acceptable.
A grid-connected set of distributed energy resources that supply power for electric vehicle charging and hydrogen production is investigated through detailed simulation studies. This work uses a genetic algorithm to find the minimum cost for a set of distributed energy resources, the component sizes and energy management strategy are optimized simultaneously. It was found that the optimal component sizes and optimal energy management strategy have a significant influence on each other, and therefore, simultaneous optimization of the two aspects is suggested for such distributed energy resources. The presented approach yields higher time resolution in the simulation compared to previous work. Hence, the model can capture short term changes in dynamic loads and generation, making the simulated energy-management performance more representative of real conditions. The stochastically varying load from electric vehicle charging is modeled based on probabilistic data from existing charging infrastructure. With the present model, the performance of several energy management strategies can be examined. The simulation results show that using the battery storage for peak shaving minimizes the distributed energy resources overall cost while simultaneously decreasing its dependence on the utility grid. Moreover, the results of this study suggest that local energy generation with photovoltaic arrays, in combination with local energy storage and connection to the utility grid, is a viable option.
Energy system models can contribute to the transition to low-carbon energy systems by helping devise pathways and calculate costs of energy policies and targets. As such, they are intended to provide support for energy planning, especially at the national level. Since energy system models are often developed outside urban contexts, their municipal policy relevance cannot be taken for granted. It is still unclear to what extent and how energy system models are applied to create municipal energy strategies and who uses them. This exploratory study aims to shed light on these aspects by examining the use of modelling tools and their outputs by municipal planners. We conducted interviews with practitioners from Danish municipalities and evaluated them using qualitative content analysis. This paper finds that the interviewees' use of software tools depends on how they perceive the functionality and complicatedness of the tools. The planners we interviewed prefer spreadsheet-based CO2 calculation or evaluation tools to energy system models, which in turn are used by heat supply companies, consultancies or universities. The practitioners we interviewed collaborate with model developers and users to further utilize model outputs. The incorporation of results from models or spreadsheet tools takes place mostly in the beginning of energy planning projects. This study suggests that models and the modelling practice can be improved with: open data, assumptions and models, collaboration across planning levels and improving links between technical modelling and practical implementation.
In many countries around the world district heating can play an important role in decarbonisation since it provides an efficient way of displacing fossil fuels and integrating renewable energy. Simultaneously, in some countries heating is based on the burning of biomass in individual stoves, which can be considered renewable but results in both inefficient heating and high contamination. In such countries, air pollution or decontamination is a more urgent problem. This paper presents the application of a methodology to analyse how district heating could be used as an important technology for coordinated decontamination and decarbonisation purposes looking towards 2050, based on an energy system analysis using hourly simulations, and using data based on spatial analysis to be able to explicitly include a Chile-specific cost for district heating. The results show that district heating also has the potential to be an important infrastructure to reduce air pollution from biomass combustion for heating, in addition to its better understood role of enabling decarbonisation and energy efficiency.
Energy transitions change the relationships between technologies and human actors. Demand response (DR), the matching of demand to available electricity supply, is a relatively new activity, important for systems that rely on distributed renewable generation. Price-based DR is spreading among residential and small business customers, along with direct control of distributed and aggregated small loads, mostly thermal. In both types of DR, information and communication technology plays a part.
This paper contributes to the debates on implementing electricity system transition and on adoption of smart technologies. Through analysis of a large-scale demonstration of highly-distributed smart thermal storage in three European countries, using mixed methods, it supports the claim that DR can usefully be seen as an outcome of interactions between technologies, activities and service expectations. Adopting a broad scope of enquiry to take in customer experiences of DR and the contributions from a range of actors, the paper shows how it was possible to achieve useful levels of aggregated DR in some circumstances. The quality and quantity of DR were influenced by customer experiences of taking part in the demonstration, which in turn were affected by three types of communication: connectivity, control and care. Outcomes also depended on contributions from a range of actors, especially the 'middle actors' who had some direct contact with both customers and programme leaders.
Mainstream theory of DR has concentrated on deploying technologies, controls and price signals. The paper demonstrates how, in practice, effectiveness relates to social and organisational as well as technical and economic features of energy systems. It concludes with some implications for design and implementation of DR programmes, and for smart energy policy in general.
The relevance of sector coupling is increasing when shifting from the current highly centralised and mainly fossil fuel-based energy system to a more decentralized and renewable energy system. Cross-sectoral linkages are already recognized as a cost-effective decarbonisation strategy that provides significant flexibility to the system. Modelling such cross-sectoral interconnections is thus highly relevant. In this work, these interactions are considered in a long-term perspective by uni-directional soft-linking of two models: JRC-EU-TIMES, a long term planning multisectoral model, and Dispa-SET, a unit commitment and optimal dispatch model covering multiple energy sectors such as power, heating & cooling, transportation etc. The impact of sector coupling in future Europe-wide energy systems with high shares of renewables is evaluated through five scenarios. Results show that the contributions of individual sectors are quite diverse. The transport sector provides the highest flexibility potential in terms of power curtailment, load shedding, congestion in the interconnection lines and resulting greenhouse gas emissions reduction. Nevertheless, allowing combinations of multiple flexibility options such as hydro for the long-term, electric vehicles and flexible thermal units for the short-term provides the best solution in terms of system adequacy, greenhouse gas emissions and operational costs.
In the last decade, a new strand of energy and climate research emerged that links quantitative models and socio-technical transitions theories or frameworks. Linking the two enables capturing the co-evolution of society, technology, the economy and the environment. We systematically review this literature (N = 44) and describe the papers' trends, scope, temporal and spatial foci, and methodological strategies. The reviewed literature aspires to find solutions to the energy and climate challenges, to increase realism in models and theories, and to enable interdisciplinary learning between the two scholarly communities. The outcomes in this literature show benefits of interdisciplinary learning between modellers and transitions theorists. However, the literature rarely identified practical insights for energy and climate solutions or for improving realism in models and theories. We conclude by suggesting that integrative research should be continued, but redirected to provide more practical outcomes to meet energy and climate targets.
A framework to account for social acceptance in the modelling of energy-transition pathways is outlined. The geographical focus is on the Nordic-Baltic energy region and the technological focus is on onshore wind power and power transmission, which are considered key technologies in achieving carbon-neutral energy systems in northern Europe. We combine qualitative analysis of social acceptance with quantitative assessments of scenarios using techno-economic energy-system modelling. Key factors in and consequences of social acceptance are identified, especially environmental, health, and distributional factors, as well as costs for developers and society. The energy system analysis includes four scenarios illustrating the system effects and costs of low social acceptance. The results indicate that if low social acceptance were to restrict investments in onshore wind power, costlier solar photovoltaics and offshore wind power would step in. Greater social acceptance cost for onshore wind and transmission lines favours local solutions and a more balanced renewable energy mix. There are important distributional effects: no restrictions on transmission line investments benefit power producers while raising consumer prices in the Nordic-Baltic energy region, while very low social acceptance of onshore wind power would lead to 12% higher consumer costs. The results imply that socio-technical and political factors such as social acceptance may significantly affect transition pathway scenarios based on techno-economic variables alone. Therefore, the techno-economic, socio-technical and political layers of co-evolution of energy systems should be considered when analysing long-term energy transitions. It is important to link energy-system models with a consideration of the dynamics of socio-technical factors.
A reliable energy supply is fundamental to ensure energy security and support the mitigation of climate change by promoting the use of renewable sources and reducing carbon emissions. Energy system analysis provides a sound methodology to assess energy needs, allowing to investigate the energy system behavior and to individuate the optimal energy-technology configurations for the achievement of strategic energy and environmental policy targets. In this framework, the estimation of future trends of exogenous variables such as energy demand has a fundamental importance to obtain reliable and effective solutions, contributing remarkably to the accuracy of models’ input data. This study illustrates an application of regression analysis to predict energy demand trends in end use sectors. The proposed procedure is applied to characterize statistically the relationships between population and gross domestic product (independent variables) and energy demands of Residential, Transport and Commercial in order to determine the energy demand trends over a long-term horizon. The effectiveness of linear and nonlinear regression models for energy demand forecasting has been validated by classical statistical tests. Energy demand projections have been tested as input data of the bottom-up TIMES model in two applications (the TIMES-Basilicata and TIMES-Italy models) confirming the validity of the forecasting approach.