Energy System Model Research Articles

TEMOA-europe: An open-source and open-data energy system optimization model for the analysis of the European energy mix

Energy system modeling tools allow to perform comprehensive analyses for the optimal integration of supply and demand technologies in different scenarios. Open tools, in particular, increase the reliability of such tools and their policy relevance. This work aims to present TEMOA-Europe, an open-data and open-software model instance for OECD Europe. Such model is developed on a time scale up to 2050 and calibrated against acknowledged energy statistics up to 2020. This work is the first to present a net-zero emissions by 2050 trajectory envisaging the absence of Russian energy imports starting from 2030. Despite the stringent constraints, TEMOA-Europe is able to provide results for a decarbonization scenario with growing end-use demands – among which some are reduced for the effect of the elasticity to gas price – considering a larger use of renewable source already starting from the near future and reduced energy intensity. Renewable energy represents more than 60 % of total energy supply by 2050 in the computed pathway. The results are also compared to the projections of the International Energy Agency for the Announced Pledges Scenario (since results for the Net-Zero Emissions Scenario are not publicly available), showing large agreement except for the outcomes concerning wind electricity generation.

100% renewable heat supply in Berlin by 2050 – A model-based approach

The energy crisis posed major challenges, especially for the heating sector. The European gas price increased from 16€/MWh in March 2021 to 227€/MWh in March 2022, which led to a debate in Germany around reshaping the heat supply to decrease energy import dependence. However, many actors on the heating market are reluctant to make the necessary investments to reduce dependence on natural gas. Although, independence of fossil energy imports by 2050 would be possible with a switch to a 100% renewable heat supply for Berlin.Based on the linear open-source energy system model GENeSYS-MOD, a 100% renewable-based energy system is modeled. GENeSYS-MOD is extended to model district heating, which is crucial for Berlin's heat supply. Furthermore, the modeling of heat pumps and a data set for Berlin is revised.The modeling considers the tightened climate protection targets, which means a 95% CO2 reduction by 2045 compared to 1990. By 2050, 100% of the transport, industry, electricity, and building sectors` emissions are mitigated.The results suggest the immediate exploitation of the existing renewable energy potential and seasonal heat storages, as well as a necessity for a major increase in renovation rates. The upcoming coal phase-out in Berlin in 2030 should be met by a mix of heat pumps and biomass. If heat pump resources are exhausted, direct electric heating is deployed as last resort option.

Modeling and Implementation of a Hybrid Solar-Wind Renewable Energy System for Constant Power Supply

Modeling and Implementation of a Hybrid Solar-Wind Renewable Energy System for Constant Power Supply

Review of Fourth-Order Maximum Entropy Based Predictive Modeling and Illustrative Application to a Nuclear Reactor Benchmark: II. Best-Estimate Predicted Values and Uncertainties for Model Responses and Parameters

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters in the combined phase space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21,976 imprecisely known parameters. This benchmark is representative of “large-scale computations” such as those involved in the modeling of energy systems. The result (“response”) of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for “high-precision” parameters (2% standard deviations) and “typical-precision” parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st—through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems modeled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, and MC). At least second-order sensitivities should be retained in order to obtain reliable predictions.

In 50 Shades of Orange: Germany’s Photovoltaic Power Generation Landscape

Spatiotemporally resolved data on photovoltaic (PV) power generation are very helpful to analyze the multiple impacts of this variable renewable energy on regional and local scales. In the absence of such disaggregated data for Germany, numerical simulations are needed to obtain the electricity production from PV systems for a time period and region under study. This manuscript presents how a physical simulation model, which uses open access weather and plant data as input vectors, can be created. The developed PV model is then applied to an ensemble of approximately 1.95 million PV systems, consisting of ground-mounted and rooftop installations, in order to compute their electricity production in Germany for the year 2020. The resulting spatially aggregated time series closely matches the measured PV feed-in pattern of Germany throughout the simulated year. Such disaggregated data can be applied to investigate the German PV power generation landscape at various spatiotemporal levels, as each PV system is taken into account with its technical data and the weather conditions at its geo-location. Furthermore, the German PV power generation landscape is presented as detailed maps based on these simulation results, which can also be useful for many other scientific fields such as energy system modeling.

Optimization Effectiveness of Multi-Intelligence Consistent Energy System Based on Digital Advertising and Data Analysis

This paper introduces a novel approach to address the issue of overestimation of state-action values in reinforcement learning algorithms based on the Q-framework. It integrates a dual-layer Q-learning algorithm with a grey wolf intelligent optimization method, enabling rapid search for optimal allocations in unknown search spaces. This integration results in the development of a multi-agent collaborative Area-Grid Coordination (A-GC) strategy, termed the grey wolf double Q (GWDQ) strategy, tailored for multi-area energy interconnection scenarios. The proposed GWDQ strategy is evaluated through simulation experiments on comprehensive energy system models, including mixed gas turbine systems, Combined Cooling, Heating, and Power (CCHP) systems, and a multi-area energy-interconnected Northeast power grid model. A centralized architecture is established to analyze the optimization effects of digital advertising. The performance of the GWDQ strategy is compared with traditional reinforcement learning algorithms through simulation and empirical data validation. Results indicate that the GWDQ strategy exhibits stronger learning capabilities, improved stability, and enhanced control performance compared to traditional methods. It demonstrates superior optimization for digital advertising and enables swift acquisition of optimal coordination in A-GC processes across multiple regions. Additionally, the paper analyzes the environmental and economic impacts of the proposed strategy.

Machine learning as a surrogate model for EnergyPLAN: Speeding up energy system optimization at the country level

In the field of energy system modelling, increasing complexity and optimization analysis are essential for understanding the most effective decarbonization options. However, the growing need for intricate models leads to increased computational time, which can hinder progress in research and policy-making. This study aims to address this issue by integrating machine learning algorithms with EnergyPLAN and EPLANopt, a coupling of EnergyPLAN software and a multi-objective evolutionary algorithm, to expedite the optimization process while maintaining accuracy. By saving computational time, we can increase the number of evaluations, thereby enabling deeper exploration of uncertainty in energy system modelling. Although machine learning models have been widely employed as surrogate models to accelerate optimization problems, their application in energy system modeling at the national scale, while preserving high temporal resolution and extensive sector-coupling, remains scarce. Several machine learning models were evaluated, and an artificial neural network was selected as the most effective surrogate model. The findings demonstrate that incorporating this surrogate model within the optimization process reduces computational time by 64 % compared to the conventional EPLANopt approach, while maintaining an accuracy level close to that obtained by running EPLANopt without the surrogate model.

Integrated modelling of CO2 plume geothermal energy systems in carbonate reservoirs: Technology, operations, economics and sustainability

The rising CO2 content in the atmosphere calls for urgent decarbonization efforts. Carbon capture and storage (CCS) have proven effective in storing large volumes of CO2 in geological reservoirs. Nevertheless, challenges persist in identifying sustainable energy sources and efficient storage sites, especially in carbonate reservoirs. This study aims to characterize the Miocene carbonate reservoir in the Jintan Megaplatform, for CO2 storage and geothermal energy utilization. By integrating advanced technologies such as CO2 Plume Geothermal (CPG) with 3D seismic techniques, borehole analysis and numerical modelling, the study reveals the distribution of critical reservoir characteristics, including architecture, heterogeneity, karstification, fractures, porosity, permeability, temperature and pressure that are paramount for CO2 plume and energy production. The reservoir is estimated to store over 31 Gt of CO2, produce 3.824 × 1018 J of geothermal energy and ⁓44.3 GW of power. This power output can be utilized for electrification of the CCS operations, industrial and municipals, potentially generating over $1.4 billion in revenue. A flow-through model has been proposed to improve energy efficiency, reservoir management, and risk mitigation. This study advances to achieving net-zero targets and the United Nations Sustainable Development Goals on clean energy, climate action and sustainable communities.

A mathematical model of an off-grid hybrid energy system utilizing the reversible solid oxide fuel cell

Abstract The current energy transition focuses on decentralizing energy systems by implementing alternative energy sources. Integrating distributed energy sources into functional hybrid energy systems has proven to be a difficult task. To solve this problem, it is important to conduct an in-depth analysis of the energy system through the preparation of a mathematical model. The study aims to model and simulate the operation of an off-grid self-sustainable energy storage system for single-family household use by utilizing solid oxide technology. The proposed system consists of photovoltaic modules and an energy storage system including batteries, reversible solid oxide fuel cells, and hydrogen storage tanks. To integrate these energy resources into one system, an energy management system, in which the solid oxide stack is used as a backup generator or a hydrogen production device, is implemented. Finally, a numerical simulation of the system’s operation is run to estimate the system’s performance over a one-year time period. The prepared model can find many practical applications, such as optimizing system costs, environmental impact, or the energy management system.

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

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

Design and optimization of zero-carbon integrated energy system for highway service area

Abstract The challenge of achieving dual-carbon targets arises from the issue of high carbon emissions in Chinese highway service areas. To address this, a comprehensive electric-thermal-hydrogen energy system is proposed, consisting of a new energy generation system, heat pumps, electrolyzers, hydrogen fuel cells, energy storage devices, and charging stations. A dispatch model for the integrated energy system is established, with the objective of minimizing the total operating cost while satisfying the constraints of each device and the electric-thermal-hydrogen balance. The model is implemented in MATLAB, and the ideal optimal solution for each decision variable is obtained using the Gurobi solver. The investment and operating costs, as well as the scheduling results for electricity, heat, and cooling, are analyzed. The results indicate that the electrolyzer-fuel cell system can improve the system’s operational stability and flexibility in dispatch optimization. Due to the instability of photovoltaic and wind power devices and the fluctuating nature of loads, energy storage devices are required for peak load shaving in the system. The zero-carbon integrated energy system model can calculate the most economical investment plan based on the load and meteorological conditions of different service areas.

Integrating district heating potentials into European energy system modelling: An assessment of cost advantages of renewable and excess heat

This paper takes a novel modelling approach by considering high spatial resolution heat generation potentials for district heating and integrating them into a European energy system model. Subsequently, a modelling analysis of an integrated energy system including district heating, electricity and hydrogen supply for 25 EU Member States and the year 2050 is carried out. In contrast to existing approaches, the modelling approach captures the heterogeneous resource availability in district heating. The results show multivalent district heating networks based on a wide range of renewable and excess heat sources used directly or in combination with large-scale heat pumps. The high spatial resolution of the heat generation potentials allows a detailed cost comparison of different possible future technology mixes in district heating. The paper finds that the use of heat pumps, geothermal energy and industrial excess heat offer slight cost advantages for the energy system as a whole. Geothermal heat can also provide cost advantages for district heating generation.

Towards a sustainable energy future: Modeling Morocco’s transition to renewable power with enhanced OSeMOSYS model

Developing countries encounter numerous challenges, such as limited access to reliable electricity and a heavy reliance on imported fossil fuels. To satisfy the rising energy demand, which is essential for economic growth, these regions are shifting towards sustainable energy solutions. Solar and wind power have emerged as key and secure energy sources. This research develops an enhanced OSeMOSYS energy system model to examine long-term energy supply strategies, using Morocco as a case study. The proposed model addresses the specific needs of decision-makers in developing countries, enabling the achievement of renewable energy targets and optimal temporal resolution. A baseline and three alternative scenarios were developed, exploring both a restructuring plan to address existing gaps and a forward-looking policy targeting nearly 100 % renewable energy. These scenarios are assessed in terms of installed capacity, generation mix, investment costs, variable and fixed costs, storage capacity utilization, and carbon emissions. The results indicate that a 92 % integration rate for renewables is feasible at an additional cost of $32 billion, thanks to new energy efficiency measures reducing demand by 15 % between 2030 and 2050 compared to baseline forecasts. Furthermore, an ambitious energy strategy by 2050 could achieve the lowest emissions rate of 0.29 Mt, paving the way for complete decarbonization. Technologically, investment in pumped-storage hydroelectric plants is the most viable backup option for a country dependent on natural gas imports. Our findings emphasize that a diversified energy mix and robust energy efficiency plans are essential for a rapid and feasible transition of the country’s energy system. This paper offers an enhanced energy model to help decision-makers in developing countries set targets on the share of renewables in total installed capacity, thereby increasing their integration rate at minimal additional cost.

Sustainability Indicators Correlation Matrix

Abstract: This study explores the complex interplay between economic growth, environmental preservation, and energy efficiency in the context of sustainable development. Leveraging analytical tools such as the OSeMOSYS model and Pearson correlation coefficient, the research investigates how economic decisions impact energy resource utilization and environmental quality. Key sustainability indicators, including Energy Payback Time (EPBT), Internal Rate of Return (ITR), and Climate Change Impact Mitigation (IMPcc), are analyzed within the framework of the Atlantis energy system. The study emphasizes the need to balance economic priorities with environmental considerations to achieve sustainability objectives effectively. Furthermore, the research evaluates the correlation between sustainability indicators when integrated into the optimization process of the OSeMOSYS energy modeling system. This approach, advocated in previous studies, underscores the importance of incorporating sustainability metrics into decision-making processes. In conclusion, the findings highlight the necessity of reevaluating the weighting of indicators within optimization functions to prioritize sustainability goals. By providing insights into the complex relationships between economic, energy, and environmental factors, the study contributes to advancing sustainable development practices for policymakers, businesses, and society as a whole.

The role of digital social practices and technologies in the Swiss energy transition towards net-zero carbon dioxide emissions in 2050

Digitalization can be an enabler of new and less energy-intensive lifestyles. However, it can be associated with rebound effects in energy consumption. For example, home office can reduce the mobility demand but increase residential energy consumption. Here, we analyze the role of households' social practices induced by information and communication technologies (ICTs) in Switzerland's transition to net-zero carbon dioxide emissions in 2050. We couple the Swiss TIMES Energy Systems Model with a socio-technical-economic agent-based model (SEED) to perform long-term pathway analysis by accounting for the socio-economic and technical aspects of technology adoption. We find an overall net-positive contribution of digitalization to the Swiss energy transition. A society adopting more digital practices and ICTs requires 10–20% less energy in 2050 than a society with a digitalization rate frozen at 2020 levels. We show that any rebound effects in energy consumption that digital practices can induce are counterbalanced by more efficient technologies and optimized processes enabled by ICTs. While digitalization helps to meet the Swiss energy and climate targets for 2050 and reduces the energy transition costs for society, policy support is necessary to develop the population's digital skills, services' digitalization, and network infrastructures.

Revealing technological entanglements in uncertain decarbonisation pathways using bayesian networks

To effectively meet the ambitious objectives set by the Paris Agreement, gaining a deeper understanding of the relationships between the key technologies involved in mitigation activities is pivotal. This research uses Bayesian Network (BN) methodology on a large ensemble of energy system model runs, aiming to shed light on the complex interdependencies, and related uncertainties, among the various technologies within the pathways. We specifically focus on tracking the evolution and interconnectedness of technology portfolios over time, enabling dynamic assessments of the impacts linked to specific deployment strategies. The results suggest that prioritizing early-stage transitions within the building sector is imperative and the consistent deployment of district heating emerges as a pivotal element in the long-term plans for decarbonisation. In the power sector, the rising trends in electrification and the substantial growth in low-carbon power plants and wind energy deployment, underscore the urgency for adaptable strategies within the power sector. Notably, the integration of bioenergy with carbon capture and storage (BECCS) also emerges as a crucial technology, offering a means to counterbalance emissions from carbon-intensive industries. The BN-based approach provides decision makers a powerful tool for comprehensive, informed, and systematic planning as they navigate towards a carbon-neutral future, but it is also crucial to acknowledge the reliance of our analysis on assumptions inherent in energy system models. Studies using different assumptions and model structures are needed to confirm the generalizability of our findings.

District Heating Deployment and Energy-Saving Measures to Decarbonise the Building Stock in 100% Renewable Energy Systems

Achieving a zero-emission building heating sector requires numerous strategies and detailed energy planning, in order to identify the optimal decarbonisation pathway. This work aims to assess the impact of district heating expansion and the implementation of energy-saving measures on the decarbonisation of the Italian building stock by 2050, analysing their combined impact, reciprocal effects, and technical–economic implications on the entire national energy system. The scenarios have been implemented and simulated with the H2RES software, a long-term energy planning optimisation model, built for the Italian national energy system. Results indicate that it is possible to decarbonise the heating system in an efficient and cost-effective manner by the year 2040. Heat pumps represent the optimal technology at both centralised and decentralised levels. District heating expansion is a priority for the decarbonisation of the building stock, allowing us to reduce costs, exploit thermal storage systems and provide system flexibility. In the best scenario, 40% of the Italian heat demand can be supplied by fourth-generation district heating. Energy-saving measures can reduce heat demand and primary energy but at higher annual costs and with a significant increase in investment. The combined simulation of the strategies within an optimisation model of the entire energy system enables the accurate assessment of the real impact of the various measures, considering their reciprocal effects and technical–economic implications.

Designing a cost-effective policy mix for transition toward net-zero emissions: a case study of the mid-term plan by 2035 of Taiwan

This study explores the design of a cost-effective climate policy mix for transition toward net-zero emissions in the mid-term (by 2035). Its novelty lies in the methodological advancements through the development of a soft-linking procedure that integrates a top-down computable general equilibrium model with a bottom-up energy system model. The integrated assessment focuses on the climate policy mix under the framework of Taiwan’s Climate Change Response Act passed in 2023. The analysis shows that a policy mix combining investments in energy transition with user-pay hybrid pricing measures can facilitate cost-effective mitigation and enhance public acceptance. The proposed hybrid pricing includes cost-based electricity pricing and a two-step carbon pricing strategy that encourages early action (before 2030) for larger emitters. Finally, we demonstrate that accelerating the transition is crucial, as it reduces the economic costs of meeting emission targets by inducing further investments in energy efficiency improvement and lowering the carbon pricing levels required to close the abatement gap.

A comparison of multi-source power supply systems for autonomous marine vehicles: The SWAMP case study

Extensive research on zero-emissions autonomous surface vehicles has been recently carried out, aiming at increasing autonomy. Due to their small size, unmanned vehicles present unique difficulties in terms of weight and dimensions when powering the vehicle with renewable energy sources. In this paper, photovoltaic source, fuel cell and Li-ion battery multi-source configurations are proposed, demonstrating by physical layout and simulation results the feasibility of the prototype, compliant with strict constraints of the vehicle under test. The energy system model of the vehicle under test is implemented in MATLAB/Simulink. A comparison of multi-source energy system configurations is proposed and validated by simulation results in terms of endurance, weight and size. If compared with the original battery-powered vehicle, as shown by comparing simulation results, the endurance is doubled, extended up to 12 h on the most favorable day of the year. The solution even complies with payload and physical dimensions constraints.

How does energy modelling influence policymaking? Insights from low- and middle-income countries

Energy system models are widely used to explore, analyse and plan energy futures and sustainable transitions. These models, typically developed in high-income countries, have more recently been applied in low- and middle-income countries (LMICs). As a result, the role that models play in informing decisions in such contexts lacks adequate exploration. Drawing on 35 qualitative interviews with energy system modellers and policymakers, this research examines the experiences of developing and using energy system models that support decision-making in LMICs. The findings suggest that many conventional modelling approaches do not account for the political economy influences and developmental challenges specific to LMICs, with implications for modelling processes and outcomes. The interviews highlighted the need to understand the roles played by diverse stakeholders in shaping modelling processes, as well as the communication, interpretation and use of energy models. This indicates that, particularly in modelling projects which bring in external modellers, a good understanding of the country context is essential to design appropriate model scenarios and for their interpretation in policymaking. Finally, more in-country capacity is needed to foster local ownership of modelling projects.