Energy System Optimization Model Research Articles

Integration of disamenity costs and equality considerations regarding onshore wind power expansion and distribution into energy system optimization models

BackgroundSocial acceptance of energy infrastructure projects affects public support for the energy transition and is essential for the transition’s sustainability and success. Despite extensive research focusing on the social acceptance of renewable energy, and on the acceptance of onshore wind power in particular, energy system models have largely prioritized techno-economic aspects. This focus has created a gap between model results and decision-makers’ needs. In this study, we offer recommendations for integrating disamenity costs and equality considerations—two critical social aspects related to onshore wind power—into energy system optimization. To achieve this, we use a spatially distributed model from a climate-neutral Germany and explore various implementations and trade-offs of these two social aspects.ResultsWe identified effective linear formulations for both disamenity costs and equality considerations as model extensions, notably outperforming quadratic alternatives, which exhibit longer solution times (+ 50–115%). Our findings reveal that the endogenous consideration of disamenity costs in the optimization approach can significantly reduce the human population’s exposure to wind turbines, decreasing the average disamenity per turbine by 53%. Drawing on notions of welfare economics, we employ two different approaches for integrating equality into the optimization process, permitting the modulation of the degree of equality within spatial distributions in energy system models. The trade-offs of the two social aspects compared to the cost-optimal reference are moderate, resulting in a 2–3% increase in system costs.ConclusionsDisamenity costs emerge as a predominant factor in the distribution of onshore wind power in energy system optimization models. However, existing plans for onshore wind power distribution in Germany underscore equality as the driving factor. The inclusion of social aspects in energy system models facilitates the identification of socially superior wind turbine locations. Neglecting disamenity costs and equality considerations leads to an overestimation of the practical solution space for decision-makers and, consequently, the resulting energy system designs.

Energy system models should consider evolving charging profiles

Abstract Globally, sales of battery electric vehicles (BEVs) are surpassing records every year, and their growing charging needs will ultimately reshape power infrastructure planning practices. While studies have analyzed the impact of light-duty BEVs on the electricity sector, they have overlooked the prospective evolution in charging profiles. We developed a framework for analyzing passenger vehicle electrification futures accounting for the evolution in charging infrastructure, BEVs technical features, and socio-demographics. We soft-link a BEV charging profiles generator with an energy system optimization model to analyze a light-duty vehicle (LDV) electrification scenario in the U.S. from 2020-2050. Compared to static charging profiles, common in prior work, evolving profiles lead to substantial differences in projected power plant installed capacity (up to ~300 GW more solar PV) and activity (up to ~460 TWh more solar PV generation). Hence, future studies should consider not only different charging profiles (e.g., day, night, uncontrolled) but also how these evolve over time.

Modeling energy storage in long-term capacity expansion energy planning: an analysis of the Italian system

This paper presents a framework to represent short-term operational phenomena associated with renewables capacity factors and final service demand distributions in a capacity-expansion and integrated energy system optimization model. The aim is to study the potential role of energy storage technologies coupled with renewable energy sources aiding the decarbonization of the overall energy system. The proposed methodology is implemented in an energy system optimization model named Tools for Energy Model Optimization and Analysis (TEMOA) and then tested in a case study focused on the Italian energy system. We examine a collection of scenarios that includes reference time scale scenarios, time scale sensitivity scenarios, and technology alternative scenarios. This paper's findings indicate that energy storage is crucial for fully decarbonizing the Italian power sector by 2050 in the absence of a low-carbon baseload. Additionally, it suggests that approximately 10 % of Italy's electricity generation in 2050 should be routed through short-term energy storage devices.

Diverse decarbonization pathways under near cost-optimal futures

Energy system optimization models offer insights into energy and emissions futures through least-cost optimization. However, real-world energy systems often deviate from deterministic scenarios, necessitating rigorous uncertainty exploration in macro-energy system modeling. This study uses modeling techniques to generate diverse near cost-optimal net-zero CO2 pathways for the United States’ energy system. Our findings reveal consistent trends across these pathways, including rapid expansion of solar and wind power generation, substantial petroleum use reductions, near elimination of coal combustion, and increased end-use electrification. We also observe varying deployment levels for natural gas, hydrogen, direct air capture of CO2, and synthetic fuels. Notably, carbon-captured coal and synthetic fuels exhibit high adoption rates but only in select decarbonization pathways. By analyzing technology adoption correlations, we uncover interconnected technologies. These results demonstrate that diverse pathways for decarbonization exist at comparable system-level costs and provide insights into technology portfolios that enable near cost-optimal net-zero CO2 futures.

Asymmetric Nash bargaining model for operation optimization of multi-integrated energy systems considering peer-to-peer energy trading

Energy interactions across integrated energy systems constitute crucial means to enhance energy efficiency and match supply and demand. However, the cooperative operation and benefit distribution among multiple integrated energy systems still need in-depth exploration. To fill this gap, this paper presents an asymmetric Nash bargaining optimization model for multiple integrated energy systems considering peer-to-peer trading. First, a scheduling model for multi-integrated energy systems is formulated considering carbon trading. Then, the model is incorporated into Nash bargaining framework and transformed into the alliance cost minimization subproblem and peer-to-peer trading payment bargaining subproblem. The bargaining power factor is introduced to measure the contribution of participants in energy sharing. The alternating direction multiplier method is utilized to handle the proposed model. Finally, a case study is carried out to validate the validity of the proposed strategy. The results show that compared with the independent operation mode, the performances of three integrated energy systems in collaborative operation mode are enhanced by 11.7 %, 9.0 %, and 4.8 % respectively. The distributed algorithm can reduce the computation time by 30 % and obtain highly efficient solutions while protecting private information of each participant. This research provides support and practical tools for conducting peer-to-peer transactions of multiple integrated energy systems.

Multi-objective coordinated optimization of low-carbon building energy systems based on high renewable energy penetration

In the context of China's targets to achieve a "carbon peak" by 2030 and "carbon neutrality" by 2060, the operation and maintenance of buildings with low-carbon and zero-energy consumption emerge as a critical technology for accomplishing the "dual carbon" goals in the building sector. As a novel energy supply system, low-carbon building energy systems can provide low-carbon, efficient, and economical energy supplies. Accordingly, this paper initially constructs a low-carbon building energy system based on a high penetration of renewable energy sources. Subsequently, considering the benefits of economic efficiency, carbon emissions, and renewable energy utilization, a multi-objective cooperative optimization model for low-carbon building energy systems with high renewable energy penetration is established. Finally, a multi-objective particle swarm optimization algorithm is applied to solve the model, and the system is studied and analyzed using a large domestic park as a case study. The results indicate that the low-carbon building energy system with high renewable energy integration, aiming for economic efficiency, carbon reduction, and increased renewable energy consumption, achieves supply energy costs of $ 19.2/m2 and carbon emissions of 49.1 kg/m2. These values represent reductions of $ 7.4/m2 and 60.9 kg/m2 compared to separate supply systems. Lastly, the study of the low-carbon building energy system provides theoretical references for renewable energy integration and energy transition in the building sector.

Decoupling framework for large-scale energy systems simultaneously addressing carbon emissions and energy flow relationships through sector units: A case study on uncertainty in China's carbon emission targets

The energy system requires meticulous planning to achieve low-carbon development goals cost-effectively. However, optimizing large-scale energy systems with high spatial-temporal resolution and a rich variety of technologies has always been a challenge due to limited computational resources. Therefore, this study proposes a soft-linkage framework to deconstruct large-scale energy system optimization models based on sectors while ensuring the total carbon emission limit and the electricity supply-demand balance. Using China's energy system as a case study, the impact of uncertainty on emission reduction targets is analyzed. A long-term emission target curve is only described by the total carbon budget and its temporal distribution. Results show that different carbon budget time series can lead to total transition cost variations of up to nearly 100 trillion yuan. Moreover, although a lower carbon budget would increase the total cumulative transition cost quadratically, excessively high carbon budgets raise national natural gas demand, threatening energy security.

Strengthening energy system resilience planning under uncertainty by minimizing regret

This study applies the concept of regret in decision-making under uncertainty to an energy system optimization model to identify optimal robust and stochastic solutions amongst several design options. The approach is demonstrated on the case study of Accra, Ghana, considering uncertainties pertinent to the city, particularly under climate change. The evaluated uncertainty scenarios consider volatile fossil fuel supply, reduced hydropower generation, rising demand due to climate change-driven rural-urban migration and global warming, unplanned power outages due to increasing natural disasters, and currency depreciation. The evaluated systems include Pareto-optimal system solutions typically under consideration by planners, which balance costs and CO2 emissions. The regret performance is evaluated for each system subject to each uncertainty scenario. A near-CO2-minimized system is the optimal robust and stochastic least-regret solution. Two factors drive this result: (1) a diverse technology set, which provides generation and cross-sectoral flexibility for adaptation under uncertainty, and (2) effectively balancing rising investment and operation costs with decreasing unmet demand costs. The demonstrated method provides energy planners and policymakers with a pragmatic, effective and fast approach, which offers new insights into long-term energy system planning to improve resilience under uncertainty, supporting the aims of the United Nations Sustainable Development Goals 7 and 11.

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.

Exploring European decarbonisation pathways in the Power Decisions Game

BackgroundArticle 12 of the Paris Agreement summons the signing parties to co-operate in improving the education of their citizens on climate change and related matters. The article thereby acknowledges the importance of citizens’ support and understanding of climate change and needed measures to fight climate change. This work aims to inform European citizens on how climate change-related policies affect the power sector in Europe. For this purpose, a serious game, based on sound principles of energy systems analysis, has been developed to allow players to explore how key policy decisions affect capacity mix, investment needs, and electricity costs.ResultsThe game is based on more than 1700 scenarios run through an open-source and accessible, yet technologically detailed, myopic energy system optimisation model for the electricity supply in the EU27 + 3. The game allows the user to take the role of a decision-maker and make decisions in 2020, 2030, and 2040 regarding the usage of CCS, biomass imports, cross-border electricity transmission and the pace of emission reductions. The user is then presented with economic, social, and environmental impacts of these choices. These impacts are, for example, measured and illustrated in the development of accumulated CO2 emissions per capita, levelised cost of electricity, and investment need per citizen.ConclusionThe Power Decisions Game provides a first-of-its-kind open-source infrastructure that allows non-modellers to explore the impact of key decisions and preferences on the design of the future European power system. Furthermore, it provides insights on the consequences of short-sighted decision making. The game can be used to facilitate policy-science discussions.

Operation Optimization of Regional Integrated Energy Systems with Hydrogen by Considering Demand Response and Green Certificate–Carbon Emission Trading Mechanisms

Amidst the growing imperative to address carbon emissions, aiming to improve energy utilization efficiency, optimize equipment operation flexibility, and further reduce costs and carbon emissions of regional integrated energy systems (RIESs), this paper proposes a low-carbon economic operation strategy for RIESs. Firstly, on the energy supply side, energy conversion devices are utilized to enhance multi-energy complementary capabilities. Then, an integrated demand response model is established on the demand side to smooth the load curve. Finally, consideration is given to the RIES’s participation in the green certificate–carbon trading market to reduce system carbon emissions. With the objective of minimizing the sum of system operating costs and green certificate–carbon trading costs, an integrated energy system optimization model that considers electricity, gas, heat, and cold coupling is established, and the CPLEX solver toolbox is used for model solving. The results show that the coordinated optimization of supply and demand sides of regional integrated energy systems while considering multi-energy coupling and complementarity effectively reduces carbon emissions while further enhancing the economic efficiency of system operations.

Flexible copper: Exploring capacity-based energy demand flexibility in the industry

Different forms of flexibility can help in balancing variable generation. This work focuses on industrial demand-side flexibility applied in copper production, which is expected to grow for the build-out of green technologies.This study assesses the potential of capacity-based energy demand flexibility (over-sizing production processes) in an industry embedded in fully renewable energy systems. For this, an optimization model for multi-vector energy systems planning is extended so that it also includes the sizing and operation of a production process. A case study is presented for copper production, with greenfield results until 2050.Results show that flexibility at the concentration and refining stages belongs to the cost-optimal system design, at least over the next decade. At current costs, the potential cost savings in the energy system for the production process through capacity-based demand flexibility range from 5 % to 12 %, depending on the technology scenario. These potential savings are expected to decrease over time if cost reductions of renewable energy supply and storage technologies materialize. Technology scenarios considering seawater pumped-hydro energy storage yield lower costs over the entire projected period.

The long term price elastic demand of hydrogen – A multi-model analysis for Germany

Hydrogen and its derivatives are important components to achieve climate policy goals, especially in terms of greenhouse gas neutrality. There is an ongoing controversial debate about the applications in which hydrogen and its derivatives should be used and to what extent. Typically, the estimation of hydrogen demand relies on scenario-based analyses with varying underlying assumptions and targets. This study establishes a new framework consisting of existing energy system simulation and optimisation models in order to assess the long-term price-elastic demand of hydrogen. The aim of this work is to shift towards an analysis of the hydrogen demand that is primarily driven by its price. This is done for the case of Germany because of the expected high hydrogen demand for the years 2025–2045. 15 wholesale price pathways were established, with final prices in 2045 between 56 €/MWh and 182 €/MWh. The results suggest that – if climate targets are to be achieved - even with high hydrogen prices (252 €/MWh in 2030 and 182 €/MWh in 2045) a significant hydrogen demand in the industry sector and the energy conversion sector is expected to emerge (318 TWh). Furthermore, the energy conversion sector has a large share of price sensitive hydrogen demand and therefore its demand strongly increases with lower prices. The road transportation sector will only play a small role in terms of hydrogen demand, if prices are low. In the decentralised heating for buildings no relevant demand will be seen over the considered price ranges, whereas the centralised supply of heat via heat grids increases as prices fall.

Is District Heating a cost-effective solution to decarbonise Irish buildings?

Achieving decarbonisation of the heating sector is a major challenge, especially in Ireland, which has the lowest proportion of renewable heat in Europe. In economically pursuing ambitious and equitable climate targets, Ireland’s Climate Action Plan 2023 set a goal of reaching 2.5 TWh of district heating by 2030, starting from a limited existing capacity. This pioneering study explores supply sources and heat density demand cohorts to develop a model to understand district heating in a low-deployment region, using Ireland as a case study. We also conduct a sensitivity analysis considering different connection rates.Our research demonstrates that district heating is pivotal in achieving optimal decarbonisation pathways. Incorporating district heating reduces marginal CO2 emission prices by 13%–25%. Additionally, with feasible growth limits applied, total savings for the energy system are anticipated to reach €17.2 billion by 2050. This reduction of emissions in the building sector, facilitated by district heating, allows for greater emissions allowances in other sectors and reduces reliance on electricity while meeting climate targets. These insights highlight the need for spatial energy policies and deepen our understanding of integrating district heating within an energy system optimisation model.

Comparing energy system optimization models and integrated assessment models: Relevance for energy policy advice.

The transition to a climate neutral society such as that envisaged in the European Union Green Deal requires careful and comprehensive planning. Integrated assessment models (IAMs) and energy system optimisation models (ESOMs) are both commonly used for policy advice and in the process of policy design. In Europe, a vast landscape of these models has emerged and both kinds of models have been part of numerous model comparison and model linking exercises. However, IAMs and ESOMs have rarely been compared or linked with one another. This study conducts an explorative comparison and identifies possible flows of information between 11 of the integrated assessment and energy system models in the European Climate and Energy Modelling Forum. The study identifies and compares regional aggregations and commonly reported variables. We define harmonised regions and a subset of shared result variables that enable the comparison of scenario results across the models. The results highlight how power generation and demand development are related and driven by regional and sectoral drivers. They also show that demand developments like for hydrogen can be linked with power generation potentials such as onshore wind power. Lastly, the results show that the role of nuclear power is related to the availability of wind resources. This comparison and analysis of modelling results across model type boundaries provides modellers and policymakers with a better understanding of how to interpret both IAM and ESOM results. It also highlights the need for community standards for region definitions and information about reported variables to facilitate future comparisons of this kind. The comparison shows that regional aggregations might conceal differences within regions that are potentially of interest for national policy makers thereby indicating a need for national-level analysis.

Can a new power system create more employment in China?

The transition from the coal-dominated power sector to the renewable energy-dominated new power system will inevitably result in significant social impacts. Taking China as the study case, we employed an integrated methodology combining the energy system optimization model, input‒output and structural path analysis, to explore the employment effects of the new power system, including the energy source, grid, load and storage. The results show that growth in solar power, wind power, energy storage and power transmission and distribution will have positive impacts on employment, while coal power shrinkage will result in negative impacts. Under new power system scenarios, renewable power can create additional 673–730 thousand jobs by 2030 and additional 2,431∼2,706 thousand jobs by 2060 compared with the business-as-usual scenario. However, jobs losses due to coal power shrinkage will reach 665–747 thousand jobs by 2030 and 3,127∼3,398 thousand jobs by 2060 compared with the business-as-usual scenario. In the meantime, power grid and energy storage can create additional 443–604 thousand jobs by 2030 and additional 2,138∼2,764 thousand jobs by 2060. Though the new power system will have positive employment impact in general, social justice should still be considered when there is an employment structure transition alongside the power structure shift.

Multi-timescale available flexibility assessment of integrated energy systems considering different proportions of wind power installations

The available flexibility capacity of the integrated energy system can be used as one of the indicators of proportions of system wind power installations, which, in turn, affects the maximum installed capacity of the system wind power, and this paper proposes a method for assessing the available flexibility of the integrated energy system at multiple timescales considering different proportions of system wind power installations. First, the framework of the integrated energy system is constructed, and based on the coupling relationship between the electrical and thermal systems, the mathematical models of the P2G, combined heat and power (CHP), energy storage equipment, and wind power generation equipment within the integrated energy system are established, and the Monte Carlo method is used to predict the wind power output in a typical scenario. Second, an integrated energy system optimization model is constructed to obtain the optimal dispatch operation of the system; the empirical mode decomposition (EMD) algorithm is used to decompose the flexibility demand curve of the system in multiple timescales. The flexibility supply capacity model of different types of flexibility resources in the system at different timescales is established, and through the comparative analysis of flexibility supply and demand at the same timescale, the upward and downward flexibility shortage probability and shortage expectation indexes at each timescale can be intuitively calculated and then weighted to constitute a comprehensive index of system flexibility assessment. Finally, the available flexibility analysis of the integrated energy system under different installed wind power capacities shows that the proposed methodology can more comprehensively assess the available flexibility capacity of the integrated energy system under different timescales, and the maximum installed wind power capacity that the system can withstand can be obtained while guaranteeing sufficient available flexibility capacity.

Integration of Land Use Potential in Energy System Optimization Models at Regional Scale: The Pantelleria Island Case Study

In the context of the energy transition, the integration of land use considerations into energy planning can provide significant improvements. In energy system optimization models (ESOMs), land use aspects can be integrated at the cost of a finer spatial resolution and a more detailed characterization of land, tailored to regional constraints and specificities. Additionally, an assessment of trade-offs with alternative land uses is necessary. Nevertheless, they are commonly neglected. This study addresses the challenge of incorporating land use aspects into ESOMs, with a focus on the unique context of Pantelleria Island. It aims to bridge the gap in methodologies for renewable energy potential assessment and model integration, considering the critical role of land pricing and availability. It combines geospatial data aggregation with model adaptation to include detailed land use aspects. The findings highlight the substantial impact of land costs on renewable energy planning, with land pricing significantly altering model outcomes. This research offers key insights for sustainable energy planning and underscores the importance of considering land use in energy transition strategies.

Study on optimal allocation of energy storage in multi-regional integrated energy system considering stepped carbon trading

Abstract In this study, an energy storage configuration optimization model of multi regional integrated energy system based on integrated scheduling and stepped Carbon emission trading is proposed. By analyzing the steady-state, dynamic, and variable operating characteristics of multi regional integrated energy systems, a distributed energy planning modeling method was adopted to construct energy storage configuration constraint objects and optimization models. The target scheme of energy storage configuration is optimized by using the results of integrated scheduling scheme and dynamic distribution analysis of ladder Carbon emission trading, and the parameters are optimally estimated by using Monte Carlo method and quadratic fitting algorithm. The simulation results show that this method has a strong scheduling capability in the context of stepped Carbon emission trading, can accurately assess the load demand and supply demand relationship, reduce carbon emissions, and improve energy efficiency, with an average of 0.9121.

EnergyScope Pathway: An open-source model to optimise the energy transition pathways of a regional whole-energy system

Due to the imperative nature of addressing climate change, the energy transition is currently underway, prompting the recognition of its urgency. Energy system optimisation models have emerged as crucial tools to assist policymakers in formulating laws and regulations that facilitate the transition towards carbon neutrality. While numerous models have been developed to explore various scenarios and define long-term objectives, only a few models focus on optimising the specific pathway to achieve these objectives. Many existing models lack the necessary time resolution to capture the integration of intermittent renewable energies; or are not open-source, creating a challenge in terms of transparency and reproducibility.This paper introduces EnergyScope Pathway, an open-source and documented model that addresses these limitations. It specifically optimises investment strategies for the whole-energy system over a 30-year period, or more, and optimising its hourly operation. This approach allows for a comprehensive evaluation of the effective integration of intermittent renewable energy sources. The model has a concise and efficient formulation, enabling its execution on personal laptops within approximately 15 min. By applying the model to the case study of Belgium, which presents challenges due to limited potential for renewable energy, we illustrate the importance of four pillars: energy efficiency, renewable energies, sector coupling, electrification and imports. The result pave the way to a new and incremental tool to support decision makers. In comparison to non open-source models, we verified the model’s results with similar studies and found consistency in terms of technico-economic estimations.