Energy System Model Research Articles

The role of carbon capture and storage to achieve net-zero energy systems: Trade-offs between economics and the environment

Carbon capture and storage can both reduce greenhouse gas emissions and provide negative emissions to contribute to the transition to a net-zero society. The contribution of carbon capture and storage has been investigated within cross-sectorial energy system models. However, such models commonly focus on cost and greenhouse gas emissions, while broader environmental impacts are investigated for individual technologies only. Here, we analyze economic and environmental impacts of the transition to net-zero emissions by combining energy system modeling with life-cycle assessment. We focus on the system-wide implications of carbon dioxide storage on economic or environmental impacts. In our investigation of the transition of the German energy system until 2045, net-zero emissions require a minimal amount of carbon capture and storage. However, increasing carbon dioxide storage beyond the minimum amount significantly lowers cost and environmental impacts in up to 13 out of 16 impact categories by avoiding investments into material-intensive technologies, such as power-to-methane or renewable power plants in areas with low generation potential. In scenarios without electricity imports, carbon dioxide storage ranges between 118Mt to 379Mt in 2045 with cost increasing by 105% when carbon dioxide storage is minimized. 84% of the cost increase is incurred for eliminating the final 23Mt of carbon dioxide stored.The benefits of applying carbon capture and storage are robust to variations in the amount of renewable electricity imports and residual emissions that require compensation. Hence, the results suggest that carbon capture and storage can offer economic and environmental benefits in the transition to net-zero energy systems beyond greenhouse gas emission mitigation.

Might future electricity generation suffice to meet the global demand?

Electricity supply is one of the critical issues in the energy field. Due to the high shares of greenhouse gases emissions, the electricity sector is experiencing a transition towards a progressively wider use of low-carbon technologies. At the same time, electrification of end-use sectors is identified as one of the most suitable mitigation strategies, although requiring larger electricity production. This paper relates the historical development trends for installed capacity of electricity production technologies to the theory of the S-curves, building a method to depict plausible developments in the electricity sector. Projections are performed considering the existence of an upper limit for industrial capacity development, and according to a path envisaging a revolutionary, an evolutionary and a maturity phase for technologies showing considerable growth trends. Oppositely, stagnation is taken into account for those not showing any remarkable progress. The computed curves are used to perform forecasts about electricity generation potentials until 2050, showing how the projected growth trend of electricity generation technologies would result in a production sufficient to meet the expected global demand, even excluding the contribution of fossil fuels in some cases. In perspective, the presented method can be applied to retrieve maximum capacity constraints for energy system models.

Validation of a Method to Select a Priori the Number of Typical Days for Energy System Optimisation Models

Studying a large number of scenarios is necessary to consider the uncertainty inherent to the energy transition. In addition, the integration of intermittent renewable energy sources requires complex energy system models. Typical days clustering is a commonly used technique to ensure the computational tractability of energy system optimisation models, while keeping an hourly time step. Its capability to accurately approximate the full-year time series with a reduced number of days has been demonstrated (i.e., a priori evaluation). However, its impact on the results of the energy system model (i.e., a posteriori evaluation) is rarely studied and was never studied on a multi-regional whole-energy system. To address this issue, the multi-regional whole-energy system optimisation model, EnergyScope Multi-Cells, is used to optimise the design and operation of multiple interconnected regions. It is applied to nine diverse cases with different numbers of typical days. A bottom-up a posteriori metric, the design error, is developed and analysed in these cases to find trade-offs between the accuracy and the computational cost of the model. Using 10 typical days divides the computational time by 8.6 to 23.8, according to the case, and ensures a design error below 17%. In all cases studied, the time series error is a good prediction of the design error. Hence, this a priori metric can be used to select the number of typical days for a new case study without running the energy system optimisation model.

Operation optimisation of integrated energy system

To effectively improve the absorption capacity of the integrated energy system (IES) to clean energy, reduce the carbon dioxide emissions of the system, and improve the economic benefits of the system, this paper constructs a comprehensive energy system model that includes both electricity to hydrogen and hydrogen to gas and introduces the carbon dioxide trading mechanism, the green certificate trading mechanism and the response mechanism to the load demand. Different scenarios are set for example analysis and the model is solved using Matlab software's CPLEX solver. The results show that the operation strategy of the IES considering demand response under the carbon dioxide–green certificate joint trading mechanism performs best, reducing the total cost of the system by 65.8% and the carbon dioxide emissions by 23.1%, which can provide a strong reference and guidance for the low carbon dioxide economy operation of the IES.

Transition toward net zero emissions - Integration and optimization of renewable energy sources: Solar, hydro, and biomass with the local grid station in central Italy

Clean energy technologies are playing a vital role in energy transition, realization of Net-Zero emission goals, and satisfying increasing energy demands. The objective of this work is to investigate the potentials of renewable energy sources and propose a state-of-the-art new grid connected energy system model, which increases the penetration of renewable energies in the medium-size Municipality of Perugia, Central Italy. The presented energy system model integrates and optimises renewable energy sources mainly consisting of hydro, solar, and biomass, by implementing new dispatch strategies and advanced mathematical model. The developed model maximises the power generation of each source based on the least Net Present Cost and the lowest Levelized Cost of Energy and balances the fluctuating load demand by using combined dispatch strategies. Furthermore, demand-side load management strategies are implemented for efficient energy management. An economic and environmental analysis has been performed to evaluate the impact of the proposed system. The results show that a significant amount of clean energy (33.2 GWh/year) is produced at the lowest Levelized Cost of Energy of 0.067 €/kWh. Moreover, up to 78% of the energy demand of the selected community is covered by renewable energies, which evades 13,452 tons of CO2 emission yearly.

Techno-economic feasibility analysis of hydrogen production by PtG concept and feeding it into a combined cycle power plant leading to sector coupling in future

Power-to-Gas offers an efficient opportunity to turn renewable energy into “green” hydrogen, establishing an inevitably remunerative spectrum of end-uses. In this spirit, the current paper intends to establish a techno-economic perspective by evaluating a hybrid energy system model for sizing hydrogen generation by water electrolysis from renewable energy and using it in thermal power plants for electricity production for localized grids in Pakistan. Modeling of this hybrid system is done using HOMER Pro. Hydrogen is produced using a hybrid energy system and then fed into a hypothetical combined cycle power plant to produce electricity. Hydrogen has economic dominance over batteries in terms of energy storage and cost of energy, as per results. Four scenarios regarding proposed system configurations are considered in this study pondering upon how hydrogen can be produced from the perspective of cleaner production and assessing it techno-economically. Firstly, base case scenario consisting of wind turbine generators, genset, solar panels, batteries, convertor, electrolyzer and hydrogen tank is evaluated. Scenario − 1 is analyzed on the basis of techno-economic evaluations, excluding Li-Ion batteries from the base case. For scenario – 2, Li-Ion batteries have been included and genset is excluded from the system architecture to provide backup power to the electrolyzer during hours of intermittency aiming at least interruption. Lastly, scenario – 3 is exclusive of the photovoltaics and all rest remain intact. The economic analysis is constructed on economic parameters like payback period, internal rate of return, and cost of energy, and multiple configurations of the system. The most feasible configuration yielded levelized cost of energy being 0.465 USD/kWh, internal rate of return being 17.3 %, and payback period of 2.3 years. Renewable energy sources are thoroughly assessed for coupling with the desalination plant and electrolyzer array. This system can further be expanded on a larger scale to transform the region into a hydrogen hub to produce green, high-energy–density hydrogen to be supplied to and utilized by multiple sectors.

The Double Dividend of International Cooperation for Climate Mitigation Cost Effectiveness and Public Health Cobenefits.

Current National Determined Contributions require strengthening to achieve the 2-degree target set in the Paris Agreement. Here, we contrast two mitigation effort strengthening ideas: the "burden-sharing" principle, which requires each region to meet the mitigation goal through domestic mitigation with no international cooperation, and the cooperation focused "cost effective conditional-enhancing" principle, which combines domestic mitigation with carbon trading and low-carbon investment transfer. By applying a burden-sharing model covering several equity principles, we analyze the 2030 mitigation burden for each region, then the energy system model generates the results for the carbon trade and the investment transfer for the conditional-enhancing plan, and an air pollution cobenefit model is used to analyze the cobenefit on air quality and public health. Here, we show that the conditional-enhancing plan leads to an international carbon trading volume of 339.2 billion USD per year and reduces the marginal mitigation cost of the quota-purchase regions by 25%-32%. Furthermore, the international cooperation incentivizes a faster and deeper decarbonization in developing and emerging regions, raising the air pollution health cobenefits by 18% to 731,000 avoided premature deaths annually compared to the "burden-sharing" principle, amounting to a reduction in the life value loss of 131 billion dollars per year.

Improving the representation of cost of capital in energy system models

Improving the representation of cost of capital in energy system models

Planning for the integration of renewable energy systems and productive zone in Remote Island: Case of Sebira Island

Sebira Island is an Indonesian remote island that relies on its energy source mostly from diesel-generated electricity. The island's main economic activities include activities related to the coastal and fishery sectors. This research paper evaluates long-term planning of a mini-grid hybrid energy system from solar, wind, and diesel in island level based on technical and economic aspects. The mini-grid hybrid system is designed not only to meet the electricity demands of residential, community, but also integrated with the development of productive zone to advance the local economy by enhancing the added value of local products and services. This study used TIMES model to obtain optimized energy system which will be used as an input for developing productive zone of the island. The optimization result is a portfolio of the energy system of Solar PV and wind turbine generators and Li-Ion battery storage. The total investment cost of the energy system is USD 130,000. In order for the designed hybrid-mini grid to be financially feasible, a minimum of 25% of the grant is required. Innovative financing lies in a low-rate crowdfunding scheme. Most schemed productive activities are financially feasible and competitively priced in the market. Designed energy system model integrated with the productive zone generates a yearly net profit of USD 103,700 and create jobs for 51 islanders.

Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System

Improvements in modelling energy systems of populous emerging economies are highly decisive for a successful global energy transition. The models used–increasingly open source–still need more appropriate open data. As an illustrative example, we take the Brazilian energy system, which has great potential for renewable energy resources but still relies heavily on fossil fuels. We provide a comprehensive open dataset for scenario analyses, which can be directly used with the popular open energy system model PyPSA and other modelling frameworks. It includes three categories: (1) time series data of variable renewable potentials, electricity load profiles, inflows for the hydropower plants, and cross-border electricity exchanges; (2) geospatial data on the administrative division of the Brazilian federal states; (3) tabular data, which contains power plant data with installed and planned generation capacities, aggregated grid network topology, biomass thermal plant potential, as well as scenarios of energy demand. Our dataset could enable further global or country-specific energy system studies based on open data relevant to decarbonizing Brazil’s energy system.

Life-cycle assessment of current and future electricity supply addressing average and marginal hourly demand: An application to Italy

The increase in overall electricity demand in recent years, together with the rapid and significant changes in electricity generation has motivated increased research addressing environmental impacts of current and future electricity generation and supply systems. This article presents a comprehensive life-cycle assessment (LCA) of electricity generation and supply for the Italian current mix and future scenarios, for a wide set of environmental impacts and indicators, addressing intra-year variations, and including average and marginal demand impact perspectives. The generation mix was modelled for a 3-year period (2018–2020) with hourly and country-specific data, for the main generation technologies; and two future scenarios (for 2030) were modelled applying the EPLANopt energy system model. While future scenarios - with larger renewable energy shares - resulted in reduced environmental impacts in most categories, they were associated with burden shifts. In particular, increased shares of solar photovoltaic generation in future scenarios resulted in significant contributions to mineral and metal resource depletion and to land use impacts. The high penetration of renewable energy sources in future scenarios was also associated with important seasonal and hourly variations, demonstrating the importance of addressing intra-year temporal variations. Moreover, complementing an average with a marginal demand perspective provided insight on potential impacts of demand changes. When considering a marginal demand perspective, future scenarios offered no clear benefits in terms of global warming, for example. The research demonstrated the need for comprehensive and detailed environmental impact assessments with fine time resolution, which can assess seasonal and intra-daily variations, and the evaluation of environmental impacts with both average and marginal demand perspectives highlighted the complementary nature of the two in providing insightful results. The approach is fully replicable to other countries and regions - it can improve LCAs of electricity generation and supply and provide robust results to adequately inform decision-making on electricity generation and demand management, toward more sustainable energy systems.

Modeling and control of islanded DC microgrid fed by intermittent generating resources

In this paper an islanded microgrid fed through the wind and solar energy resources is presented. The power flow within the microgrid is controlled using a Neutral Point Clamped Dual Active Bridge (NPC-DAB) converter. In the proposed dc microgrid, the solar energy source is associated at the low voltage (LV) bus and the wind energy source is connected at the high voltage (HV) bus. A permanent magnet synchronous generator (PMSG) machine is used in wind energy conversion system. The real time solar radiation and wind speed data of Rupangarh, Rajasthan, India is used as an input for renewable energy resource. The NPC-DAB will work as a power electronics juncture for expediting the energy exchange in the islanded DC Microgrid. The proposed closed loop controller based on the capacitor voltage and load voltage will expedite a complete automatic operation of the islanded DC-microgrid considering various load changes. The system is studied without storage element as the automatic control of energy generation and load feeding is carried out by the NPC-DAB, also this makes the scheme cost effective. The optimum duty ratios for NPC-DAB operation are obtained and thus the increased load demand is met. The modeling of PMSG, NPC-DAB and wind energy system is discussed in details in this work. The proposed system is studied in MATLAB/Simulink environment and results are obtained for different load variations. All the wind control parameters, NPC-DAB waveforms, load waveforms are also plotted using MATLAB.

A Novel Flexible Method for Optimal Operation Modeling of Multi-Carrier Energy Systems

This research suggests a new flexible method for the optimal operation of multi-carrier energy systems based on optimization techniques for different weather conditions and seasons. The results apply to different modes (a hot summer day, a cold winter day and a normal day). A new cost function from the operator’s point of view is suggested considering the constraints on consumable gas supply and electrical energy in the cold and hot seasons respectively. Furthermore, a novel algorithm is suggested that gives the economical values of energy carriers, final cost, and the percentage of the direct electrical energy supply and outside the hub. In this method, load data, related prices, constraints, and penalties are applied to the suggested model, and afterward hub optimization assists in finding the optimal operation and costs. MATLAB software is used to check the results and the simulation outputs confirming the effectiveness of the proposed method.

The impact of cautious coal power phase-out on decarbonization of China's iron and steel industry

Existing studies are probably overly optimistic about the coal power phase-out and lack analysis of the environmental impact of uncertainty in power mix optimization. This will weaken the reliability of the iron and steel industry (ISI)'s low carbon pathway and undermine the targets of CO2 emission reduction. To fill this gap, this study constructs a bottom-up energy system model that includes iron and steel (IS) production, power generation, and hydrogen production. By incorporating five representative CO2 reduction measures and two electricity structure development paths, namely the orderly phase-out of coal power scenario (OP scenario) and the cautious phase-out of coal power scenario (CP scenario), the integrated impact on CO2 emissions is analyzed. Compared with existing studies, this study focuses more on the uncertainty of power mix optimization and devotes to enhance the ability of the ISI to cope with this uncertainty. The results show that the power mix with a high proportion of coal power will significantly increase the difficulty of decarbonization of the ISI. In 2050, the ISI will emit at least 600.53 Mt of CO2 in the CP scenario, i.e., 124.10 Mt CO2 higher than in the OP scenario, and will require greater reliance on CO2 emissions capture, utilization, and storage (CCUS) and carbon sinks to achieve zero carbon emissions. Among the five measures considered, the high proportion of coal power in the power mix will entail a loss in the emissions reduction effect of hydrogen metallurgy, which will cumulatively emit 235.18 Mt CO2 in the period 2020–2050. The active promotion of energy-saving technologies and the increase of the scrap ratio can abate the negative impact of the cautious phase-out of coal power. These findings are expected to provide the government with references to formulate mitigation policies for the ISI in China and in other countries under the uncertainty of renewable energy development.

Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling.

Background: Energy system optimisation models (ESOMs) are commonly used to support long-term planning at national, regional, or continental scales. The importance of recognising uncertainty in energy system modelling is regularly commented on but there is little practical guidance on how to best incorporate existing techniques, such as global sensitivity analysis, despite some good applications in the literature. Methods: In this paper, we provide comprehensive guidelines for conducting a global sensitivity analysis of an ESOM, aiming to remove barriers to adopting this approach. With a pedagogical intent, we begin by exploring why you should conduct a global sensitivity analysis. We then describe how to implement a global sensitivity analysis using the Morris method in an ESOM using a sequence of simple illustrative models built using the Open Source energy Modelling System (OSeMOSYS) framework, followed by a realistic example. Results: Results show that the global sensitivity analysis identifies influential parameters that drive results in the simple and realistic models, and identifies uninfluential parameters which can be ignored or fixed. We show that global sensitivity analysis can be applied to ESOMs with relative ease using freely available open-source tools. The results replicate the findings of best-practice studies from the field demonstrating the importance of including all parameters in the analysis and avoiding a narrow focus on particular parameters such as technology costs. Conclusions: The results highlight the benefits of performing a global sensitivity analysis for the design of energy system optimisation scenarios. We discuss how the results can be interpreted and used to enhance the transparency and rigour of energy system modelling studies.

The Impacts of Energy Efficiency Modelling in Policy Making

Bottom-up energy system models have been used extensively to analyse future energy scenarios, addressing a wide variety of policy questions. This paper focuses on energy efficiency, a key energy, climate, and economic policy area where several examples of energy system model applications can be found in the literature. This paper analyses how different studies implement energy efficiency scenarios in energy system models and explains how the approach taken can affect the results significantly, potentially affecting policy decisions. This analysis contributes to understanding how this type of modelling framework considers energy efficiency policy issues and the extent of insight provided, or not, on different dimensions. With the aim of identifying ‘best practice’ in using energy system models to inform effective analysis of energy efficiency policy, the UK TIMES energy system model is used to implement five different energy efficiency scenarios for residential heating following different modelling approaches and replicating scenarios available in the literature. The study concludes that energy efficiency scenarios, implementing the same target, produce significantly different results in terms of technology mix, energy use, emissions and costs. Additionally, the outcomes show that there is no overall best energy efficiency scenario, as each impacts on different policy targets, which could come into conflict with each other.

Development of fuzzy logic-based demand-side energy management system for hybrid energy sources

Demand-side energy management techniques, such as load shielding, shifting, and delaying appliance operation during peak periods, are typically used to reduce electricity costs at the expense of users’ comfort. To address these challenges, especially where operational delays are unacceptable, distributed generation (DG) is frequently incorporated into the grid system to improve power balance and total energy costs. However, dynamic load demands and varying outputs from renewable DG sources such as solar photovoltaic (PV) systems make energy management in microgrids (MGs) extremely challenging. Moreover, most of the existing studies in this domain focus on objective functions that are geared on optimising the economic balance between cost and value of MG operation over a certain time period. Nevertheless, research that took into consideration the stochastic behaviour of DG’s subsystems in addition to cost and benefit of MG operation are still limited. This current study proposed a fuzzy logic control (FLC) integrated energy management system (EMS) for commercial loads with hybrid grid-solar PV/battery energy system. The proposed technique intelligently selects energy sources using the grid energy cost and the state of charge (SoC) of the solar PV/battery at any time of the day. The EMS operate the loads at a reduced cost without any operational delay or shifting. The system was implemented in the MATLAB/Simulink environment, and the techno-economic feasibility of energy cost savings was investigated by comparing the developed scheme with the Homer hybrid energy system model for a hotel building. The developed EMS reduced energy costs by an average of 11.87 % per day and 7.94 % over a 20-year lifetime.

Influence of hydrogen production on the CO2 emissions reduction of hydrogen metallurgy transformation in iron and steel industry

The transformation of hydrogen metallurgy is a principal means of promoting the iron and steel industry (ISI) in reaching peak and deep emissions reduction. However, the environmental impact of different hydrogen production paths on hydrogen metallurgy has not been systemically discussed. To address this gap, based on Long-range Energy Alternatives Planning System (LEAP), this paper constructs a bottom-up energy system model that includes hydrogen production, iron and steel (IS) production, and power generation. By setting three hydrogen production structure development paths, namely the baseline scenario, business-as-usual (BAU) scenario, and clean power (CP) scenario, the carbon dioxide (CO2) emissions impact of different hydrogen production paths on hydrogen metallurgy is carefully evaluated from the perspective of the whole industry, and each IS production process. The results show that, under the baseline scenario, the hydrogen metallurgy transition will help the CO2 emissions of ISI peak at 2.19 billion tons in 2024, compared to 2.08 billion tons in 2020, and then gradually decrease to 0.78 billion tons in 2050. However, different hydrogen production paths will contribute to the reduction or inhibit the reduction. In 2050, the development of electrolysis hydrogen production with renewable electricity will reduce CO2 emissions by an additional 48.76 million tons (under the CP scenario), while the hydrogen production mainly based on coal gasification and methane reforming will increase the additional 50.04 million tons CO2 emissions (under the BAU scenario). Moreover, under the hydrogen production structure relying mainly on fossil and industrial by-products, the technological transformation of blast furnace ironmaking with hydrogen injections will leak carbon emissions to the upstream energy processing and conversion process. Furthermore, except for the 100% scrap based electric arc furnace (EAF) process, the IS production process on hydrogen-rich shaft furnace direct reduced iron (hydrogen-rich DRI) have lower CO2 emissions than other processes. Therefore, developing hydrogen-rich DRI will help the EAF steelmaking development to efficiently reduce CO2 emissions under scrap constraints.

Maximising the benefits of renewable energy infrastructure in displacement settings: Optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda

Humanitarian organisations typically rely on expensive, polluting diesel generators to provide power for services in refugee camps, whilst camp residents often have no access to electricity. Integrating solar and battery storage capacity into existing diesel-based systems can provide significant cost and emissions savings and offer an opportunity to provide power to displaced communities. By analysing monitored demand data and using computational energy system modelling, we assess the savings made possible by the integration of solar (18.4 kWp) and battery (78 kWh) capacity into the existing diesel-powered mini-grid in Mahama Refugee Camp, Rwanda. We find that the renewables infrastructure reduces fuel expenditure by $41,500 and emissions by 44 tCO2eq (both 74%) over five years under the generator’s current operational strategy. An alternative strategy, with deeper battery cycling, unlocks further savings of $4100 and 12.4 tCO2eq, using 33% of battery lifetime versus 15% under the original strategy. This reduces the cost of electricity by 33% versus diesel generation alone, whilst more aggressive cycling strategies could prove economical if moderate battery price decreases are realised. Extending the system to businesses in the camp marketplace can completely offset the system fuel costs if the mini-grid company charges customers the same tariff as the one it uses in the host community, but not the national grid tariff. Humanitarian organisations and the private sector should explore opportunities to integrate renewables into existing diesel-based infrastructure, and optimise its performance once installed, to reduce costs and emissions and provide meaningful livelihood opportunities to displaced communities.

Modeling and optimization of a hybrid renewable energy system integrated with gas turbine and energy storage

The inherent fluctuation and intermittence of wind power and solar photovoltaics pose great difficulty for stable power grid operation. Aiming at enhancing their exploitation efficiency, this paper presents a modeling study of a large-scale renewable energy system that is backed by gas turbine power plant and energy storage. From a full-life-cycle perspective, the system configuration and power dispatch strategy were optimized by coordinating system economy and carbon emission. The results indicate that the integration of gas turbine and hydrogen energy storage reduces carbon emissions and renewable curtailment but with high costs. To improve the system economy, extra electrochemical energy storage is incorporated to share the compensation when the grid shortfall is below the minimum gas turbine load. The collaborative hydrogen and electrochemical energy storage scheme improves the operating conditions of the gas turbine and significantly saves natural gas consumption, resulting in better system economy and carbon reduction. The minimum carbon emission of the proposed hybrid renewable energy system is 37.2% lower than the scenario with no renewable power.