Renewable Scenarios Research Articles

Projected changes in seasonal and extreme summertime temperature and precipitation in India in response to COVID-19 recovery emissions scenarios

Fossil fuel and aerosol emissions have played important roles on climate over the Indian subcontinent over the last century. As the world transitions toward decarbonization in the next few decades, emissions pathways could have major impacts on India’s climate and people. Pathways for future emissions are highly uncertain, particularly at present as countries recover from COVID-19. This paper explores a multimodel ensemble of Earth system models leveraging potential global emissions pathways following COVID-19 and the consequences for India’s summertime (June–July–August–September) climate in the near- and long-term. We investigate specifically scenarios which envisage a fossil-based recovery, a strong renewable-based recovery and a moderate scenario in between the two. We find that near-term climate changes are dominated by natural climate variability, and thus likely independent of the emissions pathway. By 2050, pathway-induced spatial patterns in the seasonally-aggregated precipitation become clearer with a slight drying in the fossil-based scenario and wetting in the strong renewable scenario. Additionally, extreme temperature and precipitation events in India are expected to increase in magnitude and frequency regardless of the emissions scenario, though the spatial patterns of these changes as well as the extent of the change are pathway dependent. This study provides an important discussion on the impacts of emissions recover pathways following COVID-19 on India, a nation which is likely to be particularly susceptible to climate change over the coming decades.

Integrative techno-economic and environmental assessment for green H2 production by alkaline water electrolysis based on experimental data

Alkaline water electrolysis has been in the spotlight for green H2 production using electricity generated from renewable energy because of its high technology readiness level and low investment. However, commercialization of alkaline water electrolysis is challenging because of the low current density. Therefore, a techno-economic and environmental assessment was conducted for green H2 production by alkaline water electrolysis with experimental data from the advanced alkaline water electrolysis system of the Korea Institute of Energy Research (KIER) to identify the potential of this technology. Based on techno-economic analysis results, the stack cost and unit electricity price must be below 0.035 $ kWh−1 and 406 $ kW−1, respectively, to make this technology cost-competitive compared to the conventional H2 production method (< 2 $ kgH2−1). Furthermore, CO2 emissions of 30.7, 17.4, 7.04, and 3.12 kgCO2-eq kgH2−1 were obtained for the current, 2030, 2050, and renewable Korean electricity mix scenarios, respectively, indicating that both scenarios (Korean electricity mix in 2050 and renewable) have lower CO2 emissions than the conventional H2 production method (11.5 kgCO2-eq kgH2−1). In addition, environmental impact reductions of 89.8%, 83.6%, 65.9%, and 91.0% for CO2 emissions, stratospheric ozone depletion, fine particulate matter formation, and fossil resource scarcity, respectively, were obtained for the renewable scenario compared to the current Korean electricity mix. Therefore, the electricity source has significant effects on the economic feasibility and environmental impacts for green H2 production.

Decarbonizing the Chilean Electric Power System: A Prospective Analysis of Alternative Carbon Emissions Policies

In this paper, we investigate potential pathways for achieving deep reductions in CO2 emissions by 2050 in the Chilean electric power system. We simulate the evolution of the power system using a long-term planning model for policy analysis that identifies investments and operation strategies to meet demand and CO2 emissions reductions at the lowest possible cost. The model considers a simplified representation of the main transmission network and representative days to simulate operations considering the variability of demand and renewable resources at different geographical locations. We perform a scenario analysis assuming different ambitious renewable energy and emission reduction targets by 2050. As observed in other studies, we show that the incremental cost of reducing CO2 emissions without carbon capture or offset alternatives increases significantly as the system approaches zero emissions. Indeed, the carbon tax is multiplied by a factor of 4 to eliminate the last Mt of CO2 emissions, i.e., from 2000 to almost 8500 USD/tCO2 in 2050. This result highlights the importance of implementing technology-neutral mechanisms that help investors identify the most cost-efficient actions to reduce CO2 emissions. Our analysis shows that Carbon Capture and Storage could permit to divide by more than two the total system cost of a 100% renewable scenario. Furthermore, it also illustrates the importance of implementing economy-wide carbon emissions policies that ensure that the incremental costs to reduce CO2 emissions are roughly similar across different sectors of the economy.

Integration of seawater pumped storage and desalination in multi-energy systems planning: The case of copper as a key material for the energy transition

Copper is required for the transformation of global energy systems, and its production is intensive in water and energy. Several studies have investigated the design of renewable energy systems for copper production, aiming at reducing its environmental footprint. Here, we present the first integrated design for desalinated water and energy supply that considers all forms of energy required in the copper production process. For this, we develop an optimization model for planning integrated multi-vector energy and water systems. The model includes -for the first time in an energy system planning model- a concept for integrated pumped-hydro storage using sweater and reverse osmosis desalination. Our results show that water-energy systems for copper production based exclusively on renewables can today achieve costs as low as those of conventional fossil-based systems, when integrating multi-vector planning and seawater pumped-hydro storage. For a case study in Chile and in fully renewable scenarios, the specific cost of supplying energy and desalinated water decreases from 520–670 € per ton of copper at current costs to 330–360 by 2050. By 2030, using seawater pumped-hydro storage makes a fully renewable, multi-energy scenario the least-cost alternative. Such an integrated system is an enabler for reducing the environmental footprint that copper brings into the global energy transition.

Comparative Environmental, Economic, and Jobs Impacts in the USA of Renewable Energy Compared to Carbon Capture, Utilization, and Storage

This paper assesses the relative economic and jobs benefits of retrofitting an 847 MW USA coal power plant with carbon capture, utilization, and storage (CCUS) technology compared to replacing the plant with renewable (RE) energy and battery storage. The research had two major objectives: 1) Estimate the relative environmental, economic, and jobs impacts of CCUS retrofit of the coal plant compared to its replacement by the RE scenario; 2) develop metrics that can be used to compare the jobs impacts of coal fueled power plants to those of renewable energy. The hypotheses tested are: 1) The RE option will reduce CO2 emissions more than the CCUS option. We reject this hypothesis: We found that the CCUS option will reduce CO2 emissions more than the RE option. 2) The RE option will generate greater economic benefits than the CCUS option. We reject this hypothesis: We found that the CCUS option will create greater economic and jobs benefits than the RE option. 3) The RE option will create more jobs per MW than the CCUS option. We reject this hypothesis: We found that the CCUS option will create more jobs per MW more than the RE option. We discuss the implications of these findings.

Sun, Wind and Waves: EV Fossil Fuel Use and Emissions on an Isolated, Oil-Dependent Hawaiian Island

Electric power grids in remote communities around the world tend to be highly oil-dependent, unlike large, interconnected grids. Consequently, self-contained power grids such as the Hawaiian Islands’ have become testbeds for aggressive renewable energy integration (PV, wind, and ocean energy) and transportation electrification. However, there remains a lack of critical analysis for remote communities to determine the benefits of transitioning from internal combustion engine (ICE) vehicles to plug-in electric vehicles (EVs). This case study examines the impacts of this transition to EVs and renewable power generation on fossil fuel use and CO2 emissions on the oil-dependent Island of Oahu, Hawaii. Average passenger EVs were found to consume seven times less fossil fuel (the equivalent of 66 gallons of gasoline (GGe), than their gasoline-powered counterparts (455 gallons) in 2020. Average EVs also cut emissions in half, (2 MTCO2 versus 4 MTCO2). Several renewable power and EV transition scenarios were modeled to assess impacts out to 2050. Fossil fuel use and emissions plummet with more clean power and increasing EV numbers. By 2045, in the most ambitious scenario, all gasoline- and diesel-powered vehicles (passenger and freight) will consume a total of 8.8 billion GGe, and EVs 0.090 billion GGe (1%). ICE CO2 emissions will total 80 MMT, and EVs 4.4 MMT (5.5%). By 2050, the anticipated transition to electric passenger and freight vehicles combined with renewable power will lead to 99% less fossil fuel consumed, and 93% less CO2 emitted.

The future of coal investment, trade, and stranded assets

The future of coal investment, trade, and stranded assets

LEAP simulated economic evaluation of sustainable scenarios to fulfill the regional electricity demand in Pakistan

Some planning problems in the power sector of Pakistan have generated several issues ranging from gradual reliance on thermal-based expensive energy mix, supply shortages, subsidization, inefficiencies at different levels of the value chain, and resultantly, the circular debt. This situation in the power sector has also hampered the growth of allied economic sectors. This study uses the Long-range Energy Alternative Planning (LEAP) system for simulating Pakistan’s electrical system from 2016 to 2040. It has forecasted the electricity demand at the provincial level. At the supply side, five scenarios Business as Usual Scenario (BAUS), Coal Scenario (CS), Gas Scenario (GS), Nuclear Scenario (NS), and Renewable Scenario (RS) have been analysed based on the techno-economic and environmental parameters. The study has also included environmental externality costs for evaluating the actual costs and benefits of different scenarios. Contrary to the expectations, RS has a lower generation than even BAUS. The study has concluded that the generation from RS has five times lesser costs than BAUS, CS, and GS. NS can also be an alternative for the sustainable future of Pakistan. Generation from imported coal is not a good option, however, indigenous coal with clean coal technologies should be promoted. This paper proposes energy planners of the country to devise incentives for the utilization of indigenous energy resources including renewables on priority and then clean coal to reduce the energy crises of Pakistan.

Closing the void in energy planning modeling: Integrating local realities

Closing the void in energy planning modeling: Integrating local realities

Optimal storage systems for residential energy systems in British Columbia

In recent years, deployment of low carbon energy systems to supply electricity in residential buildings has increased. These energy systems typically integrate different renewable energy resources with energy storage systems to meet electrical energy demand. This paper applies the ”energy hub” model to various energy systems for residential buildings in British Columbia considering several scenarios. We explore the energy system changes in on-grid, off-grid and 100% renewable scenarios. In on-grid systems, the trade off between grid connection and energy storage is explored; the results shows that even the cheapest energy storage system is not feasible with the current cost of grid electricity. For off-grid systems with a diesel generator, storage technologies are used in some energy systems, however the systems still have carbon emissions. Finally, for 100% renewable off-grid systems, both Li-ion and pumped hydro storage systems are used to handle diurnal and seasonal intermittency.

Moving the system boundaries in decarbonization of large islands

During the last few years, the majority of the EU Governments are implementing suitable energy policies, promoting a transition toward full decarbonized sustainable energy systems by 2050. This paper focuses on creating comprehensive decarbonization scenarios for the island systems, using EnergyPLAN software. The island of Sardinia, Italy, was selected as a case study. The presented method uses scenario approach and seeks to push the boundary of the island energy system and use the locally available resources to partially supply the maritime transport connecting the island to the mainland. The reference model was validated using the data provided by EDGAR and IEA for 2017. The Business as Usual Scenario and High Renewable Scenario by 2030 and 2050 were modelled. The latter considers the conversion of the Sardinia energy system into a smart, highly efficient and integrated system. For the power system, improvements are made by the installation of small decentralized renewable systems, mainly including photovoltaic and wind technologies, and increase of storage technologies use. Thermal demand is reduced by building energy refurbishment actions, including the envelope insulation and use of heat pumps. For the transport sector, raise of the share of electrical vehicles is expected, as well as the possibility to introduce biofuels in both maritime and road transport sectors. Results clearly show that carbon neutrality by 2050 is a reasonable target in different decarbonization scenarios. The study also provides useful suggestions to be implemented in order to achieve this goal.

Implementing single- and multi-year sensitivity analyses to propose several feasible solutions for meeting the electricity demand in large-scale tourism sectors applying renewable systems

The tourism sectors consume a high amount of electrical power, which in most cases is supplied by the electrical grid and diesel generator. As a result, the electricity supply of large-scale tourism sectors is an important challenge in many countries. In current study, the feasibility study for applying the hybrid renewable energy systems to provide the power demand in social areas is investigated. Since Iran has a lot of tourism areas with a high amount of electricity consumption, a popular and crowded tourism sector in the Qom (a city in the center of Iran) is selected as the case study. The electricity consumption of this social area is found to be 23,808.91 kWh/day, which in the current system is supplied by the electricity grid. Also, the values of cost of energy and carbon dioxide emissions of current system are calculated to be 0.0931$/kWh and 5,741,214 kg/year, respectively. Regarding the available renewable resources of Qom city and the space limitations of this tourism sector, several combined renewable scenarios are simulated and assessed applying the HOMER Pro simulator. In continue, to identify the optimal scenarios, the techno–economic–environmental evaluation is implemented. The hybrid photovoltaic (PV)/wind turbine (WT)/battery (Bat)/diesel generator (DG)/biogas generator (BG), and grid/PV/WT are found as the optimum configurations for the off-grid and grid-connected systems, respectively. It was found that the Grid/PV/WT has a net present cost of 6.63 M$ and a cost of energy of 0.0234 $/kWh. Also, the cost of energy and net present cost of hybrid PV/WT/Bat/DG/BG are accounted for 0.1251$/kWh and 18.1 M$, respectively. By using this stand-alone system instead of current system, the CO2 emissions reduce by 61.87% (2,189,231 kg/year). In following, to assess the effects of load growth and PV degradation on system performance, the multi-year module of this software is employed. Based on the obtained findings, by applying the hybrid PV/WT/Bat/DG/BG instead of the current system, the contribution of emissions penalties to the overall net present cost diminishes from 15.47% to 0.73%. Finally, the heat map sensitivity analyses are applied to the economic and environmental parameters.

Day‐ahead renewable scenario forecasts based on generative adversarial networks

With the increasing penetration of renewable resources such as wind and solar, especially in terms of large-scale integration, the operation and planning of power systems are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised distribution learning method for renewable scenario forecasts that considers spatiotemporal correlation based on generative adversarial network (GAN), which has been shown to generate realistic time series for stochastic processes. We first utilize an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generate a large number of forecasted scenarios using stochastic constrained optimization. For validation, we use power generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The simulation results show that the generated trajectories not only reflect the future power generation dynamics, but also correctly capture the temporal, spatial, and fluctuant characteristics of the real power generation processes. The experimental comparisons verify the superiority of the proposed method and indicate that it can reduce at least 50% of the training iterations of the generative model for scenario forecasts.

On the road to net zero-emission cement: Integrated assessment of mineral carbonation of cement kiln dust

Climate change mitigation entails reaching net-zero emissions, which in turn requires unprecedented actions in all aspects of society. These include the cement industry, which represents 7% of global CO2 emissions. A proposed option for reducing CO2 emissions in the cement industry is the use of CO2-containing flue gas and cement kiln dust (CKD) for producing mineral carbonates that serve as non-reactive fillers for blending cement. This work proposes the mineral carbonation of CKD (MCCKD process) to convert CO2 to synthetic carbonates and analyzes under which conditions the MCCKD process can be a promising concept for the cement industry in Colombia. For this purpose, an existing conventional cement plant (base case) is compared with a hypothetical cement plant with MCCKD. Both systems are compared in an integrated techno-economic and environmental assessment and in eight emission scenarios with no full-abatement (NA) and full-abatement (FA) parameters, assuming a conventional energy mix and a renewable energy mix. Key findings show that implementing the MCCKD process increases by 27–30% the cost of goods manufactured (COGM) for a 7–8% reduction in CO2-emissions for fossil and renewable scenarios, respectively, in the NA scenario. In contrast, the FA scenarios result in a reduction of COGM by13–14% for fossil and renewable scenarios. The evaluation supports the economic feasibility of implementing the MCCKD within a conventional cement plant from a net-zero emissions perspective, with total CO2-captured costs ranging from 37.2 to 43.9USD/tCO2-captured (FA). The analysis shows that the MCCKD process, combined with transport and storage with renewable energy sources, has significant economic and environmental advantages compared to the same process based on fossil energy sources.

The contribution of distributed flexibility potentials to corrective transmission system operation for strongly renewable energy systems

Corrective operation of electrical transmission systems requires flexibility degrees of freedom to be reliably available when the system is in a critical state. In this study, we develop a method to quantify flexibility potentials from distributed and sector-coupling energy resources. We develop key performance indicators (KPIs) that correlate these potentials with the occurrence of critical transmission corridor loadings and, by that, quantify how often flexibility degrees of freedom are available when they are required. The method is based on a spatially and temporally resolved techno-economic dispatch optimization model and is tested on the example of Germany embedded in a central European energy system in the year 2030. In the considered strongly renewable scenario (208 GW installed renewable capacity) the supply often exceeds the demand. This leads to large-scale curtailments that can be used as a source for flexibility, particularly flexibility to re-increase generation. We find that, in total, curtailed onshore wind parks in the 110 kV-systems would have the potential to increase their feed-in power by 5 GW (upward flexibility) in the 10% most critical transmission scenarios. Central power-to-heat plants can provide 16.1 GW upward flexibility potential in these scenarios. Analyzing each transmission corridor separately, we find that corrective setpoint adjustments from the same technology combination, wind and power-to-heat, can completely compensate a sudden loss of 2 GW transmission capacity for up to 40% of critical timesteps on the transmission corridors within the given model. These findings indicate that flexibility from sector-coupling elements and decentral energy resources are a relevant resource and can be applied for corrective actions in transmission system operations.

Cenários para a matriz de geração de eletricidade do Ceará em 2050

Based on the worldwide proposition of energy matrix scenarios for 2050, the objective of this article is to present scenarios of electricity generation for Ceará in 2050, showing the participation of sources already used in the state's electric matrix and adding new ones. The article proposes three scenarios: a conservative one, where the present proportion of electricity generation sources of the state is maintained, a transitional one, with 50% of electricity generation from non-renewable sources and the other 50% from renewable sources, and 100% renewable, without the use of fossil or nuclear fuels. Estimates for 2050 in the state are obtained by extrapolating generation data from 2011 to 2017, reaching an estimated 94,775 GWh. In the conservative scenario, it is observed that half of this generation is made by thermoelectric plants and the other half by wind farms. In the transition scenario, dominated by the use of natural gas, the exponential growth of photovoltaic generation stands out. In the 100% renewable scenario, dominated by wind farms, in addition to the similar growth of photovoltaic generation as in the transition scenario, we highlight the use of urban solid waste and solar thermal concentration plants.

Comparing negative emissions and high renewable scenarios for the European power system

Emerging literature highlights the essential role played by decarbonised electricity generation in future energy systems consistent with the Paris Climate Agreement. This analysis compares the impacts of high levels of renewable electricity and negative emissions technologies on exploratory visions of the future EU power system (2050) in terms of emissions reduction, technical operation and total system costs. The analysis shows that high renewable power system scenarios coupled with low levels of negative emissions technologies, such as biomass carbon capture and storage (< 2% of installed capacity), can deliver a net-negative European power system at lower comparable cost without breaching published sustainable biomass potentials in Europe (or requiring imports) or geological storage potentials while also contributing to power system inertia. Direct air capture has the capability to further decrease overall carbon emissions. While carbon capture and storage and power-to-gas must overcome market, regulatory and social acceptance challenges, given their potential benefits to emissions reduction, costs and system operation their role in a future power system should be further explored.

Lessons from Modeling 100% Renewable Scenarios Using GENeSYS-MOD

The main aim of models has never been to provide numbers, but insights. Still, challenges prevail for modelers to use the best configuration of their models to provide helpful insights. In the case of energy system modelling, this becomes even more complicated due to increasing complexity of the energy system transition through the potential and need for sector coupling. This paper therefore showcases specific characteristics and challenges for energy system modelling of 100% renewable scenarios. The findings are based on various applications and modifications of the framework GENeSYS-MOD examining different regional characteristics for high renewable configurations in the world, China, India, South-Africa, Mexico, Europe, Germany, and Colombia. The paper elaborates on our experiences of the last years of choosing the best, yet still computable, configuration of GENeSYS-MOD with respect to spatial and time resolution as well as sufficient detailed description of the energy system transition effects. The aim of this paper is therefore twofold, to better understand and interpret existing models as well as to improve future modeling exercises.

Renewable scenario generation using stable and controllable generative adversarial networks with transparent latent space

With the growing penetration of renewable energy sources in power systems, it becomes increasingly important to characterize their inherent variability and uncertainty. Scenario generation is a key approach to provide a series of possible power scenarios in the future for the system planner and operator to make decisions. In this paper, a data-driven method is presented for renewable scenario generation using stable and controllable generative adversarial networks with transparent latent space (ctrl-GANs). The machine learning based algorithm can capture the nonlinear and dynamic renewable patterns without the need for modeling assumptions and complicated sampling techniques. The orthogonal regularization and spectral normalization are adopted to improve the training stabilization of the GAN model. To control the generation process, a relationship is built between features of the generated scenarios and latent vectors on the manifold. Moreover, several new metrics for GANs are used to evaluate the quality of the scenarios. The proposed approach is applied to generate realistic time series data of wind and photovoltaic power. The results demonstrate that our method has a better performance on numerical stabilization and is able to control the generation process with latent space.

A framework for estimating flexible resources according to future Korean renewables scenario: Robust optimization approach considering multiple uncertainties

This study presents a robust optimization model to secure flexibility under a high penetration of renewable energy systems in a future grid. An increase in renewable energy into a power system causes difficulties and complexities with regard to power system planning and operation owing to an increase in uncertainty. This trend can create an issue in the flexibility of a power system under a high penetration of renewable energy. To acquire sufficient flexibility, numerous flexible resources such as conventional generators with a ramping capability, energy storage systems and demand response program should be procured. Herein, we propose estimating the flexible resource capacity required to prevent a flexibility deficit when considering multiple uncertainties such as the effective capacity and 1-min power fluctuation rate of the renewable energy systems. To solve this problem, including uncertainties, through an optimization technique, we adopt a robust optimization to deal with uncertainty by constructing an uncertainty set and provide a robust solution considering the worst case within such a set. The robust optimization model was tested using data from the Korean electric power system for the year 2030. In addition, the results from a robust optimization are compared with the results from a deterministic approach.