Renewable Scenarios Research Articles

A 100% Renewable Energy Scenario for Finiq Municipality

The municipality of Finiq, located in southern Albania, is facing several challenges, such as population shrinkage, isolation, and loss of community identity. One of the main issues is related to a non-efficient supply of energy. In this regard, Finiq municipality is affected by frequent blackout of electricity. This is a dramatic issue both for households and companies. Such issue is a paradox since two big hydropower plants are present within the municipality. However, most of the electricity produced by the hydropower plants is supplied outside the municipality. The study is focused on the identification of the most suitable renewable energy sources as well as of their location within the municipality in order to transform Finiq in a 100% renew- able municipality. In addition to offering an affordable source of energy, renewable energy sources provide a wide range of socioeconomic and environmental benefits. The transformation to a 100% renewable energy system in all end-uses would generate new jobs, improve health due to cleaner air and water, as well as increase energy independence and economic growth. Fur- thermore, a 100% renewable scenario would allow to meet the European requirements in terms of energy efficiency and decarbonisation of energy, that can help Albania to make a further step towards the entrance into European Union. Due to agricultural character of the municipality, agrivoltaics (i.e. the simultaneous use of areas of land for both solar photovoltaic power generation and agriculture) can be a valuable option as many crops are suitable to be planted below the photovoltaic panels. Forest heritage is very huge in Finiq municipality, especially in the mountain area. A sustainable management of the forest heritage would allow to create a large amount of biomass that can be exploited for heating purpose, mostly in the area with higher heating demands.

P2M systems based on proton-conducting solid oxide cells: Future prospects and costs of renewable methanol production

As consequence of the transition towards sustainable energy sources, the future production of liquid energy carriers (e.g. methanol) via H2 supply pathways utilizing water electrolyzers (power-to-liquid) will most likely be based on fluctuating grid electricity or islanded renewable inputs. As a result, these production processes are subject to fluctuating operating conditions and varying production capacities, ultimately leading to uncertainties with respect to their process economics and profitability. Therefore, the impact of different electricity supply-side scenarios (static grid and intermittent grid or renewable supply) on the production economics of power-to-liquid processes needs to be assessed thoroughly for the upcoming decades. Methanol is considered as an essential base chemical which is widely known for its versatility and broad potential use contexts in future chemical industries and energy storage applications. Methanol production pathways powered by renewable electricity sources, also known as power-to-methanol processes, are characterized by low specific life-cycle emissions and are therefore of paramount interest. One possible renewable process chain features proton-conducting high temperature electrolyzers combined with a direct hydrogenation of CO2. In this paper, a techno-economic forecast study of this process chain is presented and specific production costs of renewable methanol under different electricity supply scenarios are determined and discussed for the years 2030 and 2050. The studies showed that flexible grid-supported scenarios through direct spot-market participation and renewable scenarios based on wind onshore production enable the largest production cost reduction potential in the upcoming decades. Minimum production costs of 740 € t−1MeOH (2030) and 415 € t−1MeOH (2050) are determined for a flexible operation of the system with spot-market participation, benefitting from times of low or even negative electricity prices. Among the renewable production scenarios, islanded power-to-methanol systems coupled to wind onshore plants were identified as the most beneficial configuration with ascertained production costs as low as 820 € t−1MeOH and 353 € t−1MeOH by 2030 and 2050, respectively.

Smart energy planning for the decarbonization of Latin America and the Caribbean in 2050

Latin America and the Caribbean is a region that has grown enormously in public policies and especially in the implementation of renewable energy generation plants, which is why it deserves to study its growth to provide long-term deep decarbonization routes. It has approximately 634 million inhabitants, and it is expected that by 2050 it will reach 784 million inhabitants, which worries researchers to establish a new model of energy supply in accordance with future demand. The concern does not end here, the new energy supply must be friendly to the environment and renewable energies are the appropriate option to avoid the increase in global temperature. The challenge of this research is to provide an adequate option for the decarbonization of Latin America and the Caribbean to achieve 100 % renewable energy by 2050. For this purpose, the energyPLAN tool is used, a flexible tool, very useful to transform large energy systems based on fossil fuels and achieve comprehensive 100 % renewable markets. Power systems are considered a challenging task and are widely studied around the world to avoid the great negative effects of altering the climate. Modeling the zero carbon energy system was implemented using the concept of smart energy. The combination shows that it is necessary to use a wide variety of energy sources and that no energy source more than 35 % stake, to increase energy security. The historic hydroelectric technology will continue to be an essential part of the Latin American energy mix. However, in 2050, the technologies that are destined to have the greatest participation will be wind turbines and solar photovoltaic with 32.7 % and 31 % respectively. Technologies with less participation such as renewable thermal and geothermal are also identified. Achieving a 100 % renewable energy mix by 2050 will prove to be economically beneficial. Based on the results obtained, it will be effective to achieve a 100 % renewable scenario when Latin America and the Caribbean form a solid regional alliance exclusive to fight for the environment and legislate favorably so that its member countries contemplate important budgets in a conscious, responsible and supportive way.

Economic evaluation of decarbonizing the electricity sector in the Dominican Republic

This paper presents an in-depth analysis of decarbonizing the electricity sector in the Dominican Republic, pivotal for addressing climate change and fostering economic growth. Employing the robust-decision making methodology, we studied multiple scenarios via computational models, capturing inputs from stakeholders and evaluating each scenario across 1,000 futures to capture deep uncertainty. Four scenarios were examined: baseline, reference, natural gas, and renewable. The renewable scenario emerged as the most advantageous, proposing the replacement of coal-fired power generation with renewable sources, primarily solar and wind, coupled with batteries. A significant investment, averaging US$3.3 billion, is necessary for this shift toward renewable energy; however, these investments are overcompensated by savings in operational costs. Crucially, this transition promises substantial benefits by 2050: an estimated cumulative average net economic gain of US$2.7 billion, an 8% reduction in average generation costs in 2050, the creation of 160,000 direct jobs, and the avoidance of circa 140 million tons of CO2. The findings underscore the feasibility and economic viability of transitioning to a 55% renewable energy generation by 2050. The study offers a critical roadmap for policymakers, highlighting renewable energy expansion, transmission grid strengthening, and strategic coal generation replacement, thus offering a comprehensive blueprint for the nation's energy transition.

Using deep learning to unravel the structural evolution of block-scale green spaces in urban renewal

Most urban green space (UGS) studies focus on city-level analysis, which is not conducive to urban renewal. We developed a new deep learning approach for characterising the structural evolution of block-scale UGS in urban renewal scenarios. Taking Beijing and Shanghai as models, we first segmented the study areas into block-scale grids and trained a random forest model based on a local climate zone framework to identify the spatial morphological classes of the grids. Second, we established a VGG16-based deep learning model to identify UGS structural types. Finally, we examined the associated patterns of grid spatial morphology and UGS structure under different renewal scenarios. We found that six distinct types of UGS structure are associated with different urban morphological classes. Spatial and temporal variations of UGS structures were observed in terms of direction of conversion, activity level, and extent of improvement to service level. Holistic and micro-renewal approaches drove positive evolution of block-scale UGS structures mainly by reducing building density and via precise optimization, respectively. Our results provide a reference for urban renewal policies and planning guidelines for block-scale UGSs in high-density cities.

A novel scenario generation method of renewable energy using improved VAEGAN with controllable interpretable features

With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features.

Sustainable Energy Transitions in Uganda: Influential Determinants of the Renewable Energy Landscape

This comprehensive review explores Uganda's journey towards sustainable energy transitions, emphasizing the pivotal factors influencing their evolution and acceptance. A thorough examination of the current state of Uganda's renewable energy sector is presented, shedding light on both the driving forces propelling its growth and the formidable challenges it confronts. The paper meticulously dissects the intricate interplay between governmental policies, technological advancements, financial mechanisms, and societal awareness, illuminating the dynamic landscape within which Uganda's sustainable energy initiatives unfold. Furthermore, an exploration of the socioeconomic and environmental implications of these transitions is undertaken, recognizing their profound impact on livelihoods, economies, and the ecological equilibrium. In addition to its analytical depth, this paper proffers a set of insightful and pragmatic recommendations, articulating a strategic roadmap aimed at accelerating progress towards a more sustainable, resilient, and environmentally conscious energy future in Uganda. By synthesizing robust research with actionable guidance, this review stands as an invaluable resource for policymakers, stakeholders, and individuals with a vested interest in Uganda's sustainable energy trajectory. It not only elucidates the current landscape but also provides a forward-looking perspective, positioning itself as a catalyst for positive change in the realm of sustainable energy. Keywords: Sustainable Energy, Renewable Energy, Energy Transition, Uganda, Policy, Socio-Economic Impacts, Environmental Sustainability, Renewable Scenario

Electrification of public buses in Jakarta, Indonesia: A life cycle study

Indonesia plans to mitigate the environmental emissions, particularly the carbon emissions, from the transport by replacing conventional buses with battery electric buses (BEBs). However, there are limited studies on the potential environmental benefits of BEBs and mostly focused on carbon emissions. In this study, the environmental impacts of adopting BEBs in Jakarta's public transportation system were examined using Life Cycle Assessment (LCA) to better understand its potential environmental impacts. Using LCA, the environmental impacts of BEBs were also compared with conventional buses across their life cycles, which included raw materials extraction until the end of life stages. The results showed diesel buses have generally lower environmental impacts than BEBs due to the high share of fossil fuels in the electricity generation in Indonesia. Scenario analysis showed that extending the life cycle, using different battery disposal methods, and using battery reuse could lead to higher environmental benefits in using BEBs. Among the scenarios considered in the study, prolonging the lifespan of the bus to 32 years, using electricity mix with a higher share of renewable energy and reusing the lithium-ion batteries, BEBs would have lesser environmental impact per kilometre. In particular, the particulate matter formation (PM2.5) dropped 21 %, while the overall life cycle of BEB using the highest renewable scenario showed an average of 25 % improvement compared to the baseline scenario regarding environmental impact.

Condition-based maintenance considering imperfect inspection for a multi-state system subject to competing and hidden failures

Unexpected failures may cause huge economic losses and bring about uncontrolled risks and damage to humans and the environment. Condition-based maintenance is considered one of the most effective maintenance methods, which can reduce and eliminate possible failures and consequently ensure the safety and operating process effectiveness of systems. Inspection quality is an important factor affecting the effect of condition-based maintenance since imperfect inspection may result in inappropriate maintenance decisions. However, the factor of imperfect inspection is rarely considered when maintaining multi-state systems subjected to competing and hidden failures. In this paper, a periodical inspection policy is utilized to identify all states of a system subject to a three-state degradation process and random shocks. Inspections for all system states are considered imperfect. Upon the inspection outcome, the system may be replaced by a failure-based corrective replacement once it is detected as failed, or is renewed by a defect-based preventive replacement if a defect is identified. Moreover, an age-based preventive replacement is also required if the system is still normal up to the preset number of inspections. Subsequently, a condition-based maintenance model is constructed based on the probabilities of different renewal scenarios. Eventually, the applicability and effectiveness of the proposed model are verified by a numerical example. The results indicate that minimizing the constructed maintenance model can achieve the optimal inspection interval, which is greatly influenced by the factor of imperfect inspection. This research can provide references for decision-makers to optimize inspection and maintenance strategies.

Optimal investment portfolio strategy for carbon neutrality of power enterprises

The reduction of carbon emissions in the power sector is a key step towards achieving the carbon neutrality target. This study explores the optimal investment strategy for power generation enterprises of China to achieve carbon neutrality. A portfolio optimization model with carbon emission penalty function is proposed. The real option is integrated into the model to obtain the investment value under uncertainties. Four scenarios are used in an empirical study, including baseline scenario, renewables scenario, cleaning fossil fuels scenario, and non-coal comprehensive energy scenario. The results detail the proportion of generation technology, portfolio revenue, and risk value. The generation of renewables is expected to account for more than 42 % in all scenarios that include both renewable energy and fossil energy, with wind generation accounting for the largest proportion. The cleaning fossil fuels scenario has the maximum conditional value at risk when the carbon neutrality target is included. Although fossil energy power generation is riskier, it occupies at least 40 % in the portfolio to safeguard stability. The electricity price and its volatility are positively correlated with the investment value and the conditional value at risk. The impact of the carbon dioxide price and its volatility on power generation projects is not monotonous.

Increasing the Energy Performance of Existing Buildings in Cold Climate Regions

High energy consumption in regions with harsh climatic conditions makes it difficult to reach energy consumption targets. This study considers design decisions affecting energy consumption for cold climate regions; the position of the building, its orientation, building envelope elements and building materials were examined. In line with these decisions, a field study was conducted by researching the energy-efficient renovation of existing social housing structures in cold climate regions. For the field study, social housing in Erzurum was selected, and the current heating energy consumption of the building was simulated using the Design Builder program. By creating energy-efficient renewal scenarios that can be economically and physically enacted, the U-values and thicknesses of the building envelope elements were changed, and 14 renewal simulations were carried out for five scenarios. The effects of each renovation scenario on the current heating energy consumption were examined, and the results were compared. With the most efficient replacement scenario, it was observed that the heating energy consumption decreased by an average of 31%. The data obtained informed decisions for building envelope element selection within the scope of energy-efficient renewal studies in cold climate regions and their impact on energy consumption.

Optimization-based expansion planning for power and hydrogen systems with feedback from a unit commitment model

This paper presents a novel long-term model for the joint expansion planning of power and hydrogen systems with short-term operational considerations. We propose linear reserve constraints with adjustable parameters within the planning model to address the reliability requirements from significant levels of variable renewable generation. These adjustable parameters are calibrated using an iterative training methodology that connects the long-term planning model with a short-term unit commitment model. A crucial aspect of our methodology is the iterative improvement of investment decisions through the feedback obtained by evaluating the operational performance of the investment decisions in different scenarios computed under the unit commitment model. Computational experiments in a case study of a large-scale Chilean power-hydrogen system show the effects of the proposed methodology on capacity expansion recommendations, where a correct balance between variable and flexible technologies is obtained. Moreover, when simulating the operation of the system for a full year, we observe that the proposed methodology generates an investment plan that achieves a lower total cost compared to other methodologies commonly used in the literature. Additional experiments in 100% renewable scenarios show how conventional methodologies reaches levels of not-supplied-energy between 2%–7%, while the proposed methodology achieves less than 1% in the same metric.

Virtual Inertia Response and Frequency Control Ancillary Services From Hydrogen Electrolyzers

This paper presents the modeling foundations to study the capabilities of hydrogen electrolyzers (HEs) to provide frequency control ancillary services (FCAS), including virtual inertia and primary and secondary frequency response. To do so, we propose a general, unified HE dynamic model for the electrolyzer stack circuit, power-electronics interface (PEI), and relevant converter-level control loops. The equivalent circuit of the stack is derived from its current transfer function, with poles and zero obtained from its step response characteristics (e.g., rise/settling time). The stack model also considers relevant physical nonlinearities and downstream hydrogen buffer/process operational constraints. The PEI control loops account for stack model parameters, hydrogen production operational constraints, stack temperature dynamics, and active power reference generation strategy for contingency and regulation frequency support services. Further, we propose a virtual synchronous machine (VSM) control approach to study the VSM HE capabilities to also provide virtual inertia response. We apply the modeling to both alkaline and proton exchange membrane (PEM) technologies, including design of appropriate control schemes. Finally, we assess the FCAS performance of alkaline and PEM HEs via dynamic simulation of the Australian south-east interconnection in a 50%-renewable scenario, also discussing comparison and cooperation with battery energy storage systems.

Renewable Scenario Generation Based on the Hybrid Genetic Algorithm with Variable Chromosome Length

Determining the operation scenarios of renewable energies is important for power system dispatching. This paper proposes a renewable scenario generation method based on the hybrid genetic algorithm with variable chromosome length (HGAVCL). The discrete wavelet transform (DWT) is used to divide the original data into linear and fluctuant parts according to the length of time scales. The HGAVCL is designed to optimally divide the linear part into different time sections. Additionally, each time section is described by the autoregressive integrated moving average (ARIMA) model. With the consideration of temporal correlation, the Copula joint probability density function is established to model the fluctuant part. Based on the attained ARIMA model and joint probability density function, a number of data are generated by the Monte Carlo method, and the time autocorrelation, average offset rate, and climbing similarity indexes are established to assess the data quality of generated scenarios. A case study is conducted to verify the effectiveness of the proposed approach. The calculated time autocorrelation, average offset rate, and climbing similarity are 0.0515, 0.0396, and 0.9035, respectively, which shows the superior performance of the proposed approach.

Conditional Style-Based Generative Adversarial Networks for Renewable Scenario Generation

Day-ahead scenario generationof renewable power plays an important role in short-term power system operations due to considerable output uncertainty included. In this paper, a deep renewable scenario generation model using style-based generative adversarial networks followed by a sequence encoder network, is developed to generate accurate and reliable day-ahead scenarios directly from historical data through different-level scenario style controlling and mixing, thus achieving better characterization of renewable spatial-temporal dynamics. Meanwhile, the integration of meteorological information serving as conditions enables our model to precisely capture the complex diurnal pattern and seasonality difference of renewable power. From wind and photovoltaic power perspectives, the effectiveness of the proposed model is validated on two real-world datasets reflecting region aggregation level and distributed power station level respectively. Numerical results demonstrate the superiority of model performance through both the statistical and power system scheduling analysis, compared to three benchmarks.

Integrated assessment on the implementation of sustainable heat technologies in the built environment in Harbin, China

Heating in built environments is an essential factor regarding energy consumption and CO2 emissions. Thus, the application of sustainable heating technologies is vital for reducing CO2 emissions. The literature indicates the requirement for a comprehensive assessment of the technical, economic, and environmental performances of various sustainable heating technologies and their implementation feasibility at the local level. Accordingly, this study presents a quantitative assessment relative to Harbin, a typical northern city with a coal-dominated heating system. Seven sustainable heating technologies were examined using current policy and future renewable scenarios. The results indicate that the examined heating technologies are technically feasible. Biomass heating saves costs and emissions (CO2 avoidance costs of 24–47 €/t), although fuel availability and storage management limit its implementation. Solar heating is a promising technology with reduced costs and low CO2 emissions (CO2 avoidance costs can decline by 50% from 2020 to 2050). However, its current resident acceptance is relatively low as lengthy investigations and periods for underground construction are required. Electric heating is preferable in terms of implementation feasibility; however, its economic competitiveness and environmental impact depend heavily on electricity prices and grid cleanliness (CO2 avoidance costs of 120–463 €/t). This study contributes to the existing literature on sustainable heat transition in China by providing informative local circumstances in Harbin and presenting assumption-making methods in detail when local data is not transparent. The integrated assessment provides solid evidence to facilitate decision-making in the clean heating transition in northern cities of China. The methods are applicable to other countries with similar heat-supply structures and climate conditions.

Modeling alternative scenarios for Egypt 2050 energy mix based on LEAP analysis

This study independently analyses various scenarios of electrical energy generation and consumption in Egypt for the period between 2020 and 2050 using LEAP and utilizing data provided by the Egyptian authorities. The energy mix case is distinguished by two features; future demand is not certain and existence of relatively high surplus capacity. As for various expectations of demand increase, the present study assumed the continuation of the same demand change for the period between the years 2009 and 2019 to be extended until 2050, (REF A) and an annual increase of 4.46%, (REF B). Three more scenarios were analyzed. (NON) scenario assumes cancellation of plans to construct a nuclear power facility. Two other scenarios examined the increase of renewable share to 42% by the year 2035 (REN 42), and to 70% by the year 2050, (REN 70). The results show that the natural gas contribution will decline till reaching its peak of 61.5% by 2050 for (NON) scenario. However, this share will be less than 16.5% by 2045 for (REN.70) scenario. Whilst, nuclear share will be 4.6% by 2050 for (REF.A) scenario. Moreover, it is found that the renewable scenarios have the lowest long-term production cost compared to other scenarios.

High energy capacity or high power rating: Which is the more important performance metric for battery energy storage systems at different penetrations of variable renewables?

Studies exploring the role and value of energy storage in deep decarbonization often overlook the balance between the energy capacity and the power rating of storage systems—a key performance parameter that can affect every part of storage operation. Here, we quantitatively evaluate the system-wide impacts of battery storage systems with various energy-to-power ratios (EPRs) and at different levels of renewable penetration. We take Jiangsu province in China as our case study, due to its high electricity consumption and aggressive renewable energy targets. Our results show the evolving role of storage: as renewable penetration increases, higher EPRs are favored, as they lead to system-wide cost reductions, lower GHG emissions, and higher power system reliability. Whereas existing studies make exogenous assumptions about the lifetime of storage, we show that lifetimes across EPRs and renewable scenarios span 10 to 20 years. Existing research can thus send false signals to investors and grid planners, delaying the deployment of storage and retarding the energy transition. By showing how different EPRs yield different benefits at different stages of the energy transition, our results help investors, policy makers, and system planners design forward-thinking and dynamic policies that encourage prudent storage uptake.

Evaluation of Australia’s Generation-Storage Requirements in a Fully Renewable Grid With Intermittent and Flexible Generation

Energy storage is crucial for grids with high renewable penetration to ensure reliable power supply during low renewable generation periods and address the intermittency associated with weather-dependent resources. However, sizing grid-scale storage is challenging due to its interdependence on renewable generation and load profiles. This paper investigates the minimization of storage requirements for Australian grids as a case study in a fully renewable scenario and examines how inflexible generation (such as solar and wind) and flexible generation (such as hydro) affect the capacity requirements. We extend our analysis to the economic trade-off between investing in increased storage versus intentional excess renewable generation. Subsequently, the optimum generation-storage requirement is analyzed, and regional requirements with and without interconnectors are estimated. Finally, we analyze storage annual utilization and present a sensitivity analysis to variations in technology costs. The quantitative results suggest that optimal storage size is contingent upon the renewable mix, and while solar generation is cost-competitive, higher contributions from wind generation and strategic dispatch of hydro generation are required to achieve an optimum generation-storage solution. We show that storage with a power capacity slightly lower than the mean annual demand with a duration of one day is required for Australia’s National Electricity Market (NEM). These findings underscore the importance of carefully balancing the renewable mix to achieve an efficient and cost-effective grid. Based on estimated future costs for long-duration storage and generation technology, the optimum generation-storage solution will translate into an investment of approximately 9.8% of the country’s Gross Domestic Product (GDP)—this investment is achievable when amortized over 10–15 years for the transition to a near-100% renewable grid.

Enhancing drought resilience and energy security through complementing hydro by offshore wind power—The case of Brazil

During atypical droughts, power systems with a heavy reliance on hydropower risk increased greenhouse gas emissions if they are balanced with fossil-fired generation. This work investigates the role of offshore wind energy in reducing the vulnerability of power systems dependent on such hydrological patterns, thereby eliminating this emission increase, using Brazil as a case study. Offshore wind potential and its complementarity with hydro resources are addressed by considering bias-corrected reanalysis data. Then, a cost-minimizing model is built to analyze the effect of integrating wind farms (considering bottom-fixed and floating structures and distance to shore) into the existing power system in Brazil. Applying a lower, median, and upper bias correction factor, potentials are reduced by 8%–44% compared to uncorrected data. Irrespective of systematic bias, the findings indicate a high complementarity between Northeastern wind regimes and most hydropower basins. The share of offshore wind energy grows in scenarios with reduced costs, but wind farms are part of the optimal system even with the current costs. With increasing wind power capacity, dynamic dispatch changes, and natural gas no longer plays a role in the dry season as it currently does, but only in the rainy season on a significantly reduced scale. Existing reservoirs support the integration of offshore wind farms into highly renewable scenarios, but they are insufficient in a complete fossil fuel phaseout, where other electricity storage must be deployed to help balance the system. Yet, power systems in the scenarios with large wind capacity have less stored hydropower in the dry season than in the current system, while they store more in the rainy season, implying a reduced risk of empty reservoirs. The Brazilian power system with offshore wind farms can eliminate 54.4 Mton CO2eq/year (97% of current power sector emissions) without additional electricity storage.