Renewable Energy Scenario Research Articles

Correction: Zahraoui et al. Current Status, Scenario, and Prospective of Renewable Energy in Algeria: A Review. Energies 2021, 14, 2354

In the original publication [...]

Geographical balancing of wind power decreases storage needs in a 100% renewable European power sector

To reduce greenhouse gas emissions, many countries plan to massively expand wind power and solar photovoltaic capacities. These variable renewable energy sources require additional flexibility in the power sector. Both geographical balancing enabled by interconnection and electricity storage can provide such flexibility. In a 100% renewable energy scenario of 12 central European countries, we investigate how geographical balancing between countries reduces the need for electricity storage. Our principal contribution is to separate and quantify the different factors at play. Applying a capacity expansion model and a factorization method, we disentangle the effect of interconnection on optimal storage capacities through distinct factors: differences in countries' solar PV and wind power availability patterns, load profiles, as well as hydropower and bioenergy capacity portfolios. Results indicate that interconnection reduces storage needs by around 30% in contrast to a scenario without interconnection. Differences in wind power profiles between countries explain around 80% of that effect.

Impacts of Green Energy Expansion and Gas Import Reduction on South Korea’s Economic Growth: A System Dynamics Approach

South Korea, ranking ninth among the largest energy consumers and seventh in carbon dioxide emissions from 2016 to 2021, faces challenges in energy security and climate change mitigation. The primary challenge lies in transitioning from fossil fuel dependency to a more sustainable and diversified energy portfolio while meeting the growing energy demand for continued economic growth. This necessitates fostering innovation and investment in the green energy sector. This study examines the potential impact of green energy expansion (through integrating renewable energy and hydrogen production) and gas import reduction on South Korea’s economic growth using a system dynamics approach. The findings indicate that increasing investment in green energy can result in significant growth rates ranging from 7% to 35% between 2025 and 2040. Under the expansion, renewable energy scenario (A) suggests steady but sustainable economic growth in the long term, while the gas import reduction scenario (B) displays a potential for rapid economic growth in the short term with possible instability in the long term. The total production in Scenario B is USD 2.7 trillion in 2025 and will increase to USD 4.8 trillion by 2040. Scenario C, which combines the effects of both Scenarios A and B, results in consistently high economic growth rates over time and a substantial increase in total production by 2035–2040, from 20% to 46%. These findings are critical for policymakers in South Korea as they strive for sustainable economic growth and transition to renewable energy.

An Action‐Oriented Approach to Make the Most of the Wind and Solar Power Complementarity

AbstractSolar and wind power are called to play a main role in the transition toward decarbonized electricity systems. However, their integration in the energy mix is highly compromised due to the intermittency of their production caused by weather and climate variability. To face the challenge, here we present research about actionable strategies for wind and solar photovoltaic facilities deployment that exploit their complementarity in order to minimize the volatility of their combined production while guaranteeing a certain supply. The developed methodology has been implemented in an open‐access step‐wise model called CLIMAX. It first identifies regions with homogeneous temporal variability of the resources, and then determines the optimal shares of each technology over such regions. In the simplistic application performed here, we customize the model to narrow the monthly deviations of the total wind‐plus‐solar electricity production from a given curve (here, the mean annual cycle of the total production) across five European domains. For the current shares of both technologies, the results show that an optimal siting of the power units would reduce the standard deviation of the monthly anomalies of the total wind‐plus‐solar power generation by up to 20% without loss in the mean capacity factor as compared to a baseline scenario with an evenly spatial distribution of the installations. This result further improves (up to 60% in specific regions) if the total shares of each technology are also optimized, thus encouraging the use of CLIMAX for practical guidance of next‐generation renewable energy scenarios.

Pharmacophore-Modeling-Based Optimal Placement and Sizing of Large-Scale Energy Storage Stations in a Power System including Wind Farms

The world is targeting fully sustainable electricity by 2050. Energy storage systems have the biggest role to play in the 100% renewable energy scenario. This paper presents an optimal method for energy storage sizing and allocation in a power system including a share of wind farms. The power system, which is used as a test system, is a modified version of the IEEE 39 bus system. The optimization is applied using novel pharmacophore modeling (PM), which is compared to state-of-the-art techniques. The objective of the optimization is to minimize the costs of power losses, peak demand and voltage deviation. The PM optimization is applied using two methods, namely, weighting factor and normalization. The optimization and simulation are applied in the DIgSILENT power factory software application. The results show that normalization of PM optimization drives the power system to less cost in terms of total power losses by up to 29% and voltage deviation by up to 4% and better covers peak demand than state-of-the-art optimization techniques.

Analysis of Smart Energy Systems and High Participation of V2G Impact for the Ecuadorian 100% Renewable Energy System by 2050

This research presents a 100% renewable energy (RE) scenario by 2050 with a high share of electric vehicles on the grid (V2G) developed in Ecuador with the support of the EnergyPLAN analysis tool. Hour-by-hour data iterations were performed to determine solutions among various features, including energy storage, V2G connections that spanned the distribution system, and long-term evaluation. The high participation in V2G connections keeps the electrical system available; meanwhile, the high proportions of variable renewable energy are the pillar of the joint electrical system. The layout of the sustainable mobility scenario and the high V2G participation maintain the balance of the electrical system during most of the day, simplifying the storage equipment requirements. Consequently, the influence of V2G systems on storage is a significant result that must be considered in the energy transition that Ecuador is developing in the long term. The stored electricity will not only serve as storage for future grid use. Additionally, the V2G batteries serve as a buffer between generation from diversified renewable sources and the end-use stage.

Environmental impact scenarios of organic fraction municipal solid waste treatment with Black Soldier Fly larvae based on a life cycle assessment

Biowaste treatment with Black Soldier Fly (BSF) larvae is an alternative option for organic waste valorization. Its environmental impacts should be assessed and compared with conventional treatment options. The research aims to evaluate the treatment of organic fraction of municipal solid waste (OFMSW) with BSF larvae through a life cycle assessment (LCA). This study employed data inventories from literature and aimed to provide a wide range of production parameter values to identify the potentialities of BSF treatment in the best-case and worst-case scenarios. The SimaPro9, the database Ecoinvent3.5, and the impact assessment method IMPACT 2002+ have been employed for the analysis. A sensitivity analysis of relevant parameters was conducted, considering the avoided impacts that can be obtained thanks to the exploitation of larvae proteins for bioplastics or fishmeal production. Research findings highlight six main environmental impact indicators: respiratory inorganics (kg PM2.5-eq), ozone layer depletion (kg CFC-11-eq), terrestrial ecotoxicity (kg TEG soil), land occupation (m2 organic arable), global warming (kg CO2-eq), and non-renewable energy (MJ primary). The most relevant process generating impacts is BSF breeding, followed by boiling, storage, and OFMSW treatment. The environmental performance is better when the conventional fishmeal substituted, thanks to BSF larvae production, is made from areas 10,000 km far, implementing a 100% renewable energy scenario, reducing the energy consumption by 50%, increasing the lifespan of the equipment to 15 years, and products are employed locally. The current study represents the first attempt to evaluate the global higher or lower environmental impact scenario related to OFMSW treatment through BSF larvae.Graphical

Developing an Integrated Energy Demand-Supply Modeling Framework for Scenario Analysis of the Low Carbon Emission Energy System in Zambia

This study develops an integrated modeling approach to analyze the effects of different scenarios compared to a baseline (2019) scenario on energy demand and supply in Zambia. Aiming at minimizing costs, the model combines available resources and technologies, adhering to techno-economic and environmental limits. On the demand side, the study uses time series analysis to forecast future electricity demand in Zambia. The supply-side energy model identifies the optimal combination of resources and technologies needed to satisfy exogenously specified electricity demand levels at the least cost. The baseline scenario results revealed that the power outages in Zambia, especially at peak times, have been attributed to reliance on imported fuel for power generation, the slow-paced integration of renewable resources, and not fully utilizing resource potential for the supply side. To overcome the power outages and meet the electricity demand in 2035, a comprehensive scenario analysis was conducted, including the main scenarios of 30% integration of renewable energy, no coal power generation, and low emission targets (10%, 20%, 30%, 40%, and 50% reduction from the baseline). The expected CO2 reductions from the renewable energy scenario, no coal scenario, and 50% low emission target are estimated at 5222 kilotons in 2035, respectively.

Similarity-Based Optimization Framework for Curtailment Service Providers Through Collaborative Filtering and Generalized Dynamic Factor Model

We propose a novel demand response framework based on two similarities in building electricity consumption patterns for curtailment service providers (CSP)s to optimally manage the aggregated building energy. We find the first similarity in spatio-temporal correlations of building demand. To avoid conflicts of interests among buildings, we synthesize building demand and renewable energy scenarios based on that similarity by using the modified generalized dynamic factor model. We find the second similarity in hidden cost functions of discomfort indices for temperature and light. The cost function of a new building is extracted through the collaborative filtering based on the second similarity. Extracted cost functions allow us to align electricity and discomfort costs in the objective function and to cluster buildings to reduce computation time. Two similarities are used to improve the co-optimized day-ahead and real-time market decision process and respond to regulation signals through a two-stage stochastic optimization. Based on building energy statistics, we verify the optimality, efficiency, and comfortability of our strategy by showing that it has a lower cost, longer time period when residents feel comfortable, and lower computation time than existing strategies, particularly when a new building is aggregated.

Current Scenario of Solar Energy Applications in Bangladesh: Techno-Economic Perspective, Policy Implementation, and Possibility of the Integration of Artificial Intelligence

Bangladesh is blessed with abundant solar resources. Solar power is considered the most desirable energy source to mitigate the high energy demand of this densely populated country. Although various articles deal with solar energy applications in Bangladesh, no detailed review can be found in the literature. Therefore, in this study, we report on the current scenario of renewable energy in Bangladesh and the most significant potential of solar energy’s contribution among multiple renewable energy resources in mitigating energy demand. One main objective of this analysis was to outline the overall view of solar energy applications in Bangladesh to date, as well as the ongoing development of such projects. The technical and theoretical solar energy potential and the technologies available to harvest solar energy were also investigated. A detailed techno-economic design of solar power applications for the garment industry was also simulated to determine the potential of solar energy for this specific scenario. Additionally, renewable energy policies applied in Bangladesh to date are discussed comprehensively, with an emphasis on various ongoing projects undertaken by the government. Moreover, we elaborate global insight into solar power applications and compare Bangladesh’s current solar power scenario with that of other regions worldwide. Furthermore, the potential of artificial intelligence to accelerate solar energy enhancement is delineated comprehensively. Therefore, in this study, we determined the national scenarios of solar power implementation in Bangladesh and projected the most promising approaches for large-scale solar energy applications using artificial intelligence approaches.

A Review on Renewable Energy Scenario in Ethiopia

Although Ethiopia is one of the world's fastest-growing economies, access to sustainable energy and cutting-edge clean energy technology remains a major concern. The government is making significant efforts to generate renewable energy and provide more access to its citizens. Despite this, traditional fuels (charcoal, fuel wood, dung cakes, and agricultural waste) account for around 87 percent of Ethiopia's energy use, and they pose a range of health and environmental risks. Solar, hydro, wind, and geothermal resources abound in the nation, but only 5% of the country's total hydroelectric capacity is being used; while, the rest is either underutilized or underdeveloped. An in-depth look at Ethiopia's renewable energy potential, as well as the opportunities and problems it faces, is presented in this review. With a combined installed capacity of over 7000 MW, hydropower and wind power are the most promising renewable energy sources in Ethiopia as of yet. It is hoped that this assessment will shed light on how Ethiopia can harness and maximize the use of its abundant renewable energy sources.

Green Energy -Overview

Abstract: Green energy definition, Source of Green energy, India’ s Energy scenario and demand of Renewable energy, Green energy types, Renewable Energy -outlook, Financing option for Green Energy and Importance of Green Energy. Green Energy is the primary source of reducing the India’s Carbon foot print.

Renewable Energy Scenarios for Sustainable Electricity in Malaysia and the Application of Analytical Hierarchy Process (AHP) for Decision-making

Renewable energy (RE) is an important addition to existing energy systems, indispensable to the sustainable development of human society now and in the future. Renewable energy is advantageous as it is clean, renewable and sustainable. Although the global power supply mainly relies on fossil fuels, as fuels are consumed and the formation cycle is extremely long, resources will gradually decrease and costs will increase accordingly. There are many types of renewable energy, and the selection of which RE should be chosen in a specific situation depends on many factors. The objective of this study is to develop a decision-making framework based on the Analytical Hierarchy Process (AHP) which covers four available renewable energy technologies in Malaysia. Through this process, the most critical issues in implementing the renewable energy systems can and have been identified which includes technology, ecological environment and market issues that must be overcome. The AHP results show that hydropower has great potential for electricity generation in Malaysia, while biomass and wind are ranked second and third respectively, and solar is ranked fourth. Nevertheless, this research had only considered four criteria with limitations in terms of the sub-criteria chosen. The accuracy of this framework may be improved if additional criteria are included and analyzed which can closely simulate the actual scenario of renewable energy development in Malaysia. Hence, through continuous optimization of the energy structure, energy security can be ensured while regional development strategies can be promoted for a balanced development of sustainable energy systems in the region.

The PRISMI Plus Toolkit Application to a Grid-Connected Mediterranean Island

Islands are a constrained environment due to their geographical peculiarities and their land use accounting for, especially in the touristic locations, strong variability during the year. Consequently, the variation of energy demand to be met by variable renewable energy leads to a complex issue. This study aims at investigating the PRISMI Plus approach applied to the Island of Procida to drive the transition towards low-carbon and high-renewable energy system. The toolkit involves the analysis of local renewable energy potential, their potential matching of the energy demand, and the prioritization of the technological solutions to achieve the decarbonization targets set by the energy planning strategies. Three scenarios are designed for 2030 considering low, middle, and high penetration of renewable energy in the systems, results indicate that the amount of power production in low, middle, and high penetration of renewable energy scenarios are 0.18, 14.5, 34.57 GWh/year, respectively. The environmental and landscape constraints lead to a restricted set of available solutions. The decarbonization of the electricity supply is foreseen thanks to the available local solar resources plus the electrification of other sectors, i.e. heating by using Heat Pumps and transport by using Electric Vehicles.

Energy reliability enhancement of a data center/wind hybrid DC network using superconducting magnetic energy storage

The progressive penetrations of sensitive renewables and DC loads have presented a formidable challenge to the DC energy reliability. This paper proposes a new solution using series-connected interline superconducting magnetic energy storage (SCI-SMES) to implement the simultaneous transient energy management and load protection of DC doubly-fed induction generator (DC-DFIG)/internet data center (IDC) composited medium-voltage direct-current (MVDC) network. The SCI-SMES mainly consists of two current source converters (CSCs) sharing a common SMES link. It can implement the transient energy conversion and interaction between sensitive sources and loads in two individual DC feeders, even they are with different voltage and current levels. To prove the above-mentioned functions, a kW-class experimental SCI-SMES prototype is established. For actual renewable energy scenarios, six different cases of power flow and energy interaction between DC-DFIG and IDC are presented in this paper, and a MW-class with SCI-SMES-integrated hybrid DC network is built in simulation for verification. A techno-economic assessment, considering capital investment, operating cost, and financial losses of voltage sags, shows that the total investment payback period is in the range of 5.44–18.41 years, which proves the SCI-SMES has both economic and technical advantages in large-scale data centers and renewable generation sectors.

Application of Data-Driven Tuned Fuzzy Inference System for Static Equivalencing of Power Systems with High Penetration of Renewable Energy

To reduce the complexity of multiarea power systems during power flow study and security assessment, equivalencing of external power systems is essential. In this paper, external power systems are modeled as adaptive loads representing the tie-line flows varying with system operating conditions. The fuzzy inference system tuned by hybrid genetic-simulated annealing (HGSA-FIS) is proposed to predict the active and reactive power of adaptive loads from forecasted renewable energy (RE) generation and loads demand. The performance of proposed equivalent has been evaluated with an RTS-GMLC by comparing the power flow results in the internal system before and after equivalencing under varying RE and load demand scenarios. The results demonstrate the robustness of HGSA-FIS-based equivalent under varying RE generation and load demand. Furthermore, the proposed equivalent performs close to ANN-based equivalent and outperforms ANFIS-based equivalent. To practically implement the proposed approach, the neighboring system operators are required to exchange only the forecast data of RE generation and load demand, and the equivalent needs to be updated upon major grid changes.

An Integrated Renewable Energy System for the Supply of Electricity and Hydrogen Energy for Road Transportation Which Minimizes Greenhouse Gas Emissions

Greenhouse gas emissions produced by the energy sector, including the transportation sector, are a problem that must be resolved. One way to solve this problem is to provide energy in the transportation sector in a sustainable way, by using renewable energy. An integrated renewable energy system has been implemented through an optimization model for the supply of electricity and hydrogen energy for road transportation. The proposed model is in the form of mixed-integer linear programming with two objective functions: planning costs and greenhouse gas emissions. The multi-objective model was solved using the linear weighted-sum method. In this article, three scenarios are developed, namely the business-as-usual scenario, the renewable energy scenario, and the renewable energy with energy storage system scenario. The business-as-usual scenario is used to analyze the supply of electricity and hydrogen by prioritizing the objective function of planning costs. The renewable energy scenario prioritizes the objective function of greenhouse gas emissions in the optimization calculation, but without an energy storage system. The optimization calculation with the renewable energy with energy storage system scenario prioritizes the objective function of greenhouse gas emissions by including the energy storage system. The proposed model in a multi-objective form is implemented in a case study of road transportation in the Province of Yogyakarta, Indonesia. The results obtained indicate that the renewable energy with energy storage system scenario produces the lowest emission level of 56.55 Mt CO2 Equivalent, but with the highest planning cost of 192.13 x 109 Billion USD.

Balancing India’s 2030 Electricity Grid Needs Management of Time Granularity and Uncertainty: Insights from a Parametric Model

With some of the world's most ambitious renewable energy (RE) growth targets, especially when normalized for scale, India aims more than quadrupling wind and solar by 2030. Simultaneously, coal dominates the electricity grid, providing roughly three-quarters of electricity today. We present results from the first of a kind model to handle high uncertainty, which uses parametric analysis instead of stochastic analysis for grid balancing based on economic despatch through 2030, covering 30-minute resolution granularity at a national level. The model assumes a range of growing demand, supply options, prices, and other uncertain inputs. It calculates the lowest cost portfolio across a spectrum of parametric uncertainty. We apply simplifications to handle the intersection of capacity planning with optimized despatch. Our results indicate that very high RE scenarios are cost-effective, even if a measurable fraction would be surplus and thus discarded ("curtailed"). We find that high RE without storage as well as existing slack in coal- and gas-powered capacity are insufficient to meet rising demand on a real-time basis, especially adding time-of-day balancing. Storage technologies prove valuable but remain expensive compared to the 2019 portfolio mix, due to issues of duty cycling like seasonal variability, not merely inherent high capital costs. However, examining alternatives to batteries for future growth finds all solutions for peaking power are even more expensive. For balancing at peak times, a smarter grid that applies demand response may be cost-effective. We also find the need for more sophisticated modelling with higher stochasticity across annual timeframes (especially year on year changes in wind output, rainfall, and demand) along with uncertainty on supply and load profiles (shapes).

Hydroelectricity Modeling for Low‐Carbon and No‐Carbon Grids: Empirical Operational Parameters for Optimization and Dispatch Models

AbstractHydroelectric power has unusual technical characteristics that could become more valuable as the penetration of variable generation renewables grows, but its use for electricity generation is constrained by complex physical, safety, and socioenvironmental considerations. Hydroelectricity can therefore be difficult to represent in national‐scale energy models, and is frequently presumed to be either overly flexible or inflexible. While a few grid models address this complexity via detailed hydraulic process models, more simplified optimization and dispatch models could benefit from the use of empirical parameter values. In this study, we combine a new data set comprising 7.8 million flow‐hours of data from 2011 to 2016 at 158 dams across the United States with monthly and hourly generation data from the U.S. Energy Information Administration (EIA) to elucidate such empirical constraints for the continental United States. We introduce an approach for estimating power generation from hourly water discharge, then present regionally resolved interannual seasonal and diurnal generation patterns; frequency analyses of ramp rates; minimum and maximum generation rates; daily reversals; and load duration curves, all available interactively with a new data visualization tool. We suggest that due largely to hydropower's role as power generation that also serves non‐energy purposes, it acts as a predictable variable generator with constrained dispatchability, more like a supply side analog to demand response resources than like a battery. This observation is particularly relevant for high penetration renewable energy scenarios, given hydroelectric generators' expected value for grid stabilization and load balancing.

Urban emissions and land use efficiency scenarios towards effective climate mitigation in urban systems

There are critical mitigation opportunities in urban areas that can be better understood through urban level analyses in the context of climate scenarios. This research work develops an approach for emissions scenarios for specific urban areas based on Shared Socioeconomic and Representative Concentration Pathways with a focus on the green growth paradigm at the lowest radiative forcing outcomes. The scenario characteristic for compact urban form is further complemented with improvements in existing land use efficiencies that are based on the Global Human Settlement Layer. Urban emissions scenarios for 420 urban areas among those with the highest emissions footprint totaling about 10.7 GtCO2eq in 2020 and land use efficiency scenarios for the top 10 urban areas in 9 regions are analysed. The 90 urban areas that represent about 6.2 GtCO2eq of urban emissions and 83.3 thousand km2 of built-up area in 2020 are then compared in a two-dimensional scenario space. Co-benefits are quantified for a new 100% renewable energy scenario that represents electrification combined with sector coupling. Based on Monte Carlo simulations, random performances that near the most stringent emissions pathways in a range of ±10% can still approach net-zero emissions by 2050 in alignment with the temperature goal of the Paris Agreement. Yet there is a penalty of about 0.95 GtCO2 even in the best land use efficiency scenario due to possible impacts of urban land expansion on the sequestration potential of local biomes. The results have widespread ramifications for guiding urban areas towards integrated action for reducing emissions, limiting the growth in urban extent, and providing co-benefits for urban inhabitants when effective action is needed urgently.