Future Electricity Generation Research Articles

Forecasting Electricity Consumption Trends in India Using ARIMA-Based Regression Analysis: A Focus on Historical Lags

The ability to forecast electricity generation is crucial for effective energy management and policy planning. This study investigates the use of historical electricity generation data as a predictor for future generation trends using an autoregressive integrated moving average (ARIMA)-based regression model. Focusing on lagged values of electricity generation, we assess the predictive accuracy and statistical significance of the lagged variable (LAG1) for forecasting. The results indicate a strong positive relationship between past and future electricity generation, with the LAG1 coefficient being statistically significant at the 1% level. The regression model explains 97% of the variation in electricity generation, demonstrating its high utility for future forecasting. This analysis provides valuable insights for energy policymakers and stakeholders in preparing for future electricity demand.

Modeling Electricity Generation and Consumption in Cameroon

Currently, there is a significant gap between electricity generation and consumption in Cameroon. Research has shown that electricity consumption in the country is estimated to increase by 965.7 GWh in five years, from 2020 to 2024 due to demographic and economic growth. Hence, this study aims to find methods that can be useful in developing strategies to balance the energy supply and demand in the country. This is done by developing models that can predict future electrical power consumption and generation. Correlation analysis and regression analysis were performed by using data obtained from various databases, and related models were developed accordingly. The model parameters were carbon dioxide emissions, electricity consumption per capita, final consumption expenditures, electricity installed capacity, fossil fuel installed capacity, labor force, and GDP. The models' results demonstrated excellent performance coefficients with RMSE of 0.17041, 0.23893, 0.27571, and 0.2465 for hydroelectricity generation, fossil fuel electricity generation, net electricity generation, and net electricity consumption respectively. Also, hydroelectricity generation, net electricity generation, and net electricity consumption models showed very good RRMSE performance indicating that the models can make predictions with only 4.26%, 5.26%, and 5.77% deviation from the mean values of hydroelectricity generation, net electricity generation, and net electricity consumption, respectively.

Quantitative Estimation of Albedo and Tilt Angle Variation in Bifacial Perovskite Solar Cells.

Bifacial perovskite solar cells (Bi-PSCs) have attracted substantial attention within the photovoltaic (PV) community due to their potential for enhanced power generation, suitability for integration into building structures and applicability in multijunction PV systems. This study presents the fabrication of efficient Bi-PSCs and investigates their unique properties using various characterization techniques, including Lambertian reflection effects through tilt angle arrangements and bottom albedo illuminations. The control device achieved a maximum power conversion efficiency (PCE) of 17.46% under front-side 1 Sun AM1.5G illumination. A significant influence of ground Lambertian reflection is observed with tilt angle variations, resulting in an increase in PCE from 17.46% → 18.82% as the tilt angle reached 20°. Additionally, enhancing the rear-side albedo to 0.5 Sun yielded a maximum PCE of 26% with a bifaciality factor of ∼90% at a tilt angle of 20°. Consequently, the synergistic effect of 0.5 Sun albedo and a 20° angular light inclination led to the development of Bi-PSCs with an efficiency of 26.46%. SCAPS-1D simulations are further employed to validate the experimental Lambertian reflection effects. Moreover, the Bi-PSCs exhibited intrinsic self-encapsulation and chemical robustness (T80 for 2000 h in the N2 atmosphere). This study anticipates that cost-effective and highly efficient Bi-PSCs will emerge as a leading PV technology in both single-junction and tandem PV configurations for electricity generation in the near future.

Health benefits of US light-duty vehicle electrification: Roles of fleet dynamics, clean electricity, and policy timing

We present a dynamic perspective to quantify the air quality-related health impacts of the electrification of light-duty vehicles in the United States between 2022 and 2050. Using a fleet turnover model and future electricity generation mix scenarios, we compare ambitious vehicle electrification to fleet renewal relying on newer internal combustion engine vehicles, without electric vehicles. The model includes vehicle-level pollutant emission factors and a reduced complexity air quality and valuation model and covers direct (tailpipe, brake wear, and tire wear) and indirect (production of electricity and liquid fuels) emissions of NOx, SO2, PM2.5, NH3, and VOCs, with a breakdown at the county level to identify geographical disparities in the distribution of health impacts. Short-term health benefits are mostly generated by reductions in NOx emissions from newer gasoline vehicles, while fleet electrification generates further benefits in the long term. The electricity mix plays a crucial role in the success of electrification policies. With continued grid decarbonization, electrification would reduce harmful air quality-related health impacts cumulatively by 84 to 188 billion USD over the study period, compared with fleet renewal without electric vehicles. In contrast, artificially freezing the 2022 grid would make electrification responsible for 32 to 71 billion USD additional health disbenefits compared with fleet renewal. Finally, we show that while fleet electrification achieves most of its benefits over fleet renewal in the long term, delaying the implementation of such policies would sacrifice meaningful cumulative benefits.

Increasing the efficiency of CIGS solar cells due to the reduced graphene oxide field layer of the back surface

Copper indium gallium selenide solar cells (CIGS-SCs) have gained attention due to their cost-effectiveness and environmentally friendly characteristics, making them a promising option for future electricity generation. The efficiency of CIGS-SCs can be enhanced by adding a back surface field layer (BSFL) under the absorber layer to reduce recombination losses. In this study, the electrical parameters, such as the series resistance, shunt resistance, and ideality factor, are calculated for CIGS-SCs with an advanced design, using the SC capacitance simulator (SCAPS) software. The detailed model used in the simulations considers the material properties and fabrication process of BSFL. By utilizing a reduced graphene oxide (rGO) BSFL, a conversion efficiency of 24% and a significant increase in the fill factor are predicted. This increase is primarily attributed to the ability of the rGO layer to mitigate the recombination of charge carriers and establish a quasi-ohmic contact at the metal-semiconductor interface. At higher temperatures, BSFL can become less effective due to an increased recombination and, in turn, a decreased carrier lifetime. Overall, this study provides valuable insights into the underlying physics of CIGS-SCs with BSFL and highlights the potential for improving their efficiency through advanced design and fabrication techniques.

Variability in wind energy on interannual and decadal scales and its influence on hydrogen energy storage

AbstractForecasting future wind electricity generation requires diverse methods and tools to estimate potential output at specific locations. This study utilizes historical meteorological data and simple models at particular sites, along with past electricity production records from selected wind farms. The interannual and decadal oscillations of wind energy production at the grid level are computed. The large‐scale, long‐term energy storage needed to achieve dispatchable electricity, addressing generation variability is assessed. For the continental United States, the estimated storage requirement is approximately 1300 GWh per GW of installed capacity. The inclusion of solar power generation and the round‐trip efficiency of energy storage positively or negatively impact this estimation.

Conducting a Geographical Information System-Based Multi-Criteria Analysis to Assess the Potential and Location for Offshore Wind Farms in Poland

Offshore wind energy is becoming an increasingly important element in the decarbonisation of energy systems. This study aims to estimate the technical potential for offshore wind energy in the Polish exclusive economic zone (EZZ) of the Baltic Sea. Such estimates are necessary to plan the expansion of energy infrastructure. This paper proposes a multi-criteria analysis for the estimation of offshore wind potential and site selection using QGIS software (QGIS Desktop 3.28.6). The criteria include wind velocity, water depth, distance from nature conservation areas, distance from submarine cables and gas pipelines, distance from shipping routes, distance from fishing areas, distance from shipwrecks, and distance from the coastline. The best sites are identified through the Analytic Hierarchy Process (AHP). The results show that the total suitable marine area of 16,218.67 square kilometres can be divided into three parts, in which the highly suitable area for offshore wind farms accounts for 17.55%, the moderately suitable area accounts for 48.56%, and the marginally suitable area accounts for 10.26%. Two different wind turbine placement configurations are being considered, in which wind turbines with a nominal capacity of 15 MW are laid out: 2.5 km and 2 km apart from each other. The corresponding overall electrical capacities only in highly suitable areas equal ca. 9 GW and 15 GW, respectively. If this area is extended to also include the moderately suitable parts, these values increase to ca. 31 GW and 50 GW. The results indicate that the technical potential of offshore wind in the Polish EEZ is significant and should be taken into account when designing the future electricity generation mix.

The potential of multiapartment rooftop PV systems as citizen’ energy communities in Latvia

Electricity generation from photovoltaic (PV) in Latvia is currently below the necessary capacity required to contribute to achieving climate neutrality by 2050. However, photovoltaic offer households and companies the opportunity to align their electricity consumption with environmental goals while supporting the European Green Deal objectives. In recent years, there was a growing interest in the installation of photovoltaic microgenerators in Latvia. By June 2023, the total number of households microgenerators connected to the grid has reached 15,000, boasting a collective production capacity exceeding 120 MW. This significant increase is in stark contrast to the situation 5 years ago when Latvia had a mere 3 MW of photovoltaic capacity. Several factors contributed to this growth, including a surge in electricity prices, at times surging more than tenfold, and the implementation of renewable energy (RE) use support measures to facilitate the acquisition of RE systems. Furthermore, 2023 witnessed numerous amendments to Latvia’s energy policy documents and the introduction of the concept of citizen energy communities (CEC), indicating a growing emphasis on harnessing solar energy potential in the country. However, it remains crucial to base these endeavors on accurate, economically viable information regarding solar technologies, their costs and their anticipated long-term outcomes. This manuscript aims to provide an overview of the grid-connected potential of rooftop photovoltaic systems within a Latvian urban setting. Through extensive research, a model has been developed, employing a thoroughly tested simulation program for evaluating the generation capacity of photovoltaic systems. This model considers real electricity consumption data, the existing infrastructure and economic factors. The findings affirm the technical and economic viability of urban rooftop photovoltaic systems within the Latvian context. It has been established that the implementation of such citizen energy communities energy systems holds significant potential. These systems have the potential to be a promising solution for future electricity generation, addressing some of the demands while relieving strain on external power grids. However, the full potential can only be realized with improved infrastructure, and the system’s profitability is heavily contingent on market dynamics and political conditions. This study may also be applicable to other photovoltaic systems facing similar climate conditions.

Integrated life cycle sustainability assessment of the electricity generation sector in Bangladesh: Towards sustainable electricity generation

The electricity sector of Bangladesh has experienced a drastic expansion over the last decade, which will continue in the future. However, the country is not attaining optimal benefit from the sector due to discrepancies among its different components. Increased generation capacity based on fossil fuel sources is not only causing negative environmental impacts but also raising future energy security concerns. Stakeholders are unable to address these interconnected issues due to lack of adequate knowledge, which is leading to policy inconsistencies. This paper aims to evaluate the integrated sustainability of Bangladesh’s electricity generation, considering environmental, economic and social aspects using a life cycle approach. Eighteen sustainability indicators have been assessed for five electricity generation technologies currently in operation. Applying a multi-criteria decision analysis, all the technologies have been ranked considering equal as well as differing preferences for the three aspects. Solar power followed by hydropower is the most sustainable option when all aspects are given equal importance and priority is given to economic and social aspects. However, hydropower becomes the most sustainable option when the environmental aspect is prioritized. Among fossil sources, gas is the best and oil power is found as the least sustainable option in all preferences except the environmental aspect when coal becomes the worst. By identifying the disputes between different indicators, this study demonstrates the variations in the results of the integrated sustainability assessment that can be considered while planning improved energy policies for sustainable future electricity generation.

Beyond price taker: Conceptual design and optimization of integrated energy systems using machine learning market surrogates

Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.

Inclusion of key social indices for a comparative assessment of the sustainability of the life cycle of current and future electricity generation in Spain: A proposed methodology

The purpose of this work is to offer a methodology for the introduction and consolidation of social indexes within a Life Cycle Sustainability Assessment for the evaluation of large-scale electricity production. This methodology is based on an interrelation of the UNEP subcategories with the global indicator framework for the Sustainable Development Goals, resulting in 9 categories of social impact quantified by 10 indexes. To evaluate the introduction of this methodology in an LCSA a study case is used. It is applied to obtain social indexes for the 2019, 2030 and 2050 Spanish electricity scenarios, which involve different electricity generation and storage technologies: involve coal, natural gas, nuclear, hydropower, wind, solar photovoltaic, concentrated solar, biomass and storage technologies. Within the study case, an environmental life cycle assessment is also conducted, while the impacts of the economic dimension are estimated by the levelized cost of energy. This study reveals that even before important qualitative information is brought to light, as should be done in any decision-making process, there are important gaps that can be filled in terms of quantitative information in an LCSA. To do so, it provides with a methodology to expand the number of social indexes normally used in this type of assessments with the purpose of contributing to decision processes with clarity and transparency. Labour rights, salary differences for the same position, diversity in executive positions, child labour, and R&D expenditure complement the previously used occupational injuries, fatalities due to large accidents, and direct employment. The proposed methodology is applied to a Spanish case study, demonstrating the benefits and disadvantages associated with the modification of the electricity mix. However, it also highlights significant information gaps in availability and transparency that need to be addressed in future research, as part of the path towards standardizing LCSA.

A robust optimization model for microgrid considering hybrid renewable energy sources under uncertainties.

Hybrid renewable energy sources and microgrids will determine future electricity generation and supply. Therefore, evaluating the uncertain intermittent output power is essential to building long-term sustainable and reliable microgrid operations to fulfill the growing energy demands. To address this, we proposed a robust mixed-integer linear programming model for the microgrid to minimize the day-ahead cost. To validate the proposed model piecewise linear curve is to deal with uncertainties of wind turbine, photovoltaic, and electrical load. The proposed solution is demonstrated through a case study compared under a robust worst-case scenario, deterministic model, and max-min robust optimization that aim to find optimal robustness. So, a piecewise linear curve is considered to obtain uncertain parameters in order to deal with uncertainties and predict the day-ahead cost. This study illustrates how the Uncertainty Budget Set selection used to integrate renewable energy sources into a microgrid, which manages the energy system. Therefore, the model complexity was slightly modified by adjusting the Uncertainty Budget Set to obtain the optimal decision and control the load demand and uncertainty of renewable energy sources. The comparative results demonstrate that the proposed robust optimization can achieve high solutions under microgrid's availability and is intended to confirm that the proposed method is more cost-effective than alternative optimization techniques. Additionally, the effectiveness and advantage of the proposed methodology in the IEEE 33-node system are validated in this case study by comparing it to the existing optimization. The comparison results show that the proposed robust optimization methods illustrate the model's efficiency, concluding remarks, and managerial insights of the research.

A techno-economic and environmental assessment of a low-carbon power generation system in Cameroon

In attempt to address the power sector issues, provide energy necessary to attain its developmental goals and meet its international commitments, Cameroon has developed several policies and masterplans. However, these policy directives haven't had the desired effect with the issues persisting and the nation falling short of intended milestones. This research analyses the implications of stated and clean energy policies on the future electricity generation system of Cameroon. The study uses the Schwartz's methodology for scenario development and the Low Emissions Analysis Platform (LEAP) to model the reference scenario and three alternative scenarios that describe various policy directives. These scenarios are assessed based on total installed capacity, economic competitiveness, and associated environmental benefits. The results indicate Cameroon's generation capacity in 2045 would need to grow by over 800% under the Reference scenario. This growth would be at a cumulative cost of $3377 million and associated greenhouse gas emissions of 82.6 MTCO2e. Furthermore, only 13.11% renewable energy target would be achieved. The study also shows that higher renewable energy targets result in significant economic and emission savings compared to the Reference scenario. Therefore, Cameroon should reassess its power sector masterplans and intensify efforts to increase uptake of renewables.

ALFRED reactor and hybrid systems: A test case

Future electricity generation will likely rely on renewable energy sources with consequent power intermittency and grid instability issues that will need to be compensated for. In this work, a model of a Generation IV small modular reactor has been developed in order to study its capabilities as a grid stabilizer. The plant model has been developed in Dymola and exported in the Simulink environment for the control strategy. The hybrid system considered couples a lead-cooled fast reactor to a desalination plant, an energy storage system, renewable energy farms, a variable electrical load, a local grid, and an external interconnected grid. Different desalination plants have been considered. Reverse osmosis has proven to be the most suitable option when coupled with energy production systems. Different configurations of the hybrid system have been considered showing a higher degree of load-following capabilities for the nuclear plant studied.

Thermodynamic design and power prediction of a solar power tower integrated system using neural networks

In spite of the fact that the solar power tower system is considered as one of the most valuable power generation facilities, it still faces challenges such as insufficient utilization of the solar salt temperature range and the dependence on high solar radiation intensity. To address this issue, an integrated recompression Brayton cycle and trans-critical regenerative organic Rankine cycle in parallel layout is proposed. The comparison with other literature shows that the specific work of the integrated system (147.8 kW/kg) outperforms the partial cooling Brayton cycle (130.1 kW/kg) under the same solar salt temperature range. In order to forecast future electricity generation and serve as a reference for plant operation, a power prediction strategy using neural networks is developed. Findings indicate that the integrated system can increase power production and maximize solar salt temperature utilization by adjusting the physical constraint strategy based on the changing temperature interval. Its equivalent work and thermal efficiency reach 12.7 MW and 38.36%, respectively, and it can recover the cost in 8 years. These results suggest that the proposed integrated system is a promising solution to enhance the performance of solar power tower systems with significant economic benefits.

Might future electricity generation suffice to meet the global demand?

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

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

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

Toward more robust ignition of inertial fusion targets

Following the 3.15 MJ fusion milestone at the National Ignition Facility, the further development of inertial confinement fusion, both as a source for future electricity generation and for high-energy-density physics applications, requires the development of more robust ignition concepts at current laser facility energy scales. This can potentially be achieved by auxiliary heating the hotspot of low convergence wetted foam implosions where hydrodynamic and parametric instabilities are minimized. This paper presents the first multi-dimensional Vlasov–Maxwell and particle-in-cell simulations to model this collisionless interaction, only recently made possible by access to the largest modern supercomputers. The key parameter of interest is the maximum fraction of energy that can be extracted from the electron beams into the hotspot plasma. The simulations indicate that significant coupling efficiencies are achieved over a wide range of beam parameters and spatial configurations. The implications for experimental tests on the National Ignition Facility are discussed.

Hedging Volumetric Risks of Solar Power Producers Using Weather Derivative Smart Contracts on a Blockchain Marketplace

The vulnerability of solar power producers to sunshine fluctuations exposes them to the volumetric risk that future electricity generation may deviate from predicted generation. Weather derivatives have recently emerged as a tool for hedging the volumetric risks of these power producers. However, the state-of-the-art instruments have several shortcomings, contributing to their limited application in the industry. Therefore, novel solar radiation-based weather derivative smart contract arrangements on a blockchain marketplace are proposed to address some of the main limitations of traditional instruments. In this regard, the cash flow of solar generators is modelled to assess the weather elements causing its stochasticity. Using this information, novel smart contract arrangements on a blockchain marketplace with solar radiation days as the underlying weather index are developed and analytically valued. Thereafter, a suite of novel smart contract autonomous mechanisms compelling contracting parties to behave rationally and maintain an enduring arrangement is presented. Finally, a trading strategy based on the developed smart contract arrangements is proposed to minimize the power producers’ volatility risk. Results emanating from notional simulations indicate that the proposed approach could be more suitable for hedging the volumetric risks of solar power producers than traditional instruments.

Computational model for thermohydraulic analysis of an integral pressurized water reactor with mixed oxide fuel (Th, Pu)O2

The use of advanced generation III+ and IV nuclear reactors, and their applications, has become important, seen as a means capable of contributing to the global transition to more sustainable, affordable and reliable energy systems. This technology, which could be integrated into future carbon-free electric power generation systems with high proportions of different renewable energy sources, includes Small Modular Reactors (SMR). There are about 100 different proposed projects of Generation III+ and IV, of which about 50 are SMR concepts, in various stages of development and of different types of technologies. Other important issues for achieving the long-term sustainability of nuclear energy are the proper use of its fuel sources and the improvement of nuclear waste management. Therefore, fuels based on a mixture of oxides have been used successfully in several countries. In addition, the incorporation of thorium-based fuel is a current challenge for the new designs of advanced reactors. The present paper focuses on the analysis of a small modular integral pressurized water reactor (iPWR) with Thorium-Uranium Oxide (Th-U MOX) mixtures. A thermohydraulic model is developed using the Ansys CFX program, which allows the calculation of the temperature distribution in the section where the highest power is produced within the SMR IPWR core (critical section). The temperature distributions in the fuel, clad and coolant were calculated with the objective of verifying that they were within the safety limits.