Uncertainty Of Generation Research Articles

Mitigating Risk and Emissions in Power Systems: A Two-Stage Robust Dispatch Model with Carbon Trading

The large-scale integration of renewable energy sources is crucial for reducing carbon emissions. Integrating carbon trading mechanisms into electricity markets can further maximize this potential. However, the inherent uncertainty in renewable power generation poses significant challenges to effective decarbonization, renewable energy accommodation, and the security and cost efficiency of power system operations. In response to these challenges, this paper proposes a two-stage robust power dispatch model that incorporates carbon trading. This model is designed to minimize system operating costs, risk costs, and carbon trading costs while fully accounting for uncertainties in renewable energy output and the effects of carbon trading mechanisms. This model is solved using the column-and-constraint generation algorithm. Validation of an improved IEEE 39-bus system demonstrates its effectiveness, ensuring that dispatch decisions are both robust and cost-efficient. Compared to traditional dispatch models, the proposed model significantly reduces system risk costs, enhances the utilization of renewable energy, and, through the introduction of a ladder carbon trading mechanism, achieves substantial reductions in carbon emissions during system operation.

Empirical analysis of the influence mechanism of vegetation and environment on negative air ion in warm temperate forest ecosystems

Air pollution presents a significant threat to public health in megacities globally. Negative air ions (NAI), often referred to as “air vitamins,” are recognized for their effectiveness in alleviating the harmful effects of air pollution. Forest ecosystems serve as natural generators of NAI, with both vegetation and environmental conditions playing critical roles in the formation and persistence of NAI. Gaining a comprehensive understanding of how forest ecosystems regulate NAI production is essential for leveraging their potential to enhance air quality. However, the intricate dynamics of forest ecosystems, along with seasonal fluctuations in vegetation and environmental factors, introduce uncertainties in NAI generation. This study utilized long-term observational data to explore the relationships between environmental variables, vegetation photosynthetic capacity (using solar-induced chlorophyll fluorescence, SIF), and NAI concentrations. By employing machine learning algorithms, we analyzed the spatiotemporal distribution of NAI, identifying the key contributing factors and their relative influence within forest ecosystems. The results revealed distinct seasonal variations in NAI levels, with higher values in summer and lower in winter. SIF and PM2.5 primarily influenced NAI through direct effects across seasons, whereas ambient temperature (TA), relative humidity, photosynthetically active radiation (PAR), and soil moisture predominantly impacted SIF on NAI through indirect effects in summer. TA was the primary influencing factor in spring and winter, contributing 28% and 25%, respectively, while PAR played a more significant role in summer and autumn, accounting for 37% and 27%. Vegetation had a greater impact on NAI levels during spring and summer, contributing 66% and 62%, whereas environmental factors dominated in autumn and winter, with contributions of 83% and 89%. This study offers both a theoretical foundation and technical guidance for enhancing the role of forest ecosystems in improving air quality and human living environments.

A cooperative approach for generation and lines expansion planning in microgrid‐based active distribution networks

AbstractWith the growth of the load in the electricity networks, sufficient investment in the generation and lines expansion should be made in order to provide the energy needed by consumers with the lowest possible investment and operation costs. This issue is especially important in distribution networks, which are faced with the uncertainties of renewable energy generation and the development of microgrids and related issues.In this article, the planning of generation and lines expansion has been modeled with the aim of minimizing the total costs of microgrids, based on the cooperative approach. For this purpose, a bi‐level model has been developed; on the upper level, microgrids make investment decisions with a cooperative approach, and a constrained stochastic formulation has been developed with considering operational uncertainties on the lower level. Also, in this article, in order to ensure the supply of critical loads in island conditions, the self‐sufficiency index is defined. Three case studies have been considered to ensure the effectiveness of the developed model. In case 1, each microgrid will be able to supply its load only by generating of its units and purchasing from the retail market. In case 2, the possibility of trading with other microgrids in a non‐cooperative approach will also be available to the microgrids operators, and in case 3, microgrids can exchange energy with other microgrids in a cooperative manner. The simulation results showed that due to the possibility of using nearby microgrid resources, the cost of microgrid load supply in case 2 was reduced by 4.84% compared to case 1. Also, this cost in case 3 was reduced by 5.23% and 0.38%, respectively, compared to cases 1 and 2, due to the use of a cooperative manner in microgrid load providing.

Demand management and pricing in renewable integrated plug-in electric vehicle charging station using reinforcement learning

ABSTRACT The increasing adaption of Plug-in Electric Vehicles (PEVs) has increased the power demand for charging. Renewable generation can significantly help to meet this demand. The uncertainty of renewable generation and randomness in PEV charging makes the demand management of charging stations challenging. To address these challenges, this study proposes a novel demand management pricing strategy for renewable integrated charging stations. The proposed strategy uses reinforcement learning for charging coordination of PEVs. The renewable generation is estimated using weather data and accordingly PEVs are scheduled for demand management. The strategy decides the PEV power price based on real-time price, renewable tariff and PEV load. The strategy uses the bidirectional power flow with battery degradation cost. The distribution transformer operating cost and loading constraints are included to resemble the real-time environment. The PEV randomness and uncertainty of renewable generations are incorporated in the study. The results of the numerical case study show that the proposed strategy can manage the charging station load efficiently. The proposed strategy has optimized the cost of charging, discharging and charging station profit and increased the service capability of the charging station.

U-Net Semantic Segmentation-Based Calorific Value Estimation of Straw Multifuels for Combined Heat and Power Generation Processes

This paper proposes a system for real-time estimation of the calorific value of mixed straw fuels based on an improved U-Net semantic segmentation model. This system aims to address the uncertainty in heat and power generation per unit time in combined heat and power generation (CHPG) systems caused by fluctuations in the calorific value of straw fuels. The system integrates an industrial camera, moisture detector, and quality sensors to capture images of the multi-fuel straw. It applies the improved U-Net segmentation network for semantic segmentation of the images, accurately calculating the proportion of each type of straw. The improved U-Net network introduces a self-attention mechanism in the skip connections of the final layer of the encoder, replacing traditional convolutions by depthwise separable convolutions, as well as replacing the traditional convolutional bottleneck layers with Transformer encoder. These changes ensure that the model achieves high segmentation accuracy and strong generalization capability while maintaining good real-time performance. The semantic segmentation results of the straw images are used to calculate the proportions of different types of straw and, combined with moisture content and quality data, the calorific value of the mixed fuel is estimated in real time based on the elemental composition of each straw type. Validation using images captured from an actual thermal power plant shows that, under the same conditions, the proposed model has only a 0.2% decrease in accuracy compared to the traditional U-Net segmentation network, while the number of parameters is significantly reduced by 74%, and inference speed is improved 23%.

State-of-the-Art Probabilistic Solar Power Forecasting: A Structured Review

In recent years, the installed capacity increment with regard to solar power generation has been highlighted as a crucial role played by Photovoltaic (PV) generation forecasting in integrating a growing number of distributed PV sites into power systems. Nevertheless, because of the PV generation’s unpredictable nature, deterministic point forecast methods struggle to accurately assess the uncertainties associated with PV generation. This paper presents a detailed structured review of the state-of-the-art concerning Probabilistic Solar Power Forecasting (PSPF), which covers forecasting methods, model comparison, forecasting horizon and quantification metrics. Our review methodology leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to systematically identify primary data sources, focusing on keywords such as probabilistic forecasting, Deep Learning (DL), and Machine learning (ML). Through an extensive and rigorous search of renowned databases such as SCOPUS and Web of Science (WoS), we identified 36 relevant studies (n=36). Consequently, expert scholars decided to develop three themes: (1) Conventional PSPF, (2) PSPF utilizing ML, and (3) PSPF using DL. Probabilistic forecasting is an invaluable tool concerning power systems, especially regarding the rising proportion of renewable energy sources in the energy mix. We tackle the inherent uncertainty of renewable generation, maintain grid stability, and promote efficient energy management and planning. In the end, this research contributes to the development of a power system that is more resilient, reliable, and sustainable.

A Multi-Layer Techno-Economic-Environmental Energy Management Optimization in Cooperative Multi-Microgrids with Demand Response Program and Uncertainties Consideration.

This paper presents a multi-layer, multi-objective (MLMO) optimization model for techno-economic-environmental energy management in cooperative multi-Microgrids (MMGs) that incorporates a Demand Response Program (DRP). The proposed MLMO approach simultaneously optimizes operating costs, MMG operator benefits, environmental emissions, and MMG dependency. This paper proposed a new hybrid ε-lexicography-weighted-sum that eliminates the need to normalize or scalarize objectives. The first layer of the model schedules MMG resources with DRP to minimize operating costs (local generation and power transactions with the utility grid) and maximize MMG profit. The second layer achieves the environmental operation of the MMG, while the third layer maximizes MMG reliability. This paper also proposed a new application of a recently developed enhanced equilibrium optimizer (EEO) for solving the three-layer EM problem. In addition, the uncertainties of solar power generation, wind power generation, load demand, and energy prices are considered based on the probabilistic 2m + 1 Point estimation method (PEM) approach. Three case studies are presented to verify the proposed MLMO approach on an MMG test system. In Case I, a deterministic EM is solved to simulate the MMG as a single layer to minimize costs and maximize benefits through DRP, while Case II solves the MLMO optimization problem. Simulation results show that the proposed MLMO technique reduces environmental emissions by 2.45% and 3.5% in its optimization layer and at the final layer, respectively. The independence index is also enhanced by 2.49% and 4.8% in its layer only and as a total increase, respectively. Case III is for the probabilistic EM simulation; due to the uncertain variables effect, the mean value in this case is increased by about 2.6% over Case I.

Dynamic adaptation in power transmission: integrating robust optimization with online learning for renewable uncertainties

IntroductionThe increasing integration of renewable energy sources, such as wind and solar, into power grids introduces significant challenges due to their inherent variability and unpredictability. Traditional fossil-fuel-based power systems are ill-equipped to maintain stability and cost-effectiveness in this evolving energy landscape.MethodsThis study presents a novel framework that integrates robust optimization with online learning to dynamically manage uncertainties in renewable energy generation. Robust optimization ensures system resilience under worst-case scenarios, while the online learning component continuously updates operational strategies based on real-time data. The framework was tested using an IEEE 30-bus test system under varying levels of renewable energy integration.ResultsSimulation results show that the proposed framework reduces operational costs by up to 12% and enhances system reliability by 1.4% as renewable energy integration increases from 10% to 50%. Additionally, the need for reserve power is significantly reduced, particularly under conditions of high variability in renewable energy outputs.DiscussionThe integration of robust optimization with online learning provides a dynamic and adaptive solution for the sustainable management of power transmission systems. This approach not only improves economic and environmental outcomes but also enhances grid stability, making it a promising strategy for addressing the challenges posed by the increasing reliance on renewable energy.

Multi-objective optimal scheduling of electricity consumption in smart building based on resident classification

By the end of 2021, the urbanization rate of China had reached 64.72%. Increasing urbanization rates have led to an 80 percent share of carbon emissions in urban areas. Therefore, a load optimal scheduling model of smart buildings is proposed in this paper, which takes into account the cost of carbon emissions. The model aims to minimize the electric cost for building residents and maximize the utilization of distributed energy. The research focuses on the load optimal scheduling for two types of residents, i.e., the working residents and the retired ones, based on their electricity usage patterns and habits. To address the challenges of uneven load distribution, nonlinear behavior, and uncertainties introduced by electric vehicles and photovoltaic energy, the multi-objective beluga whale optimization algorithm with hybrid reverse learning competitive strategies (RCMBWO) is introduced. It utilizes the energy storage and discharge capabilities of electric vehicles to mitigate the uncertainties of photovoltaic energy generation. The simulation results show that load optimal scheduling for both types of residents can lead to significant cost savings for a smart building with 100 households, approximately 29,554 RMB per year. Furthermore, the optimized results can guide the determination of the appropriate size for the photovoltaic energy storage system, reducing energy waste resulting from insufficient energy consumption capability of smart buildings. The research on targeted load optimal scheduling for classified residents presents a viable solution for enhancing the cost-effectiveness and environmental benefits of smart buildings.

Day-ahead multi-criteria energy management of a smart home under different electrical rationing scenarios

With the recent global energy crisis, some countries have implemented electrical rationing (ER), making it necessary for smart homes to play a pivotal role in optimizing energy consumption and contributing to sustainable practices. To effectively manage smart home consumption, a stochastic programming approach for a grid-connected smart home energy management system (SHEMS) is proposed in this paper. The system includes PV, battery, diesel, and gas-based heating/cooling systems (HCS). Additionally, a demand response program (DRP) has been employed under time-of-use tariffs in the Syrian energy market. The main objective is to minimize the day-ahead expected cost and consumer discomfort by optimizing the operation of dispatchable units and loads. To manage the risks associated with the expected cost due to potential uncertainties in PV energy generation and electrical rationing programs, the conditional value-at-risk (CVaR) approach is adopted. Two methods are proposed to model the uncertainty in PV energy generation; interval bands and interval-based scenarios. The problem is modeled as a mixed-integer non-linear programming (MINLP) model, and coded in GAMS to test different cases. Based on the results obtained, substantial reductions reached 56.2% in worst-case cost scenarios when employing concurrent DRP-risk management.

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

Multi-objective two-stage robust optimization of wind/PV/thermal power system based on meta multi-agent reinforcement learning

The integration of renewable energy into the power grid poses significant challenges for optimization and scheduling of the power system. In recent years, methods based on deep reinforcement learning have surpassed traditional methods on the high complexity and long-term decision-making of power system optimization and scheduling. However, faced with the inherent uncertainty of renewable energy generation and the different optimization objectives in power system, the deep reinforcement learning methods are unable to effectively address them. This paper proposes a method that combines meta reinforcement learning with multi-agent reinforcement learning to solve the multi-objective two-stage robust optimization of wind/PV/thermal power system. We conducts optimization and scheduling experiments on the IEEE39 bus system. The results indicate that our method not only enhances the robustness of the scheduling strategy, but also outperforms baseline methods in terms of convergence, diversity, and uniformity of the Pareto frontier.

Short-Term Photovoltaic Power Probabilistic Forecasting Based on Temporal Decomposition and Vine Copula

With the large-scale development of solar power generation, highly uncertain photovoltaic (PV) power output has an increasing impact on distribution networks. PV power generation has complex correlations with various weather factors, while the time series embodies multiple temporal characteristics. To more accurately quantify the uncertainty of PV power generation, this paper proposes a short-term PV power probabilistic forecasting method based on the combination of decomposition prediction and multidimensional variable dependency modeling. First, a seasonal and trend decomposition using a Loess (STL)-based PV time series feature decomposition model is constructed to obtain periodic, trend, and residual components representing different characteristics. For different components, this paper develops a periodic component prediction model based on TimeMixer for multi-scale temporal feature mixing, a long short-term memory (LSTM)-based trend component extraction and prediction model, and a multidimensional PV residual probability density prediction model optimized by Vine Copula optimized with Q-Learning. These components’ results form a short-term PV probabilistic forecasting method that considers both temporal features and multidimensional variable correlations. Experimentation with data from the Desert Knowledge Australia Solar Center (DKASC) demonstrates that the proposed method reduced root mean square error (RMSE) and mean absolute percentage error (MAPE) by at least 14.8% and 22%, respectively, compared to recent benchmark models. In probability interval prediction, while improving accuracy by 4% at a 95% confidence interval, the interval width decreased by 19%. The results show that the proposed approach has stronger adaptability and higher accuracy, which can provide more valuable references for power grid planning and decision support.

A Bi-Level Peak Regulation Optimization Model for Power Systems Considering Ramping Capability and Demand Response

In the context of constructing new power systems, the intermittency and volatility of high-penetration renewable generation pose new challenges to the stability and secure operation of power systems. Enhancing the ramping capability of power systems has become a crucial measure for addressing these challenges. Therefore, this paper proposes a bi-level peak regulation optimization model for power systems considering ramping capability and demand response, aiming to mitigate the challenges that the uncertainty and volatility of renewable energy generation impose on power system operations. Firstly, the upper-level model focuses on minimizing the ramping demand caused by the uncertainty, taking into account concerned constraints such as the constraint of price-guided demand response, the constraint of satisfaction with electricity usage patterns, and the constraint of cost satisfaction. By solving the upper-level model, the ramping demand of the power system can be reduced. Secondly, the lower-level model aims to minimize the overall cost of the power system, considering constraints such as power balance constraints, power flow constraints, ramping capability constraints of thermal power units, stepwise ramp rate calculation constraints, and constraints of carbon capture units. Based on the ramping demand obtained by solving the upper-level model, the outputs of the generation units are optimized to reduce operation cost of power systems. Finally, the proposed peak regulation optimization model is verified through simulation based on the IEEE 39-bus system. The results indicate that the proposed model, which incorporates ramping capability and demand response, effectively reduces the comprehensive operational cost of the power system.

A dynamic pricing strategy and charging coordination of PEV in a renewable-grid integrated charging station

The increasing penetration of Plug-in Electric Vehicles (PEV) in the transportation system has increased the burden on the power system. This has made peak load demand management a challenging task for the power grid. To address this issue, a novel dynamic demand response pricing strategy in a grid-renewable generation integrated charging station environment is proposed in this paper. Renewable energy sources reduce the cost of generation and grid integration makes the system reliable. The proposed strategy models a Stackelberg game to provide dynamic prices for charging, discharging and grid power supplied for charging stations. Uncertainty and economics of renewable generation are considered for effective analysis and evaluation of the feasibility of the proposed strategy. The study considers the bidirectional flow of power and the battery degradation cost. Charging coordination is performed to optimize the cost of charging and discharging and support the grid in peak load demand management. Random charging behaviour and other parameters of PEVs are simulated using a random distribution function to resemble the real-time environment. A numerical case study validates that the proposed strategy has optimized the cost of charging and discharging and the serving capabilities of the charging station are enhanced with existing infrastructure.

Bi-Layer Model Predictive Control strategy for techno-economic operation of grid-connected microgrids

Effective management of renewable energy sources (RESs) alongside energy storage systems is crucial for maintaining stability and enhancing the economic performance of a grid-connected microgrid (MG). To address shortcomings in existing energy management strategies, which often overlook essential elements such as voltage support, battery degradation management, and uncertainties in load demand and solar generation, this study proposes a Bi-Layer Strategy for optimizing the operation of Battery Energy Storage Systems (BESS) within grid-connected MGs. The proposed approach maximizes economic returns, minimizes battery degradation, and provides reactive power support to maintain voltage stability. The method consists of the Energy Management Layer (EML) and the Control Layer (CL). The EML adopts a Model Predictive Control (MPC) framework to optimize the BESS power output over a 24-h rolling horizon based on forecasted solar generation, load demand, and electricity prices. The CL translates the optimized hourly setpoints from the EML into real-time operational commands, ensuring that the BESS adapts to actual grid conditions and maintains stable operation. Simulations conducted on a test MG, comprising 150 households, 500 kW rooftop solar PV, and a 300 kWh Li-Ion BESS demonstrate the strategy’s effectiveness under both fixed-rate and Time-of-Use (ToU) pricing schemes. The strategy reduces costs, mitigates battery degradation, and enhances voltage stability by optimizing BESS operations. Compared to a similar method in the literature, it reduces battery degradation by 24%, leading to net savings of $10,000 AUD, while the compared strategy resulted in a $5000 AUD loss. Real-time validation using the OPAL-RT platform further confirms the strategy’s effectiveness in optimizing BESS operations under real-world conditions.

A decentralized energy management scheme for a DC microgrid with correlated uncertainties and integrated demand response

Secure operation of a DC microgrid with high penetration of renewables and electric vehicle load is challenging. This paper proposes a decentralized energy management scheme for a grid-connected renewable integrated community DC microgrid considering the water-energy nexus. Uncertainties of renewable generation, plug-in electric vehicle load, electricity demand, electricity price in the day-ahead wholesale market, ambient temperature, and water demand are modelled using a probabilistic approach. Correlation between the input random variables is modelled using Copula theory. Flexibilities on the consumer side across multiple entities (temperature-dependent loads, electric vehicles, and water supply system storage) are coordinated to form an “integrated demand response entity”, which is further coordinated with the DC microgrid operator side flexibility (electrochemical storage) to support system operation. A distributed dynamic pricing scheme is used to implement the integrated demand response program. The objectives of the energy management scheme are to minimize the DC microgrid operator’s operating cost and consumers’ electricity cost. The decentralized algorithm is solved by the “alternating direction method of multipliers”. Simulation studies on a six-bus DC microgrid test system demonstrate that the proposed strategy reduces the operating cost of the DC microgrid operator by ∼19.28% and the electricity cost of the consumers by ∼13.82%.

Two-Stage Distributed Robust Optimization Scheduling Considering Demand Response and Direct Purchase of Electricity by Large Consumers

The integration of large-scale wind power into power systems has exacerbated the challenges associated with peak load regulation. Concurrently, the ongoing advancement of electricity marketization reforms highlights the need to assess the impact of direct electricity procurement by large consumers on enhancing the flexibility of power systems. In this context, this paper introduces a Distributed Robust Optimal Scheduling (DROS) model, which addresses the uncertainties of wind power generation and direct electricity purchases by large consumers. Firstly, to mitigate the effects of wind power uncertainty on the power system, a first-order Markov chain model with interval characteristics is introduced. This approach effectively captures the temporal and variability aspects of wind power prediction errors. Secondly, building upon the day-ahead scenarios generated by the Markov chain, the model then formulates a data-driven optimization framework that spans from day-ahead to intra-day scheduling. In the day-ahead phase, the model leverages the price elasticity of the demand matrix to guide consumer behavior, with the primary objective of maximizing the total revenue of the wind farm. A robust scheduling strategy is developed, yielding an hourly scheduling plan for the day-ahead phase. This plan dynamically adjusts tariffs in the intra-day phase based on deviations in wind power output, thereby encouraging flexible user responses to the inherent uncertainty in wind power generation. Ultimately, the efficacy of the proposed DROS method is validated through extensive numerical simulations, demonstrating its potential to enhance the robustness and flexibility of power systems in the presence of significant wind power integration and market-driven direct electricity purchases.

Real-time techno-economical operation of preserving microgrids via optimal NLMPC considering uncertainties

In the modern era, managing optimal real-time control of microgrids during the operation phase has been a significant challenge, requiring careful consideration of both technical and economic factors. This paper introduces a framework for the real-time control of islanded microgrids using a preserving network. This structure incorporates various distributed generation sources, including rotating and non-rotating resources, along with energy storage systems. The optimization function within model predictive control (MPC) manages essential network parameters, such as frequency and voltage, while addressing real-time economic and technical objectives. To enhance precision and account for uncertainties in generation and consumption parameters, the integration of continuous power flow and the preserving network model is employed. This approach aims to create a model that closely mirrors real-world conditions, ensuring a more accurate representation of microgrid dynamics. The proposed structure demonstrates significant improvements in both technical and economic performance compared to Standard MPC and Adaptive MPC, highlighting its potential for more efficient islanded microgrid management. The proposed framework achieves notable reductions in total voltage deviation of 85.87% and 87.62% compared to Standard MPC and Adaptive MPC, respectively. Additionally, it delivers impressive enhancements in frequency deviation of 99.46% and 96.62% compared to Standard MPC and Adaptive MPC, respectively. Economically, the proposed framework significantly outperforms both, reducing costs by 39.29% compared to Standard MPC and by 28.12% compared to Adaptive MPC.

Mediating role of energy uncertainty for environmental management in electricity generation: The evidence from Pakistan

This groundbreaking study examines the relationship between CO2 intensity, aggregated and disaggregated fossil fuels, clean and nuclear energy, and, as a mediating variable, energy uncertainty for Pakistan during 2019M01 and 2022M10 with monthly data. To this end, the ARDL Bound Testing method is used to identify the long-run relationship of the studied factors. The empirical results suggest that under the mediating effect of energy uncertainty, renewables and nuclear energy in electricity generation have a negative association with CO2 intensity. In contrast, fossil fuels in generating electricity influence positively CO2 intensity in the aggregated analysis. Moreover, the disaggregated results under the mediating role of energy uncertainty reveal that only hydro energy reduces CO2 intensity as renewables, bioenergy, wind, and solar energy do not impact CO2 intensity. Both coal and gas energies cause a rise in CO2 intensity. Regarding nuclear energy, it also has a negative relation with CO2 intensity. The increase in energy uncertainty leads to a fall in CO2 intensity in aggregated and disaggregated analyses as well. Wavelet coherence analysis shows that CO2 intensity and energy uncertainty depend on each other dynamically for almost the whole employed period.