Energy Integration Research Articles

A model for assessing the economic impact of renewable energy adoption in traditional oil and gas companies

The adoption of renewable energy presents significant economic implications for traditional oil and gas companies. This paper proposes a model for assessing the economic impact of integrating renewable energy into traditional oil and gas operations, focusing on cost efficiency, revenue potential, and overall financial performance. The model evaluates key metrics, including capital investment, operational cost savings, revenue generation from renewable energy projects, and potential impacts on profitability and shareholder value. By incorporating various renewable technologies such as solar, wind, and geothermal, the model provides a comprehensive framework for understanding the financial benefits and challenges associated with transitioning to cleaner energy sources. The model incorporates both direct and indirect economic impacts, such as reduced energy costs, enhanced energy security, and improved corporate reputation. It also considers the effects of regulatory incentives, including tax credits and subsidies, which can influence the financial viability of renewable energy projects. Additionally, the model examines potential risks and uncertainties, such as market volatility and technological limitations, which can affect the economic outcomes of renewable energy investments. Case studies of traditional oil and gas companies that have successfully integrated renewable energy are analyzed to illustrate the practical application of the model. These case studies demonstrate how companies have achieved cost reductions, diversified revenue streams, and enhanced their long-term financial resilience through renewable energy adoption. The paper concludes that while the initial investment in renewable energy technologies can be substantial, the long-term economic benefits—including cost savings, revenue growth, and alignment with sustainability goals—can significantly outweigh the challenges. By adopting the proposed model, oil and gas companies can better assess the economic implications of renewable energy integration and make informed decisions to drive sustainable growth and profitability.

Leveraging machine learning to optimize renewable energy integration in developing economies

The integration of renewable energy sources into power grids is a critical challenge for developing economies, where infrastructure limitations, unpredictable energy demand, and policy gaps hinder effective energy transitions. Machine learning (ML) offers transformative potential in addressing these challenges, enabling more efficient and reliable energy systems through advanced data analytics, predictive modeling, and real-time decision-making. This review explores how ML can optimize renewable energy integration by improving forecasting accuracy, enhancing grid stability, and optimizing resource allocation in solar, wind, and hydropower systems. Machine learning algorithms are particularly effective in predicting energy demand and renewable resource availability, allowing for better alignment between energy supply and consumption patterns. By leveraging ML-based predictive models, grid operators can mitigate the risks of energy shortages or oversupply, improve grid stability, and reduce operational costs. Furthermore, the application of ML in renewable energy systems provides opportunities for developing economies to leapfrog traditional energy infrastructure limitations by adopting smart grids that integrate real-time data to enhance decision-making and efficiency. This review also reviews case studies from Africa and Latin America, highlighting successful implementations of ML in renewable energy systems. These examples underscore the potential for ML to accelerate the deployment of sustainable energy solutions, while also addressing technical, economic, and policy barriers that exist in developing contexts. With continued advancements in machine learning, combined with supportive regulatory frameworks and investment in digital infrastructure, developing economies have the potential to realize substantial gains in renewable energy integration. This review concludes by discussing future trends, challenges, and opportunities for leveraging machine learning to optimize renewable energy integration, ultimately contributing to sustainable development and energy security in emerging markets.

Robust integral super-twisting controller for enhanced photovoltaic integration with hybrid battery and supercapacitor storage in DC microgrid

This study relates to the enhancement in performance of a Hybrid Energy Storage System (HESS) over a DC microgrid, particularly toward a fast-accurate controller of the DC bus voltage. Conventional control methods are particularly difficult to implement in order to reduce start-up time with minimum overshoot, resulting in a significant disturbance of the DC bus voltage. To circumvent these problems, we propose an innovative hybrid controller method that combines the Integral Super-Twisting Algorithm (ISTA) controller technique with adaptive gain in regulating DC bus voltage and current. The capacity of ISTA to greatly enhance the system's dynamic responsiveness, guaranteeing steady operation with effective output voltage control, is by far its greatest benefit. Comparative testing found that the conventional PI controller had a rise time of 0.0666 s, a settling time of 0.165 s, and a 17 % overshoot with a steady-state error of 0.02 %. The proposed ISTA controller had a rise time of 0.0016 s, a settling time of 0.0667 s, zero percentage overshoot, and a minimal steady-state error of only 0.0003 %. The above-mentioned issues mostly focused on better sharing power in HESS and a more effective controller of the DC bus voltage. Furthermore, we evaluate the practical feasibility of the suggested controller through hardware-in-the-loop testing. This research lays a foundation for advancing renewable energy integration in DC microgrid, offering a promising avenue for sustainable and resilient power systems. Finally, the effectiveness and applicability of the proposed ISTA controller for DC microgrid use are proven with Controller Hardware-in-the-Loop (C-HIL) simulations.

A transformer-less DC-DC converter for grid-connected PV systems using TSTS-ZSI voltage boost-buck technique

This research introduces an innovative design for a grid-connected photovoltaic (PV) system utilizing a transformer-less DC-DC converter. The converter is based on the TSTS-ZSI (Three-Switch Transformer-less Z-Source Inverter) voltage boost-buck technique, which enhances voltage regulation and energy transfer, making it ideal for micro-inverter applications. The primary goal of the study is to improve energy efficiency while ensuring seamless integration with the grid. The proposed system not only boosts voltage but also ensures efficient buck operation, leading to enhanced power injection into the grid under fluctuating conditions. By utilizing advanced control techniques, the converter minimizes power losses, reduces stress on the components, and maintains stability in a wide range of grid scenarios. Simulations performed using MATLAB/Simulink demonstrate that the converter delivers stable and efficient power with minimal energy loss. The results validate its effectiveness in small-scale grid-connected systems, achieving enhanced power management under various operating conditions. The converter's ability to operate efficiently without a transformer reduces magnetic losses and electromagnetic interference, making it a cost-effective and compact solution. The research presents a valuable contribution to the field of renewable energy integration, with its applications extending to both residential and commercial grid-connected PV systems.'

Comparative Analysis of Hybrid Geothermal-Solar Systems and Solar PV with Battery Storage: Site Suitability, Emissions, and Economic Performance

Renewable energy integration has become a critical focus in the global effort to reduce carbon emissions and diversify energy sources. In regions with distinct geographic features, such as Türkiye, combining different renewable technologies can offer enhanced energy security. This study investigates the site suitability and economic and environmental performance of hybrid geothermal-solar systems and solar PV systems with battery storage across the provinces of Osmaniye, Hatay, and Kilis, of Türkiye. Using the fuzzy-AHP method, site suitability is evaluated, addressing a key gap in comparing these systems' adaptability to varying geographic conditions. This study is the first to directly compare these two renewable energy technologies in terms of site suitability. The findings reveal significant differences in site suitability, with solar PV systems with battery storage demonstrating broader applicability across the region. The suitable sites (20-100% suitability) cover 1260.82 km² for solar PV systems with battery storage and only 122.18 km² for hybrid geothermal-solar systems. In terms of environmental impact, hybrid geothermal-solar systems exhibit significantly lower carbon emissions, averaging 44.6 kg CO₂/MWh, compared to 123.8 kg CO₂/MWh for solar PV systems with battery storage. Economically, hybrid geothermal-solar systems also outperform with a lower levelized cost of electricity of $0.091/kWh versus $0.254/kWh for solar PV systems. These results highlight the environmental and economic advantages of hybrid geothermal-solar systems, while also emphasizing their limited scalability to regions with geothermal activity. Conversely, solar PV systems, despite their higher emissions and costs, offer greater flexibility and potential for widespread deployment.

Revisiting empirical solar energetic particle scaling relations

Aims. The space radiation environment conditions and the maximum expected coronal mass ejection (CME) speed are assessed by investigating scaling laws between the peak proton flux and fluence of solar energetic particle (SEP) events with the speed of the CMEs. Methods. We used a complete catalog of SEP events, covering the last ∼25 years of CME observations (i.e., 1997–2017). We calculated the peak proton fluxes and integrated event fluences for events that reached an integral energy of up to E > 100 MeV. For a sample of 38 strong SEP events, we first investigated the statistical relations between the recorded peak proton fluxes (IP) and fluences (FP) at a set of integral energies of E > 10 MeV, E > 30 MeV, E > 60 MeV, and E > 100 MeV versus the projected CME speed near the Sun (VCME) obtained by the Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph (SOHO/LASCO). Based on the inferred relations, we further calculated the integrated energy dependence of both IP and FP, assuming that they follow an inverse power law with respect to energy. By making use of simple physical assumptions, we combined our derived scaling laws to estimate the upper limits for VCME, IP, and FP by focusing on two cases of known extreme SEP events that occurred on 23 February 1956, (GLE05) and in AD774/775, respectively. Based on the physical constraints and assumptions, several options for the upper limit VCME associated with these events were investigated. Results. A scaling law relating IP and FP to the CME speed as VCME5 for CMEs ranging between ∼3400–5400 km/s is consistent with values of FP inferred for the cosmogenic nuclide event of AD774/775. At the same time, the upper CME speed that the current Sun can provide possibly falls within an upper limit of VCME ≤ 5500 km/s.

Novel Recuperated Power Cycles for Cost-Effective Integration of Variable Renewable Energy

The ongoing transition to energy systems with high shares of variable renewables motivates the development of novel thermal power cycles that operate economically at low capacity factors to accommodate wind and solar intermittency. This study presents two recuperated power cycles with low capital costs for this market segment: (1) the near-isothermal hydrogen turbine (NIHT) concept, capable of achieving combined cycle efficiencies without a bottoming cycle through fuel combustion in the expansion path, and (2) the intercooled recuperated water-injected (IRWI) power cycle that employs conventional combustion technology at an efficiency cost of only 4% points. The economic assessment carried out in this work reveals that the proposed cycles increasingly outperform combined cycle benchmarks with and without CO2 capture as the plant capacity factor reduces below 50%. When the cost of fuel storage and delivery by pipelines is included in the evaluation, however, plants fired by hydrogen lose competitiveness relative to natural gas-fired plants due to the high fuel delivery costs caused by the low volumetric energy density of hydrogen. This important but uncertain cost component could erode the business case for future hydrogen-fired power plants, in which case the IRWI concept powered by natural gas emerges as a promising solution.

A Comprehensive Review of Multi-Use Platforms for Renewable Energy and Aquaculture Integration

A Comprehensive Review of Multi-Use Platforms for Renewable Energy and Aquaculture Integration

Application of cascaded neural network for prediction of voltage stability margin in a solar and wind integrated power system

Voltage stability is a paramount concern in the management of renewable-rich power systems. As the diffusion of renewable energy sources continues to increase, accurately estimating voltage stability margins becomes essential. This paper addresses the critical need for effective voltage stability margin estimation and presents a novel approach to predict the same on the solar and wind integrated power system. A new machine learning methodology is developed based on the cascaded neural network (CNN). The proposed CNN methodology effectively estimates the loadability margin in a renewable-rich power system under normal operating conditions, varied loading directions, and various contingency situations. The results of CNN are compared with different machine learning techniques and tested in the IEEE 30 bus system and IEEE 118 bus system under various operational circumstances. The results illustrate the efficiency of the suggested method for online assessment of a power system's Voltage Stability Margin. The proposed approach gives real-time insights into voltage stability margins by leveraging the power of data and powerful algorithms, allowing grid operators to proactively manage and optimize renewable energy integration while ensuring grid dependability and stability.

Strategic investments and portfolio management in interdependent low-carbon electricity and natural gas markets

Addressing global warming necessitates carbon emissions reduction and renewable energy integration within the energy sector. Gas-Fired Power Plants (GFPP) are appealing to investors due to their low emissions and operational flexibility, which are considered necessary characteristics within low-carbon power systems with increasing renewable energy uncertainty. Investing in GFPPs presents intricate challenges due to the increasingly interdependent electricity and natural gas markets, especially in the light of a low-carbon economy. This work addresses these challenges for a strategic agent by proposing a bi-level optimization framework. The upper-level model derives the optimal electricity portfolio management regarding new investments and strategic biddings, while in the lower-level model, the electricity and gas markets are cleared sequentially under a Carbon Emission Trading Scheme (CETS). Case studies on a Pennsylvania-New Jersey- Maryland (PJM) 5-bus power system and an IEEE 24-bus test system demonstrate the applicability and efficacy of the proposed model in capturing the impact of a transitional integrated market framework on GFPPs investments. Also, introducing stochasticity to the model provides a better insight into the contrasting effects of emission allowance trading and gas prices on investment and bidding strategies.

Bilevel low-carbon coordinated operation of integrated energy systems considering dynamic tiered carbon pricing methodology

The coordinated operation and management of energy and carbon emissions in an integrated energy system (IES) can effectively promote overall energy efficiency and reduce carbon emissions. However, allocating carbon emission responsibilities between transmission-level integrated energy system (TIES) and regional integrated energy system (RIES) is difficult. On the basis of carbon emission flow theory, a model for allocating carbon emission responsibility for IES is developed in this paper. Moreover, the Shapley value method is adopted to obtain the carbon emissions responsibility intervals at the RIES. A dynamic tiered carbon pricing methodology is proposed for the RIES on the basis of equitable carbon emission responsibility intervals. In addition, a bilevel coordinated operation model that imposes carbon price to more fully excavate the low-carbon benefits obtained from operating an IES is proposed. The upper-level model, namely, the TIES, explores the optimal low-carbon schedule for energy networks by imposing fixed carbon price on the energy production costs. The lower-level model, namely, the RIES, investigates the optimal low-carbon energy supply scheme of multienergy coupling equipment and imposes dynamic tiered carbon pricing to adjust the amount of electricity and natural gas consumed. After the whole bilevel model is solved iteratively, equilibrium is reached. Case studies verify the potential and efficacy of the proposed bilevel model, demonstrating superior effectiveness in reducing carbon emissions compared with existing methods.

Leveraging Deep Learning For Enhancing Sustainability In Environmental Engineering: Recent Advances In Zero-Emission Technologies And Integration Of Alternative Energies

Leveraging Deep Learning For Enhancing Sustainability In Environmental Engineering: Recent Advances In Zero-Emission Technologies And Integration Of Alternative Energies

Accelerating transmission capacity expansion by using advanced conductors in existing right-of-way

As countries pursue decarbonization goals, the rapid expansion of transmission capacity for renewable energy (RE) integration poses a significant challenge due to hurdles such as permitting and cost allocation. However, we find that large-scale reconductoring with advanced composite-core conductors can cost-effectively double transmission capacity within existing right-of-way, with limited additional permitting. This strategy unlocks a high availability of increasingly economically viable RE resources in close proximity to the existing network. We implement reconductoring in a model of the US power system, showing that reconductoring can help meet over 80% of the new interzonal transmission needed to reach over 90% clean electricity by 2035 given restrictions on greenfield transmission build-out. With $180 billion in system cost savings by 2050, reconductoring presents a cost-effective and time-efficient, yet underutilized, opportunity to accelerate global transmission expansion.

Investigating different scenarios of integrating solar-assisted carbon capture with combined cycle power plant and water desalination system

In this work, an optimal algorithm-based integration of solar thermal energy with carbon capture and desalination processes were proposed. The stripper columns, identified as the most energy-consuming equipment, were designed with constant capacities to ensure practicality and applicability. Results show that, without affecting the power plant's efficiency, using a solvent tank as an energy storage system (scenario-2) increases total CO2 capture from 45 % to 98 % compared to a part-time solar power system (scenario-1). Utilizing two solvent tanks reduces waste energy from 87 % to 67 % (scenario-3), while achieving 100 % CO2 capture. Using three different stripper columns (scenario-4) lowers the levelized cost of energy by 20 %. Incorporating an additional multiple effect distillation system (scenario-5) reduces levelized cost of energy and energy loss by 26 % and 74 %, respectively. Approximately 28 % of the total solar energy collected (4.64 × 106 MWh) was used to capture 1.57 × 106 ton.year−1 of CO2. The remaining energy was used in desalination to produce 9.4 × 106 m³.year−1 of fresh water, increasing overall energy efficiency from 28 % to 81 %, and reducing waste energy to 0.88 × 106 MWh, just 26 % of previous scenarios' waste.

Multi-Objective Short-Term Operation of Hydro–Wind–Photovoltaic–Thermal Hybrid System Considering Power Peak Shaving, the Economy and the Environment

In recent years, renewable, clean energy options such as hydropower, wind energy and solar energy have been attracting more and more attention as high-quality alternatives to fossil fuels, due to the depletion of fossil fuels and environmental pollution. Multi-energy power systems have replaced traditional thermal power systems. However, the output of solar and wind power is highly variable, random and intermittent, making it difficult to integrate it directly into the grid. In this context, a multi-objective model for the short-term operation of wind–solar–hydro–thermal hybrid systems is developed in this paper. The model considers the stability of the system operation, the operating costs and the impact in terms of environmental pollution. To solve the model, an evolutionary cost value region search algorithm is also proposed. The algorithm is applied to a hydro–thermal hybrid system, a multi-energy hybrid system and a realistic model of the wind–solar–hydro experimental base of the Yalong River Basin in China. The experimental results demonstrate that the proposed algorithm exhibits superior performance in terms of both convergence and diversity when compared to the reference algorithm. The integration of wind and solar energy into the power system can enhance the economic efficiency and mitigate the environment impact from thermal power generation. Furthermore, the inherent unpredictability of wind and solar energy sources introduces operational inconsistencies into the system loads. Conversely, the adaptable operational capacity of hydroelectric power plants enables them to effectively mitigate peak loads, thereby enhancing the stability of the power system. The findings of this research can inform decision-making regarding the economic, ecological and stable operation of hybrid energy systems.

Applications of blockchain technology in peer-to-peer energy markets and green hydrogen supply chains: a topical review

Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed.

Life cycle and techno-economic analyses of biofuels production via anaerobic digestion and amine scrubbing CO2 capture

The global energy landscape highlights the importance of the hydrogen economy, emphasizing its critical role in worldwide energy policies. This study explores a system designed for producing biomethanol and biomethane by integrating anaerobic digestion, biogas upgrading, and high-temperature electrolysis. The system builds on real-scale industrial data from an existing anaerobic digestion plant, which has been expanded to include biogas upgrading, an oxy-fuel gas turbine, a Solid Oxide Electrolysis Cell (SOEC), and a methanol production unit. Hydrogen, generated through electrolysis, synthesizes biomethanol by reacting with CO2. Additionally, the system produces biomethane through biogas upgrading. The system incorporates thermal energy integration with an oxy-fuel gas turbine. Life cycle assessment (LCA) results demonstrate the system’s environmental potential, achieved negative CO2 emissions of −0.0075 kgCO2eq/kgBiomass in case of photovoltaic panels and −0.0096 kgCO2eq/kgBiomass in case of wind turbines as electricity sources, attributed to efficient conversion of sewage sludge into valuable biofuels. Thermodynamic modeling in Aspen Plus shows an energy efficiency of 58.09 %, with outputs of 188 kg/h of biomethane and 269 kg/h of biomethanol. The techno-economic analysis reveals a payback period of 6.11 years and a levelized cost of biomethanol of 294.37 € per ton, indicating the system’s economic viability. The LCA further underscores the system’s sustainability, supporting its environmental benefits. This comprehensive analysis provides valuable insights into the viability and environmental impact of the proposed biofuel production system.

Deep neural network based distribution system state estimation using hyperparameter optimization

In the past decade, distribution system state estimation has become a crucial topic in power system research due to the increasing importance of distribution networks amidst the decline of centralized energy production. This paper addresses a gap in the literature regarding the application of modern hyperparameter optimization techniques in low-voltage distribution system state estimation using deep neural networks. In particular, it demonstrates the use of the Tree-structured Parzen Estimator algorithm, which is a Bayesian hyperparameter optimization method, for distribution system state estimation on real Hungarian low-voltage networks. The study uses data from four real-life low-voltage supply areas in Hungary, which were modeled to address the challenges in obtaining network information. Compared to traditional methods like the weighted least squares method, the Tree-structured Parzen Estimator algorithm significantly improves the accuracy of the voltage amplitude and angle estimations, reducing the relative error by 14–73%. Additionally, it is shown that TPE outperforms simpler methods like Random Search in hyperparameter optimization. The results also reveal connections between the distribution system size and optimal hyperparameters, such as batch size, learning rate, and hidden layer configuration. The proposed non-iterative algorithm, combined with the parallel computation capabilities of deep neural networks utilizing GPU, resulted in four orders of magnitude improvement in runtime. These advancements make the proposed approach a valuable tool for renewable energy integration planning and real-time monitoring, highlighting its potential for practical applications in the power industry.

Cooperative energy interaction for neighboring multiple distribution substation areas considering demand response

With the growing integration of renewable energy into medium- and low-voltage distribution networks, the distribution substation area (DSA) has emerged, encompassing energy storage and loads. This paper introduces an energy interaction framework for multiple DSAs aimed at enhancing local renewable energy consumption. The energy interaction issue among various DSAs is modeled as a Nash bargaining problem to encourage energy exchanges. However, the variability in pricing and internal demand response may influence scheduling decisions, necessitating further investigation. To address price forecast errors, scenarios are developed using a stochastic programming approach to represent price uncertainties while adjusting the DSA’s load accordingly. Optimal power flow constraints are integrated into the model to bolster power system operation security. Additionally, the transmission capacity can impact scheduling outcomes and operational costs. The influence of transmission limitations on operational strategies is examined within the allowable capacity. To solve this issue, the bargaining model is divided into two subproblems, and an enhanced alternating direction multiplier method (ADMM) is used to maintain the privacy of DSAs. The simulation results obtained using the IEEE-33 bus system indicate that energy interaction among multiple DSAs significantly lowers operating costs and facilitates the integration of renewable energy.

A Novel Wind Power Prediction Model That Considers Multi-Scale Variable Relationships and Temporal Dependencies

Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relationships of multi-scale variables and temporal dependencies. In this paper, a multi-scale frequency decomposition module is designed to split the raw data into high-frequency and low-frequency parts. Subsequently, features are extracted from the high-frequency information using a multi-scale temporal graph neural network combined with an adaptive graph learning module and from the low-frequency data using an improved bidirectional temporal network. Finally, the features are integrated through a cross-attention mechanism. To validate the effectiveness of the proposed model, extensive comprehensive experiments were conducted using a wind power dataset provided by the State Grid. The experimental results indicate that the MSE of the model proposed in this paper has decreased by an average of 7.1% compared to the state-of-the-art model and by 48.9% compared to the conventional model. Moreover, the improvement in model performance becomes more pronounced as the prediction horizon increases.