Demand For Renewable Energy Research Articles

Assessment of energy, economic and environmental aspects of the methanol-fueled Allam cycle in cross-border renewable energy supply chain

This study addresses the challenge of uneven renewable energy distribution by proposing a renewable energy supply chain using MeOH-fueled Allam Cycle for power generation. With the escalating demand for renewable energy, regions with a surplus can produce MeOH using green H2 and recycled CO2, which can then be transported to energy-deficient regions. In these importing regions, MeOH is used for electricity generation via the Allam Cycle, with captured CO2 recycled for MeOH synthesis, creating a closed-loop carbon system. This study evaluates the energy, economic and environmental aspects of this supply chain. It projects that electricity costs in importing regions could decrease from USD 233.02/MWhimpt to USD 101.33/MWhimpt, and MeOH costs could drop from USD 585.39/t to USD 173.40/t, assuming a reduction in renewable electricity costs in exporting regions from USD 45/MWhexpt to USD 15/MWhexpt. Comparative analysis shows acceptable energy conversion and transportation costs, with an electricity conversion rate of 30.65 %-37.07 %. This design offers a promising, cost-effective solution for a sustainable future, with the onshore wind supply chain exhibiting lower emissions of 111.3–97.2 kg/MWhimpt.

An experimental investigation of various trickle collector structures to enhance solar water heater efficiency

This study aimed to enhance the efficiency of the solar water heater (SWH) due to the increasing demand of renewable energy. It compared use three different solar collector models, namely Model A (square-shaped polycarbonate), Model B (v-corrugated zinc), and Model C (trapezoidal aluminium) to identify the most cost-effective configuration. The models were subjected to experiments in real operating conditions during the summer season in Indonesia. Various parameters, including solar radiation intensity, wind speed, inlet and outlet temperatures, and flow rate, were measured every 10 min from morning to afternoon. Additionally, the study employed a trickle and one-way flow rate system. The results showed that Model B achieved the highest total efficiency at 50%, followed by Models A and C at 47% and 34%, respectively. The 120 Lph flow rate exhibited better performance in absorbing useful heat energy than the 240 Lph flow rate. Based on these findings, all three models were recommended for the household-scale SWH applications. Model A showed the most promising economic value but had a shorter lifespan due to the tendency of polycarbonate to deform. In contrast, Model B and Model C, using zinc and aluminium, offered longer lifespans.

The Material and Energy Conversion Potential from Municipal Waste in Padang City: Business Chance and Future Prospective

Padang City, the capital city of West Sumatra Province, is expected to grow and reach a population of 1 million by 2035. However, this growth also means an increase in municipal solid waste, projected to be about 256,000 tons annually. At the same time, there is potential for waste-to-energy conversion in Padang City due to the demand for renewable energy and business opportunities. This study is based on existing literature and data, which were analyzed using a statistical approach and municipal solid waste management calculations. The study conducted scenarios that show potential energy conversions. The first scenario involves converting solid waste to gas and using it directly as biogas. The second scenario involves converting biogas from solid waste to electricity and distributing it for household use. The last scenario consists of using small and medium-sized biodigesters to convert gas into electricity, which will then be used as a charging station for electric vehicles (EVs). The first scenario can generate the most significant profit, while the third scenario suits small and medium businesses. However, each scenario has its pros and cons. Still, it's crucial to consider regulation issues, government support, and public awareness of the environmental problems to implement these waste-to-energy conversion practices on-site successfully.

P2P Optimization Operation Strategy for Photovoltaic Virtual Power Plant Based on Asymmetric Nash Negotiation

Under the guidance of the “dual-carbon” target, the utilization of and demand for renewable energy have been growing rapidly. In order to achieve the complementary advantages of renewable energy in virtual power plants with different load characteristics and improve the rate of consumption, an interactive operation strategy for virtual power plants based on asymmetric Nash negotiation is proposed. Firstly, the photovoltaic virtual power plant is proposed to establish the optimal scheduling model for the operation of the virtual power plant, and then the asymmetric Nash negotiation method is adopted to achieve the fair distribution of benefits. Finally, the ADMM distribution is used to solve the proposed model in the solution algorithm. The simulation results show that the revenue enhancement rates are 28.27%, 1.09%, and 12.37%, respectively. The participating subjects’ revenues are effectively enhanced through P2P power sharing. Each subject can obtain a fair distribution of benefits according to the size of its power contribution, which effectively improves the enthusiasm of the PV virtual power plant to participate in P2P interactions and thus promotes the development and consumption of renewable energy.

Ultrahigh Potassium Storage Capacity of Ca2Si Monolayer with Orderly Multilayered Growth Mechanism.

As the rising renewable energy demands and lithium scarcity, developing high-capacity anode materials to improve the energy density of potassium-based batteries (PBBs) is increasingly crucial. In this work, a unique orderly multilayered growth (OMLG) mechanism on a 2D-Ca2Si monolayer is theoretically demonstrated for potassium storage by first-principles calculations. The global-energy-minimum Ca2Si monolayer is a semiconductor with isotropic mechanical properties and remarkable electrochemical properties, such as a low potassium ion migration energy barrier of 0.07eV and a low open circuit voltage ranging from 0.224 to 0.003V. Most notably, 2D-Ca2Si demonstrates an ultrahigh theoretical specific capacity of 5459mAhg-1 and a total specific capacity of 610mAhg-1, reaching up to 89% of the capacity of a potassium metal anode. Remarkably, the OMLG mechanism facilitates stable, dendrite-free deposition of hcp-K metal layers on the 2D-Ca2Si surface, where the ultrahigh and gradually converging lattice match as the layers increase is the key to achieving theoretically near-infinite growth. The study theoretically demonstrates the Ca2Si monolayer a highly promising anode material, and offers a novel potassium storage strategy for designing 2D anode materials with high specific capacity, rapid potassium-ion migration, and good safety.

Boosting Acidic Water Oxidation on Hematite Photoanodes with Synergistic Effects of Ce‐Doped Co3O4 Nanoparticles

AbstractPhotoelectrochemical (PEC) water splitting is pivotal for addressing the clean and renewable energy demands. However, developing low‐cost, efficient, and robust non‐precious metal catalysts for the acidic oxygen evolution reaction (OER) remains a significant challenge. In this study, we introduced Ce to modulate the electronic structure of Co3O4, enhancing the stability of Co3O4 without compromising its activity. Ce‐doped Co3O4 nanoparticles were electrodeposited onto Ti‐doped hematite surfaces (Ce : Co3O4/Fe2O3), significantly enhancing the PEC performance for water splitting under acidic conditions. These catalysts achieved high photocurrent densities and exhibited prolonged stability. Functioning as p‐type semiconductors, the Ce : Co3O4 nanoparticles not only boosted light absorption but also formed a p‐n heterojunction with the Ti‐doped hematite. This heterojunction generated a built‐in electric field that facilitated the separation and transfer of photogenerated carriers, thereby improving charge separation efficiency. Additionally, these nanoparticles expanded the active surface area of hematite and served as a co‐catalyst, markedly accelerating the OER kinetics. The Ce : Co3O4/Fe2O3 photoanode achieved an impressive photocurrent density of 1.08 mA cm−2 at 1.23 VRHE in acidic media, demonstrating its enhanced activity and stability.

Production of Biofuels from Agricultural Waste

Agricultural waste represents a largely untapped resource that could be utilized for the production of biofuels through various conversion pathways. As the global demand for renewable and sustainable energy grows, biofuels offer solutions to mitigate climate change impacts while improving waste management. This review analyzes using agricultural residues and by-products as feedstocks for biofuel production through biological, thermochemical and chemical conversion processes. The different types of lignocellulosic biomass available from agricultural activities are discussed, along with their compositions. While agricultural waste has advantages like wide availability and low cost, challenges relating to heterogeneous composition, pre-existing contamination and seasonal availability must be addressed. Fermentation, anaerobic digestion, pyrolysis and gasification are examined as established routes for converting agricultural waste into liquid biofuels and biogas. Pretreatment methods, enzyme production pathways and synthesis of fuels like ethanol, butanol and diesel substitutes are outlined. Environmental benefits of biofuels from waste, including greenhouse gas mitigation and recycling of soil nutrients, are evaluated against fossil fuel alternatives. Case studies on operational plants and feasibility studies provide insights into technical and economic viability at scale. Challenges regarding feedstock logistics, conversion efficiency, commercial scale-up and sustainability assessment are identified for future research focus. In conclusion, the review finds that agricultural waste is a promising renewable resource for biofuel production when integrated with appropriate thermochemical, biochemical or anaerobic digestion technologies. While the field is advancing, further improvements in areas such as feedstock supply, pretreatment technologies, and demonstration of sustainability will be critical to realize the full potential of this emerging bioeconomy sector. The review recommends steps to accelerate commercialization and policy frameworks to incentivize waste-to-energy solutions.

Towards Sustainable Energy Communities: Integrating Time-of-Use Pricing and Techno-Economic Analysis for Optimal Design—A Case Study of Valongo, Portugal

This paper presents a comprehensive analysis of optimal energy community design, leveraging time-of-use pricing mechanisms and techno-economic parameters. Focusing on a case study of Valongo, Portugal, this study explores the intricate interplay between energy infrastructure planning, economic considerations, and pricing dynamics. Through a systematic approach, various factors, such as renewable energy integration, demand–response strategies, and investment costs, are evaluated to formulate an efficient and sustainable energy community model. Time-of-use pricing schemes are incorporated to reflect the dynamic nature of energy markets and consumer behavior. By integrating techno-economic analyses, this study aims to optimize energy resource allocation while ensuring cost-effectiveness and environmental sustainability. The influence of optimized sizes of photovoltaics (PV), battery storage, and electrical vehicles (EVs) on self-sufficiency rates, self-consumption rates and CO2 savings is analyzed. The findings offer valuable insights into the design and implementation of energy communities in urban settings, highlighting the importance of adaptive strategies in the transition towards a resilient and low-carbon energy future. The novelty of this paper lies in its comprehensive approach to energy community design, which integrates time-of-use pricing mechanisms with techno-economic parameters. By focusing on the specific case of Valongo, Portugal, it addresses the unique challenges and opportunities present in urban settings. Additionally, the analysis considers the interaction between renewable energy production, demand profiles and investment costs, providing valuable insights for optimizing resource allocation and achieving both cost-effectiveness and environmental sustainability.

Hybrid Grey Wolf Optimizer for Efficient Maximum Power Point Tracking to Improve Photovoltaic Efficiency

Today, the demand for Renewable Energy (RE) sources has increased a lot; out of all Renewable Energy Sources (RES), Solar Energy (SE) has emerged as a better solution due to its sustainability and abundance. However, energy sources from the sun directly depend on the efficiency of the photovoltaic (PV) systems employed, whose efficiency depends on the variability of solar irradiance and temperature. So harvesting the maximum output from PV panels requires optimized Maximum Power Point Tracking (MPPT) systems. The traditional MPPT systems that involved Perturb and Observe (P&O) and Incremental Conductance (IncCond) are the most widely used models. However, those models have limited efficiency due to rapidly changing environmental conditions and their tendency to oscillate around the Maximum PowerPoint (MPP). This paper proposes a Hybrid Heuristic Model (HHM) called the Hybrid Grey Wolf Optimizer (HGWO) Algorithm, which employs the Genetic Algorithm (GA) model for optimizing the Grey Wolf Optimizer (GWO) algorithm for effectively utilizing MPPT in PV systems. The simulation decreases fluctuation, boosting how the system responds to shifts in the surrounding atmosphere. The framework evolved through several experiments, and its ability to perform was assessed concerning the results of different models for the factors that were considered seriously throughout several solar radiation and temperature scenarios. During all of the tests, the recommended HGWO model scored more effectively than the other models. This succeeded by accurately following the MPP and boosting the power supply.

The driving forces behind renewable energy consumption: A comprehensive analysis of key determinants and environmental concerns in ASEAN countries.

Renewable energy consumption is a crucial solution to addressing pressing environmental issues, particularly climate change and air pollution. Investigating the factors that drive its adoption is highly significant, as it provides policymakers and stakeholders with valuable insights to accelerate the transition to renewable energy sources. Through this approach, we can minimise the negative consequences of our reliance on fossil fuels, thereby protecting the integrity of the environment. Therefore, the primary goal of this study is to thoroughly investigate the main factors that influence renewable energy consumption and environmental change in six specifically chosen ASEAN countries. The stationarity of the 1990-2019 data was tested using panel data techniques such as Levin, Lin, and Chu (LLC), Im Pesaran (IPS), and the Shin W-stat test. According to the stationarity tests, after the first order, all variables exhibit stationarity. Additionally, Pedroni's co-integration test result confirmed that there was a long-term relationship among the variables. Different methods, such as dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and pooled ordinary least squares (POLS), are used for cointegration estimating. The results suggest that there is a positive co-integration between renewable energy use and GDP in six ASEAN countries, indicating a long-term relationship. The positive relationship between GDP and renewable energy use suggests that economic growth is the primary driving force behind ASEAN's renewable energy adoption. However, factors like carbon emissions, population density, and foreign direct investment (FDI) negatively impact the demand for renewable energy. The limited availability of renewable energy in certain ASEAN countries may discourage foreign direct investment (FDI) due to the inverse relationship between FDI and renewable energy use. The studies also revealed that carbon emissions, which contribute to environmental pollution, do not motivate industries to invest in renewable energy. This finding would challenge the Environmental Kuznets Curve (EKC) hypothesis. According to the EKC, there is a significant transition towards renewable energy as a response to environmental degradation. However, it is worth noting that several ASEAN countries have experienced economic growth while also experiencing higher levels of carbon emissions. Given that economic expansion might not be environmentally beneficial, this research has implications for ASEAN energy policies. The ASEAN region faces a challenge in investing in renewable energy due to the excessive dependence on fossil fuels. Therefore, an in-depth evaluation of the main factor behind ASEAN's environmental concerns, which promotes the adoption of renewable energy, can greatly influence policy decisions, particularly in attaining net zero emissions. Policymakers can utilise this comprehensive analysis to establish informed objectives for policies related to renewable energy and develop strategic plans, i.e. reforming fuel subsidies. The goal is to encourage the development of environmentally friendly and sustainable energy plans for the future in the ASEAN region.

Empowering Bangladesh: A Study on Synergizing Renewable Energy and Smart Grid Technologies for Sustainable Development

This paper shows a study of the Smart Grid in Bangladesh, emphasizing the incorporation of renewable energy sources. Its aim is to provide an overview of global renewable energy demand and highlight the significance of a smart grid system in fostering sustainability and dropping CO2 emissions.The total renewable energy generation of Bangladesh stood at a meager 1.34% in 2009, representing its low comparative position with other nations. Unfortunately, there has been a little improvement over time, as the total renewable energy generation was 1.51% in 2014 and only slightly increased to 1.57% by 2019. To overcome the addressed issue, it requires massive potential of renewable energy sources to ensure a lucrative, secure, and high-quality supply. This can be achieved through effective monitoring and control mechanisms within the grid system.The integration of cutting-edge technologies such as bi-directional current flow, bi-directional communication systems, automatic working capacity, automatic control systems, and rear systems are required if the upgradation of the current power structure is needed. These advancements will contribute to the enhanced performance, reliability, and security of the renewable energy infrastructure. By embracing a smart grid system and optimizing the integration of renewable energy, Bangladesh can pave the way towards a sustainable future. This will not only boost the country's renewable energy generation but also play a vital role in mitigating CO2 emissions. IUBAT Review—A Multidisciplinary Academic Journal, 7(1): 26-44

Renewable energy resource management using an integrated robust decision making model under entropy and similarity measures of fuzzy hypersoft set

The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing long-lasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi-criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set-based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub-parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.

A multi-timescale and chance-constrained energy dispatching strategy of integrated heat-power community with shared hybrid energy storage

The community in the future may develop into an integrated heat-power system, which includes a high proportion of renewable energy, power generator units, heat generator units, and shared hybrid energy storage. In the integrated heat-power system with coupling heat-power generators and demands, the key challenges lie in the interaction between heat and power, the inherent uncertainty of renewable energy and consumers’ demands, and the multi-timescale scheduling of heat and power. In this paper, we propose a game theoretic model of the integrated heat-power system. For the welfare-maximizing community operator, its energy dispatch strategy is under chance constraints, where the day-ahead scheduling determines the scheduled energy dispatching strategies, and the real-time dispatch considers the adjustment of generators. For utility-maximizing consumers, their demands are sensitive to the preference parameters. Taking into account the uncertainty in both renewable energy and consumer demand, we prove the existence and uniqueness of the Stackelberg game equilibrium and develop a fixed-point algorithm to find the market equilibrium between the community operator and community consumers. Numerical simulations on integrated heat-power community show that the proposed solution outperforms the constant curtailment strategy with the daily cost reduced by 14.38%.

Chance constrained optimal power-water flow: An iterative algorithm assisted by deep neural networks

The optimal power-water flow (OPWF) is a fundamental tool for operating integrated power and water networks. To address uncertainties in renewable energy and load demands, researchers have used chance-constrained models to investigate OPWF under uncertainty. However, existing work has limitations, such as oversimplified network representations, intractable problem formulations, and high computational burdens. This paper presents a chance-constrained OPWF formulation that incorporates detailed network constraints and stochastic uncertainties from wind speeds and electricity/water demands. By leveraging the concept of quantiles of stochastic variables and convexification techniques, the model is recast into a deterministic mixed-integer second-order cone programming (MISOCP) problem with uncertain margins. To efficiently solve this problem, we propose a novel iterative algorithm assisted by deep neural networks (DNNs). The algorithm integrates two DNNs: one designed to solve power and water flow equations, accelerating the probabilistic evaluation of uncertain margins; and the other to predict pipeline flows and refine variable ranges, facilitating a warm-start approach to the MISOCP model. Case studies conducted on the IEEE 123-node feeder coupled with a 67-node water distribution network validate the two DNNs and the overall iterative algorithm. The findings demonstrate that the proposed DNN-assisted approach effectively balances system security and economy. Compared to existing algorithms, the relative error of the optimization solution is less than 0.03%, with a speedup ratio exceeding 200.

Research on the starting-up process of a prototype reversible pump turbine with misaligned guide vanes: An energy loss analysis

With fossil fuel depletion and rising demand for renewable energy, reversible pump turbines (RPTs) are crucial for balancing energy supply. Understanding the dynamics of RPTs during starting-up is essential for optimal performance and stability in renewable energy systems. The unique “S" curve of RPTs can lead to instability under transient conditions. This study aimed to investigate the impacts of misaligned guide vanes (MGV) devices on prototype RPT stability, pressure pulsation, and performance improvement during the starting-up process. Using CFD simulation, flow characteristics and energy loss during start-up were analyzed and validated against experimental data. The results show that MGV enhances stability but induces significant energy loss. The analysis of entropy production, vorticity transport, and pressure fluctuation revealed high energy loss regions and the evolution of vortices during the starting-up process. This study provides new insights into hydrodynamic challenges and energy loss with MGV in prototype RPT start-up, with implications for optimizing pumped storage hydropower operations. Overall, this study has valuable engineering significance for the development and application of RPT in renewable energy systems.

Nexus of Human Development and Environmental Quality in Low-Income and Developing Countries: Do Renewable Energy and Good Governance Matter?

The relationship between human development and environmental quality has been explored in this study by examining the human-development status and carbon (CO2)-emissions levels of 60 countries from the low, lower-middle, and upper-middle income categories. The roles of renewable energy and some economic and institutional factors such as GDP, the rule of law, regulatory quality, and corruption control have also been investigated to ascertain their impacts on the relationship. The empirical investigations apply the generalized method of moments (GMM), fixed effects (FE), and random effects (RE) methods, and the long-run associations among the variables are investigated by applying the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) techniques. The robust findings support the trade-off relationship between human development and environmental quality in the selected low-income and developing countries. With evidence of an environmental Kuznets’s relationship between economic growth and environmental quality, these findings reveal that the measures pursued to improve human-development status have a contributory impact on CO2 emissions in the selected countries. However, an increased demand for renewable energy, effective enforcement of the rule of law, and improved control over corruption have a mitigating effect on CO2 emissions. The result has also highlighted the policy issues instrumental to increased emissions levels in these countries. Consequently, it is recommended to formulate policies for resolving disparities within the various dimensions of human development while also making deliberate investments in the socio-economic aspects of human development to ensure both sustainable human development and environmental quality.

Distributed Cooperative Optimal Operation of Multiple Virtual Power Plants Based on Multi-Stage Robust Optimization

This paper develops a distributed cooperative optimization model for multiple virtual power plant (VPP) operations based on multi-stage robust optimization and proposes a distributed solution methodology based on the combination of the alternating direction method of multipliers (ADMMs) and column-and-constraint generation (CCG) algorithm to solve the corresponding optimization problem. Firstly, considering the peer-to-peer (P2P) electricity transactions among multiple VPPs, a deterministic cooperative optimal operation model of multiple VPPs based on Nash bargaining is constructed. Secondly, considering the uncertainties of photovoltaic generation and load demand, as well as the non-anticipativity of real-time scheduling of VPPs in engineering, a cooperative optimal operation model of multiple VPPs based on multi-stage robust optimization is then constructed. Thirdly, the constructed model is solved using a distributed solution methodology based on the combination of the ADMM and CCG algorithms. Finally, a case study is solved. The case study results show that the proposed method can realize the optimal scheduling of renewable energy in a more extensive range, which contributes to the promotion of the local consumption of renewable energy and the improvement of the renewable energy utilization efficiency of VPPs. Compared with the traditional deterministic cooperative optimal operation method of multiple VPPs, the proposed method is more resistant to the risk of the uncertainties of renewable energy and load demand and conforms to the non-anticipativity of real-time scheduling of VPPs in engineering. In summary, the presented works strike a balance between the operational robustness and operational economy of VPPs. In addition, under the presented works, there is no need for each VPP to divulge personal private data such as photovoltaic generation and load demand to other VPPs, so the security privacy protection of each VPP can be achieved.

Safe reinforcement learning based optimal low-carbon scheduling strategy for multi-energy system

Multi-energy system with distributed energy resources has become the inevitable trend in recent years due to their potential for creating the efficient and sustainable energy infrastructure, with a strong ability on carbon emission reduction. To accommodate the uncertainties of renewable energy generation and energy demand, model-free deep reinforcement learning methods are emerging for energy management in multi-energy system. However, traditional reinforcement learning methods still have operation safety issue of violating the physical constraints of multi-energy system. To address the challenges, a low-carbon scheduling strategy based on safe soft actor-critic algorithm is proposed in this paper. Firstly, an electricity-thermal-carbon joint scheduling framework is constructed, where carbon trading mechanism is incorporated to further motivate carbon emission reductions. Secondly, the energy cost and carbon trading cost are simultaneously integrated in the objective function, and the dynamic optimization problem of multi-energy system is modeled as a constrained Markov decision process by taking into account the diverse uncertainties. Then, a novel safe soft actor-critic method is proposed to achieve the benefits of economic and carbon emissions, where the security networks and Lagrangian relaxation are introduced to deal with operation constraints. The case study validates that the proposed scheduling strategy can reduce the energy cost and carbon trading cost by up to 26.24% and 33.73% within constraints, compared with existing methods.

Balancing Possibilist-probabilistic risk assessment for smart energy hubs: Enabling secure peer-to-peer energy sharing with CCUS technology and cyber-security

This paper introduces the Possibilistic-Probabilistic Risk-based Smart Energy Hub (PPR-SEH) scheduling model, addressing cybersecurity challenges in the transition to advanced energy systems characterized by decarbonization, decentralization, and digitalization. The model integrates cutting-edge technologies like Carbon Capture Utilization and Storage (CCUS) and demand response programs. It addresses uncertainties from renewable energy sources, demand variability, fuel supply, and energy price fluctuations using a Z-number-based approach. Covering various energy types—electricity, heat, cooling, gas, and water—the PPR-SEH model offers a comprehensive energy management solution. Employing a mixed-integer linear programming (MILP) framework optimized with the CPLEX solver in GAMS software, it uses an epsilon constraint method and a fuzzy satisfying approach for solution selection. The model also evaluates the economic and environmental impacts of peer-to-peer energy-sharing markets linked with carbon emission trading, enhancing the efficiency and sustainability of energy distribution. The findings reveal that incorporating robust cybersecurity measures and integrating demand response and CCUS significantly influence operational efficiency and sustainability, evidenced by an increase in costs and pollution by approximately 11.43 % and 1.8 %, respectively, compared to deterministic approaches. This study underscores the importance of robust planning and the beneficial impact of a carbon system on load curve management and economic returns in smart energy systems.

A Review of Coupled Geochemical–Geomechanical Impacts in Subsurface CO2, H2, and Air Storage Systems

Increased demand for decarbonization and renewable energy has led to increasing interest in engineered subsurface storage systems for large-scale carbon reduction and energy storage. In these applications, a working fluid (CO2, H2, air, etc.) is injected into a deep formation for permanent sequestration or seasonal energy storage. The heterogeneous nature of the porous formation and the fluid–rock interactions introduce complexity and uncertainty in the fate of the injected component and host formations in these applications. Interactions between the working gas, native brine, and formation mineralogy must be adequately assessed to evaluate the efficiency, risk, and viability of a particular storage site and operational regime. This study reviews the current state of knowledge about coupled geochemical–geomechanical impacts in geologic carbon sequestration (GCS), underground hydrogen storage (UHS), and compressed air energy storage (CAES) systems involving the injection of CO2, H2, and air. Specific review topics include (1) existing injection induced geochemical reactions in these systems; (2) the impact of these reactions on the porosity and permeability of host formation; (3) the impact of these reactions on the mechanical properties of host formation; and (4) the investigation of geochemical-geomechanical process in pilot scale GCS. This study helps to facilitate an understanding of the potential geochemical–geomechanical risks involved in different subsurface energy storage systems and highlights future research needs.