Green Grid Research Articles

Smart grid and blockchain: Bibliometric analysis

The interest in smart grids and new technologies is growing around the world. Countries are investing in the development of new technologies that will help achieve environmental goals, energy supply efficiency, improve energy efficiency and increase consumer involvement in the energy generation. One of such technology is a blockchain. It is believed that the blockchain, combined with a smart grid, provides an opportunity to integrate the activities of all stakeholders, including: generators, distributors and consumers of electricity. The aim of the article is to identify the key research areas discussed by the researchers of both the smart grid and the blockchain issues. An analysis of the Scopus database from 2015 to 2023 was conducted. Using a created bibliometric query, a systematic literature review was conducted. 476 scientific publications relating to the issues addressed were identified. Using the VOSviewer software, a bibliometric analysis was performed using the author’s keywords. The bibliometric maps obtained allowed for the identification of key research areas. The article also presents potential future directions of scientific considerations, which should be focused on the issue of green smart grid and green blockchain. The results presented in the article can inspire researchers looking for research gaps or describing the current state of knowledge in the field of the smart grid and the blockchain issues.

Optimal allocation method of oxygen enriched combustion-carbon capture low-carbon integrated energy system considering uncertainty of carbon-source-load

In the context of green grid, it is crucial to enhance the low-carbon transformation of integrated energy systems. To improve the degree of low-carbon in these systems, this paper proposes an optimal allocation method of oxygen enriched combustion-carbon capture low-carbon integrated energy system considering the uncertainty of carbon-source-load. Firstly, this paper provides an analysis of uncertainties of carbon-source-load and uses budget uncertainty sets to model the three uncertain parameters. Secondly, in the integrated energy system, the by-product O2 of hydrogen production by electrocution and the oxygen storage device are used to provide oxygen-enriched combustion conditions for the gas turbine to reduce the CO2 generated in the combustion process. At the same time, the carbon capture device is used to collect CO2, and the oxygen enriched combustion-carbon capture integrated energy system is formed. Finally, an improved multi-populations quantum-behaved particle swarm optimization algorithm is proposed to solve the proposed model. This method considers the uncertainty of carbon emission intensity, which makes the optimal allocation of integrated energy system more inclined to low-carbon rather than economic. In addition, in terms of model structure, this method integrates oxygen enriched combustion-carbon capture into the integrated energy system to form a low-carbon model, which directly improves the low-carbon performance of the system. The simulation results show that the carbon emission of the proposed method is reduced by 38.8 % compared with other methods.

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

Techno-economic analysis of deploying a short or mixed energy storage strategy in a 100 % green power grid

A fully decarbonised electricity grid with extensively deployed renewable systems is a fundamental step in transitioning to a net-zero world. Unlike fossil energy, renewable energy systems are subject to meteorological intermittency. However, few studies have investigated the techno-economic performance of integrating short- and promising long-duration energy storage into a 100 % renewable energy grid to balance short-term and inter-seasonal demand.This research developed an economic model to investigate the techno-economic performance of standalone and combined energy storage solutions for a fully green grid in three defined scenarios. Lithium-ion batteries (Li-ion), hydrogen, and electrical thermal energy storage (ETES) were selected as promising storage technologies due to their maturity, commercial availability, and scalability. The three examined scenarios are: 1) Li-ion battery only, 2) Li-ion battery with ETES, and 3) Li-ion battery with hydrogen. The research aims to determine whether combining long-duration energy storage (e.g., ETES and hydrogen) with Li-ion batteries offers greater economic and technical benefits, resulting in a more affordable, resilient, and secure power supply.The results show that the Li-ion battery only scenario (Scenario 1) requires a capacity of 234,956 MWh. Adding ETES and hydrogen reduces this capacity to 53,304 MWh (Scenario 2) and 7020 MWh (Scenario 3). The addition of ETES and hydrogen improves power supply flexibility, increasing the proportion of generated electricity used to meet demand from 32.7 % in Scenario 1 to 41.2 % and 52.3 % in Scenarios 2 and 3, respectively. These technologies also help avoid electricity deficits, reduce the loss of power probability (LOPP), and lower the cost of electricity supply losses from $8365 million in Scenario 1 to $1793 million in Scenario 2 and only $4 million in Scenario 3. Power delivery costs decrease from $99.5/MWh in Scenario 1 to $77.6/MWh and $76.6/MWh in Scenarios 2 and 3, respectively. The research concludes with four policy recommendations to advance long-duration energy storage and transition to a fully green power supply system.

Internet of Things-Based Robust Green Smart Grid

Renewable energy sources play a critical role in all governments’ and organizations’ energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the generation of green smart grids. Smart grids are critical for modernizing electricity distribution by using new communication technologies that improve power system efficiency, reliability, and sustainability. Smart grids assist in balancing supply and demand by allowing for real-time monitoring and administration, as well as accommodating renewable energy sources and reducing outages. However, their execution presents considerable problems. High upfront expenditures and the need for substantial and reliable infrastructure changes present challenges. Despite these challenges, shifting to green smart grids is critical for a resilient and adaptable energy future that can fulfill changing consumer demands and environmental aims. To this end, this work considers developing a reliable Internet of Things (IoT)-based green smart grid. The proposed green grid integrates traditional grids with solar energy and provides a control unit between the generation and consumption parts of the grid. The work deploys intelligent IoT units to control energy demands and manage energy consumption effectively. The proposed framework deploys the paradigm of distributed edge computing in four levels to provide efficient data offloading and power management. The developed green grid outperformed traditional grids in terms of its reliability and energy efficiency. The proposed green grid reduces energy consumption over the distribution area by an average of 24.3% compared to traditional grids.

Securing the green grid: A data anomaly detection method for mitigating cyberattacks on smart meter measurements

Smart meters, being a vital component in the advanced metering infrastructure (AMI), provide an opportunity to remotely monitor and control power usage and act like a bridge between customers and utilities. The installation of millions of smart meters in the power grid is a step forward towards a green transition. However, it also constitutes a massive cybersecurity vulnerability. Cyberattacks on AMI can result in inaccurate billing, energy theft, service disruptions, privacy breaches, network vulnerabilities, and malware distribution. Thus, utility companies should implement robust cyber-security measures to mitigate such risks. In order to assess the impact of cybersecurity breaches on AMI, this paper presents a cyber-attack scenario on grid measurements obtained via smart meters and assesses the stochastic grid estimations under attack. This paper also presents an efficient method for the detection and identification of anomalous data within the power grid by leveraging the distance between measurements and the confidence ellipse centered around the estimated value. To assess the proposed method, a comparative analysis is done against the chi-square test for detection and the largest normalized distribution test for the identification of bad data. Furthermore, by using a Danish low-voltage grid as a base case, this paper introduces two test cases to evaluate the performance of the proposed method under single and multiple-node cyber-attacks on the grid state estimation. Results show a notable improvement in accuracy when using the proposed method. Additionally, based on these numerical results, protective countermeasures are presented for the grid.

Liquid-solid interface diffusion behaviors of furfural in the insulation systems of natural ester-cellulose paper and mineral oil-cellulose paper

Furfural as an ageing product of cellulose paper can be detected by the furfural content in the oil. With the construction of green power grid, more and more power equipment adopts environmentally friendly insulating fluids. The investigation of the diffusion behaviors of furfural at different oil-paper interfaces can provide a basis for fault detection in power transformers. The liquid-solid interface diffusion behaviors of furfural in natural ester-cellulose paper and mineral oil-cellulose paper insulating systems are investigated by molecular dynamics simulation in this paper. Results show that the range of center-of-mass movement distances of furfural in mineral oil-cellulose paper is about 5 times that of natural ester-cellulose paper. The main reason for this is that the long-chain macromolecules composing the natural ester undergo curling in the extended flow. As polar molecules, natural ester can attract furfural, which is also a polar molecule. And the electrostatic potential mapping interval of furfural is about 4.4 times that of mineral oil, but 1.1 times that of natural ester, with which it overlaps more. These hinder the diffusion of furfural molecules in the natural ester-cellulose paper liquid-solid interface. This research provides theoretical support for the diffusion of furfural in the insulation systems of natural ester-cellulose paper and mineral oil-cellulose paper. It also provides useful guidance for fault detection in transformers using natural ester insulating fluids.

Spatial synergistic effect of urban green space ecosystem on air pollution and heat island effect

It is crucial for urban greening studies to comprehend the spatial synergy effect of the urban green space ecosystem on air pollution and the heat island effect. However, there are limited studies that have considered the impact of green space ecosystems on different air pollutants and urban heat island effects across different seasons from an urban space perspective. Therefore, this study took an urban space perspective and established a high-precision spatial grid with a resolution of 500 m. The spatial econometric regression and bivariate local Moran's I test were employed to analyze the impacts of urban green space ecosystems on air pollutants (including summer ozone (O3) concentration, winter fine particulate matter (PM2.5) concentration and winter inhalable particulate matter (PM10) concentration) and heat islands in summer and winter.The results showed that the spatial regression coefficients between green space and heat islands, PM2.5 concentration and PM10 concentration are significantly negative, −0.00049***, −0.00329***, and −0.00365*, respectively. The urban green space ecosystems are observed to effectively mitigate the heat island effect and help to reduce PM2.5 and PM10 pollution in winter. There are 88 high-high clustering in the spatial interactions between green space and ozone concentration in summer, that is, the grids with high O3 concentration surrounded by the high green space grids. It implies that urban green space ecosystems can significantly increase O3 pollution in summer. This study reveals that low BVOCs tree species should be widely planted in urban green spaces, while large-scale green space parks should be established in the urban region to continuously increase the proportion of green space. This study would contribute to improving the awareness of the government and relevant employees on scientific urban greening methods.

Equity-oriented power sharing and demand response for enhancing green energy access in mixed communities

The distribution of renewable energy resources (RERs) is currently uneven and is largely influenced by their affordability across various communities. This has led to disparities in access to green energy for different community members. To address these inequalities, this study proposes an equity-oriented power-sharing mechanism that incorporates demand response (DR). The implementation of this approach will increase the availability of green energy for all members of the community in mixed neighborhoods. First, a DR program is introduced for conventional homes (i.e., homes without RERs) to maximize the consumption of RERs by shifting their load to RER excess intervals. Then, a proportionally fair mechanism is introduced to distribute the available resources among community members during times of high excess. The objective is to minimize the grid dependence of community members by reducing their reliance on purchasing power from the grid. To ensure renewable energy homes (i.e., homes with installed RERs) maintain their green energy access, additional constraints are put in place to prioritize their energy needs when compared to the non-sharing case. Three indices (green energy access, grid dependence, and cost reduction) are introduced to evaluate the performance of the proposed method in different cases: the base case, the power-sharing-only case, and the case of power-sharing with the DR. Finally, the impact of different seasons, day types, and user-defined parameters is analyzed. Simulations have shown that the proposed method can significantly increase the green energy access of conventional homes without affecting the access to green energy of renewable energy homes. It also decreases the grid dependence and the electricity cost of the entire community.

New trends in datacenter energy efficiency: beyond PUE

The energy efficiency is one of the great challenges of the IT industry. This challenge is even greater for supercomputing centers, since they are very intensive in energy consumption. The FCSCL is a center that was born in 2008 and its infrastructures were designed to achieve higher energy efficiency. In addition, energy efficiency is the priority line of research in FCSCL. To solve the problem of energy efficiency it is necessary to establish metrics, and organizations as The Green Grid PUE has established the PUE as the primary measure of energy efficiency. The FCSCL has developed the MONICA project in collaboration with the company Caton S. L. and the University Jaume I, and the first objective of the project has been active PUE monitoring. The PUE is the main indicator of the energy balance of a datacenter, and it is obvious that the first thing to do is monitor it to keep it under control But the analysis of the data acquired in MONICA project has shown that taking measures to improve the PUE can worsen the overall efficiency of the datacenter and IT equipment, as will be demonstrated in the article.

Global building life cycle assessment: Comparative study of steel and concrete frames across European Union, USA, Canada, and Australia building codes

The construction industry has one of the worst carbon footprints among the major industries and accounts for 39% of the world’s total greenhouse gas emissions. One of the main determinants of the carbon footprint of a building is the type of frame used in its structure. The most commonly used types of structural frames are steel and concrete frames, each having its own advantages and drawbacks. This study estimates and compares the carbon footprint of two buildings designed with a steel frame and a concrete frame, respectively, based on the building structural design codes of the European Union (EC), USA (ACI, AISC), Canada (CSA), and Australia (AS). The primary objective of this study is to conduct a comprehensive comparison of carbon emissions from these buildings. The focus extends beyond operational emissions to include an assessment of embodied emissions using the life cycle assessment (LCA) methodology. This comparison aims to gauge the overall emissions of the buildings and also evaluate the impact of major building codes on their carbon footprint. The results show that among the steel frame structures designed based on different building codes, the one designed according to the codes of the European Union has the highest carbon emission (142 kgCO2-eq/m2 floor area), and the one designed based on the Australian code has the lowest carbon emission (119 kgCO2-eq /m2). Among the concrete frame structures, the one designed based on the Canadian codes had the highest carbon emission (221 kgCO2-eq/m2), but again, the structure designed according to the Australian code had the lowest carbon emission (134 kgCO2-eq /m2). Carbon emission estimations for the operational phase showed that, while Canadian Cities exhibit the highest portion of energy consumption, its utilization of a green electricity grid results in the lowest operational carbon emissions. In contrast, Sydney, despite having the lowest energy consumption, exhibits the highest level of operational carbon emissions due to its reliance on coal for electricity generation. The findings of this study can serve as a valuable benchmark for policymakers in various areas. They can identify the shortcomings and advantages of their region's building codes, paving the way for targeted modifications that contribute to achieving a more sustainable built environment in their respective locales.

Study on coupling and coordinated development of green development of power grid and ecosystem—from the perspective of energy industry chains

The advancement of green power grid technology, driven by the Tenth Five-Year Plan and the "Double Carbon" objective, is transforming the energy sector and promoting a greener economy. Understanding the link between green power grid development and the ecosystem is crucial. This study establishes an evaluation system to assess their coordination using a model. The analysis shows a progression towards coordinated development since 2006, with green initiatives playing a key role. This evolution highlights green development as a driving force behind power grid progress, rather than a constraint.

Study on coupling factors between green development of power grid and ecosystem—from the perspective of energy industry chains

The green power grid development, with industry and regional characteristics, impacts energy systems and eco-friendly production. Ecosystem services are vital for sustainable development. Urgent research is needed to adapt to new situations. This study establishes index systems for power grid green development and ecology. Key factors affecting their coordination are identified, guiding the promotion of green power grid development. Focus on enhancing cross-regional clean energy allocation, waste disposal, noise monitoring, and increasing electric energy use in terminal consumption.

Future Green Mobile Communication Technology Facing the “Double Carbon” Goal

The goal of “double carbon” (namely “peak carbon dioxide emissions” and “carbon neutrality”) proposed by China for the first time is an important layout in the Tenth Five-Year Plan, and it is also the key goal to realize the green and sustainable development of mobile communication networks in the future, and it is also the foundation for China’s international carbon asset pricing right and the world carbon trading platform. Among them, the difficulty in realizing green communication lies in maintaining the growth of business volume. Reduce network energy consumption and carbon emissions. This paper studies the green communication technology from the perspective of energy saving and emission reduction on the mobile communication network side and the perspective of the integrated architecture of communication network and multi-energy energy network. The research results show that the key to realize green communication technology lies in the mutual matching of network resources, energy resources and business distribution, while the existing technology can only achieve one-way matching of network resources and business distribution. Or the one-way matching of energy resources and service distribution. Based on this, this paper proposes a native green grid architecture with communication, perception and energy fusion, which has the ability of energy perception and service perception, supports the two-way matching method of network resources, energy resources and service distribution, and realizes the continuous growth of service while significantly reducing the energy consumption and carbon emissions on the mobile communication network side by eliminating the randomness and suddenness of service distribution and energy distribution.

An IoT Assimilated Distributed Control Method for Green Electrical Transmission Grids

Green electrical grids utilize renewable energy to ensure sustainable transmission from natural resources. Internet of Things (IoT) like pervasive platforms is integrated with the grids for improving the automation in such power grids. This article considers the IoT control over the green grids for uninterrupted power transmission. The proposed method named Assimilated Distributed Control (ADC) balances the generated and distribution of electrical power based on demand. The IoT paradigm monitors the rising demand for recommending multi-renewable power source assimilation for meeting the distribution demands. In this process, linear decision-making for distribution management and assimilation is performed. The decision-making process relies on power generation and distribution ratio from low to peak demand intervals. Therefore, the number of resource assimilations relies on the distributed control for handling peak demands. The proposed method is analyzed using distribution ratio, peak demand, and recommendation assimilation.

A novel energy storage and demand side management for entire green smart grid system for NEOM city in Saudi Arabia

AbstractHybrid renewable energy systems (HRES) are gaining high interest in supplying electric energy for remote communities. Energy storage systems (ESS) are utilized by green autonomous HRESs to accommodate the variability of renewable resources such as wind and solar energy systems. The lack of any traditional energy source is adding a great reliability challenge which should be compensated using expensive ESS. This challenge can be avoided by using a pumped hydro energy storage system (PHES) in harmony with batteries. The PHES is an excellent option to be used in NEOM city due to the perfect topographical characteristic of this site. The minimum cost of energy and the highest reliability is used as an objective for sizing the proposed entire green HRES. Using smart grid principles (SGP) and demand‐side management (DSM) in the design and operation stages will minimize system size and cost, which can result in a significant reduction in consumer bills. As a result, this paper introduces an innovative DSM based on a dynamic tariff. The suggested DSM technique was developed utilizing a unique fuzzy logic that takes into account the present and day‐ahead ESS situations to intelligently determine the ideal tariff for the lowest cost and maximum reliability of the HRES. This paper introduces a modified grey wolf optimization (MGWO) technique to shorten convergence time while preserving the best accuracy. The suggested MGWO is assessed against 10 swarm optimization techniques. The payback period of the project is 7 years. The findings acquired from this unique program demonstrated its superiority, with conversion times reduced by 22% to 80% when compared to previous optimization procedures. Furthermore, as compared to the flat rate pricing tariff, the usage of the dynamic tariff lowered the LCOE by 53%.

Process Design and Techno-ECONOMIC Evaluation of a Decarbonized Cement Production Process Using Carbon Capture and Utilization

To address a decarbonized cement production process (DCPP), a calcium looping process is connected to an industrial cement production process (CPP) for capturing CO2 by 93.5~96%. Since the captured CO2 purity is up to 99.9 wt%, the carbon capture and utilization (CCU) process is connected to generate the additional products of urea and methanol. An integration of DCPP and CCU, named the DCPP-based polygeneration system, is being developed for three scenarios. To meet the power demand for producing high-purity hydrogen and oxygen, Scenario 1 adopts water electrolysis and the full green electricity grid; Scenario 2 adopts the Cu-Cl thermochemical cycle and the partial green electricity grid; and Scenario 3 adopts water electrolysis and the heat recovery steam generator (HRSG). Through the techno-economic analysis and comparisons, the CO2 avoided costs of three scenarios are estimated between 16.53 and 21.42 USD/ton, which are lower than the conventional DCPP of around 40 USD/ton. It is due to the fact that the polygeneration scheme could reduce the LCOP (levelized cost of producing 1 ton of clinker) due to the production of valorized products. It is noted that Scenario 2 is superior to other scenarios since the RenE2P cost in Scenario 2 is lower than it is in Scenario 1 and the captured CO2 rate in Scenario 2 is lower than it is in Scenario 3.

Theoretical Investigation on Atmospheric Chemistry of Nitryl Cyanide: A Novel Fluorine-Free Dielectric Gas.

In view of the significant greenhouse effect of sulfur hexafluoride and the potential biotoxic hazard of perfluorinated substances, we proposed that nitryl cyanide (NCNO2), a nearly nonpolar molecule with a unique combination of two strongly electronegative and polarized functional groups, is a novel fluorine-free replacement to be used as the insulating gas in green electrical grids. Atmospheric chemistry of NCNO2 has been investigated theoretically to assess its environmental impact if emitted into the atmosphere. Potential energy surfaces for the reaction of NCNO2 with OH in the presence of O2 were calculated using the restricted open-shell complete basis set quadratic Becke3 and Gaussian-4 methods on the basis of the density functional (M06-2X) and couple-cluster (CCSD) optimized geometrical parameters. The oxidation of NCNO2 takes place via the nearly zero-barrier association of OH with the cyano-C to form energy-rich adducts NC(OH)NO2, followed by C-N bond rupture to the major HOCN + NO2 and the minor HONO + NCO products. Interception of the adduct by O2 can result in OH-regeneration together with further degradation to CO and NOx. Moreover, photolysis of NCNO2 under tropospheric sunlight conditions might compete with OH-oxidation. The atmospheric lifetime and radiative efficiency of NCNO2 were computed to be far less than those of either nitriles or nitro compounds. The global warming potential of NCNO2 was estimated to be in the range of 0-5 for a 100 year time horizon. However, the secondary chemistry of NCNO2 should be treated with caution in view of the production of NOx in the atmosphere.

A two-phase multi-objective metaheuristic for a green UAV grid routing problem

A two-phase multi-objective metaheuristic for a green UAV grid routing problem

Research on Plug-and-Play Technology of Distributed Renewable Energy

With the application of distributed renewable energy access in the power distribution system, the automation and digitalization of the power grid is effectively promoted. This greatly improves the level of energy utilization and promotes green and sustainable grid development. However, there are problems of edge-to-end authentication and information interaction in the process of accessing massive and various types of renewable energy sources. Therefore, we propose a distributed renewable energy plug-and-play approach for distribution local area networks and study a topology identification-based plug-and-play technology. We establish a distributed renewable energy access architecture with reference to the “cloud-side-end” hierarchy of the distribution IoT. Based on this, we propose a topology-based renewable energy plug-and-play mechanism to verify the energy access and realize the plug-and-play and interoperability functions. The experimental results show that the method has an accuracy rate of 90% in the verification of renewable energy access, and has a significant improvement in the efficiency of plug-and-play access for various types of renewable energy, fully realizing the automatic operation and maintenance of plug-and-play and topological correlation for massive renewable energy.