Grid Level Research Articles

Qualitative-quantitative identification and functional zoning analysis of production-living-ecological space: a case study of Urban Agglomeration in Central Yunnan, China.

Territorial space exhibits multiple functional attributes, which comprise production, living, and ecological functions usually. Optimizing the production-living-ecological space (PLES) has become the key to territorial and spatial planning; the scientific identification of the PLES lays a foundation for space optimization and has important guiding significance in territorial spatial zoning. To achieve the integration of macro-scale and micro-scale PLES, with the Urban Agglomeration in Central Yunnan as the research area in this study, the PLES functional identification systems from the administrative unit scale and the grid scale are constructed. The types of PLES are determined by integrating qualitative and quantitative evaluation results and using an improved primacy index model from a composite spatial perspective. On that basis, the division of comprehensive zoning is achieved for land use functions through kernel density analysis. As indicated by the results, the model is capable of reflecting the macro background of the PLES functions in administrative regions while characterizing the micro differences at the grid level in administrative units. There are significant differences in the production, living, and ecological functional spaces in the Urban Agglomeration. Production functions are concentrated in the central and northeastern, living functions are concentrated in the central, and ecological functions are concentrated in the western and northeastern, with significantly consistent or complementary spatial distributions of each other. The PLES of Urban Agglomeration includes production space (PS), ecological space (ES), production-living space (P-LS), production-ecological space (P-ES), living-ecological space (L-ES), and production-living-ecological space (P-L-ES), placing a focus on ES, P-ES, and P-L-ES, which marks significant differences in spatial distribution among different spatial types. The study area is divided into 24 functional zones, which are classified into 6 categories, and optimization paths are proposed. This study will provide a reference for territorial and spatial planning in spatial functional zoning, spatial pattern optimization, and land management applications.

Overcoming challenges in life cycle assessment of smart energy systems – A map of solution approaches

Current research underlines the benefits of digitalization to integrate renewable energy sources (RES) efficiently and to coordinate generation and supply. This development towards smart energy systems (SES) includes emerging use cases at the distribution grid level, e.g., smart charging strategies for electric vehicles or energy management systems in buildings. An environmental assessment of such, however, is challenging. The standardized Life Cycle Assessment (LCA) approaches seem insufficient since SES are emerging systems at the interface of digitalization and energy with multiple intended and unintended environmental effects. This article aims to derive solution approaches for LCA professionals that investigate use cases enabled by information and communication technology (ICT), specifically in SES. To identify challenges during the first two phases of an LCA and solution approaches, we examine seven recent research projects and conduct a real-time Delphi workshop with LCA experts in the field of SES. As a result, the article provides approaches on setting up an LCA, filling data gaps, and methods to draw upon the long-term impacts of use cases in SES. Key findings include a modified taxonomy of potential environmental effects. Results show that systemic effects in SES have potentially the highest impact on the LCA, followed by effects caused by user behavior. Assessing these effects of ‘higher-order’ poses an increased complexity. Determined solution approaches include combining the standardized LCA with energy system modeling or surveys on user behavior. Overall, this article reveals orientation strategies and recommendations to guide LCA professionals toward a holistic environmental assessment of use cases in SES.

Predictive source management for low power domestic direct current grids

The decrease in the price of solar photo voltaic (PV) panels has led to the widespread adoption of the solar power as a renewable energy source, not only at the grid level but also on the roof tops of the residential buildings. The solar PV panels produce direct current (DC) and can be readily used to drive DC powered loads or charge batteries. Direct powering of the loads from the solar panels is hindered by the highly variant nature of solar power generation which depends on a number of external as well as internal factors. The paper proposes a prediction based direct connection between PV panel and DC loads, only when the panel will be able to supply the required power to the load. The loads have been categorized based on how they need to be powered and their priority levels. The data specific to the rooftop was collected over two years using which the prediction of the panel output was carried out. Combining the load categorization and the power prediction a smart management system was designed which was able to decide on how the loads need to be powered and hence were connected to the appropriate source.

Soil erosion effects and mechanisms in the karst peak-cluster depression area from a multi-scale landscape perspective

The karst regions in southwest China are integral to maintaining regional ecological security. However, escalating human activities in these areas have intensified soil erosion and landscape fragmentation. Clarifying the role of landscape patterns in karst regions as drivers of soil erosion processes is essential for providing crucial support in mitigating regional soil erosion. By utilizing unmanned aerial vehicle detection, this study examined the soil erosion characteristics, driving factors, and interaction effects in peak-cluster depression landscapes. The investigation was conducted at multiple scales, including peak-cluster depression, grid, and patch levels, under the combined ecology–agriculture, ecology–economy and near–nature landscape utilization types. Furthermore, an optimal landscape pattern for alleviating soil erosion was identified. The research findings indicate that the proportions of mild and moderate erosion were higher in the ecology-agriculture landscape utilization type compared to the other two landscapes. In the ecology-economy type, slight soil erosion predominated. Increasing vegetation cover led to a decrease in soil erosion risk in the near-nature landscape utilization type. The interaction effects among factors contributed to an enhanced explanatory power for soil erosion. Particularly, the synergistic effect between vegetation coverage and land use exhibited the most significant influence on soil erosion. The mosaic distribution pattern of soil erosion sources and sinks, as well as the distribution pattern of fragmented source and aggregated sink distribution pattern, effectively mitigated soil loss, leading to reduced soil erosion rates.

Cation Modified Highly Soluble Active Materials for Redox Flow Batteries

Renewables such as solar and wind energy are making increased penetration into modern electrical grids.1 However, the reliability of such grids is challenged by the intermittent nature of renewable sources and variable atmospheric conditions, posing a major roadblock in transition toward carbon-neutral energy sources. Grid level energy storage has long been seen as a solution to the intermittency problem.2 Among various storage technologies, redox flow batteries (RFBs) stand out, due to their ability to accommodate various needs of renewable-powered grids.3 RFBs based on non-aqueous chemistry (NRFBs) have a potential to greatly increase the energy density, however, a major setback in NRFB implementation arises from instability and low solubility of active materials.4,5 Herein, we present a strategy to design a flow battery utilizing anionic active materials which exhibit high solubility and high stability. To this end, we utilized a highly stable bio-inspired complex, vanadium-bis-hydroxyiminodiacetate (VBH) as a catholyte and anthraquinone-sulfonate (AQS) as an anolyte scaffold. Cycling performance of a flow battery based on VBH/AQS active materials was investigated in a full cell and a detailed chemical and electrochemical analysis of electrolytes before and after cycling was performed. Post-cycling analysis of electrolyte revealed that the active materials are highly stable even under deep charge-discharge cycling. However, lack of suitable membrane resulted in modest capacity fade. To overcome the crossover issue, a full cell comprising compositionally symmetric electrolytes, made by mixing anolyte and catholyte, was performed and various performance metrics of such cell were evaluated.

Accelerated convergence for city-scale flow fields using immersed boundaries and coupled multigrid

Fast and accurate prediction of Chemical, Biological and Radiological (CBR) dispersion patterns and toxic extents is important for public safety as conventional and non-conventional airborne threats released into the built environment can pose serious dangers to human health ranging from difficulty breathing and eye irritation to incapacitation and death. Computational fluid dynamics (CFD) is routinely used for gas dispersion modeling when increased accuracy over traditional Gaussian-based methods is required, although CFD requires greater modelling efforts. The combination of structured Cartesian meshes and immersed boundary methods reduces this effort and has been resurging in popularity over the past decade.Due to a lack of related literature, a simple and robust methodology was developed by the authors to treat immersed boundaries at multigrid levels, and this integration focused on a segregated multigrid approach using structured Cartesian meshes with adaptive mesh refinement. In the current work, a coupled multigrid treatment is adapted to the integrated immersed boundary methodology and adaptive mesh approach and compared to the segregated technique in terms of convergence rates. To accelerate convergence, the coupled approach selectively applies grid refinement at coarse grid levels, and by design, neither methodology detects the immersed boundaries on coarse grid levels.The two multigrid approaches are compared at three flow scales: wind tunnel, mock-urban and full-urban. At the wind tunnel scale, the coupled multigrid solver shows 39.3 times speed up over single grid and 10.4 times improvement over the baseline segregated approach. At the full urban scale, the coupled approach is shown to converge wind fields with a 6.3 times speed improvement over the previous segregated approach and therefore is suitable for CFD-based CBR assessment tools.

The green divide: A spatial analysis of segregation-based environmental inequality in Vienna

Cities, as the human habitat of the 21st century, will increasingly face climate change-related risks. Extreme weather events, hot spells, and rising air pollution already have widespread impacts on people, affecting their health and well-being. Urban vegetation is a proposed nature-based solution to address these challenges. However, the equitable distribution of urban vegetation is not always ensured. The study applies two innovative segregation-based inequality indices, recently developed by Schaeffer and Tivadar (2019), which reference to the residential segregation literature as a source of insight for the measurement of environmental inequalities within urban areas. Using high-resolution satellite data and demographic data on a 250 m2 grid level, this study is able to deliver robust evidence on the existence of environmental inequalities with respect to ethnic minorities and urban vegetation. It is observed that minorities consistently experience higher levels of segregation from urban vegetation compared to non-minorities, even when accounting for interactions with wider neighbourhoods. Additionally, the application of a Jackknife simulation provides insights for local policy interventions, mapping hotspots of urban inequalities and provides a solid starting point to tackle these issues on the ground.

Population spatialization in Zhengzhou city based on multi-source data and random forest model

Fine-scale population map plays an essential role in numerous fields, including resource allocation, urban planning, disaster prevention and response. Point of Interest (POI) data is widely used for population spatialization, but the types of POI are ignored. Since different types of POI data have different impacts on population distribution, this paper used typed POI data and other multi-source data to map population distributions at fine scales. At the township level, three random forest models were used to generate the population maps of 150 m, 300 m, and 500 m in 2020, enabling the downscaling of county-level population distribution to the grid level. The main influencing factors of population distribution were extracted and analyzed based on the feature importance output from the model. Zhengzhou city was used as a case for study. The experiments show the results of population spatialization for all three scales in this study have better fitting accuracy than that of the GPWv4 and LandScan datasets. The coefficient of determination (R2) is 0.8333 for 150 m gridded population, 0.8295 for 300 m, and 0.8224 for 500 m; POI types related to residence information have greater contributions to population spatialization than other features; typed POI data are more conducive to population spatialization.

Prediction method of peak carbon emission in power grid based on energy consumption elasticity coefficient

With the implementation of China’s “two-carbon” target strategy, the status of renewable energy in the power system is gradually improving. In order to improve the carbon emission control level of power grid, it is necessary to predict the peak carbon emission of power grid. A prediction method of grid carbon emission peak based on energy elasticity coefficient is proposed. A time series model of grid carbon emission samples was constructed, and a combination of combinatorial sorting and machine learning was used to reconstruct the time series of grid carbon emission peaks. Based on the results of time series reconstruction, feature extraction and classification training of grid carbon emission peak are carried out, and the measurement error correction of grid carbon emission under the constraint of energy consumption elasticity is realized. Combined with the association rule mining method, the fusion processing of grid carbon emission peak samples under the elastic constraint of energy consumption is realized. Through characteristic detection and fusion processing of energy consumption elasticity coefficient, the reconstituted carbon emission samples of power grid constrained by energy consumption elasticity were reconstructed, and the peak carbon emission of power grid was predicted. The simulation results show that this method has high accuracy and convergence in predicting the peak carbon emission of power grid, and improves the error correction ability in the prediction process.

Mapping high-resolution energy consumption CO2 emissions in China by integrating nighttime lights and point source locations

High-resolution CO2 emission inventories are essential to accurately assess spatiotemporal patterns of carbon emissions, analyze factors affecting carbon emissions, and develop sound emission reduction policies. The top-down approach is often used to map CO2 emissions from energy consumption due to its simplicity. However, the spatial proxy variables commonly used in this method, such as nighttime light (NL), land use, and population, are difficult to reflect the spatial distribution of CO2 emissions from large point sources. Therefore, this study uses the active fire product provided by Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite to extract the location of industrial heat sources in China, and then develops an improved CO2 emission estimation model by integrating industrial heat sources, Global Energy Monitor (GEM) power plant location and nighttime lights. The model is used to map CO2 emissions from energy consumption at a resolution of 1 km*1 km from 2012 to 2019 in China. It is found that the overall accuracy of the model is greatly improved at the provincial level, the R2 value is >0.75, and RMSE is distributed in 40–110 Mt. At the grid level, the improved model allocates more carbon emissions to the grid where the point source is located, which makes the spatial distribution of CO2 emissions more reasonable.

Verification of the uninterrupted transition from grid parallel to island grid operation of the Virtual Synchronous Machine in a microgrid

The classic energy supply system is mainly based on the stabilising properties of electromechanical synchronous generators. The structural change of the electrical energy supply system towards a decentralised grid structure means that ancillary services must now also be provided at the distribution grid level. The Virtual Synchronous Machine (VISMA) was developed at the Institute of Electrical Power Engineering at the Clausthal University of Technology with the aim of making a decentralised generation unit in the grid behave like a real synchronous machine and thus actively provide relevant ancillary services. The VISMA is based on a three-phase inverter that can comply with grid requirements by adding an energy storage device in the DC link and using a special control algorithm. The focus of the investigations shown here is on the uninterrupted transition from grid-parallel operation of the VISMA to island grid operation and the subsequent stable continued operation of this island grid. A controller changeover, as most feed-in inverters would require in order to change the operating mode, is not necessary here. It is shown that the transients have subsided after 5 ms, which means that the VISMA has completely taken over the

Nowcasting Multiparameter Phased-Array Weather Radar (MP-PAWR) Echoes of Localized Heavy Precipitation Using a 3D Recurrent Neural Network Trained with an Adversarial Technique

Abstract We present nowcasts of sudden heavy rains on meso-γ scales (2–20 km) using the high spatiotemporal resolution of a multiparameter phased-array weather radar (MP-PAWR) sensitive to rain droplets. The onset of typical storms is successfully predicted with 10-min lead time, i.e., the current predictability limit of rainfall caused by individual convective cores. A supervised recurrent neural network based on long short-term memory with 3D spatial convolutions (RN3D) is used to account for the horizontal and vertical changes of the convective cells with a time resolution of 30 s. The model uses radar reflectivity at horizontal polarization ZH and the differential reflectivity. The input parameters are defined in a volume of 64 × 64 × 8 km3 with the lowest level at 1.9 km and a resolution of 0.4 × 0.4 × 0.25 km3. The prediction is a 10-min sequence of ZH at the lowest grid level. The model is trained with a large number of observations of summer 2020 and an adversarial technique. RN3D is tested with different types of rapidly evolving localized heavy rainfalls of summers 2018 and 2019. The model performance is compared to that of an advection model for 3D extrapolation of PAWR echoes (A3DM). RN3D better predicts the formation and dissipation of precipitation. However, RN3D tends to underestimate heavy rainfall especially when the storm is well developed. In this phase of the storm, A3DM nowcast scores are found slightly higher. The high skill of RN3D to predict the onset of sudden localized rainfall is illustrated with an example for which RN3D outperforms the operational precipitation nowcasting system of Japan Meteorological Agency (JMA). Significance Statement Temporal extrapolation of radar observations is a means of nowcasting sudden heavy rains, i.e., forecasts with a few tens of minutes and a high spatial resolution better than 500 m. They are necessary to set up warning systems to anticipate damage to infrastructure and reduce the fatalities these storms cause. It is a difficult task due to the storm suddenness, restricted area, and nonlinear behavior that are not well captured by current operational observation and numerical systems. In this study, we use a new high-resolution weather radar with polarimetric information and a 3D recurrent neural network to improve 10-min nowcasts, the current limit of operational systems. This is a first and essential step before applying such a method for increasing the prediction lead time.

Modeling Near-Surface Turbulence in Large-Eddy Simulations of a Tornado: An Application of Thin Boundary Layer Equations

Abstract Unsteadiness and horizontal heterogeneities frequently characterize atmospheric motions, especially within convective storms, which are frequently studied using large-eddy simulations (LES). The models of near-surface turbulence employed by atmospheric LES, however, predominantly assume statistically steady and horizontally homogeneous conditions (known as the equilibrium approach). The primary objective of this work is to investigate the potential consequences of such unrealistic assumptions in simulations of tornadoes. Cloud Model 1 (CM1) LES runs are performed using three approaches to model near-surface turbulence: the “semi-slip” boundary condition (which is the most commonly used equilibrium approach), a recently proposed nonequilibrium approach that accounts for some of the effects of turbulence memory, and a nonequilibrium approach based on thin boundary layer equations (TBLE) originally proposed by the engineering community for smooth-wall boundary layer applications. To be adopted for atmospheric applications, the TBLE approach is modified to account for the surface roughness. The implementation of TBLE into CM1 is evaluated using LES results of an idealized, neutral atmospheric boundary layer. LES runs are then performed for an idealized tornado characterized by rapid evolution, strongly curved air parcel trajectories, and substantial horizontal heterogeneities. The semi-slip boundary condition, by design, always yields a surface shear stress opposite the horizontal wind at the lowest LES grid level. The nonequilibrium approaches of modeling near-surface turbulence allow for a range of surface-shear-stress directions and enhance the resolved turbulence and wind gusts. The TBLE approach even occasionally permits kinetic energy backscatter from unresolved to resolved scales. Significance Statement The traditional approach of modeling the near-surface turbulence is not suitable for a tornado characterized by rapid evolution, strongly curved air parcel trajectories, and substantial horizontal heterogeneities. To understand the influence of statistically unsteady and horizontally heterogeneous near-surface conditions on tornadoes, this work adopts a fairly sophisticated approach from the engineering community and implements it into a widely used atmospheric model with necessary modifications. Compared to the traditional approach, the newly implemented approach produces more turbulent near-surface winds, more flexible surface-drag directions, and stronger wind gusts. These findings suggest a simulated tornado is very sensitive to the modeling approach of near-surface turbulence.

Investigation of Iron(III) Tetraphenylporphyrin as a Redox Flow Battery Anolyte: Unexpected Side Reactivity with the Electrolyte.

Redox flow batteries (RFBs) present an opportunity to bridge the gap between the intermittent availability of green energy sources and the need for on-demand grid level energy storage. While aqueous vanadium-based redox flow batteries have been commercialized, they are limited by the constraints of using water as an electrochemical solvent. Nonaqueous redox flow battery systems can be used to produce high voltage batteries due to the larger electrochemical window in nonaqueous solvents and the ability to tune the redox properties of active materials through functionalization. Iron porphyrins, a class of organometallic macrocycles, have been the subject of many studies for their photocatalytic and electrocatalytic properties in nonaqueous solvents. Often, iron porphyrins can undergo multiple redox events making them interesting candidates for use as anolytes in asymmetrical redox flow batteries or as both catholyte and anolyte in symmetrical redox flow battery systems. Here the electrochemical properties of Fe(III)TPP species relevant to redox flow battery electrolytes are investigated including solubility, electrochemical properties, and charge/discharge cycling. Commonly used support electrolyte salts can have reactivities that are often overlooked beyond their conductivity properties in nonaqueous solvents. Parasitic reactions with the cations of common support electrolytes are highlighted herein, which underscore the careful balance required to fully assess the potential of novel RFB electrolytes.

An Analysis of Prescribed Fire Activities and Emissions in the Southeastern United States from 2013 to 2020.

Prescribed burning is a major source of a fine particular matter, especially in the southeastern United States, and quantifying emissions from burning operations accurately is an integral part of ascertaining air quality impacts. For instance, a critical factor in calculating fire emissions is identifying fire activity information (e.g., location, date/time, fire type, and area burned) and prior estimations of prescribed fire activity used for calculating emissions have either used burn permit records or satellite-based remote sensing products. While burn permit records kept by state agencies are a reliable source, they are not always available or readily accessible. Satellite-based remote sensing products are currently used to fill the data gaps, especially in regional studies; however, they cannot differentiate prescribed burns from the other types of fires. In this study, we developed novel algorithms to distinguish prescribed burns from wildfires and agricultural burns in a satellite-derived product, Fire INventory from NCAR (FINN). We matched and compared the burned areas from permit records and FINN at various spatial scales: individual fire level, 4 km grid level, and state level. The methods developed in this study are readily usable for differentiating burn type, matching and comparing the burned area between two datasets at various resolutions, and estimating prescribed burn emissions. The results showed that burned areas from permits and FINN have a weak correlation at the individual fire level, while the correlation is much higher for the 4 km grid and state levels. Since matching at the 4 km grid level showed a relatively higher correlation and chemical transport models typically use grid-based emissions, we used the linear regression relationship between FINN and permit burned areas at the grid level to adjust FINN burned areas. This adjustment resulted in a reduction in FINN-burned areas by 34%. The adjusted burned area was then used as input to the BlueSky Smoke Modeling Framework to provide long-term, three-dimensional prescribed burning emissions for the southeastern United States. In this study, we also compared emissions from different methods (FINN or BlueSky) and different data sources (adjusted FINN or permits) to evaluate uncertainties of our emission estimation. The comparison results showed the impacts of the burned area, method, and data source on prescribed burning emission estimations.

The impact of clustering strategies to site integrated community energy and harvesting systems on electrical demand and regional GHG reductions

The current work examines the Integrated Community Energy and Harvesting system (ICE-Harvest), an integration of thermal and electrical distributed energy resources. The system prioritizes the harvesting of community waste energy resources—for example, residual heat rejected from cooling processes and peak electricity fossil-fuel fired generators, as well as energy from curtailed clean grid electricity resources—to help in satisfying the heating demands of commercial and residential buildings. As such, ICE-Harvest systems provide a solution that can minimize greenhouse gas emissions from high-energy-consumption buildings in cold-climate regions such as North America and Northern Europe. The current work focuses on where to locate these systems and introduces different clustering methods for integrated energy systems that focus on thermal load diversity among buildings in each cluster. In the first technique, buildings with the highest amount of rejected heat from their cooling systems (anchor buildings) are clustered with nearby high heating demands buildings to create a large opportunity for harvesting wasted energy however, buildings not located close to anchor buildings are clustered using density-based clustering methods. The energy harvesting capabilities of this technique are compared to those provided by full DB clustering without specifying the anchor buildings, as well as to full density-based clustering with a post-processing step in which the nearest anchor building is added to each cluster. The selected clustering method also demonstrates the grid level potential of the ICE-Harvest systems to impact greenhouse gas emissions, heating, and electricity consumption by presenting a reduced model for the ICE-Harvest system that can be applied to any number of clusters on a municipal or provincial/state scale. Specifically, the model is applied to a database of 14,832 high-energy-consumption buildings with the potential for forming 1,139 clusters in the province of Ontario, Canada. The results of this case study reveal that density-based clustering with post processing resulted in the largest emission reduction per unit piping network length of 360 t CO2eq /km/year. In addition, the use of ICE-Harvest systems can displace the energy required from the gas-fired heating resources by 11 TWh, accounting for over 70% of the clusters’ total heating requirements. This results in a 1.9 Mt CO2eq reduction in overall sites’ emissions, which represents around 60% of the clusters’ emissions.

Optimization-based framework for low-voltage grid reinforcement assessment under various levels of flexibility and coordination

The rapid electrification of residential heating and mobility sectors is expected to drive the existing distribution grid assets beyond their planned operating conditions This change will also reveal new potentials through sector coupling, flexibilities, and the local exchange of decentralized generation. This paper thus presents an optimization framework for multi-modal energy systems at the low voltage (LV) distribution grid level. In this, we focus on the reinforcement requirements of the grid and the techno-economic assessment of flexibility components and coordination between agents. By employing a multi-level approach, computational complexity is reduced, and various levels of coordination and flexibilities are implemented. We conclude the work with a case study for a representative rural grid in Germany, in which we observe high economic potential in the flexible operation of buildings, majorly thanks to better integration of photovoltaics. Across all paradigms barring a best-case benchmark, grid reinforcement based on a mix of passive and active measures was necessary. A synergy effect is observed between flexibilities and coordination, as their combination reduces the peaks to the extent of completely avoiding grid reinforcement. The presented framework can be applied with a wide range of grid and component types to outline the broad landscape of future LV distribution grids.

Synergies and Trade-Offs among Different Ecosystem Services through the Analyses of Spatio-Temporal Changes in Beijing, China

Increasing global urbanization has not only resulted in economic development but it has also caused a number of ecological issues, such as haze, global warming, and storm surges, which can end up hindering the development of human society in the long term. One method of maintaining the long-term growth of human ecosystems is by considering ecosystem services (ES) when making decisions over land use. This study provides information to aid with decision making in the maintenance of Beijing’s ES provision in the long term. Firstly, three key ES, namely, carbon storage (CS), habitat quality (HQ), and water yield (WY), were evaluated by the InVEST model. Then, the spatial patterns of synergies and trade-offs among three ES at the city and grid scales were explored through the correlation coefficients analysis and geographically weighted regression (GWR). Finally, the strength of trade-offs among ES was calculated based on root mean squared error (RMSE), and the potential ecological risk areas are recognized. We discovered that (1) the total carbon storage decreases from 3.74 million tons in 2000 to 3.66 million tons in 2020, and HQ has the same trend, with its average value decreasing from 0.72 to 0.67; in contrast, water yield is more stable, increasing slightly from 8.22 × 1010 m3 in 2000 to 8.23 × 1010 m3 in 2020. (2) The synergies and trade-offs of ES are spatially heterogeneous. Among them, the correlation coefficients at the city-level indicated synergistic relationships among the three ES, but CS-WY and WY-HQ always have trade-off relationships at the grid level, where 37.88% of WY-HQ and 14.59% of CS-WY were trade-offs in 2020. (3) At the urban-rural interface, the trade-offs among ES are stronger than those in other regions. In rural-urban areas, the RMSE in CS-HQ, CS-WY and WY-HQ always had high values (>0.5), accounting for 16.72%, 9.33%, and 26.94% of the entire area, respectively; these areas are identified as potential ecological risk areas, which will be the focus area for future ES regulation. These findings provide opportunities for clear trade-offs among ES and promote positive synergies. In addition, land-use management may use the results to guide ecosystem service use, identify critical areas, and ensure regional sustainability in urban development.

Simulation and Analysis of Harmonic High Voltage Filters for Wind Farm

With the rapid development of the wind power industry, the total installed capacity of wind power has increased dramatically worldwide, and a large number of wind power installations need to be connected to the grid. In wind turbines, voltage flicker due to output power fluctuations caused by wind speed changes, and the application of a Voltage Source Converter (VSC) cause harmonic richness and other power quality problems to become more and more prominent. In this regard, the research is focused on the harmonic and reactive current distribution, impedance frequency characteristics, and voltage distortion rate of several offshore wind farm access systems, including the establishment of a harmonic current simulation model of the offshore wind farm delivery system, simulation analysis of network impedance frequency characteristics, simulation analysis of harmonic currents, and proposed a preferred filtering scheme. Taking into full consideration the characteristic harmonics of the wind farm, the system resonance generated by the submarine cable and the background harmonic level of the power grid, combined with the simulation results of the harmonic voltage, harmonic current, and network impedance frequency characteristics of each node, the technical solution of adding a direct-mounted high-voltage filtering device to the 220 kV bus at the metering station is proposed, which can destroy the system resonance conditions of the 3rd to 6th times of the submarine cable. Various wind farms and system operating conditions are able to form a stable harmonic absorption point to avoid the problem of harmonic amplification.

An Assessment of Energy Flexibility Solutions from the Perspective of Low-Tech

The energy transition is a multidisciplinary challenge that warrants solutions that are robust and sustainable. Energy flexibility, one of the key pillars of the energy transition, is an umbrella term that covers multiple innovative solutions implemented at all levels of the electric grid to ensure power quality standards, amongst other objectives. Low-tech, on the other hand, emphasizes designing, producing, and sustainably implementing solutions. Therefore, considering the multidisciplinary nature of energy transition and the existing energy flexibility solutions, the purpose of this research work is multilateral: first, it presents the concept of low-tech and its associated mechanisms; then, it addresses the misconceptions and similarities that low-tech might have with other innovation approaches; and finally, it provides an assessment of existing flexibility solutions using low-tech as a tool. The result of this assessment is presented qualitatively and indicates that indirect energy flexibility solutions rank higher on a low-tech scale relative to supply-side energy flexibility solutions and energy storage flexibility solutions.