Demand Scenarios Research Articles

Evaluating and aggregating the grid-support capability of energy storage clusters by considering the peak regulation requirements

With the rapid progression of Energy Storage Systems (ESSs), the capability of extensively distributed and heterogeneous ESSs to support the power grid remains largely underexplored. To better exploit the potential of these numerous ESSs and enhance their service to the power grid, this paper proposes a model for evaluating and aggregating the grid-support capability of energy storage clusters by considering the peak regulation requirements. To begin with, the proposed model employs subjective and objective combination weighting methods to establish a grid-support capability matrix between ESSs indicators and grid demand scenarios, thereby facilitating the identification of the ESSs with a strong ability to regulate peak power. Next, based on the dual-peak pattern of grid load and diverse characteristics of ESSs, the ESSs in the peak regulation cluster are evaluated by clustering again. In addition, taking into account the operational constraints of the ESSs and the peak regulation requirements, a grid-support capability aggregation model for energy storage clusters based on the revised Chino polytope is proposed. The case study results demonstrate that the proposed model not only balances computational efficiency and aggregation accuracy to a certain extent but also enhances the capability of energy storage clusters to participate in peak regulation of the power grid.

Techno-economic analysis of fifth-generation district heating and cooling combined with seasonal borehole thermal energy storage

Fifth-generation district heating and cooling (5GDHC) is a promising solution to decarbonise future thermal energy systems. This paper investigates the potential of 5GDHC integrated with borehole thermal energy storage (BTES) in unlocking the synergies between heating and cooling via a bidirectional thermal network. A model for system design and operation simulation of 5GDHC is developed. In a case study, the proposed 5GDHC supplied by BTES facilitates the long-term balancing of heating and cooling demands despite the poor simultaneity between them. This enables 5GDHC to achieve overall advantages over modern heat pump driven systems, including lower levelized cost of energy, less greenhouse gas emission, higher exergy efficiency, and lower electricity peak load. One possible barrier of adopting 5GDHC is the high upfront cost, which mostly attributes to borehole drilling and booster heat pump installation. Demand scenarios with varying heating and cooling demand structures are analysed and show that the ratio of cooling to heating substantially affects the economic performance of 5GDHC coupled with BTES. When cooling to heating ratio is within the optimal range of 0.4–1.0, 5GDHC reaches lower levelized cost compared with modern heat pump driven systems and present-day gas-fired heating systems.

Creación de un parque urbano Nuevo índice ASD con un coeficiente de distribución espacial (Estudio de caso: región 1 de Teherán)

The literature study demonstrates how GIS uses techniques like network analysis and fuzzy logic to carry out analyses in accordance with spatial balance. The aforementioned analytical techniques are technically difficult and costly since they need a large database. This study aims to measure geographical disparity, which indicates the lack of access to park services, identify circumstances where population demand is the main driver of demand, and identify general causes for the lack of service. The results of the GIS study suggest that the warning range is displayed in the parks with new­ ASD index of -25> new ASD> 170, while the range is displayed in the parks with a new ASD index of 2000> new ASD>74250. It also demonstrates how adverse the supply and demand scenario is for parks with tiny areas and close proximity to one another. This is because smaller parks have less ability to draw visitors from far away.

WEAP Model Based Evaluation of Future Scenarios and Strategies for Sustainable Water Management in the Chennai Basin, India

Abstract The gap between water supply and demand is increasing in several urban clusters of the world. This study uses the water evaluation and planning model to assess the water supply and demand dynamics in one of the large metropolitan regions of the Chennai hydrological basin. The primary water supply sources, including reservoirs, groundwater, inter-basin transfer, and desalination plants, were integrated into the model to simulate the current and future water demand and supply scenario. Three rainfall scenarios (excess, normal, and deficit) were utilized to assess their impacts on water supply. The study highlights the increase in unmet demand for normal and deficit rainfall scenarios. In response, various mitigation options were explored, including increasing groundwater recharge, reservoir capacity enhancement, water treatment plant expansion, additional storage, and utilization of water stored in rock quarries. The findings provide valuable insights for policymakers and stakeholders to develop sustainable water management strategies in the Chennai Basin.

Material requirements and impacts of the building sector in the Shared Socioeconomic Pathways

The significant weight of the building sector in global material demand has been addressed in several publications, but a lack of consideration of the energy-materials nexus was identified. To fill this gap, we developed a stock-flow dynamic model from 1950 to 2100, using scenarios of the International Energy Agency and the Shared Socioeconomic Pathways of the academic literature. We find that (i) the increasing stocks in the less developed countries results in a rise of the inflows; (ii) strong inequalities are observed in the in-use stocks per capita of materials in the SSPs, despite optimistic material intensities projection, (iii) a growing materials demand translates into larger final energy demand of material production, which could question the feasibility of some low-energy demand scenarios, and that (iv) the recycled concrete aggregated display a significant potential to enhance the reduction of accumulated concrete stocks in landfills and green concrete could decrease the energy demand of concrete production. We furthermore highlight the crucial temporal aspect of policies to successfully implement these solutions, as long lifetimes are observed in the building sector.

Potential changes of regional natural gas market in China amidst liberalization: A mixed complementarity equilibrium simulation in 2030

Ensuring the continuous efficient use of natural gas is crucial for China to achieve its carbon peak goal by 2030. This study scrutinizes the prospective effects of an intensified liberalization of China’s natural gas sector in 2030. To accomplish this, a mixed complementarity equilibrium model is employed, capturing the interdependencies among relevant sectors within the industrial chain. Multiple scenarios of liberalization are simulated, encompassing variations in gas demand, pipeline capacity expansion, and the escalation of imported Liquefied Natural Gas (LNG), to identify potential modifications. Scenario results reveal that the insufficient capacity of long-distance pipeline network will significantly hinder the distribution of gas resources and cause a shortage of gas supplies in Eastern region. Furthermore, the expansion of pipeline capacity from Northwest to East is beneficial in low demand scenario, while increasing the receiving and transfer capacity of LNG in Northern and Eastern regions is crucial in face of strong demand growth. Moreover, a decline in LNG imports will put upward pressure on regional prices, and further affect the security of natural gas supply. The findings will offer critical data for policymakers and present a diversified view of natural gas liberalization market under China’s peak carbon emission target in 2030.

BCH modelling studies on biocementation process in mitigating leaks from a CO2 sequestrated aquifer

BCH modelling studies on biocementation process in mitigating leaks from a CO2 sequestrated aquifer

Dual-network hydrogels based on dynamic imine and borate ester bonds with antibacterial and self-healing properties.

Polymeric hydrogel materials with multiple functions are in great demand in practical biomedical scenarios. In this work, a self-healing hydrogel with both antimicrobial properties was prepared using a strategy that combines dynamic imine and borate ester bonds. In this hydrogel, polyvinyl alcohol (PVA) is used as the base network, and borax solution as the cross-linking agent, and borate ester bonds can be formed between these two. Dialdehyde carboxymethyl cellulose (DCMC) was selected to cross-link with the amino groups in carboxymethyl chitosan (CMCS) and polyethyleneimine (PEI) to form dynamic imine bonds. The PVA/PEI/DCMC/CMCS hydrogels prepared by double chemical cross-linking have good mechanical properties (maximum tensile strength up to 289 KPa and strain at the break up to 1025%). Due to the uniqueness of the two chemical bonds, the hydrogel material is self-healing at room temperature without additional stimulation. In addition, the inherent antibacterial properties of the raw materials in this hydrogel confer antibacterial properties, with a kill rate of up to 99% against E. coli and S. aureus. The multifunctional hydrogels developed in this study provide more ideas and references for the future application of hydrogel materials in practical scenarios.

What would a US green hydrogen energy economy look like?

Abstract Detailed description is given for a hypothetical US hydrogen economy with solar and wind energy supplying virtually all current energy needs and with electrolytic hydrogen the energy carrier and storage medium. Fossil fuels provide nonfuel products (plastics, chemicals, cement and asphalt). Only current technologies are considered and hydrogen storage accommodates generation intermittency and variability, using pit storage of high-pressure vessels in open air, yielding daily storage round-trip energy installation costs of 722 and 538 $/kWh for electric and thermal, respectively; and for power, 2351 and 2240 $/kW for electric and thermal, respectively. For long-duration storage, the costs are 94.1 and 23.8 $/kWh and 937 and 845 $/kW, respectively. Increased energy generation 20% over baseline accommodates low-season generation, obviates much required storage and ensures that reserves are topped off; 96% of US 2022 total energy consumption is provided for. In the default scenario (demand energy portions: half photovoltaic, quarter onshore wind and quarter offshore wind), the surface area for the farms (including offshore surface) requires ~4.6% of the US 48-state land area. About 350 pit storage sites provide both daily and long-duration storage, with the latter accounting for complete loss of generation for 4 days over a quarter of the nation. Hydrogen pipelines and a renewed electric grid transmit and distribute energy. The installation cost of the public infrastructure is ~$27.8 trillion for the default scenario. Alternative scenarios show significant infrastructure and cost savings when batteries are used for transportation and/or utility storage, provided current insufficiencies can be overcome. Broadly, cost levels in money, surface and infrastructure are within existing levels already achieved in historical events and modern living.

Decomposing texture and semantic for out-of-distribution detection

The out-of-distribution (OOD) detection task assumes samples that follow the distribution of training data as in-distribution (ID), while samples from other data distributions are considered OOD. In recent years, the OOD detection tasks have made significant progress since many studies observed that the distribution mismatch between training and real datasets can severely deteriorate the reliability of AI systems. Nevertheless, the lack of precise interpretation for the in-distribution (ID) limits the application of the OOD detection methods to real-world systems. To tackle this, we decompose the definition of the ID into texture and semantics, motivated by the demands of real-world scenarios. We also design new benchmarks to measure the robustness that OOD detection methods should have. Our proposed benchmark verifies not only the precision but also the robustness of the detection models. It is crucial to measure both factors in OOD detection as they indicate different traits of the model. For instance, precision is relevant to scenarios that detect minor cracks in the conveyor belt of a smart factory, whereas robustness pertains to maintaining performance under diverse weather conditions, as required by autonomous driving. To achieve a good balance between the OOD detection performance and robustness, our method takes a divide-and-conquer approach. Specifically, the proposed model first handles each component of the texture and semantics separately and then fuses these later. This philosophy is empirically proven by a series of benchmarks including both the proposed and the conventional counterpart. By decomposing the prior “unclear” definition of the ID into texture and semantic components, our novel approach better suits the demands of a reliable machine learning system, which requires robustness and consistent performance across varied scenarios. Unlike prior works, our approach does not rely on any extra datasets or labels. This prevents our proposed framework from being dependent on a particular dataset distribution.

Chance-constrained multi-stage stochastic energy system expansion planning with demand satisfaction flexibility

A classic multi-period stochastic energy system expansion planning (ESEP) model aims to address demand uncertainty by requiring immediate demand satisfaction for all scenarios. However, this approach may result in an expensive system that deviates from the planner’s long-term goals, especially when facing unexpectedly high demand scenarios. To address this issue, we propose a chance-constrained stochastic multi-stage ESEP model that allows for a portion of demand to remain unmet in specific periods while still ensuring complete demand satisfaction during most of the planning horizon, including the final period. This approach provides more time flexibility to build infrastructure and assess needs, ultimately reducing costs and allowing for a broader view of infrastructure planning options. To solve the chance-constrained stochastic model, we introduce a binary-search-based progressive hedging algorithm heuristic, which is particularly useful for large-scale models. We demonstrate the effectiveness and benefits of implementing the chance-constrained model through a case study of Rwanda using real-world data.

Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities

Evolution in geoscientific data provides the mineral industry with new opportunities. A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity. This direction is more significant where traditional geochemical data are not ideal, which is the case for evaluating unconventional resources, such as tailing storage facilities (TSFs), because they are not static due to sedimentation, compaction and changes associated with hydrospheric and lithospheric processes (e.g., erosion, saltation and mobility of chemical constituents). In this paper, we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin (South Africa). Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TSF, we trained a machine learning model to predict in-situ gold grades. Subsequently, we deployed the model to the Lindum TSF, which is 3 km away, over a period of a few years (2015-2019). We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF. Additionally, we were able to infer extraction sequencing (to the resolution of the data), acid mine drainage formation and seasonal migration. These findings suggest that dynamic mineral resource models and live geochemical monitoring (e.g., of elemental mobility and structural changes) are possible without additional physical sampling.

A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment

The increasing population migration to urban and peri-urban areas increases basic service needs for cities worldwide. Residential electricity demand increases with more customers and varies with novel uses, such as charging electric vehicles, which may add additional dynamics to the residential electricity demand profile. Widespread installation of smart electricity meters provides fine temporal resolution data reflecting current demands and supports predicting future demands. As part of a demand-side management program, understanding the main drivers of current electricity usage based on demand-driven and exogenous predictors represents a valuable tool for utilities facing new demand scenarios. This work presents the application of multiple models to forecast electricity demand based on the input data and the forecasting horizon. Models with exogenous variables as predictors are part of the input–output category, including a Feed Forward Neural Network, Random Forest, and a Linear Gaussian State Space model. The second category is demand-driven models, where predictors include only previous demand values. The demand-driven models in our analysis include a univariate Nonlinear Autoregressive Neural Network and a Linear Gaussian State Space. Using smart electricity meter data from the greater Chicago area, we compare the performance of the models on two different types of accounts: single- and multi-family residential users when forecasting one and multiple steps. Results show that the Linear Gaussian model reports an R2 of 0.99 compared to an average R2 of 0.92 from the Feed Forward Neural Networks and Random Forest when forecasting single- and multi-family electricity demand one step ahead. However, Nonlinear Autoregressive Neural Networks report an average R2 of 0.85 compared to the Linear Gaussian R2 of 0.58 when forecasting 48 steps. We also found that the most important predictors for single-family demand are temporal variables like weekdays, working and non-working days, and day hours. For multi-family demand, electricity demand at the same hour as the previous week replaces weekdays as a significant predictor. Different forecasting models can assist utilities and city planners to manage demand under different and novel residential electricity usage conditions.

Impact of Higher Airspace Operations on Air Traffic in Europe

Historically, higher airspace has been used for military exercises and as transit for space vehicles. Riding on commercial space operations’ coattails, more and more vehicles are under development that will make use of higher airspace resources. This will lead to increasing interactions with conventional air traffic since these new vehicles will have to transit through lower airspaces. The management of these operations is necessary to ensure the safe and practicable shared usage of these airspaces. This paper outlines an assessment of the impact of higher airspace operations on conventional air traffic in Europe. Initially, a synthesis of possible use cases was performed, and demand scenarios were developed that served as input to a fast-time simulation. The impact on air traffic was measured by means of flight efficiency parameters. The simulation results showed that the impact is dependent on the type of operation. High-altitude platform system flights and orbital launches cause the largest deviations in flight distance, flight duration and fuel consumption. Higher airspace operation parameters, including location, time, and duration, strongly affect the impact on the conventional air traffic.

Robust Optimization Model for Sustainable Supply Chain Design Integrating LCA

Supply chain management is the basis for the operations in an organization. The development of realistic supply chain designs that work effectively in the presence of disturbances in a stochastic environment and incorporate sustainability factors, is a complex challenge being investigated in recent years. However, the inclusion of a methodological structured framework to evaluate environmental impacts constitutes a knowledge gap in the literature on supply chain design. This study developed a model for sustainable supply chain design, integrating Life Cycle Assessment and based on a robust optimization approach. The study follows a 4-stage methodology beginning with data collection and the execution of a Life Cycle Assessment. Then, the deterministic modeling is proposed, concluding with a robust model. A bi-objective model is proposed to maximize utility and minimize environmental impact based on demand scenarios. The model was validated with real data from a medium-sized enterprise that produces antibacterial gel, generating as a result, different configuration alternatives for the supply chain to transport the products and raw materials between its elements. The conclusions of this work highlight the importance of including sustainability factors during supply chain design, the consequences and costs of its inclusion, as well as the priority actions that promote sustainable designs.

MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems

Nowadays, automatic modulation classification (AMC) has become a key component of next-generation drone communication systems, which are crucial for improving communication efficiency in non-cooperative environments. The contradiction between the accuracy and efficiency of current methods hinders the practical application of AMC in drone communication systems. In this paper, we propose a real-time AMC method based on the lightweight mobile radio transformer (MobileRaT). The constructed radio transformer is trained iteratively, accompanied by pruning redundant weights based on information entropy, so it can learn robust modulation knowledge from multimodal signal representations for the AMC task. To the best of our knowledge, this is the first attempt in which the pruning technique and a lightweight transformer model are integrated and applied to processing temporal signals, ensuring AMC accuracy while also improving its inference efficiency. Finally, the experimental results—by comparing MobileRaT with a series of state-of-the-art methods based on two public datasets—have verified its superiority. Two models, MobileRaT-A and MobileRaT-B, were used to process RadioML 2018.01A and RadioML 2016.10A to achieve average AMC accuracies of 65.9% and 62.3% and the highest AMC accuracies of 98.4% and 99.2% at +18 dB and +14 dB, respectively. Ablation studies were conducted to demonstrate the robustness of MobileRaT to hyper-parameters and signal representations. All the experimental results indicate the adaptability of MobileRaT to communication conditions and that MobileRaT can be deployed on the receivers of drones to achieve air-to-air and air-to-ground cognitive communication in less demanding communication scenarios.

Analyzing spatiotemporal land use change using an urban growth model based on multilevel logistic regression and future land demand scenarios

This study establishes an urban growth model using future land demand scenarios based on multilevel logistic regression (MLR) in the Seoul metropolitan area and then explores the effects associated with spatiotemporal land use changes in different scenarios. We divide urban growth factors into spatial allocation and demand variables and use the MLR to identify spatial allocation factors and distribute the probability of urbanization. Then, we estimate the land required for urbanization depending on future population and density scenarios. This study predicts the spatially urbanized land area in 2030 using data collected across a span of 40 years. We obtain mean absolute error values of 6.52%, 6.55%, and 5.15%, Cohen's kappa (k) values of 0.60, 0.66, and 0.79, and mean demand error values of 30.30%, 20.41%, and 12.17 for the late 1990s, late 2000s, and late 2010s, respectively. Our analysis finds that the largest portions of urbanized land were previously agricultural and forest land, and we predict such urbanization patterns will continue until 2030. In 2030, urbanized land is expected to expand around principal cities. Although agricultural land will tend to be urbanized near cities with extensive plains, high ratios of forest lands will be urbanized in cities near Seoul.

Optimization of BESS placement, technology selection, and operation in microgrids for minimizing energy losses and CO2 emissions: A hybrid approach

This research paper addresses the issue of placement, technology selection and operation of BESS energy storage systems (BESS) in microgrids under a variable distributed generation (DG) and energy demand scenario for an average year of operation. To address this issue, a mixed-integer nonlinear programming (MINLP) model was formulated. The model considers two different objective functions, which are optimized using a mono-objective approach: (i) total energy losses and (ii) total CO2 emissions. The MINLP model was solved using a master–slave methodology based on metaheuristic algorithms. In the master stage, the Chu & Beasley parallel genetic algorithm (PCBGA) was used to solve the BESS placement and technology selection problem, while in the slave stage, the vortex search algorithm (VSA) was used to solve the BESS operation problem. A matrix-based successive approximation power flow was also used in this stage to determine the objective functions and evaluate the technical and operational constraints defined in the mathematical model. To verify the results, the 33-node test system was adjusted to the generation and demand characteristics of the city of Medellín. In addition, the proposed methodology was compared with two optimization techniques used in the master stage, namely the Parallel Monte Carlo (PMC) method and the Parallel Particle Swarm Optimizer (PPSO). Simulation results obtained using MATLAB software for the test scenario showed that the CBGA-VSA methodology outperformed the other techniques in terms of solution quality, repeatability and processing times to minimize total annual operating costs.

Quadrant Roadway Intersections: Tradeoffs between Control Delay Savings and Extra Travel Time

A quadrant roadway intersection (QRI) reduces congestion relative to a four-phase intersection. (Note: this study relates to traffic systems where vehicles drive on the right-hand side of the road.) It does this by removing left-turn traffic from the main intersection, resulting in a two-phase signal. Nevertheless, there is a lack of clear understanding of the tradeoffs between savings in control delay versus extra travel time experienced by the rerouted movements. This research compared the operational performance of five QRI designs with the counterpart conventional intersection (CI) under various traffic demand scenarios via TransModeler microsimulation modeling. Three measures-of-effectiveness (MOEs) were employed: time-in-system (TIS), control delay, and intersection capacity utilization. Simulation results show that all QRI designs outperform CI design for all three MOEs under all demand scenarios. QRIs with direct left-turn design have a smaller average TIS than those with loop left-turn design, indicating that savings in control delays did not offset the extra travel times. Under a relatively low demand condition, a single QRI design can generally balance the tradeoffs between control delay and extra travel time. Under a high demand scenario, a dual or full QRI with direct left-turns is preferred, since it reroutes or partially reroutes left- and right-turn traffic to secondary intersections, thus the main intersection has a lower capacity utilization and can accommodate more through-traffic demands than CI, single QRI, and dual or full QRIs with loop left-turns.

Comparative analysis of CMIP5 and CMIP6 in conjunction with the hydrological processes of reservoir catchment, Chhattisgarh, India

Study regionMahanadi reservoir project complex (MRP), Chhattisgarh, India Study focusThis study assesses and compares the performance of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models in modeling the hydrological regime. The study compared the performance of 13 CMIP6 GCMs in two Shared Socioeconomic Pathways (SSEPs) with that of 16 CMIP5 GCMs in two Representative Concentration Pathways (RCPs). Before being used to drive the Soil and Water Assessment Tool (SWAT) for streamflow prediction, the raw CMIP outputs were adjusted and downscaled using Bias Correction and Spatial Disaggregation methods (BCSD). In addition, the water deficits were also assessed between the mean monthly released by the optimized model and the mean monthly water demand. Results of this research projected the domestic water demand for future scenarios (2023–2099). New hydrological insights for the study regionThe results of this study revealed that the genetic algorithm (GA) outperforms the various optimization techniques for CMIP5, with deficits observed by the GA algorithm. In addition, CCCmaCanESM2 was found to be the most efficient among CMIP5 GCMs, whereas MPI-ESM1–2-HR was used for CMIP6 GCMs. Overall, the CMIP6 multi-model mean ensemble (MMME) outperformed the CMIP5 MMME in simulating streamflow over the study area at annual and seasonal timescales. CMIP6 MMME also reduced maximum and minimum temperature biases over the study region significantly. In overall conclusion, the CMIP6 ensemble offers a lower margin of uncertainty for future climate projections and better credibility for hydrological effect analysis. The results further indicate that satisfactory steam flows can be obtained from the SWAT model while slight underestimations can be seen for higher discharges. In addition, the projected domestic water demand based on future population and irrigation demand clearly showcases significant annual increases.