Variable Demand Research Articles

Examining the Long-Run and Short-Run Relationship between Water Demand and Socio-Economic Explanatory Variables: Evidence from Amman

This study investigates the key factors that influence household water usage in Amman, Jordan, with the aim of improving water management practices in a region facing significant scarcity. The research focuses on factors such as temperature, water pricing, system input, and family size. The Vector Error Correction Model with Exogenous Variables (VECMX) is applied to data from 1980 to 2015 to provide insights into consumption patterns, both in the short-term and long-term. The results show that family size and marginal costs significantly impact long-term water demand, while system input and family size influence short-term water demand. The study also finds that water pricing has a limited impact on consumer behavior, indicating inelasticity. Temperature and income, however, did not emerge as significant determinants. These findings highlight the need for water management policies in arid areas like Amman to prioritize factors other than price, such as household size and water infrastructure, to establish more effective strategies for conserving water.

Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)

Abstract. This paper presents an extension of industry modelling within the REMIND integrated assessment model to industry subsectors and a projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse-gas-emitting energy demand sector and is considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon capture and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy–economy–climate system, covering mitigation options ranging from reduced demand for industrial goods, fuel switching, and electrification to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for use with the REMIND model in baseline scenarios, based on short-term continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the Shared Socioeconomic Pathways (SSPs).

Performance analysis of machine learning based prediction models in assessing optimal operation of microgrid under uncertainty

Of late, the exponential rise in the global population is driving higher energy demand. However, the rapid depletion of conventional fossil fuels and growing environmental concerns have prompted the evolution of alternative energy sources. To this end, Microgrid (MG) with Renewable Energy Sources (RES) has emerged as popular means of small-scale localized power grid. However, planning of MG operation poses challenges due to the inherent variability and stochasticity in RES power output and energy demand. On account of this, the present study introduces a Stochastic Energy Management Strategy (SEMS) for a grid-connected MG incorporating Micro-Turbine, Fuel-Cell, RES, Battery Energy Storage, and electrical and heat energy demand. The stochasticity of RES is forecasted through a hybrid prediction model (sARIMA-GRU) and the uncertain demand is estimated via 'Monte Carlo Simulation.' The proposed problem is formulated as a dynamic non-linear stochastic optimization problem. It seeks to minimize the expected value of MG operational cost satisfying the practical constraints. Addressing this, a newly developed ‘Artificial Electric Field Algorithm (AEFA)' is utilized. Several case studies are performed to assess MG operation under varied operating conditions. Moreover, the present study analyses the impact of uncertainty on energy contribution from DER, grid dependency, and MG operation cost. Comparative analysis reveals that sARIMA-GRU outperforms other contemporary prediction models. It is noteworthy that the superior prediction accuracy of sARIMA-GRU leads to lower MG operation costs. Moreover, statistical analysis and convergence confirm the proficiency of applied AEFA over state-of-the-art Grey Wolf Optimization and Firefly Algorithm in solving the proposed problem.

Multi-objective design of off-grid low-enthalpy geothermal generation systems considering partial-load operations

This work addresses the multi-objective design of an organic Rankine cycle system for low-enthalpy applications. The design is generated from end-user demand variations. The model uses the reference equations of state of four working fluids commonly used in the literature: R1234yf, R1234ze (E), R134a and R245fa. This approach allows computing the optimal sizing of each unit of the equipment that composes the cycle, obtaining a suitable configuration for each working fluid. That is, the comparison between configurations is linked to the thermodynamic properties of the fluid. In addition, it addresses the phase changes present in each operation and the operational adjustments associated with changes in demand. Therefore, the optimal design captures partial load operation through a multi-period model. Given the off-grid operating conditions, the effects of coupling with energy storage systems are analyzed. The considered objective functions are the total annual cost and energy efficiency of the system. Results indicate that the sizing of the heat exchangers is significantly affected by phase change effects and that the R1234yf and R1234ze (E) fluids are promising since their thermodynamic properties allow for trade-offs between the economic and energy performance of the system. System efficiencies, considering part-load operation and demand variability, can be increased by up to 2 % with the inclusion of energy storage, significantly improving economic performance and reducing costs by up to 10 %.

Battery degradation: Impact on economic dispatch

AbstractBatteries are crucial to manage the rising share of intermittent energy sources and variability in demand. Most technoeconomic models in the literature oversimplify battery degradation representation. Accounting properly for battery degradation allows for better cost tradeoffs and optimal battery usage, especially in dynamic settings. We propose a highly accurate and scalable formulation for battery degradation that considers the combined impact of cycle depth (CD) and state of charge on calendar and cycle aging. This includes a novel way to track charge‐discharge cycles. We test the consequences of battery degradation in a stylized price arbitrage model on battery operation and solution times. When ignoring battery degradation, ex post calculations reveal hidden degradation costs that exceed revenues and hence turn seemingly profitable trades into losing trades. Considering battery degradation leads to smaller CDs and lower average states of charge. Overall, we show that a much‐improved representation of battery degradation is possible at modest computational cost, allowing better decisions and higher profits.

Towards clean energy: Design and integration strategy of photovoltaics for residential electrification

Rapid global population growth and rising energy demands are causing environmental damage, particularly in developing countries dealing with climate change, pollution, and fuel shortages. Adopting cleaner, more sustainable energy sources could be one solution to this problem. In the context of hybrid energy systems, one proposition is to incorporate renewables into residential developments. This manuscript proposes a grid-connected hybrid (GCH) photovoltaic (PV) system for residential electrification in Dhaka, Bangladesh, and evaluates the technical feasibility. The proposed design includes four scenarios for PV and battery energy storage systems (BESS). The primary goals of the assessment include lowering net present cost (NPC), cost of energy (COE), and unmet load, as well as reducing grid dependence, lowering greenhouse gas (GHG) emissions, and providing grid support. Solar irradiation fluctuations, demand variations, and voltage sag are all factors to consider when evaluating technical feasibility. The four cases were simulated using hybrid optimization with HOMER Pro, to achieve a system that maximizes renewable energy utilization. The analysis concludes that the GCH PV system is best suited for residential power needs, with lower COE, NPC, and grid dependency. The system achieves a COE of $0.0643/kWh, lowers GHG emissions, and attains a renewable fraction of 40%. Technical feasibility is verified using MATLAB-Simulink simulations at the point of common coupling (PCC), which include solar irradiation variations, active and reactive power adjustments based on demand, and voltage sag analysis for grid integration. The proposed system is cost-effective, convenient, and sustainable, with few salvageable components.

A price-regulated scheduling of electric vehicle with grid supporting photovoltaic and battery storage

With solar photovoltaic (PV) sources integrated into microgrids and electric vehicle (EV) charging stations, users have a variety of charging mode options to meet their needs. However, overloading of the distribution transformer, higher peak demand, and voltage instability are all potential drawbacks associated with EV charging loads. Microgrid optimisation becomes more difficult when dealing with intermittent PV generation and uncertain EV charging and discharging profiles. A Smart Home Energy Management System (SHEMS) is proposed for efficient power flow from PV sources, battery energy storage system (BESS), and an EV to maximise economic advantages for a consumer using the Artificial Hummingbird Algorithm (AHBA)-based optimisation approach. This approach considers the EV's unpredictable energy consumption, variations in residential load demand and seasonal variations in the solar insolation profile throughout the year. Consequentially, potential EV requirements such as initial and predetermined state of charge (SOC) arrangements and arrival and departure hours are also considered. Three energy management scenarios satisfying the allowable loading limit of the distribution transformer are examined, namely power transfers from home to electric vehicle (H2EV), EV to home (EV2H), and EV to grid (EV2G). The reduction in yearly operating costs (YOC) of 49.5 %, 53.2 %, and 70 % was achieved in H2EV, EV2H, and EV2G modes, respectively. This demonstrates that an effective EV and BESS management strategy can reduce the annual operating costs of a residential consumer by up to 70 % while simultaneously achieving peak load shaving.

Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources

<span lang="EN-US">This paper suggests a modified version of the former economic load dispatch (MELD) problem with the integration of wind power plant (WPP) and solar power plants (SPP) into thermal units (TUs). The target of the whole study is to cut the total producing electricity cost (TPEC) as much as possible. Three meta-heuristic algorithms, including particle swarm optimization (PSO), jellyfish search (JS) and salp swarm algorithm (SSA), are applied to solve the MELD. The real performance of these optimization tools is tested on the first system with six thermal units considering prohibited zones, and the second system with the combination of the first system and one solar, and two WPPs. In addition, the variation of load demand in 24 hours per day is also taken into account in the second system. JS is proved to be the most effective method for dealing with MELD. Furthermore, JS can also reach lower or the same TPEC as other previous algorithms. Hence, JS is a recommended to be a strong computing method for dealing with the MELD problem. </span>

Estimating domestic water usage in a tropical environment: exploring socio-demographic perspectives

Water demand at any given time, particularly at the household level, depends on various factors, including climatic variables, social, economic, and demographic factors. Achieving consistent availability and accessibility, coupled with effective resource management, is crucial, particularly in tropical regions. Understanding all these variables is essential to achieving these goals. This research aimed to assess the impact of ten socio-demographic variables on predicting household water demand in Iwo. Two hundred and twenty-five households were randomly selected, with 196 completing and returning the survey. Both descriptive and multivariate analyses, specifically Factor and Regression Analysis, were employed to analyse the data. Factor analysis (FA) identified four variables: (i) housing characteristics; (ii) marital status; (iii) income level; and (iv) gender distribution, in that sequence. These four variables collectively accounted for 68.608% of the variance in household water demand in Iwo. This outcome underscores the importance of giving due consideration to these variables in water supply planning by relevant authorities. The study contributes to a better understanding and quantification of the significant variables influencing household water demand. However, it is advisable that future investigations into household water use incorporate additional variables beyond socio-demographic factors to comprehensively comprehend factors influencing water demand at the household level.

Dynamic-Deep-Ensemble-Learning Scheme for Probabilistic Voltage Stability Margin Estimation to Enhance Resilient Power Grid Monitoring

Modern power grids are characterized by significant penetration of renewable energy sources (RES), variable power demand, and aging transmission infrastructure, all of which contribute to a high degree of operational uncertainty. Such uncertainty complicates the assessment of the static voltage stability in power grids. In response to this challenge, this paper proposes a novel deep ensemble learning-based approach to assess the probabilistic voltage stability margin (PVSM) for strengthening the resilience of power grid monitoring. First, the estimation of the PVSM is formulated as a quantile regression problem. Then, an improved deep quantile regression (iDQR) is utilized to generate a set of quantiles under specific nominal proportions. Next, a dynamic deep ensemble learning (D2EL) scheme based on diverse iDQR models and an improved Choquet fuzzy integral (iCFI) algorithm is proposed to enhance the overall performance of predictive quantiles for the PVSM. The proposed D2EL-based PVSM estimation approach is capable of accommodating system changes in a timely manner, thus providing higher estimation accuracy and stronger adaptability than conventional approaches. A comprehensive numerical study of several test systems is carried out, taking into account uncertain RES and loads, as well as topology changes. The results reveal the impressive performance of the proposed approach in the PVSM assessment.

Recourse strategy for the routing problem of mobile parcel lockers with time windows under uncertain demands

It is expected that mobile parcel lockers (MPLs) will provide more responsive and flexible service to the increasing variation of demand than fixed parcel lockers. However, the service provided by MPLs following planned routes can be degraded and even fail due to the demand uncertainty, such as demand volume and pickup time. In this study, we propose an optimization approach integrated with the recourse strategies to overcome this barrier. Three types of uncertain demands and two types of service failures are addressed in this study, with consideration of uncertain demands and time window constraint. To optimally determine the a priori routes for a set of MPLs, we propose a tailored tabu search algorithm by taking into account the recourse cost. A series of numerical experiments were conducted to examine the effectiveness of the proposed approach in enhancing route reliability with a comparison to ordinary express vehicles. The results reveal that the service reliability can be significantly improved with only a marginal increase in the operational cost by the proposed approach.

Combining visual interpretation and image segmentation to derive canopy cover index from high resolution satellite imagery in functionally diverse coniferous forests

Forest canopy cover is one of the most significant structural parameters of the forest stand that can be estimated using of aerial and satellite remote sensing. Even though sub-pixel analysis can be used to estimate the index on low-resolution imagery, high-resolution imagery provides more accurate details on forest canopy variability for ecological and forestry applications. However, the high variability of the images demands a more advanced approach to canopy cover measurement than the visual interpretation of single images or stereo pairs. These traditional methods are inefficient and limited in providing a comprehensive and accurate canopy cover assessment. An improvement of the method could involve classifying high spatial resolution images, separating and extracting the areas corresponding to canopy gaps, and generating canopy cover maps. This study offers valuable insights and reveals key differences between three methods for estimating canopy cover: ground measurements, visual photo interpretation and automatic extraction from classified images using pixel and object-based methods. The texture analysis approach was used for separating the “shadow” objects corresponding to gaps in the canopy from the shades cast on the lower trees. The sample plot-based visual interpretation of the images revealed comparable results between ground and satellite image canopy cover values (correlation coefficient of 0.74 for all plots), with lower correlations (r = 0.39) for uneven-aged stands. The results encourage the use of the texture analysis method, with satisfying accuracy (forest canopy cover differences of maximum 0.06 between ground, photo interpreted and extracted datasets). The method could be further integrated with complementary data like LIDAR or hyperspectral images.

Probabilistic forecast of electric vehicle charging demand: analysis of different aggregation levels and energy procurement

Electric vehicles (EVs) are expected to be vital in transitioning to a low-carbon energy system. However, integrating EVs into the power grid poses significant challenges for grid operators and energy suppliers, especially regarding the uncertainty and variability of EV charging demand. Accurate forecasting of EV charging demand is essential for optimal power system integration, yet previous studies have often only considered point predictions that are inadequate for risk assessment. Therefore, this paper compares different probabilistic forecasting models for the short-term prediction of EV charging demand at various aggregation levels, using a large and novel dataset of over 350,000 charging processes at more than 500 locations across Germany. The performance of both machine learning and deep learning methods is evaluated against a naïve benchmark model, and the impact of data availability on the forecasting models is investigated. Further, the paper examines the effects of forecast accuracy on energy procurement, which has so far received minor attention in the literature. The results show that machine learning methods such as Ada Boosting and Random Forest yield robust results with a normalized root mean square error of 0.42 and 0.41 and a mean absolute scaled error of 0.36 and 0.34 at the highest aggregation level. Furthermore, the results show the influence of different site compositions on the forecast quality and how many charging points are likely to yield a robust forecast. Energy and fleet managers can use the described method to reliably predict the required energy quantities for fleets of sufficient size and procure them at low risk.

Sources of Supply Chain Volatility: A Literature Review

Purpose: The purpose of this study was to identify the main source of supply chain volatility based on empirical literature, addressing the gap in existing research where consensus on this matter has been lacking.
 Methodology: Employing an interpretivist approach, this study utilized a bibliographic and qualitative research method. The researchers systematically reviewed literature from top publishing sites and journals, focusing on titles and abstracts containing the keyword 'supply chain volatility' spanning from 2013 to 2023. Through this process, a taxonomy of 15 articles was developed to synthesize existing knowledge on the subject.
 Findings: The results of the study indicate that demand variability emerges as the primary source of supply chain volatility, with 60% of the analyzed articles highlighting its significance. This finding underscores the critical role of demand fluctuations in driving supply chain disruptions and challenges.
 Unique Contribution to Theory, Practice and Policy: This study makes a unique contribution to existing literature by providing empirical evidence and consensus on the main source of supply chain volatility. By synthesizing and categorizing findings from diverse sources, it advances theoretical understanding of the factors underlying supply chain disruptions. The identification of demand variability as the primary source of supply chain volatility offers valuable insights for practitioners seeking to enhance supply chain resilience and mitigate disruptions. Understanding the central role of demand dynamics can inform strategic decision-making and risk management practices within organizations. The findings of this study have implications for policy-makers involved in shaping regulatory frameworks and industry standards related to supply chain management. By recognizing demand variability as a key driver of volatility, policymakers can tailor interventions and incentives to promote stability and efficiency in supply chains.

GIS-based service network optimisation for location of postal delivery system

ABSTRACT Public postal organisations aim to determine the optimal locations for postal delivery service branches to enhance service quality, increase operational efficiency, reduce costs, and optimise resource utilisation. In this paper, we consider how to enhance the process of identifying suitable sites for these branches in Istanbul, Turkey, through a case study that evaluates land-use categories – and their influence on demand for postal services – within different districts so as to pinpoint the most appropriate locations for service branches. We employ Kernel Density Estimation (KDE) analyses in conjunction with Geographic Information Systems (GIS) to optimise the service network. This optimisation aids in deciding whether to close underperforming branches or to establish new branches in neighbourhoods with higher demand. Turkish postal delivery system statistics serve as the basis for our case study. Consequently, this study provides valuable insights into the selection of postal service branch locations and offers practical implications for improving branch accessibility in relation to spatial variability in demand. Such applied insights can be crucial for administrators and policymakers when making informed decisions regarding long-term location planning and initiatives across various sectors.

Mitigating drought mortality by incorporating topography into variable forest thinning strategies

Abstract Drought-induced productivity reductions and tree mortality have been increasing in recent decades in forests around the globe. Developing adaptation strategies hinges on an adequate understanding of the mechanisms governing the drought vulnerability of forest stands. Prescribed reduction in stand density has been used as a management tool to reduce water stress and wildfire risk, but the processes that modulate fine-scale variations in plant water supply and water demand are largely missing in ecosystem models. We used an ecohydrological model that couples plant hydraulics with groundwater hydrology to examine how within-stand variations in tree spatial arrangements and topography might mitigate forest vulnerability to drought at individual-tree and stand scales. Our results demonstrated thinning generally ameliorated plant hydraulic stress and improved carbon and water fluxes of the remaining trees, although the effectiveness varied by climate and topography. Variable thinning that adjusted thinning intensity based on topography-mediated water availability achieved higher stand productivity and lower mortality risk, compared to evenly-spaced thinning at comparable intensities. The results from numerical experiments provided mechanistic evidence that topography mediates the effectiveness of thinning and highlighted the need for an explicit consideration of within-stand heterogeneity in trees and abiotic environments when designing forest thinning to mitigate drought impacts.

Robot-aided tailoring of flame spray coatings for high-temperature barrier application

The flame spraying of refractory ceramics such as alumina is a promising technique for the modification of manifold substrate materials for high-temperature application. Its high potential for automation and feedstock design allows for swift adaption to variable demands. Consequentially, a tailoring of flame-sprayed barrier coatings can be realised in terms of the phase composition and the mechanical performance. In order to isolate the effects of the addition of zirconia and titania, as well as the coating thickness, the present study focused the biaxial flexure of self-supporting flame-sprayed alumina bodies, produced by a fully automated and robot-aided flame spray station (on the lab-scale).

Stability Study of Grid-Connected Power System for Wind Farms Considering Power Control

Wind energy has emerged as a pivotal practice in the contemporary energy landscape, generated through grid-connected power sources aligning with the vernacular principles of systemic approaches. This study explores the surge in initiation rates, offering insights into various factors impacting sustainable electricity production. Intriguingly, this research delves into the intricacies of managing the variability and uncertainty inherent in energy demand, catalysing the integration of grid-based solutions that enhance sustainability. It probes the dynamic nature of power supply paradigms, revealing a journey of continuous enhancement by applying cutting-edge resource methodologies. Amidst the backdrop of global shifts in electricity dynamics, this study uncovers the profound implications of energy depletion and wasteful consumption practices, spotlighting a burgeoning movement towards optimising grid electricity resources on a macro scale. The intricacies and nuances of power supply challenges are comprehensively dissected, offering valuable insights. Furthermore, the study explores the pivotal role played by information technology innovators in consolidating the predictability of wind energy, augmenting its viability. It also aligns with forward-looking reviews, underscoring the actionable strategies taken. The culmination of these efforts not only enhances predictability but also unlocks a spectrum of reflective and adaptive resources in wind energy utilisation.

Revisiting the TeV flare of PKS 2155-304 in 2006

ABSTRACT Blazars, a subclass of active galactic nuclei (AGN), are known to be bright γ-ray sources, frequently exhibiting active (flaring) periods. Blazar PKS 2155-304 is a high synchrotron-peaked BL Lac object located at redshift z = 0.116. On 2006 July 28, an extremely remarkable outburst of VHE γ-ray emission from this blazar was reported by the H.E.S.S. experiment, with an average flux more than 10 times the low-state level. The variability time-scale of this extraordinary flare was as short as approximately 200 s. In order to guarantee the transparency of the emission region for TeV photons, the fast variability demands an extremely high Doppler factor δD > 50 of the jet within the classical one-zone model, leading to the so-called ‘Doppler factor crisis’. Here, we demonstrate that the stochastic dissipation model, which is a multiblob scenario for blazars, can self-consistently explain the giant TeV flares of PKS 2155-304 and the low-state emission before and after the flares, in terms of both multiwavelength spectral and variability characteristics. The required Doppler factor in this model can be as low as 20, which is a reasonable and typical value for blazar jets. The obtained model parameters may shed some light on the physical properties of the relativistic jet.

Housing market, oil prices, and macroeconomic volatility in the G7

AbstractIn this paper, we investigate house price shocks on the macroeconomic variables (financial market, inflation, and real sector) of the G7 economies. We use the GVAR to capture the spillover effects from the U.S. housing market and oil prices on these economies from 1991M3–2022M10. We identify the U.S. house price shock using the Structural Generalized Impulse Response Function, house supply and demand variables, and regional divergence. We find that the domestic stock markets and industrial production are the most sensitive to house price shocks. We further compare the importance of house and oil prices on domestic fluctuations. The estimates reinforce the previous findings: U.S. house prices are responsible for a quarter of the domestic volatility of the stock markets and industrial production. In the other macroeconomic segments, the effects of house prices are present, but in lower values. Our results show that house prices provoke more domestic fluctuations than oil prices. Finally, we also found that short and long‐term credit markets, as well as stock markets, transmit the house price shock to industrial production. Consequently, we provide potential channels to comprehend the spillover effect of U.S. house prices on international markets.