Demand Scenarios Research Articles

A Geospatial Framework of Food Demand Mapping

Spatial mapping of food demand is essential for understanding and addressing disparities in food accessibility, which significantly impact public health and nutrition. This research presents an innovative geospatial framework designed to map food demand, integrating individual dietary behaviors with advanced spatial analysis techniques. This study analyzes the spatial distribution of eating habits across Lithuania using a geospatial approach. The methodology involves dividing Lithuania into 60,000 points and interpolating survey data with Shepard’s operator, which relies on a weighted average of values at data points. This flexible approach allows for adjusting the number of points based on spatial resolution and sample size, enhancing the reliability and applicability of the generated maps. The procedure includes generating a structured grid system, incorporating measurements into the grid, and applying Shepard’s operator for interpolation, resulting in precise representations of food demand. This framework provides a comprehensive understanding of dietary behaviors, informing targeted policy interventions to improve food accessibility and nutrition. Traditional food spatial mapping approaches are often limited to specific polygons and lack the flexibility to achieve high granular detail. By applying advanced interpolation techniques and ensuring respondent location data without breaching privacy concerns, this study creates high-resolution maps that accurately represent regional differences in eating habits. The methodology’s flexibility allows for adjustments in spatial resolution and sample size, enhancing the maps’ validity and applicability. This novel approach facilitates the creation of detailed food demand maps at any granular level, providing valuable insights for policymakers and stakeholders. These insights enable the development of targeted strategies to improve food accessibility and nutrition. Additionally, the obtained information can be used for computer simulations to further analyze and predict food demand scenarios. By leveraging spatial data integration, this study contributes to a deeper understanding of the complex dynamics of food demand, identifying critical areas such as food deserts and swamps, and paving the way for more effective public health interventions and policies aimed at achieving equitable food distribution and better nutritional outcomes.

Integrated energy hub dispatch with a multi-mode CAES–BESS hybrid system: An option-based hierarchical reinforcement learning approach

The high penetration of renewable energy sources (RES) in power generation has driven demand for advanced integrated energy management systems (IEMS). In this study, to address the challenges of insufficient adaptability to dynamic supply–demand, a multi-type energy IEMS combining compressed air energy storage (CAES) and a battery energy storage system (BESS) is proposed, which operates under a multi-mode energy storage (MES) mechanism with rapid response, long-term balance, and synergic adjustment modes. To address the complexity of sequential decisions, an option-critic based twin delayed deep deterministic policy gradient (OCTD3) algorithm is firstly proposed within the hierarchical reinforcement learning (HRL) framework, enhancing efficiency through encapsulation of subtasks within ”options”. Additionally, model precision is refined by fitting the electricity–gas–heat conversion dynamics of CAES under off-design conditions. Dispatch tasks are modeled as an option-based Semi-Markov Decision Process (SMDP) and optimized by the OCTD3 to improve the power fluctuations index (PFI), comprehensive costs index (CCI), and system response synergy index (SRSI). Comparative simulations reveal that the MES mechanism boosts SRSI by 91.8%, showcasing high adaptability to varied supply–demand scenarios. The OCTD3 algorithm develops five hybrid strategies for CAES–BESS across three modes, effectively cutting costs by reducing electricity purchases and fluctuations expenses, and lowering PFI by 42.2% through balancing peak–valley loads and swiftly responding to transient shifts.

Microsyringe-assisted visual volume detection based on phase separation: the case of chymosin milk-clotting activity study.

The constantly diverse demand scenarios for rapid on-site analysis have put forward high requirements for developing low-cost and user-friendly visual detection methods. Therefore, developing a visual detection method with simple operation and intuitive results has important practical value in the field of analysis and detection, but it is also challenging. In this work, we propose a microsyringe-assisted visual volume detection method based on phase separation, and apply it to analyze the milk-clotting activity of chymosin. Chymosin can cause phase separation of milk with whey in the mobile phase and curd in the gel state. The network structures of casein in curd can trap water molecules, resulting in separation of whey from curd gradually. Therefore, the analysis of chymosin milk-clotting activity can be realized according to the volume of whey measured using a portable microsyringe. This method shows a good linear correlation when the concentration of chymosin ranges from 1.02 U L-1 to 1020 U L-1 and the limit of detection of this method for chymosin is calculated to be 0.03 U mL-1. This work successfully realizes the visual analysis of chymosin milk-clotting activity based on the enzyme-triggered phase separation. It also shows great promise to be applied in other phase separation-based detection systems with the advantages of high accuracy, great portability and user-friendliness.

A simulation‐based impact assessment of autonomous vehicles in urban networks

AbstractThe behavioural differences between autonomous vehicles (AVs) and human‐driven vehicles (HDVs) can significantly impact traffic efficiency, safety, and emissions. Simulation‐based impact assessments using microscopic traffic models often modify car‐following (CF) and lane‐changing (LC) configurations to differentiate AVs from HDVs. Typically, researchers adjust CF model parameters to replicate AV driving behaviour, but these assumptions can lead to varying conclusions on AV impacts. The scope of each study (e.g., freeways, highways, urban links, intersections) also influences the outcomes. This research conducts an impact assessment utilizing optimized AV driving behavior rather than assumptions on a city network level (Munich) using a simulation‐based platform. The particle swarm optimization (PSO) algorithm is used to calibrate the base model and run simulation experiments under various penetration rates (PRs) and demand scenarios. Results show significant safety improvements throughout the network under higher PRs, while lower PRs might lead to deteriorating safety. At 100% AV PR, the total number of conflicts decreased by around 25% compared to a fully HDV environment. Considering AVs' sensing capabilities, additional safety improvements are found in almost any AV PR. However, AVs might not improve traffic efficiency; in some cases, they may slightly increase average network travel time, though this change is minimal.

Summary characteristics for multivariate function‐valued spatial point process attributes

SummaryPrompted by modern technologies in data acquisition, the statistical analysis of spatially distributed function‐valued quantities has attracted a lot of attention in recent years. In particular, combinations of functional variables and spatial point processes yield a highly challenging instance of such modern spatial data applications. Indeed, the analysis of spatial random point configurations, where the point attributes themselves are functions rather than scalar‐valued quantities, is just in its infancy, and extensions to function‐valued quantities still remain limited. In this view, we extend current existing first‐ and second‐order summary characteristics for real‐valued point attributes to the case where, in addition to every spatial point location, a set of distinct function‐valued quantities are available. Providing a flexible treatment of more complex point process scenarios, we build a framework to consider points with multivariate function‐valued marks, and develop sets of different cross‐function (cross‐type and also multi‐function cross‐type) versions of summary characteristics that allow for the analysis of highly demanding modern spatial point process scenarios. We consider estimators of the theoretical tools and analyse their behaviour through a simulation study and two real data applications.

Combined axicon design based on a structural parameter optimization algorithm

This study proposes a combined axicon (CA) design method based on a structural parameter optimization algorithm designed to rapidly address the demands of practical application scenarios, precisely tailor structural parameters, and produce high-quality Bessel beams (HQ-QBBs) that satisfy specific requirements. Compared to generating an HQ–QBB using an axicon, our method effectively overcomes the shortcomings of fewer tunable factors, a large number of high-energy side-lobes, and limited non-diffractive regions. Through detailed analyses of the transmission characteristics, imaging characteristics, and thick-sample detection ability of the generated HQ-QBB, the significant advantages of the proposed method are demonstrated. The proposed method is not only relevant to current research but also demonstrates wide-ranging application potential in future lens designs.

A novel method for long-term power demand prediction using enhanced data decomposition and neural network with integrated uncertainty analysis: A Cuba case study

This study developed a methodological approach for long-term electricity demand forecasting and applied it to the electricity demand in Cuba, which is crucial for transitioning from a fossil fuel-dependent system to renewable energy sources. The methodology employs enhanced complete ensemble empirical mode decomposition with adaptive noise (ECEEMDAN) applied for obtaining long-term trends from historical electricity usage data decomposition, combined with a long short-term memory (LSTM) deep learning model for prediction. Comprehensive datasets, including historical electricity consumption, economic indicators, and demographic data, are utilized in the analysis. Monte Carlo simulations, then, are integrated to address uncertainties in prediction and explore 50 different scenarios of future electricity demand. The study forecasts varying scenarios for the energy demand of Cuba by 2050, with the extreme low scenario projecting a decrease of up to 7.9% compared to the 2019 level. This research offers a groundbreaking framework specifically designed to aid Cuba's energy sector stakeholders in informed decision-making during this critical energy transition. The adaptability of the methodology makes it applicable for long-term projections in various sectors, offering a reliable tool for global decision makers.

A novel fuzzy finite-horizon economic lot and delivery scheduling model with sequence-dependent setups

This paper addresses the economic lot and delivery scheduling problem (ELDSP) within three-echelon supply chains, focusing on the complexities of demand uncertainty, limited shelf-life of products, and sequence-dependency of setups. We develop a novel mixed-integer non-linear programming (MINLP) model for a supply chain comprising one supplier, multiple manufacturers with flexible flow shop (FFS) production systems, and multiple retailers, all operating over a finite planning horizon. The common cycle (CC) strategy is adopted as the synchronization policy. Our model employs fuzzy set theory, particularly the “Me measure,” to effectively handle the retailers’ demand uncertainty. Our findings indicate that total supply chain costs escalate with an increase in demand, final components’ holding costs, and sequence-dependent setup costs, but decrease with increasing production rates. Furthermore, while total costs are significantly sensitive to changes in demand, they are relatively insensitive to fluctuations in sequence-dependent setup times. The models developed offer valuable managerial insights for optimizing costs in synchronized multi-stage supply chains, aiding managers in making informed decisions about production lot sizes and delivery schedules under both deterministic and fuzzy demand scenarios. Additionally, the proposed models bridge key research gaps and provide robust decision-making tools for cost optimization, enhancing supply chain synchronization in practical settings.

Smart grid enterprise decision-making and economic benefit analysis based on LSTM-GAN and edge computing algorithm

As the next-generation power system, smart grid presents challenges to enterprises in managing and analyzing massive data, meeting complex operational and decision-making demands, and predicting future power demand for grid optimization. This paper aims to proposed a fusion algorithm for smart grid enterprise decision-making and economic benefit analysis, enhancing the accuracy of decision-making and predictive capability of economic benefits. The proposed method combines techniques such as Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and edge computing. The LSTM model is employed to model historical data of the smart grid. The GAN model generates diverse scenarios for future power demand and economic benefits. The proposed method is evaluated on four public datasets, including the ENTSO-E Dataset, and outperforms several traditional algorithms in terms of prediction accuracy, efficiency, and stability. Notably, on the ENTSO-E Dataset, the proposed algorithm achieves a reduction of over 46.6% in FLOP, and a decrease in inference time by over 48.3%, and an improvement of 38% in MAPE. The novel fusion algorithm proposed in this paper demonstrates significant advantages in accuracy and predictive capability, providing a scientific basis for smart grid enterprise decision-making and economic benefit analysis while offering practical value for real-world applications.

Adaptive energy management strategy for optimal integration of wind/PV system with hybrid gravity/battery energy storage using forecast models

Hybrid energy systems, including hybrid power generation and hybrid energy storage, have attracted considerable attention as eco-friendly solutions to meet the increasing global energy demands while minimizing environmental impacts. The economic viability and resilience of hybrid energy system solutions depend on careful consideration of economic and reliability factors during the design phase. This paper explores the optimization and design of a wind turbine (WT)/photovoltaic (PV) system coupled with a hybrid energy storage system combining mechanical gravity energy storage (GES) and an electrochemical battery system. An adaptive energy management strategy linked to an optimization process has been proposed for the optimal integration of the WT/PV system with the hybrid Gravity/Battery storage system. Forecast models have been employed to predict solar and wind generation. Simulation models are developed for each component of the multi-source power plant to predict energy flow behavior based on real-world industrial load demand scenarios. The renewables contribution to 100 % reliability is approximately 18 % for photovoltaic and 92 % for wind turbine, primarily attributed to the great wind energy potential of the selected site. The optimal cost of energy is 0.28 €/kWh for a reliability of 100 %, which reduces to 0.03€/kWh when reliability level is decreased to 20 %. The incorporation of forecast models into the study and the hybridization of storage options improve the design accuracy and optimal operation of the multi-source power system leading to reliable and efficient use of renewable energy.

Explicit coordinated signal control using soft actor–critic for cycle length determination

AbstractExplicit signal coordination carries prior knowledge of traffic engineering and is widely accepted for global implementation. With the recent popularity of reinforcement learning, numerous researchers have turned to implicit signal coordination. However, these methods inevitably require learning coordination from scratch. To maximize the use of prior knowledge, this study proposes an explicit coordinated signal control (ECSC) method using a soft actor–critic for cycle length determination. This method can fundamentally solve the challenges encountered by traditional methods in determining the cycle length. Soft actor–critic was selected among various reinforcement learning methods. A single agent was administered to the arterials. An action is defined as the selection of a cycle length from among the candidates. The state is represented as a feature vector, including the cycle length and features of each leg at every intersection. The reward is defined as departures that indirectly minimize system vehicle delays. Simulation results indicate that ECSC significantly outperforms the baseline methods, as evident in system vehicle delay across nearly all demand scenarios and throughput in high demand scenarios. The ECSC revitalizes explicit signal coordination and introduces new perspectives on the application of reinforcement learning methods in signal coordination.

Frequency and intensity of maximal and submaximal demanding scenarios in U19 professional soccer players

ABSTRACT This study examined the frequency of the most demanding scenarios (MDS) during official soccer matches using rolling averages over 1, 5, and 10 min. Forty-two Under-19 players from different positions (central defenders, full-backs, central midfielders, wide midfielders, offensive midfielders, and forwards) were monitored across 27 matches using GPS to track distance covered, high-speed running, sprint distance, accelerations, and decelerations. Intensity thresholds were established based on percentiles (0–25, 25–50, 50–75, 75–100, and ≥100). The main findings suggest that: (i) Peak Demands occur in less than 1% of all time windows and variables for all positions; (ii) Most efforts occur below peak demands, with around 95% for high-speed running and sprint distance, and 85% for accelerations, decelerations, and total distance; (iii) Significant differences in intensity distributions were found between positions, particularly at medium-low and high intensities. Regarding training prescription, relying solely on MDS may be limited, highlighting the need to supplement MDS with other metrics for a comprehensive understanding of match demands. This approach ensures better-informed training programs for soccer players.

Orthogonal Radical and Cationic Single-Unit Monomer Insertions for Engineering Polymer Architectures.

The single-unit monomer insertion (SUMI), derived from living/controlled polymerization, can be directly functionalized at the end or within the chain of polymers prepared by living/controlled polymerization, offering potential applications in the preparation of polymers with complex architectures. Many scenarios demand the simultaneous incorporation of monomers suitable for different polymerization methods into complex polymers. Therefore, it becomes imperative to utilize SUMI technologies with diverse mechanisms, especially those that are compatible with each other. Here, we reported the orthogonal SUMI technique, seamlessly combining radical and cationic SUMI approaches. Through the careful optimization of monomer and chain transfer agent pairs and adjustments to reaction conditions, we can efficiently execute both radical and cationic SUMI processes in one pot without mutual interference. The utilization of orthogonal SUMI pairs facilitates the integration of radical and cationic reversible addition-fragmentation chain transfer (RAFT) polymerization in various configurations. This flexibility enables the synthesis of diblock, triblock, and star polymers that incorporate both cationically and radically polymerizable monomers. Moreover, we have successfully implemented a mixing mechanism of free radicals and cations in RAFT step-growth polymerization, resulting in the creation of a side-chain sequence-controlled polymer brushes.

Orthogonal Radical and Cationic Single‐Unit Monomer Insertions for Engineering Polymer Architectures

AbstractThe single‐unit monomer insertion (SUMI), derived from living/controlled polymerization, can be directly functionalized at the end or within the chain of polymers prepared by living/controlled polymerization, offering potential applications in the preparation of polymers with complex architectures. Many scenarios demand the simultaneous incorporation of monomers suitable for different polymerization methods into complex polymers. Therefore, it becomes imperative to utilize SUMI technologies with diverse mechanisms, especially those that are compatible with each other. Here, we reported the orthogonal SUMI technique, seamlessly combining radical and cationic SUMI approaches. Through the careful optimization of monomer and chain transfer agent pairs and adjustments to reaction conditions, we can efficiently execute both radical and cationic SUMI processes in one pot without mutual interference. The utilization of orthogonal SUMI pairs facilitates the integration of radical and cationic reversible addition‐fragmentation chain transfer (RAFT) polymerization in various configurations. This flexibility enables the synthesis of diblock, triblock, and star polymers that incorporate both cationically and radically polymerizable monomers. Moreover, we have successfully implemented a mixing mechanism of free radicals and cations in RAFT step‐growth polymerization, resulting in the creation of a side‐chain sequence‐controlled polymer brushes.

An Assessment Method of Deviation Electricity Responsibility in China’s Forward Electricity Market Based on Time-Sharing Trading Prices

Electricity forward market contract trading constitutes a principal trading method in the electricity market. To address deviations from or non-compliance with contracts in China’s forward electricity market, an Energy Deviation Settlement (EDS) mechanism has been implemented. However, the current EDS mechanism is marred by unscientific penalties and imprecise deviation cost assessments. Consequently, this paper proposes a deviation responsibility assessment method based on time-sharing trading prices. Initially, the total annual contracted power is decomposed to derive the hourly deviation power for all 8760 h of the year. Subsequently, considering the power supply cost and load level, this paper proposes four pricing strategies for typical supply–demand scenarios and constructs a time-sharing trading pricing model based on cost pricing. Finally, this paper conducts a simulation using actual electricity data from China’s Province H. The results demonstrate that, compared to the traditional EDS mechanism, the proposed method can more accurately and reasonably measure the value of deviation responsibility. Concurrently, it can be better integrated with the future development of real-time spot market operations.

A practical and robust approach for solving the multi‐compartment vehicle routing problem under demand uncertainty using machine learning

AbstractThe multi‐compartment vehicle routing problem is an extension of the vehicle routing problem and consists of designing a set of routes to perform the collection or delivery of different product types from customers with minimal costs. The product types are incompatible with each other and must be transported separately in multiple compartments. In practice, several uncertainties such as uncertainty in demands can arise, where the exact demand of customers is not known at the time of planning. To deal with these uncertainties, decision‐makers have to rely on robust solutions. A solution is considered robust when it can resist perturbations in every possible demand scenario as much as possible. In the day to day business of most logistic companies, historical data about each customer can be stored and used to make intelligent decisions regarding the expected demands. In this article, we propose an adaptive large neighborhood search for solving the robust multi‐compartment vehicle routing problem under demand uncertainty and present a robust solution approach for the problem in practical settings by employing machine learning. We show that by using our approach, the solutions obtained have lower recourse costs and have a lower gap between expected and actual costs, which is a favorable outcome to have in practice.

Assessing the use of Fisher knowledge in oceanic eco-social networks of Peru: A comprehensive approach to sustainable fisheries management and governance

This study is the first attempt to determine the local stability and resilience of the highly exploited oceanic ecosystem of Peru, integrating the use of fisher knowledge (FK) by government institutions (GI), which could increase sustainable governance and improve the design of management strategies. To do this, five simplified semi-quantitative eco-social models with different levels of complexity were constructed. The Routh-Hurwitz local stability criterion 1 (RH1) showed that all models (with and without the link between FK and GI) exhibited the highest percentages of transient stable states in the scenario of demand and fishers in self-negative feedback (D- & F-). Only the least complex eco-social models (models 1 and 2) exhibited an increase in the average values of Fs (stability) and Fn (resilience) when connecting FK with GI, whereas the more complex models showed the opposite trend. Although the link that connects FK by GI would allow increasing the number of stable transient states in the most complex eco-social models (models 3, 4 and 5), however FK would be self-controlled, that is, an evaluation of the quantity and quality of the information available is required. Since the models constructed are simplifications of the eco-social system studied, they allowed us to identify the conditions of stable/resilient (sustainable) scenarios, which would help us in decision-making and governance based on the use of FK in Peruvian fisheries management and monitoring.

Dyna algorithm-based reinforcement learning energy management for fuel cell hybrid engineering vehicles

Fuel cell hybrid engineering vehicles utilize green energy with high energy conversion efficiency. Optimizing the utilization of hybrid energy storage systems is crucial for improving energy efficiency and promoting energy conservation. This study introduces an innovative energy management framework that employs reinforcement learning to regulate a fuel cell/battery hybrid engineering vehicle. This novel framework facilitates the system achieve optimal energy management, even when confronted with demanding and ever-changing operational scenarios. The proposed strategy integrates the concepts of the Dyna algorithm and the deep Q-network (DQN) algorithm. A working conditions predictive model is developed using the gated recurrent unit (GRU) neural network. The aim of this predictive model is to generate a virtual depiction of the environment and accelerate the process of model updating. The results from simulations and hardware-in-the-loop test illustrate that the predictive model exhibits effective prediction capabilities, enabling the development of an accurate environmental model even when limited sample data is available. The proposed strategy exhibits superior performance compared to the DQN and Q-Learning algorithms regarding adaptability, real-time optimization, training efficiency, convergence speed, and energy conservation. When interacting with the environment, our proposed learning algorithm exhibits a faster convergence rate. Moreover, this strategy achieves a fuel economy of 95 %, as determined by the dynamic programming-based method. The model-based deep reinforcement learning approach is practical and does not require extensive training. An added benefit of this energy management architecture is its use of a more accurate world model.

Economic Dispatch of Combined Cycle Power Plant: A Mixed-Integer Programming Approach

In this article, we present a modification to the Economic Dispatch (ED) model that addresses the non-convex nature of the cost curves associated with a Combined Cycle Power Plant (CCPP). Incorporating a binary variable provides greater precision in solving the combinatorial problem in only one simulation and, most importantly, demonstrates cost minimization among the three different cost curve models for dispatching the CCPP. Our results highlight the importance of considering different demand scenarios based on a reference forecast for one day ahead. Therefore, piecewise modeling is more feasible for solving the non-convex problem, showing greater accuracy regarding the operational state of the CCPP and avoiding the cost overestimation that occurs with traditional models. Moreover, it allows the operators to manage costs better and optimize generation potential, ultimately showing economic benefits for the system operator.

Identifying critical demand scenarios for the robust capacitated network design problem using principal component analysis

AbstractIn this paper, we consider the single‐commodity robust network design problem. Given an undirected graph with capacity installation costs on its edges and a set of scenarios with associated flow balance vectors that represent different scenarios of node supplies and demands, the goal is to find integer edge capacities that minimize the total installation cost and permit a feasible single commodity flow for each scenario. This problem arises, for example, in the design of power networks, which are dimensioned to accommodate many different load scenarios. We propose a new method to identify a small subset of the given scenarios, such that solving the robust network design problem for the smaller scenario set leads to almost the same capacities as solving it for the full scenario set . By considering only the scenarios in , the size of the model that needs to be solved can be reduced substantially, while the error introduced by neglecting the remaining scenarios is kept very small. Our method only employs simple techniques from statistical data analysis, namely principal component analysis (PCA), and convex hull computations in low dimensions. Thus, its computational effort is very small and it is easily applicable to more complex network design problems. We evaluate the effectiveness of the method in computational experiments for instances stemming from offshore power grid planning and telecommunication networks. Our results show that the proposed techniques are indeed well suited to identify small scenario subsets that lead to significantly reduced models with high quality solutions.