Demand Uncertainty Research Articles

Prediction uncertainty of greenhouse electrical power and gas demand: Part 2, the role of parametric and weather forecast error

This study breaks new ground by assessing the influence of individual and collective errors in weather forecast variables and errors in model parameters on the prediction error of greenhouse power and gas demand. To achieve this a sample-based and a Polynomial Chaos based sensitivity analysis using higher order sensitivity indices is proposed. This is accompanied by a sensitivity analysis of the impact of reducing individual weather forecast errors on greenhouse energy demand prediction error. The findings of this study show that weather forecast errors have a far greater role in creating mean gas (27Wm−2) and power (24Wm−2) prediction uncertainty than parametric errors (5.7Wm−2 and 4.6Wm−2). In addition, and crucially, weather forecast and parameter errors were found to be independent factors. Reducing weather forecast error exhibited large diminishing returns with the reduction in prediction error. For instance, a scenario where the forecast error of all variables is reduced by 80–90 % resulted in only a 50 % decrease in gas and electrical power prediction error. The radiation forecast errors emerged as the primary contributor to power demand prediction errors, exhibiting the potential to reduce the power demand prediction error by approximately 60 %. Reductions of forecast errors in wind and outdoor air temperature were identified as the predominant contributors, offering a respective potential for a 17 % reduction in gas demand prediction error.

Emergency Logistics Facilities Location Dual-Objective Modeling in Uncertain Environments

The uncertainty of post-earthquake disaster situations can affect the efficiency of rescue site selection, material, and personnel dispatching, as well as the sustainability of related resources. It is crucial for decision-makers to make decisions to mitigate risks. This paper first presents a dual-objective model for locating emergency logistics facilities, taking into account location costs, human resource scheduling costs, transportation time, and uncertainties in demand and road conditions. Then, stochastic programming and robust optimization methods are utilized to cater to decision-makers with varying risk preferences. A risk-preference-based stochastic programming model is introduced to handle the potential risks of extreme disasters. Additionally, robust models are constructed for two uncertain environments. Finally, the study uses the Wenchuan earthquake as a case study for the pre-locating of emergency logistics facilities and innovatively compares the differences in the effects of models constructed using different uncertainty methods. Experimental results indicate that changes in weight coefficients and unit transportation costs significantly impact the objective function. This paper suggests that decision-makers should balance cost and rescue efficiency by choosing appropriate weight coefficients according to the rescue stage. It also shows that risk level and robust conservatism can significantly alter the objective function. While stochastic programming models offer economic advantages, robust optimization provides better robustness.

A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources

This paper presents a novel approach to solve the optimal power flow (OPF) problem by utilizing a modified white shark optimization (MWSO) algorithm. The MWSO algorithm incorporates the Gaussian barebones (GB) and quasi-oppositional-based learning (QOBL) strategies to improve the convergence rate and accuracy of the original WSO algorithm. To address the uncertainty associated with renewable energy sources, the IEEE 30 bus system, which consists of 30 buses, 6 thermal generators, and 41 branches, is modified by replacing three thermal generators with two wind generators and one solar PV generator. And the IEEE 57-bus system, which consists of 57 buses, 7 thermal generators, and 80 branches, is also modified by the same concept. The variability of wind and solar generation is described using the Weibull and lognormal distributions, and its impact on the OPF problem is considered by incorporating reserve and penalty costs for overestimation and underestimation of power output. The paper also takes into account the unpredictability of power consumption (load demand) by analyzing its influence using standard probability density functions (PDF). Furthermore, practical conditions related to the thermal generators, such as ramp rate limits are examined. The MWSO algorithm is evaluated and analyzed using 23 standard benchmark functions, and a comparative study is conducted against six well-known techniques using various statistical parameters. The results and statistical analysis demonstrate the superiority and effectiveness of the MWSO algorithm compared to the original WSO algorithm for addressing the OPF problem in the presence of generation and demand uncertainties.

Monitoring Energy-Loss-Driven-Cost by Using Earned Value Simulation in Complex Systems

The economic impact of energy loss stemming from end-user electricity consumption is a significant concern, with historical trends revealing escalating costs. Effectively managing both peak and off-peak demands remains a formidable challenge due to the unpredictable nature of consumer behaviors, leading to energy wastage. This study delves into the nexus of demand uncertainty, financial repercussions, and potential strategies to mitigate energy losses in the evolving landscape of electricity consumption.that causes financial loss. This simulation measure of time series data serves the purpose of determining what possibly contributes to policy and regulatory reforms and its notion as an economic growth pathway in Australia. The objective of this study is to build a relationship between social factors and financial aspects and discuss the issue of energy loss that emerges from the lack of leverage between end-users, providers and suppliers of electricity. Recognising the financial burdens associated with energy loss as electricity demand continues to rise, the investigation aims to elucidate the complexities underlying the difficulties in controlling these losses. The distinctiveness of the electricity industry, characterised by its prototypical nature, introduces dynamics that contribute to energy losses, thereby impacting electricity prices. Employing quantitative analysis, this research employs the Earned Value Method (EVM) tool to scrutinise the influential role of consumer behavior in precipitating financial losses. The study provides a comprehensive examination of the interplay between electricity demand and the adverse effects of energy loss during peak and off-peak consumption periods. Utilising time series data through simulation measures, the research identifies key metrics influencing the formation of electricity costs and prices. The findings not only contribute to a deeper understanding of the energy loss parameter but also offer insights into potential policy and regulatory reforms. With a focus on Australia, the research aims to establish a relationship between social factors and financial considerations, emphasising the issue of energy loss arising from the lack of alignment between end-users, providers, and suppliers of electricity. The study concludes by proposing pathways for economic growth through strategic interventions and collaborative efforts within the electricity ecosystem.

Optimization on Photovoltaics and Energy Storage Integrated Flexible Direct Current Distribution Systems of Buildings Considering Load Uncertainty Using Scenario Generation Method

This study explores the intricate challenge of energy demand uncertainty in the design of Photovoltaics and Energy Storage integrated Flexible Direct Current Distribution (PEDF) systems. Our objective is to examine the impact of different scenario generation methods on PEDF system optimization. We compare four approaches, including probabilistic techniques based on Monte Carlo simulation, Latin Hypercube Sampling for base scenario sampling, and a simulation-based method using building performance modeling. We evaluate these approaches using the Independent Scenario Optimization (ISO) and Two-Stage Stochastic Programming (TSSP) models, aiming to minimize the annual total cost within PEDF systems while addressing uncertainties. Our findings shed light on the optimal PEDF design under uncertainty, offering valuable insights for future decision-making in dynamic energy systems.

Major government customers and organizational resilience during the COVID-19 pandemic: evidence from China

ABSTRACT The COVID-19 pandemic disrupted global supply chains and threatened firm survival. By analysing Chinese listed private firms from 2017 to 2022, we examine the impact of government customers on organizational resilience during this crisis and find that firms with major government customers demonstrate higher resilience than those without. Moreover, government customers support private suppliers by providing additional trade credit and reducing demand uncertainty. This support substitutes for specific internal resources in assisting private suppliers during the pandemic. Finally, the effect is amplified when government customers are affiliated with higher-level governments.

Optimal economic dispatch of a virtual power plant based on gated recurrent unit proximal policy optimization

The intermittent renewable energy in a virtual power plant (VPP) brings generation uncertainties, which prevents the VPP from providing a reliable and user-friendly power supply. To address this issue, this paper proposes a gated recurrent unit proximal policy optimization (GRUPPO)-based optimal VPP economic dispatch method. First, electrical generation, storage, and consumption are established to form a VPP framework by considering the accessibility of VPP state information. The optimal VPP economic dispatch can then be expressed as a partially observable Markov decision process (POMDP) problem. A novel deep reinforcement learning method called GRUPPO is further developed based on VPP time series characteristics. Finally, case studies are conducted over a 24-h period based on the actual historical data. The test results illustrate that the proposed economic dispatch can achieve a maximum operation cost reduction of 6.5% and effectively smooth the supply–demand uncertainties.

Dynamic price optimization of a solar integrated cogeneration system considering uncertainties of building demands

Introducing renewable and high-efficiency devices into conventional cogeneration systems has the potentials to reduce fossil fuel consumption and mitigate environmental issues. This study aims to incorporate solar power and thermal energy into the power and waste heat generated by the gas turbine, respectively, while utilizing a double-effect absorption heat pump to meet the total heat demands. Following the creation of thermodynamic models, a trading mechanism is introduced involving an operating participant known as the product seller, which is analogous to electricity companies in power trading models. To maximize the interests of the participants with different confidence levels, the master-slave game is employed, using Genetic and Quadratic programming algorithms. The hourly outputs of devices and responded demands are evaluated based on the dynamic purchasing and selling price of products. The results indicate that the optimal interest of the cogeneration system is the lowest among all the participants. The power proportions from gas turbine and power grid exceed 60 %, while the contribution of gas turbine decreases by 11.0 % due to the lower heat demands caused by reduced confidence level. Overall, this study offers valuable insights into the market-oriented development of solar assisted cogeneration systems.

Implementation of Forecasting with the Monte Carlo Simulation Method to Predict Supply and Demand for Psychotropic Drug Products

The availability of drugs is one of the needs that supports the presence of health in the community. Patient certainty regarding the availability of drugs becomes increasingly important for patients who use psychotropic drugs. This type is a dangerous drug and needs to be controlled. Control is carried out to avoid drug abuse outside of medical purposes. Two conflicting sides between the importance of ensuring the availability and controlling their use can be resolved, one of which is by measuring supply and demand. The availability of psychotropic drugs should be adjusted to demand. The problem is that we cannot know demand that has not yet occurred, drugs have an expiration date, and procuring drugs takes time. One way is to predict the demand when supplying drugs so that the order quantity is appropriate. The prediction method used is the Monte Carlo Simulation Method. One example of implementation is the Economic Order Quantity (EOQ) Method. As a result, the Monte Carlo method successfully made predictions based on existing data. In addition, it was found that the Monte Carlo Method tended to the distribution of the data used. The closer the distance between the data, the higher the prediction accuracy obtained. Uncertainty in actual demand is also a big challenge for producing accurate predictions based on patterns. Prediction results will be more accurate with more data patterns and variations. Implementation of prediction results with EOQ makes the number of drugs that should be ordered based on demand that is likely to occur.

Incentive-based coordination for scheduled delivery in prefab construction

An increasing number of projects are adopting prefabrication to economize on time, labor, and materials in fixed-position layout operations, such as construction, ship building, and aircraft manufacturing. In such contexts, independent contractor and fabricator make interdependent decisions, which calls for prudent supply chain management because performance relies on coordination between their decisions. Many studies have developed integrated systems and propose various algorithms for scheduling efficiency and reliability. Nevertheless, they pay scant attention to conflicting interests amongst independent partners, which may result in subpar performance not only for the supplier but for the contractor as well. Coordination of conflicting interests has been extensively studied in economics and supply chain management; yet, those studies focus on order-quantity decisions under demand uncertainty for profit maximization, while managers in fixed-position operations are more concerned about delivery decisions under scheduling uncertainty for cost minimization. We consider the case of construction and explore a contractual scheme that aligns the agents’ decisions for coordination in a construction supply chain. Specifically, we propose a supplier rebate for coordination: the supplier grants a rebate if the contractor accepts the shipment in accordance with the delivery schedule that the contractor initially chose. We show that the optimal rebate fully coordinates the supply chain to minimize the joint supply chain costs. Thus, both the contractor and supplier benefit from the coordination by negotiating a mutually acceptable way to allocate the savings in joint costs between them. We further show that the rebate motivates the contractor to enhance its work scheduling.

A robust dynamic hydrogenation network layout model considering station capacity expansion and flexible transportation modes

The development of the hydrogen-fueled vehicle industry needs a mature hydrogenation network to provide a guaranteed source of hydrogen in the future. We propose a dynamic hydrogen supply network optimization model under demand uncertainty. The model incorporates the dynamic characteristics of the network, takes into account the linkages of units within the network as well as the hydrogenation network between cycles. Hydrogenation networks can be planned over multiple cycles and allow for capacity expansion of hydrogen production and refueling stations, and shifts in the way hydrogen is transported between sites. Robust optimization is used to deal with demand uncertainty. In the results of the case study obtained, resources within the network were fully utilized, costs within the network show economies of scale; the average resource utilization rate at each site reached 85.6 %; and the rate of meeting user demand reached more than 98.5 %. A hydrogenation network with sustainable operations was formed.

A simheuristic approach towards supply chain scheduling: Integrating production, maintenance and distribution

This study attempts to integrate production, maintenance, and delivery operations among supply chain members. Despite numerous studies in the field of supply chain management, researchers have often overlooked crucial aspects, such as uncertainties in demand and production. For instance, the significant impact of maintenance activities on production flow has been underrepresented in supply chain management literature. This study investigates these gaps in the context of a fertilizer producer case study, which is characterized by seasonal demand and the functional silos syndrome due to old-fashioned management approaches. This study proposes a mathematical model and two multi-objective simheuristics for the Integrated Production, Maintenance, and Distribution Scheduling Problem (IPMDSP) considering demand variation for multiple products and product delivery time-windows using a heterogeneous fleet of vehicles. The IPMDSP is solved using the ϵ-constraint method and simheuristics linking the simulation model to customized and tuned versions of Particle Swarm Optimization (MOPSO) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). The optimization objectives include minimizing maintenance duration, distribution costs, and customer dissatisfaction due to delivery tardiness. The results demonstrate the superiority of the simheuristic empowered by NSGA-II over the MOPSO in solving the IPMDSP. The comparison between the performance of deterministic and stochastic approaches in addressing the problem reveals that neglecting uncertainty caused by maintenance activities can lead to an increase in optimization objectives. Furthermore, the proposed simheuristics achieved significant improvements in minimizing objectives in solving the fertilizer producer case study.

Stochastic energy management of a microgrid incorporating two-point estimation method, mobile storage, and fuzzy multi-objective enhanced grey wolf optimizer

In this study, the stochastic energy management, and scheduling of a renewable microgrid involving energy sources and dynamic storage is performed considering energy resource and demand uncertainties and demand response (DR) using the two-point estimation method (2 m + 1 PEM). The three-dimensional objective function is defined as maximizing the renewable hosting capacity and minimizing the operation cost, and emission cost minimization. The decision variables include installation location and size of the renewable resources and mobile energy storage system (MESS), determined using a multi-objective enhanced grey wolf optimizer (MOEGWO) improved based on the logistic chaotic mapping integrated with fuzzy decision-making approach. The simulations are implemented for several cases of employing MESS, DR, and uncertainties to investigate the proposed approach's efficacy. The MOEGWO performance is confirmed to solve the ZDT and CEC'09 functions according to some well-known algorithms. Then, the performance of the MOEGWO is evaluated on the stochastic energy management and scheduling of the renewable microgrid. The results indicate that considering the dynamic MESS causes reducing the operation and emission costs by 23.34% and 34.78%, respectively, and increasing the renewable hosting capacity by 7.62% in contrast to using the static MESS. Also, the stochastic problem-solving considering uncertainties showed that operation and emission costs are raised, the renewable hosting capacity is decreased, and the uncertainty impact is reduced in the condition of DR application. So, the results validated the proposed methodology's effectiveness for minimizing the operation and emission costs and maximizing the renewable hosting capacity. Moreover, the superior capability of the MOEGWO is confirmed in comparison with the multi-objective particle swarm optimization to obtain lower operation and emission costs and higher renewable hosting capacity.

Cross-channel influences in mobile-app-website e-commerce supply chains: When to weaken the influence?

Today, e-commerce has entered the mobile era in which consumers can shop through both mobile app (MA) and website (WS) channels. By strategically designing the linkage between the two channels based on the cross-channel influences, companies can better handle the demand uncertainty of seasonal products. In this paper, we consider a dual channel e-commerce supply chain with an e-tailer and a manufacturer selling a seasonal product. We analytically examine how the e-tailer can (i) adopt risk pooling by aggregating the demands from both the MA and WS channels and (ii) invest in forecast-enhancement technology (FET) to improve inventory management. We uncover that when the magnitude of cross-channel influences increases, the impacts on the optimal inventory decision vary greatly depending on four different “model cases”; interestingly, it does not affect the optimal FET decision. Considering the impacts of cross-channel influences on the e-tailer’s expected profit, we reveal that the optimal weakening or strengthening strategy, i.e., “creating barriers” or “enhancing linkage” between two channels, depends on the e-tailer’s channel operations models. Then, we find that the optimal weakening or strengthening strategy follows the same pattern as in the basic model when the dual channel e-commerce supply chain is coordinated under a Nash bargaining framework. Last but not least, we study the potential use of blockchain technology to improve the effectiveness of FET investment. We surprisingly find that using blockchain or not does not affect the optimal MA and WS design decisions on weakening or strengthening linkages.

Advance selling of fresh produce e‐tailer with uncertain demand and risk‐averse consumers

AbstractAiming to investigate whether a fresh produce e‐tailer should implement the strategy of advance selling considering the behavior of consumers' risk aversion, we build a two‐stage newsvendor decision‐making model and further explore how the advance selling value is affected by factors, such as the freshness‐keeping cost and the effect of demand information updating on reducing demand uncertainty. The results indicate that adopting advance selling strategy benefits the e‐tailer or not depends on consumer risk aversion and the effect of demand information updating on reducing demand uncertainty. The increasing effect of demand information updating on reducing demand uncertainty is conducive to improving the value of advance selling, and the freshness‐keeping cost may increase or decrease the value of advance selling. Besides, adopting the strategy of advance selling may benefit both the e‐tailer and the consumers under certain conditions.

A multi-period multi-objective closed-loop blood supply chain configuration and optimisation under disruption

The COVID-19 pandemic has highlighted the stability of the blood supply chain in case of interruption and other emergencies. Blood is a precious and perishable substance, and any disruption in its supply can cause serious consequences. Current research on the blood supply chain mostly focuses on a multi-objective perspective, and rarely involves both multi-objective and multi-period methods simultaneously. Therefore, we develop a multi-period multi-objective closed-loop blood supply chain model that considers disruptions and uncertainties of demand and recovery, aiming at achieving a balance among various objectives, and coordination of various periods. Fuzzy chance-constraint, meta-heuristic approaches and exact solution (CLPEX) are adopted to mitigate the impact of uncertainties and calculate the optimal solution of a calculating case. The calculation outcomes indicate that: With regard to economic costs and social benefit, the multi-period system is better than the single-period system; the comprehensive performance of the multi-target system is better than that of the single-target system; and the multi-period multi-objective system is more in line with the real situation. Meanwhile, the mitigation strategies, such as maintaining emergency inventory and implementing lateral transshipment, can significantly enhance the performance of the system in disruptions. This paper mainly contributes the follows: Firstly, it provides a reference for configuration and optimisation of a cross-region closed-loop supply chain under disruptions and uncertainties. Secondly, the comprehensive trade-off of multi-objective and multi-period in this paper is especially suitable for perishable production with short cycles, large fluctuations of supply and demand.

Energy Security Faces Critical Global Attention

Ensuring a stable and sustainable supply of energy has emerged as a critical global concern in the face of increasing energy demand, environmental degradation and geopolitical uncertainty. As all countries rely heavily on energy to drive economic growth and technological progress, ensuring a stable and accessible supply of energy has become a paramount imperative. This study aims to explore the multifaceted concept of energy security and its derivative topics for follow-up development in the future. The research approach combines bibliometric analysis and the analytical hierarchy process which was developed to analyze the derivatives of the concept of energy security. Data sourced from scientific literature collected from journal publications published from 2018-2022. The results of the study found three priority topics that have the potential to have novelty in the development of energy security in the future, including clean energy, energy harvesting and energy equity. This priority topic underscores the importance of a transition towards sustainable energy system that not only guarantees a reliable supply of energy but also promotes environmental stewardship and people's well-being.

Improving efficiency in supply chains with a capital-constrained app developer under the agency contract

We investigate a supply-chain model with a capital-constrained app developer. The developer interacts with a distribution platform via an agency contract, and the demand is uncertain and dependent on the app’s quality level and selling price. Given the amount of capital that the developer commits to investing in the creation of quality, the platform offers the developer a loan at a certain interest rate, based on which the developer determines the app’s quality level and subsequently also the selling price. We analytically prove that: (i) the interest rate offered by the platform decreases in the developer’s capital and, interestingly, the platform subsidizes the developer’s efforts either when the platform’s share of the revenue or the developer’s capital are sufficiently large; (ii) there is an amount of capital assigned to the creation of quality that is optimal in terms of maximizing the developer’s expected profit; and (iii) the total channel’s expected profit increases in the developer’s capital, which enables us to propose an improved contract leading to Pareto improvement and full coordination by using a side payment that compensates the developer for his loss of profit. In addition, we compare the supply-chain measures of the platform-financing model with those of four common benchmark models: a self-financing developer, a centralized system, no financing, and bank financing. Finally, we extend the analysis in three directions: setting price under demand uncertainty; analyzing a multiplicative demand form with regard to price and quality; and exploring the case of bank financing in addition to platform financing.

Day-ahead bidding and dispatch strategy of novel battery charging and swapping station under multiple uncertainties

The novel battery charging and swapping station (NBCSS) has great operational flexibility due to its integration of wind power, photovoltaic power, gas turbine and energy storage. This paper presents a day-ahead bidding and dispatch strategy of NBCSS under multiple uncertainties, which consists of battery demand, market price, renewable energy and load. Firstly, the optimized backup method is used to address the uncertainty of battery demand. Then, a two-stage stochastic robust optimization model is further applied to address the uncertainties of market price, renewable energy and load. The model’s goal is to minimize the total operational cost in the worst-case scenario, which is limited by the corresponding uncertainty set. Especially, to control the conservatism of the model, the total adjustable budget and temporal adjustable budget of uncertainties are considered together. Next, the two-stage model is solved efficiently based on the strong duality theory and column-and-constraint generation (C&CG) method. Finally, case studies are given to verify the effectiveness and performance of the proposed model. The results demonstrate that the NBCSS can be efficiently deployed, enabling market arbitrage and reducing the overall cost. Additionally, the conservatism of the proposed model can be adjusted according to the budgets of uncertainties.

Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining. But uncertainties, including uncertain demands of crude distillation units (CDUs), might make the production plans made by the traditional deterministic optimization models infeasible. A data-driven Wasserstein distributionally robust chance-constrained (WDRCC) optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling. First, a new deterministic crude oil scheduling optimization model is developed as the basis of this approach. The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands. A cross-validation method is advanced to choose suitable radii for these ambiguity sets. The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets. The proposed WDRCC model is transferred into an equivalent conditional value-at-risk (CVaR) representation and further derived as a mixed-integer nonlinear programming (MINLP) counterpart. Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method. Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.