Supply Uncertainty Research Articles

An application of simultaneous stochastic optimisation on an open-pit copper mining complex with supply, recovery, and market uncertainties

ABSTRACT A mining complex is an integrated value chain that consists of multiple mines, stockpiles, processors, waste dumps, and tailing facilities, and where the in-situ materials mined are transformed into products sold to customers. Simultaneous stochastic optimisation provides a strategic mine planning framework that incorporates all components of a mining complex into a single mathematical representation, capitalising on the synergies between components while considering uncertainty. An application of this optimisation framework at a coper mining complex demonstrates that the incorporation of material supply, market demand, and processing recovery uncertainties into the modelling leads to production schedules that yield a higher NPV and metal recovery.

The Economic Impact of Covid-19 pandemic on Labor Market and Stock Market

The pandemic has caused severe unrest and uncertainty in the labor market. Share prices have fallen sharply. However, the reasons behind it and the related policy effects still lack a unified explanation and summary. Therefore, the topic of this paper is the impact of the epidemic on the labor force and the stock market. The research method of this paper is as follows: firstly, the relevant data such as unemployment rate, hiring ratio and purchasing manager index are collected, and then the data are analyzed. The study found that the main reasons for the decline in labor demand may include business shutdowns, more cautious business investment and the psychological impact on workers. Fears of a potential recession, supply chain disruptions and uncertainty over the duration and severity of the outbreak have driven the rapid fall in share prices. The relevant policy recommendations further encourage local governments to use the surplus funds from unemployment insurance and other sources to support enterprises in maintaining employment stability. This paper will encourage the implementation of policies that delay or partially refund social security contributions. And strengthen the strategy of partially refunding unemployment insurance premiums, promoting local employment opportunities, and providing subsidies to ensure the overall stability of the employment situation. These policies are constantly evaluated and adjusted in light of changing economic conditions and employment trends to help maintain employment levels and support businesses during difficult times.

Probabilistic Mathematical Model for Dietary Consumption and Exposure Assessment in the Absence of Data from National Nutrition Surveys: A Case Study on Saudi Arabia

Introduction: Noncommunicable diseases, which account for 71% of annual global deaths, necessitate the implementation of targeted legislative measures to reduce exposure to risk factors, particularly those related to nutrition. Accurately assessing these risk factors is challenging owing to infrequent national surveys, high costs, and uncertainties from human behavior. Thus, addressing these issues is crucial. This study aimed to develop a model for estimating dietary exposure. Methods: Our study utilizes the National Nutrient Consumption (NNC) model, which employs a probabilistic and cost-effective approach. This model leverages open data and focuses on supply chain dynamics and uncertainties, integrating import, production, and stock levels. It estimates commodity supply, consumption, and nutrient intake using predictive coefficients from historical data. Model accuracy was assessed using mean absolute error, correlation coefficient, and one-sample t test. The findings were evaluated against the recommended intake outlined in the Technical Regulation of Food Labeling (SFDA.FD 2233). Results: The model demonstrated high accuracy in estimating dietary intake, with minimal error margins. For both genders, the 50th percentile nutrient exposure exceeded the reference values – with males consuming 3,310 kcal (+65%), 144 g of total fat (+105%), 33.8 g of saturated fat (+69%), 86 g of free sugar (+72%), and 2,545 mg of sodium daily (+0.06%); females consumed 3,306 kcal (+65%), 143 g of total fat (+104%), 33.5 g of saturated fat (+67%), 86 g of free sugar (72%), and 2,500 mg of sodium (+4%). Conclusion: The model, both cost-effective and accurate, identifies elevated nutritional risk factors in Saudi Arabia, informing targeted health policies.

Multi-Stage Engineered Co-Free Li[Ni1-X-Y Ti x Al y ]O2 Cathode for High-Energy Li-Ion Batteries

Since the global blueprint for carbon net zero emissions has been drawn, a part of the internal combustion engine (ICE) market share of vehicles has been continually shifting toward electric powered public and personal transportation. However, the serious remaining challenges regarding the price and performance issues of lithium-ion batteries (LIBs), which are the primary power source for electric vehicles (EVs), have impeded the large-scale adoption of EVs. Solving the affordability issue of current layered cathodes requires reducing the reliance on Co, which is extremely expensive with supply uncertainty. The aim of developing Co-poor or Co-free cathodes has led to an interest in LiNiO2 (LNO).1 LNO is one of the most promising cost-effective Co-free cathodes for LIBs because the cathode can achieve a high specific capacity of over 250 mAh g-1 with a typical upper voltage of 4.3 V versus Li/Li+. However, its poor cycling durability and thermal instability make its practical use difficult.2 In this study, we propose a highly stable Co-free Ni-enriched layered cathode based on LNO through a new doping strategy that incorporates heteroelements at different doping stages, i.e., the introduction of Ti during Ni(OH)2 synthesis and doping excess amounts of Al during the lithiation step. In combination with cathode surface protection, i.e., electrolyte modification or cathode surface coating, the proposed cathode retains 72.0% of its initial capacity after 3,500 cycles, which is the state-of-the-art for Co-free layered cathodes. Reference s : [1] M. Bianchini, M. Roca-Ayats, P. Hartmann, T. Brezesinski, J. Janek, Angew. Chem. Int. Ed., 58 (2019) 10434-10458.[2] H.-J. Noh, S. Youn, C.S. Yoon, Y.-K. Sun, J. Power Sources, 233 (2013) 121-130.

Sourcing from risky reverse channels: Insights on pricing and resilience strategies in sustainable supply chains

This study examines the resilience and sustainability of supply chains amid global disruptions, with a particular focus on the essential role of reverse logistics. Through a game-theoretic approach, we explore manufacturer decisions to source from either reliable but expensive raw materials or cost-effective yet riskier recycled or recyclable materials from the reverse logistics channel. Our analysis outlines three primary sourcing strategies: sourcing exclusively from suppliers (SS), sourcing solely through retailer reverse channel (RS), and a balanced dual sourcing (DS) approach. Our findings reveal the economic viability that recycling outsourcing is influenced by market demand and disruption risks. Notably, in scenarios of constrained market potential, the cost advantage of using recycled materials from less reliable reverse logistics channels surpasses the risks associated with supply chain disruptions, suggesting a complex cost-benefit landscape amidst supply uncertainties. Moreover, the stability of suppliers emerges as a pivotal factor in strategic sourcing decisions, underscoring the need to consider both economic efficiencies and supply reliability. The study also evaluates the dynamic competition between manufacturers and retailers, shedding light on how strategic adjustments driven by sustainability and resilience goals can enhance profitability and sustainability. It was found that despite the threat of disruptions, manufacturers benefit more from engaging with risky reverse channels under specific conditions, underscoring the nuanced decision-making required in uncertain supply scenarios. This research advances sustainable supply chain management by highlighting strategic complexities and the need for understanding economic efficiencies and supply stability, offering insights for navigating disruptions and fostering resilient, sustainable supply chains.

Enhanced bioleaching of spent Li-ion batteries using A. ferrooxidans by application of external magnetic field

Recycling spent batteries is increasingly important for the sustainable use of Li-ion batteries (LIBs) and for countering the supply uncertainty of critical raw minerals (Li, Co, and Ni). Bioleaching, which uses microorganisms to extract valuable metals, is both economical and environmentally safe compared to other recycling methods, but its practical application is impaired by slow kinetics. Accelerating the process is a key for bioleaching spent LIBs on an industrial scale. Acidithiobacillus ferrooxidans (A. ferrooxidans), which thrives in extremely low pH conditions, has long been explored for bioleaching of spent LIBs. Metabolism of A. ferrooxidans involves the oxidation of magnetic Fe2+ and produces intracellular magnetic nanoparticles. The possibility of accelerating the leaching kinetics of A. ferrooxidans by the application of an external magnetic field is explored in this work. A weak static magnetic field is applied during the bioleaching of spent LIBs to recover Li, Ni, and Co using A. ferrooxidans. It is determined that 3 mT is the optimal field strength which allows the leaching efficiency of Li to reach 100% after only 2 days of leaching at a pulp density of 3 w/v % while without the external magnetic field, the leaching efficiency is limited to 57% even after 4 days. The leaching efficiency of Ni and Co also increases by nearly three-fold to >80% after 4 days of leaching. The proposed magnetic field-assisted bioleaching of spent LIBs using A. ferrooxidans substantially improves the leaching kinetics and thus the cost-effectiveness of the bioleaching process with minimal environmental impact, hence enabling environment-friendly recycling of raw materials that are increasingly becoming scarce. The positive effect of an external magnetic field on the metabolism of A. ferrooxidans demonstrated in this work provide a new set of tools to engineer the bioleaching process and the possibility for genetic modification of acidophile bacteria, especially targeted for magnetic enhancement.

Decision-focused neural adaptive search and diving for optimizing mining complexes

Optimizing industrial mining complexes, from extraction to end-product delivery, presents a significant challenge due to non-linear aspects and uncertainties inherent in mining operations. The two-stage stochastic integer program for optimizing mining complexes under joint supply and demand uncertainties leads to a formulation with tens of millions of variables and non-linear constraints, thereby challenging the computational limits of state-of-the-art solvers. To address this complexity, a novel solution methodology is proposed, integrating context-aware machine learning and optimization for decision-making under uncertainty. This methodology comprises three components: (i) a hyper-heuristic that optimizes the dynamics of mining complexes, modeled as a graph structure, (ii) a neural diving policy that efficiently performs dives into the primal heuristic selection tree, and (iii) a neural adaptive search policy that learns a block sampling function to guide low-level heuristics and restrict the search space. The proposed neural adaptive search policy introduces the first soft (heuristic) branching strategy in mining literature, adapting the learning-to-branch framework to an industrial context. Deployed in an online fashion, the proposed hybrid methodology is shown to optimize some of the most complex case studies, accounting for varying degrees of uncertainty modeling complexity. Theoretical analyses and computational experiments validate the components’ efficacy, adaptability, and robustness, showing substantial reductions in primal suboptimality and decreased execution times, with improved and more robust solutions that yield higher net present values of up to 40%. While primarily grounded in mining, the methodology shows potential for enabling smart, robust decision-making under uncertainty.

The Analysis of Fruit Carving, an Art Qualified Culinary by The Student of Culinary Art Study Program

This research is a qualitative research aim to analysis fruit carving, with participant observation and literature study. The research method was participant observation means researchers immerse themselves in the natural environment or social setting of the participants to do, observe and document behaviors, interactions, and cultural practices. The exploration of fruit carving through participatory observation in qualitative research unveils a unique blend of artistic expression, cultural tradition, health awareness, sustainability, and educational significance. This amalgamation renders fruit carving a multifaceted and enriching area of study, offering novel insights into its diverse dimensions. This approach is particularly useful for studying social dynamics and cultural phenomena. The result was fruit carving's uniques lie in its strength, such as widespread popularity, art and cultural heritage. Fruit carving weaknesses include price instability and dependency on high-quality fruit carving. Opportunities for business growth include creating variations and marketing to culinary tourists, while threats include intense competition and uncertainty in raw material supply. Overall, leveraging its strengths while addressing weaknesses and seizing opportunities will be crucial for the success of fruit carving existence in the future.

Matching inventory and demand in a Fast Moving Consumer Goods company: A Decision Support System

Demand fulfilment and its core, order promising, is a key business function that directly impacts the company’s ability to generate revenue through sales. For companies operating under a Make-To-Stock (MTS) production strategy, it is tantamount to matching both on-hand and projected stock to customer demand. The prevalent manner to carry out this task is to use either First-Come-First-Served policies, or simple allocation rules embedded in commercially available software. However, order promising is, in general, quite complex due to uncertainties both in demand and supply, and it can become extremely challenging when demand exceeds the stock. Therefore, several contributions have established the advantages of order promising using optimisation models and/or sophisticated algorithms. However, these works assume that customers can be clustered according to their profitability/importance and it remains open to test whether this also holds for homogeneous customers. Furthermore, due to the exploratory nature of most studies, they assume rather stylised scenarios (such as e.g. single product-orders, partial shipments) that do not clarify how model-based order promising would perform in a realistic setting.In this paper we present a decision framework for order promising in a Fast Moving Consumer Goods (FMCG) company in the brewery sector where customers cannot be clustered and that incorporates many realistic features (such as e.g. multi-product orders, on-full delivery, or perishability constraints). The decision framework relies on several iterations of a MILP model that allocates the stock to customer and reserves some portion of the stock for orders arriving later on. These functionalities are embedded in a Decision Support System (DSS) that also helps with the renegotiation of the orders that cannot be served. Results reported from the usage of the DSS show its ability to obtain high service levels and to favourably compare to other order promising policies. Furthermore, in order to analyse the instance sizes that can be solved in reasonable time, and the parameters that influence the performance and results of the MILP model, an extensive computational experience has been carried out using a testbed that covers a wide range of business scenarios.

Optimization of straw supply chain considering carbon emissions, supply uncertainty and facility disruption risk

Optimization of straw supply chain considering carbon emissions, supply uncertainty and facility disruption risk

Resilient and sustainable semiconductor supply chain network design under trade credit and uncertainty of supply and demand

Supply Chain (SC) performance appraisal extends beyond economic evaluation to encompass environmental and social impacts, as well as resilience to supply and demand disruptions. Thus, SC network design decisions should simultaneously optimize all these performance metrics as a primary objective. However, existing studies optimized these objectives independently or in a limited scope, failing to comprehensively address the interconnected nature of these performance metrics. Therefore, this study proposes a mixed integer linear programming (MILP) model with four objectives to address a multi-tier resilient and sustainable semiconductor SC network design problem under trade credit, supply, and demand uncertainties. The model aims to determine the optimal number and location of various facilities, the amount to order from suppliers, economic production quantities, quantity allocation between SC entities, and ideal supplier selections. The proposed supply chain network contributes to the global effort to reduce carbon emissions and enhance the circular economy by considering carbon trading, recycling, and the proper disposal of end-of life products. Uncertainties in input parameters are addressed using a fuzzy programming approach, while lexicographic optimization and the ɛ-constraint method are used to solve the proposed model. This paper presents a numerical example to illustrate the applicability of the proposed model and provide managerial insights. The numerical analysis results show that provided an appropriate confidence level for the uncertain parameters, the sets of Pareto optimal solutions can be generated. And it also shows that the total SC cost increases with decreasing carbon emissions level and increasing job opportunities created. This research advances sustainable and resilient supply chain management practices while offering practical guidance for decision-makers in real-world contexts.

Characterizing the multisectoral impacts of future global hydrologic variability

There is significant uncertainty in how global water supply will evolve in the future, due to uncertain climate, socioeconomic, and land use change drivers and variability of hydrologic processes. It is critical to characterize the potential impacts of uncertainty in future water supply given its importance for food and energy production. In this work, we introduce a framework that integrates stochastic hydrology and human-environmental systems to characterize uncertainty in future water supply and its multisector impacts. We develop a global stochastic watershed model and demonstrate that this model can generate a large ensemble of realizations of basin-scale runoff with global coverage that preserves the mean, variance, and spatial correlation of a historical benchmark. We couple this model with a well-known human-environmental systems model to explore the impacts of runoff variability on the water and agricultural sectors across spatial scales. We find that the impacts of future hydrologic variability vary across sectors and regions. Impacts are felt most strongly in the water and agricultural sectors for basins that are expected to have unsustainable water use in the future, such as the Indus River basin. For this basin, we find that the variability in future irrigation water withdrawals and irrigated cropland increase over time due to uncertainty in renewable water supply. We also use the Indus basin to show how our stochastic ensemble can be leveraged to explore the global multisector consequences of local extreme runoff conditions. This work introduces a novel technique to explore the propagation of future hydrologic variability across human and natural systems and spatial scales.

Modeling of agricultural water policies to guarantee water supply under climate change

The efficient and sustainable use of water has become a necessity in regions prone to drought and water scarcity. One such region is the Fars province of Iran, where farmers often face uncertainties in irrigation water supply due to frequent droughts and declining groundwater levels. This study employed a quantitative research methodology, utilizing surveys and questionnaires to collect data. Specifically, the study used the choice experiment (CE) methodology to evaluate policy attributes aimed at guaranteeing agricultural water supply. The research was conducted in Marvdasht County within the Fars province, with a sample size of 170 farmers and 4080 observations collected in 2015. The collected data were analyzed using the conditional logit (CL) model. The sample size was determined using the stratified random sampling method. The results of the study indicate that age has a negative effect on farmers' willingness to pay (WTP) for guaranteed water supply, while education has a positive effect. Additionally, the study found that farmers' WTP for different policies varied, with the highest WTP observed for the use of water-saving technologies (estimated at 254.89 IRR per m3) across all areas. Consequently, the study recommends that policies promoting the adoption of water-saving technologies should be prioritized globally. It is worth noting that water policies can significantly differ between countries and regions due to various factors, including local water challenges, legal frameworks, cultural norms, and socio-economic conditions. Therefore, when formulating water policies, it is crucial to consider the specific context and tailor them to the unique circumstances of each region or country.

Optimal wind power capacity decision consider commitment contracts under uncertain power supply and electricity demand in China

China has been introducing reforms in the electricity market. Since 2016, electricity retailers are allowed to operate in the market once monopolized by traditional power companies. This regulatory environment demands new types of purchase contracts to remunerate the energy agents and deliver better prices for consumers fairly and sustainably. This paper proposes two new electricity purchase contracts with risk sharing mechanism and studies on wind power supplier's optimal capacity investment problem under supply and demand uncertainty. Firstly, a basic model for fixed demand contract is developed, and the optimal capacity is obtained. Secondly, the fixed commitment order contract and the forecast order contract with risk sharing mechanism are proposed to manage the fluctuation of wind power supply randomness. With backward induction, the optimal investment capacity of wind power supplier is solved, and then the fixed order quantity or forecast demand quantity of the electricity retailer can be obtained. Our results show that the fixed order quantity may have a positive influence on the wind power supplier's capacity investment decision. While, the forecast order quantity may have a negative influence on the wind power supplier's capacity investment decision. In addition, wind density has a strong influence on wind power capacity investment, and it has a positive impact on wind power supplier's profit, but it does not always have a positive impact on electricity retailer's profit. At last, based on numerical analysis, we examine how the wind density distribution coefficient change affect members' profit in the electricity supply chain. We also find that even with wind curtailment punishment, the forecast order contract can make a Pareto improvement on the whole electricity supply chain.

Global warming’s grip on agriculture: Strategies for sustainable production amidst climate change using regression based prediction

The intersection of climate change and food production is emerging as a critical area of research, focusing on both the potential benefits and the significant challenges posed by changing climate conditions. Elevated levels of carbon dioxide alongside rising global temperatures could theoretically boost crop yields, benefiting both human and animal consumption. This study examines the impact of various climate variables—temperature, humidity, precipitation, and soil moisture—on the primary production of essential foods such as rice, wheat, livestock, milk, eggs, vegetables, and fruits. Utilizing data from different countries spanning from 2000 to 2020, drawn from world development indicators, this research employs econometric analysis coupled with deep learning-based cluster analysis. Additionally, it projects future production trends up to 2100 using the moving average time series forecasting method. The findings reveal a direct correlation between climate variables and the production levels of vegetables and other food items, highlighting the immediate effects of climatic changes on agriculture. The study also points out the uneven distribution of these climate impacts, with developing countries facing more severe challenges due to their limited resources and adaptive capacities. This uneven impact contributes to increased uncertainty in food supply and affects market stability. Furthermore, concerns about food safety are intensifying under the influence of climate change, although some regions have implemented effective food conservation and control measures to mitigate these risks. This research underscores a complex landscape where the risks and benefits of climate change on food production are not uniformly distributed, but rather are influenced by a myriad of factors including geographic location, economic conditions, and the level of technological advancement in food safety practices. The nuanced understanding of these dynamics is crucial for developing targeted strategies to enhance food security in the face of a changing climate.

Designing a resilient-sustainable integrated broiler supply chain network using multiple sourcing and backup facility strategies dealing with uncertainties in a disruptive network: A real case of a chicken meat network

Increasing supply and demand uncertainty, coupled with unforeseen disruptions, pose challenges to the resilience of today's critical sectors in the global food industry, including the broiler supply chain. This study introduces a resilient model to enhance the sustainability and resilience of the broiler supply chain in the face of uncertainties and disruptions. The model integrates backup facilities and employs multiple sourcing strategies to reinforce resilience. Using mixed integer linear programming with bi-objective, multi-period, and multi-product features, the model aims to minimize carbon dioxide (CO2) emissions from transportation while maximizing overall supply chain profit. The goal programming, and the ε-constraint methods optimize decision-making and yield Pareto solutions, achieving a balanced approach to conflicting objectives. Also, robust optimization and stochastic programming provide practical solutions for handling uncertainties. Validation and sensitivity analysis confirm that the proposed model optimizes the broiler supply chain, enhancing resilience, sustainability, and profitability.

Neuroevolution reinforcement learning for multi-echelon inventory optimization with delivery options and uncertain discount

The advanced information technology has enabled supply chain to make centralized optimal decision, allowing to make a global optimal solution. However, dealing with uncertainty is important in inventory management. Besides demand and supply uncertainties, supplier discounts also often arise unexpectedly. Further, suppliers or third-parties typically offer various delivery options in which trade-off occurs between cost and lead time. Thus, this study introduces new problem namely Multi-Echelon Inventory Optimization with Delivery Options and Uncertain Discount (MEIO-DO-UD). As a solution, Neuroevolution Reinforcement Learning (NERL) framework is developed for minimizing total system cost. The environment is modeled via System Dynamics (SD) and actor is presented by integration of Artificial Neural Network and Evolutionary Algorithm (EA), creating an effective decision-making model under dynamic uncertainty. The experimental study has been conducted where two different supply chain networks are given namely serial and divergence. Three EA algorithms are compared namely Differential Evolution (DE), Memetic Algorithm (MA), and Evolution Strategy (ES). Furthermore, NERL is also compared with the EA-optimized classical continuous review model namely (s,Q). The result shows that regardless what EA type is used, the proposed NERL always outperforms EA-optimized (s,Q) model. The more complex the problem, the further improvement can be made i.e. cost reduction up to 58%, followed by the fill rate improvement. The result also shows that NERL can avoid overfitting. Managerial implications are highlighted where NERL provides the more stable inventory level among all supply chain partners and bull-whip effect can be damped.

A multi-period optimal distribution model of emergency resources for responding to COVID-19 under uncertain conditions

Ideally, optimal emergency resource allocation would have been vital for effective relief work during the COVID-19 outbreak. However, the suddenness of the epidemic and uncertainty of its spread added some difficulties to distributing emergency resources. First, this study introduces triangular fuzzy numbers to describe the uncertainty of supply and demand of emergency resources, and interval numbers to describe the time required for resource transportation under disaster conditions. To minimize the total delivery time and difference in the total satisfaction rate, this study constructs an optimal model for emergency resource distribution under uncertain conditions that considers both efficiency and equity. Subsequently, an improved genetic algorithm (IMGA) is proposed to obtain the optimal decision scheme. Finally, a case study on emergency resource distribution during the COVID-19 pandemic is conducted for model verification. The results demonstrate that the proposed model can improve the efficiency and effect of emergency resource distribution. The model allocates some emergency resources to each demand site during each emergency period, which can help avoid large losses caused by extreme shortages of resources at a certain demand point. The emergency resource allocation scheme considers the transportation time and degree of impact, which is beneficial for enhancing the flexibility of decision-making and practical applicability of distribution operations. A comparative analysis of the algorithms shows that the proposed IMGA is an effective method for managing emergency resource distribution optimization problems because it has higher solving efficiency, better convergence, and stronger stability. These findings can provide decision support for the optimal distribution of large-scale, multiperiod emergency resources during the COVID-19 pandemic.

Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag

A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.

Conjunctive surface water and groundwater management in a multiple user environment

The conjunctive use of surface water and groundwater is practiced worldwide as a measure to address water supply uncertainty. This paper focuses on a scenario in which an aquifer is open to multiple users and examines the impact of such an open-access environment on the stabilization function of conjunctive management. We construct a non-cooperative stochastic dynamic game model where multiple users utilize groundwater intake from a common aquifer as a complement to fluctuating surface water. We also propose a simpler baseline applicable to dynamic environments to compute the benefits of the stabilization function when users are unable to adjust groundwater intake to surface water uncertainties. We then apply the model to the real-world case of the water supply environment of the Cao’e River in China. Simulation results show that open access leads to diminishing stabilization values as higher pumping costs due to declining stocks lead to a weakening of users’ incentive to utilize groundwater flexibly. Furthermore, the stabilization function itself is destabilized as the number of users increases. This is because greater groundwater intake and a decline in stocks amplify variations in pumping costs caused by different patterns of surface-water fluctuation.