Demand Uncertainty Research Articles

Optimal Placement and Sizing of Distributed Generation in Electrical DC Distribution Networks Using a Stochastic Mixed-Integer LP Model

AbstractThis paper presents a stochastic mixed-integer linear mathematical model for finding the optimal placement and sizing of distributed generation in a DC distribution network, considering the uncertainty of electrical demand and distributed renewable sources. The proposed model accurately represents the original mixed-integer nonlinear model, obtaining a globally optimal solution in less computational time with low errors. The mathematical model allows for considering constraints related to the maximum limits for the penetration of distributed generation, such as those specified by Resolution CREG 174 of 2021. Furthermore, the uncertainties of the electrical demand, wind energy-based distributed generation (DG), and solar energy-based DG are considered in the mathematical models using a two-stage stochastic programming approach. The accuracy and efficiency of the proposed model were tested and validated on a 21-node DC test system from the specialized literature, and the effectiveness and robustness were assessed on a 69-node DC test system. The obtained results show that the proposed stochastic mixed-integer linear mathematical model performs well.

Modeling and optimization of two-stage emergency supply chain network under uncertain environment

In order to formulate an effective emergency supply chain network planning scheme, improve the efficiency of emergency organization rescue and realize the reasonable allocation of resources and materials, considering the uncertainty of supply and demand, a distribution mode combining multiple transportation modes is adopted, and the optimization objectives are to minimize network response time, cost and carbon emissions. A two-stage emergency supply chain mixed integer programming model is constructed. At the same time, an adjustable robust optimization model is constructed based on robust optimization theory to enhance the network's ability to cope with uncertain factors, and the constraints with uncertain parameters are transformed through linear duality theory. In order to improve the solution effect of the model, an optimize cuckoo search (OCS) algorithm is proposed, and a benchmark example is introduced to verify the superiority and applicability of the OCS algorithm in solving multi-objective functions. Finally, the emergency material distribution data during the COVID-19 epidemic in Wuhan is used to study the emergency supply chain network decision-making problem with uncertain parameters, and the effective suppression of the robust control coefficient on uncertain disturbances is proved through sensitivity analysis.

The Influence of Big Data Analytics on Supply Chain Operations Efficiency

This paper aims to validate the feasibility of establishing a risk assessment system. Delphi and Entropy Weighting for Indicator Weighting was conducted by experts in the field of economics on the assessment data of Haier's supply chain indicators to analyze and derive a risk assessment system based on Haier's supply chain indicators. With a comprehensive analysis of risk assessment indicators using the Delphi and entropy weighting methods, the feasibility of the evaluation system for supply chain risk analysis in the context of big data gets demonstrated. The establishment of this assessment system will help Haier to analyze and respond to supply chain risks in the future, and will solve the related problems in avoiding supply chain market risk. These problems encompass difficulties related to managing substantial volumes of data and handling uncertainties in demand and supply. The template makes relevant conclusions and suggestions based on analysis, which could have effect on supply chain risk circumvention in a certain extent.

The effects of load demand uncertainty on cascading overload failures in power systems

The effects of load demand uncertainty on cascading overload failures in power systems

A Multi-Layer Techno-Economic-Environmental Energy Management Optimization in Cooperative Multi-Microgrids with Demand Response Program and Uncertainties Consideration.

This paper presents a multi-layer, multi-objective (MLMO) optimization model for techno-economic-environmental energy management in cooperative multi-Microgrids (MMGs) that incorporates a Demand Response Program (DRP). The proposed MLMO approach simultaneously optimizes operating costs, MMG operator benefits, environmental emissions, and MMG dependency. This paper proposed a new hybrid ε-lexicography-weighted-sum that eliminates the need to normalize or scalarize objectives. The first layer of the model schedules MMG resources with DRP to minimize operating costs (local generation and power transactions with the utility grid) and maximize MMG profit. The second layer achieves the environmental operation of the MMG, while the third layer maximizes MMG reliability. This paper also proposed a new application of a recently developed enhanced equilibrium optimizer (EEO) for solving the three-layer EM problem. In addition, the uncertainties of solar power generation, wind power generation, load demand, and energy prices are considered based on the probabilistic 2m + 1 Point estimation method (PEM) approach. Three case studies are presented to verify the proposed MLMO approach on an MMG test system. In Case I, a deterministic EM is solved to simulate the MMG as a single layer to minimize costs and maximize benefits through DRP, while Case II solves the MLMO optimization problem. Simulation results show that the proposed MLMO technique reduces environmental emissions by 2.45% and 3.5% in its optimization layer and at the final layer, respectively. The independence index is also enhanced by 2.49% and 4.8% in its layer only and as a total increase, respectively. Case III is for the probabilistic EM simulation; due to the uncertain variables effect, the mean value in this case is increased by about 2.6% over Case I.

Liner fleet deployment and empty container repositioning under demand uncertainty: A robust optimization approach

This paper investigates a robust optimization problem concerning the integration of fleet deployment and empty container repositioning in a shipping line network, where a fleet of vessels is dispatched to transport both laden and empty containers, aiming to fulfill a predetermined set of requests over a defined time horizon. The sizes of customer demands are uncertain and are characterized by a budgeted uncertainty set. This study aims to ascertain the vessel types assigned to each shipping route, the routing of laden containers, and the repositioning of empty containers in a manner that minimizes the total cost. Simultaneously, it ensures the feasibility of all transportation plans for any realization of demand within the uncertainty set. We introduce a path-based two-stage robust formulation for addressing the problem. In the first stage, the assignment of vessel types to each shipping route is determined, and the second stage focuses on establishing the routing of laden containers and repositioning of empty containers under a worst-case scenario. We propose the Column-and-Constraint Generation algorithm for solving the proposed robust formulation. To address large-scale size instances, we propose an acceleration technique, i.e., the piece-wise affine policy, which reduces the dimensions of the uncertainty set while maintaining a bounded compromise in solution quality. Comprehensive numerical experiments derived from real-world industries, such as the Shanghai port and CMA CGM, are conducted to validate the proposed formulation and solution methodologies.

Selling mode choice and return policy for advance selling in an e-commerce platform with social influence

Advance selling is increasingly employed to mitigate demand uncertainty, raise consumer awareness of products, and even exert social influence on subsequent sales. However, consumers may hesitate to pre-order products due to uncertainty about product valuation. As a response, sellers have begun implementing return policies and utilizing online retail platforms to alleviate the uncertainty. This study aims to investigate the interaction between return policies for advance selling and selling formats of e-commerce platforms considering the impact of social influence. We examine a two-period platform supply chain that includes agency selling, reselling, and hybrid selling formats with and without return policies. Using game theory models, we analyze pricing decisions and profits of each player under six possible scenarios. The results indicate that the manufacturer consistently prefers adopting a Money-Back Guarantee (MBG) return policy regardless of the selling format. However, the MBG return policy is not always advantageous for the platform, except in the case of low social influence. In such a case, the platform may implement the MBG return policy to boost advance sales, thereby enhancing the penetration power from advance sales to regular sales. Regarding selling formats, the platform and the manufacturer opt for the reselling and agency selling, respectively, when the commission rate is low; otherwise, they adopt the agency selling and hybrid selling, respectively. Furthermore, we explore the impact of critical drivers on the pricing decisions and profits within the platform supply chain. Our study offers valuable insights into implementing return policies and determining selling formats.

A robust financing theory of ICOs under demand uncertainty of products of token platforms

PurposeThe market products produced by Initial Coin Offerings (ICO) platforms are often relatively new and have no previous transaction records and therefore are hard to estimate for its demand. The purpose is to study the impacts of the degree of ambiguity aversion of entrepreneurs to demand uncertainty on the ICO financing ratio, the optimal expected output, the optimal efforts and the token price.Design/methodology/approachIn an optimal ICO design, we introduce demand uncertainty of the product and establish a robust optimization method to solve the ICO optimal design. We compare ICO financing and the general venture capital (VC) financing model. We analyze the impact of demand uncertainty on the optimal ICO financing ratio.FindingsFindings include that the ICO financing ratio is positively related to the degree of ambiguity aversion, the token price is negatively related to the degree of ambiguity aversion and the “ambiguity premium” exists in the ICO market, the optimal effort levels are negatively related with the ICO financing ratio, but positively related with token price, and in the environment of high production cost, VC financing is not as good as ICO financing.Originality/valueWe develop a robust ICO financing model by assuming that the entrepreneur is ambiguity aversive to the demand uncertainty. Analyze the impact of the degree of ambiguity aversion on the ICO financing ratio in theory and find that the entrepreneur can raise funds with the higher ICO token ratio when she has a larger degree of ambiguity aversion to the demand uncertainty. Extend the impact analysis of the degree of ambiguity aversion on the expected token price and find a negative relationship between the expected token price and the degree of ambiguity aversion of the entrepreneur to the demand uncertainty.

Transmission Network Expansion Planning Considering Uncertainty in Demand with Global and Nodal Approach

Transmission Network Expansion Planning Considering Uncertainty in Demand with Global and Nodal Approach

Managing platelets supply chain under uncertainty: A two-stage collaborative robust programming approach

This study investigates the role of horizontal collaboration among blood centers in mitigating the negative impacts of fluctuations in blood platelet supply and demand during normal circumstances and after sudden-onset disasters. Specifically, four scenarios are studied: no collaboration, collaboration among blood centers, collaboration among hospitals, and collaboration among blood centers and hospitals. To formulate these scenarios, we propose a two-stage robust programming model that enables decision-makers to monitor the collaborative behavior of blood centers and hospitals before and after changes in supply and demand occur. In the first stage, a robust optimization approach is developed to cope with supply and demand uncertainties, where collaboration is limited to blood centers. In the second stage, the uncertain parameters (supply and demand) are treated as deterministic. The key decision is how to transfer platelets among hospitals located in a collaborative cluster. This research uses benchmark data from previous studies to test and validate the developed model and solution approach. Comparing the results obtained in different scenarios, we systematically select the most favorable scenario in terms of model performance under normal and emergency situations. The final results indicate that the network achieves the highest performance when blood centers and hospitals participate in the grand coalition.

Net-zero carbon emission oriented Bi-level optimal capacity planning of integrated energy system considering carbon capture and hydrogen facilities

The integrated energy system (IES) is considered an efficient energy provision paradigm to improve the utilization efficiency of renewable generation (RG), reduce energy cost and improve energy supply reliability. However, the multi-dimensional uncertainties and the hydrogen utilization have posed enormous challenges to the optimal planning of the IES. This paper proposes a net-zero carbon emission oriented bi-level optimal planning model for the IES with full consideration of the multiple operational uncertainties in the IES (RG, multiple demands). The upper level is an optimal capacity configuration model, and the economic costs, energy selling profits, the penalty for power abandonment and energy supply shortage are settled as the objectives. The lower level is an optimal energy dispatch model with the objective function of minimizing the total operating cost of the IES on typical days. An integrated energy system framework consisting of multiple energy flows is formulated in the presence of carbon capture system (CCS) and hydrogen-to-power (H2P) facilities. To fully characterize the uncertainties of RG and multiple demands, a data-driven scenario generation method is adopted to expand the operational scenarios, in which probability distribution assumptions and prior knowledge are not necessary. Finally, the effectiveness of the proposed solution is extensively demonstrated through numerical simulation, and a sensitivity analysis is carried out to assess the impact of changes in electricity and gas prices on the IES planning results.

Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.

Multi-period emergency facility location-routing problems under uncertainty and risk aversion

This paper addresses a multi-period emergency facility location-routing problem, in which the uncertainties in material demands and transportation time, as well as dynamic inventory replenishment and carryover are incorporated in the design of multi-level emergency logistics networks. To measure the risks stemming from uncertain transportation time, mean-CVaR method is used. Then, a risk-averse stochastic programming model for the presented problem is formulated to minimize the total rescue time of the network. Moreover, a genetic algorithm is developed to solve the proposed model. Extensive numerical experiments including the randomly generated instances and a case study on the Wenchuan earthquake in China are conducted to verify the effectiveness of the presented model and algorithm. Experimental results show that the genetic algorithm significantly performs better than the Gurobi solver in terms of both solution quality and solution time.

Data-Driven Decision-Making for Flexible Natural Gas Allocation Under Uncertainties: An Agent-Based Modelling Approach

Despite the anticipated growth in the global demand for energy commodities, the frequently changing market dynamics imposed by environmental regulations and political sanctions create end-user demand uncertainties. This imposes the need for prompt quantitative decision-making approaches to understand how various market structures affect the planning of current natural gas projects. Agent-based modelling (ABM) emerges as a powerful approach to facilitate expedited and well-informed decisions amidst limited timeframes. This study deploys agent-based modelling to investigate natural gas allocation across various utilisation routes under diverse economic and environmental scenarios. Results from four main cases and two sub-scenarios imply that the allocation strategy is driven by utilisation routes considered in each case, followed by the allocation target (i.e., economic or environmental) and the operational bounds. The results reveal that cases prioritising natural gas monetisation for export outperform those meeting power requirements in average annual profitability. In case 4, considering a full network with power, the average annual profitability in the economic scenario reduces by approximately 47% compared to case 3, representing the optimal network configuration with $5.22 billion in average annual profitability. However, the economic scenario of case 3 demonstrates the second-highest rate of emissions (0.66 CO2-eq t/y), following the hydrogen-rich process routes in case 2. Overall, this study presents an innovative data-driven framework for enhancing strategic resource allocation in dynamic business environments. By integrating empirical evidence and technical data with an advanced technical tool (i.e., ABM), the framework provides decision-makers and policymakers with valuable insights for managing uncertainties and shifts in market structures, particularly in existing natural gas projects.

Coupling geospatial suitability simulation and life cycle carbon emissions towards uncertain optimization planning for wind-photovoltaic-hydrogen multi-energy complementary system

Building clean and modern multi-energy complementary systems is a direct path to decarbonization in energy sector. The introduction of hydrogen energy with high operational flexibility enables the multi-energy complementary system to fully accommodate renewable energy. The key issues of the optimization planning of the wind-photovoltaic-hydrogen multi-energy complementary system that need to be solved are how to finely characterize the carbon reduction potential of the system, consider the uncertainty of supply and demand in the modeling process, and incorporate the energy supply business of electric vehicles into the planning. This study constructs optimization planning model with the following three progressive stages: geospatial suitability simulation based on geographic information system, uncertainty analysis based on probabilistic scene generation technology, and capacity configuration optimization. Unlike previous studies, this model measures and optimizes the carbon emissions level of the system from a lifecycle perspective. The empirical study results indicate that carbon emissions optimization from the perspective of the entire life cycle would directly affect the system configuration results. From a lifecycle perspective, the carbon emissions accounting result is about 9 times higher than that obtained only during the operation phase. Compared to the traditional distribution system, the carbon emissions of the system constructed in this study decreased by 82.75 %. In addition, considering that the system provides charging services for electric vehicles, which can reduce carbon emissions by about 93.6 % but only increase costs by about 29.4 %. The computational results justify the proposed optimization framework. This study provides optimization models for the system, and the implementation framework can support technical reference for practice.

DESIGNING A MICROSERVICES BASED ENTERPRISE ARCHITECTURE USING TOGAF 10: A CASE STUDY APPROACH

PT Umbara Karya Sejati, operating in the home living and industrial product sectors, faced significant digital transformation challenges, marked by inefficiencies due to system integration issues and vendor dependencies that incurred cost penalties of 10-15% each month. Addressing these challenges, this research developed an Enterprise Architecture using TOGAF 10's Microservices Architecture (MSA) from the Preliminary Phase to Phase F: Migration Planning. This approach aimed to enhance system integration and operational efficiency, thereby improving modularity and scalability while reducing reliance on external vendors. A pivotal component of this architecture, an API gateway, provided robust monitoring capabilities for integration processes, enabling quick identification and prioritization of critical issues, which will assist the internal IT team's workflow. Furthermore, the research planned the establishment of a DevOps team, incorporating Agile methodologies, and scheduled IT governance and data security training to prepare for future policy development and strengthen internal controls. This strategic design equips the organization to navigate the volatility, uncertainty, complexity, and ambiguity (VUCA) of market demands with agile and effective responses, projecting a 10% increase in both operational and cost efficiency for PT Umbara Karya Sejati.

Apply Honey Barger Algorithm to double integral sliding mode observer for stable load frequency in multi-area power system

A randomness of load demand and plant uncertainties causes frequency deviation in linked power plants. Based on the double-integral sliding mode observer (DISMO) integrated Honey Badger algorithm (HBA), a novel LFC for a multi-area power plant (MAPP) is introduced in this article. The main aim is to minimize frequency offset, finite time, and aggregate instability of controllable loads subject to the balance of the power on the MAPP. Firstly, the suggested observer is developed to ensure an exact estimation of the actual signal for the controller input signal. Secondly, a double-integral sliding surface (DISS) is introduced based on estimated signals the designed observer gains. Then, a new SMC rule is designed to ensure finite time reachability and eliminate the chattering and oscillating troubles. Thirdly, in this regard, the proposed sliding surface is designed with four selected parameters. Regarding this, to maximize the ability of the suggested SMCer, the optimized controller parameters are determined by HBA. The robustness of the suggested scheme is examined by various case studies on the LFC models in a single-area power plant (SAPP) and MAPP under different load disturbances and parameter uncertainties.

Optimizing Drone Delivery Paths from Shared Bases: A Location-Routing Problem with Realistic Energy Constraints

Recently, Amazon patented fulfillment centers for drones on a large scale in densely populated areas. A network of such shared centers can be used for landing and launching drones as an alternative to the traditional private bases in near future. This paper studies how a user of such a shared delivery infrastructure can optimally determine the required bases among the existing bases to perform her delivery tasks. To this end, a location-routing problem is studied where the problem determines the drone launching centers and their routes to deliver parcels. A realistic energy function is used that incorporates the effect of load weight for calculating energy consumption in all flight phases including take-off, level flight, hovering, and landing. Although the energy consumption is nonlinear, the problem is formulated as a mixed-integer linear programming model and strengthened by valid inequalities and a pre-processing algorithm that enables us to solve instances with up to 100 customers using off-the-shelf optimization solvers. Moreover, a heuristic method is presented to solve instances with up to 200 customers. Results indicate the importance of incorporating the load-dependent energy formula in contrast to using fixed flight duration constraints for drones. The value of allowing multiple visits on each trip versus only simple back-and-forth trips is also assessed. The advantage of using an integrated location-routing approach is shown over the sequential approach in which the locations of bases are decided first and the routes are constructed next. Moreover, to manage future demand uncertainty, the model is extended for the case that a number of demand scenarios are given.

Optimizing sustainable multiple biomass-to-biofuel conversion network with integrated water resource management utilizing data-driven robust planning

Until now, the biofuel supply chain has lacked an integrated approach to address its fundamental challenges in real-world implementation. The first challenge is maintaining a continuous supply flow in the biofuel network, which can significantly impact economic outcomes and performance measures. The second challenge involves managing water resources for biofuel production to preserve freshwater reserves. The third challenge is managing operational risks, such as uncertainty in biofuel demand, which leads to conservative planning and underscores the need for realistic uncertain programming approaches. To address these challenges, the proposed study designs a biofuel conversion network that utilizes third-generation feedstocks and non-edible seed sources, sourcing necessary water from wastewater resources. This practical solution addresses water-energy relationship concerns, reducing dependency on limited freshwater supplies. To achieve a continuous and cost-effective feed supply, this study incorporates a pre-processing phase using a hybrid Multi-Criteria Decision-Making model. This approach ensures the feed supply remains consistent, economical, and sustainable. To manage the uncertainty in biofuel demand, we developed a robust optimization model with an intersected uncertainty set using a data-driven approach. This approach aims to reduce conservatism in the obtained solutions, making the model more practical for real-world implementation. The numerical results reveal some practical implementation for policymakers. The proposed robust optimization using the configured uncertainty set offers a tool to manage the conservatism of renewable energy demand uncertainty and operational risks more effectively than traditional approaches (0.914%). This formulation can help policymakers to maintain greater robustness against fluctuations in confidence levels. Additionally, the simultaneous use of third-generation and non-edible feedstocks creates a multi-biomass-to-biofuel conversion network, offering practitioners a practical solution that significantly reduces reliance on freshwater resources. Furthermore, policymakers can leverage the proposed pre-processing phase to promote economic and continuous supply feed based on geographical, resource accessibility, and infrastructure criteria.

Robust cooperative hub location optimization considering demand uncertainty and hub disruptions

Amidst the rise of economic globalization and increased commodity trading, the logistics industry is experiencing rapid growth, encountering intricate transportation demands and fierce market competition. Simultaneously, it faces challenges related to infrastructure development and resource allocation efficiency. To enhance the adaptability and robustness of transportation networks when facing uncertain demands and potential hub disruptions, this paper proposes a two-stage robust optimization model for cooperative hub location problem. The model utilizes a hybrid algorithm that effectively combines the global search capability of genetic algorithms with the step-by-step optimization efficiency of Benders decomposition. Case study analyses demonstrate that, irrespective of the uncertainty environment, the cooperative model maintains lower total costs. Particularly in the case of large demand fluctuations, its cost advantages over non-cooperative models become notably prominent, showcasing remarkable performance in meeting service demands and enhancing resource utilization. Additionally, in cooperative networks, hub disruptions have a more significant impact on hub location decisions, with penalty and supplementary costs further exacerbating this influence. These research findings offer crucial insights for management practices: in uncertain market environments, adopting cooperative and robust planning strategies is pivotal for mitigating operational risks. When making decisions on cooperative hub location selection, carriers should comprehensively consider the interaction between uncertainty and economic benefits to achieve an optimal balance between risk and cost, ensuring the sustained economic feasibility of cooperative ventures.