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Shipping, the backbone of economic development, poses a significant environmental threat. Many government agencies have implemented regulations to mitigate ship air pollution. Three commonly used methods for compliance during berthing are marine diesel oil, scrubber, and shore power. Scrubber and shore power have greater emission reduction potential but can be costlier than marine diesel oil. To encourage their adoption, we optimize the subsidy plan through a bi-level mixed integer programming model where the government at the upper level minimizes the total subsidy amount while ship operators at the lower level choose the most cost-effective energy supply. The problem's complexity arises from the interdependence in the bi-level structure and the nonlinearity in the model. We address this by first converting the model into an equivalent single-level form and then reformulating the model by linearization. Numerical experiments are conducted to assess the model's performance. Results suggest that the promotion of scrubber or shore power starts with large ships in the initial stage. Increasing the number of ships with these technologies reduces subsidies. Additionally, each subsidy corresponds to a specific utilization range, allowing the government to adjust amounts based on target utilization levels. [ABSTRACT FROM AUTHOR]

Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing task diversity will enhance the generalization ability of meta-learning models. However, this paper challenges this view through empirical and theoretical analysis. We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models; (ii) over-constraining task diversity may incur the risk of under-fitting or over-fitting during training; and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty. Based on this insight, we design a novel task sampler, called Adaptive Sampler (ASr). ASr is a plug-and-play module that can be integrated into any meta-learning framework. It dynamically adjusts task weights according to task diversity, task entropy, and task difficulty, thereby obtaining the optimal probability distribution for meta-training tasks. Finally, we conduct experiments on a series of benchmark datasets across various scenarios, and the results demonstrate that ASr has clear advantages. The code is publicly available at https://github.com/WangJingyao07/Adaptive-Sampler. [ABSTRACT FROM AUTHOR]

The bidirectional uncertainty of source and load creates scarcity in the reserve resources of the distribution network. Therefore, it is highly significant for the safe and economic operation of the system to harness spare energy storage capacity from prosumers to provide reserves. This paper proposes a bi-layer optimal scheduling model of "distribution network–prosumer group–prosumer" that considers the flexible reserve resources of a prosumer. The upper layer is the "distribution network–prosumer group" optimization model, in which the distribution network sets the electricity price and reserve price according to its own economic benefit and sends it to the prosumer group and guides it to participate in the scheduling of the resources of the prosumer. The lower layer is the "prosumer group–prosumer" optimization model, where the prosumer group incentivizes the prosumer to adjust its energy storage charging and discharging plans through prices and mobilize its own resources to provide flexible reserve resources. The results show that the optimal method proposed in this paper can fully utilize flexible reserve resources from prosumers, improve the economy of distribution network operations, and reduce the pressure of providing reserves using the upper grid. [ABSTRACT FROM AUTHOR]

The collaboration of a multiple unmanned underwater vehicles (multi-UUVs) system has attracted widespread attention in recent years, as it can overcome the limitations of a single UUV and enhance mission completion efficiency. Oriented towards patrol and exploration missions with multiple waypoints, this paper proposes a balanced mission planning strategy, aiming to improve mission quality while reducing mission time for multi-UUVs. Firstly, due to the uneven performance of the two optimization objectives, a quick initialization screening method is employed specifically for mission quality to reduce the mission space. Secondly, to ensure mission load distribution and collaboration among multi-UUVs, and ease the difficulty in solving the issues of mission allocation and route planning, a balanced bi-level mission planning method based on regional segmentation is proposed. Finally, applicable weight evaluation criteria are utilized to evaluate the feasible solution set and determine the optimal solution. The efficacy of the balanced mission planning strategy is substantiated through comprehensive numerical simulations in a complex 2D marine environment. [ABSTRACT FROM AUTHOR]

In group decision making, the decision participants may exhibit various interaction behaviors, some of them are efficient at facilitating the efficiency of consensus reaching process. Motivated by the view of this, a bi-level consensus model with maximum return and minimum cost considering interaction and transaction behaviors (MRMC-IT) is proposed in the way of adopting the Choquet integral. The bi-level feedback mechanism based on the adjustment of opinions guarantees the interaction between the moderator in the upper level and the decision makers in the lower level. Meanwhile, a transaction mechanism of discourse weights is elicited to present the trading behaviors between the pairwise decision makers. Additionally, in order to solve the complex proposal, a bi-level interactive approach based on satisfactory degree is applied to obtain a satisfactory solution for all decision participants. Finally, an application of sewage discharge rights trading is conducted to demonstrate the validity and the rationality of the proposal. [ABSTRACT FROM AUTHOR]

Malicious apps use a variety of methods to spread infections, take over computers and/or IoT devices, and steal sensitive data. Several detection techniques have been proposed to counter these attacks. Despite the promising results of recent malware detection strategies, particularly those addressing evolving threats, inefficiencies persist due to potential inconsistency in both the generated malicious malware and the pre-specified detection rules, as well as their crisp decision-making process. In this paper, we propose to address these issues by (i) considering the detection rules generation process as a Bi-Level Optimization Problem, where a competition between two levels (an upper level and a lower one) produces a set of effective detection rules capable of detecting new variants of existing and even unseen malware patterns. This bi-level strategy is subtly inspired by natural evolutionary processes, where organisms adapt and evolve through continuous interaction and competition within their environments. Furthermore, (ii) we leverage the fundamentals of Rough Set Theory, which reflects cognitive decision-making processes, to assess the true nature of artificially generated malicious patterns. This involves retaining only the consistent malicious patterns and detection rules and categorizing these rules into a three-way decision framework comprising accept, abstain, and reject options. Our novel malware detection technique outperforms several state-of-the-art methods on various Android malware datasets, accurately predicting new apps with a 96.76% accuracy rate. Moreover, our approach is versatile and effective in detecting patterns applicable to a variety of cybersecurity threats. [ABSTRACT FROM AUTHOR]

In many industrial real-world environments, scheduling necessitates continual reactive adjustments due to unpredictable perturbations, leading to the dynamic transformation of predefined static schedules. In this paper, we introduce a new framework named a two-level evolutionary algorithm (2LEA) as a comprehensive approach for addressing the dynamic flexible job shop scheduling problem. The 2LEA is based on a bi-level optimization design, where the upper level is dedicated to solving the general flexible job shop scheduling problem, and the lower level is used as a new evolutionary operator guided by a probability rate in the upper level, focusing on the optimization of operation sequences. This framework is capable of handling four dynamic events job insertion, job cancellation, machine breakdown, and job replacement using a predictive-reactive rescheduling strategy. By addressing the previously unexplored dynamic event of job replacement, this paper fills a significant gap in the literature and opens avenues for further research. Extensive computational experiments conducted on well-known benchmark instances from the Brandimarte and Hurink datasets demonstrate the effectiveness and efficiency of our proposed scheduling algorithm. Our results showcase the superior performance of 2LEA over state-of-the-art approaches in terms of solution quality, affirming its potential as a leading solution for both static and dynamic scheduling challenges. [ABSTRACT FROM AUTHOR]

Abstract This study presents a novel method for planning the expansion of transmission lines and energy storage systems while considering the interconnectedness of electricity and gas networks. We developed a two-level stochastic planning model that addresses both the expansion of transmission and battery systems in the electrical grid and the behavior of the gas network. This research explores the challenges and effects of integrating high levels of renewable energy sources while ensuring security within both networks. Our model uses a stochastic mixed-integer non-linear programming approach. To solve this complex model, we applied the Human Evolutionary Model (HEM). We tested our approach on two case studies: a simple 6-node network and the more complex IEEE RTS 24-bus network for the electricity grid, combined with 5-node and 10-node gas networks, respectively. The results demonstrate the effectiveness of our model, particularly in scenarios where connections in the power and gas networks are disrupted, preventing load shedding even when integrated network connections are cut.

This article discusses the need for a multi-grade pricing strategy for emergency power supply services provided by mobile energy storage systems. The authors argue that the existing uniform pricing model fails to meet the differentiated demands of customers and undermines their enthusiasm for purchasing emergency supply services. They propose a hierarchical trading framework and a bi-level pricing optimization model based on a Stackelberg game to obtain tiered prices of emergency power supply and increase the revenue of mobile energy storage while reducing the cost for customers. The article provides insights and discussions on this pricing strategy, considering power supply capacity, duration, and response time as metrics for differentiating power supply levels. The document also suggests future research directions, such as integrating real-time scheduling of mobile energy storage into the pricing model. The research was supported by the Guangdong Basic and Applied Basic Research Foundation and the Scientific Research Startup Fund for Shenzhen High-Caliber Personnel. Additionally, there is a list of references cited in an article or research paper, which includes various sources related to the topic of energy storage systems, such as studies on optimal configuration and operation methods, pricing strategies, and the use of mobile energy storage in distribution networks. The document provides a range of perspectives and approaches to the subject, allowing library patrons to explore different aspects of energy storage for their research. [Extracted from the article]

Supply or service networks are vulnerable to hazards that can stem from both unintentional and intentional human actions, as well as natural calamities. To ensure vital infrastructure resilience in these networks, address the interdiction facility location problem. Currently, diverse groups of attackers target supply and service systems to cause maximum disruption. Collaboration among attackers improves system vulnerability detection accuracy and realism. This research examines the challenges of interdiction location with different defense systems and heterogeneous attackers. To address this challenge, a mixed-integer non-linear bi-level programming model was considered. Heuristic optimization methods including variable neighborhood search, simulated annealing, and hybrid variable neighborhood search are used to efficiently solve the suggested model. The outcomes of our investigation suggest that implementing various protective methods leads to an escalation in system damage when attackers collaborate. Furthermore, the findings illustrate the efficacy of the suggested algorithms in resolving interdiction location issues within supply or service networks. [ABSTRACT FROM AUTHOR]

We examine the phenomenon of impact assessment in the practice of scientific research, paying attention to the context in which project evaluation is used in federally funded research on plant health in the United States. Our analysis, which is derived from systems theory, carries out a particular view of the research process. For the purposes of this review, our use of the term systems theory references the body of interdisciplinary work that deals with the organization and function of complex structures in nature and human society. Key concepts in this body of theory are that both the components and the interactions among components are important in understanding behavior and that, frequently, systems are seen to be hierarchical in structure. The aim of our analysis is to bring to the attention of the plant health community several concepts from the social sciences that might help in understanding how researchers have responded to the increased expectations from funders to provide project evaluations and impact assessments. We generate a synthesis of these theories, which have not previously been used in a unified way, to explain choices in response to newly imposed goals. Although our analysis is motivated by a specific disciplinary focus on plant health, the issues we discuss are general. Thus, we hope the review is useful to a wide range of scientists, science program managers, and policymakers. [ABSTRACT FROM AUTHOR]

Developing cooperative relationships between neighboring ports has emerged as an effective strategy for enhancing overall productivity. Two key factors to consider in such collaborations are: cooperative motivation (which drives the decision to cooperate between ports) and cooperation type (which defines the business scope of collaborative activities). Cooperative motivation can be categorized into regional welfare and competition, while cooperation type encompasses activities related to hinterland access and terminal management. This study aims to determine the most optimal types of cooperation based on different motivations. To achieve this, a bi-level optimization model was developed to analyze the relationship between shippers and ports, specifically focusing on the cooperation between the Kobe and Osaka ports. Competition with the Busan port was examined as a case study to provide a comprehensive understanding of the dynamics involved. The findings revealed that cooperation plays a crucial role in enhancing regional welfare for both North American and Southeast Asian cargo, particularly when cooperation levels are relatively low. Furthermore, it was observed that an effective cooperative strategy for competition in the Kobe and Osaka ports is contingent upon specific conditions. Hence, for successful competition-based cooperation, careful consideration of market dynamics and prevailing circumstances is essential. [ABSTRACT FROM AUTHOR]

Risk budgeting is one of the recent and successful strategies for asset portfolio selection. In this strategy, risk budgets are associated with assets, and the amount of investment is adjusted so that the contribution of each asset to the portfolio risk is proportional to its risk budget. To the best of our knowledge, no specific method has been presented in the literature to systematically determine the value of risk budgets. To fill this research gap, in this article, we consider the risk budgets as decision variables and present a bi-level programming model where the upper level decides the risk budgets and the lower level determines the risk budgeting portfolio. Three approaches are introduced to solve the model. The first is a single-level reformulation of the bi-level model, the second is a novel gradient-based algorithm, and the third is the particle swarm optimization algorithm. Moreover, the k-means clustering method is utilized to determine the assets involved in the portfolio. Computational results over real-world datasets demonstrate the significance of the bi-level model. In addition, the results confirm the proficiency of our gradient-based algorithm from both solution quality and running time. [ABSTRACT FROM AUTHOR]

This study presents a novel method for planning the expansion of transmission lines and energy storage systems while considering the interconnectedness of electricity and gas networks. We developed a two-level stochastic planning model that addresses both the expansion of transmission and battery systems in the electrical grid and the behavior of the gas network. This research explores the challenges and effects of integrating high levels of renewable energy sources while ensuring security within both networks. Our model uses a stochastic mixed-integer non-linear programming approach. To solve this complex model, we applied the Human Evolutionary Model (HEM). We tested our approach on two case studies: a simple 6-node network and the more complex IEEE RTS 24-bus network for the electricity grid, combined with 5-node and 10-node gas networks, respectively. The results demonstrate the effectiveness of our model, particularly in scenarios where connections in the power and gas networks are disrupted, preventing load shedding even when integrated network connections are cut. [ABSTRACT FROM AUTHOR]

Pseudo-labeling methods are popular in semi-supervised learning (SSL). Their performance heavily relies on a proper threshold to generate hard labels for unlabeled data. To this end, most existing studies resort to a manually pre-specified function to adjust the threshold, which, however, requires prior knowledge and suffers from the scalability issue. In this paper, we propose a novel method named Meta-Threshold, which learns a dynamic confidence threshold for each unlabeled instance and does not require extra hyperparameters except a learning rate. Specifically, the instance-level confidence threshold is automatically learned by an extra network in a meta-learning manner. Considering limited labeled data as meta-data, the overall training objective of the classifier network and the meta-net can be formulated as a nested optimization problem that can be solved by a bi-level optimization scheme. Furthermore, by replacing the indicator function existed in the pseudo-labeling with a surrogate function, we theoretically provide the convergence of our training procedure, while discussing the training complexity and proposing a strategy to reduce its time cost. Extensive experiments and analyses demonstrate the effectiveness of our method on both typical and imbalanced SSL tasks. [ABSTRACT FROM AUTHOR]

To enhance the efficient utilization of space resources, it is critical to integrate information from various systems of the space station and formulate scientific and effective methods for planning cargo supplies. Considering the large-scale, multi-objective, complex nonlinear, non-convex, non-differentiable, and mixed-integer characteristics, this study decomposes the space station cargo supply planning problem into a bi-level optimization problem involving cargo manifest and loading layout iterations. A new CILPSO algorithm is proposed to solve this by integrating particle coding, reliability priority, and random generation mechanisms of population initialization, global and local versions of particle updating, and a local search strategy. The experimental results show that the CILPSO algorithm outperforms other algorithms regarding search performance and convergence efficiency. The proposed approach can effectively reduce the cargo supply cost of the space station and improve the output of space science and application achievements. It provides a decision-making basis for the responsible department to develop cargo supply schemes, for the cargo supply systems to submit cargo demands, and for the cargo spaceship system to design loading schemes. This study advances the logistics sustainability of the space station. [ABSTRACT FROM AUTHOR]

Reducing procurement risks to ensure the supply of emergency supplies is crucial for mitigating the losses caused by mine thermodynamic disasters. The risk preference of decision-makers and supply chain collaboration are the important aspects for this reductiom. In this study, a novel P-CVaR (Piecewise conditional risk value) distributionally robust optimization model is proposed to accurately assist the decision-makers' decision of risk preference for reducing procurement risks. Meanwhile, the role of cooperation between procurement and reserves are considered for the weakening procurement risks. A risk-averse bi-level optimization model is proposed to obtain the optimal procurement strategy. Furthermore, by applying the Lagrange duality theorem, the complexity of the bi-level optimization model is simplified then solved using a PSO algorithm. Using empirical analysis, it has been verified that the model presented in this paper serves as a valuable guideline for mine thermodynamic pre-disaster emergency material procurement strategies for the prevention of thermodynamic disasters. [ABSTRACT FROM AUTHOR]

To address the issue of high auxiliary service costs caused by the grid connection of new energy generation, a dual layer optimization scheduling method for high penetration new energy power grids is proposed, taking into account the auxiliary service costs of grid connection. The correlation between the output fluctuations and load fluctuations of new energy generation is analyzed, and a calculation model for auxiliary service costs caused by grid connection of new energy generation is established. Taking into account the timescale division requirements for grid energy trading and auxiliary service market transactions, a new high penetration energy grid two-level optimization scheduling model is established. The upper level scheduling model aims to minimize the sum of grid energy procurement costs and auxiliary service costs caused by new energy generation and completes the optimization scheduling of the grid for several hours. The lower level scheduling model aims to closely follow the upper level scheduling plan for new energy power plants/groups. Optimization and scheduling of the power grid for tens of minutes is completed, taking into account the balanced allocation of wind and solar losses among various power plants within the new energy power plant group. The simulation results show that the method proposed in this paper can reduce the energy transaction costs and auxiliary service transaction costs of the power grid during low and peak load periods. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Electric vehicles (EVs) are currently a focal point in distribution network research due to their potential benefits. Integrating EVs into distribution networks can enhance voltage profile stability. However, unplanned EV connection and charging can lead to increased peak load power consumption, voltage profile irregularities, and heightened network losses. Addressing the placement of charging stations in distribution networks is crucial to prevent voltage drop crises and mitigate other issues. This study proposes a Bi-level particle programming optimization programming approach, utilizing the Particle Swarm Optimization (PSO) algorithm to minimize losses and determine optimal station locations. The algorithm iteratively minimizes losses while considering location and operating costs until a stopping condition is met. Additionally, an EV charging planning algorithm is introduced to manage EV connections without exacerbating peak load or causing severe voltage drops. Simulations conducted on an IEEE 33-bus system using MATLAB analyze the proposed design's impact on power consumption, system losses, and network voltage. Simulations in an IEEE 33-bus system show that the proposed model shifts peak power consumption by about 4.6 % to about 11.6 % under different EV penetration scenarios. It achieves a reduction in the network losses by up to 0.39 %, while the voltage deviations in critical buses are minimized to enhance stability during periods of high demand for EVs. Results demonstrate that the programmed charging algorithm effectively reduces power consumption at peak load, minimizes network losses, and smooths the daily voltage profile.

As the penetration level of renewable energy is continuously growing, it is essential for transmission and distribution system operators to collaborate on optimizing the siting and sizing of distributed energy storage to enhance the operational flexibility and economic efficiency. Given the frequent occurrence of extreme weather in recent years, the planning should also account for such factors. Hence, a planning method of distributed energy storage with the coordination of transmission and distribution systems considering extreme weather is proposed. Firstly, a Gaussian mixture model-based chance constraint is established to describe the uncertainty of wind and solar power, ensuring high confidence that the bus voltage of the distribution system is within a safe range. Secondly, aiming to maximize the social welfare, a bi-level planning model for distributed energy storage is developed. The upper-level addresses the siting and sizing issues of distributed energy storage, while the lower-level characterizes the day-ahead clearing problem of power market. By leveraging Karush-Kuhn-Tucker (KKT) conditions and linearization techniques, the bi-level model is transformed into a single-level mixed integer linear programming model that is easier to solve. Finally, numerical analysis is conducted on a modified IEEE 24-node system combined with two IEEE 33-node systems. The case study verifies the effectiveness of the proposed model.

The rapid growth of renewable energy is integrated into the distribution system. The creation of both new market mechanisms and investment models has critical effects on the economics and security of the distribution market. Mobile energy storage has been used to increase the resilience of distribution grids due to their advantages in mobility and flexibility, which offer electricity arbitrage options for merchant investments. This paper presents a bi-level optimization framework based on location marginal pricing settlement of mobile energy storage financial rights revenue in active distribution systems. In the developed framework, the upper-level problem determines the optimal capacity, routing, and dispatching of mobile energy storage to maximize revenue in the liberalized electricity market. The lower-level problem performs the joint optimization of energy and reserve market clearing as well as renewable energy capacity optimization based on the alternating current optimal power flow model. The non-convexity of the line flow and location marginal pricing is linearized via second-order cone relaxation and Karush-Kuhn-Tucker. The bi-level programming is reformatted as mathematical programming with equilibrium constraints. Further, the revenue risk due to renewable energy uncertainty is measured via conditional value-at-risk. Finally, the effectiveness of the optimization framework and solution method is verified using the modified IEEE-33 test system. The results show that mobile energy storage promotes renewable energy and reduces distribution network costs by 2.3%.

This study explores the enhancement of electric grid flexibility and the realization of smart grid objectives through the integration of renewable energy (RE) resources and energy storage systems (ESS). While prior research has mainly concentrated on optimizing ESS operations either within the transmission network as a versatile power source or within the distribution network using small-scale batteries, our approach offers a more holistic perspective. We introduce a bi-level stochastic model for integrated energy management that encompasses renewable energy, demand side management (DSM), transmission, and distribution networks as interconnected entities. To tackle the binary variables inherent in the model, we employ a precise method based on reformulation and decomposition techniques to ensure globally optimal solutions. We evaluate the efficacy of our proposed model and the influence of ESS on the networks using various integrated transmission and distribution network systems. Our findings demonstrate the model's efficiency and underscore the cost-saving benefits of integrating energy storage systems. Specifically, incorporating ESS into the distribution grid results in a 13 % reduction in distribution network costs, while deploying large batteries in the transmission grid leads to an impressive 83 % cost reduction.

In this paper, a new structure, the so-called FBSCUCFDIP-P/EV is proposed as flexibility-based security constrained unit commitment (SCUC) in the presence of false data injection (FDI) attack into the communication infrastructure of electric vehicle parking lot (EVPL). Herein, the uncertain EVPLs are integrated into the SCUC problem with the aim of reducing the operation cost. It is notable that growing integration of unspecified EVPLs can introduce novel challenges to the power system, significantly impacting its flexibility. In this study, electric vehicles are leveraged as a means to enhance system flexibility. Meanwhile, the FDI attacks in EVPLs can distort the system’s flexibility and lead to inaccurate assessments of the power system’s ability to adapt to changing conditions. In order to model the FDI attack, a bi-level optimization problem based on mixed integer linear programming is formulated. At the upper level, the impact of EVPLs on the flexibility indices of the SCUC is evaluated, and the false data injected into the EVPL is calculated at the lower level. Since both levels of the proposed FBSCUCFDIP-P/EV include discrete variables, a reformulation and decomposition technique is utilized to achieve the optimal solution. Instead, an extreme gradient boosting (XGBoost)-based machine learning method is considered to detection and correction of FDI attack. The proposed approach is tested on the IEEE 24-bus system. The simulation results initially indicate the improvement of the flexibility of the power system in proposed structure. Further, injecting false data into all available EVPLs causes to increase the system operation cost. Besides, false data leads to distorted charging and discharging scheduling of EVPLs; likewise, scheduling and commitment of power generation units also changes. Subsequently, the application of the XGBoost algorithm effectively mitigates the impact of FDI attacks, achieving a maximum accuracy of 85.41%.

In order to enable a multi-energy coupling integrated energy system (IES) to meet the needs of load diversity in low-carbon economic operation, a bi-level optimal configuration method for low-carbon economic operation of multi-energy coupling IES in different charging modes of electric vehicles (EVs) is proposed. First, an IES including cold-thermal-electric-gas coupling is established. Then, in the day-to-day operation stage, factors such as hierarchical carbon trading mechanism and different charging modes of EVs are considered to achieve the lowest daily scheduling cost. In the configuration planning stage, based on the daily operation cost, the equipment capacity is configured with the lowest equipment investment cost and annual operation cost. Finally, Cplex is used to solve the above two-stage objective functions and obtain the optimal configuration scheme and scheduling results through mutual iteration. The results show that the charging method considering the remaining charge of EVs and carbon trading mechanism can significantly reduce carbon emissions and operating costs of the system. The proposed configuration approach can well realize low-carbon economic operation of the multi-energy coupling IES.

In this paper, we will provide a detailed explanation of the limitations behind differentiable architecture search (DARTS). Algorithms based on the DARTS paradigm tend to converge towards degenerate solutions. A degenerate solution corresponds to an architecture with a shallow graph containing mainly skip connections. We have identified 6 sources of errors that could explain this phenomenon. Some of these errors can only be partially eliminated. Therefore, we will propose an innovative solution to remove degenerate solutions from the search space. We will demonstrate the validity of our approach through experiments conducted on the CIFAR10 and CIFAR100 databases. Our code is available at the following link: [ABSTRACT FROM AUTHOR]

Multi-objective bi-level optimization (MOBLO) addresses nested multi-objective optimization problems common in a range of applications. However, its multi-objective and hierarchical bi-level nature makes it notably complex. Gradient-based MOBLO algorithms have recently grown in popularity, as they effectively solve crucial machine learning problems like meta-learning, neural architecture search, and reinforcement learning. Unfortunately, these algorithms depend on solving a sequence of approximation subproblems with high accuracy, resulting in adverse time and memory complexity that lowers their numerical efficiency. To address this issue, we propose a gradient-based algorithm for MOBLO, called gMOBA, which has fewer hyperparameters to tune, making it both simple and efficient. Additionally, we demonstrate the theoretical validity by accomplishing the desirable Pareto stationarity. Numerical experiments confirm the practical efficiency of the proposed method and verify the theoretical results. To accelerate the convergence of gMOBA, we introduce a beneficial L2O (learning to optimize) neural network (called L2O-gMOBA) implemented as the initialization phase of our gMOBA algorithm. Comparative results of numerical experiments are presented to illustrate the performance of L2O-gMOBA. [ABSTRACT FROM AUTHOR]

AbstractSupply chain members can intelligently learn their decisions based on historical data by using Machine-Learning (ML) algorithms. To coordinate the supply chain, the data-driven contract design problems for three contracts—buyback, quantity flexibility, and combined quantity flexibility and rebate—were investigated for a supply chain with one manufacturer and multiple retailers under algorithm sharing and algorithm competition. The problems were formulated as bi-level optimization models by introducing nonlinear mapping from historical demand data to ordering decisions and using ML algorithms to learn the mapping parameters. The bi-level optimization models were transformed into semi-infinite programming models and solved using the (nested) cutting plane methods. Empirical studies using data from two databases showed that algorithm sharing or algorithm competition, the type of contract used, and learning algorithms were the three factors influencing the performance of supply chain coordination when using a data-driven contract design. Algorithm sharing was found to be more beneficial to the supply chain members than algorithm competition in promoting supply chain coordination. An effective incentive mechanism, such as an individualized buyback ratio and a rebate from the manufacturer to the retailers with a good forecast performance, can encourage the retailers to participate in algorithm sharing and improvement. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Microgrid (MG) integrated with renewable energy sources (RES) has become increasingly popular, especially when the lack of resources and environmental pollution are serious. However, the uncertainty of RES is the major problem when operating MG. The hydrogen energy storage system (HESS) is a prominent solution for the RES uncertainty since it can store excess energy, supply when in shortage, help to regulate power consumption, and improve MG's flexibility. In this research, a bi-level optimization model for planning the microgrid is proposed. The upper layer of the optimization model aims to minimize the total operation cost of the MG, while the lower level focuses on minimizing the operation cost of the hydrogen energy storage system. The results demonstrate that the proposed bi-level model can help to reduce the operation cost of MG by 42.75% in comparison with typical single level optimization model. Additionally, the electricity generated by diesel generators has also decreased by 52.43%, resulting in a significant reduction in CO 2 emissions and contributing to environmental protection. • Green hydrogen can be effectively stored from surplus renewable energy. • HESS operation in Day-Ahead economic optimization scheduling model for MG with high penetration of RES. • A novel bi-level optimization model for optimal scheduling of MG with 42.75% reduction in operation cost. • The interaction between the distributed sources and the behavior of HESS are incorporated into the optimal scheduling. • The electricity generated by diesel generators decreases by 52.43%, which leads to the CO 2 emission reduction. [ABSTRACT FROM AUTHOR]

Since microgrids can effectively integrate renewable energy, energy storage devices, and controllable loads, this advantage promotes the rapid development and application of microgrid technology. However, with the high proportion of renewable energy access, only considering how energy is optimally distributed in microgrids can no longer meet the actual demand. How to aggregate user-side controllable loads to form regulation resources has become a research hotspot, and the users, as a passive party in the load scheduling process, should also be an important consideration in their perception of the use of electricity. First, a control model for temperature-controlled loads and a time-shift model for time-shiftable loads are developed. Then, the comprehensive electricity satisfaction model of users is established, and the two-layer optimal scheduling model of microgrids considering users' satisfaction is proposed, with users as the upper layer and microgrids as the lower layer, and the two-layer model is transformed into a single-layer model according to the KKT condition for solving. Finally, the effect of the weighting factor for satisfaction on the economy is discussed through the analysis of examples, which verifies the effectiveness of the two-layer model. [ABSTRACT FROM AUTHOR]

This paper proposes a pricing model to ensure the budget balance of a company responsible for collecting and treating solid household waste in a city. The model used is of the Principal-Agent type which considers two actors at play, namely the leader (the service provider company) and the follower (the households and establishments that are the beneficiaries of the waste collection service). The model is a constrained bi-level optimization program. The model was applied in the case of Benin to the Grand Nokoué waste management and healthiness Company (SGDS-GN) whose intervention area, in the beginning, covers five communes in the southern part of the country. The solving of the optimization program was made with three (03) scenarios: the company spends two (02) times a week for waste collection (scenario 1), the company spends three (03) times a week for waste collection (scenario 2) and the company spends four (04) times a week for waste collection (scenario 3). The simulation results show that scenario 2 with three (03) passages per week appears more optimal for both the company (the operating result is higher) and the beneficiaries of waste collection services (the rate to be paid is lower). [ABSTRACT FROM AUTHOR]

We consider rather a general class of multi-level optimization problems, where a convex objective function is to be minimized subject to constraints of optimality of nested convex optimization problems. As a special case, we consider a trilevel optimization problem, where the objective of the two lower layers consists of a sum of a smooth and a non-smooth term. Based on fixed-point theory and related arguments, we present a natural first-order algorithm and analyze its convergence and rates of convergence in several regimes of parameters. [ABSTRACT FROM AUTHOR]

The material behavior intrinsically possesses the aleatory uncertainty (i.e., the natural variability). Against uncertainty in a given data set of elastic material responses, this paper presents a data-driven approach to reliability-based truss topology optimization under the compliance constraint. We utilize the order statistics to guarantee the confidence level of the probability that the reliability on the compliance constraint is no smaller than the target reliability, and formulate the truss optimization problem in a bi-level optimization form. By using the duality of linear optimization, we recast this bi-level optimization problem as a single-level optimization problem, which can be solved with a standard nonlinear optimization approach. Numerical examples illustrate the validity, as well as the characteristic of optimal solutions, of the proposed method.

Aiming to address power consumption issues of various equipment in metro stations and the inefficiency of peak shaving and valley filling in the power supply system, this study presents an economic optimization scheduling method for the multi-modal “source-network-load-storage” system in metro stations. The proposed method, called the Improved Gray Wolf Optimization Algorithm (IGWO), utilizes objective evaluation criteria to achieve economic optimization. First, construct a mathematical model of the “sourcenetwork- load-storage” joint system with the metro station at its core. This model should consider the electricity consumption within the station. Secondly, a two-layer optimal scheduling model is established, with the upper model aiming to optimize peak elimination and valley filling, and the lower model aiming to minimize electricity consumption costs within a scheduling cycle. Finally, this paper introduces the IGWO optimization approach, which utilizes meta-models and the Improved Gray Wolf Optimization Algorithm to address the nonlinearity and computational complexity of the two-layer model. The analysis shows that the proposed model and algorithm can improve the solution speed and minimize the cost of electricity used by about 5.5% to 8.7% on the one hand, and on the other hand, it improves the solution accuracy, and at the same time effectively realizes the peak shaving and valley filling, which provides a proof of the effectiveness and feasibility of the new method.

This paper introduces a comprehensive three-stage strategic framework designed to enable home microgrids (H-MGs) to collaborate effectively within multiple interconnected electrical and thermal grids. By forming coalitions, H-MGs can enhance their competitiveness in the energy market. The framework is supported by a bi-level optimization model that addresses the optimal management of both electrical and thermal energy within home microgrids, while also integrating electricity and heat distribution networks. A key aspect of this model is the incorporation of a specialized demand-side management strategy, which focuses on solving the optimization problem to maximize the overall system’s profit, despite the presence of variable uncertainties. The bi-level optimization model operates on two levels. The upper-level model focuses on maximizing the profit of the network operator, taking into account Combined Heat and Power (CHP) resources. The lower-level model, on the other hand, is designed to minimize the cost of electricity supply for H-MGs. To solve this complex optimization problem, the paper proposes a Multi-Stage Stochastic Programming approach based on the Golf Optimization Algorithm (MSSP-GOA). The effectiveness of the proposed method is demonstrated through a detailed simulation study. The results show that the method significantly reduces the market clearing price (MCP) for approximately 26% of the time intervals. Additionally, it leads to a 42% increase in the consumption of responsive loads within H-MGs and a threefold increase in local generation. The MSSP-GOA algorithm not only enhances market participation but also significantly boosts profits for all participants involved, underscoring its potential as a robust solution for optimizing energy management in collaborative microgrid environments.

The rise of distributed energy resources (DERs) in the energy landscape underscores the pivotal role of prosumers in the ongoing energy transition. With the significant investment required for individual energy storage (IES), community energy storage (CES) emerges as a key facilitator, enabling the smooth incorporation of renewable energy sources and strengthening grid flexibility. This paper explores the dynamic interplay between CES owners, who serve as key economic actors in local energy communities, and prosumers within these communities through a Stackelberg game framework. A bi-level optimization framework aimed at empowering CES systems is introduced to address the proposed Stackelberg game framework. At the upper level of this framework, the goal is to maximize the profit of the CES system while the flexibility limits of the upstream electricity grid and other constraints are addressed. On the other hand, individual prosumers at the lower level optimize their actions to minimize billing costs and enhance comfort in alignment with their preferences. The bi-level optimization problem is reformulated into a linear single-level optimization problem using the Karush-Kuhn-Tucker (KKT) approach. The results indicate that implementing flexibility constraints can effectively smooth energy exchanges with the upstream grid. However, the distribution system operator must account for the potential reduction in the CES system’s operational profit to incentivize the CES owner to participate in the flexibility enhancement program.

In this paper, an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system is considered, where the ISAC base station (BS) serves the communication user equipments (UEs) and tracks sensing targets simultaneously. Unfortunately, it is challenging to obtain the perfect channel state information (CSI) of user equipments (UEs)-related links and target directions in practice. To address the imperfection, we investigate the robust beamforming design under the statistical CSI and bounded target direction uncertainty models. With these considerations, we formulate a Cramér-Rao bound (CRB) of sensing targets minimization problem, subject to the signal-to-interference-plus-noise ratio (SINR) outage probability constraint, the maximum transmit power limits and the unit-modulus constraint of each IRS element. In order to solve this non-convex problem, we formulate the robust beamforming design as a bi-level optimization (BLO) problem and then apply the double-loop deep unfolding (DU) approach to solve this problem. Specifically, we formulate the problem into a primal-dual problem by integrating the SINR outage probability constraint into the objective function which is further approximated by using the worst-case SINR. The projection method and Riemannian manifold optimization method are used to deal with the maximum transmit power constraint and the unit-modulus constraint, respectively. Therefore, the problem can be reformulated as the bi-level optimization (BLO) problem. The lower level (LL) problems aim to find the worst-case CSI and angle of sensing targets. The upper level (UL) problem is the primal-dual problem solved by beamforming design and the Lagrange multipliers optimization with the certain CSI and angle. Then, we apply double-loop DU neural network which unfolds the iterative gradient descent into multi-layer structure, and each layer consists of UL loop and LL loop. In addition, the trainable step sizes and offset parameters are introduced as the network parameters, and the network ultimately outputs optimal optimization variables. Simulation results demonstrate the effectiveness of the proposed robust beamforming design algorithm.

Considering the flexibility of shared energy storage and the different electricity demand of multiple types of users, a bi-level optimization method is proposed to achieve the optimal overall economic benefits of users and improve the photovoltaic consumption capacity for industrial users. Aiming at the lowest monthly power consumption cost of users, the upper layer model is established considering the system energy balance, energy storage charging and discharging constraints and energy storage state of charge constraints, so as to reduce the monthly maximum electricity demand and decrease demand charge. Taking the optimized monthly demand from the upper layer model, the day-ahead model is established, and the charging and discharging power of the shared energy storage, as well as the interactive power between users and the power grid are output to optimize comprehensive benefits of multiple types of industrial users and improve the photovoltaic absorption ability. Strategies for shared energy storage charging and discharging power, as well as user interaction power with the grid, are proposed to optimize the comprehensive benefits for various types of industrial users, ensuring daily economic efficiency under the premise of monthly overall economic optimality. Finally, the effectiveness of the proposed method is verified with the case study.

The bidirectional uncertainty of source and load creates scarcity in the reserve resources of the distribution network. Therefore, it is highly significant for the safe and economic operation of the system to harness spare energy storage capacity from prosumers to provide reserves. This paper proposes a bi-layer optimal scheduling model of “distribution network–prosumer group–prosumer” that considers the flexible reserve resources of a prosumer. The upper layer is the “distribution network–prosumer group” optimization model, in which the distribution network sets the electricity price and reserve price according to its own economic benefit and sends it to the prosumer group and guides it to participate in the scheduling of the resources of the prosumer. The lower layer is the “prosumer group–prosumer” optimization model, where the prosumer group incentivizes the prosumer to adjust its energy storage charging and discharging plans through prices and mobilize its own resources to provide flexible reserve resources. The results show that the optimal method proposed in this paper can fully utilize flexible reserve resources from prosumers, improve the economy of distribution network operations, and reduce the pressure of providing reserves using the upper grid.

The collaboration of a multiple unmanned underwater vehicles (multi-UUVs) system has attracted widespread attention in recent years, as it can overcome the limitations of a single UUV and enhance mission completion efficiency. Oriented towards patrol and exploration missions with multiple waypoints, this paper proposes a balanced mission planning strategy, aiming to improve mission quality while reducing mission time for multi-UUVs. Firstly, due to the uneven performance of the two optimization objectives, a quick initialization screening method is employed specifically for mission quality to reduce the mission space. Secondly, to ensure mission load distribution and collaboration among multi-UUVs, and ease the difficulty in solving the issues of mission allocation and route planning, a balanced bi-level mission planning method based on regional segmentation is proposed. Finally, applicable weight evaluation criteria are utilized to evaluate the feasible solution set and determine the optimal solution. The efficacy of the balanced mission planning strategy is substantiated through comprehensive numerical simulations in a complex 2D marine environment.

An innovative strategy for an optimal design of planar frames able to resist seismic excitations is proposed. The optimal design is performed considering the cross sections of beams and columns as design variables. The procedure is based on genetic algorithms (GA) that are performed according to a nested structure suitable to be implemented in parallel on several computing devices. In particular, this bi-level optimization involves two nested genetic algorithms. The first external one seeks the size of the structural elements of the frame which corresponds to the most performing solution associated with the highest value of an appropriate fitness function. The latter function takes into account, among other considerations, the seismic safety factor and the failure mode that are calculated by means of the second internal algorithm. The proposed procedure aims at representing a prompt performance-based design procedure which observes earthquake engineering principles, that is displacement capacity and energy dissipation, although based on a limit analysis, thus avoiding the need of performing cumbersome nonlinear analyses. The details of the proposed procedure are provided and applications to the seismic design of two frames of different size are described. [ABSTRACT FROM AUTHOR]

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Deep neural networks, specifically deep convolutional neural networks (DCNNs), have been highly successful in machine learning and computer vision, but a significant challenge when using these networks is choosing the right hyperparameters. As the number of layers in the network increases, the search space also becomes larger. To overcome this issue, researchers in deep learning have suggested using deep compression techniques to decrease memory usage and computational complexity. In this paper, we present a new approach for compressing deep CNNs by combining filter and channel pruning methods based on Evolutionary Algorithms (EA). This method involves eliminating filters and channels in order to decrease the number of parameters and computational complexity of the model. Additionally, we propose a bi-level optimization problem that interacts between the hyperparameters of the convolution layer. Bi-level optimization problems are known to be difficult as they involve two levels of optimization tasks, where only the optimal solutions to the lower-level problem are considered as feasible candidates for the upper-level problem. In this work, the upper-level problem is represented by a set of filters to be pruned in order to minimize the number of selected filters, while the lower-level problem is represented by a set of channels to be pruned in order to minimize the number of selected channels per filter. Our research has focused on developing a new method for solving bi-level problems, which we have named Bi-CNN-Pruning. To achieve this, we have adopted the Co-Evolutionary Migration-Based Algorithm (CEMBA) as our search engine. The Bi-CNN-Pruning method is then evaluated using image classification benchmarks on well-known datasets such as CIFAR-10 and CIFAR-100. The results of our evaluation demonstrate that our bi-level proposal outperforms state-of-the-art architectures, and we provide a detailed analysis of the results using commonly employed performance metrics. [ABSTRACT FROM AUTHOR]