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Due to the increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as large industrial consumers can arbitrage energy prices and provide reserve capacity by exploiting the inherent flexibility in selected industrial processes. A large enough industrial consumer can capitalize on this flexibility through optimized bidding in electricity markets. In this work, the day-ahead cost minimization of a risk-averse pulp and paper mill is formulated as a two-stage stochastic problem, considering thermodynamic and electrical constraints. The bids in the energy and reserve markets are jointly optimized subject to price uncertainty as well as uncertainty of frequency realization. The results of a case study in Sweden display a significant economic benefit in exploiting the flexibility of integrated pulp and paper mills with electric boilers. The expected cost of the pulp and paper mill resulting from different strategies are compared and the risk-aversion of the pulp and paper mill is investigated. Reserve offers are mainly facilitated by fast-acting electric boilers and supported by flexibility in the steam network. We show that reserve offers can significantly improve the profitability of the pulp and paper mill. • Pulp and paper mills with electric boilers can supply primary frequency reserves. • Electric boilers can provide up to 50 MW flexibility of 100 MW rating. • An algorithm is developed to optimize the energy & reserve bids of the mill. • Expected cost savings range between 7.4% in summer and 2.3% in winter. [ABSTRACT FROM AUTHOR]

Deploying renewable generation to replace conventional fossil-fuel-based energy supplies provides an important pathway for the decarbonization of steelmaking plants. Meanwhile, it is also crucial to improve the flexibility of blast-furnace-gas-fired combined-cycle power plants (BFGCCs) to ease the accommodation of uncertain renewable generation. To this end, this paper proposes the deployment of a molten salt heat storage (MSHS) system in BFGCCs to store the heat of gas turbine flue gas so that the power–heat coupling of these BFGCCs can be unlocked to enhance the flexibility of the energy supply. A stochastic capacity optimization of an integrated BFGCC–MSHS–wind–solar (BMWS) energy system is presented to determine the optimal installed capacities of a BFG holder, MSHS, wind turbine, and PV panel, aiming to achieve an economic and safe energy supply for the entire system. Multiple scenarios considering uncertain fluctuations in load demands and renewable generation are generated with the Monte Carlo method based on a typical scenario. These scenarios are then reduced to representative scenarios using the synchronous substitution and reduction method for stochastic capacity optimization to enhance the reliability of the results. The case study results demonstrate that configuring MSHS reduces the total annualized cost of the BMWS system by 2.28%. Furthermore, considering the uncertainties of the power/heating load and wind/PV generation can reduce the expected annualized total cost of the BMWS system and the corresponding standard deviation by 5.66% and 81.45%, respectively. The BMWS system can achieve 730.68 tons of equivalent CO2 reduction in 24 h due to the successful utilization of renewable energy. This paper provides an effective approach for the decarbonization of energy generation systems in steelmaking plants. [ABSTRACT FROM AUTHOR]

Featured Application: Optimal operation for renewable energy integrated smart microgrid under uncertainty. Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park's peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids. [ABSTRACT FROM AUTHOR]

There exists a temporal and spatial coupling effect among the hydropower units in cascade hydropower stations which constitutes a complex planning problem. Researching the multi-objective optimization scheduling of cascade hydropower stations under various spatiotemporal inflow impacts is of significant importance. Previous studies have typically only focused on the economic dispatch issues of cascade hydropower stations, with little attention given to their coupling mechanism models and the uncertainty impacts of inflows. Firstly, this paper establishes a coupled optimization scheduling model for cascade hydropower stations and elaborates on the operational mechanism of cascade hydropower stations. Secondly, according to the needs of actual scenarios, two types of optimization objectives are set, considering both the supply adequacy and peak-shaving capacity as indicators, with the total residual load and the peak-valley difference of the residual load as comprehensive optimization objectives. Subsequently, considering the uncertainty impact of the inflow side, a stochastic optimization model for inflow is established based on a normal distribution probability. Finally, case study analyses demonstrate that the proposed model not only effectively achieves supply stability but also reduces the peak-valley difference in load, and can achieve optimized scheduling under the uncertain environment of inflow. [ABSTRACT FROM AUTHOR]

To maximize the expected profits and manage the risks of renewable energy system under electricity market environment, scenario-based- stochastic optimization model can be established to generate energy bidding strategies, in which the probabilistic scenarios of risk parameters are usually obtained by using statistical or machine learning methods. This paper proposes a practical multivariate statistical method for risk parameter scenario generation, which is used by a wind energy system faced with uncertain electricity prices and wind power productions, and it considers the correlation between dependent risk parameters by using historical data directly. The probabilities of scenarios containing correlated risk parameters are calculated by using multivariate histograms, in which the asymmetric correlation between different parameters existing in the historical data are preserved. Additionally, in order to make the stochastic optimization problem with large numbers of scenarios tractable, a multivariate scenario reduction method is used to trim down the scenario number. By solving the stochastic optimization problem, optimal day-ahead bidding curves for the wind energy system are generated, and Douglas-Peucker algorithm is used to fit the bidding curves according to market requirements. Case studies based on real world data in electricity markets are performed to prove the effectiveness of the proposed risk parameter scenario generation method and energy bidding strategies. Finally, conclusions and practical suggestions on future research works are provided. [ABSTRACT FROM AUTHOR]

The wind turbine power curve model is critical to a wind turbine's power prediction and performance analysis. However, abnormal data in the training set decrease the prediction accuracy of trained models. This paper proposes a sample average approach-based method to construct an interval model of a wind turbine, which increases robustness against abnormal data and further improves the model accuracy. We compare our proposed methods with the traditional neural network-based and Bayesian neural network-based models in experimental data-based validations. Our model shows better performance in both accuracy and computational time. [ABSTRACT FROM AUTHOR]

Recognizing the importance of incorporating different risk measures in the portfolio management model, this paper examines the dynamic mean-risk portfolio optimization problem using both variance and value at risk (VaR) as risk measures. By employing the martingale approach and integrating the quantile optimization technique, we provide a solution framework for this problem. We demonstrate that, under a general market setting, the optimal terminal wealth may exhibit different patterns. When the market parameters are deterministic, we derive the closed-form solution for this problem. Examples are provided to illustrate the solution procedure of our method and demonstrate the benefits of our dynamic portfolio model compared to its static counterpart. [ABSTRACT FROM AUTHOR]

Robust face clustering enjoys a wide range of applications for gate passes, surveillance systems and security analysis in embedded sensors. Nevertheless, existing algorithms have limitations in finding accurate clusters when data contain noise (e.g., occluded face clustering and recognition). It is known that in subspace clustering, the ℓ 1 - and ℓ 2 -norm regularizers can improve subspace preservation and connectivity, respectively, and the elastic net regularizer (i.e., the mixture of the ℓ 1 - and ℓ 2 -norms) provides a balance between the two properties. However, existing deterministic methods have high per iteration computational complexities, making them inapplicable to large-scale problems. To address this issue, this paper proposes the first accelerated stochastic variance reduction gradient (RASVRG) algorithm for robust subspace clustering. We also introduce a new momentum acceleration technique for the RASVRG algorithm. As a result of the involvement of this momentum, the RASVRG algorithm achieves both the best oracle complexity and the fastest convergence rate, and it reaches higher efficiency in practice for both strongly convex and not strongly convex models. Various experimental results show that the RASVRG algorithm outperformed existing state-of-the-art methods with elastic net and ℓ 1 -norm regularizers in terms of accuracy in most cases. As demonstrated on real-world face datasets with different manually added levels of pixel corruption and occlusion situations, the RASVRG algorithm achieved much better performance in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Precipitation modeling holds significant importance in various fields such as agriculture, animal husbandry, weather derivatives, hydrology, and risk and disaster preparedness. Stochastic precipitation generators (SPGs) represent a class of statistical models designed to generate synthetic data capable of simulating dry and wet precipitation stretches for a long duration. The construction of Hidden Markov Models (HMMs), which treat latent meteorological circumstances as hidden states, is an efficient technique for simulating precipitation. Considering that there are many choices of emission distributions used to generate positive precipitation, the characteristics of different distributions for simulating positive precipitation have not been fully explored. The paper includes a simulation study that demonstrates how the Pareto distribution, when used as the distribution for generating positive precipitation, addresses the limitations of the exponential and gamma distributions in predicting heavy precipitation events. Additionally, the Pareto distribution offers flexibility through adjustable parameters, making it a promising option for precipitation modeling. We can estimate parameters in HMMs using forward–backward algorithms, Variational Bayes Expectation-Maximization (VBEM), and Stochastic Variational Bayes (SVB). In the Xilingol League, located in the central part of the Inner Mongolia Autonomous Region, China, our study involved data analysis to identify crucial locations demonstrating a robust correlation and notable partial correlation between the Normalized Difference Vegetation Index (NDVI) and annual precipitation. We performed fitting of monthly dry days ratios and monthly precipitation using seasonal precipitation and year-round precipitation data at these crucial locations. Subsequently, we conducted precipitation predictions for the daily, monthly, and annual time frames using the new test dataset observations. The study concludes that the SPG fits the monthly dry-day ratio better for annual daily precipitation data than for seasonal daily precipitation data. The fitting error for the monthly dry day ratio corresponding to annual daily precipitation data is 0.053 (exponential distribution) and 0.066 (Pareto distribution), while for seasonal daily precipitation data, the fitting error is 0.14 (exponential distribution) and 0.15 (Pareto distribution). The exponential distribution exhibits the poorest performance as a model for predicting future precipitation, with average errors of 2.49 (daily precipitation), 40.62 (monthly precipitation), and 130.40 (annual precipitation). On the other hand, the Pareto distribution demonstrates the best overall predictive performance, with average errors of 0.69 (daily precipitation), 34.69 (monthly precipitation), and 66.42 (annual precipitation). The results of this paper can provide decision support for future grazing strategies in the Xilingol League. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a stochastic optimization technique that imitates the evolution process in nature. This paper uses an improved adaptive differential evolution to solve gravity inversion with multiplicative regularization. Compared with additive regularization, the advantage of multiplicative regularization is that it does not require the regularization parameter in the search process. The contributions in this paper mainly focus on two aspects: accelerating the convergence speed of adaptive DE and balancing the effect of model and data misfits in the objective function. The effectiveness of the proposed inversion method is verified by synthetic and field cases. For the synthetic cases, it is concluded that, based on the obtained results and analysis, the presented DE method is superior and competitive with its original version. Additionally, the designed parameter adaptation for multiplicative regularization is useful for trading off the effect of data and model misfits. For the field cases, two successful applications from China were conducted, and the obtained density source distributions were in accordance with those obtained from drilling wells. The synthetic and practical examples demonstrate that high-quality inversion results can be obtained using improved adaptive differential evolution and multiplicative regularization. [ABSTRACT FROM AUTHOR]

Scenario generation has attracted wide attention in recent years owing to the high penetration of uncertainty sources in modern power systems and the introduction of stochastic optimization for handling decision-making problems. These include unit commitment, optimal bidding, online supply–demand management, and long-term planning of integrated renewable energy systems. Simultaneously, the installed capacity of solar power is increasing due to its availability and periodical characteristics, as well as the flexibility and cost reduction of photovoltaic (PV) technologies. This paper evaluates scenario generation methods in the context of solar power and highlights their advantages and limitations. Furthermore, it introduces taxonomies based on weather classification techniques and temporal horizons. Fine-grained weather classifications can significantly improve the overall quality of the generated scenario sets. The performance of different scenario generation methods is strongly related to the temporal horizon of the target domain. This paper also conducts a systematic review of the currently trending deep generative models to assess introduced improvements, as well as to identify their limitations. Finally, several research directions are proposed based on the findings and drawn conclusions to address current challenges and adapt to future advancements in modern power systems. [ABSTRACT FROM AUTHOR]

In this paper, we develop a three-stage stochastic network-based optimization model for the provision of 5G services with Unmanned Aerial Vehicles (UAVs) in the disaster management phases of: preparedness, response and recover/reconstruction. Users or devices on the ground request services of a fleet of controller UAVs in flight and the requested services are executed by a fleet of UAVs organized as a Flying Ad-Hoc Network and interconnected via 5G technology. A disaster scenario can create difficulties for the provision of services by service providers. For this reason, in the first stage, service providers make predictions about possible scenarios in the second stage. Therefore, the first stage represents the preparedness phase, the second stage represents the response phase, followed by the recovery/reconstruction phase, represented by the third stage. In each of the three stages, service providers seek to maximize the amount of services to be performed, assigning each service a priority. They also aim to, simultaneously, minimize the total management costs of requests, the transmission and execution costs of services, the costs to increase the resources of the pre-existing network and, if need be, to reduce them in the recovery/reconstruction phase. For the proposed multi-stage stochastic optimization model, we provide variational formulations for which we investigate the existence and uniqueness of the solution. Finally, a detailed numerical example is solved in order underline some of the key aspects of the model. This paper adds to the literature on the rigorous mathematical modeling of advanced technologies for disaster management. [ABSTRACT FROM AUTHOR]

As the penetration of renewable distributed generation (RDG) continues to grow, the stochastic and intermittent nature of its output imposes significant challenges on distribution networks (DNs), such as source-load mismatch and voltage fluctuations, which seriously affects the safety and reliability of the system. Thus, this paper presents a stochastic optimal allocation method for a battery energy storage system (BESS) in the DN, with the consideration of annual load growth, BESS degradation, and DN operation, aiming to minimize the overall cost of DNs and harvest more renewable energy. Based on the rainflow-counting concept, BESS degradation is efficiently modeled and linearized to improve solvability. Additionally, to address the uncertainties of RDG outputs and loads, a stochastic optimization (SO) method is adopted. Furthermore, considering that a large number of integer variables of the BESS allocation model may cause a heavy computational burden, a feasibility pump-based solution algorithm is introduced to accelerate the solving speed. Finally, the effectiveness of the proposed BESS allocation method and the solution algorithm is verified on a 33-bus DN system through comparative analyses, showing high efficiency and performance. [ABSTRACT FROM AUTHOR]

The paper provides a precise error estimate for an asymptotic expansion of a certain stochastic control problem related to relative entropy minimization. In particular, it is shown that the expansion error depends on the regularity of functionals on path space. An efficient numerical scheme based on a weak approximation with Monte Carlo simulation is employed to implement the asymptotic expansion in multidimensional settings. Throughout numerical experiments, it is confirmed that the approximation error of the proposed scheme is consistent with the theoretical rate of convergence. [ABSTRACT FROM AUTHOR]

Developing renewable energy has become a major strategy for China to accelerate the energy transition and combat climate change. Accordingly, a guarantee mechanism for renewable energy consumption with renewable portfolio standards (RPS) has been set in China. However, currently, the top-down allocation of regional renewable energy consumption targets often has issues of unfairness and inefficiency. It is necessary to investigate the issue of how to stimulate the renewable energy consumption potential on the demand side and reasonably formulate the consumption responsibility weights of various market entities. This paper aimed to develop a new methodology for the weight allocation of renewable energy consumption responsibilities. In doing so, an integrated model of an evolutionary game and stochastic optimization was constructed between market entities and governments. Then, the equilibrium strategies of market entities and governments were obtained through the evolutionary game. Furthermore, based on the equilibrium strategies, this paper optimized the renewable energy consumption weight of each market entity, which constitutes the optimal allocation scheme of renewable energy consumption responsibility weights. Finally, using the data of 7069 market entities in Hubei Province in 2021, this study simulated the model to verify its effectiveness and practicability. The results indicate that the willingness of market entities to assume more consumption responsibility is positively correlated with the government's incentives and the maturity of the green electricity trading market. This study provides important implications for optimizing government regulations and promoting renewable energy consumption. [ABSTRACT FROM AUTHOR]

The objective of this special issue is to introduce a new theme, the use of game and decision theory in reliability modeling and risk analysis, which was the focus of the First Symposium on Games and Decisions in Reliability and Risk (GDRR) held at the George Washington University on May 27-28, 2009. The issue considered papers presented at the Second Symposium on GDRR (http://www.mi.imati.cnr.it/conferences/gdrr11.html), held at the Hotel Villa Carlotta, Belgirate (VB), Lake Maggiore, Italy, on May 19-21, 2011, and also was open to the public for submission of papers relevant to the theme. The contributors to the special issue include Sevillano, Rios Insua, and Rios; Delquié; Foschi and Spizzichino; French; Bhattacharjya and Deleris; Bhattacharjya and Shachter; Lejeune; Cheung and Zhuang; Smith and Dodd; Sri Bhashyam and Montibeller; and Paté-Cornell. [ABSTRACT FROM AUTHOR]

Summary: Power systems face more uncertainty by increasing photovoltaic system installations on the roof of buildings. To optimally manage energy and available flexibility in a building, stochastic optimization is used to take an optimal decision under uncertainty and minimize the operational cost. In stochastic optimization, a scenario set is used as an input to represent the uncertainty in a random variable, PV energy generation in this case. In this paper, a data‐driven method is proposed to obtain the distribution of the random variable and later generate scenario sets representing the uncertainty on day‐ahead PV energy generation installed on the roof of a building. This method is only based on historical PV generation and it does not require any other external data such as weather forecasts. A machine learning‐based technique is applied to forecast the PV energy production following by generating a scenario set for day‐ahead decision‐making. Later, the day‐ahead PV‐battery system management problem is formulated as a two‐stage stochastic optimization while the generated scenario set is the input of this optimization. The proposed algorithm is tested in the day‐ahead scheduling of a PV‐battery system for a commercial building, informed by real‐life measurement data. The results show that the proposed algorithm is able to capture the uncertainty in PV system while providing a cost‐optimal and reliable solution to the application problem. Without using any weather data, the error of the proposed PV energy forecast method reaches to NRMSE = 11.89, while there is 5% reduction in the operational cost of the proposed two‐stage stochastic optimization. Moreover, the proposed algorithm is easy to implement in the energy management system of a building to manage PV‐battery system. [ABSTRACT FROM AUTHOR]

China's carbon neutrality strategy has expedited a transition towards greener and lower-carbon integrated energy systems. Faced with the problem that the central position of thermal power cannot be transformed quickly, utilizing traditional thermal power units in a low-carbon and efficient manner is the premise to guarantee green energy development. This study focuses on the integrated energy production system (IEPS) and a stochastic optimization model for capacity configuration that integrates carbon capture storage and power-to-gas while considering source-load uncertainty. Firstly, carbon capture storage and power-to-gas technologies are introduced, and the architecture and models of the IEPS are established. The carbon and hydrogen storage equipment configuration enhances the system's flexibility. Also, source-load uncertainty is considered, and a deterministic transformation is applied using the simultaneous backward reduction algorithm combined with K-means clustering. The paper simulates the optimal capacity configuration of the IEPS in a park energy system in Suzhou, China. Furthermore, the research performs a sensitivity analysis on coal, natural gas, and carbon tax prices. Case studies verified that IEPS can realize the recycling of electricity, gas, hydrogen, and carbon, with remarkable characteristics of low-carbon, flexibility, and economical. Stochastic optimized capacity allocation results considering source-load uncertainty are more realistic. Sensitivity intervals for energy prices can reference pricing mechanisms in energy markets. This study can provide ideas for the transition of China's energy structure and offer directions to the low-carbon sustainable development of the energy system. [ABSTRACT FROM AUTHOR]

In this paper, a weighted multivariate generalized Gaussian mixture model combined with stochastic optimization is proposed for point cloud registration. The mixture model parameters of the target scene and the scene to be registered are updated iteratively by the fixed point method under the framework of the EM algorithm, and the number of components is determined based on the minimum message length criterion (MML). The KL divergence between these two mixture models is utilized as the loss function for stochastic optimization to find the optimal parameters of the transformation model. The self-built point clouds are used to evaluate the performance of the proposed algorithm on rigid registration. Experiments demonstrate that the algorithm dramatically reduces the impact of noise and outliers and effectively extracts the key features of the data-intensive regions. [ABSTRACT FROM AUTHOR]

This paper investigates the stochastic optimization problem focusing on developing scalable parallel algorithms for deep learning tasks. Our solution involves a reformation of the objective function for stochastic optimization in neural network models, along with a novel parallel computing strategy, coined the weighted aggregating stochastic gradient descent (WASGD). Following a theoretical analysis on the characteristics of the new objective function, WASGD introduces a decentralized weighted aggregating scheme based on the performance of local workers. Without any center variable, the new method automatically gauges the importance of local workers and accepts them by their contributions. Furthermore, we have developed an enhanced version of the method, WASGD+, by (1) implementing a designed sample order and (2) upgrading the weight evaluation function. To validate the new method, we benchmark our pipeline against several popular algorithms including the state-of-the-art deep neural network classifier training techniques (e.g., elastic averaging SGD). Comprehensive validation studies have been conducted on four classic datasets: CIFAR-100, CIFAR-10, Fashion-MNIST, and MNIST. Subsequent results have firmly validated the superiority of the WASGD scheme in accelerating the training of deep architecture. Better still, the enhanced version, WASGD+, is shown to be a significant improvement over its prototype. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic model predictive control approach combined with a time-series forecasting technique to tackle the problem of microgrid energy management in the face of uncertainty. The data-driven nonparametric chance constraint method is used to formulate chance constraints for stochastic model predictive control, while removing the dependency on probability density assumptions of uncertain variables and retaining the linear structure of the resulting optimization problem. The proposed approach is suitable for implementation on systems with limited computational power or limited memory storage, thanks to its simple linear structure and its ability to provide accurate results within pre-defined confidence levels, even when using small data batches. The proposed forecasting and stochastic model predictive control approaches are applied on a numerical example featuring a small gridconnected microgrid with PV generation, a battery storage system, and a noncontrollable load, showing the ability to reduce costs by reducing the confidence level, and to satisfy pre-defined confidence levels. [ABSTRACT FROM AUTHOR]

The aim of this paper is to address the challenges regarding the safety and economics of power system operation after the integration of a high proportion of wind power. In response to the limitations of the literature, which often fails to simultaneously consider both aspects, we propose a solution based on a stochastic optimization scheduling model. Firstly, we consider the uncertainty of day-ahead wind power forecasting errors and establish a multi-scenario day-ahead stochastic optimization scheduling model. By balancing the reserve capacity and economic efficiency in the optimization scheduling, we obtain optimized unit combinations that are applicable to various scenarios. Secondly, we account for the auxiliary service constraints of thermal power units participating in deep peak shaving, and develop an intra-day dynamic economic dispatch model. Through the inclusion of thermal power units and energy storage units in the optimization scheduling, the accommodation capacity of wind power is further enhanced. Lastly, in the electricity market environment, increasing wind power capacity can increase the profits of thermal power peak shaving. However, we observe a trend of initially increasing and subsequently decreasing wind power profits as the wind power capacity increases. Considering system flexibility and the curtailed wind power rate, it is advisable to moderately install grid-connected wind power capacity within the power system. In conclusion, our study demonstrates the effectiveness of the proposed scheduling model in managing day-ahead uncertainty and enhancing the accommodation of wind power. [ABSTRACT FROM AUTHOR]

With the development of 6G wireless communication technologies, various resource-intensive and delay-sensitive vehicle application tasks are generated. These application tasks can be offloaded to Mobile Edge Computing (MEC) which deploys computing resources at the edge of networks. Besides, the recent proposed Cybertwin, as the digital representation of the complicated physical end-systems, can help the terminals obtain the required services from networks. Vehicles enabled by Cybertwin can offload their tasks to MEC and achieve better performance. In this paper, we focus on the study of a hybrid energy-powered multi-server MEC system with Cybertwin. Vehicles enabled by Cybertwin and edge servers send the current network status and unprocessed vehicle application tasks to the macro base station (MBS) to achieve the better allocation of resources. Energy harvesting (EH) devices are deployed on edge servers to form a “green energy-grid” hybrid energy supply model. We formulate a stochastic offloading optimization problem, and the goal is to minimize the system cost. The stochastic optimization problem is decomposed into three sub-problems. Then, we design an efficient multi-vehicle task offloading (EMT) algorithm to achieve the trade-off between system cost and task queue length. Theoretical analysis shows that EMT algorithm can optimize the total cost of the MEC system and guarantee the system performance. According to experimental evaluation, we verify the performance of the EMT algorithm. [ABSTRACT FROM AUTHOR]

Stochastic optimization can be used to model predictable but uncertain element failures, in an attempt to enhance system reliability in power system operation and planning. In practical applications, such as preventive operation during severe weather, the uncertainty set is often very large. This will lead to two challenges: (i) every possible scenario cannot be practically identified; and (ii) the computational demands of stochastic optimization with a large scenario set cannot be met. To address these challenges, this paper develops a multidimensional scenario selection method, which creates a rather small but representative set of scenarios. The developed method makes use of failure features as well as network features of the elements that may fail, to achieve a superior performance. The simulations studies on a synthetic large-scale Texas system, show the dominant performance of the method compared to existing algorithms in the literature, as well as common industry practices. Due to its effectiveness, the method presented in this paper enables computationally efficient implementation of stochastic power system operation and planning software tools. Such stochastic tools will improve system reliability and efficiency through enhanced use of the existing resources, without requiring any expensive system upgrade. [ABSTRACT FROM AUTHOR]

Most of the existing caching solutions for wireless networks rest on the ideal assumption that the file popularity distribution is perfectly known. In this paper, we consider optimal random caching designs for perfect, imperfect, and unknown file popularity distributions in a large-scale multi-tier wireless network. First, in the case of perfect file popularity distribution, we formulate the successful transmission probability (STP) optimization problem, which is nonconvex. We propose an efficient parallel iterative algorithm to obtain a stationary point based on parallel successive convex approximation (SCA). Then, in the case of imperfect file popularity distribution, we formulate the worst-case STP maximization problem. To solve this challenging robust optimization problem, we transform it into an equivalent complementary geometric programming (CGP) and propose an efficient iterative algorithm to obtain a stationary point based on the SCA. To the best of our knowledge, this is the first work explicitly considering the estimation error of file popularity distribution in the optimization of caching design. Next, in the case of unknown file popularity distribution, we formulate the stochastic STP (i.e., the STP in the stochastic form) maximization problem. This is a challenging nonconvex stochastic optimization problem, and we propose an efficient iterative algorithm to obtain a stationary point based on stochastic parallel SCA. As far as we know, this is the first work considering stochastic optimization in a large-scale wireless network. Finally, by numerical results, we show that the proposed solutions achieve notable gains over existing schemes in all three cases and reveal the values of the robust caching optimization and stochastic caching optimization in the cases of imperfect file popularity distribution and unknown file popularity distribution, respectively. [ABSTRACT FROM AUTHOR]

The optimal sizing of an energy storage system (ESS) in a power generation system that incorporates photovoltaic (PV) generation is crucial in a power grid for which the reduction of CO2 emissions is important. This problem is particularly challenging when it relates to the power system of a ship because it involves uncertain meteorological and load data along a navigation route. This paper proposes a novel method for multi-objective minimization of investment/replacement cost, fuel cost, and CO2 emissions, to find the optimal size of the ESS considering life-span of the ESS. The generation of power by PV modules on a ship is affected by temporal and geographical variations of irradiation along the navigation route. In particular, operating load conditions and irradiation are uncertain. This paper proposes a novel algorithm for partitioning high-dimensional uncertain data into tractable clusters solved by deterministic optimization method. Case studies of an all-electric ship along a route from Dalian in China to Aden in Yemen are shown to demonstrate the applicability of the proposed clustering-based stochastic optimization method. [ABSTRACT FROM PUBLISHER]

In recent years, there is massive development of new metaheuristics as stochastic methods. Meanwhile, there is not any metaheuristics is powerful to handle all problems as stated in the no-free-lunch (NFL) theory. Based on this circumstance, this paper introduces a new swarm-based metaheuristics with the main strategy moving toward the resultant of better swarm members and avoiding the resultant of worse swarm members called group better-worse algorithm (GBWA). It consists of five searches: moving toward the best swarm member, moving toward the resultant of better swarm members, moving away from the resultant of worse swarm members, searching locally, and jumping to the opposite area. GBWA is then evaluated in three ways. The first evaluation is a comparative evaluation where GBWA is compared to five recent metaheuristics: coati optimization algorithm (COA), average and subtraction-based optimization (ASBO), clouded leopard optimization (CLO), total interaction algorithm (TIA), and osprey optimization algorithm (OOA). The second evaluation is the individual search evaluation. The third evaluation is hyperparameter test. The collection of 23 classic functions is chosen as the use case in all evaluations. The result of the first evaluation shows that GBWA is better than COA, ASBO, CLO, TIA, and OOA in 20, 21, 20, 21, and 21 functions consecutively. Meanwhile, the result of the second evaluation shows the equal contribution between the motion toward the best swarm member and the motion toward the resultant of better swarm members. [ABSTRACT FROM AUTHOR]

In this paper, we study the classic submodular maximization problem subject to a group equality constraint under both nonadaptive and adaptive settings. It is shown that the utility function of many machine learning applications, including data summarization, influence maximization in social networks, and personalized recommendation, satisfies the property of submodularity. Hence, maximizing a submodular function subject to various constraints can be found at the heart of many of those applications. On a high level, submodular maximization aims to select a group of most representative items (e.g., data points). However, the design of most existing algorithms does not incorporate the fairness constraint, leading to underrepresentation or overrepresentation of some particular groups. This motivates us to study the submodular maximization problem with group equality, in which we aim to select a group of items to maximize a (possibly nonmonotone) submodular utility function subject to a group equality constraint. To this end, we develop the first constant-factor approximation algorithm for this problem. The design of our algorithm is robust enough to be extended to solving the submodular maximization problem under a more complicated adaptive setting. Moreover, we further extend our study to incorporating a global cardinality constraint and other fairness notations. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The online supplement is available at https://doi.org/10.1287/ijoc.2022.0384. [ABSTRACT FROM AUTHOR]