Optimal Allocation Scheme Research Articles

Peer-to-peer energy sharing model considering multi-objective optimal allocation of shared energy storage in a multi-microgrid system

A novel peer-to-peer (P2P) energy sharing model incorporating shared energy storage (SES) is proposed in order to effectively utilize renewable energy sources and facilitate flexible energy trading among microgrids. The model is divided into three main blocks. In the first block, a multi-objective optimal allocation scheme for SES is developed to maximize SES benefits, minimize carbon emissions, and enhance the sustainability of the energy sharing zone (ESZ). The second block introduces a new internal pricing mechanism based on the energy supply and demand ratio. Further, an innovative optimal scheduling strategy for the ESZ is presented in the third block, which includes demand response strategies for residential microgrids and control strategies for SES, with the goal of minimizing the energy cost of a multi-microgrid system (MMS). To solve the proposed complex and constrained model, a multi-objective slime mould algorithm based on generalized opposition-based learning and reference points is proposed. Simulation results demonstrate that the introduction of SES and energy sharing can reduce MMS's energy cost by 89.45 % and increase the sustainability index of ESZ by 9.08 %. The proposed P2P trading mechanism can strictly lock the internal trading prices within the grid prices, which fully mobilizes the microgrids to participate in P2P trading.

Optimal Attack Energy Allocation Against Remote Estimation in Cyber-Physical Systems

This paper focuses on designing an optimal attack energy allocation strategy to disrupt the performance of remote state estimation with unknown gains for cyber-physical systems (CPSs). To deal with the unknown gains, the Cramér-Rao Bound (CRB) is introduced to obtain a lower bound of the secondary moment of the estimate error. Then, by solving an optimization problem that maximizes the average CRB over a finite time horizon with energy constraint, an optimal attack energy allocation strategy is derived. Compared with the existing works, the rigorous assumption that the attacker has full knowledge of the system is removed, and the computational complexity of solving the optimization problem is significantly reduced. Finally, numerical results validate the presented strategy.

A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue

Unmanned aerial vehicles (UAVs) have the potential to establish flexible and reliable emergency networks in disaster sites when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrusted environment and open-access UAV networks. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable for heavy security provisioning operations when performing complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-assisted disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs’ honest behaviors. Furthermore, we introduce a novel vehicular fog computing (VFC)-based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs’ heavy data processing and storage tasks. To offload computational tasks from the UAVs to ground vehicles with idle computing resources, an optimal allocation strategy is developed by choosing payoffs that achieve equilibrium in a Stackelberg game formulation of the allocation problem. For lack of sufficient knowledge on network model parameters and users’ private cost parameters in practical environment, we also design a two-tier deep reinforcement learning-based algorithm to seek the optimal payment and resource strategies of UAVs and vehicles with improved learning efficiency. Simulation results show that RescueChain can effectively accelerate consensus process, improve offloading efficiency, reduce energy consumption, and enhance user payoffs.

Energy Aware Task Allocation for Semi-Asynchronous Mobile Edge Learning

This paper extends the paradigm of "mobile edge learning (MEL)" by designing an energy-aware optimal task allocation scheme for training a machine learning (ML) model in a semi-asynchronous manner across multiple learners connected via the resource-constrained wireless edge network. The tasks are allocated such that the local dataset size selected at each learner ensures completion within a given global delay constraint and a local maximum energy consumption limit. Hence, the designed method is heterogeneity aware (HA) because it offers a trade-off between resource consumption and MEL performance by directly relating the time and energy consumption to the heterogeneous communication/computational capabilities of learners. Because the resulting optimization is an NP-hard quadratically-constrained integer linear program (QCILP), a two-step suggest-and-improve (SAI) solution is proposed. The proposed HA semi-asynchronous (HA-Asyn) approach is compared against the HA synchronous (HA-Sync) scheme and the heterogeneity unaware (HU) synchronous/asynchronous (HU-Sync/Asyn) equal batch allocation schemes. Results from a system of 20 learners tested for various completion time and energy consumption constraints show that the proposed HA-Asyn method works better than the HU-Sync/Asyn approaches and can even provide gains of up-to 25% compared to the HA-Sync scheme.

A novel approach for utilizing waste heat resources in the steel industry

The efficient utilization of waste heat resources plays a pivotal role in enhancing energy efficiency and curbing carbon emissions. To address this, effective planning for waste heat recovery (WHR) utilization becomes imperative, guiding consumers in device installation and capacity allocation. This paper introduces a novel approach to WHR utilization planning, tailored specifically for steel factories, with the goal of achieving optimal WHR solutions. The approach automates device selection, capacity allocation, and operational strategies while considering their impact on the regular manufacturing processes of the factories to maximize overall benefits. Unlike existing methods, this approach introduces discrete capacity selection modeling, considering the constraints of the limited product range during device selection. A numerical study illustrates the effectiveness of the proposed model in delivering optimal WHR device selection, capacity allocation, and operational strategies under various economic conditions. These enhancements contribute to the increased practicality and realism of the proposed method in comparison to existing approaches.

Quantization Bits Allocation for Wireless Federated Learning

Federated learning (FL) enables multiple clients to collaborate on a common learning task via only exchanging model updates. With the progressive improvements in deep learning models, communication is becoming a primary bottleneck of FL. Quantization of model updates before transmitting is an effective technique to reduce communication overhead. Most prior literature assumes lossless transmission, but in practice, quantized model updates are distorted by wireless channels due to the variation of client locations. Therefore, this paper focuses on analysis and design of personalized model update quantization with explicitly incorporating channel diversity in wireless FL. We present a novel convergence analysis of quantized FL, which encompasses full and partial client participation, single and multiple local training iterations, and convex and non-convex loss functions. This analysis explicitly embodies the impact of personalized quantization error, channel diversity and model aggregation in FL, and also elucidates their tradeoff on tightening a convergence rate upper bound. An optimization framework, which seeks an optimal allocation scheme given a total budget of quantization bits, is proposed by minimizing an upper bound with respect to channel quality. A nearly optimal solution is derived for this non-convex integer programming problem via analytically solving Karush–Kuhn–Tucker (KKT) optimality conditions and linear search. From a perspective of outlier detection, this channel-aware allocation scheme is also extended to robust model aggregation against client dropouts. Comprehensive numerical evaluation demonstrates the performance enhancement of the proposed scheme over the vanilla allocation scheme with equal quantization bits, particularly in terms of training stability, test accuracy, and robustness.

Dynamic multi-period sustainable water resources optimal allocation strategies: A case study of China

Global climate change and escalating water demand are exacerbating the burden on watershed management. In pursuit of a resilient water management framework, supportive of local economic growth and social stability, it is prudent for watershed management authorities to sequentially address the intricacies. The foremost step involves resolving supply–demand conflicts stemming from the seasonal ebb and flow before embarking on the development of an optimal dynamic allocation strategy. Therefore, this study has crafted a dynamic, seasonally responsive, multi-objective, multi-period water allocation system that harmonizes three pivotal objectives: economic efficiency, equity, and utilization efficiency. Then, to effectively mitigate the economic risks associated with uncertainty, the dynamic allocation framework incorporates the VaR and accounts for uncertainties in available surface water resources. Employing the improved adaptive self-constrained NSGA-III algorithm, we endeavored to simulate the optimal equilibrium solution in the face of various risks and facilitate a tradeoff between these objectives through a multi-period interaction mechanism. This iterative process displayed convergence within the range of 1.75 × 104 and 2.45 × 104 iterations. The proposed model was implemented to address the pressing issue of unsustainable water allocation within the MRB system in western China. Its validity and practicability were confirmed, and water allocation strategies were simulated across three distinct risk scenarios: high, medium, and low. Significantly, these findings have highlighted Chengdu as the focal area for considerable water apportionment. In light of this revelation, an emergency cooperation system has been established to address water shortages and uncertainties. Furthermore, some policy recommendations designed to guide watershed managers in the effective allocation of water resources.

Multi-objective optimization and demand variation analysis on a water energy food nexus system

The interdependence of water, energy, and food resources demands a holistic approach to sustainable resource management. This study focuses on the mathematical optimization of the water-energy-food nexus system in Sinaloa state, Mexico. Using a multi-objective optimization model, we aimed to identify optimal resource allocation strategies and assess the implications of demand variation scenarios in the domestic sector. By considering various scenarios, ranging from reduced to increased demand, the study analyzes the trade-offs between resource consumption, economic benefits, and environmental impacts. The results reveal Pareto fronts that provide insights into efficient resource allocation, highlighting the relationships between water, energy, and food demand. The study provides recommendations for policymakers and contributes to knowledge on water-energy-food nexus optimization, emphasizing the need for integrated approaches to support sustainable resource management.

Research on power fluctuation strategy of hybrid energy storage to suppress wind-photovoltaic hybrid power system

In this paper, an adaptive hybrid energy storage power optimal allocation strategy is proposed. The strategy aims to suppress the fluctuation of grid-connected power under different operation conditions and improve the operation reliability of a high proportion of renewable energy connected to the grid. Firstly, an online control strategy of grid-connected power fluctuation rate based on model predictive control (MPC) is established. This strategy can realize the grid-connected target power dynamic generation of wind-photovoltaic-energy storage (Wind-PV-ES) hybrid power system and the optimal allocation of energy storage (ES) output power. Secondly, an adaptive hybrid ES power allocation strategy based on the variational mode decomposition (VMD), Hilbert-Huang transform (HHT) and filtering algorithm is constructed to realize adaptive adjustment and optimal allocation of hybrid ES output power. Finally, the hybrid ES capacity configuration is verified by simulation analysis and theoretical calculation. The simulation results show that the proposed hybrid ES output power optimal allocation strategy has certain engineering practical value to suppress the grid-connected power fluctuation.

A game theory based optimal allocation strategy for defense resources of smart grid under cyber-attack

A deep integration of cyber and physical systems will lead to severe risk and security challenges in the new power system. To address this problem, an optimal defense resource allocation method based on game theory is proposed to against potential cyber-attacks in smart grid proactively. Based on this method, a two-layer game model is designed to optimal resource allocation. One layer is consisted by an evolutionary game between defense nodes and attackers, and the anther layer is consisted by a noncooperative game between multiple defense nodes. Further up, the offensive and defensive evolution results for all scenarios have been discussed, then the solution to resource allocation problem among multiple nodes is formulated. Different from previous work, characteristics of the bounded rationality of the attacker are considered according to the indexes of integrity, usability and confidentiality. Meanwhile, quantum response equalization is introduced to quantify player gains. Finally, specific algorithms are employed to demonstrate the feasibility and effectiveness of the method proposed in this paper.

Deep Learning Framework for Two-Way MISO Wireless-Powered Interference Channels

In this paper, we present a realistic and novel protocol for two-way communication in multiple-input-single-output (MISO) wireless-powered interference channels, namely, a simultaneous wireless information and power transfer (SWIPT) then wireless information transfer (WIT) protocol. In the protocol considered, transmitters first perform SWIPT in a forward link (FL) and receivers and then execute WIT using the harvested energy in a backward link (BL). Given that the operation of SWIPT in the FL affects the performance of WIT in the BL, our aim is to find a resource allocation strategy that maximizes the sum spectral efficiency (SE) of the FL and BL, which requires the joint optimization of the transmit beamforming vector, transmit power, and energy harvesting (EH) ratio in the FL and the receive beamforming vector in the BL. To deal with the non-convexity of the optimization problem, a deep learning (DL) framework is devised, in which the optimal resource allocation strategy is approximated by a well-designed deep neural network (DNN) model consisting of six independent DNN modules where the sigmoid and softmax functions are jointly utilized to properly model each control parameter. Furthermore, a two-stage training method is proposed where the DNN model is initialized using suboptimal solutions that are found using a low complexity algorithm in a supervised manner before it is fine-tuned using the main training based on unsupervised learning. Through intensive simulations performed in various environments, we confirm that the proposed method improves the training performance of the DNN model while reducing the training overhead. As a result, the proposed DL-based resource allocation achieves a near-optimal performance in terms of the sum SE with a low computation time.

Optimal Allocation Strategy of Virtual Inertia and Damping for Improving the Small-signal Stability

The application of the virtual synchronous generator (VSG) control technology to engage the inertia response of renewable energy generation systems is an effective means of coping with the problem of weak inertia in power systems. To deal with the problem of small-signal stability in the weak inertia power system, this paper studies how the strength of virtual inertia and damping affect the small-signal stability, and proposes an optimal allocation strategy of virtual inertia and damping for improving the small-signal stability. The optimal allocation model of virtual inertia is constructed to minimize the energy imbalance of the system under small-signal by selecting the virtual inertia and damping as optimization variables. Regarding the aforementioned model, the evolutionary algorithm is used to obtain the optimal allocation of virtual inertia and damping. Simulations on a virtual synchronized microgrid system show the effectiveness of the proposed strategy in improving small-signal stability.

Research on Multi UAVS Task Allocation Method for Post Disaster Emergency Goods Distribution

When a natural disaster occurs, ensuring timely and efficient delivery of emergency supplies to every affected location is crucial in mitigate the losses caused by the disaster. This study focuses on the distributing emergency supplies to heterogeneous multi-drone (UAV) swarms in the post-disaster scenario. Given the known distribution stations and disaster locations, a model is proposed that enables multiple drones to distribute various emergency supplies to multiple disaster zones. The model incorporates multiple objective constraints, including UAV range, priority of emergency goods, and UAV losses, resulting in a multi-objective optimization problem. To enhance the solving ability of genetic algorithm, we improve genetic algorithm by introducing adaptive cross operators, and its feasibility is compared with traditional genetic algorithms. Simulation experiments demonstrate that the improved genetic algorithm is significantly superior to traditional genetic algorithms in solving the optimal allocation scheme, thus providing a scientific basis for decision-making in emergency supply distribution.

The optimal decision of e-retailer based on return-freight insurance – considering the loss aversion of customers

PurposeThe return behavior of customers has a great impact on the e-retail industry and has resulted in the emergence of return-freight insurance (RI). Additionally, customer loss aversion arising from returns affects e-retailers' decisions and manufacturers' profits. Therefore, the main purpose of the authors' study is to determine how e-retailers and manufacturers choose their RI strategy and pricing according to customers' loss aversion.Design/methodology/approachThe authors propose three scenarios: no RI, customer purchase RI and free e-retail RI (FRI). Meanwhile, the authors also model a Stackelberg game between e-retailers and manufacturers for analysis. Then, according to customer return behavior and loss aversion, the authors study the optimal pricing decision and RI premium allocation scheme for e-retailers and manufacturers under different scenarios.FindingsIt was found that the loss sensitivity reduces customers' willingness to buy RI, which is not conducive to the development of e-retailers and manufacturers. Additionally, with higher loss sensitivity, e-retailers and manufacturers offer FRI to gain higher profits, which supports the implementation of the FRI strategy.Originality/valueThe authors introduce customers' loss aversion into RI to analyze the optimal pricing decisions and profits of e-retailers and manufacturers, enriching the application of loss aversion theory. In addition, this study analyzes the two-way cost-sharing mechanism between manufacturers and e-retailers to provide FRI, which provides a theoretical basis for RI premium sharing.

A two-stage stochastic programming approach for reserving and allocating of emission trading permits under uncertainties

ABSTRACT The Emissions Trading Scheme (ETS) is a market-based approach aiming at reducing greenhouse gas (GHG) emissions. It involves the allocation of emission allowances to entities that are permitted to emit certain levels of GHG, as well as the re-sale and re-allocation of these allowances among entities. Considering the uncertainty of urban traffic congestion, the problem of reserving and allocating Emission Trading Permits (ETP) under stochastic demand is investigated. The ETP reserve of each link is determined before congestion, the optimal allocation scheme of ETP is determined after congestion. A two-stage stochastic programming model is formulated to minimize the sum of the reserve cost of ETP before congestion and the expected total loss of ETP after congestion. First, the effectiveness of the stochastic programming model is verified by comparing with the traditional method. Second, the total cost increases with the increase of initial reserve cost and shortage cost, and decreases with the increase of selling price and the capacity of links by sensitivity analysis. The unit reserve cost has a negative effect on the reserve quantity, while shortage cost, selling price, and link capacity have a positive effect on the reserve quantity. At last, the validity of the stochastic programming solution can be verified by numerical analysis of the Nguyen-Dupuis network. The stochastic programming solution proposed in this study shows an 89% reduction in ETP reserve compared to the optimal initial capacity solution. When level E occurs, the total cost is correspondingly reduced by 11%.

Optimal battery energy storage planning and control strategy for grid modernization using improved genetic algorithm

The flexible operation of battery energy storage systems (BESS) to support electricity grid modernization requires optimal planning and an efficient control strategy. This paper proposes the optimal allocation of BESS with photovoltaic systems for microgrids to enhance grid reliability and flexibility. An adaptive BESS control strategy is developed to allow the BESS to operate in both redundant and peak shaving modes. An evolutionary programming-based genetic algorithm implemented with an optimal power flow control is proposed to determine the BESS’s optimal location, sizing, and charge/discharge operation. The objective function is to maximize the benefits of energy loss reduction and peak shaving enhancement while minimizing the installation cost. The proposed approach is conducted with MATLAB and DIgSILENT PowerFactory software, which uses an intelligent automatic data exchange process to solve the optimal solution. A practical 22 kV grid-connected microgrid of Thailand is used as a test system to demonstrate the effectiveness of the planning and control strategy for the BESS installation. The simulation test results show that the proposed optimal BESS allocation and control strategy can significantly reduce the microgrid’s energy loss and peak demand and increase energy shaving leading to enhanced grid resiliency and reliability.

Multi-Objective Optimal Scheduling of Generalized Water Resources Based on an Inter-Basin Water Transfer Project

For inter-basin water transfer (IBWT) projects, the conflict between social, economic, and ecological objectives makes water allocation processes more complex. Specific to the problem of water resource conflict in IBWT projects, we established an optimal allocation model of generalized (conventional) water resources (G (C) model) to demonstrate the advantages of the G model. The improved multi-objective cuckoo optimization algorithm (IMOCS) was applied to search the Pareto frontiers of the two models under normal, dry, and extremely dry conditions. The optimal allocation scheme set of generalized (conventional) water resources (G (C) scheme set) consists of ten Pareto optimal solutions with the minimum water shortage selected from the Pareto optimal solutions of the G (C) model. The analytic hierarchy process (AHP) combined with criteria importance using the inter-criteria correlation (CRITIC) method was used to assign weights of evaluation indexes in the evaluation index system. The non-negative matrix method was employed to evaluate the G (C) scheme set to determine the best G (C) scheme for the Jiangsu section of the South-to-North Water Transfer (J-SNWT) Project. The results show that (1) the Pareto frontier of the G model is better than that of the C model, and (2) the best G scheme shows better index values compared to the best C scheme. The total water shortages are reduced by 254.2 million m3 and 827.9 million m3 under the dry condition, respectively, and the water losses are reduced by 145.1 million m3 and 141.1 million m3 under the extremely dry condition, respectively. These findings could not only provide J-SNWT Project managers with guidelines for water allocation under normal, dry, and extremely dry conditions but also demonstrate that the G model could achieve better water-allocation benefits than the C model for inter-basin water transfer projects.

Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency

Satellite edge computing has attracted the attention of many scholars, but the limited resources of satellite networks bring great difficulties to the processing of edge-computing-dependent tasks. Therefore, under the system model of the satellite-terrestrial joint network architecture, this paper proposes an efficient scheduling strategy based on task degrees and a resource allocation strategy based on the improved sparrow search algorithm, aiming at the low success rate of application processing caused by the dependency between tasks, limited resources, and unreasonable resource allocation in the satellite edge network, which leads to the decline in user experience. The scheduling strategy determines the processing order of tasks by selecting subtasks with an in-degree of 0 each time. The improved sparrow search algorithm incorporates opposition-based learning, random search mechanisms, and Cauchy mutation to enhance search capability and improve global convergence. By utilizing the improved sparrow search algorithm, an optimal resource allocation strategy is derived, resulting in reduced processing latency for subtasks. The simulation results show that the performance of the proposed algorithm is better than other baseline schemes and can improve the processing success rate of applications.

Role Mechanism of Enterprise Management Efficiency Improvement Based on Improved Drosophila Algorithm

INTRODUCTION: With today's intelligent technology in full swing, many companies and enterprises need to catch up to the reality of internal management. To make the company better adapt to society and realize its sustainable development, it is essential to optimize the internal management means and improve the management efficiency of the enterprise.
 OBJECTIVES: To find the optimal staff allocation scheme and the best decision path by utilizing the improved fruit fly algorithm and establishing the enterprise's regular task and staff allocation model.
 METHODS: We analyze the standard swarm intelligence algorithms, and then we compare the differences between the basic fruit fly algorithm, the optimized fruit fly algorithm, and the above swarm intelligence algorithms. The fruit fly algorithm is utilized, and the algorithm is optimized to fit the actual enterprise management model. At the same time, the influence of Levy's flight on the Drosophila algorithm in enterprise management efficiency improvement is studied. Finally, it points out the application fields, the optimized Drosophila algorithm's current situation, and the existing problems and shortcomings.
 RESULTS: By comparing the performance of the Drosophila algorithm with that of the other three optimization Drosophila algorithms, the influence factors of Levy flight on the enterprise's internal management allocation are obtained, making the algorithm a good fit for the model. The results of the optimal allocation scheme under known conditions and the best decision were obtained.
 CONCLUSION: The experimental results show that the optimized fruit fly algorithm can solve the multi-parameter allocation problem of enterprise management, which has high reference value and significance for general enterprise internal management.

An improved bi-level programming model for water resources allocation under multiple uncertainties

Nowadays, with the scarcity of water resources, competition for water resources among different levels and water sectors is growing increasingly fierce. Furthermore, uncertainties are unavoidable in the water resources system. To address the aforementioned issues, a fuzzy max-min decision bi-level multi-objective interval programming model was proposed, which can not only focus on water conflicts at the same level or between different levels, but also pay attention to optimal allocation of water resources under uncertainty. The developed model was then applied to a case study in Wuwei City, Gansu Province, China, which selected fairness of water distribution and agricultural economic benefits as planning objectives. Based on the developed model, different water resources optimal allocation schemes under different representative hydrological years were provided. From the result, as representative hydrological years changed from wet (P = 25%) to dry (P = 75%), agricultural economic benefit and Gini coefficient of agriculture would vary from [35.19, 37.78] × 108 yuan to [31.12, 31.99] × 108 yuan and from [0.468, 0.429] to [0.505, 0.503], which indicates that as available water resources decrease, agricultural economic benefit would decrease and fairness of water distribution would also decrease. And the water distribution fairness of the upper bound water allocation scheme is higher than that of the lower bound water allocation scheme when in the same representative hydrological year. In addition, no matter what representative hydrological year, the results of the established bi-level programming model are always in the middle of the results of the upper and lower level individual objective, which means that the developed bi-level programming model has great advantage to deal with water competing conflict among different levels. Furthermore, based on the results of developed model, the reasonable water resources optimization schemes can be determined by the decision-makers when faced with multi-objective, bi-level and multiple uncertainties problems.