Optimal Allocation Model Research Articles

An optimised resource allocation scheme based on NOMA-SWIPT in cooperative network

ABSTRACT A cooperative network could improve energy efficiency by combining non-orthogonal multiple access (NOMA) with simultaneous wireless information and power transfer (SWIPT). This work proposes a novel model for optimal resource allocation for specified users through combined time switching and power splitting protocol. The system uses an amplified and forward-based energy harvesting protocol to broadcast the data in the Nakagami-m fading channel. An algorithm that provides the power coefficients for all NOMA users as a Levy Aquila optimiser is exploited to maximise the signal-to-noise ratio for far and near users. Further, the parameters of combined time switching and power splitting are brought by a game theoretic approach, which supports better energy harvesting in relay nodes with improved energy efficiency and high outage probability. The simulated results show that the employed scheme’s energy efficiency has increased by 11.3%, improved from the GS-Dinkelbach algorithm.

Bi-objective robust planning model for optimal allocation of soft open points in active distribution network: A flexibility improvement approach

Improving system flexibility is one of the requirements of today's power grids in response to existing uncertainties and fluctuations. High penetration of renewable energy sources (RESs) poses many technical challenges to the system, making it difficult to solve unless flexibility is provided by network-side tools efficiently. In this regard, this paper uses the possibility of changing the network topology in the optimal presence of intelligent switching tools, such as soft open points (SOPs), to improve the network's operational flexibility. The penetration capacity range of RESs is defined as an index to evaluate the active distribution network (ADN) flexibility considering the different operating conditions caused by the inherent uncertainty of RESs. The more compatible a network is with a larger penetration capacity range of RESs without violating system security and power flow constraints, the more flexible the network will be in dealing with the uncertain production of such sources. The optimal site and size of SOPs, final network topology, and penetration capacity range of RESs are determined by solving a bi-objective robust optimization model cost-effectively and in a flexibility improvement approach while considering supply-side uncertainty. The proposed model is conducted on IEEE 33-bus system, and the results are presented and discussed.

Integrated Reactive Power Optimisation for Power Grids Containing Large-Scale Wind Power Based on Improved HHO Algorithm

Large-scale wind power grid integration will greatly change the system current distribution, making it difficult for the reactive power regulator to adjust to the optimal state. In this paper, an integrated reactive power optimisation method based on the improved Harris Hawk (HHO) algorithm is proposed. Firstly, a reactive power regulation model is constructed to solve the reactive power regulation interval of wind turbines, and the reactive power margin of wind turbines is used to participate in the system’s reactive power optimisation. Finally, a reactive power compensation capacity allocation optimisation model considering nodal voltage deviation, line loss and equipment investment cost, is established, and a reactive power optimisation scheme is obtained using the Harris Hawk optimisation algorithm on the basis of considering the constraints of the wind turbine reactive power output interval. The improved HHO algorithm is used to solve the reactive power optimisation scheme considering the constraints of tidal power, machine end voltage, a conventional generator and wind farm reactive power. In the simulation, the effects of the improved Harris Hawk optimisation algorithm and the particle swarm optimisation algorithm are compared, and the experimental results prove that compared to the particle swarm algorithm, the optimisation result of the improved Harris Hawk optimisation algorithm reduces the average loss of the system by 42.6% and reduces the average voltage deviation by 30.3%, which confirms that the improved Harris Hawk intelligent optimisation algorithm is effective in proving its superiority and solving the multi-objective model for reactive power optimisation.

An optimal allocation model based on the regional ecological water requirements and multi-water supply characteristics: A case study of Shandong Province, China

Study regionShandong Province in China. Study focusMulti-water supply can alleviate regional water scarcity. However, the regional water usage weakens ecological water requirements owing to cost management. And the simplified optimal allocation model usually considers regional external water purchase cost or departmental water consumption cost individually, while neglecting differences of multi-water sources in water quality, quantity, and overall costs, resulting in unreasonable utilization. The present work calculated both rigid and flexible water requirements based on different ecological objectives. Meanwhile, the improved model considering water quantity, cost, and quality was constructed, and in comparison to simplified model and actual allocation scheme. New hydrological insights for the regionTaking Shandong Province in 2020 as a case study, the rigid and flexible ecological water requirements are 1.35 and 1.74 times higher than the actual usage, respectively, reflecting the insufficient actual ecological water use. Compared to the simplified models that do not distinguish water source, the improved model can not only consider departmental water consumption costs, but also regional external water purchase costs, and result in a more reasonable water allocation. Compared to the actual water usage scheme, the present scheme reduces the water usage of the Yellow River and increases the water usage of Eastern Route, resulting in a more reasonable water allocation overall improved efficiency.

A volunteer allocation optimization model in response to major natural disasters based on improved Dempster–Shafer theory

Nowadays, factors such as global climate change, environmental damage, and the impact of human activities have led to an increase in natural disasters, and the frequency of natural disaster problems is increasing, which poses a great threat to the safety of people’s lives and property. The increased frequency of natural disasters has increased the need to focus on the ability to prevent and respond to disasters. The increased frequency of natural disasters has increased the need to focus on the capacity for disaster prevention and mitigation. All parties need to take appropriate measures to reduce losses and provide assistance after a disaster occurs. Among them, volunteers are a newly emerged important force for rescue, but volunteers have characteristics that normal rescue organizations do not have, such as voluntary and fragmented nature, therefore, volunteer allocation becomes an important issue today. This study tries to construct a volunteer assignment method, which takes into account several factors in the assignment, such as volunteers’ own willingness to the disaster site, the competency of volunteers’ various abilities, the demand of the disaster site for the task and the time satisfaction of the disaster victims, etc. L-T2FNs are introduced in the assignment process to enhance the degree of certainty; Prospect theory’s value function to consider the disaster victims’ psychology; This study proposes an algorithm to reduce evidence fusion conflicts. This algorithm uses a differential evolutionary algorithm based on the Dempster–Shafer theory to train the lowest conflict index (determined by K and evidence distance) for BPA fusion. Subsequently, the assignment is demonstrated by using the 7.8 magnitude earthquake in Turkey in 2023 as an arithmetic example to provide a solution to the problem of how to allocate volunteers to the appropriate disaster sites.

Portfolio allocation and borrowing constraints

Using the US Survey of Consumer Finances, we explore the empirical relationship between borrowing constraints and financial portfolio allocation by American households. To help motivate our empirical analysis we initially draw insights from a mean-variance model of optimal portfolio allocation with three tradable asset classes defined by increasing risk, and establish a link between borrowing restrictions and portfolio allocation in the presence of background risk. Our main contribution, however, lies in estimating the role that borrowing constraints play in shaping households' financial portfolio allocation. This is achieved using an ordered fractional probit model. In addition to revealing the significant empirical role played by household borrowing constraints in determining portfolio allocation, our analysis helps us to resolve ambiguity surrounding the behaviour of the medium-risk asset in our motivational theoretical framework. Further, the empirical distribution of medium-risk assets is found to be remarkably similar to that for high-risk assets, suggesting the presence of a more general ‘risk puzzle’, which our borrowing constraints measures partially explain.

A real-time market-based framework for basin-wide surface water pricing and allocation considering the available water uncertainty

Market-based approaches are increasingly considered reallocating instruments that put water consumption at its highest economic value among competing water users. Setting up a water market can have a lot of environmental, social, economic, and legal complexities. One of the main issues is the uncertain nature of the available water, which can cause the failure of markets, especially during drought conditions. Therefore, there is a need for market mechanisms to consider and reduce the adverse impacts of available water uncertainty on market outcomes. Accordingly, this paper proposes a new real-time seasonal smart water market framework for basin-wide surface water pricing and allocation. The framework uses the results of the reservoir water allocation optimization models and ANFIS-based monthly river discharge forecasts to better assist the water users with their bidding. The market manager uses updated available information at the beginning of each season to provide users with a more accurate understanding of available water to adjust their tradings for the rest of the year. The applicability and efficiency of the proposed framework are evaluated by applying it to the Gorganrood River basin in Iran. According to the results, the framework increased users' benefits from 721 to 1050 billion rials, which is more efficient than an annual market. Water markets can use this framework to improve their ability to cope with the uncertainty of available water, increase their users' benefits, and encourage them to improve their efficiency. Furthermore, the proposed framework allows the decision-makers in water sectors (e.g., industrial, agricultural, etc.) to discover time and location specific water allocation and price for different water users.

Energy Storage Sharing for Multiple Services Provision: A Computable Combinatorial Auction Design

Given the profound integration of the sharing economy and the energy system, energy storage sharing is promoted as a viable solution to address the underutilization of energy storage and the challenges associated with cost recovery. While energy storage sharing offers various services for system operation, a significant question remains regarding the development of an optimal allocation model for shared energy storage in diverse application scenarios and the proposal of efficient solving algorithms. This paper presents the design of a computable combinatorial mechanism aimed at facilitating energy storage sharing. Leveraging the distinct characteristics of buyers and sellers engaged in energy storage sharing, we propose a combinatorial auction solving algorithm that prioritizes and incorporates the offers of shared energy storage, accounting for temporal variations in the value of energy resources. The numerical results demonstrate that the proposed solving algorithm achieves a computation time reduction of over 95%, adequately meeting the practical requirements of industrial applications. Importantly, the proposed method maintains a high level of computational accuracy, ranging from 92% to 98%, depending on the participants and application scenarios. Hopefully, our work is able to provide a useful reference for the further mechanism design for energy storage sharing.

China's value-added tax policy and intertemporal optimal assets allocation of enterprises--Based on the dual perspectives of VAT input refund and VAT rate.

The article sought to detect the impact of the value-added tax (VAT) policy on the enterprises' asset allocation from the dual perspectives of the VAT input refund and the VAT rate. Based on the influenced mechanism of the VAT input refund and the tax burden effect (and the price effect) caused by the VAT rate, enterprises' intertemporal optimal asset allocation models are constructed under the states of adopting the VAT input refund and maintaining the theoretical tax (non-)neutrality of VAT. When VAT rates of the general taxpayers are predicted to be reduced, we also use China's manufacturing and economic data to simulate specific cases to verify propositions under different states. The results show that: (1) When the VAT output tax rate decreases: if returns to scale are diminishing, enterprises will increase the number of productive material assets and labor and reduce financial assets. (2) When the VAT input tax rate reduces: under the state of adopting the VAT input refund and maintaining the theoretical tax (non-)neutrality of VAT, if returns to scale are decreasing, enterprises will reduce the number of productive material assets and labor and increase financial assets. Under the state of adopting the VAT input refund and maintaining the theoretical tax neutrality of VAT, if returns to scale are increasing and the expected rate of return of financial assets is lower than the additional tax rate, or the enterprise has diminishing returns to scale and the expected rate of return of financial assets is higher than the additional tax rate, enterprises will increase the number of productive material assets and labor. (3) When VAT output and input tax rates reduce simultaneously: under the state of adopting the VAT input refund and maintaining the theoretical tax neutrality of VAT, if returns to scale are increasing and the expected return rate of financial assets is higher than the additional tax rate, the enterprise will reduce the number of productive material assets and labor and increase financial assets. Under the diminishing returns to scale in China's national economy, the research conclusions endorse the rational necessity of the VAT policy change-VAT rate reduction to develop the entity economy and provide a reference for enterprises to make asset allocation decisions. The conclusions also provide possible changes in VAT policy for different countries according to their actual economic conditions.

Optimal Capacity Configuration of Wind–Solar Hydrogen Storage Microgrid Based on IDW-PSO

Because the new energy is intermittent and uncertain, it has an influence on the system’s output power stability. A hydrogen energy storage system is added to the system to create a wind, light, and hydrogen integrated energy system, which increases the utilization rate of renewable energy while encouraging the consumption of renewable energy and lowering the rate of abandoning wind and light. Considering the system’s comprehensive operation cost economy, power fluctuation, and power shortage as the goal, considering the relationship between power generation and load, assigning charging and discharging commands to storage batteries and hydrogen energy storage, and constructing a model for optimal capacity allocation of wind–hydrogen microgrid system. The optimal configuration model of the wind, solar, and hydrogen microgrid system capacity is constructed. A particle swarm optimization with dynamic adjustment of inertial weight (IDW-PSO) is proposed to solve the optimal allocation scheme of the model in order to achieve the optimal allocation of energy storage capacity in a wind–hydrogen storage microgrid. Finally, a microgrid system in Beijing is taken as an example for simulation and solution, and the results demonstrate that the proposed approach has the characteristics to optimize the economy and improve the capacity of renewable energy consumption, realize the inhibition of the fluctuations of power, reduce system power shortage, and accelerate the convergence speed.

Bi-level Planning Model for Optimal Battery Energy Storage Allocation Considering Optimal Daily Scheduling Using Mixed-Integer Particle Swarm Optimization

Bi-level Planning Model for Optimal Battery Energy Storage Allocation Considering Optimal Daily Scheduling Using Mixed-Integer Particle Swarm Optimization

Low-opportunity-cost feed can reduce land-use-related environmental impacts by about one-third in China.

Feeding animals more low-opportunity-cost feed products (LCFs), such as food waste and by-products, may decrease food-feed competition for cropland. Using a feed allocation optimization model that considers the availability of feed sources and animal requirements for protein and energy, we explored the perspectives of feeding more LCFs to animals in China. We found that about one-third of the animal feed consisted of human-edible products, while only 23% of the available LCFs were used as feed during 2009-2013. An increased utilization of LCFs (45-90 Mt) could potentially save 25-32% of feed-producing cropland area without impairing livestock productivity. Parallelly, about one-third of feed-related irrigation water, synthetic fertilizer and greenhouse gas emissions would be saved. Re-allocating the saved cropland could sustain the food energy demand of 30-185 million people. Achieving the potentials of increased LCF use requires improved technology and coordination among stakeholders.

Методика стратегического управления проектами

The article presents strategic project management methodology developed to meet the relevant requirements and challenges faced by organizations in current business environment. The methodology includes an algo-rithm for strategic project management, a system of indicators for assessing the results of project implementa-tion and a mathematical model for optimal resource allocation. The relevance of the methodology develop-ment is determined by the importance of strategic project management in current conditions of instability, un-certainty and innovations implementation necessity. It enables organizations to effectively plan, analyze, moni-tor and coordinate project activities to achieve their overall strategic objectives. The study also provides an ex-tended list of prospective project management principles, which served as the basis for the development of an algorithm and a system of indicators for evaluating the results of such activities. The latter are categorized into groups, including assessment of the achievement of strategic objectives, the quality of task performance, the use of resources and the level of risks. The mathematical model for optimal resource allocation enables effi-cient resource management in projects, taking into account requirements and limitations. The said model is based on minimizing the use of available capacity while meeting the availability restrictions for each project task.

Retracted: Optimization Model of Logistics Task Allocation Based on Genetic Algorithm

Retracted: Optimization Model of Logistics Task Allocation Based on Genetic Algorithm

Pareto-improving seat allocation for high-speed railway networks with equilibrium flows

This paper develops optimization models for Pareto-improving seat allocation schemes in high-speed railway (HSR) networks, where time-dependent demand and equilibrium travel choices are incorporated. Under the Pareto-improving seat allocation scheme, at least some passengers are strictly better off, and no passenger is worse off, when compared to the situation with no seat allocation scheme. In particular, two scenarios with fixed and elastic passenger demands are considered. The passenger travel choice equilibrium conditions are taken as constraints for the seat allocation optimization model, which can be further reformulated into a mixed-integer linear programming (MILP) with the help of linearization, relaxation and outer-approximation techniques. The optimal solution then can be obtained by solving the proposed MILP. The numerical examples on two real-world regional HSR systems are presented to illustrate the proposed Pareto-improving seat allocation optimization approach.

A Bilevel Optimal Water Allocation Model Considering Water Users’ Satisfaction Degree and Water Rights Transaction: A Case Study in Qingzhang River Basin, China

The contradiction between water supply and demand in China is becoming increasingly prominent. A water allocation scheme that satisfies various water users can effectively solve it. In this paper, considering both individual rationality and collective rationality, a bilevel optimal allocation model for river basin water resources is established. Firstly, water users’ satisfaction degree was defined, to characterize their satisfaction with the water resource allocation scheme, and principles of water users’ satisfaction degree were mathematically expressed, to represent water users’ negotiation activities in the initial water rights allocation. Then, based on the initial allocation results, water users’ water intake quantity, water-saving amount, and water-trade amount were optimized by water rights trading. Finally, an algorithm based on the response surface was put forward for solving the proposed bilevel optimal allocation model. The validity and feasibility of the model and algorithm were verified by a case study in the Qingzhang River Basin in China.

Study on optimal configuration of EV charging stations based on second-order cone

The Electric Vehicle(EV) charging station frequently participates in the daily regulation and operation of the power distribution system, whether the installation position is reasonable and the configuration capacity is appropriate, it will directly affect the operation strategy and operating conditions of the distribution system, and then affect the economic and environmental benefits of the distributed power supply and EV load connected to the power grid, based on the analysis of fuel vehicles, EV travel characteristics and electrical geography coupled state, by fitting the charging data of EV with travel chain, more accurate EV charging load can be obtained. Secondly, the optimal allocation model of EV charging stations is established to optimize the grid loss, and the nonconvex problem is solved by combining the second-order cone relaxation technique with the distflow power flow model, the configuration scheme of EV charging station is obtained. Finally, taking an EV charging scene in a certain area as an example, the optimal configuration of the charging station is simulated by setting up two types of charging station: conventional charging station and conventional charging station and fast charging station, the effectiveness of the proposed configuration scheme is verified. The optimal configuration scheme of EV charging station described in this paper provides a theoretical basis for charging station planning and capacity expansion.

Optimal allocation model of forest fire detection towers in protected areas based on fire occurrence risk: Where and how to act?

Forest fire detection towers are crucial in supporting rapid firefighting actions in conservation units and thus reducing environmental, social, and economic damages. Thus, the aim was to evaluate scenarios for optimal allocation of forest fire detection towers, according to the risk of occurrence, in the Caparaó National Park, Brazil. Thus, by geotechnological analysis, the areas most susceptible to forest fires and the optimal locations for installation of detection and monitoring for these events were delimited. To run the proposed models, biological, physical, socioeconomic, and meteorological variables were used. From the application of the methodologies, it was observed that 76.70% of the study area was covered by low, moderate, and shallow fire risk classes, while high and very high-risk classes were concentrated in the buffer zone. The scenario with 45 towers was considered the most advantageous, given that they presented viewing levels above 70% and a lower cost per hectare viewed than the scenario with 48 towers. Results showed no critical risks of fire occurrence within the conservation unit, but preventive measures are still needed to avoid fire spread, particularly near the buffer zone. The study's methodologies can be applied in other areas to improve forest fire prevention and control efforts.

A hybrid grey wolf optimizer using opposition-based learning, sine cosine algorithm and reinforcement learning for reliable scheduling and resource allocation

As the number of space debris in geosynchronous Earth orbits continues to grow, the threat posed by space debris to satellites surveillance is increasing, and the available orbital resources are also decreasing. Thus, reasonably scheduling and allocating the resources for space object tracking has become vital. This paper establishes an optimization model for the resource allocation and scheduling problem for space debris tracking. A fusion algorithm that combines the grey wolf optimizer, opposition-based learning, sine cosine search strategy, and reinforcement learning was proposed and used to solve the problem. Six groups of realistic data were selected based on the relevant background information of space debris tracking to test the validity and effectiveness of the proposed algorithm. The performance of the state-of-the-art optimization algorithms was compared with that of the proposed algorithms. The result of the experiment indicates that the proposed algorithm effectively solves the resource allocation and scheduling problem for space debris tracking.

Sustainable open pit fleet management system: integrating economic and environmental objectives into truck allocation

ABSTRACT Fleet management systems in open pit mines make two important semi-dynamic and dynamic decisions to maximize utilization of available equipment: the decision of allocation and the decision of dispatching the trucks to the shovels. In this paper, we propose a bi-objective mathematical model that incorporates the minimization of carbon emission into the allocation optimization model. We also consider different inputs that might impact upon truck allocation decisions such as the fleet size, truck velocity, truck age groups, etc. The presented mathematical model is examined using two different case studies from an iron mine and a copper mine containing a different number of shovels, dumps, and trucks. The results reveal that the developed model enhances the production performance while controlling emissions. It is indicated that the average truck velocity and, the age of trucks are among the critical factors, which can highly affect the amount of carbon emissions.