Allocation Of Renewable Resources Research Articles

Towards urban energy sustainability and resiliency through smart agriculture and effective renewable resource allocation

Urban energy sustainability and resiliency are paramount in addressing the challenges of modern urbanization. This paper introduces a novel framework that seamlessly integrates smart agriculture practices with an effective renewable resource allocation strategy to optimize energy utilization in urban and suburban areas. The optimization models employ a Multifactorial Evolutionary Algorithm, providing a dynamic and adaptive approach to fine-tune system parameters. The results, presented through numeric tables and colorful figures, showcase optimized agricultural production levels, renewable resource utilization, and the impact of specific factors and parameters on algorithmic performance. Notably, the study reveals the intricate balance achieved through the integration of smart agriculture and renewable energy, offering actionable insights for sustainable urban development. The visualizations not only enhance result comprehension but also serve as powerful tools for effective communication. This research contributes to the evolving discourse on urban energy sustainability, providing a systematic approach for future investigations at the intersection of agriculture, renewable energy, and optimization algorithms.

Allocation of renewable resources with radial distribution network reconfiguration using improved salp swarm algorithm

This paper presents an optimal and simultaneous allocation of the photovoltaic panel (PV) and wind turbine (WT) with the reconfiguration of radial distribution networks to reduce power losses cost and improve reliability. Determining the optimal optimization variables are very important to obtain the maximum benefits of renewable resource allocation and network reconfiguration, i.e. achieving the lowest losses and reliability cost. Problem optimization variables are the location, size and power factor of PV and WT as well as network tie-line switches states. Operational constraints include renewable sources size and radiality constraints. The optimization is conducted using an improved salp swarm algorithm (ISSA). Operators of the differential evolutionary (DE) algorithm are used to enhance the performance of conventional salp swarm algorithm (SSA) to prevent trapping in local optima and to increase the convergence speed in achieving the global optimal solution. The methodology is implemented on IEEE 33 and 69 bus distribution networks in various scenarios of renewable sources allocation and reconfiguration and simulation results including the cost of losses, cost of reliability improvement and net saving is presented before and after the optimization. The simulation results show that the ISSA is easily able to find the location and size of the renewable units optimally as well as the best network configuration in different scenarios. The results clear that optimal and simultaneous allocation of WTs and reconfiguration using ISSA has the lowest losses and reliability improvement cost, more net saving and better network performance than the other scenarios. The superiority of the ISSA is also confirmed in solving the problem in comparison with conventional SSA and also the previous studies.

Unrelated Parallel Machine Scheduling with Job and Machine Acceptance and Renewable Resource Allocation

In this paper, an unrelated parallel machine scheduling problem with job (product) and machine acceptance and renewable resource constraints was considered. The main idea of this research was to establish a production facility without (or with minimum) investment in machinery, equipment, and location. This problem can be applied to many real problems. The objective was to maximize the net profit; that is, the total revenue minus the total cost, including fixed costs of jobs, job transportation costs, renting costs of machines, renting cost of resources, and transportation costs of resources. A mixed-integer linear programming (MILP) model and several heuristics (greedy, GRASP, and simulated annealing) are presented to solve the problem.

Managing academic performance by optimal resource allocation

In this paper, we develop and study a complex data-driven framework for human resource management enabling (i) academic talent recognition, (ii) researcher performance measurement, and (iii) renewable resource allocation maximizing the total output of a research unit. Suggested resource allocation guarantees the optimal output under strong economic assumptions: the agents are rational, collaborative and have no incentives to behave selfishly. In reality, however, agents often play strategically maximizing their own utilities, e.g., maximizing the resources assigned to them. This strategic behavior is typically mitigated by implementation of performance-driven or uniform resource allocation schemes. Next to the framework presentation, we address the cost of such mitigation.

Towards a Cleaner and Developed World: A Birds Eye view on Renewables Distribution and Strategy Around the World

This paper gives a bird’s eye view on the allocation of renewable resources across the world using the data from IRENA (International Renewable Energy Agency) from 2000 to 2019. It depicts many graphs for major countries and regions on how to develop renewables and thus address policy suggestions with a focus on renewables and so fossil fuels can exit the market.

Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously.

Estimation and Optimization of the Efficiency of a Recursive Conveyor

The efficiency of a recursive conveyor is conceptually formalized, and the optimal allocation of renewable resources is obtained by maximizing efficiency via reduction to an integer linear programming problem. The recursive functions for calculating the process schedule under an arbitrary allocation of resources are defined. The results of this paper can be used for designing conveyors or for calculating bounds in the case of combinatorial scheduling algorithms.

English

English

English

English

Renewable resources portfolio optimization in the presence of demand response

Demand response is viewed as a practical and relatively low-cost solution to increasing penetration of intermittent renewable generation in bulk electric power systems. This paper examines the question of what is the optimal installed capacity allocation of renewable resources in conjunction with demand response. We introduce an integrated model for total annual system cost that can be used to determine a cost-minimizing allocation of renewable asset investments. The model includes production, uncertainty, emission, capacity expansion and mothballing costs, as well as wind variability and demand elasticity to determine the hourly cost of electricity delivery. The model is applied to a 2024 planning case for British Columbia, Canada. Results show that cost is minimized at about 30% wind generation. We find that the optimal amount of renewable resource is as sensitive to installation cost as it is to a carbon tax. But we find the inter-hourly demand response magnitude is much less helpful in promoting additional renewables than intra-hourly demand elasticity.

Classroom Games: The Allocation of Renewable Resources Under Different Property Rights and Regulation Schemes

The authors describe a renewable resource allocation game designed to stimulate students' interest in and understanding of market failure associated with open-access types of resource use. They also use the game to discuss the advantages and disadvantages of various property rights and regulation schemes. Because this exercise demonstrates the power of unregulated and regulated economic incentives, many types of students may benefit from this game, including undergraduates enrolled in the standard introductory and in more advanced microeconomics classes, as well as courses in environmental economics and natural resource economics. This game was specifically designed so that noneconomics majors in natural resource management and environmental courses could also benefit.

Sustainable management of renewable resources: a comparison of alternative decision approaches

SUMMARY There has been an active political and academic debate about the meaning of sustainable development, which has led to a growing number of definitions and policy paradigms. This paper explores the traditional, economic definition of sustainable development along with a multiobjective framework for temporal decision-making. The fields of operations research and ecology are used to provide a perspective on issues of intergenerational equity and ecological integrity as well as economic efficiency. A simple, dynamic renewable resource allocation model is used to look at how resource management objectives and decisions change under varying definitions and problem scope. Impacts from temporal manipulation of how decisions are made, using a multiobjective approach, have suggested adjustments to decision-time steps according to initial conditions and time-horizon. Potential use of this form of policy alternative includes the regulation of many open access resources, including water resources.

A DSS for Ressource—Constrained Project Scheduling under Uncertainty

ABSTRACT This paper presents a decision support system, called FPS (Fuzzy Project Scheduling), for solving a problem of an allocation of renewable resources among nonpreemtable and dependent activities, talking into account three criteria: project completion time, maximum lateness and mean flow time, under uncertain time parameters. By time parameters we understand: durations, ready times and due dates of particular activities. Uncertainty of these parameters is modelled by means of L—R fuzzy numbers. A general procedure for transforming the fuzzy scheduling problem into a set of associate are generated for given α-cuts. Optimistic and pessimistic schedules are generated for given α-levels. The comparison of obtained fuzzy result is based on the compensation of areas determined by membership function. The FPS system is described and illustrated using a real, agricultural example.

Stochastic task selection and renewable resource allocation

A class of renewable-resource-allocation problems is studied for the processing of dynamically arriving tasks with deterministic deadlines. The model presented explicitly considers time available, time required, resources available, resources required, stochastic arrivals of multiple types of tasks, importance of tasks, timeliness of processing, and accuracy of resource allocation. After state augmentation, the problem becomes a Markovian decision problem, and can be solved, at least in principle, by using a stochastic dynamic programming (SDP) method. Effects of key system parameters on optimal decisions are investigated and analyzed through numerical examples. >

A dynamic linear programming approach to market allocation of renewable resources in the italian energy system: The case of solar thermal and biogas technologies

In Italy solar thermal energy and energy from biogas are two possible means of reducing dependence on energy imports. Using a multiperiod LP model (MARKAL) the authors assessed the likely potential of both technologies under various circumstances. The study covered the period 1980–2005, in five segments of five years. It focused only on the subsystem of the energy end-uses which can be substituted for by solar thermal and biogas technologies. The overall non-renewable sources which can be saved in 20 years by these technologies total 450 PJ (1 PJ = 101 5 J) if the fuel prices rise at 0 per cent average annual, 1450 PJ if the fuel prices rise at 4.2 per cent average annual, 1860 PJ if the fuel prices rise at 7.2 per cent average annual and 3780 PJ if the fuel prices rise at 15 per cent average annual. However the most competitive technologies appear to be solar water heaters used mainly in the private and commercial sectors and biogas systems used mainly in the agricultural sector. The study was carried out by APRE under ENEA (formerly CNEN) contract and was intended to serve as an analytical basis for establishing an overall development and demonstration strategy for end-use renewable technologies in Italy.

Uncertainty and markets for renewable resources

The market allocation of renewable resources is examined when growth rates are affected by random disturbances. Given free access to a renewable resource, environmental disturbances are shown to affect the biological survival of the resource. The optimal solution is then examined and it is shown to be achieved by a competitive allocation when property rights are clearly defined given rational expectations or a complete set of contingent futures markets. The stochastic dynamics are shown in each case to differ considerably from the deterministic model.