Battery Energy Storage Systems (BESS) are expected to play an important part in the power system of the future to meet an increasing share of the electricity demand from renewable energy sources. This paper deals with...
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ISBN:
(数字)9798350372380
ISBN:
(纸本)9798350372397
Battery Energy Storage Systems (BESS) are expected to play an important part in the power system of the future to meet an increasing share of the electricity demand from renewable energy sources. This paper deals with the sizing and operation optimization of a hybrid a PV system with different battery technologies, namely lithium-ion, vanadium redox flow, and sodium-sulfur batteries, to cover a specified proportion of the electricity demand. Based on PV production and electricity consumption profiles a linear optimization was executed for the whole year of 2022 with a 15-minute resolution to calculate the optimal size of PV and BESS. The hybrid systems with optimal sizing offer cost savings between 10 and 25% compared to the case where all electricity is purchased from the day-ahead market. The results also reveal that PV overbuilding offers a cheaper way to increase the share of the demand covered by renewables at current investment cost levels, while the implementation of BESS is only feasible at lower investment costs.
linear programming is the seminal optimization problem that has spawned andgrown into today\'s rich and diverse optimization modeling and algorithmiclandscape. This article provides an overview of the recent devel...
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In this work, we propose a new algorithm for solving linear programs. This algorithm starts by an initial support feasible solution, then it moves from one feasible point to a better one following a new hybrid directi...
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In this work, we propose a new algorithm for solving linear programs. This algorithm starts by an initial support feasible solution, then it moves from one feasible point to a better one following a new hybrid direction. The constructed direction gives a better local improvement of the objective function than the direction of the adaptive method with hybrid direction (AMHD) algorithm proposed in Bibi MO, Bentobache M. A hybrid direction algorithm for solving linear *** Journal of Computer Mathematics, 2015;92(2):200-216. In order to stop the algorithm, a suboptimality criterion is used and the long step rule is developed for changing the current support. The proposed algorithm is implemented with C++, then a numerical study is conducted on randomly generated test problems and some instances of an optimal control problem. The obtained numerical results show that our algorithm is competitive with AMHD and the primal simplex algorithm of GNU linear programming kit (GLPK).
In this paper, we introduce an algorithm designed to solve a Multilevel MOnoObjective linear programming Problem (ML(MO)OLPP). Our approach is a refined adaptation of Sinha and Sinha’s linear programming method, inco...
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We consider the following classical conjecture of Besicovitch: a 1-dimensional Borel set in the plane with finite Hausdorff 1-dimensional measure H1 which has lower density strictly larger than 1/2 almost everywhere m...
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Procurement is a common problem in the field of economy and finance. In practice, an enterprise’s procurement strategy will directly affect its production activities, and even affect its subsequent income and develop...
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ISBN:
(数字)9798350393682
ISBN:
(纸本)9798350393699
Procurement is a common problem in the field of economy and finance. In practice, an enterprise’s procurement strategy will directly affect its production activities, and even affect its subsequent income and development. Therefore, enterprises should formulate reasonable ordering plans and transportation plans in advance to meet future production activities, which are closely related to suppliers and forwarders. In order to make the results more reasonable, accurate and reliable, we set the evaluation index, use the entropy weight method to determine the corresponding weight, and use TOPSIS model to establish the evaluation system. After ranking the suppliers with this evaluation model, the suppliers with unqualified order rate will be eliminated, and the final scores will be sorted in ascending order, that is, the lower the score, the better, and the most important 50 suppliers will be obtained. How many suppliers can the enterprise choose to supply at least to meet the production demand, and formulate the most economical weekly ordering scheme and the least loss transshipment scheme 24 weeks in advance. Using the 0-1 programming in linear programming, using meeting the weekly demand and the actual situation as constraints, and taking the minimum number of suppliers as the objective function to establish the minimum number of suppliers model, the final minimum number of suppliers is 47. The actual situation is used as the constraint condition, and the “most economical” is used as the objective function to solve the optimal ordering scheme. Similarly, the transportation scheme with less loss is solved by satisfying the production and selecting only one forwarder as the constraint condition, and taking “least loss” as the objective function.
This paper presents a comprehensive approach for forecasting vegetable sales and devising pricing strategies using the ARIMA algorithm and linear programming models. The study aggregates data on a weekly basis and emp...
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ISBN:
(数字)9798350354560
ISBN:
(纸本)9798350354577
This paper presents a comprehensive approach for forecasting vegetable sales and devising pricing strategies using the ARIMA algorithm and linear programming models. The study aggregates data on a weekly basis and employs cost-plus pricing for different vegetable categories. A univariate linear regression model is constructed to analyze the relationship between sales volume and cost-plus pricing, followed by the establishment of a seasonal time series ARIMA algorithm through first-order differencing. This algorithm predicts the replenishment quantities for various vegetable categories over the upcoming week, considering factors such as loss rates and wholesale prices to propose pricing strategies. The paper also integrates a profit formula to maximize returns. Additionally, the study utilizes Item-based collaborative filtering to recommend a range of saleable items. By employing Jaccard similarity, the research calculates the similarity of bestselling items between different periods and can identify multiple bestselling items, and predicts the future vegetable replenishment quantity based on last week's data. Subsequently, improvements are made in pricing decisions, resulting in the derivation of a formula and pricing strategy for individual items, culminating in the use of linear programming to maximize revenue.
The purpose of this paper is to explore the application of K-Means cluster analysis and linear programming model in agricultural production. Firstly, the linear programming model is used to formulate the planting stra...
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ISBN:
(数字)9798350368208
ISBN:
(纸本)9798350368215
The purpose of this paper is to explore the application of K-Means cluster analysis and linear programming model in agricultural production. Firstly, the linear programming model is used to formulate the planting strategy in the region, taking the actual situation in North China as an example. Secondly, on the basis of the optimization of the linear programming model, a preliminary crop planting strategy is given. A robust optimization model is introduced to construct the uncertainty set and incorporate it into the objective function to obtain the optimal planting strategy in the worst case. Finally, the objective function was improved by K-Means cluster analysis model and the model was refined by integrating the substitutability and complementarity among crops. The model aims to improve the economic efficiency of local farmers and provide a reference for agricultural planning in other regions.
Sensor Nodes (SNs) in Wireless sensor networks (WSNs) continuously monitor the environment and accumulate data. But due to the limited memory and processing capabilities of the sensor nodes, the data must be transmitt...
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Maintaining resource fairness while achieving optimization for various performance metrics such as resource utilization, turnaround time and job latency is a well-known resource scheduling challenge in cloud computing...
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ISBN:
(数字)9798350367201
ISBN:
(纸本)9798350367218
Maintaining resource fairness while achieving optimization for various performance metrics such as resource utilization, turnaround time and job latency is a well-known resource scheduling challenge in cloud computing. Despite the significant progress made with the introduction of dominant resource fairness by Ghodsi et al., which ensures major allocation properties such as sharing incentive, strategy-proofness, envy-freeness and Pareto efficiency to be achieved, in practice, strict adherence to fairness may still lead to non-optimized utilization of resources when a series of jobs are scheduled on a shared computing infrastructure. On the other hand, prioritizing optimization without consideration for fairness is not acceptable. Therefore, there is a need for an approach that can address both fairness and optimization. In this paper, we present LPWD, an enhanced Dominant Resource Fairness based scheduling approach that utilizes linear programming based calculated weights to balance fairness with user-specific configurable optimization objective. LPWD maintains relative and proportional fairness and employs linear programming to conduct dynamic resource allocation planning for arriving jobs on-the-fly and generate weights for each job based on a multi-objective optimization. These calculated weights are then applied to the weighted dominant resource fairness policy that guides the reordering of the jobs in the queue before deployment to the cloud. We have implemented and evaluated LPWD in a Kubernetes cluster hosted on the Chameleon cloud infrastructure, which is a configurable platform widely used by scientific communities. Our workloads are based on normalized Google trace data and Alibaba trace data and capture characteristics of resource demands in a real-world production setting. The experimentation with LPWD shows that it leads to resource utilization improvement up to 7.62% and overall turnaround time improvement up to 8.21% while maintaining job latency.
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