Energy Curtailment Scheduling MILP Formulation for an Islanded Microgrid with High Penetration of Renewable Energy

The efficiency of photovoltaic (PV) cells has improved significantly in the last decade, making PV generation a common feature of the sustainable microgrid. As the PV-powered microgrid reaches high penetrations of intermittent PV power, optimum scheduling of over-production is necessary to minimize energy curtailment. Failure to include an accurate assessment of curtailed energy costs in the scheduling process increases wasted energy. Moreover, applying an objective function without considering the cost coefficients results in an inefficient concentration of curtailed power in a specific time interval. In this study, we provide an optimization method for scheduling the microgrid assets to evenly distribute curtailment over the entire daily period of PV generation. Each of the curtailment intervals established in our optimization model features the application of different cost coefficients. In the final step, curtailment costs are added to the objective function. The proposed cost minimization algorithm preferentially selects intervals with low curtailment costs to prevent the curtailment from being concentrated at a specific time. By inducing even distribution of curtailment, this novel optimization methodology has the potential to improve the cost-effectiveness of the PV-powered microgrid

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A Robust Optimization Method for Systems With Significant Nonlinear Effects

19th Design Automation Conference: Volume 1 — Mechanical System Dynamics; Concurrent and Robust Design; Design for Assembly and Manufacture; Genetic Algorithms in Design and Structural Optimization ◽

10.1115/detc1993-0325 ◽

1993 ◽

Author(s):

Jyh-Cheng Yu ◽

Kosuke Ishii

Keyword(s):

Heat Treatment ◽

Robust Optimization ◽

Nonlinear Effects ◽

Optimization Method ◽

Post Heat Treatment ◽

Worst Case ◽

Estimation Scheme ◽

Optimization Methodology ◽

Major Factors ◽

The Cost

Abstract This paper describes a robust optimization methodology for design involving either complex simulations or actual experiments. The proposed procedure optimizes the worst case response that consists of a weighted sum of expected mean and response variance. The estimation scheme for expected mean and variance adopts the modified 3-point Gauss quadrature integration to assure superior accuracy for systems with significant nonlinear effects. We apply the proposed method to the robust design of geometric parameters of heat treated parts to minimize the cost of post heat treatment operations. The paper investigates the major factors influencing geometric distortions due to heat treatment and the rules of thumb in design. The study focuses on relating dimensional distortion to the design of part geometry. To illustrate the utility of the proposed method, we present the formulation of a case study on allocation of dimensions of preheat treated (green) shafts to minimize the cost of post heat treatment operations. The final result is not presented yet pending the completion of further experiments.

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Reducing the Cost of Electricity by Optimizing Real-Time Consumer Planning Using a New Genetic Algorithm-Based Strategy

Mathematics ◽

10.3390/math8071144 ◽

2020 ◽

Vol 8 (7) ◽

pp. 1144 ◽

Cited By ~ 1

Author(s):

Laurentiu-Mihai Ionescu ◽

Nicu Bizon ◽

Alin-Gheorghita Mazare ◽

Nadia Belu

Keyword(s):

Genetic Algorithm ◽

Cost Minimization ◽

Renewable Energy Sources ◽

Time Interval ◽

Renewable Sources ◽

Total Cost ◽

Cost Of Electricity ◽

Cost Of Energy ◽

Input Parameters ◽

The Cost

To ensure the use of energy produced from renewable energy sources, this paper presents a method for consumer planning in the consumer–producer–distributor structure. The proposed planning method is based on the genetic algorithm approach, which solves a cost minimization problem by considering several input parameters. These input parameters are: the consumption for each unit, the time interval in which the unit operates, the maximum value of the electricity produced from renewable sources, and the distribution of energy production per unit of time. A consumer can use the equipment without any planning, in which case he will consume energy supplied by a distributor or energy produced from renewable sources, if it is available at the time he operates the equipment. A consumer who plans his operating interval can use more energy from renewable sources, because the planning is done in the time interval in which the energy produced from renewable sources is available. The effect is that the total cost of energy to the consumer without any planning will be higher than the cost of energy to the consumer with planning, because the energy produced from renewable sources is cheaper than that provided from conventional sources. To be validated, the proposed approach was run on a simulator, and then tested in two real-world case studies targeting domestic and industrial consumers. In both situations, the solution proposed led to a reduction in the total cost of electricity of up to 25%.

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Precision Allocation Method of Large-Scale CNC Hobbing Machine Based On Precision-Cost Comprehensive Optimization

10.21203/rs.3.rs-1069776/v1 ◽

2021 ◽

Author(s):

Zongyan Hu ◽

Shilong Wang ◽

Chi Ma

Keyword(s):

Machine Tool ◽

Optimization Algorithm ◽

Large Scale ◽

Cost Minimization ◽

Optimization Method ◽

Manufacturing Cost ◽

Multi Objective ◽

Allocation Method ◽

Tool Performance ◽

The Cost

Abstract In modern machine tool design, precision is an important index to characterize machine tool performance. and precision allocation has become a key task. Since middle 20th century, the precision allocation method using optimization technology to balance manufacturing cost and quality has gradually developed. But most methods mainly take the cost minimization as the goal to optimize the precision allocation. As the precision and manufacturing cost are a pair of factors to be comprehensively considered, balance between them is needed to meet different design requirements. This paper proposes a comprehensive optimization method to trade-off between precision and cost. A multi-object precision allocation optimization model aiming at minimizing fuzzy manufacturing cost and comprehensive precision of machine tool is constructed. A multi-object optimization algorithm to solve the model is designed, combining the multi-objective grey wolf optimization algorithm with multi-objective decision analysis method TOPSIS. A case study based on a large-scale hobbing machine shows that the comprehensive optimization of manufacturing cost and machining precision is realized by using the proposed multi-object precision allocation optimization method.

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Developing Schedule With Linear Programming (Case Study: STTF II Project Komplek Sukamukti Banjaran)

International Journal of Innovation in Enterprise System ◽

10.25124/ijies.v4i02.77 ◽

2020 ◽

Vol 4 (02) ◽

pp. 34-45

Author(s):

Naufal Dzikri Afifi ◽

Ika Arum Puspita ◽

Mohammad Deni Akbar

Keyword(s):

Linear Programming ◽

Objective Function ◽

Project Scheduling ◽

Time Limit ◽

Minimum Time ◽

Service Cost ◽

Trade Off ◽

Overtime Work ◽

The Cost

Shift to The Front II Komplek Sukamukti Banjaran Project is one of the projects implemented by one of the companies engaged in telecommunications. In its implementation, each project including Shift to The Front II Komplek Sukamukti Banjaran has a time limit specified in the contract. Project scheduling is an important role in predicting both the cost and time in a project. Every project should be able to complete the project before or just in the time specified in the contract. Delay in a project can be anticipated by accelerating the duration of completion by using the crashing method with the application of linear programming. Linear programming will help iteration in the calculation of crashing because if linear programming not used, iteration will be repeated. The objective function in this scheduling is to minimize the cost. This study aims to find a trade-off between the costs and the minimum time expected to complete this project. The acceleration of the duration of this study was carried out using the addition of 4 hours of overtime work, 3 hours of overtime work, 2 hours of overtime work, and 1 hour of overtime work. The normal time for this project is 35 days with a service fee of Rp. 52,335,690. From the results of the crashing analysis, the alternative chosen is to add 1 hour of overtime to 34 days with a total service cost of Rp. 52,375,492. This acceleration will affect the entire project because there are 33 different locations worked on Shift to The Front II and if all these locations can be accelerated then the duration of completion of the entire project will be effective

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Cost-minimization analysis of drugs used in the treatment of Asthma and COPD diseases in India

Current Drug Therapy ◽

10.2174/1574885515999200813193250 ◽

2020 ◽

Vol 15 ◽

Author(s):

Billu Payal ◽

Anoop Kumar ◽

Harsh Saxena

Keyword(s):

Generic Drugs ◽

Cost Minimization ◽

Cost Savings ◽

Chronic Obstructive ◽

Pulmonary Medicine ◽

Potential Cost ◽

Maximum Cost ◽

Cost Minimization Analysis ◽

Cost Variation ◽

The Cost

Background: Asthma and Chronic Obstructive Pulmonary Diseases (COPD) are well known respiratory diseases affecting millions of peoples in India. In the market, various branded generics, as well as generic drugs, are available for their treatment and how much cost will be saved by utilizing generic medicine is still unclear among physicians. Thus, the main aim of the current investigation was to perform cost-minimization analysis of generic versus branded generic (high and low expensive) drugs and branded generic (high expensive) versus branded generic (least expensive) used in the Department of Pulmonary Medicine of Era Medical University, Lucknow for the treatment of asthma and COPD. Methodology: The current index of medical stores (CIMS) was referred for the cost of branded drugs whereas the cost of generic drugs was taken from Jan Aushadi scheme of India 2016. The percentage of cost variation particularly to Asthma and COPD regimens on substituting available generic drugs was calculated using standard formula and costs were presented in Indian Rupees (as of 2019). Results: The maximum cost variation was found between the respules budesonide high expensive branded generic versus least expensive branded generic drugs and generic versus high expensive branded generic. In combination, the maximum cost variation was observed in the montelukast and levocetirizine combination. Conclusion: In conclusion, this study inferred that substituting generic antiasthmatics and COPD drugs can bring potential cost savings in patients.

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Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽

10.3390/electronics10121452 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1452

Author(s):

Cristian Mateo Castiblanco-Pérez ◽

David Esteban Toro-Rodríguez ◽

Oscar Danilo Montoya ◽

Diego Armando Giral-Ramírez

Keyword(s):

Genetic Algorithm ◽

Objective Function ◽

Programming Model ◽

Distribution Networks ◽

Optimization Method ◽

Optimal Placement ◽

Mixed Integer ◽

Power Balance ◽

Continuous Version ◽

Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

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Topology Optimization of Hard-Coating Thin Plate for Maximizing Modal Loss Factors

Coatings ◽

10.3390/coatings11070774 ◽

2021 ◽

Vol 11 (7) ◽

pp. 774

Author(s):

Haitao Luo ◽

Rong Chen ◽

Siwei Guo ◽

Jia Fu

Keyword(s):

Topology Optimization ◽

Objective Function ◽

Composite Plates ◽

Optimization Method ◽

Hard Coating ◽

Design Variables ◽

Coating Composite ◽

Damping Materials ◽

Topology Optimization Method ◽

Loss Factors

At present, hard coating structures are widely studied as a new passive damping method. Generally, the hard coating material is completely covered on the surface of the thin-walled structure, but the local coverage cannot only achieve better vibration reduction effect, but also save the material and processing costs. In this paper, a topology optimization method for hard coated composite plates is proposed to maximize the modal loss factors. The finite element dynamic model of hard coating composite plate is established. The topology optimization model is established with the energy ratio of hard coating layer to base layer as the objective function and the amount of damping material as the constraint condition. The sensitivity expression of the objective function to the design variables is derived, and the iteration of the design variables is realized by the Method of Moving Asymptote (MMA). Several numerical examples are provided to demonstrate that this method can obtain the optimal layout of damping materials for hard coating composite plates. The results show that the damping materials are mainly distributed in the area where the stored modal strain energy is large, which is consistent with the traditional design method. Finally, based on the numerical results, the experimental study of local hard coating composites plate is carried out. The results show that the topology optimization method can significantly reduce the frequency response amplitude while reducing the amount of damping materials, which shows the feasibility and effectiveness of the method.

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An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

Applied Sciences ◽

10.3390/app11020850 ◽

2021 ◽

Vol 11 (2) ◽

pp. 850

Author(s):

Dokkyun Yi ◽

Sangmin Ji ◽

Jieun Park

Keyword(s):

Artificial Intelligence ◽

Cost Function ◽

Numerical Experiments ◽

Global Minimum ◽

Optimization Method ◽

Learning Method ◽

Adaptive Optimization ◽

The Cost ◽

Proof Of Convergence ◽

Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

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Reverse Modeling and Topological Optimization for Lightweight Design of Automobile Wheel Hubs with Hollow Ribs

International Journal of Computational Methods ◽

10.1142/s0219876219500646 ◽

2019 ◽

Vol 17 (09) ◽

pp. 1950064

Author(s):

P. F. Xu ◽

S. Y. Duan ◽

F. Wang

Keyword(s):

Finite Element ◽

Lightweight Design ◽

Element Model ◽

Optimization Method ◽

Modeling Technique ◽

Topological Optimization ◽

Time Interval ◽

Braking Performance ◽

Existing Structures ◽

Physical Conditions

Lightweight of wheel hubs is the linchpin for reducing the unsprung mass and improving the vehicle dynamic and braking performance of vehicles, thus, sustaining stability and comfortability. Current experience-based lightweight designs of wheel hubs have been argued to render uneven distribution of materials. This work develops a novel method to combine the reverse modeling technique with the topological optimization method to derive lightweight wheel hubs based on the principles of mechanics. A reverse modeling technique is first adopted to scan and reproduce the prototype 3D geometry of the wheel hub with solid ribs. The finite element method (FEM) is then applied to perform stress analysis to identify the maximum stress and its location of wheel hub under variable potential physical conditions. The finite element model is then divided into optimization region and nonoptimized region: the former is the interior portion of spoke and the latter is the outer surface of the spoke. A topology optimization is then conducted to remove the optimization region which is interior material of the spokes. The hollow wheel hub is then reconstructed with constant wall thickness about 5[Formula: see text]mm via a reverse modeling technique. The results show that the reconstructed model can reduce the mass of 12.7% compared to the pre-optimized model. The present method of this paper can guarantee the optimal distribution of wheel hub material based on mechanics principle. It can be implemented automatically to shorten the time interval for optimal lightweight designs. It is especially preferable for many existing structures and components as it maintains the structural appearance of optimization object.

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