Three Scenarios in Microgrid to Solve Management Problem for Residential Application Using Genetic Algorithms

2014 ◽  
pp. 568-588
Author(s):  
Faisal A. Mohamed
Keyword(s):  
Genetic Algorithms ◽  
Operating Cost ◽  
Economic Problem ◽  
Management Problem ◽  
Total Load ◽  
Operation And Maintenance ◽  
Online Management ◽  
Power Utilities ◽  
Model Components ◽  
The Cost

This chapter discusses online management of the MicroGrid components. A major challenge for all power utilities is not only to satisfy the consumer demand for power, but to do so at minimal cost and low emissions. Any given power system can be comprised of multiple generating units each of which has its own characteristic operating parameters. The operating cost and emission level of these generators usually correlate proportionally with their outputs, therefore the challenge for power utilities is to balance the total load among generators that are running as efficiently as possible. One of the important applications of the MicroGrid (MG) units is the utilization of small-modular residential or commercial units for onsite service. Genetic Algorithms (GA) optimization is well-suited to solve the environmental/economic problem of the MG. The proposed problem is first formulated as a nonlinear constrained optimization problem. Prior to the optimization, system model components from real industrial data are constructed. The model takes into consideration the operation and maintenance costs as well as the reduction in NOx, SO2, and CO2 emissions. The optimization is aimed at minimizing the cost function of the system while constraining it to meet the customer demand and safety of the system. The results ensure the efficiency of the proposed approach to satisfy the load and to reduce the cost and the emissions in one single run.

scholarly journals Cost of goods sold analysis and recommendations to reduce costs of co-packaged mifepristone–misoprostol for medical abortion

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Lester Chinery ◽  
Chadia Allaouidine ◽  
Alessandra Tomazzini ◽  
Melanie Larson ◽  
A. Metin Gülmezoglu
Keyword(s):  
Operating Cost ◽  
Medical Abortion ◽  
Business Case ◽  
Active Pharmaceutical Ingredients ◽  
Manufacturing Companies ◽  
Direct Production ◽  
Pharmaceutical Ingredients ◽  
Manufacturing Location ◽  
Model Components ◽  
The Cost

Abstract Objective Understanding the price components of the mifepristone/misoprostol (combi-pack) for medical abortion to improve access is critical for identifying strategies to reduce product costs for quality-assured formulations and expanding its availability and use. Methods We constructed a cost of goods sold analysis using data collected from manufacturing companies in Bangladesh, China and India supported by publicly available information related to the product formulation, active pharmaceutical ingredients (API), manufacturing location, manufacturer profiles and other individual model components. Key model components were the active pharmaceutical ingredients (quality-assured or not), excipients, labour cost, operating cost and packaging. Results Combi-pack direct production cost ranges from US$1.08 for finished products which are not quality assured to US$3.05 for products containing quality assured active pharmaceutical ingredients, which means that with a 30% administrative fee applied to those prices, it could be made available between US$1.40 and US$3.97 depending on location, manufacturer’s profile, optimal market situation and the quality of the active pharmaceutical ingredients. The main model component impacting on the cost range is the purchase price of mifepristone active pharmaceutical ingredient and the current differential between quality-assured material supported by adequate documentation and API for which quality assurance cannot be demonstrated. Compared to India cost of goods sold is lower in Bangladesh primarily due to lower operating costs, including the cost of labour. Conclusions It is feasible to lower the cost of quality-assured combi-packs, through reducing mifepristone API cost and selection of the manufacturing location. However, manufacturers need to be incentivised to achieve WHO pre-qualification with a carefully built business case and require support in identifying and sourcing competitively priced material and manufacturing products to the necessary standard.

scholarly journals A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

2021 ◽  
Vol 11 (23) ◽  
pp. 11116
Author(s):  
Ke Zheng ◽  
Guozhu Jia ◽  
Linchao Yang ◽  
Chunting Liu
Keyword(s):  
Fault Diagnosis ◽  
Data Distribution ◽  
Imbalanced Data ◽  
Data Driven ◽  
Total Cost ◽  
Operation And Maintenance ◽  
Diagnosis Method ◽  
Overall Performance ◽  
Diagnosis Model ◽  
The Cost

In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible approach to determine the cost of the misdiagnosis of different fault modes that accounts for the interference of data distribution. The performance of the original cost-insensitive flight data-driven fault diagnosis models also needs to be improved. In response to this requirement, this paper proposes a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV. According to the fault criticality from FMECA information, we defined a misdiagnosis hazard value and calculated the misdiagnosis cost. By using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis results. A two-step ensemble cost-sensitive method based on the MetaCost framework was proposed using stratified bootstrapping, choosing LightGBM as meta-classifiers, and adjusting the ensemble form to enhance the overall performance of the diagnosis model and reduce the occupation of the computing resources while optimizing the total misdiagnosis cost. The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost incurred by misdiagnosis, without putting forward excessive requirements on the computing equipment under the condition of ensuring a certain overall level of diagnosis performance.

scholarly journals Cost-Effectiveness Analysis of a Large-Scale Crèche Intervention to Prevent Child Drowning in Rural Bangladesh

2021 ◽  
Author(s):  
Y. Natalia Alfonso ◽  
Adnan A Hyder ◽  
Olakunle Alonge ◽  
Shumona Sharmin Salam ◽  
Kamran Baset ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Societal Perspective ◽  
Large Scale ◽  
Operating Cost ◽  
Cost Effective ◽  
Economic Benefits ◽  
Program Cost ◽  
Rural Bangladesh ◽  
Life Years ◽  
The Cost

Abstract Drowning is the leading cause of death among children 12-59 months old in rural Bangladesh. This study evaluated the cost-effectiveness of a large-scale crèche intervention in preventing child drowning. Estimates of the effectiveness of the crèches was based on prior studies and the program cost was assessed using monthly program expenditures captured prospectively throughout the study period from two different implementing agencies. The study evaluated the cost-effectiveness from both a program and societal perspective. Results showed that from the program perspective the annual operating cost of a crèche was $416.35 (95%C.I.: $222 to $576), the annual cost per child was $16 (95%C.I.: $9 to $22) and the incremental-cost-effectiveness ratio (ICER) per life saved with the crèches was $17,803 (95%C.I.: $9,051 to $27,625). From the societal perspective (including parents time valued) the ICER per life saved was -$176,62 (95%C.I.: -$347,091 to -$67,684)—meaning crèches generated net economic benefits per child enrolled. Based on the ICER per disability-adjusted-life years averted from the societal perspective (excluding parents time), $2,020, the crèche intervention was cost-effective even when the societal economic benefits were ignored. Based on the evidence, the creche intervention has great potential for reducing child drowning at a cost that is reasonable.

scholarly journals Integrated Offline and Online Decision Making under Uncertainty

2021 ◽  
Vol 70 ◽  
pp. 77-117
Author(s):  
Allegra De Filippo ◽  
Michele Lombardi ◽  
Michela Milano
Keyword(s):  
Optimization Problems ◽  
Greedy Heuristic ◽  
Management Problem ◽  
Quality Improvements ◽  
Solution Quality ◽  
Convex Optimization Problem ◽  
Routing Problem ◽  
Multi Stage ◽  
The Cost ◽  
Parametric Convex Optimization

This paper considers multi-stage optimization problems under uncertainty that involve distinct offline and online phases. In particular it addresses the issue of integrating these phases to show how the two are often interrelated in real-world applications. Our methods are applicable under two (fairly general) conditions: 1) the uncertainty is exogenous; 2) it is possible to define a greedy heuristic for the online phase that can be modeled as a parametric convex optimization problem. We start with a baseline composed by a two-stage offline approach paired with the online greedy heuristic. We then propose multiple methods to tighten the offline/online integration, leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase. Overall, our methods provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios. To test our methods, we ground our approaches on two real cases studies with both offline and online decisions: an energy management problem with uncertain renewable generation and demand, and a vehicle routing problem with uncertain travel times. The application domains feature respectively continuous and discrete decisions. An extensive analysis of the experimental results shows that indeed offline/online integration may lead to substantial benefits.

scholarly journals Multiobjective Electrical Power Dispatch of Thermal Units with Convex and Non-Convex Fuel Cost Functions for 24 Hours Load Demands

2020 ◽  
Vol 9 (3) ◽  
pp. 1534-1542
Keyword(s):  
Power Systems ◽  
Electrical Power ◽  
Economic Dispatch ◽  
Operating Cost ◽  
Load Condition ◽  
Minimum Cost ◽  
Electricity Sector ◽  
Operating Characteristics ◽  
Total Load ◽  
Gaseous Emission

There are a host of difficult issues with scheduling, operation, and control of integrated power systems. The electricity sector is changing rapidly, and one of the most important concerns is deciding operational strategies to meet electricity demand. It is a greater challenge to satisfy customer demand for power at a minimum cost. The operating characteristics of all generators may be different. In general, operating cost is not proportionate to the performance of these generators. Therefore challenge for power utilities to balance the total load between generators. For a specific load condition on energy systems, Economic Dispatch(ED) seeks to reduce the fuel costs of power generation units. Moreover, energy utilities have also an important task to reduce gaseous emission. So the ED problem can be recognized as a complicated multi-objective optimization problem (MOOP) with two competing targets, the minimal cost of fuel and the minimum emissions effects. This paper presented an efficient method, hybrid of particle swarm optimization (PSO) and a learning-based optimization (TLBO) for combined environmental issues because of gaseous emission and economic dispatch (CEED) problems. The results were shown and verified by PSO and TLBO for standard 3 and 6-generator frameworks with combined issues of emission and economic dispatch taking into account line losses and prohibited zones (POZs) on hourly demand for 24 hours

The Changing Retail Cost Model

2020 ◽  
pp. 145-157
Author(s):  
Alan Treadgold ◽  
Jonathan Reynolds
Keyword(s):  
Business Models ◽  
Cost Model ◽  
Operating Cost ◽  
Sustainable Performance ◽  
Additional Costs ◽  
New Entrants ◽  
To Come ◽  
Established Firms ◽  
The One ◽  
The Cost

This chapter examines the changing retail cost model. As established firms re-think existing business models, most will need to come to terms with a rather different operating cost model than the one they have been used to in a pre-internet era, when retailing was conducted entirely out of physical stores. Equally, new entrants may struggle to achieve sustainable performance without understanding the full implications of their evolving cost base. In an omni-channel world where shoppers are, as we have discussed, showing much more appetite to shop online and across multiple touchpoints, the implications for the cost model of traditional retailers are considerable. The extent to which any additional costs of omni-channel retailing become ‘baked in’ to the model is also up for discussion.

scholarly journals A Study of an EOQ Model under Lock Fuzzy Environment

Mathematics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 75 ◽  
Author(s):  
Suman Maity ◽  
Sujit Kumar De ◽  
Sankar Prasad Mondal
Keyword(s):  
Inventory Management ◽  
Numerical Study ◽  
Management Problem ◽  
Final State ◽  
Eoq Model ◽  
Fuzzy Environment ◽  
Demand Rate ◽  
Computer Based ◽  
The Cost ◽  
Cost Vector

The present article was developed for the economic order quantity (EOQ) inventory model under daytime, non-random, uncertain demand. In any inventory management problem, several parameters are involved that are basically flexible in nature with the progress of time. This model can be split into three different sub-models, assuming the demand rate and the cost vector associated with the model are non-randomly uncertain (i.e., fuzzy), and these may include some of the retained learning experiences of the decision-maker (DM). However, the DM has the option of revising his/her decision through the application of the appropriate key vector of the fuzzy locks in their final state. The basic novelty of the present model is that it includes a computer-based decision‐making process involving flowchart algorithms that are able to identify and update the key vectors automatically. The numerical study indicates that when all parameters are assumed to be fuzzy, the double keys of the fuzzy lock provide a more accurate optimum than other methods. Sensitivity analysis and graphical illustrations are made for better justification of the model.

A Systems Approach to the Design of Training Devices: A Model and its Application

1978 ◽  
Vol 22 (1) ◽  
pp. 151-151
Author(s):  
K. Ronald Laughery ◽  
Robert C. Sugarman
Keyword(s):  
Systems Approach ◽  
The State ◽  
Simulation Technology ◽  
Operation And Maintenance ◽  
The Cost ◽  
Operational Device ◽  
Technology Advances

Due to the increasing costs of operation and maintenance of hardware, the use of training devices as an alternative to the use of the operational device is taking on an increasingly important role. As the state of simulation technology advances, much “better” devices, from the standpoint of training utility, are available. Unfortunately, the cost of a device increases almost exponentially as a function of its complexity. The goal of designing a set of training devices for a given system, therefore, is to maintain maximum training utility while minimizing overall device costs (i.e., device complexity). This paper discusses a methodology developed by Calspan Corporation which approaches this problem in a systematic manner.

Potential Benefits of Plug-In Hybrid Electric Vehicles for Consumers and Electric Power Utilities

2011 ◽  
Vol 133 (3) ◽  
Author(s):  
Jim B. Himelic ◽  
Frank Kreith
Keyword(s):  
Cost Effectiveness ◽  
Co2 Emissions ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Unit Commitment ◽  
Electric Utility ◽  
Battery Pack ◽  
Hybrid Electric ◽  
Power Utilities ◽  
The Cost

Plug-in hybrid electric vehicles (PHEVs) have the potential of substantially reducing petroleum consumption and vehicular CO2 emissions relative to conventional vehicles. The analysis presented in this article first ascertains the cost-effectiveness of PHEVs from the perspective of the consumer. Then, the potential effects of PHEVs to an electric utility are evaluated by analyzing a simplified hypothetical example. When evaluating the cost-effectiveness of a PHEV, the additional required premium is an important financial parameter to the consumer. An acceptable amount for the additional upfront costs will depend on the future costs of gasoline and the on-board battery pack. The need to replace the on-board battery pack during the assumed vehicle lifetime also affects the allowed premium. A simplified unit commitment and dispatch model was used to determine the costs of energy and the CO2 emissions associated with PHEVs for different charging scenarios. The results show that electricity can be used to charge PHEVs during off-peak hours without an increase in peak demand. In addition, the combined CO2 emissions from the vehicles and the electric generation facilities will be reduced, regardless of the charging strategy.

scholarly journals Evolutionary Design Optimization of an Alkaline Water Electrolysis Cell for Hydrogen Production

2020 ◽  
Vol 10 (23) ◽  
pp. 8425
Author(s):  
Damien Le Bideau ◽  
Olivier Chocron ◽  
Philippe Mandin ◽  
Patrice Kiener ◽  
Mohamed Benbouzid ◽  
...  
Keyword(s):  
Hydrogen Production ◽  
Current Density ◽  
Operating Cost ◽  
Cell Voltage ◽  
Electrolysis Cell ◽  
Alkaline Water Electrolysis ◽  
Financial Investment ◽  
Operation Conditions ◽  
Optimal Current ◽  
The Cost

Hydrogen is an excellent energy source for long-term storage and free of greenhouse gases. However, its high production cost remains an obstacle to its advancement. The two main parameters contributing to the high cost include the cost of electricity and the cost of initial financial investment. It is possible to reduce the latter by the optimization of system design and operation conditions, allowing the reduction of the cell voltage. Because the CAPEX (initial cost divided by total hydrogen production of the electrolyzer) decreases according to current density but the OPEX (operating cost depending on the cell voltage) increases depending on the current density, there exists an optimal current density. In this paper, a genetic algorithm has been developed to find the optimal evolution parameters and to determine an optimum electrolyzer design. The optimal current density has been increased by 10% and the hydrogen cost has been decreased by 1%.

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