Sensor System Optimization Under Uncertainty

2019 ◽  
pp. 283-316
Author(s):  
Wolfgang Granig ◽  
Lisa-Marie Faller ◽  
Hubert Zangl
Keyword(s):  
System Optimization ◽  
Optimization Under Uncertainty ◽  
Sensor System

Optimization Under Uncertainty: A Case Study of a Solar Absorption Cooling and Heating System for a Medium-Sized Office Building in Atlanta

2012 ◽  
Author(s):  
Yin Hang ◽  
Ming Qu ◽  
Fu Zhao
Keyword(s):  
Uncertainty Analysis ◽  
Heating System ◽  
System Optimization ◽  
Optimization Under Uncertainty ◽  
Office Building ◽  
Medium Size ◽  
Solar Absorption ◽  
Absorption Cooling ◽  
Solar Absorption Cooling

Solar absorption cooling and heating (SACH) systems currently still stay at development and demonstration stage due to the nature of the complex system. It is critical for practitioners and engineers to have a correct and complete performance analyses and evaluation for SACH systems with respects of energy, economics, and environment. Optimization is necessarily involved to find the optimal system design by considering these three aspects. However, many assumptions made in the optimization are sensitive to the energy, economic, and environmental variations, and thus the optimization results will be affected. Therefore, the sensitivity and uncertainty analysis is important and necessary to make optimization robust. This paper uses a case study to explore the influence of the uncertainties on the SACH system optimization results. The case is a SACH system for a medium size office building in Atlanta. The one parameter at a time (OAT) sensitivity analysis method was applied firstly to determine the most sensitive inputs. Monte Carlo statistical method was utilized to generate the data sets for uncertainty analysis. The optimization problem under uncertainty was then formulated and solved. Due to the uncertainty associated with system inputs, the optimization solutions were found with certain types of the distributions. In addition, the scenario analysis on electricity price does not show large sensitivity to the objectives.

Mission-Level Optimization: Complex Systems Design for Highly Stochastic Life Cycle Use Case Scenarios

2020 ◽  
Author(s):  
Brian Chell ◽  
Steven Hoffenson ◽  
Benjamin Kruse ◽  
Mark R. Blackburn
Keyword(s):  
Life Cycle ◽  
Systems Design ◽  
System Optimization ◽  
Optimization Under Uncertainty ◽  
Modeling And Analysis ◽  
Mission Success ◽  
Large Numbers ◽  
Key Stakeholders ◽  
System Life ◽  
Engineering Modeling

Abstract Mission engineering is a growing field with many practical opportunities and challenges. The goal of mission engineering is to increase system effectiveness, reduce life cycle costs, and aid in communicating system capabilities to key stakeholders. Optimizing system designs for their mission context is important to achieving these goals. However, system optimization is generally done using multiple key performance indicators (KPIs), which are not always directly representative of, nor easily translatable to, mission success. This paper introduces, motivates, and proposes a new approach for performing mission-level optimization (MLO), where the objective is to design systems that maximize the probability of mission success over the system life cycle. This builds on previous literature related to mission engineering, modeling, and analysis, as well as optimization under uncertainty. MLO problems are unique in their high levels of design, operational, and environmental uncertainty, as well as the single binary objective representing mission success or failure. By optimizing for mission success, designers can account for large numbers of KPIs and external factors when determining the best possible system design.

scholarly journals 3Es System Optimization under Uncertainty Using Hybrid Intelligent Algorithm: A Fuzzy Chance-Constrained Programming Model

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Jiekun Song ◽  
Kaixin Zhang ◽  
Zijian Cao
Keyword(s):  
Energy Consumption ◽  
Industrial Structure ◽  
Programming Model ◽  
System Optimization ◽  
Ammonia Nitrogen ◽  
Optimization Under Uncertainty ◽  
Chance Constrained Programming ◽  
Intelligent Algorithm ◽  
Total Energy Consumption ◽  
Hybrid Intelligent Algorithm

Harmonious development of 3Es (economy-energy-environment) system is the key to realize regional sustainable development. The structure and components of 3Es system are analyzed. Based on the analysis of causality diagram, GDP and industrial structure are selected as the target parameters of economy subsystem, energy consumption intensity is selected as the target parameter of energy subsystem, and the emissions of COD, ammonia nitrogen, SO2, andNOXand CO2emission intensity are selected as the target parameters of environment system. Fixed assets investment of three industries, total energy consumption, and investment in environmental pollution control are selected as the decision variables. By regarding the parameters of 3Es system optimization as fuzzy numbers, a fuzzy chance-constrained goal programming (FCCGP) model is constructed, and a hybrid intelligent algorithm including fuzzy simulation and genetic algorithm is proposed for solving it. The results of empirical analysis on Shandong province of China show that the FCCGP model can reflect the inherent relationship and evolution law of 3Es system and provide the effective decision-making support for 3Es system optimization.

Sensor system optimization to meet reliability targets

2018 ◽  
Vol 87 ◽  
pp. 113-124 ◽  
Author(s):  
Wolfgang Granig ◽  
Lisa-Marie Faller ◽  
Hubert Zangl
Keyword(s):  
System Optimization ◽  
Sensor System

scholarly journals Demand response power system optimization in presence of renewable energy sources

2017 ◽  
Vol 11 (1) ◽  
pp. 218-226 ◽  
Author(s):  
Virgil Dumbrava ◽  
George Cristian Lazaroiu ◽  
Gabriel Bazacliu ◽  
Dario Zaninelli
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Wind Power ◽  
Demand Response ◽  
Demand Function ◽  
Renewable Energy Sources ◽  
System Optimization ◽  
Optimization Under Uncertainty ◽  
Energy Sources ◽  
Large Customer

Abstract This paper optimizes the price-based demand response of a large customer in a power system with stochastic production and classical fuel-supplied power plants. The implemented method of optimization, under uncertainty, is helpful to model both the utility functions for the consumers and their technical limitations. The consumers exposed to price-based demand can reduce their cost for electricity procurement by modifying their behavior, possibly shifting their consumption during the day to periods with low electricity prices. The demand is considered elastic to electricity price if the consumer is willing and capable to buy various amounts of energy at different price levels, the demand function being represented as purchasing bidding blocks. The demand response is seen also by the scientific literature as a possible source of the needed flexibility of modern power systems, while the flexibility of conventional generation technologies is restricted by technical constraints, such as ramp rates. This paper shows how wind power generation affects short term operation of the electricity system. Fluctuations in the amount of wind power fed into the grid require, without storage capacities, compensating changes in the output of flexible generators or in the consumers’ behavior. In the presented case study, we show the minimization of the overall costs in presence of stochastic wind power production. For highlighting the variability degree of production from renewable sources, four scenarios of production were formulated, with different probabilities of occurrence. The contribution brought by the paper is represented by the optimization model for demand-response of a large customer in a power system with fossil fueled generators and intermittent renewable energy sources. The consumer can reduce the power system costs by modifying his demand. The demand function is represented as purchasing bidding blocks for the possible price forecasted realizations. The consumer benefit function is modelled as a piecewise linear function.

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