scholarly journals Flexible Continuous-Time Modeling for Multi-Objective Day-Ahead Scheduling of CHP Units

2021 ◽  
Vol 13 (9) ◽  
pp. 5058
Author(s):  
Elnaz Davoodi ◽  
Salar Balaei-Sani ◽  
Behnam Mohammadi-Ivatloo ◽  
Mehdi Abapour
Keyword(s):  
Real Time ◽  
Bernstein Polynomials ◽  
Superior Performance ◽  
Affine Function ◽  
Multi Objective ◽  
Time Modeling ◽  
Environmental Criteria ◽  
Discrete Time Approximation ◽  
Sample Test ◽  
Electrical Loads

Increasing applications of CHP units have turned the problem of finding the best optimization model into a significant subject for scholars. In this respect, this paper is aimed at driving a novel formulation to the multi-objective day-ahead scheduling of CHP units using Bernstein polynomials, which more optimally schedules power and heat generations as well as ramping trajectories. This procedure includes yielding an affine function that closely approximates real-time net-load and generation trajectories, which is demonstrated to have a superior performance to the conventional hourly day-ahead scheduling of CHP units based on discrete-time approximation. The problem of how to handle various objective functions by function space method is also addressed. The simulations conducted on the sample test systems, which consist of CHP systems, thermal and heat-only units, as well as thermal and electrical loads, show that the suggested multi-objective model can perfectly cover the total heat and electrical loads in terms of economic and environmental criteria. More importantly, the results indicate that the accuracy of the proposed approach renders cost saving of 1.67% and emission saving of 1.46% in comparison with the conventional hourly-based model, apart from leading to fewer ramping scarcities in real-time operations.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

scholarly journals Handling Initial Conditions in Vector Fitting for Real Time Modeling of Power System Dynamics

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2471
Author(s):  
Tommaso Bradde ◽  
Samuel Chevalier ◽  
Marco De Stefano ◽  
Stefano Grivet-Talocia ◽  
Luca Daniel
Keyword(s):  
Dynamical Systems ◽  
Power System ◽  
System Dynamics ◽  
Real Time ◽  
Initial Conditions ◽  
Test System ◽  
Initial State ◽  
Power System Dynamics ◽  
Vector Fitting ◽  
Time Modeling

This paper develops a predictive modeling algorithm, denoted as Real-Time Vector Fitting (RTVF), which is capable of approximating the real-time linearized dynamics of multi-input multi-output (MIMO) dynamical systems via rational transfer function matrices. Based on a generalization of the well-known Time-Domain Vector Fitting (TDVF) algorithm, RTVF is suitable for online modeling of dynamical systems which experience both initial-state decay contributions in the measured output signals and concurrently active input signals. These adaptations were specifically contrived to meet the needs currently present in the electrical power systems community, where real-time modeling of low frequency power system dynamics is becoming an increasingly coveted tool by power system operators. After introducing and validating the RTVF scheme on synthetic test cases, this paper presents a series of numerical tests on high-order closed-loop generator systems in the IEEE 39-bus test system.

Real-time multi-objective optimisation for electric vehicle charging management

2021 ◽  
Vol 292 ◽  
pp. 126066
Author(s):  
Ridoy Das ◽  
Yue Wang ◽  
Krishna Busawon ◽  
Ghanim Putrus ◽  
Myriam Neaimeh
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Multi Objective ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

scholarly journals Elite Exploitation: A Combination of Mathematical Concept and EMO Approach for Multi-Objective Decision Making

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 136
Author(s):  
Wenxiao Li ◽  
Yushui Geng ◽  
Jing Zhao ◽  
Kang Zhang ◽  
Jianxin Liu
Keyword(s):  
Decision Making ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Decision Makers ◽  
Superior Performance ◽  
Test Problems ◽  
Mathematical Function ◽  
Hyperbolic Tangent ◽  
Nsga Ii ◽  
Multi Objective

This paper explores the combination of a classic mathematical function named “hyperbolic tangent” with a metaheuristic algorithm, and proposes a novel hybrid genetic algorithm called NSGA-II-BnF for multi-objective decision making. Recently, many metaheuristic evolutionary algorithms have been proposed for tackling multi-objective optimization problems (MOPs). These algorithms demonstrate excellent capabilities and offer available solutions to decision makers. However, their convergence performance may be challenged by some MOPs with elaborate Pareto fronts such as CFs, WFGs, and UFs, primarily due to the neglect of diversity. We solve this problem by proposing an algorithm with elite exploitation strategy, which contains two parts: first, we design a biased elite allocation strategy, which allocates computation resources appropriately to elites of the population by crowding distance-based roulette. Second, we propose a self-guided fast individual exploitation approach, which guides elites to generate neighbors by a symmetry exploitation operator, which is based on mathematical hyperbolic tangent function. Furthermore, we designed a mechanism to emphasize the algorithm’s applicability, which allows decision makers to adjust the exploitation intensity with their preferences. We compare our proposed NSGA-II-BnF with four other improved versions of NSGA-II (NSGA-IIconflict, rNSGA-II, RPDNSGA-II, and NSGA-II-SDR) and four competitive and widely-used algorithms (MOEA/D-DE, dMOPSO, SPEA-II, and SMPSO) on 36 test problems (DTLZ1–DTLZ7, WGF1–WFG9, UF1–UF10, and CF1–CF10), and measured using two widely used indicators—inverted generational distance (IGD) and hypervolume (HV). Experiment results demonstrate that NSGA-II-BnF exhibits superior performance to most of the algorithms on all test problems.

Real-Time Profitability of Published Anomalies: An Out-of-Sample Test

2013 ◽  
Vol 03 (03n04) ◽  
pp. 1350016 ◽  
Author(s):  
Jing-Zhi Huang ◽  
Zhijian Huang
Keyword(s):  
Asset Pricing ◽  
Transaction Costs ◽  
Real Time ◽  
Empirical Evidence ◽  
Equity Market ◽  
Data Snooping ◽  
Asset Pricing Anomalies ◽  
Pricing Anomalies ◽  
Out Of Sample ◽  
Sample Test

Empirical evidence on the out-of-sample performance of asset-pricing anomalies is mixed so far and arguably is often subject to data-snooping bias. This paper proposes a method that can significantly reduce this bias. Specifically, we consider a long-only strategy that involves only published anomalies and non-forward-looking filters and that each year recursively picks the best past-performer among such anomalies over a given training period. We find that this strategy can outperform the equity market even after transaction costs. Overall, our results suggest that published anomalies persist even after controlling for data-snooping bias.

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