scholarly journals Two-Stage Physical Economic Adjustable Capacity Evaluation Model of Electric Vehicles for Peak Shaving and Valley Filling Auxiliary Services

2021 ◽  
Vol 13 (15) ◽  
pp. 8153
Author(s):  
Dunnan Liu ◽  
Tingting Zhang ◽  
Weiye Wang ◽  
Xiaofeng Peng ◽  
Mingguang Liu ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Evaluation Model ◽  
Ancillary Services ◽  
Learning Ability ◽  
Time Sharing ◽  
Two Stage ◽  
Peak Shaving ◽  
Second Stage ◽  
Capacity Evaluation ◽  
Auxiliary Services

A large number of renewable energy and EVs (electric vehicles) are connected to the grid, which brings huge peak shaving pressure to the power system. If we can make use of the flexible characteristics of EVs and effectively aggregate the adjustable resources of EVs to participate in power auxiliary services, this situation can be alleviated to a certain extent. In this paper, a two-stage physical and economic adjustable capacity evaluation model of EVs for peak shaving and valley filling ancillary services is constructed. The main steps are as follows: with the help of the deep learning ability of the AC (Actor-Critic) algorithm, the optimal physical charging scheme of EV fleet is determined to minimize the grid fluctuation under the travel constraints of private EVs, and the optimized charging power is transferred to the second stage. In the second stage, load aggregators encourage users to participate in ancillary services by setting subsidy prices. In this stage, the model constructs a user decision model based on a logistic function to describe the probability of users accepting dispatching instructions. With the goal of maximizing the revenue of load aggregators, the wolf colony algorithm is used to solve the optimal solution of the time-sharing subsidy level, and finally the economic adjustable capacity of the EV fleet considering the subjective decision of users is obtained.

Two-Stage Data Envelopment Analysis Efficiency of Indian General Insurance Companies

2021 ◽  
pp. 097215092110476
Author(s):  
Ram Pratap Sinha
Keyword(s):  
Data Envelopment Analysis ◽  
Asset Management ◽  
Evaluation Model ◽  
Insurance Companies ◽  
Data Envelopment ◽  
Efficiency Evaluation ◽  
Two Stage ◽  
Network Dea ◽  
Second Stage ◽  
General Insurance

The present study compares efficiency-related performance of 15 Indian general insurance companies using a two-stage efficiency evaluation model. Efficiency evaluation has been made for the span 2009–2010 to 2017–2018 using network DEA (data envelopment analysis). The results indicate that the in-sample private sector general insurance companies outcompeted the public sector insurers with regard to first-stage activity (premium mobilization), while the reverse was observed in terms of the second-stage activity (asset management and provision of claim benefits). The study also carried out regression of efficiency scores on several contextual variables. The results indicate that ownership is an influential contextual variable in both stages of productivity while solvency significantly impacts efficiency in the second stage.

Revisiting Ellsberg’s and Machina’s Paradoxes: A Two-Stage Evaluation Model Under Ambiguity

2021 ◽  
Author(s):  
Ying He
Keyword(s):  
Decision Making ◽  
Model Uncertainty ◽  
Evaluation Model ◽  
Subjective Expected Utility ◽  
Uncertainty Aversion ◽  
Two Stage ◽  
Second Stage ◽  
Different Types ◽  
Small Domain ◽  
Different Sources

In this paper, a two-stage evaluation (TSE) model for decision making under ambiguity is proposed. Events in state space are classified into risky and ambiguous events, which correspond to different types of uncertainty generated by different sources. In this TSE model, uncertainty of two different types are evaluated by decision maker (DM) in different stages. In the first stage, DM evaluates more uncertain consequences of an act locally by applying local subjective expected utility (SEU) models, which are then embedded into the second-stage evaluation based on SEU defined globally over all events. To axiomatize such a model, the small domain SEU over risky acts is extended to both risky and nonrisky (ambiguous) acts. When evaluating a risky act, TSE model reduces to Savage’s SEU with one stage. When evaluating an ambiguous act, local SEU with a different uncertainty aversion defined on ambiguous events gives TSE model some flexibility in describing preferences. It can be shown that TSE model can accommodate Ellsberg’s paradoxes and Machina’s paradoxes in the literature. When applied to portfolio selection problem, TSE model enjoys some nice properties other models do not have. This paper was accepted by Manel Baucells, decision analysis.

An Enhanced Two Stage MMSE Equalizer for Coded FBMC/OQAM Systems

2019 ◽  
Vol 10 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Mohammad Rizk Assaf ◽  
Abdel-Nasser Assimi
Keyword(s):  
Error Propagation ◽  
Channel Model ◽  
Error Correcting Codes ◽  
Two Stage ◽  
Probability Of Error ◽  
Second Stage ◽  
Different Types ◽  
Mmse Equalizer ◽  
And Performance ◽  
Ici Cancellation

In this article, the authors investigate the enhanced two stage MMSE (TS-MMSE) equalizer in bit-interleaved coded FBMC/OQAM system which gives a tradeoff between complexity and performance, since error correcting codes limits error propagation, so this allows the equalizer to remove not only ICI but also ISI in the second stage. The proposed equalizer has shown less design complexity compared to the other MMSE equalizers. The obtained results show that the probability of error is improved where SNR gain reaches 2 dB measured at BER compared with ICI cancellation for different types of modulation schemes and ITU Vehicular B channel model. Some simulation results are provided to illustrate the effectiveness of the proposed equalizer.

Personalised attraction recommendation for enhancing topic diversity and accuracy

2021 ◽  
pp. 016555152199980
Author(s):  
Yuanyuan Lin ◽  
Chao Huang ◽  
Wei Yao ◽  
Yifei Shao
Keyword(s):  
Real World ◽  
Information Overload ◽  
Two Stage ◽  
Misclassification Cost ◽  
Second Stage ◽  
Low Visibility ◽  
Recommendation Diversity ◽  
Optimisation Model ◽  
Definition Of ◽  
Improved Methods

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.

scholarly journals Two-stage multi-tasking transform framework for large-scale many-objective optimization problems

2021 ◽  
Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma
Keyword(s):  
Large Scale ◽  
Original Problem ◽  
Optimization Problems ◽  
Reference Points ◽  
Optimization Strategy ◽  
Two Stage ◽  
Decision Space ◽  
Second Stage ◽  
Decision Variables ◽  
Objective Space

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

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