Data-Driven Chance Constrained Optimization under Wasserstein Ambiguity Sets

We present a data-driven approach for distributionally robust chance constrained optimization problems (DRCCPs). We consider the case where the decision maker has access to a finite number of samples or realizations of the uncertainty. The chance constraint is then required to hold for all distributions that are close to the empirical distribution constructed from the samples (where the distance between two distributions is defined via the Wasserstein metric). We first reformulate DRCCPs under data-driven Wasserstein ambiguity sets and a general class of constraint functions. When the feasibility set of the chance constraint program is replaced by its convex inner approximation, we present a convex reformulation of the program and show its tractability when the constraint function is affine in both the decision variable and the uncertainty. For constraint functions concave in the uncertainty, we show that a cutting-surface algorithm converges to an approximate solution of the convex inner approximation of DRCCPs. Finally, for constraint functions convex in the uncertainty, we compare the feasibility set with other sample-based approaches for chance constrained programs.Comment: A shorter version is submitted to the American Control Conference, 201

Consistency of Distributionally Robust Risk-and Chance-Constrained Optimization under Wasserstein Ambiguity Sets

We study stochastic optimization problems with chance and risk constraints, where in the latter, risk is quantified in terms of the conditional value-at-risk (CVaR). We consider the distributionally robust versions of these problems, where the constraints are required to hold for a family of distributions constructed from the observed realizations of the uncertainty via the Wasserstein distance. Our main results establish that if the samples are drawn independently from an underlying distribution and the problems satisfy suitable technical assumptions, then the optimal value and optimizers of the distributionally robust versions of these problems converge to the respective quantities of the original problems, as the sample size increases

A Novel Global MPP Tracking of Photovoltaic System Based on Whale Optimization Algorithm

To harvest maximum amount of solar energy and to attain higher efficiency, photovoltaic generation (PVG) systems are to be operated at their maximum power point (MPP) under both variable climatic and partial shaded condition (PSC). From literature most of conventional MPP tracking (MPPT) methods are able to guarantee MPP successfully under uniform shading condition but fails to get global MPP as they may trap at local MPP under PSC, which adversely deteriorates the efficiency of Photovoltaic Generation (PVG) system. In this paper a novel MPPT based on Whale Optimization Algorithm (WOA) is proposed to analyze analytic modeling of PV system considering both series and shunt resistances for MPP tracking under PSC. The proposed algorithm is tested on 6S, 3S2P and 2S3P Photovoltaic array configurations for different shading patterns and results are presented. To compare the performance, GWO and PSO MPPT algorithms are also simulated and results are also presented. From the results it is noticed that proposed MPPT method is superior to other MPPT methods with reference to accuracy and tracking speed.Article History: Received July 23rd 2016; Received in revised form September 15th 2016; Accepted October 1st 2016; Available onlineHow to Cite This Article: Kumar, C.H.S and Rao, R.S. (2016) A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm. Int. Journal of Renewable Energy Development, 5(3), 225-232.http://dx.doi.org/10.14710/ijred.5.3.225-23

Data-driven chance constrained optimization under wasserstein ambiguity sets

We present a data-driven approach for distri-butionally robust chance constrained optimization problems (DRCCPs). We consider the case where the decision maker has access to a finite number of samples or realizations of the uncertainty. The chance constraint is then required to hold for all distributions that are close to the empirical distribution constructed from the samples (where the distance between two distributions is defined via the Wasserstein metric). We first reformulate DRCCPs under data-driven Wasserstein ambiguity sets and a general class of constraint functions. When the feasibility set of the chance constraint program is replaced by its convex inner approximation, we present a convex reformulation of the program and show its tractability when the constraint function is affine in both the decision variable and the uncertainty. For constraint functions concave in the uncertainty, we show that a cutting-surface algorithm converges to an approximate solution of the convex inner approximation of DRCCPs. Finally, for constraint functions convex in the uncertainty, we compare the feasibility set with other sample-based approaches for chance constrained programs.</p

Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition

Partial shading condition is one of the adverse phenomena which effects the power output of photovoltaic (PV) systems due to inaccurate tracking of global maximum power point. Conventional Maximum Power Point Tracking (MPPT) techniques like Perturb and Observe, Incremental Conductance and Hill Climbing can track the maximum power point effectively under uniform shaded condition, but fails under partial shaded condition. An attractive solution under partial shaded condition is application of meta-heuristic algorithms to operate at global maximum power point. Hence in this paper, an Enhanced Grey Wolf Optimizer (EGWO) based maximum power point tracking algorithm is proposed to track the global maximum power point of PV system under partial shading condition. A Mathematical model of PV system is developed under partial shaded condition using single diode model and EGWO is applied to track global maximum power point. The proposed method is programmed in MATLAB environment and simulations are carried out on 4S and 2S2P PV configurations for dynamically changing shading patterns. The results of the proposed method are analyzed and compared with GWO and PSO algorithms. It is observed that proposed method is effective in tracking global maximum power point with more accuracy in less computation time compared to other methods.Article History: Received June 12nd 2017; Received in revised form August 13rd 2017; Accepted August 15th 2017; Available onlineHow to Cite This Article: Kumar, C.H.S and Rao, R.S. (2017 Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition. Int. Journal of Renewable Energy Development, 6(3), 203-212.https://doi.org/10.14710/ijred.6.3.203-21

Influence of host plant (Terminalia arjuna)defences on the evolution of feeding behaviourin the tasar silkworm

Both under indoor and outdoor rearing conditions, early instars of Antheraea mylitta showed differential preference for eating towards developmentally different leaves of host plant, Terminalia arjuna. Semi-mature leaves were preferred by first, second and third instar of A. mylitta. Nutritional-value study of leaves of different age groups with respect to soluble protein and electrophoretic profile showed that young leaves are nutritionally rich compared to semi-mature and mature leaves. However, growth response and survival of larvae were better on semi-mature leaves compared to young and mature leaves. When analysed, semi-mature leaves showed protease inhibitor activity intermediate between young and mature leaves. This observation suggests optimal defence theory, where young and semi-mature leaves having high fitness and high probability of attack tend to have higher concentration of defence metabolites. Differential inhibition of midgut and bovine proteases by host plant protease inhibitor indicates that the tasar silkworm might have detoxified or evolved proteases that are insensitive to the leaf protease inhibitor of the host plant. Thus the differential feeding behaviour of larvae of tasar silkworm is an adaptation for coexistence of the insect and its host plant

Prediction of Soakout Time Using Analytical Models

In precision manufacturing enterprises, machine parts at nonstandard temperatures are often soaked to standard temperature prior to making any dimensional measurements. The soakout times are usually determined using lumped heat-transfer models where the part temperatures are assumed to be uniform. This article discusses conditions under which lumped model assumptions are valid by comparing lumped analyses for various shapes and materials with the more general finite element results. In addition, the effect of ambient temperature cycling on part response is also studied