Kumaraswamy Ponnambalam

University of Waterloo | UWaterloo · Department of Systems Design Engineering

A thorough understanding of the impact of climatic factors on agricultural production is crucial for improving crop models and enhancing predictability of crop prices and yields. Fluctuations in crop yield and price can have significant implications for the market sector and farming community. Given the projected increase in frequency and intensity...

The model predictive control (MPC) approach can be implemented in either a reactive (RE-) or predictive (PR-) manner to control the operation of urban drainage systems (UDSs). Previous research focused mostly on the RE-MPC, as the PR-MPC, despite its potential to improve the performance of the UDS operations, requires additional computational resou...

According to the World Meteorological Organization, since 2000, there has been an increase in global flood-related disasters by 134 percent compared to the previous decades. Efficient flood risk management strategies necessitate a holistic approach to evaluating flood vulnerabilities and risks. Catastrophic losses can occur when the peak flow value...

According to the World Meteorological Organization, since 2000, there has been an increase in global flood-related disasters by 134 percent as compared to the two previous decades. Efficient flood risk management strategies necessitate a holistic approach to evaluating flood vulnerabilities and risks. Catastrophic losses can occur when the peak flo...

The Fletcher-Ponnambalam (FP) method is an explicit stochastic optimization method for design and operations management of real-world storage systems including surface water reservoir and groundwater management problems. The FP method faces no curse of dimensionality and no need for scenario generation. The paper introduces a novel implementation f...

The DC microgrid (DC MG) concept enables the hosting of DC-type renewable energy resources. However, their intermittent nature means that a high penetration of renewables can jeopardize supply adequacy and voltage provision during islanding. The work presented in this paper was therefore directed at developing a probabilistic graphical approach bas...

This paper presents an acoustic leak detection system for distribution water mains using machine learning methods. The problem is formulated as a binary classifier to identify leak and no-leak cases using acoustic signals. A supervised learning methodology has been employed using several detection features extracted from acoustic signals, such as p...

In this study, a comparison of time-series modeling with linear and nonlinear ML tools is conducted for fresh produce (FP) yield forecast. The consecutive monthly weather and yield dataset of Oxnard, California, corresponding to the years 2007 to 2014, are applied for models' development and training by examining the diverse combinations of predict...

Adequately priced orders and time for fresh produce (FP) are two factors that bring commercial benefits to vendors and minimizes waste. However, many factors, such as income, labor, and other trade issues, affect the price that include uncertainties due to climate change, making decisions on FP procurement prices and quantities extremely challengin...

Stochastic dynamic programming (SDP) is a widely-used method for reservoir operations optimization under uncertainty but suffers from the dual curses of dimensionality and modeling. Reinforcement learning (RL), a simulation-based stochastic optimization approach, can nullify the curse of modeling that arises from the need for calculating a very lar...

This paper presents basic definitions and challenges/opportunities from different perspectives to study and control water cycle impacts on society and vice versa. The wider and increased interactions and their consequences such as global warming and climate change, and the role of complex institutional- and governance-related socioeconomic-environm...

Boukan Dam reservoir is the largest infrastructure constructed on the Zarineh-Roud River regulating streamflow for different uses including supplying water to Lake Urmia, the second largest salt lake in the world. This paper presents a forecast-based adaptive real-time optimal operation model (ARTOM) for Boukan reservoir with the aim of maximizing...

The future smart grid encompasses ac–dc clusters known as ac–dc microgrids. For reliability and security purposes, each microgrid hosts a mix of synchronous-based and converter-based distributed resources. However, synchronous-based generators, in particular, are characterized by their limited reactive power capabilities because of the limitation o...

To deal reliably with the cognitive uncertainty experienced by decision-makers when facing problems involving linguistic group decision-making, we investigate a new research perspective: cognitive familiarity is regarded as a measure of cognitive reliability. The linguistic variables examined in this work are quantified with the use of several gran...

We propose a new formulation for controlling inventory in a two-echelon distribution system consisting of one warehouse and multiple non-identical retailers. In such a system, customer demand occurs based on a normal distribution at the retailers and propagates backward through the system. The warehouse and the retailers have a limited capacity for...

This paper presents a new explicit stochastic hydro-economic optimization model for reservoir-irrigation district systems design affected by multiple interdependent sources of uncertainties. The model solution determines both optimal design and long-term operation policies of the systems while accounting for uncertainties of reservoir inflow, rainf...

The stiff competition for water between agriculture and non-agricultural production sectors makes it necessary to have effective management of irrigation networks in farms. However, the process of selecting flow control structures in irrigation networks is highly complex and involves different levels of decision makers. In this paper, we apply mult...

Investments in wind and solar power are driven by the aim to maximize the utilization of renewable energy (RE). This results in an increased concentration of wind farms at locations with higher average wind speeds and of solar panel installations at sites with higher average solar insolation. This is unfavourable for energy suppliers and for the ov...

This paper presents a new optimal power flow (OPF) formulation based on loadability maximization for islanded converter-dominated AC/DC hybrid microgrids. Hybridizing AC and DC at the distribution level brings the merits of AC and DC together as a valuable future layout for AC and DC technologies. Nevertheless, most recent AC/DC distributed resourc...

In the planning of isolated microgrids aiming for a small carbon footprint, the penetration of renewable energy resources is expected to be high. Energy supply from renewable sources are highly variable and renewable energy sources have relatively a large capital investment although with a positive impact on the environment. In planning and designi...

The future smart grid can have an AC/DC hybrid structure that enables the integration of AC/DC energy supply and demand, thus permitting the formation of AC/DC hybrid microgrids (HMGs). The AC/DC HMG is a promising concept that provides the envisioned smart grid with the plug-and-play feature. Nevertheless, understanding such hybrid systems, especi...

A promising configuration for future smart grids is an AC/DC hybrid topology that enables the integration of AC/DC energy resources and modern loads, thus permitting the consequent formation of AC/DC hybrid microgrids (HMGs). An understanding of AC/DC HMGs and their operational premise during islanding will certainly pave the way toward the realiza...

This paper presents a two-stage stochastic centralized dispatch scheme for AC/DC hybrid smart grids. The developed dispatch scheme coordinates the operations of a variety of distributed energy resources (DERs), such as distributed generators (DGs) and energy storage systems (ESSs). It also ensures the coordinated charging of electric vehicles (EVs)...

This study proposes a robust approach for pricing a European option using the binomial tree method. This method considers stock up and down prices in a closed and convex region, called the uncertainty region, defined by the covariance matrix of high and low stock prices. The option model uses this uncertainty region for pricing instead of spot pric...

This paper presents stochastic energy management system (S-EMS) in hybrid ac/dc smart grids. The developed S-EMS coordinates the operations of various distributed resources (DRs), i.e., distributed generators (DGs) and energy storage systems (ESSs). It also ensures coordinated charging of the plug-in electric vehicles (PEVs). The energy management...

The smart grid allows its consumers to participate in producing cost effective, sustainable, and environmentally friendly electricity. The consumers in a smart grid, for example, can plug their Electric Vehicles (EVs) into the grid to charge and discharge their vehicles' batteries. However, charging of the electric vehicles, especially during the p...

Stochastic optimization methods are used for optimal design and operation of surface water reservoir systems under uncertainty. Chance-constrained (CC) optimization with linear decision rules (LDRs) is an old approach for determining the minimum reservoir capacity required to meet a specific yield at a target level of reliability. However, this app...

In this paper, we present different opposition schemes for four reinforcement learning methods: Q-learning, Q(λλ), Sarsa, and Sarsa(λλ) under assumptions that are reasonable for many real-world problems where type-II opposites generally better reflect the nature of the problem at hand. It appears that the aggregation of opposition-based schemes wit...

In this study, the power scheduling problem in μ-grids is investigated taking the uncertainties in power demand and wind power into account. The problem is formulated as a stochastic mixed-integer linear optimization problem with the objective being minimizing the total μ-grid cost. The objective is subject to a set of operational constraints impos...

In this paper, a recently developed stochastic programming technique that includes reliability constraints is used to solve the operations optimization problem of the Parambikulam-Aliyar project (PAP), a multireservoir system in India. The use of reliability constraints as chance constraints in reservoir operations optimization have been around for...

This paper presents a new statistical design method for maximizing the manufacturing yield of engineering systems for which the realizations of design parameters are assumed to be dependent random variables. Like in many practical situations, the method assumes that the joint distribution of design parameters is unknown and their marginal distribut...

The purpose of this study is to find a computationally inexpensive calibration method of a hydrologic model for predicting river flows. The approach is called the surrogate model optimization (SMO), which relies on optimizing a surrogate model instead of the original model that requires significantly more computing time. The proposed SMO method com...

K KE EY YW WO OR RD DS S ABSTRACT This paper presents a new formulation for warehouse inventory management in a stochastic situation. The primary source of this formulation is derived from FP model, which has been proposed by Fletcher and Ponnambalam for reservoir management. The new proposed mathematical model is based on the first and the second...

This paper presents an approach to time series prediction based on Asymmetric Subsethood-Product Fuzzy Neural Inference System (ASuPFuNIS). The standard time series techniques have standard averaging where a fixed weight is added to the past values. In this paper we present a novel neuro-fuzzy inference system based on asymmetric subsethood with in...

This paper describes the implementation of a new solution approach — Fletcher-Ponnambalam model (FP) — for risk management in hydropower system under deregulated electricity market. The FP model is an explicit method developed for the first and second moments of the storage state distributions in terms of moments of the inflow distributions. This m...

Fletcher–Ponnambalam presented a new model for considering the balance equation of the storage volume of the reservoir using indicator functions. For stochastic inflows, the two storage moments of this balance equation, namely, the mean and variance, calculated using a random release policy were found to be quite accurate unlike any known models. S...

Renewable energy systems such as solar and wind are notorious for their varying energy production. Joint use of a battery system can mitigate this problem to meet constant demand. In this paper, we compare three methods, namely, (i) Monte Carlo simulation based optimization, (ii) Stochastic programming, and (iii) Optimization using Storage moment e...

Epilepsy is a serious neurological disorder characterized by recurrent unprovoked seizures due to abnormal or excessive neuronal activity in the brain. An estimated 50 million people around the world suffer from this condition, and it is classified as the second most serious neurological disease known to humanity, after stroke. With early and accur...

Recently in Ontario, Canada new installations of renewable distributed generation are being encouraged. Incentives are provided to producers directly by the government when energy is fed to the grid. This however has brought the attention of operators of local distribution companies to the issue of assuring the quality of supply to downstream custo...

Pool electricity markets are cleared under the strong assumption of having a perfectly known future; in real life, this is anything but true. The inability to predict the random parameters of the supply and the demand function introduces risk into the market clearing process. Therefore, the main interest is to minimize such risk by means of a trade...

Risk minimization in stochastic systems is a challenging problem and this paper compares results of three different techniques in reservoir management. Two-stage stochastic programming (TSP) for maximizing expected benefits is a well-known method, Fletcher and Ponnambalam (FP) and Q-Learning are the two new methods in reservoir management, all of w...

This paper presents a statistical framework for the design of flip-flops under process variations in order to maximize their timing yield. In nanometer CMOS technologies, process variations significantly impact the timing performance of sequential circuits which may eventually cause their malfunction. Therefore, developing a framework for designing...

This paper presents a method for designing component values of electrical devices when design values are random. A polyhedral approximation of the feasible region is used to contain the high yield region of the random distributions modeled by the Kumaraswamy distribution. Evaluation of designed system is done with a Monte Carlo simulation.

Despite growing research interest in evaluating optimal decisions for a load-serving entity (LSE), the literature contains only a few studies that deal with the task of determining strategic power purchases for a distribution company, a retailer, or even a large customer. The scope of this paper is limited to a discussion of the incorporation of di...

This paper proposes a numerical method for the asset allocation problem based on the conventional Advanced First-Order Second Moment (AFOSM) reliability analysis. The proposed method separates the space of decision problems from the space of uncertain returns. By this separation, an uncertain asset allocation problem can be converted into two recur...

This paper presents a design optimization method for MEMS parallel-plate capacitors under fabrication uncertainties. The objective of the optimization problem is to maximize the production yield considering the fabrication tolerances. The method utilizes aspects of the advanced first-order second-moment (AFOSM) reliability method in probabilistic d...

In our previous works, deterministic release policies were considered for the development of approximations of the two lower moments of the storage volume defined by the dynamic equation of the reservoir in discrete time but in continuous state space. Important innovation in that work was the incorporation of the lower and upper bounds of reservoir...

Intrinsic uncertainties of MEMS fabrication processes can severely affect the performance of devices because the tolerance ranges of these processes are relatively large and improvement of process accuracy is very expensive. Therefore, the analysis of fabrication uncertainties and their outcome on a device performance is a vital task before finaliz...

Water resource management is one of the important issues for most governmental and private agencies around the globe. many mathematical and heuristic optimization or simulation techniques have been developed and applied to capture the complexities of the problem; however, most of them suffered from the curse of dimensionality. Q-learning as a popul...

This work proposes a hybrid of stochastic programming (SP) approaches for an optimal midterm refinery planning that addresses three sources of uncertainties: prices of crude oil and saleable products, demands, and yields. An SP technique that utilizes compensating slack variables is employed to explicitly account for constraints’ violations to incr...

This paper addresses the strategic planning, design, and optimization of a network of petrochemical processes under uncertainty and risk considerations. In this work, we extend the deterministic model proposed by Al-Sharrah et al. [Ind. Eng. Chem. Res. 2001, 40, 2103; Chem. Eng. Res. Des. 2006, 84, 1019] to account for parameter uncertainty in proc...

In this approach, exploration of the cost function space was performed with an inexpensive surrogate function, not the expensive original function. To increase the efficiency of the process the surrogate model was combined with one of the dimensionality reduction techniques called Principle Component Analysis (PCA). The case study of this document...

In this paper, a new model for generation and transmission expansion is presented. This new model considers as random events the demand, the equivalent availability of the generating plants, and the transmission capacity factor of the transmission lines. In order to incorporate these random events into an optimization model, stochastic programming...

In the ideal world, electricity markets are solved under the assumption that all quantities are deterministic. This is the equivalent of assuming that one can perfectly predict future levels of demand and supply. In the real world, the biggest source of uncertainty comes precisely from demand and supply levels. A clear understanding of the economic...

In this approach, exploration of the cost function space was performed with an inexpensive surrogate function, not the expensive original function. The Design and Analysis of Computer Experiments(DACE) surrogate function, which is one type of approximate models, which takes correlation function for error was employed. The results for Monte Carlo Sa...

Broadband satellite based IP network congestion control is becoming a serious issue towards commitment of service level agreements (SLAs) to honor the guaranteed quality of service (QoS). In satellite communication systems, the channel performance might be severely degraded due to the dynamic weather conditions leading to network congestion. The in...

In an idealized restructured electricity industry, market participants always find economic incentives to invest in new generation and in new transmission. Intuitively, one can say that generation and transmission expansion projects can be a complement or a substitute of each other. This paper studies to what extent generation and transmission expa...

The methods of ordinary least-squares regression (OLSR), fuzzy regression (FR), and adaptive network-based fuzzy inference system (ANFIS) are compared in inferring operating rules for a reservoir operations optimization problem. Dynamic programming (DP) is used as an example optimization tool to provide the input–output data set to be used by OLSR,...

Opposition-based learning (OBL) is a new scheme in machine intelligence. In this paper, an OBL version Q-learning which exploits opposite quantities to accelerate the learning is used for management of single reservoir operations. In this method, an agent takes an action, receives reward, and updates its knowledge in terms of action-value functions...

In this paper a fuzzy dynamic Nash game model of interactions between water users in a reservoir system is presented. The model represents a fuzzy stochastic non-cooperative game in which water users are grouped into four players, where each player in game chooses its individual policies to maximize expected utility. The model is used to present em...

Planning of reservoir management and optimal operations of surface water resources has always been a critical and strategic concern of all governments. Today, many equipments, facilities, and substantial budgets have been assigned to carry out an optimal scheduling of water and energy resources over long or short periods. Many researchers have been...

Deficit irrigation has been suggested as a way to increase system benefits, at the cost of individual benefits, by decreasing the crop water allocation and increasing the total irrigated land. Deterministic methods are common for determining optimal irrigation schedules with deficit irrigation because considering the inherent uncertainty in crop wa...

In this work, the problem of transmission expansion under risk from demand uncertainty and capacity of the lines is addressed. A deterministic model is expanded into a two-stage stochastic model with fixed recourse by means of considering the various foreseeable levels of demand as random. After this model is analyzed, a way of quantifying risk usi...

Compared to the Pacific Ocean, tsunamis are rare both in the Atlantic and Indian Oceans. However, the December 26, 2004, tsunami demonstrated that, no matter how rare they may be, when a major tsunami occurs, it could be very disastrous. The most basic information in tsunami warning center requires are charts showing tsunami travel times to various...

A new method of reliability analysis for crop water production function is presented considering crop water demand uncertainty. The procedure uses an advanced first-order second moment (AFOSM) method in evaluating the crop yield failure probability. To determine the variance and the mean of actual evapotranspiration as the component of interest for...

When electricity prices were regulated, hydropower optimization often considered only the inflow uncertainty. In a deregulated electricity market, price uncertainty must be also considered in addition to inflow uncertainty. This makes the operation problem more challenging due to inclusion of the objective of minimizing risk. It also makes the obje...

Data mining is the process of automatic classification of cases based on data patterns obtained from a dataset. A number of algorithms have been developed and implemented to extract information and discover knowledge patterns that may be useful for decision support. Once these patterns are extracted they can be used for automatic classification of...

This paper proposes a family of robust counterpart for uncertain linear programs (LP) which is obtained for a general definition of the uncertainty region. The relationship between uncertainty sets using norm bodies and their corresponding robust counterparts defined by dual norms is presented. Those properties lead us to characterize primal and du...

The fabrication of MEMS tunable capacitors faces many uncertainties in which the fabricated dimensions differ from nominal values. This deviation in a tunable capacitor may cause significant variation in the capacitance-voltage response. In this paper, the effect of uncertainty in parallel-plate tunable capacitors is studied to maximize the yield u...

In this work, the problem of power plant expansion for electricity generation under risk from demand uncertainty and supply is addressed. We begin with a deterministic model. Then, this model is expanded to a stochastic model by means of considering the various demands for different operation modes as random. After this model is analyzed, a way of...

A dynamic programming fuzzy rule–based (DPFRB) model for optimal operation of reservoirs system is presented in this paper. In the first step, a deterministic dynamic programming (DP) model is used to develop the optimal set of inflows, storage volumes, and reservoir releases. These optimal values are then used as inputs to a fuzzy rule–based (FRB)...

Engineering systems are usually designed deterministically, but if there are uncertainties in parameters, an appropriate approach is to use probabilistic methods. For reliability estimation it is necessary to have at least the first two moments including means and covariances of the output variables. Most of the existing methods applied to problems...

A designer often over-designs in solving a problem. This is necessary to minimize risk and to ensure safety during the lifetime of the product. This conservative design approach might not be too farfetched if robustness of the design is evaluated. One way to combat this approach is to set up a controller to identify the system correctly. A solution...

Soft computing based tools including fuzzy inference systems (FIS), artificial neural networks (ANN), and genetic algorithms (GA) are used here to tackle the minimization of variance of benefits from reservoir operation. Variance reduction is a very hard optimization problem and solvable only using implicit methods like simulation, especially if th...

A discussion about data sharing with P2PdataShare (peer-to-peer) is presented. P2P systems are based on a distributed computing model in which peers share computer resources and exchange services directly. P2PdataShare's architecture addresses issues such as data sharing among peers, local autonomy, query construction and a general model that is fl...

Monte Carlo simulation is a popular method of risk and uncertainty analysis in oceanographic, atmospheric, and environmental applications. It is common practice to introduce a stochastic part to an already existing deterministic model and, after many simulations, to provide the user with statistics of the model outcome. The underlying deterministic...

This paper extends the recently developed hybrid method to find the optimal designs of systems with correlated non-gaussian random parameters. A double-bounded density function is used to approximate marginal distribution and a Frank copula is used to define dependence (a more general concept than correlation) among the random parameters. We use a...

In this paper, we consider a quantitative approach in modeling customer access pattern and traffic load of the E-Business website and present a scheme of resource allocation based on the customer visits. We model the session of the customer visit as a discrete Markov proce