W. Rosehart

The University of Calgary, Calgary, Alberta, Canada

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Publications (89)119.55 Total impact

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    ABSTRACT: The capability of life-long learning is a stable set of attributes and skills related to interest in and self-regulation of continuous learning. This and other attributes related to professional skill development have been increasingly viewed as a priority for the development in post-secondary education, rather than solely focusing on technical and discipline-specific knowledge acquisition. In the current study we examined the role of life-long learning as an antecedent of academic engagement in a university course using student engineering project teams with extensive team-related deliverables. We adopted multilevel longitudinal methodology and analytics to support several novel contributions. First, the general trend over the course of the semester was a decrease in academic engagement, but only for students low on the attribute of life-long learning. Second, life-long learning was a significant predictor of all 12 indicators of academic engagement over three time periods. Third, life-long learning was more important for academic engagement than other dispositional variables known to be relevant, namely conscientiousness and its facet of achievement-striving. As such, this adds unique evidence in support of recent accreditation initiatives, interventions, and learning structures that promote life-long learning development.
    Learning and Individual Differences 04/2015; 39:124-131. DOI:10.1016/j.lindif.2015.03.022 · 1.58 Impact Factor
  • Paras Mandal, Hamidreza Zareipour, William D. Rosehart
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    ABSTRACT: SUMMARY This paper describes the problem of short-term wind power production forecasting based on meteorological information. Aggregated wind power forecasts are produced for multiple wind farms using a hybrid intelligent algorithm that uses a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on neural network (NN), which is optimized by using particle swarm optimization (PSO) algorithm. To demonstrate the effectiveness of the proposed hybrid intelligent WT + NNPSO model, which takes into account the interactions of wind power, wind speed, wind direction, and temperature in the forecast process, the real data of wind farms located in the southern Alberta, Canada, are used to train and test the proposed model. The test results produced by the proposed hybrid WT + NNPSO model are compared with other SCMs as well as the benchmark persistence method. Simulation results demonstrate that the proposed technique is capable of performing effectively with the variability and intermittency of wind power generation series in order to produce accurate wind power forecasts. Copyright © 2014 John Wiley & Sons, Ltd.
    International Journal of Energy Research 10/2014; 38(13). DOI:10.1002/er.3171 · 2.74 Impact Factor
  • Logan Rakai, William Rosehart
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    ABSTRACT: The optimal power flow problem (OPF) has been of importance to power system operators for many decades. Being able to quickly determine optimal operating points and analyzing larger networks can lead to advantages for operators from reliability, stability, cost and market fairness perspectives. This work aims at achieving those ends by solving OPF problems by utilizing hardware acceleration capabilities of graphical processing units (GPUs). At present, nearly all desktop and laptop computers ship with general-purpose GPUs that can be harnessed to accelerate analysis. This work will present important concepts regarding effective use of GPUs as it pertains to OPF problems and illustrate the types of problems that stand to benefit most from their use. The benefits of GPU acceleration are demonstrated by implementing a predictor-corrector interior-point method with the majority of the computation offloaded onto a GPU. Experiments are used to validate the developments by analyzing well-known power systems.
    Proceedings of the 2014 47th Hawaii International Conference on System Sciences; 01/2014
  • Peng Wang, Hamidreza Zareipour, W.D. Rosehart
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    ABSTRACT: In addition to electric energy, ancillary services, such as operating reserves and frequency regulation service, are also traded in a competitive electricity market. With the emerging smart grid technologies and large scale integration of variables resources into the grid, the market for ancillary services is expected to grow, and thus, their prices become increasingly important. The prices of ancillary services feature different patterns and characteristics when compared to electric energy prices. The differences include lower price level, higher variability, and more frequent and extreme spikes. While electric energy prices have been broadly studied in the literature, research on features and modelling of ancillary services prices is limited. This paper investigates the application of established stochastic approaches for modelling the behavior of operating reserve and regulation prices in North American electricity markets. Such descriptive stochastic models are necessary for risk management and derivative pricing of these commodities. Mean-reverting jump-diffusion (MRJD) and Markov regime-switching (MRS) models with various specifications are analyzed. Historical prices from the Ontario and New York markets have been used for model calibration and simulation analysis. The performance of the two classes of models has been compared using various statistical measures.
    IEEE Transactions on Smart Grid 01/2014; 5(1):471-479. DOI:10.1109/TSG.2013.2279890 · 4.33 Impact Factor
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    ABSTRACT: In this paper, a Latin supercube sampling (LSS) combined with Monte Carlo simulation is presented to efficiently sample random variables in the probabilistic power flow (PPF) problem. The results of the LSS method are compared with other techniques, namely Latin hypercube sampling (LHS) and simple random sampling (SRS), using bin-by-bin histogram comparison. The simulation results are presented for the case of IEEE 118-bus test system.
    IEEE Transactions on Power Systems 05/2013; 28(2):1550-1559. DOI:10.1109/TPWRS.2012.2214447 · 3.53 Impact Factor
  • Mohammad Rasouli, David Westwick, William Rosehart
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    ABSTRACT: In this paper, the Hammerstein identification problem with correlated inputs is studied in a prediction error framework using separable least squares methods. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. A sufficient condition is derived that guarantees that the identification problem is quasiconvex with respect to the parameters that describe the nonlinearity. Simulations using both IID and correlated inputs are used to illustrate the result.
    Automatica 01/2013; 50(1). DOI:10.1016/j.automatica.2013.11.004 · 3.13 Impact Factor
  • M. Monishaa, M. Hajian, M.F. Anjos, W.D. Rosehart
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    ABSTRACT: This paper deals with the development of an efficient iterative method to solve the chance-constrained generation expansion planning (GEP) problem. Reliability in an economic manner is the main criterion when addressing GEP. The algorithm proposed here minimizes the cost of achieving the required reliability. Computational results based on the IEEE 30- and 118-bus test systems are presented. The proposed method decreases the cost in comparison to existing methods for the same reliability level.
    Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP), 2013 IREP Symposium; 01/2013
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    ABSTRACT: In this paper, an approach based on the concept of Model Predictive Control (MPC) is used to control transmission voltages and prevent long-term voltage instability. The MPC model is based on a linearized steady-state system model derived from power flow equations. Simulation results are presented for the case of Nordic32 test system.
    System Sciences (HICSS), 2013 46th Hawaii International Conference on; 01/2013
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    ABSTRACT: Wind-powered electricity generation is growing at a high rate around the world, mainly driven by the associated environmental benefits. However, because of the uncertain nature of wind energy, large-scale integration of wind-powered generators into power systems introduces uncertainty of supply in the operation and planning of electric energy systems. A chance-constrained model is presented to study the strategic behavior of power suppliers (firms) with respect to the wind generation uncertainty in an electricity market. The linear complementarity problem of a Nash-Cournot competition is extended to account for the wind generation uncertainty. An iterative solution approach is introduced to solve the resultant joint chance-constrained programming problem. Five studies were conducted to show the impact of the amount, location and standard deviation of the wind generation and transmission line limits on the suppliers' profit in the Nash-Cournot game for different confidence levels. The value of stochastic solution index is used to evaluate the suppliers' loss of profit. The numerical results for a three-bus test system are given to verify the formulation and the solution approach. Copyright © 2011 John Wiley & Sons, Ltd.
    European Transactions on Electrical Power 01/2013; 23(1). DOI:10.1002/etep.650 · 0.63 Impact Factor
  • Han Yu, W.D. Rosehart
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    ABSTRACT: Consideration of uncertain injections in optimal power flow (OPF) calculation is increasingly important because more renewable generators, whose outputs are variable and intermittent, are connected into modern power systems. Since it is often difficult to predict the variations of both load and renewable generator output accurately, this paper proposes an OPF algorithm to make optimized results not only have a high probability to achieve minimized generation cost, but also robust to the uncertain operating states. In this paper, the objective of the OPF is to minimize the generation cost of the scenario which has the largest probability to appear in the future. In order to make the OPF result be able to accommodate other possible scenarios, the OPF constraints are modified. Considering the probabilistic distributions of both load and renewable energy output, the modified constraints are derived from Taguchi's orthogonal array testing and probabilistic power flow calculation. The effectiveness of the proposed OPF method is demonstrated by the cases up to the system with 2736 buses.
    IEEE Transactions on Power Systems 11/2012; 27(4):1808-1817. DOI:10.1109/TPWRS.2012.2194517 · 3.53 Impact Factor
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    J. MacCormack, H. Zareipour, W.D. Rosehart
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    ABSTRACT: A general model of long-term equilibrium in energy only electricity markets that allows for generation additions and retirements is developed. The model is then applied with different assumptions regarding generator behaviors. The distribution of electricity prices, reliability of supply, and overall CO2 emissions at equilibrium are analytically found for fully competitive markets with and without inflexible generation. A method to determine long-term equilibrium in markets with a mix of competitive and inflexible generators and a strategic supplier is proposed and illustrated.
    IEEE Transactions on Power Systems 11/2012; 27(4):2291-2292. DOI:10.1109/TPWRS.2012.2193490 · 3.53 Impact Factor
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    ABSTRACT: In this paper, a chance-constrained optimization (CCO) is presented to handle uncertainty in control of transmission voltages. A control scheme is proposed using a steady-state system model to achieve the goal of online voltage control and preventing long-term voltage instability. In order to model steady-state system response, the long-term model of governors and Automatic Voltage Regulators are employed in the control scheme. The Nordic32 test system is selected to show the simulation results of the proposed technique.
    IEEE Transactions on Power Systems 08/2012; 27(3):1568-1576. DOI:10.1109/TPWRS.2011.2181431 · 3.53 Impact Factor
  • M. Rasouli, D. T. Westwick, W. D. Rosehart
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    ABSTRACT: Reliable induction motor modeling is critical in power system planning and operation. This paper considers the identifiability of induction motor parameters, with a particular emphasis placed on using subset selection and shrinkage methods to allow the identification methods to focus on the most significant parameters. The proposed approach is validated using experimental data and the results found are compared to those of a recently proposed method based on sensitivity analysis.
    Electric Power Systems Research 07/2012; 88:1–8. DOI:10.1016/j.epsr.2012.01.011 · 1.60 Impact Factor
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    Amir Motamedi, Hamidreza Zareipour, William D. Rosehart
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    ABSTRACT: In future smart grids, consumers of electricity will be enabled to react to electricity prices. The aggregate reaction of consumers can potentially shift the demand curve in the market, resulting in prices that may differ from the initial forecasts. In this paper, a hybrid forecasting framework is proposed that takes such dynamics into account when forecasting electricity price and demand. The proposed framework combines a multi-input multi-output (MIMO) forecasting engine for joint price and demand prediction with data association mining (DAM) algorithms. In this framework, a DAM-based rule extraction mechanism is used to determine and extract the patterns in consumers' reaction to price forecasts. The extracted rules are then employed to fine-tune the initially generated demand and price forecasts of a MIMO engine. Simulation results are presented using Australia's and New England's electricity market data.
    IEEE Transactions on Smart Grid 06/2012; 3(2):664-674. DOI:10.1109/TSG.2011.2171046 · 4.33 Impact Factor
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    ABSTRACT: Forecasting electricity prices plays a significant role in making optimal scheduling decisions in competitive electricity markets. Predominantly, price forecasting is performed from a “point forecasting” perspective, i.e., forecasting the exact values of future prices. However, in some applications, such as demand-side management, operation decisions are made based on certain price thresholds. It is, hence, desirable to obtain the “classes” of future prices, which can be cast as an electricity price classification problem. In this paper, we investigate the application and effectiveness of several data mining approaches for electricity market price classification. In addition, we propose a new data model for forming the initial data set for price classification. Simulation results for New York, Ontario, and Alberta electricity market prices are provided. Finally, the application of the generated numerical results to a demand-side management case study is demonstrated.
    IEEE Transactions on Smart Grid 06/2012; 3(2):808-817. DOI:10.1109/TSG.2011.2177870 · 4.33 Impact Factor
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    ABSTRACT: In this paper, the impact of large-scale integration of intermittent generation resources on electricity markets is studied through a supply function equilibrium model. A new formulation for determining supply function equilibria in uniform electricity markets is presented, where uncertainty of intermittent generation resources is taken into account. In the case of an unconstrained market model where generation limits are ignored, a closed form solution for market equilibrium is derived and its uniqueness is proved. In the case of a generation-constrained market model, an algorithm is proposed for determining supply function equilibria and the existence and uniqueness of the equilibrium is discussed. Case studies are presented for demonstrating the proposed approaches.
    IEEE Systems Journal 06/2012; 6(2):220-232. DOI:10.1109/JSYST.2011.2162895 · 1.75 Impact Factor
  • M. Hajian, S. Manish, H. Zareipour, W.D. Rosehart
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    ABSTRACT: Increasing wind generation in power systems brings up operational challenges such as mitigating variability. Plug-in hybrid Electric Vehicles (PHEVs) have been considered in the literature to play a role as a potential means to enable the integration of more wind generation. In this paper, different case studies are performed to examine the capability of PHEVs to support wind integration considering variations in wind generation and demand of PHEVs. A cost analysis is conducted to compare the cost of employing PHEVs to enable wind generation versus the cost of adding peak generation units.
    Power and Energy Society General Meeting, 2012 IEEE; 01/2012
  • Majid Oloomi Buygi, Hamidreza Zareipour, William D. Rosehart
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    ABSTRACT: In this paper, the impact of large-scale integration of intermittent generation resources on electricity markets is studied through a supply function equilibrium model. A new formulation for determining supply function equilibria in uniform electricity markets is presented, where uncertainty of intermittent generation resources is taken into account. In the case of an unconstrained market model where generation limits are ignored, a closed form solution for market equilibrium is derived and its uniqueness is proved. In the case of a generation-constrained market model, an algorithm is proposed for determining supply function equilibria and the existence and uniqueness of the equilibrium is discussed. Case studies are presented for demonstrating the proposed approaches.
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    ABSTRACT: In this paper, short-term electricity price forecasting considering residual electricity demand is investigated. Residual, or net, demand is determined by subtracting any unpredictable generation from the system load. Focusing on wind energy as the main hard-to-predict source of electricity, we first examine the dependency of short-term electricity prices and wind power using data association mining algorithms. Second, we investigate the impact of including net demand in short-term electricity price forecasting, and we propose a new electricity price forecasting model. Data from the Alberta and the Nordic electricity markets are used to conduct studies and evaluate the forecasting results.
    Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on; 01/2012
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    ABSTRACT: In this paper, a receding-horizon multi-step optimization is proposed to correct nonviable transmission voltages and prevent long-term voltage instability. The proposed control scheme is based on real-time control, inspired by model predictive control, and steady state power-flow-based equations. In order to anticipate load behavior and avoid using dynamic equations in the control scheme, explicit formulations are used to model evolution of load with time. The simulation results of the proposed technique are presented on the Nordic32 test system.
    IEEE Transactions on Power Systems 08/2011; 26(3):1641-1650. DOI:10.1109/TPWRS.2011.2105286 · 3.53 Impact Factor

Publication Stats

1k Citations
119.55 Total Impact Points

Institutions

  • 2002–2014
    • The University of Calgary
      • • Department of Electrical and Computer Engineering
      • • Schulich School of Engineering
      Calgary, Alberta, Canada
  • 2005–2006
    • University of Malaga
      Málaga, Andalusia, Spain
    • Amirkabir University of Technology
      • Department of Electrical Engineering
      Tehrān, Ostan-e Tehran, Iran
  • 1997–2004
    • University of Waterloo
      • Department of Electrical & Computer Engineering
      Ватерлоо, Ontario, Canada