Program: 2nd Session on Thursday (11:00-12:20)

← Previous Session Next Session →

T2-1Renewables Integration & Optimization – Wind

11:00 Statistical Evaluation Study for Different Wind Speed Distribution Functions Using Goodness of Fit Tests

Abdulaziz Almutairi (University of Waterloo, Canada); Mohmmed Nassar (University of Waterloo, Canada); Magdy Salama (University of Waterloo, Canada)

Modeling wind generation for use in many power system applications requires a massive database of historical wind speeds so that the stochastic nature of the wind at a particular site can be accurately captured. The alternative is to use reliable estimates of a probability distribution function (PDF) that can preserve the variable characteristics of wind speed and generate the desired synthetic data. This paper presents a statistical evaluation study for different collections of PDFs in order to find the best model to precisely reflect the variable characteristics of the wind at a particular site. The most commonly used PDFs, along with some advanced PDFs, have been verified against the observed wind data based on consideration of two well-known goodness of fit statistical tests. A further case study is conducted in order to evaluate the impact of sample size on the selection of the best-fit PDFs. From a variety of candidate PDFs, the results indicate that the Generalized Logistic and Dagum distributions are the PDFs that best maintain the main characteristics of the observed wind data.

11:20 Effect of Wind Turbine Parameters on Optimal DG Placement in Power Distribution Systems

Majed Alotaibi (University of Waterloo, Canada); Abdulaziz Almutairi (University of Waterloo, Canada); Magdy Salama (University of Waterloo, Canada)

The notion of the “smart grid” has led stakeholders in the power industry to promote more efficient technologies to the network. Distribution systems are a favorite place to host most of these technologies including Renewable-based distributed generation (DG). Wind Turbine Generators (WTGs) in particular have proved their usefulness for supplying a fair portion of power demand; however, the power output of WTGs is mainly dependent on the stochastic nature of the site’s wind speed in addition to the design parameters of WTGs. Furthermore, WTGs can only be suitably utilized when their capacities and locations are optimized in such a way to achieve certain goals. In this paper, the effect of wind generator design parameters, namely cut-in, cut-out, and rated wind speeds, on the problems of sizing and siting WTGs-based DGs is addressed. The probabilistic optimization model is used to minimize the system’s annual energy losses, and the results reveal that the design parameters of WTGs must be carefully selected due to their strong effect on system losses and DG locations and capacities.

11:40 Comparison of Wind Turbine Probabilistic Model with Negative Model in Economic Dispatch Problem Using Differential Evolution (DE)

Zakareya Hasan (Dalhousie University, Canada); Mohamed El-Hawary (Dalhousie University, Canada)

This paper use differential evolution to do a comparison between two wind turbine cost models used in solving the economic dispatch problem. The models of interest are: The probabilistic model and the negative load model. These models will be used to solve the economic dispatch problem with and without the valve point effect considering different constrains such as generators capacity, ramp rate limit, and prohibited operation zones. Three test systems with different constraints will be used to select the best cost model for the wind turbine

12:00 Unit Commitment Incorporating Wind Energy by BBO and GA

Zakareya Hasan (Dalhousie University, Canada); Mohamed El-Hawary (Dalhousie University, Canada)

This paper proposes biogeography based optimization (BBO) algorithm and genetic algorithm (GA) to solve the unit commitment problem incorporating the wind energy (UCIW) uncertainty. Unit commitment (UC) problem is mainly finding the minimum cost schedule to a set of generators by turning each one either on or off over a given time horizon to meet the demand load and satisfy different operational constraints. There are many constraints in unit commitment problem such as spinning reserve, minimum up/down, crew, must run and fuel constraints. In this paper, BBO and GA are used to solve the UCIW problem for four and six generator system, then a comparison will be done between the results of both algorithms with other algorithms in literature if possible. And if not the comparison will be between the two algorithms only.

T2-2Energy Efficiency, Demand Response, & Energy Markets

11:00 Estimated Economic Load Dispatch Based on Real Operation Logbook

Ali Al-Roomi (Dalhousie University, Canada); Mohamed El-Hawary (Dalhousie University, Canada)

The economic load dispatch (ELD) problem of electric power systems has been solved by various types of techniques including traditional and modern optimization algorithms. The main problem of achieving this stiff task is that all the exact specifications of the generating machines are required. Also, from a practical side, many of the power plants are operated without considering the ELD strategy because of the lack of experience to deal with this part, which is embedded as a package in the energy management system (EMS), and/or the hardness to construct precise constrained objective function matched with the real generating machines. Based on a fact that most of the power stations have their daily records, the estimated economic load dispatch (EELD) can be determined by using these recorded datasheets. This novel method can be applied without using any special software, and it is an optimization free technique. Moreover, this technique does not require to determine any parameter nor constraint on the generating machines, and all the candidate solutions are practical and feasible. The proposed method is tested with a real power station and it shows encouraging results.

11:20 The Impact of Essentials of Application Engineering on Conservation and Energy Efficiency Projects

Constantin Pitis (Powertech Labs Inc., Canada)

Energy conservation measures (ECMs) become a topic of increasing technical and economic importance. The objective is fulfilled by utility and government programs as part of demand-side management (DSM). The programs are designed to achieve corporate objectives by deferring the need for new power generation projects, reducing overall emission of greenhouse gases, improving efficiencies of the equipments and processes. There is a natural tendency of reducing the costs of Energy Studies on Conservation and Energy Efficiency Projects (CEEP) – as part of program expenses, in order to minimize the projects pay-back period. As a result, after CEEP commissioning undesired collateral effects might be present resulting in reduced amount of expected energy savings and sometime even financial losses. Subsequently these may reduce the impact of conservation programs at customer level. The root-cause analyze of such CEEP conducted to conclusion that some of consultants failing to consider a holistic approach methodology. Based on experience resulted from CEEP review activity, the author presents a unified mode of approaching CEEP by using the concept of 5 (five) Essentials of Application Engineering (5EAE). Paper presents fundamentals of new 5EAE concept enabling consultants, designers, manufacturers, and end-users to consistently design and evaluate new projects or retrofits of power converters (PC) and/or any industrial system drives (ISD). Application of 5 EAE concepts is relatively limited on American continent. To date there are no specific references on this subject. The concept is focused on specific ways to think outside the box in designing and/or assessing existent components of ISD. Case studies are used to prove the impact of using 5EAE on CEEP.

11:40 Evaluating Factors Responsible for Energy Consumption: Connection Weight Approach

Oludolapo Olanrewaju (University of Johannesburg, South Africa); Charles Mbohwa (University of Johannesburg, South Africa)

Various governments and stakeholders are established across the globe to respond to various energy challenges that has led to one or more energy policy development. A proper analysis of what contributes to energy consumption will assist in the development of policies needed for the conservation of energy consumption. This study made use of the connection weight approach as an instrument of the Artificial Neural Network (ANN) to evaluate the contributions of activity, structure and intensity factors to energy consumption in the Canadian industrial sector. From the evaluation, intensity contributed 46.5 %, whereas activity and structure contributed 32.6 % and 20.9 %. This is an indication that policies and strategies should be developed more on intensity to achieve energy saving.

12:00 Multi-market Bidding Strategy Considering Probabilistic Real Time Ancillary Service Deployment

Jie Li (Clarkson University, USA); Zuyi Li (Illinois Institute of Technology, USA)

Generation companies (GENCOs) are seeking for maximum economical profits in both energy and ancillary service (AS) markets. This paper proposes a methodology for obtaining the optimal bidding strategy for GENCOs in multiple electricity markets. Step-wise Supply function like bid curve with strategical bidding prices is used for the day-ahead multi-market auction. Probabilistic real time deployment of ancillary services is considered. Competition among GENCOs in such multi-market is modeled as a non-cooperative complete information game. A two-layer optimization problem is modeled, with the upper layer representing the GENCO’s profit maximization subproblem and the lower layer representing the ISO’s simultaneous multi-market clearing subproblem. Illustrative examples show advantages of considering probabilistic real time ancillary services deployment and simultaneously participating multi-market for a GENCO’s profit seeking purpose.

T2-3Cyber-Security and Big Data

11:00 Anomaly Detection in a Smart Grid Using Wavelet Transform, Variance Fractal Dimension and an Artificial Neural Network

Maryam Ghanbari (University of Manitoba, Canada); Witold Kinsner (University of Manitoba, Canada); Ken Ferens (University of Manitoba, Canada)

This paper presents a scheme for detecting anomalous power consumption patterns attack using wavelet transform, variance fractal dimension and artificial neural network for smart grid. The main procedure of proposed algorithm consists of the following steps: I) Finding normal and anomalous patterns of power consumption to train the proposed method. II) Applying Wavelet Transform to power consumption patterns to extract features. III) Applying variance fractal dimension to the extracted features from step 3 as an input. IV) Training artificial neural network with extracted features from step3. IV) Launching the trained artificial neural network from step 4 to detect the anomalous power consumption attack based on a threshold. In the simulations, the proposed method can detect anomalous power consumption attack with 51% accuracy in the worst case scenario.

11:20 Incremental Mining of Frequent Power Consumption Patterns From Smart Meters Big Data

Shailendra Singh (University of Ottawa, Canada); Yassine Abdulsalam (University of Ottawa, Canada); Shervin Shirmohammadi (University of Ottawa, Canada)

The key elements for understanding power consumption of a typical home are related to the activities that users are performing, the time at which appliances are used, and the interdependencies with other appliances that may be used concurrently. This information can be extracted from context rich smart meters big data. However, the main challenge is how to mine complex interdependencies among different appliances usage within a home where multiple concurrent data streams are occurring. Furthermore, generation of energy consumption data from a smart meter is an ongoing continuous process and over period of time inter-appliance associations can change or new ones can establish. In this paper, we propose a near real-time incremental mining of frequent power consumption patterns from smart meters big data. Our model exploits the benefits of pattern growth strategy and mine in quantum of 24 hour period, i.e. frequent patterns are extracted from data comprising of appliance usage tuples for 24 hours period, in a progressive manner. The details and the results of evaluating the proposed mechanism using real smart meters dataset are presented in this paper.

11:40 Supervised Household’s Loads Pattern Recognition

Maher Azaza (Mälardalen University (EST), Sweden)

The deployment of smart meters is a promising innovation that comes to enhance the energy efficiency measures in the smart grid. The smart meter enables distributors to better understand the electrical network and reduce complexity of the management operations. It offers to households monitoring and control possibilities to their everyday energy consumption through the distribution of detailed information on household consumption and its evolution. This involves disaggregation of individual household loads in term of their individual energy consumption known as Non intrusive loads monitoring. In this paper, we present a supervised NILM approach based on dynamic fuzzy c-means events clustering and KNN label matching. First, a filtering method is involved to enhance the edge/events detection step. Then we perform a dynamic Fuzzy c-means clustering procedures to build appliances signature data based on active and reactive power measurements taking into account the time of day usage. The data base is further refined to map potential clusters centers that best identify the different appliances. A performance evaluation of the proposed approach is conducted showing a recognition rate over 90% for high consumption loads and promising results for low consumption loads.

12:00 Dynamic Threshold Algorithm with Simplified Appliance Identification for Smart Meter Privacy

Yang Zhao (York University, Canada); Hany Farag (YorkU, Canada); Yong Lian (York University, Canada)

Smart meter is one of the key components of smart power grids. The recent interest of smart meters adoption has created two research directions that are in conflict of each other in terms of data collection and user privacy, i.e. appliance identification from the measurements of smart meters and the privacy compensation for such measurements. This paper proposes a dynamic threshold algorithm (DTA) based on the prediction results from a simplified two-step filtering appliance identification algorithm (AIA) to compensate the smart meter data. The battery capacity required by the proposed DTA to achieve the same privacy level is less than 25% of that required by fixed threshold algorithms. Furthermore, the integration of AIA and DTA provides a trade-off method to solve the conflict through advancing the simplified two-step filtering AIA before DTA.