OSG has organized the following events:
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Optimal planning and control of direct current lines in power systems, April 30, 2019
Josh Taylor University of Toronto, Canada Direct current transmission of electric power is becoming more and more common as power electronic converters improve. The advantages of DC over AC transmission include better wire utilization, decreased losses, and the ability to dynamically decouple neighboring grids. In this talk, we first consider transmission system expansion with AC and DC lines. We model both voltage-sourced and line-commutated converters, AC to DC line conversion, and installation of reactive power support. Transmission expansion planning is nonconvex due to the power flow equations, discrete installation variables, and products between power flow and installation variables. We use convex relaxations and a physics-based approximation to obtain a mixed-integer second-order cone model, which can be solved using commercially available software. In the second part of the talk, we look at control of DC-segmented power systems. DC-segmented power systems consist of multiple AC grids (e.g., microgrids, provinces, countries) that are only connected to each other by DC lines. We show that, due to the controllability of DC lines, DC segmented power systems are poset-causal. This makes the problem amenable to optimal decentralized control, which is intractable for general systems. We find in simulation that the optimal decentralized controller attains virtually the same performance as the optimal centralized controller. |
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Incentive Design for Smart Charging: Utility-Customer Interactions and Distribution Systems Impact, October 25
Kankar Bhattacharya University of Waterloo, Canada With increasing environmental concerns, the penetration of electric vehicles (EVs) is expected to increase in the future. Such electrification of the transportation sector will impact the distribution grid adversely; however, EV smart charging strategies can help mitigate the impacts. In this presentation a mathematical model will be presented that represents the total charging load at an EV charging station (EVCS) in terms of controllable parameters. A queuing model is used to construct a data set of EV charging parameters which are input to a neural network (NN) to determine the controllable EVCS load model as a function of the number of EVs charging simultaneously, total charging current, arrival rate, and time; and various class of EVs. The load model is integrated within a distribution operations framework to determine the optimal operation and smart charging schedules of the EVCS. In the second part of the presentation, a smart charging approach is proposed where the charging loads are controlled and incentivized by the local distribution company (LDC) for every unit of energy controlled. A framework is proposed, that captures the relationship between EV customers’ participation and incentives offered by LDC, to determine the optimal participation of EVs in smart charging program and optimal incentives paid by the LDC, such that both parties are economically benefited. The relationship between the expected investment deferral and hence the economic benefits from smart charging participation are considered as well. |
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Demand flexibility: Planning and scheduling of smart electric water heaters and electric baseboard thermostats, September 20 2018
Véronique Delisle & Louis-Philippe Proulx CanmetENERGY Demand flexibility is the capability to modify the electricity consumption of a load away from its original or usual shape. It can be used to balance supply and demand and can be useful for integrating renewable energy and supporting the electrical grid during peak demand periods. As smart grid technologies are being increasingly deployed, it is becoming easier for utilities to find and capture demand flexibility from their clients. This presentation will provide an overview of the current and future R&D activities related to demand flexibility conducted by Natural Resources Canada’s CanmetENERGYlaboratory. It will discuss the planning and scheduling strategies used in both laboratory and field tests conducted with smart electric water heaters and electric baseboard thermostats. Different load aggregation methods and their applicability for utilities will also be presented. |
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Very Large Scale Covering Location Problems in the Design of Advanced Metering Infrastructure, August 17 2018
Ivana Ljubic ESSEC Business School, France Smart metering is currently replacing simple billing with intelligent metering services of tremendous value to both utility companies and end users. According to Gartner, a typical family home could contain more than 500 smart devices by 2022. For covering this exceedingly increasing demand, wireless communications will be inevitable when it comes to designing and planning of the advanced metering infrastructure (AMI). In this talk we address the deployment of access points in the design of AMI. We embed a notion of proximity (or coverage radius) that specifies whether a given smart meter (representing a demand point, e.g., a household) can be served or "covered'' by a potential access point location (also referred to as potential facility location). A demand point is then said to be covered by an access point location if it lies within its coverage radius. Typically, a relatively small number of potential facility locations can be considered, while the number of demand points can run in the thousands or even millions. As such, finding the optimal placement of access points in the design of AMI remained out of reach for modern MIP solvers. In this talk we address two optimization problems relevant for the design of AMI: the maximal covering location problem (MCLP), which requires choosing a subset of facilities that maximize the demand covered while respecting a budget constraint on the cost of the facilities and the partial set covering location problem (PSCLP) which minimizes the cost of the open facilities while forcing a certain amount of demand to be covered. We propose an effective decomposition approach based on the branch-and-Benders-cut reformulation. We also draw a connection between Benders and submodular cuts. The results of our computational study demonstrate that, thanks to this decomposition technique, optimal solutions can be found very quickly, even for benchmark instances involving up to twenty million demand points. |
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Famous Quotes On Energy Systems Modelling: Key Issues In Using Modelling For Decision Support, August 1 2018
Chris Dent University of Edinburgh, United Kingdom Mathematical and computer modelling is widely used to support decisions in energy systems planning, and in development of government energy policy. This talk will explore key issues in the use of modelling for decision support (with the help of a few experts from history), with illustrations from work at the University of Edinburgh. It will further explore opportunities for collaboration between Edinburgh, IVADO and the Montreal research community on data science and energy systems. |
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Generation Scheduling under Renewable-Based Uncertainty via Two-Stage Adaptive Robust Optimization, April 19, 2018
José M. Arroyo Universidad de Castilla-La Mancha Within the context of current day-ahead electricity markets, the presentation examines the use of two-stage adaptive robust optimization as a relevant tool to handle renewable-based uncertainty in generation scheduling. Unlike alternative approaches to deal with uncertainty, neither accurate probabilistic information nor a discrete set of uncertainty realizations are required. Rather, uncertainty is modeled by decision variables within a deterministic uncertainty set. Hence, the size of the robust models does not depend on the dimension of the space of uncertainty realizations belonging to the uncertainty set, thereby providing a computationally efficient framework. In addition, an easy control of the degree of conservativeness can be implemented. The resulting robust counterparts are instances of mixed-integer trilevel programming. Practical modeling aspects allow using effective decomposition-based techniques that guarantee finite convergence to optimality. Results from several case studies illustrate the effectiveness of the two-stage robust setting. |
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Revenue and Network-Constrained Day-Ahead Market Clearing under Marginal Pricing, April 12, 2018
Natalia Alguacil Universidad de Castilla-La Mancha In this seminar a practical day-ahead auction model, where generation revenue constraints are explicitly incorporated in the problem formulation, will be presented. The revenue-constrained market-clearing procedure includes the effect of the transmission network, inter-temporal constraints associated with generation scheduling, demand-side bidding, and marginal pricing. This auction design is an instance of price-based market clearing which features two major complicating factors. First, locational marginal prices become decision variables of the optimization process. In addition, producer revenues are formulated as bilinear and nonconvex products of power outputs and market-clearing prices. The resulting problem is formulated as a mixed-integer nonlinear bilevel program with bilinear terms for which available solution techniques rely on heuristics, approximations, or modeling simplifications. This work shows a novel and exact methodology whereby the original problem is recast as an equivalent single-level mixed-integer linear program. As a consequence, finite convergence to optimality is guaranteed and the use of standard commercial software is allowed. The proposed transformation is based on duality theory of linear programming, Karush-Kuhn-Tucker optimality conditions, and integer algebra results. |
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Increasing Electric Vehicle Adoption Via Strategic Siting of Charging Stations, March 28 2018
Martim Joyce-Moniz GERAD In order to increase electric vehicle (EV) adoption it is necessary for a central authority to drive the initial investment in charging infrastructures. Recent works on optimization problems for siting and sizing of charging stations for EVs have started addressing this issue by considering strategic multi-period optimization problems. One limitation of these works, however, is that they consider the demand (i.e. number of EVs and their geographical distribution) over time to be static and given as an input. In this presentation, we present a more holistic optimization framework that considers how new infrastructure impacts EV demand growth, and how the infrastructure can be installed in a way that it properly responds to future demand. This framework has been validated by Hydro-Québec and is being applied to help achieve Quebec's objective of having 100,000 EVs on the roads by 2020, and 1,000,000 by 2030. |
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On the packing process in a shoes manufacturer, March 22 2018
Manuel V.C. Vieira Nova University of Lisbon In this presentation we describe a real life application of a container loading problem for a children’s shoes manufacturer. First, we choose the appropriate shoe box for each pair of shoes depending on the model and size before production starts. Then, the shoe boxes are packed in several cardboard boxes with variable dimensions. We present an integer programming model that chooses the shoe boxes for each model size, and a MINLP model which decides how to pack the shoe boxes and the size of cardboard boxes. This container loading problem is classified as open dimension problem, with three open dimensions. We approximate the MINLP model with a MILP and we compare it by using BARON on the former model and we run CPLEX on the latter model. |
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Assessing Wind Resource Adequacy for Peak Demand, March 15 2018
Kristen R. Schell University of Michigan There are almost 50,000 individual wind turbines currently operating in the continental United States. Federal tax credits, as well as local price differences, have helped spur past and future investment in the wind industry. While this increase in wind capacity, at times, provides a remarkable percentage of load with renewable generation, variable wind power output is not often synchronous with peak demand, which raises the issue of its contribution to overall resource adequacy. We propose a new method for assessing wind resource adequacy in the planning phase, utilizing cross-spectra analysis of wind speed and electricity system load time series. The results indicate which geographic locations in an electricity system have wind resource potential that is most able to contribute to meeting peak load. This metric gives wind farm planners information on where to site wind farms that reduce reliability risk and increase supply adequacy. Such information is particularly important as electricity systems move toward maximum levels of variable renewable power penetration. Results are shown for three major electricity markets of interest – CAISO in California, NYISO in New York and ERCOT in Texas. |
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Functional Econometrics of Multi-Unit Auctions: An Application to the New
York Electricity Market, March 01 2018
David Benatia Université de Montréal This paper proposes a novel approach for the empirical analysis of multi-unit auctions, to which participants submit supply or demand functions observable by the econometrician. The approach allows for the evaluation of firm-level market power in a private information setting, and avoids having to model the market mechanism. It relies on econometric methods that treat the observed bid functions as function-valued random elements. Notably, a functional instrumental variable estimator is developed. The method is applied to the New York electricity market using rich data on firm-level bids and marginal costs for 2013-2015. In this market, daily bids are disclosed three months later in order to limit strategic behaviors. I estimate firm-level market power and compare actual bidding behavior to profit-maximizing behavior under private information. I find consistent evidence of strategic bidding, suggesting that firms are well aware of their own market power and behave accordingly. Therefore, the late disclosure of bids is not sufficient to preclude firms from acting strategically, most likely due to the repeated nature of those auctions. |
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Regulatory, Rate, and Market Design for Energy Storage , November 1st 2017
Professor Ramteen Sioshansi Ohio State University Energy storage is a unique grid asset, in that it can provide services that are functionally similar to those provided by generation, transmission, and distribution assets. Most of our market designs and regulatory constructs assume that assets primarily fall into one of these three categories. This talk will introduce the challenges that are raised by this dichotomy between the capabilities of energy storage and the regulatory and market treatment of grid assets. It will also discuss some potential solutions to overcoming these challenges, including the use of storagecapacity rights. |
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