4 présentations

• 
  15h30 - 15h55

  Uncertainty modelling for planning and operations of multi-microgrids

  • Mehrdad Pirnia, prés., University of Waterloo

  We present a Two Stage stochastic Programming (TSSP) model for the planning of Multi-Microgrids (MMGs) in Active Distribution Networks (ADNs) and an interval-based method for their operations. The models aim to minimize the total costs while benefiting from interconnections of Microgrids (MGs), considering uncertainties associated with electricity demand and Renewable Energy Sources (RESs). In the planning model, the associated uncertainties are analyzed using Geometric Brownian Motion (GBM) and their associated probability distribution functions (pdfs). The planning model includes long-term purchase decisions and short-term operational constraints, using Geographical information Systems (GIS) to realistically estimate rooftop solar limits, and is used to study the feasibility of implementing an MMG system consisting of 4 individual MGs at an ADN in a municipality in the state of Sao Paulo, Brazil. Using the same test system, the uncertainties in the operational model are formulated in an Affine Arithmetic (AA) domain to obtain an Energy Management System (EMS) model that is robust for a range of realizations of the uncertain parameters, with no need of statistical assumptions or repeated calculations, which can be solved with relatively low computational burden, as opposed to other approaches such as Monte Carlo Simulation (MCS).
• 
  15h55 - 16h20

  Measurement-based Locational Marginal Pricing in Power Distribution Systems

  • Christine Chen, prés., The University of British Columbia

  Locational marginal pricing is widely used in wholesale electricity markets in the power transmission system, and it represents a promising solution to establish local electricity markets within distribution networks. We present a measurement-based method for calculating real-time distribution locational marginal prices (DLMPs) without the use of an offline network model. Instead, the proposed method relies only on online measurements collected at a subset of distribution system buses to estimate a linear sensitivity model mapping bus voltages to injections, which in turn is embedded in an optimal power flow (OPF) problem as an equality constraint. The proposed method completely obviates the need for an accurate distribution network model that may not be available, especially for active distribution networks with faster variations in operating point. Also, the proposed method renders the original OPF problem with nonlinear constraints a computationally efficient quadratic programming problem (with linear constraints) and provides sufficiently accurate DLMPs at buses where measurements are collected. Via numerical simulations involving a 33-bus test system, we demonstrate that the proposed method yields similar DLMPs as solving the OPF problem with an up-to-date model and greatly outperforms it when the model is out of date.
• 
  16h20 - 16h45

  Feature-driven trading of wind power and hydrogen

  • Lesia Mitridati, prés., Technical university of Denmark
  • Jalal Kazempour, Technical university of Denmark
  • Emil Helgren, Technical university of Denmark

  This talk introduces a data-driven approach to support the decisions and enhance the profits of hybrid power plants trading wind energy, in forward and balancing electricity markets, and hydrogen, in bilateral contracts. The proposed approach leverages valuable explanatory variables (so-called features) of the uncertain parameters in a data-driven model. Given historical observations of the uncertain parameters (e.g. wind power forecast error) and the corresponding explanatory variables (e.g. wind power forecast values), we fit a piecewise linear policy to those data, which is optimized to maximize the profits of the hybrid power plant under constraints on its operation. We show on a realistic case study, using historical wind production data from a wind farm in Denmark, that the proposed feature-driven approach achieves significant economic gains compared to traditional approaches.
• 
  16h45 - 17h10

  Online Dynamic Submodular Optimization for Power Systems

  • Antoine Lesage-Landry, prés., Polytechnique Montréal
  • Julien Pallage, Polytechnique Montreal, Mila & GERAD

  We propose new algorithms with provable performance for online combinatorial optimization in dynamic settings and subject to general binary constraints. We consider the subset of problems in which the objective function is submodular. First, we propose online submodular greedy algorithm (OSGA) which solves to optimality an approximation of the previous round loss function to avoid the NP-hard original problem. We then extend OSGA to a generic approximation function. We show that OSGA have a dynamic regret bound that is of the same order as the tightest bounds in OCO. Then, for cases where no approximation exists or when an efficiency-oriented implementation is desired, we formulate the online submodular projected gradient descent (OSPGD) by leveraging the Lovász extension. We obtain a dynamic regret bound that is similar to the conventional online gradient descent. Finally, we numerically test our algorithms in two power system applications: real-time distribution network reconfiguration and fast-timescale demand response.