TAILIEUCHUNG - Bi-level optimization model for calculation of LMP intervals considering the joint uncertainty of wind power and demand

In the electricity market operation, electricity prices or Locational Marginal Prices (LMP) vary according to both electric demand and the penetration level of the wind power. The variable domain identification of LMP plays a very important role for market participants to assess and mitigate the risk on account of the combined uncertainty of wind power and demand. | Journal of Science & Technology 128 (2018) 001-006 Bi-Level Optimization Model for Calculation of LMP Intervals Considering the Joint Uncertainty of Wind Power and Demand Pham Nang Van Hanoi University of Science and Technology – No. 1, Dai Co Viet Str., Hai Ba Trung, Ha Noi, Viet Nam Received: May 25, 2018; Accepted: June 29, 2018 Abstract In the electricity market operation, electricity prices or Locational Marginal Prices (LMP) vary according to both electric demand and the penetration level of the wind power. The variable domain identification of LMP plays a very important role for market participants to assess and mitigate the risk on account of the combined uncertainty of wind power and demand. Traditionally, the Monte Carlo simulation (MCS) method can be used in order to determine the variable intervals of LMP. However, in this paper, author deploys a bi-level optimization model to calculate the upper and lower bounds of LMP when considering the combined uncertainty of wind power generation and demand. The objective function of the upper-level optimization problem is to maximize (or minimize) LMP at a node whereas the objective function of the lower-level optimization problems is to calculate the optimal power generation of the units participating in supplying the load. Key words: electricity market, mathematical program with equilibrium constraints (MPEC), mixed-integer linear programming (MILP), joint uncertainty of wind power and demand, Locational Marginal Prices (LMP). 1. problem with the objective to minimize the expected operation cost. In reference [6], the influence of distributed generation on a heavily loaded distribution system with a wind forecast model based on statistics is tackled. A mixed-integer stochastic optimization model is established in [7] where the wind uncertainty is modeled with ARMA as well as Latin hypercube sampling and a scenario reduction method is adopted to simplify the computation. Introduction Currently, .

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