TY - CONF
T1 - Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning
T2 - Hawaii International Conference on Systems Science (HICSS)
Y1 - 2017/01//
A1 - Weisi Deng
A1 - Yuting Ji
A1 - Lang Tong
AB - The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach incorporates real-time measurements and historical traces to produce forecasts of joint and marginal probability distributions of future locational marginal prices, power flows, and dispatch levels, conditional on the system state at the time of forecasting. Compared with standard Monte Carlo simulation techniques, the ODL approach offers several orders of magnitude improvement in computation time, making it feasible for online forecasting of market operations. Numerical simulations on large and moderate size power systems illustrate its performance and complexity features and its potential as a tool for system operators.
JF - Hawaii International Conference on Systems Science (HICSS)
SN - 978-0-9981331-0-2
UR - http://aisel.aisnet.org/hicss-50/es/markets/10/
ER -
TY - JOUR
T1 - Probabilistic Forecasting of Real-Time LMP and Network Congestion
JF - IEEE Transactions on Power Systems
Y1 - 2016/07//
A1 - Yuting Ji
A1 - Robert J. Thomas
A1 - Lang Tong
KW - RM13-002
AB - The short-term forecasting of real-time locational marginal price (LMP) and network congestion is considered from a system operator perspective. A new probabilistic forecasting technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability distribution of the real-time LMP/congestion is obtained. The proposed method incorporates load/generation forecast, time varying operation constraints, and contingency models. By shifting the computation associated with multiparametric programs offline, the online computational cost is significantly reduced. An online simulation technique by generating critical regions dynamically is also proposed, which results in several orders of magnitude improvement in the computational cost over standard Monte Carlo methods.
JO - IEEE Trans. Power Syst.
DO - 10.1109/TPWRS.2016.2592380
ER -
TY - CONF
T1 - Probabilistic Forecast of Real-Time LMP via Multiparametric Programming
T2 - 48th Hawaii International Conference on System Sciences (HICSS)
Y1 - 2015/01//
A1 - Yuting Ji
A1 - Robert J. Thomas
A1 - Lang Tong
KW - CERTS
KW - locational marginal pricing
KW - reliability and markets
KW - RM13-002
AB - The problem of short-term probabilistic forecast of real-time locational marginal price (LMP) is considered. A new forecast technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability mass function of the real-time LMP is estimated using Monte Carlo techniques. The proposed methodology incorporates uncertainty models such as load and stochastic generation forecasts and system contingency models. With the use of offline computation of multiparametric linear programming, online computation cost is significantly reduced.
JF - 48th Hawaii International Conference on System Sciences (HICSS)
PB - IEEE
CY - Kauai, HI
UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070121&refinements%3D4260156971%26filter%3DAND%28p_IS_Number%3A7069647%29
DO - 10.1109/HICSS.2015.306
ER -
TY - CONF
T1 - Piecewise affine dispatch policies for economic dispatch under uncertainty
T2 - 2014 IEEE Power & Energy Society (PES) General Meeting
Y1 - 2014/07//
SP - 1
EP - 5
A1 - Munoz-Alvarez, Daniel
A1 - Eilyan Bitar
A1 - Lang Tong
A1 - Wang, Jianhui
KW - CERTS
KW - economic dispatch
KW - Power system modeling
KW - reliability and markets
KW - stochastic optimization
AB - Stochastic optimization has become one of the fundamental mathematical frameworks for modeling power systems with important sources of uncertainty in the demand and supply sides. In this framework, a main challenge is to find optimal dispatch policies and settlement schemes that support a market equilibrium. In this paper, the economic dispatch under linear network constraints and resource uncertainty is revisited. Piece-wise affine continuous dispatch policies and locational prices that support a market equilibrium using a two-settlement scheme are derived. We find that the ex-post locational prices are piecewise affine continuous functions of the system uncertainties.
JF - 2014 IEEE Power & Energy Society (PES) General Meeting
PB - IEEE
CY - National Harbor, MD, USA
DO - 10.1109/PESGM.2014.6939369
ER -