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.

%B Hawaii International Conference on Systems Science (HICSS) %8 01/2017 %@ 978-0-9981331-0-2 %G eng %U http://aisel.aisnet.org/hicss-50/es/markets/10/ %0 Conference Paper %B 2016 IEEE Power and Energy Society General Meeting (PESGM) %D 2016 %T Multi-proxy interchange scheduling under uncertainty %A Yuting Ji %A Lang Tong %K RM13-002 %XThe problem of inter-regional interchange scheduling using a multiple proxy bus representation is considered. A new scheduling technique is proposed for the multi-proxy bus system based on a stochastic optimization that captures uncertainty in renewable generation and stochastic load. In particular, the proposed algorithm iteratively optimizes the interchange across multiple proxy buses using a vectorized notion of demand and supply functions. The proposed technique leverages the operator's capability of forecasting locational marginal prices (LMPs) and obtains the optimal interchange schedule directly without iterations between operators.

%B 2016 IEEE Power and Energy Society General Meeting (PESGM) %I IEEE %C Boston, MA, USA %P 1 - 5 %8 07/2016 %R 10.1109/PESGM.2016.7741106 %0 Journal Article %J IEEE Transactions on Power Systems %D 2016 %T Probabilistic Forecasting of Real-Time LMP and Network Congestion %A Yuting Ji %A Robert J. Thomas %A Lang Tong %K RM13-002 %XThe 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.

%B IEEE Transactions on Power Systems %8 07/2016 %! IEEE Trans. Power Syst. %R 10.1109/TPWRS.2016.2592380 %0 Journal Article %J IEEE Transactions on Power Systems %D 2016 %T Stochastic Interchange Scheduling in the Real-Time Electricity Market %A Yuting Ji %A Zheng, Tongxin %A Lang Tong %K RM13-002 %XThe problem of inter-regional interchange schedul- ing in the presence of stochastic generation and load is consid- ered. An interchange scheduling technique based on a two-stage stochastic minimization of expected operating cost is proposed. Because directly solving the stochastic optimization is intractable, an equivalent problem that maximizes the expected social welfare is formulated. The proposed technique leverages the operator’s capability of forecasting locational marginal prices and obtains the optimal interchange schedule without iterations among op- erators. Several extensions of the proposed technique are also discussed.

%B IEEE Transactions on Power Systems %P 1 - 1 %8 08/2016 %! IEEE Trans. Power Syst. %R 10.1109/TPWRS.2016.2600635 %0 Conference Paper %B 48th Hawaii International Conference on System Sciences (HICSS) %D 2015 %T Probabilistic Forecast of Real-Time LMP via Multiparametric Programming %A Yuting Ji %A Robert J. Thomas %A Lang Tong %K CERTS %K locational marginal pricing %K reliability and markets %K RM13-002 %X 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. %B 48th Hawaii International Conference on System Sciences (HICSS) %I IEEE %C Kauai, HI %8 01/2015 %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070121&refinements%3D4260156971%26filter%3DAND%28p_IS_Number%3A7069647%29 %R 10.1109/HICSS.2015.306 %0 Conference Paper %B 2015 IEEE Power & Energy Society General Meeting %D 2015 %T Stochastic coordinated transaction scheduling via probabilistic forecast %A Yuting Ji %A Lang Tong %K RM13-002 %XThe problem of real-time interchange scheduling between two independently operated regions is considered. An optimal scheduling scheme is proposed by maximizing the expected economic surplus based on Coordinated Transaction Scheduling (CTS) mechanism. The proposed technique incorporates probabilistic forecasts of renewable generation to optimize the interchange schedule using a parametric programming formulation, from which statistical real-time generation supply offer curves are constructed.

%B 2015 IEEE Power & Energy Society General Meeting %I IEEE %C Denver, CO, USA %P 1 - 5 %8 07/2015 %R 10.1109/PESGM.2015.7286497 %0 Conference Paper %B 2013 Asilomar Conference on Signals, Systems and Computers %D 2013 %T Forecasting real-time locational marginal price: A state space approach %A Yuting Ji %A Kim, Jinsub %A Robert J. Thomas %A Lang Tong %K CERTS %K locational marginal pricing %K reliability and markets %K RM13-002 %X The problem of forecasting the real-time locational marginal price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future locational marginal prices with forecast horizons of 6-8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques. %B 2013 Asilomar Conference on Signals, Systems and Computers %I IEEE %C Pacific Grove, CA, USA %P 379 - 383 %8 11/2013 %R 10.1109/ACSSC.2013.6810300