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.

1 aDeng, Weisi1 aJi, Yuting1 aTong, Lang uhttp://aisel.aisnet.org/hicss-50/es/markets/10/01123nas a2200145 4500008003900000245005700039210005600096260003500152300001000187520066300197653001300860100001500873700001500888856007400903 2016 d00aMulti-proxy interchange scheduling under uncertainty0 aMultiproxy interchange scheduling under uncertainty aBoston, MA, USAbIEEEc07/2016 a1 - 53 aThe 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.

10aRM13-0021 aJi, Yuting1 aTong, Lang uhttps://certs.lbl.gov/publications/multi-proxy-interchange-scheduling01394nas a2200157 4500008003900000022001400039245007000053210006900123260001200192520089100204653001301095100001501108700002301123700001501146856007501161 2016 d a0885-895000aProbabilistic Forecasting of Real-Time LMP and Network Congestion0 aProbabilistic Forecasting of RealTime LMP and Network Congestion c07/20163 aThe 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.

10aRM13-0021 aJi, Yuting1 aThomas, Robert, J.1 aTong, Lang uhttps://certs.lbl.gov/publications/probabilistic-forecasting-real-time01181nas a2200169 4500008003900000022001400039245007400053210006900127260001200196300001000208520065800218653001300876100001500889700001900904700001500923856007300938 2016 d a0885-895000aStochastic Interchange Scheduling in the Real-Time Electricity Market0 aStochastic Interchange Scheduling in the RealTime Electricity Ma c08/2016 a1 - 13 aThe 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.

10aRM13-0021 aJi, Yuting1 aZheng, Tongxin1 aTong, Lang uhttps://certs.lbl.gov/publications/stochastic-interchange-scheduling01319nas a2200181 4500008003900000245007600039210006900115260002900184520065000213653001000863653003200873653002800905653001300933100001500946700002300961700001500984856013800999 2015 d00aProbabilistic Forecast of Real-Time LMP via Multiparametric Programming0 aProbabilistic Forecast of RealTime LMP via Multiparametric Progr aKauai, HIbIEEEc01/20153 aThe 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.10aCERTS10alocational marginal pricing10areliability and markets10aRM13-0021 aJi, Yuting1 aThomas, Robert, J.1 aTong, Lang uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070121&refinements%3D4260156971%26filter%3DAND%28p_IS_Number%3A7069647%2901002nas a2200145 4500008003900000245007700039210006900116260003500185300001000220520050900230653001300739100001500752700001500767856007400782 2015 d00aStochastic coordinated transaction scheduling via probabilistic forecast0 aStochastic coordinated transaction scheduling via probabilistic aDenver, CO, USAbIEEEc07/2015 a1 - 53 aThe 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.

10aRM13-0021 aJi, Yuting1 aTong, Lang uhttps://certs.lbl.gov/publications/stochastic-coordinated-transaction01582nas a2200205 4500008003900000245007600039210006900115260004200184300001400226520091200240653001001152653003201162653002801194653001301222100001501235700001601250700002301266700001501289856007201304 2013 d00aForecasting real-time locational marginal price: A state space approach0 aForecasting realtime locational marginal price A state space app aPacific Grove, CA, USAbIEEEc11/2013 a379 - 3833 aThe 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.10aCERTS10alocational marginal pricing10areliability and markets10aRM13-0021 aJi, Yuting1 aKim, Jinsub1 aThomas, Robert, J.1 aTong, Lang uhttps://certs.lbl.gov/publications/forecasting-real-time-locational