Probabilistic Forecast of Real-Time LMP via Multiparametric Programming

Ji, Yuting; Thomas, Robert J.; Tong, Lang

doi:10.1109/hicss.2015.306

Cited by 21 publications

(23 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An existing work closest to the present paper is [6], [7] where the authors proposed a probabilistic forecasting method based on a multiparametric formulation of DCOPF that has random generations and demands as parameters. From the parametric linear/quadrtic programming theory, the (conditional) probability distributions of LMP and power flows, given the current system state, reduce to the conditional probabilities that realizations of the random demand and generation fall into one of the critical regions in the parameter space.…”

Section: A Related Workmentioning

confidence: 99%

“…The main difficulty of the approach in [6], [7] is the high cost of computing the set of critical regions that partition the parameter space. Although such computations can be made off-line, the number of critical regions grows exponentially with the number of constraints, which makes even the offline computations prohibitive for large systems.…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning

Deng¹,

Tong

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

Self Cite

View full text Add to dashboard Cite

Abstract-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.Index Terms-Dictionary learning, electricity market, machine learning in power systems, power flow distributions, probabilistic price forecasting. NOMENCLATURE c(·)Real-time generation cost function. Shadow prices for max/min transmission constraints at time t.

show abstract

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning

Deng¹,

Tong

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The matrix A B is invertible and P * G is uniquely determined by A Lemma 2. Within each SPR, the vector of LMPs is unique [7] [8].…”

Section: Remarkmentioning

confidence: 99%

“…Reference [7] advances the theory of SPR from system operator's perspective where the knowledge of system topology and parameters is available. For market participants, such knowledge is not necessarily available.…”

Section: Introductionmentioning

confidence: 99%

Learning the LMP-load coupling from data: A support vector machine based approach

Geng¹,

Xie²

2017

2017 IEEE Manchester PowerTech

View full text Add to dashboard Cite

This paper investigates the fundamental coupling between loads and locational marginal prices (LMPs) in security-constrained economic dispatch (SCED). Theoretical analysis based on multi-parametric programming theory points out the unique one-to-one mapping between load and LMP vectors. Such one-to-one mapping is depicted by the concept of system pattern region (SPR) and identifying SPRs is the key to understanding the LMP-load coupling. Built upon the characteristics of SPRs, the SPR identification problem is modeled as a classification problem from a market participant's viewpoint, and a Support Vector Machine based datadriven approach is proposed. It is shown that even without the knowledge of system topology and parameters, the SPRs can be estimated by learning from historical load and price data. Visualization and illustration of the proposed data-driven approach are performed on a 3-bus system as well as the IEEE 118-bus system.

show abstract

“…Among the input factors besides historical prices, load data is most popular. Here the authors usually directly take load forecasts as an Kou et al (2015) New South Wales Spot prices, electricity load Variational heteroscedastic ACE, WS, NLPD Gaussian process with active learning Ji et al (2015) none, simulated Spot prices, electricity load DC Optimal Power Flow BS operation and contingency constraints Table 2: Detailed comparison of probabilistic and mid-to long-term electricity price articles published between 2011 and 2017. input or use a separate model for the load. The table also shows the beforementioned facts that there is only one group of authors so far working on medium-term probabilistic forecasts.…”

mentioning

confidence: 99%

Probabilistic mid- and long-term electricity price forecasting

Ziel¹,

Steinert²

2018

Renewable and Sustainable Energy Reviews

119

View full text Add to dashboard Cite

The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity prices for a short period of time provided new opportunities for market participants. However, the electricity price literature seem to be very scarce on the issue of medium-to long-term price forecasting, which is mandatory for investment and political decisions. Our paper closes this gap by introducing a new approach to simulate electricity prices with hourly resolution for several months up to three years. Considering the uncertainty of future events we are able to provide probabilistic forecasts which are able to detect probabilities for price spikes even in the long-run. As market we decided to use the EPEX day-ahead electricity market for Germany and Austria. Our model extends the X-Model which mainly utilizes the sale and purchase curve for electricity day-ahead auctions. By applying our procedure we are able to give probabilities for the due to the EEG practical relevant event of six consecutive hours of negative prices. We find that using the supply and demand curve based model in the long-run yields realistic patterns for the time series of electricity prices and leads to promising results considering common error measures.

show abstract

Probabilistic Forecast of Real-Time LMP via Multiparametric Programming

Cited by 21 publications

References 10 publications

Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning

Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning

Learning the LMP-load coupling from data: A support vector machine based approach

Probabilistic mid- and long-term electricity price forecasting

Contact Info

Product

Resources

About