This paper quantifies the economic benefits associated with the introduction of greater spatial granularity in short-term pricing in the California wholesale electricity market. On April 1, 2009, California switched to a nodalpricing market with an integrated day-ahead forward market and real-time imbalance market from its original zonal market design with bilateral day-ahead scheduling, a real...

In this study, we investigate the maximum possible profit for a commercial office building participating in New York’s Day-Ahead Demand Response (DADR) program. We formulate an optimal control problem, assuming perfect knowledge of future weather, occupancy, and day-ahead electricity price predictions to examine this potential benefit. Then, a practical control strategy based upon the framework...

The quality of short-term electricity load forecasting is crucial to the operations and trading activities of market participants in an electricity market. In this paper, a multiple equation time series model for intra-day and day-ahead load forecasting is built. The model uses lagged load and temperature as the primary explanatory variables but makes effective use of diurnal characteristics an...

In this paper, a two-stage optimal charging scheme based on transactive control is proposed for the aggregator to manage day-ahead electricity procurement and real-time EV charging management in order to minimize its total operating cost. The day-ahead electricity procurement considers both the day-ahead energy cost and expected real-time operation cost. In the real-time charging management, th...

As part of the day-ahead market clearing process, MISO solves one of the most challenging Security Constrained Unit Commitment (SCUC) models. This paper introduces several developments on MIP SCUC formulation that result in significant performance improvement. Numerical results based on MISO data are presented. Index Terms – Electricity market, mixed integer programming, security constrained un...

This project is focused on the design of price forecasting tools for market operators and for market traders, taking into account the distinct purposes, data availability, and time horizons of the distinct users. Empirical price data from the MISO and RTE have been analyzed. A combined model is developed to forecast MISO day-ahead nodal prices. An ARIMA model is constructed to forecast RTE week...

Purpose of the paper The Basel Committee regulations require the estimation of Value-at-Risk at 99% confidence level for a 10-trading-day-ahead forecasting horizon. The paper provides a multivariate modelling framework for multi-period VaR estimates for leptokurtic and asymmetrically distributed real-estate portfolio returns. The purpose of the paper is to estimate accurate 10-day-ahead 99% VaR...

electricity price predictions have become a major discussion on competitive market under deregulated power system. but, the exclusive characteristics of electricity price such as non-linearity, non-stationary and time-varying volatility structure present several challenges for this task. in this paper, a new forecast strategy based on the iterative neural network is proposed for day-ahead price...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

Electricity industries around the world have significantly restructured in order to improve their economic efficiency, reliability of power systems and accountability. Accurate prediction of day-ahead electricity nodal price has now become an important activity to address the system operations and price volatility in the restructured electricity market. This will facilitate the market participa...