Wind-integration benefits of controlled plug-in electric vehicle charging

Flexibility in plug-in electric vehicle(PEV) charging can reduce the ancillary cost effects of wind variability and uncertainty on electric power systems. In this paper, we study these benefits of PEV charging, demonstrating that controlled PEV charging can reduce costs associated with wind uncertainty and variability. Interestingly, we show that the system does not require complete control of PEV-charging loads to mitigate the negative cost impacts of wind variability and uncertainty. Rather, PEV owners giving the system a two-hour window of flexibility in which to recharge their vehicles provides much of the benefits that giving full charging control does.

A vector autoregression weather model for electricity supply and demand modeling

Weather forecasting is crucial to both the demand and supply sides of electricity systems. Temperature has a great effect on the demand side. Moreover, solar and wind are very promising renewable energy sources and are, thus, important on the supply side. In this paper, a large vector autoregression(VAR) model is built to forecast three important weather variables for 61 cities around the United States. The three variables at all locations are modeled as response variables. Lag terms are used to capture the relationship between observations in adjacent periods and daily and annual seasonality are modeled to consider the correlation between the same periods in adjacent days and years. We estimate the VAR model with16 years of hourly historical data and use two additional years of data for out-of-sample validation. Forecasts of up to six-hours-ahead are generated with good forecasting performance based on mean absolute error, root mean square error, relative root mean square error, and skill scores. Our VAR model gives forecasts with skill scoresthat are more than double the skill scores of other forecasting models in the literature. Our model also provides forecasts that outperform persistence forecasts by between6% and 80% in terms of mean absolute error. Our results show that the proposed time series approach is appropriate for very short-term forecasting of hourly solar radiation,temperature, and wind speed.

Benefits of Stochastic Optimization for Scheduling Energy Storage in Wholesale Electricity Markets

We propose a two-stage stochastic model for optimizing the operation of energy storage. The model captures two important features: uncertain real-time prices when day-ahead operational commitments are made;and the price impact of charging and discharging energy storage. We demonstrate that if energy storage has full flexibility to make real-time adjustments to its day-ahead commitment and market prices do not respond to charging and discharging decisions, there is no value in using a stochastic modeling framework, i.e., the value of stochastic solution is always zero. This is because in such a case the energy storage behaves purely as a financial arbitrageur day ahead, which can be captured using a deterministic model.We show also that prices responding to its operation can make it profitable for energy storage to "waste" energy, for instance by charging and discharging simultaneously, which is normally sub-optimal. We demonstrate our model and how to calibrate the price-response functions from historical data with a practical case study.

Using Interim Recommitment to Reduce the Operational-cost Impacts of Wind Uncertainty

Using wind-availability forecasts in day-ahead unit commitment can require expensive real-time operational adjustments.We examine the benefit of conducting interim recommitment between day-ahead unit commitment and real-time dispatch.Using a simple stylized example and a case study that is based on ISO New England,we compare system-operation costs with and without interim recommitment.We find an important tradeoff—later recommitment provides better wind-availability forecasts,but the system has less flexibility due to operating constraints.Of the time windows that we examine,hour-20 recommitment provides the greatest operational-cost reduction.