Demand response is a key element of future power systems due to its capacity defer grid investments, improve demand participation in the market, and absorb renewable energy source variations. In this regard, can play an important role delivering ancillary services systems. The lack standardization programs prepared for traditional generators have blocked these services. Nowadays, increasing nee...

Emergency resource is important for people evacuation and property rescue when accident occurs. The relief efforts could be promoted by a reasonable emergency resource allocation schedule in advance. As the marine environment is complicated and changeful, the place, type, severity of maritime accident is uncertain and stochastic, bringing about dynamic demand of emergency resource. Considering ...

Integrated resource planning has been performed by electric companies, because of its multifarious benefits in power system operation. Demand Response Resources (DRRs) can be used as a virtual demand side power plant. DRRs are clustered to IncentiveBased Programs (IBPs) and Time-Based Rate Programs (TBRPs). Precise modeling of DRRs helps the system operator to investigate the impact of responsi...

Petroleum (crude oil) is one of the most important resources of energy and its demand and consumption is growing while it is a non-renewable energy resource. Hence forecasting of its demand is necessary to plan appropriate strategies for managing future requirements. In this paper, three types of time series methods including univariate Seasonal ARIMA, Winters forecasting and Transfer Function-...

In today’s rapidly changing energy markets, the role of direct load control programs are increasingly recognized as an underutilized resource 1 capable of providing numerous benefits to our electricity grid and to our society as a whole. Although different names are used to describe these programs, we use the term Demand Response (DR) to include the overarching segment of load participation res...

Machine learning has shown tremendous success in a large variety of applications. The evolution machine-learning applications from cloud-based systems to mobile and embedded devices shifted the focus only quality-related aspects towards resource demand machine learning. For systems, dedicated accelerator hardware promises energy-efficient execution neural network inferences. Their precise terms...