Energy big data: A survey <s> A. ROLE OF BIG DATA IN THE SMART GRID <s> Intelligent and effective monitoring of power system stability in control centers is one of the key issues in smart grid technology to prevent unwanted power system blackouts. Voltage stability contingency analysis is one of the important monitoring tools for control center operation in smart grid era. With the advent of Phasor Measurement Unit (PMU) technology, real time monitoring of voltage stability of power system is now a reality. This work utilizes real-time PMU data to derive voltage stability boundary and indices to monitor the voltage stability related contingency situation in power systems. It uses PMU data to calculate two voltage stability indices that indicates relative closeness to the instability by producing numerical indices. The IEEE 39 bus system was modeled and run on a Real-time Digital Simulator (RTDS) which streamed PMU data over the Internet using IEEE C37.118 standard and was used to calculate the voltage stability boundaries and indices in real-time. <s> BIB001 </s> Energy big data: A survey <s> A. ROLE OF BIG DATA IN THE SMART GRID <s> In this paper, we introduce a hierarchical system model that captures the decision making processes involved in a network of multiple providers and a large number of consumers in the smart grid, incorporating multiple processes from power generation to market activities and to power consumption. We establish a Stackelberg game between providers and end users, where the providers behave as leaders maximizing their profit and end users act as the followers maximizing their individual welfare. We obtain closed-form expressions for the Stackelberg equilibrium of the game and prove that a unique equilibrium solution exists. In the large population regime, we show that a higher number of providers help to improve profits for the providers. This is inline with the goal of facilitating multiple distributed power generation units, one of the main design considerations in the smart grid. We further prove that there exist a unique number of providers that maximize their profits, and develop an iterative and distributed algorithm to obtain it. Finally, we provide numerical examples to illustrate the solutions and to corroborate the results. <s> BIB002 </s> Energy big data: A survey <s> A. ROLE OF BIG DATA IN THE SMART GRID <s> Integrating renewables into the mainstream energy market is pivotal for the green revolution promised by the smart grid. The real power behind realization of the smart grid goals lies in the volume, variety, velocity of the big data generated by a variety of sources. Nevertheless, the smart grid needs data centers to digest the big data for its profound green revolution. However, big data processing is the radix for data centers to be seen as energy black holes. Unless data centers are transformed into energy-efficient enterprises, big data are going to be responsible for superfluous energy burn, potentially reversing the smart grid genesis with regard to green environmental impact. This paper describes the role of the big data enterprise in envisioning the smart grid. We dissect the big data enterprise into six vital planes impacting the energy footprints of data centers. We present a survey of key strategies to make these six vital planes greener. Moreover, we present open challenges and directions in this context. We assert that a cross-plane approach toward a greener optimization is crucial. In this vein, we present a green orchestrator that is capable of incorporating different planes in an integrated fashion to boost energy profile of the big data enterprise. <s> BIB003
The data obtained from PMUs, smart meters, sensors and other IEDs have opened up a plethora of chances, such as predictive analytics, real-time vulnerability assessment, theft detection, demand-side-management, economic dispatch, energy trading, etc BIB003 . Big data can help improve the smart grid management to a higher level. For example, by enhancing the accessibility of a customer's electricity consumption data, the demand response will be expanded and energy efficiency will be improved BIB002 . Similarly, analyzing data obtained from PMUs and IEDs will help to improve customer service, prevent outages, maximize safety and ensure service reliability BIB001 ,  . Furthermore, electric utilities are using prediction data analytics for estimating several parameters which are helpful to operate the smart grid in an efficient, economical and reliable way. For instance, whether the excess energy is available from renewable sources can be predicted through accessing the ability of the smart grid to transmit it. At the same time, calculating equipment downtime, accessing power system failures, and managing unit commitment can also be done effectively in high correlation with integrating distributed generation. Thus, the grid management state will be enhanced, the efficiency will be improved and the robustness of generation and deployment will be ensured. With the integration of various distributed generating sources, refined forecasting, load planning, and unit commitment are more convenient to avoid inefficient energy transmitting or dispatching extra generation.