You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Energy big data: A survey 
Chapter Title:  A. ROLE OF BIG DATA IN THE SMART GRID 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  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.

Literature Review:
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.