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: A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems 
Chapter Title:  C. DRP Implementation 
1. Abstract of Cited Paper (BIB001):  Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes. 
2. Abstract of Cited Paper (BIB002):  This paper provides an overview of the recent developments in data-based techniques focused on modern industrial applications. As one of the hottest research topics for complicated processes, the data-based techniques have been rapidly developed over the past two decades and widely used in numerous industrial sectors nowadays. The core of data-based techniques is to take full advantage of the huge amounts of available process data, aiming to acquire the useful information within. Compared with the well-developed model-based approaches, data-based techniques provide efficient alternative solutions for different industrial issues under various operating conditions. The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view. This paper begins with a brief evolutionary overview of data-based techniques in the last two decades. Then, the methodologies only based on process measurements and the model-data integrated techniques will be further introduced. The recent developments for modern industrial applications are, respectively, presented mainly from perspectives of monitoring and control. The new trends of data-based technique as well as potential application fields are finally discussed.

Literature Review:
Customers can take part to DR programs through an intermediary or directly with the utility. In the case when end-use customers are gathered by intermediaries, generally called DR providers (DRPs), the end-use customers' aggregated capability is presented to an organized market by DRPs BIB001 . Automated response technologies, generally classified as control devices, monitoring systems, and communication systems, are required in order to actuate the functionalities of a DRP and allow remote managing of peak load demand and energy consumption. An infrastructure to implement a DRP in a smart grid is presented in BIB001 . As shown in Fig. 4 , it consists of energy management systems (EMSs) and DRP, supervisory control and data acquisition (SCADA), remote terminal units (RTUs), advanced metering infrastructure (AMI), state estimation algorithms (SEAs), and generation and load forecast system (GLFS). Measurement data provided by AMI and RTUs are transferred to the EMS by the SCADA system. The EMS performs monitoring, control, and optimization tasks by using SEAs and GLFS. An AMI network, consisting of a smart meter, wide-area networks, home area networks, meter data management systems, and neighborhood area networks, is used to measure, save, and evaluate energy usage and to communicate information among the utility, the loads, and the consumers. In summary, the tasks of monitoring, diagnostics, and predictive maintenance should play an important role in energy management systems. The model-based solutions were recognized as very successful for this purpose, but it is extremely difficult and expensive to obtain relevant models built from the first principles. Relevant approaches, including the model-based and fault-tolerant system approaches are reviewed in recent papers BIB002 , BIB003 . In the DR energy management of today, the AI principles and techniques (negotiations based on price models, bidding and contracting) are strongly explored, despite that the authors from the energy management field do not mention this explicitly. The trend of the deeper exploration of AI will continue.