Smart Grid Dynamic Control and Management with Big Data Process Platform

Successful deployment and integration of such fluctuating renewable energy into the electricity grid and the transformation into SG will require the use of advanced technologies and management strategies [3]. Conventional power grids are facing many challenges to respond to the continues growing demand for energy and to deal with the increased penetration of intermittent renewable energies into the grid, as well as to provide a reliable, stable, and efficient electric grid. These diverse challenges are the forcing drivers to the transformation of current grid into SG [4]. Smart grid can be divided into two main parts, SG infrastructure and SG applications. SG infrastructure includes smart power system, information technology, and communication system, while SG applications are divided into fundamental applications and emerging applications. The fundamental applications refer to the energy management strategies, reliability models, security and privacy, in addition to promoting demand-side management while the emerging applications include the deployment of electric vehicles and mobile charging stations [5]. SGs are electrical power grids that use two-way flow of electricity and information. They are characterized with automated energy delivery, monitoring and consumption with players from utilities, market, and customers [6]. Microgrids play an important role in the effort to make the traditional electric grid smarter, more efficient, and resilient. The challenge is to manage the large system with high number of microgrids and distributed renewable energies and be able to maintain the supply-demand balance. The dynamic control and management in SG involves smart energy-efficient controllable equipment, smart distributed energy resources, advanced dynamic control architecture, distributed optimization architecture, and integrated communications architecture. SG is supported by large number of smart meters, sensors, detectors, measurement units, etc. Those elements provide a continuous stream of data to support SG performance. Huge amount of data in obtained from different SG sources satisfy all the Big Data characteristics [7]. The success of future SG depends mainly on the effective utilization of the huge amount of the big data flow. The proposed project is aimed to provide a smart management and dynamic control systems for SG while taking the advantages of the big data information flows. The project consists of four main components, each is with specific challenges, outputs, and expected outcomes, as described below: Big Data Platform, Big Data process is divided into data management and analytics. Data management contains Big Data storage, mining, and integration to prepare and retrieve for analysis. Data analytics include analyzing the managing data to be in a useful form for decision-making. This mass of information is essential to make SG more efficient, reliable, secure, independent, and supportive during normal conditions and contingencies. By using Big Data process platform, electricity suppliers and customers can reap significant benefits with successful implementation of Big Data analytics. The challenges in that stage are security and privacy of Big Data, quality and reliability of the diverse data sources, data complexity reduction, and online information extraction in a meaningful context [8]. In addition, the big data computational algorithms should be flexible to deal with the uncertainties and customers’ behavior [9]. Electric information management is an important issue raised for effective dynamic energy and control management. Energy Resources Platform, it generally refers to identifying, matching, allocating, scheduling, and monitoring energy resources over time, which is one of the challenges. It deals effectively with the coordinated sharing of resources, balancing the availability of these resources with varying levels of electricity demand, optimizes SG assets and makes them operate efficiently and reliably. A centralized big data process platform for the grid aims to act within seconds to achieve supervisory energy management of grid energy resources together with load forecasting and scheduling during the different operational scenarios Direct Load-management Platform, aims to control and manage the demand in order to shape the load profile. In the SG environment, loads include active and passive controllable loads. Those loads can communicate with the upper control system or electrical utility providers in real-time for optimal and planned energy consumption. A decentralized big data process platform for the grid aims to act within µ-seconds to m-seconds, which should be realized at the field level to achieve safe operation of the connected equipment in the network. Demand Response Platform, the demand response program is induced from load demand elasticity by reducing or shifting consumption in response to electric utilities signals such as prices or during peak demand periods to meet installed capacity requirements. Static and dynamic tariffs implementation are designed for load management, the real-time price increases with an increase in the demand, it is needed to limit tremendous growth in customers energy demands. The customers can help electricity providers to save energy consumption through reductions in peak demand.

1- Collects huge amount of data from DERs, loads, and energy market to provide insights to better understand the characteristics of energy activities inside Qatar
2- Develop a Big Data framework for performance management, data mining, predictive analytics, and decision-making quality.
3- Integrating multiple big data strategies
4- Understand electric utilities consumers’ preferences and measure customers satisfaction
5- Increased implementation and full utilization of renewable energy sources into the power grid
6- Big data process will provide assurance to embrace new techniques and technologies such as data management and data analytics
7- Raise consumers’ awareness and behaviors for energy saving and contribution.
8- Establishing realistic platform for direct load management that allows electric power companies to control electricity load
9- Allow consumers make decisions to maximize their benefit Medium Term
10- New pathway for electric customers engagement and build a novel programs and electric services
11- Allow active customer interactions
12- Minimize outages and deliver enhanced customer services
13- Provide opportunities for energy suppliers through big Data platform
14- Raising the stakes and utility incentives for energy efficiency
15- Enhanced billing based on the big data analytics