Organic Architecture for Energy Management and Smart Grids

Abstract

An unprecedented rise of renewable and distributed energy resources imposes unprecedented challenges in terms of complexity to power grids. Multitudes of devices are not only connected to the electricity grid but need appropriate information and communication technologies for proving their services. These devices ask for novel control mechanisms on different levels and regional scales. In this paper, we show how concepts from Organic Computing may support the controlled self-organization of the future smart grid. We propose a generic hierarchical architecture as a framework for various energy management systems. This architecture is able to reflect the physical power grid structure as well as the interdependencies of its stakeholders, user objectives, subsystems, and devices. It enables adaptive responses to changing objectives as well as disturbances in the system. Various simulations of systems based on the proposed architecture show the applicability of the proposed architecture to domains of energy management in smart grids.

Two stages K‐means and PSO‐based method for optimal allocation of multiple parallel DRPs application & deployment

“…Obtained results in this paper have been achieved further to the exploration of the following areas, which were essential and part of the current conducted works: Clustering and data mining techniques serving the multiple DRP application when assessing customers and predicting their patterns thus helping in (a) detection of prospect customers and their accurate consumption behaviour suitable for implementing DRPs as in [14–16], (b) development of criteria for a good baseline for DRP as in [17]. Architectures suiting the multiple DRP applications and their characteristics (a) global along with its layers as in [18], (b) generic including functionalities like prediction, learning and other topics as in [19], (c) multiple agent system based with local and global control hubs as in [20] or macro, micro grids connectivity based as in [21]. Pricings methodology ensuring customer‐guaranteed response (a) joint online learning and time slots pricing as in [22], (b) generic demand model capturing the aggregated effect of price‐responsive prosumers as in [23], (c) scheduling day‐ahead model for elastic price‐based demand bidding as in [24], (d) Two‐stage model for the retailer energy pricing as in [25].…”

“…Architectures suiting the multiple DRP applications and their characteristics (a) global along with its layers as in [18], (b) generic including functionalities like prediction, learning and other topics as in [19], (c) multiple agent system based with local and global control hubs as in [20] or macro, micro grids connectivity based as in [21].…”