A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services

Also available for download in MDPI (open access).

Title: A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services

Authors: Sean Williams (1), Michael Short (1), Tracey Crosbie (1) and Maryam Shadman-Pajouh (2)

1 School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK;

2 Teesside University Business School, Teesside University, Middlesbrough, UK;

Abstract: This paper presents a decentralized informatics, optimization, and control framework to enable demand response (DR) in small or rural decentralized community power systems, including geographical islands. The framework consists of a simplified lumped model for electrical demand forecasting, a scheduling subsystem that optimizes the utility of energy storage assets, and an active/pro-active control subsystem. The active control strategy provides secondary DR services, through optimizing a multi-objective cost function formulated using a weight-based routing algorithm.

In this context, the total weight of each edge between any two consecutive nodes is calculated as a function of thermal comfort, cost (tariff), and the rate at which electricity is consumed over a short future time horizon. The pro-active control strategy provides primary DR services. Furthermore, tertiary DR services can be processed to initiate a sequence of operations that enables the continuity of applied electrical services for the duration of the demand side event. Computer simulations and a case study using hardware-in-the-loop testing is used to evaluate the optimization and control module. The main conclusion drawn from this research shows the real-time operation of the proposed optimization and control scheme, operating on a prototype platform, underpinned by the efectiveness of the new methods and approach for tackling the optimization problem. This research recommends deployment of the optimization and control scheme, at scale, for decentralized community energy management. The paper concludes with a short discussion of business aspects and outlines areas for future work.

Keywords: decentralized; demand response; optimization; community energy management

A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services

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