Extended Residential Power Management Interface for Flexibility Communication and Uncertainty Reduction for Flexibility System Operators

Felix Heider,Maik Plenz,Amra Jahic,Detlef Schulz

doi:10.3390/en15041257

Abstract

The high importance of demand-side management for the stability of future smart grids came into focus years ago and is today undisputed among a wide spectrum of energy market participants, and within the research community. The increasing development of communication infrastructure, in tandem with the rising transparency of power grids, supports the efforts for deploying demand-side management applications. While it is then accepted that demand-side management will yield positive contributions, it remains challenging to identify, communicate, and access available flexibility to the flexibility managers. The knowledge about the system potential is essential to determine impacts of control and adjustment signals, and employ temporarily required demand-side flexibility to ensure power grid stability. The aim of this article is to introduce a methodology to determine and communicate local flexibility potential of end-user energy systems to flexibility managers for short-term access. The presented approach achieves a reliable calculation of flexibility, a standardized data aggregation, and a secure communication. With integration into an existing system architecture, the general applicability is outlined with a use case scenario for one end-user energy system. The approach yields a transparent short-term flexibility potential within the flexibility operator system.

Highlights

Throughout the last decade, with the increasing share of renewable energy sources (RES), the demand side of the energy economy has come into focus in the research and industry activities [1,2,3,4,5,6,7,8,9]
As stated in [38,41] the linear state of charge (SOC) phase is recommended for controlling by electric vehicles (EVs) charging coordinators, because the charging behaviour in the constant voltage phase is of a heterogeneous nature
The applicability of the developed methodology was shown through a use case scenario on working days in the spring with a reduced set of end-user inputs that were easy to derive and implement into the power management system (PMS) interface

Summary

Introduction

Throughout the last decade, with the increasing share of renewable energy sources (RES), the demand side of the energy economy has come into focus in the research and industry activities [1,2,3,4,5,6,7,8,9]. The orientation of fulfilling high energy demands in times of low RES production leads to high conventional reserves or storage capacities. These high reserve facilities lead to high costs for energy infrastructure operators and, high costs for society. The integration of the rising share of electric vehicles (EVs) as time- and location-independent loads and potential storage devices adds an additional challenge to the energy infrastructure. When focusing on real-time applications, different aspects contribute to uncertainty. These aspects occur through forecast errors of production facility or load states. Additional aspects are the changing user behaviour and ambient conditions that influence the device operations [1]

Objectives

Methods

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Conclusion