The Next Generation EMS Design

摘要

The concept of Supervisory Control and Data Acquisition (SCADA) system was introducedrnin the late sixties and subsequently the Energy Management System (EMS) solution with setrnof functionalities that relied on SCADA data was introduced in the seventies. This EMSrndesign was based on the notion that power system goes through distinct states such as normal,rnalert, emergency and restorative, and hence the EMS functionalities need to supportrnoperators’ ability to monitor the system behaviour as it goes through various operating statesrnand make decisions to steer it back to the normal state. As the time went by, many things havernchanged in the area of power systems: introduction of the electricity markets to accommodaterncompetitive trading, the growth of the power system infrastructure to meet demands forrnrenewable resources, EMS technology to improve computations, communications andrnvisualization. Hence, almost 50 years after the initial proposals for the EMS solution as wernknow it today, the question whether we need a new EMS design and if so, how the nextrngeneration EMS design is going to look like may be raised. While this is a complex questionrnrequiring analysis well beyond what may be achieved through a discussion in a single paper,rnthere are a few requirements of the next generation EMS design that are becoming ratherrnobvious. They revolve around three simple questions: a) can the operator’s ability to monitorrnthe system be improved and how, b) what are the requirements for the better decision-makingrntools for the operators and how such tools may be implemented, and c) how the design mayrnevolve from the current legacy design to the stages of future EMS implementation. This paperrnis focused on trying to answer those three questions. The discussion is facilitated by usingrnspecific examples of the developments that the author was or is involved with. The examplesrninclude the discussion of spatial and temporal improvements in data management by mergingrnoperational and non-operational data, development of improved operator decision-makingrntools by better matching data and models, and introduction of a development andrnimplementation strategy that relies on gradual transition from legacy to future design.