Mavrogenou K., Degefa M. Z., Morch A., D'Arco S., Hatziargyriou N. and Merino J., "Post-Primary Voltage Control using Optimal Power Flow for Loss Minimization within Web-of-Cells", Proceedings of the 52nd International Universities Power Engineering Conference (UPEC), Heraklion, 29 Aug. – 1st Sep. 2017

 Abstract

This paper presents the decentralized Post-Primary Voltage Control (PPVC) scheme which is introduced within the Web-of-Cells (WoC) concept in the ELECTRA IRP project. The PPVC improves the traditionally known secondary and tertiary voltage control schemes to develop a robust method coping with the emerging intermittent generation and variable loading in the distribution system. PPVC aims to utilize all available resources within a defined network area for voltage control purposes by taking loss minimization as an objective. To achieve that, the re-definition of voltage set-points in all controllable nodes within the area is performed using an Optimal Power Flow (OPF) algorithm. The proposed PPVC algorithm coordinates tap changers and other reactive power resources such as PV inverters proactively altering their settings for a recurring time-window. The settings are optimally computed by using short-term forecasted load and generation values. The PPVC algorithm has been implemented with MATLAB and the General Algebraic Modeling System (GAMS) tool for optimization and evaluated on The European CIGRÉ MV network, modified with distributed energy resources (DERs). Simulation results showing the impact of PPVC compared to the business-as-usual (BaU) way of voltage control are presented. In addition, laboratory tests coupling the GAMS-based OPF with OPAL-RT have been conducted to present the efficiency of PPVC in real-time applications.

Pamela MacDougall and Bob Ran (TNO), George B. Huitema (University of Groningen), Geert Deconinck
(University of Leuven)

Abstract

Aggregated demand response for smart grid services is a growing field of interest especially for market participation. To minimize economic and network instability risks, flexibility characteristics such as shiftable capacity must be known. This is traditionally done using lower level, end user, device specifications. However, with these large numbers, having lower level information, has both privacy and computational limitations. Previous studies have shown that black box forecasting of shiftable capacity, using machine learning techniques, can be done accurately for a homogeneous cluster of heating devices. This paper validates the machine learning model for a heterogeneous virtual power plant. Further it applies this model to a control strategy to offer flexibility on an imbalance market while maintaining day ahead market obligations profitably. It is shown that using a black box approach 89% optimal economic performance is met. Further, by combining profits made on imbalance market and the day ahead costs, the overall monthly electricity costs are reduced 20%.

E. Guillo-Sansano, M.H. Syed, A.J. Roscoe, G. Burt (USTRATH), Mark Stanovich and Karl Schoder Center for Advance Power Systems, Florida State University

Abstract

With the evolution of power system components and structures driven mainly by renewable energy technologies, reliability of the network could be compromised with traditional control methodologies. Therefore, it is crucial to thoroughly validate and test future power system control concepts before deployment. In this paper, a Controller Hardware in the Loop (CHIL) simulation for a real-time distributed control algorithm concept developed within the ELECTRA IRP project is performed. CHIL allows exploration of many real-world issues such as noise, randomness of event timings, and hardware design issues that are often not present on a simulation-only system. Octave has been used as the programming language of the controller in order to facilitate the transition between software simulation and real-time control testing. The distributed controller achieved frequency restoration with a collaborative response between different controllers very fast after the unbalanced area is located.

Junjie Hu (DTU), Kai Heussen (DTU), Bert Claessens (Restore), Lei Sunx (ZheJiang University), Reinhilde D'Hulst (Vito)

Abstract

Conventional regulation reserves have fixed participation factors and are thus not well suited to utilize differentiated capabilities of ancillary service providers. This study applies linear decision rules-based (LDR) control policies, which effectively adapt the present participation factor in dependence of the imbalance signal of previous time steps. The LDR-policies are centrally computed using a robust optimization approach which takes into account both the covariances of historic imbalance signals and the operational flexibility of ancillary service providers. The concept is then extended to the cooperation of multiple cells. Two illustrating examples are presented to show the functioning of the proposed LDR method.