Adequacy of neural networks for wide-scale day-ahead load forecasts on buildings and distribution systems using smart meter data

Power system operation increasingly relies on numerous day-ahead forecasts of local, disaggregated loads such as single buildings, microgrids and small distribution system areas. Various data-driven models can be effective predicting specific time series one-step-ahead. The aim of this work is to investigate the adequacy of neural network methodology for predicting the entire load curve day-ahead and evaluate its performance for a wide-scale application on local loads. To do so, we adopt networks from other short-term load forecasting problems for the multi-step prediction. We evaluate various feed-forward and recurrent neural network architectures drawing statistically relevant conclusions on a large sample of residential buildings. Our results suggest that neural network methodology might be ill-chosen when we predict numerous loads of different characteristics while manual setup is not possible. This article urges to consider other techniques that aim to substitute standardized load profiles using wide-scale smart meters data

Building power demand forecasting using K-nearest neighbours model - practical application in Smart City Demo Aspern project

Following the ongoing transformation of the European power system, in the future, it will be necessary to locally balance the increasing share of decentralised renewable energy supply. Therefore, a reliable short-term load forecast at the level of single buildings is required. In this study, we use a forecaster, which is based on K-nearest neighbours approach and was introduced in an earlier publication, on three buildings of Smart City Demo Aspern project. The authors demonstrate how this forecaster can be applied on different buildings without any manual setup or parametrisation, showing that it is viable to replace load-profiling solutions for predicting electricity consumption at the level of single buildings

Beyond low-inertia systems: Massive integration of grid-forming power converters in transmission grids

As renewable sources increasingly replace existing conventional generation, the dynamics of the grid drastically changes, posing new challenges for transmission system operations, but also arising new opportunities as converter-based generation is highly controllable in faster timescales. This paper investigates grid stability under the massive integration of grid-forming converters. We utilize detailed converter and synchronous machine models and describe frequency behavior under different penetration levels. First, we show that the transition from 0% to 100% can be achieved from a frequency stability point of view. This is achieved by re-tuning power system stabilizers at high penetration values. Second, we explore the evolution of the nadir and RoCoF for each generator as a function of the amount of inverter-based generation in the grid. This work sheds some light on two major challenges in low and no-inertia systems: defining novel performance metrics that better characterize grid behaviour, and adapting present paradigms in PSS design.Comment: 5 pages, 7 figure

Cyber-physical framework for emulating distributed control systems in smart grids

This paper proposes a cyber-physical framework for investigating distributed control systems operating in the context of smart-grid applications. At the moment, the literature focuses almost exclusively on the theoretical aspects of distributed intelligence in the smart-grid, meanwhile, approaches for testing and validating such systems are either missing or are very limited in their scope. Three aspects need to be taken into account while considering these applications: (1) the physical system, (2) the distributed computation platform, and (3) the communication system. In most of the previous works either the communication system is neglected or oversimplified, either the distributed computation aspect is disregarded, either both elements are missing. In order to cover all these aspects, we propose a framework which is built around a fleet of low-cost single board computers coupled with a real-time simulator. Additionally, using traffic control and network emulation, the flow of data between different controllers is shaped so that it replicates various quality of service (QoS) conditions. The versatility of the proposed framework is shown on a study case in which 27 controllers self-coordinate in order to solve the distributed optimal power flow (OPF) algorithm in a dc network

WIDE-AREA CONTROL SYSTEM FOR BALANCE-ENERGY PROVISION BY ENERGY CONSUMERS

Abstract: This paper focuses on wide-area control systems for that Internet-based com-munication, although being the only economically feasible option for communication, is insufficient for reliability or transmission delay reasons. An example for such a control system is the modem electricity system, which is currently changing from the traditional hierarchical to a more and more peer-to-peer oriented structure, and thus having growing demands for modem IT and control solutions. While up to now consumers were Eonsid--ered passive players, a new generation of automated demand response emerges, where consumers can react on real-time prices, on grid parameters like frequency or on transport schedules, in terms of their energy consumption. For enabling these features, a robust-wide-area control infrastructure has to be developed, that allows for low delay transmis-sion of control commands and measurement data. Further, it is critical to find simple and consistent models of the involved processes to design the respective control infrastructure according to its needs. This paper describes a novel approach for the design of distributed wide-area control systems that utilises process-specific parameters (here: grid frequency changes) as a new means of fast and reliable communication besides conventional com-munication channels. Copyright 0 2007 IFA

Phase Balancing Home Energy Management System Using Model Predictive Control

Most typical distribution networks are unbalanced due to unequal loading on each of the three phases and untransposed lines. In this paper, models and methods which can handle three-phase unbalanced scenarios are developed. The authors present a novel three-phase home energy management system to control both active and reactive power to provide per-phase optimization. Simplified single-phase algorithms are not sufficient to capture all the complexities a three-phase unbalance system poses. Distributed generators such as photo-voltaic systems, wind generators, and loads such as household electric and thermal demand connected to these networks directly depend on external factors such as weather, ambient temperature, and irradiation. They are also time dependent, containing daily, weekly, and seasonal cycles. Economic and phase-balanced operation of such generators and loads is very important to improve energy efficiency and maximize benefit while respecting consumer needs. Since homes and buildings are expected to consume a large share of electrical energy of a country, they are the ideal candidate to help solve these issues. The method developed will include typical distributed generation, loads, and various smart home models which were constructed using realistic models representing typical homes in Austria. A control scheme is provided which uses model predictive control with multi-objective mixed-integer quadratic programming to maximize self-consumption, user comfort and grid support