Achieving the Dispatchability of Distribution Feeders through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage

We propose and experimentally validate a control strategy to dispatch the operation of a distribution feeder interfacing heterogeneous prosumers by using a grid-connected battery energy storage system (BESS) as a controllable element coupled with a minimally invasive monitoring infrastructure. It consists in a two-stage procedure: day-ahead dispatch planning, where the feeder 5-minute average power consumption trajectory for the next day of operation (called \emph{dispatch plan}) is determined, and intra-day/real-time operation, where the mismatch with respect to the \emph{dispatch plan} is corrected by applying receding horizon model predictive control (MPC) to decide the BESS charging/discharging profile while accounting for operational constraints. The consumption forecast necessary to compute the \emph{dispatch plan} and the battery model for the MPC algorithm are built by applying adaptive data driven methodologies. The discussed control framework currently operates on a daily basis to dispatch the operation of a 20~kV feeder of the EPFL university campus using a 750~kW/500~kWh lithium titanate BESS.Comment: Submitted for publication, 201

Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the gen-eration is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power con-sumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296 % this paper was successfully carried out. This pa-per can be implemented by any intensive power consuming town for predicting the fu-ture load and would prove to be very useful tool while sanctioning the load

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Application of Conventional Feedforward and Deep Neural Networks to Power Distribution System State Estimation and State Forecasting

Classical neural networks such as feedforward multilayer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. This research investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to mitigate challenges in power distribution system state estimation and forecasting based upon conventional analytic methods. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn complex functional mapping between datasets with many features. CNNs and LSTMs are considered based upon their promise to perform time-series forecasting by learning the autocorrelation of the dataset being predicted. The performance of MLPs will be presented in terms of root-mean-square error (RMSE) between actual and predicted voltage magnitude and voltage phase angles and training execution time for distribution system state estimation (DSSE). The performance of CNNs, and LSTMs will be presented in terms of RMSE between actual and predicted real power demand and execution time when performing distribution system state forecasting (DSSF). Additionally, Bayesian Optimization with Gaussian Processes are used to optimize MLPs for regression. An IEEE standard 34-bus test system is used to illustrate the proposed conventional neural network and deep learning methods and their effectiveness to perform power system state estimation and power system state forecasting respectively

A Survey on Deep Learning Role in Distribution Automation System : A New Collaborative Learning-to-Learning (L2L) Concept

This paper focuses on a powerful and comprehensive overview of Deep Learning (DL) techniques on Distribution Automation System (DAS) applications to provide a complete viewpoint of modern power systems. DAS is a crucial approach to increasing the reliability, quality, and management of distribution networks. Due to the importance of development and sustainable security of DAS, the use of DL data-driven technology has grown significantly. DL techniques have blossomed rapidly, and have been widely applied in several fields of distribution systems. DL techniques are suitable for dynamic, decision-making, and uncertain environments such as DAS. This survey has provided a comprehensive review of the existing research into DL techniques on DAS applications, including fault detection and classification, load and energy forecasting, demand response, energy market forecasting, cyber security, network reconfiguration, and voltage control. Comparative results based on evaluation criteria are also addressed in this manuscript. According to the discussion and results of studies, the use and development of hybrid methods of DL with other methods to enhance and optimize the configuration of the techniques are highlighted. In all matters, hybrid structures accomplish better than single methods as hybrid approaches hold the benefit of several methods to construct a precise performance. Due to this, a new smart technique called Learning-to-learning (L2L) based DL is proposed that can enhance and improve the efficiency, reliability, and security of DAS. The proposed model follows several stages that link different DL algorithms to solve modern power system problems. To show the effectiveness and merit of the L2L based on the proposed framework, it has been tested on a modified reconfigurable IEEE 32 test system. This method has been implemented on several DAS applications that the results prove the decline of mean square errors by approximately 12% compared to conventional LSTM and GRU methods in terms of prediction fields.©2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Integration of Energy Storage into a Future Energy System with a High Penetration of Distributed Photovoltaic Generation

Energy storage units (ESU) are increasingly used in electrical distribution systems because they can perform many functions compared with traditional equipment. These include peak shaving, voltage regulation, frequency regulation, provision of spinning reserve, and aiding integration of renewable generation by mitigating the effects of intermittency. As is the case with other equipment on electric distribution systems, it is necessary to follow appropriate methodologies in order to ensure that ESU are installed in a cost-effective manner and their benefits are realized. However, the necessary methodologies for integration of ESU have not kept pace with developments in both ESU and distribution systems. This work develops methodologies to integrate ESU into distribution systems by selecting the necessary storage technologies, energy capacities, power ratings, converter topologies, control strategies, and design lifetimes of ESU. In doing so, the impact of new technologies and issues such as volt-VAR optimization (VVO), intermittency of photovoltaic (PV) inverters, and the smart PV inverter proposed by EPRI are considered. The salient contributions of this dissertation follow. A unified methodology is developed for storage technology selection, storage capacity selection, and scheduling of an ESU used for energy arbitrage. The methodology is applied to make technology recommendations and to reveal that there exists a cost-optimal design lifetime for such an ESU. A methodology is developed for capacity selection of an ESU providing both energy arbitrage and ancillary services under a stochastic pricing structure. The ESU designed is evaluated using ridge regression for price forecasting; Ridge regression applied to overcome numerical stability and overfitting issues associated with the large number of highly correlated predictors. Heuristics are developed to speed convergence of simulated annealing for placement of distributed ESU. Scaling and clustering methods are also applied to reduce computation time for placement of ESU (or any other shunt-connected device) on a distribution system. A probabilistic model for cloud-induced photovoltaic (PV) intermittency of a single PV installation is developed and applied to the design of ESU

A Review of Graph Neural Networks and Their Applications in Power Systems

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power systems, such as fault scenario application, time series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed