Antti Mutanen

Customer Classification and Load Profiling Method for Distribution Systems

In Finland, customer class load profiles are used extensively in distribution network calculation. State estimation systems, for example, use the load profiles to estimate the state of the network. Load profiles are also needed to predict future loads in distribution network planning. In general, customer class load profiles are obtained through sampling in load research projects. Currently, in Finland, customer classification is based on the uncertain customer information found in the customer information system. Customer information, such as customer type, heating solution, and tariff, is used to connect the customers with corresponding customer class load profiles. Now that the automatic meter-reading systems are becoming more common, customer classification and load profiling could be done according to actual consumption data. This paper proposes the use of the ISODATA algorithm for customer classification. The proposed customer classification and load profiling method also includes temperature dependency correction and outlier filtering. The method is demonstrated in this paper by studying a set of 660 hourly metered customers.

Statistical Charging Load Modeling of PHEVs in Electricity Distribution Networks Using National Travel Survey Data

In this paper, statistical charging load modeling of plug-in hybrid electric vehicles (PHEVs) in electricity distribution networks is studied. Usefulness of National Travel Survey data in the modeling is investigated, and a novel modeling methodology is proposed where detailed car use habits are taken into account and statistical distributions of charging energies can be produced. Using the modeling methodology some example calculation results of a Finnish case study are presented with further analysis and sensitivity studies. The example calculations are made mostly from viewpoint of the Finnish distribution networks and their modeling traditions but the method can be applied internationally when relevant travel survey data is available. Example calculations are analyzed in order to assess reasonability and practical usability of the models. The models produced by the methodology can easily be used in network calculation tools commonly used by distribution network operators.

Enhanced Load Profiling for Residential Network Customers

Anticipating load characteristics on low voltage circuits is an area of increased concern for Distribution Network Operators with uncertainty stemming primarily from the validity of domestic load profiles. Identifying customer behavior makeup on a LV feeder ascertains the thermal and voltage constraints imposed on the network infrastructure; modeling this highly dynamic behavior requires a means of accommodating noise incurred through variations in lifestyle and meteorological conditions. Increased penetration of distributed generation may further worsen this situation with the risk of reversed power flows on a network with no transformer automation. Smart Meter roll-out is opening up the previously obscured view of domestic electricity use by providing high resolution advance data; while in most cases this is provided historically, rather than real-time, it permits a level of detail that could not have previously been achieved. Generating a data driven profile of domestic energy use would add to the accuracy of the monitoring and configuration activities undertaken by DNOs at LV level and higher which would afford greater realism than static load profiles that are in existing use. In this paper, a linear Gaussian load profile is developed that allows stratification to a finer level of detail while preserving a deterministic representation.

RTDS verification of a coordinated voltage control implementation for distribution networks with distributed generation

In weak distribution networks the amount of distributed generation (DG) is usually limited by the voltage rise effect. The voltage rise can be mitigated using passive methods such as increasing the conductor size which can, however, be quite expensive. Also active voltage control methods can be used to reduce the maximum voltage in the network. In many cases active voltage control can increase the capacity of connectable DG substantially which can lead to significantly lower connection costs. In this paper, operation of an active voltage control algorithm is viewed. The algorithm controls the substation voltage and DG reactive power and determines its control actions based on the state of the whole network. The algorithm is implemented as a Matlab program and communication between Matlab and SCADA is realized using OPC Data Access. Correct operation of the algorithm is verified using Real Time Digital Simulator (RTDS). The same algorithm could also be implemented as a part of the distribution management system (DMS).

Testing low voltage network state estimation in RTDS environment

The low voltage network operating environment is going through changes. The simultaneous introduction of intermittent renewable energy production and customer requirements for increased power quality and supply reliability are forcing utilities to rethink the role of low voltage networks. With recent advances in smart grid technology, low voltage network automation is emerging as a viable option to traditional network investments. Congestion management and demand response, for example, can be used to keep the network currents and voltages within acceptable limits. In order to control the network, we must first have a comprehensive view on the state of the network. In this paper, the low voltage network monitoring concept proposed by the FP7 European project INTEGRIS is tested. Real-Time Digital Simulator (RTDS) is used to test how well the measurements from secondary substations and smart meters can be combined in a state estimator to get a real-time view of the network state.

Case studies on impacts of plug-in vehicle charging load on the planning of urban electricity distribution networks

In this paper, the impact of plug-in vehicle charging load on electricity network planning is investigated by means of case studies which are based on statistical PHEV charging load modeling work and co-operation with two Finnish distribution network companies. According to the case studies, the impacts of plug-in vehicle charging load on Finnish urban distribution networks are modest with low penetration levels, but with high penetration levels plug-in vehicles should be taken into account in long-term network planning. Also, needs and possibilities of “smart charging” were acknowledged from the calculation results.

Change detection of electric customer behavior based on AMR measurements

Smart Grids technology is emphasized a lot in the future power system worldwide. Nowadays, the widely used Automatic Meter Reading (AMR) technology in Finland makes it possible to collect customers hourly load measurements and to use data analysis methods for customer clustering and load prediction purposes. This paper addresses the detection of possible changes in customers behavior. This could for example be a result of changed habitation, heating solution change, installation of solar panels or other equipment. Basic classification and regression methods like K-means and Fuzzy C-means are utilized to analyze the electric customer behavior. The developed method successfully detects various obvious load pattern changes on different customer types. It also offers rough time information regarding at which week the change happens. This behavior change detection method can be applied in improving load modeling accuracy by considering the most recent consumption information after the change.

Evolving smart meter data driven model for short-term forecasting of electric loads

Short-term forecasting of electric loads is an essential function required by Smart Grids. Today increasing amount of smart metering data is available enabling the development of enhanced data-driven models for short-term load forecasting. Until now, a plethora of models have been developed ranging from simple linear regression models to more advanced models such as (artificial) neural networks (NNs) and support vector machines (SVMs). Despite the relatively high accuracy obtained, the acceptance of purely data-driven models such as NN models is still remained limited due to their complexity and nontransparent nature. Therefore it is important to develop optimization schemes, which can be used to facilitate the selection of appropriate model structure resulting good forecasting accuracy with low complexity. This study presents an optimization scheme based on multi-objective genetic algorithm (GA) for designing data-driven models for short-term forecasting of electric loads. The optimization scheme is demonstrated for designing the conventional NN/MLP model using real smart metering data and weather measurements. The optimal NN model structures are identified and analyzed in terms of model complexity and forecasting accuracy.

Demonstrating coordinated voltage control in a real distribution network

The connection of distributed generation (DG) to weak distribution networks is likely to cause voltage rise problems. At present, the voltage rise is usually mitigated by reinforcing the network but as the penetration level of DG increases also active voltage control methods need to be taken into use. Active voltage control has been a subject of extensive research in the past decade but the number of real implementations is still very low. One reason for this is that only few demonstrations have been conducted in real distribution networks. In this paper, the operation of one coordinated voltage control (CVC) algorithm is successfully demonstrated in a real Finnish distribution network. The main objective of the paper is to identify the problems that may arise when academic smart grid methods are implemented in real electricity networks. The second objective is to verify the operation of the studied CVC algorithm.

Classification of electricity customer groups towards individualized price scheme design

This paper introduces classification of electricity residential customers into different groups associated with individualized electricity price schemes, such as time-of-use (TOU) or critical peak pricing (CPP). We use an unsupervised learning method, K-means, assisted by a dimensionality reduction technique and an innovative supervised learning method, extreme learning machine (ELM), to cluster daily load profiles based on hourly AMI measurements. Then, the achieved typical daily load profiles are analyzed and utilized for the design of an electricity price scheme for every subgroup based on symbolic aggregate approximation (SAX). These carefully designed and customized retail price schemes can provide a potential tool for price-based and incentive-based demand response in the Smart Grid context.