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International audience ; The main goals of smart metering are the reduction of costs, energy and CO2 by the provision of actual metering information to the providers and the customer. They allow for flexible possibilities to influence the customers' energy consumption behavior and to adapt dynamically the power generation and distribution to the requested energy by smart grids. Metering devices are under control of governmental organizations, which are responsible for the permanent correct delivery of metering data. The governmental organizations accept online metering, administration and even software download of regulated software only, if strong, lawful security requirements are fulfilled. This paper describes such a security system. It considers not only the security mechanisms of the metering devices, but also of the complete system hierarchy, which is planned for the communication system of smart metering. It supports also new use cases, which are caused by the liberalization of the energy and metering services markets.

Pseudo medium/low voltage (MV/LV) transformer loads are usually used as partial inputs to the distribution system state estimation (DSSE) in MV systems. Such pseudo load can be represented by the aggregation of smart metering (SM) data. This follows the government restriction that distribution network operators (DNOs) can only use aggregated SM data. Therefore, we assess the subsequent performance of the DSSE, which shows the impact of this restriction - it affects the voltage angle estimation significantly. The possibilities for improving the DSSE accuracy under this restriction are further studied. First, two strategies that can potentially relax this restriction's impact are studied. First, the correlations among pseudo loads' errors are taken into account in the DSSE process and a power loss estimation method is proposed. Second, the investments (i.e., either adding measurement devices or increasing the original devices' accuracy) for the satisfactory DSSE results are assessed. All these are for addressing DNOs' concerns on this restriction.

Report on the FSR Workshop held in Florence on 6 February 2009. ; The FSR Workshop on "Smart Metering" organized by the Florence School of Regulation gathered 45 participants from 15 countries. The Workshop was devoted to: (1) reviewing European progress to date in terms of smart metering technologies and deployment and (2) identifying research needs related to smart metering within the context of domestic and EU energy policies. Participants to the workshop were mostly experts from EU Institutions, National Regulatory Authorities, Energy Companies and Academic Institutions. ; I. Smart metering as an enabler of smart grids II. Views and Experiences of Regulators and the Industry III. Smart meter technologies: A fast moving frontier IV. A Research Agenda: Regulation and Smart Metering Policies

The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is driven by technological progress in trading systems and an increasing importance of intraday trading, liquidity risk, optimal order placement as well as high-frequency volatility. This book provides a state-of-the art overview on the major approaches in high-frequency econometrics, including univariate and multivariate autoregressive conditional mean approaches for different types of high-frequency variables, intensity-based approaches for financial point processes and dynamic factor models. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and presents applications to volatility and liquidity estimation, order book modelling and market microstructure analysis

The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is driven by technological progress in trading systems and an increasing importance of intraday trading, liquidity risk, optimal order placement as well as high-frequency volatility. This book provides a state-of-the art overview on the major approaches in high-frequency econometrics, including univariate and multivariate autoregressive conditional mean approaches for different types of high-frequency variables, intensity-based approaches for financial point processes and dynamic factor models. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and presents applications to volatility and liquidity estimation, order book modelling and market microstructure analysis.

Proceedings, Elforsk rapport 14:32, paper 9.6 ; Research on residential smart metering based demand response in Finland is presented. Hourly interval metering and settlement of practically every customer started in the beginning of 2014 as required by the electricity market legislation that aimed at enabling new forms of demand response. To some extent this already happened. 1) Customers are now offered home automation based demand response, including fuel switching. 2) There is about 1 GW of Time of Use (ToU) load, and new smart metering and settlement made it possible to make a substantial part of it dynamically respond to situations in the electricity market and network. Helsinki Energy and its partners developed an operational model for market based dynamic load control to replace static ToU load controls, and implemented it with smart meters. 3) Loiste in Kainuu has implemented smart metering based direct load control of residential ToU customers and the response is used as system reserve. The value of demand response depends very much on the accuracy of response forecasting. Both in Helsinki and Kainuu field tests to support response modelling were completed in winter 2014. This paper describes the field tests and related model development for forecasting and optimising the load responses ; NORDAC 2014, The 11th Nordic Electricity Distribution and Management Conference 2014, Stockholm, Sweden, 8 - 9 September 2014

Pseudo MV/LV (Medium/Low Voltage) transformer loads are usually used as partial inputs to the Distribution System State Estimation (DSSE) in MV systems. Such pseudo load can be represented by the aggregation of Smart Metering (SM) data. This follows the government restriction that Distribution Network Operators (DNOs) can only use aggregated SM data. Therefore, we assess the subsequent performance of DSSE, which shows the impact of this restriction - it affects the voltage angle estimation significantly. The possibilities for improving the DSSE accuracy under this restriction are further studied. First, two strategies that can potentially relax this restriction's impact are studied: the correlations among pseudo loads' errors are taken into account in the DSSE process; a power loss estimation method is proposed. Second, the investments (i.e., either adding measurement devices or increasing the original devices' accuracy) for the satisfactory DSSE results are assessed. All these are for addressing DNOs' concerns on this restriction.

Estimates of daily volatility are investigated. Realized volatility can be computed from returns observed over time intervals of different sizes. For simple statistical reasons, volatility estimators based on high‐frequency returns have been proposed, but such estimators are found to be strongly biased as compared to volatilities of daily returns. This bias originates from microstructure effects in the price formation. For foreign exchange, the relevant microstructure effect is the incoherent price formation, which leads to a strong negative first‐order autocorrelation ρ(1)≃40 per cent for tick‐by‐tick returns and to the volatility bias. On the basis of a simple theoretical model for foreign exchange data, the incoherent term can be filtered away from the tick‐by‐tick price series. With filtered prices, the daily volatility can be estimated using the information contained in high‐frequency data, providing a high‐precision measure of volatility at any time interval.(J.E.L.: C13, C22, C81).