Abstract
This paper presents a load estimation method applicable to complex power networks (namely, heavily meshed secondary networks) based on available network transformer measurements. The method consists of three steps: network reduction, load forecasting, and state estimation. The network is first mathematically reduced to the terminals of loads and measurement points. A load forecasting approach based on temperature is proposed to solve the network unobservability. The relationship between outdoor temperature and power consumption is studied. A power-temperature curve, a nonlinear function, is obtained to forecast loads as the temperature varies. An “effective temperature” reflecting complex weather conditions (sun irradiation, humidity, rain, etc.) is introduced to properly consider the effect on the power consumption of cooling and heating devices. State estimation is adopted to compute loads using network transformer measurements and forecasted loads. Experiments conducted on a real secondary network in New York City with 1040 buses verify the effectiveness of the proposed method.
Highlights
Power networks are one kind of the most complex arti cial network in the world
Low-voltage highly meshed secondary networks (HMSNs) are frequently used in densely populated metropolitan areas in North America to improve reliability. e unique characteristic of these networks is that the transformer secondaries are all tied together by a heavily meshed lowvoltage network from where the loads are connected
To estimate loads in such an unobservable secondary network, this paper proposes a load estimation method based on secondary transformer measurements without the installation of additional meters at loads. e method is composed of three main steps: network reduction, load forecasting, and state estimation
Summary
Power networks are one kind of the most complex arti cial network in the world. Load estimation has long been an important issue in electrical power systems for energy management and operation. Device installation is not always possible due to various reasons, in particular high cost Under this condition, pseudomeasurements, which could be a forecasted value derived from historical data or an estimated value derived from a mathematical model, are used to make the network observable. (4) Meter placement and load forecasting are helpful to estimate loads in a distribution system They mainly focused on radial or weakly meshed systems [2, 29, 30, 38,39,40,41,42] due to high cost or lack of historical data. E proposed method, which consists of three steps: network reduction, load forecasting, and state estimation, is inspired by our observations and analysis of the strong relationship existing among temperature, load consumption, and transformer measurements in a long-term investigation.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
