scholarly journals Effects of Environmental and Electrical Factors on Metering Error and Consistency of Smart Electricity Meters

2021 ◽  
Vol 11 (23) ◽  
pp. 11457
Author(s):  
Suqin Xiong ◽  
Jiahai Zhang ◽  
Baoliang Zhang ◽  
Guodong Sun ◽  
Zhen Chen ◽  
...  
Keyword(s):  
Power Grid ◽  
Electricity Consumption ◽  
Reference Value ◽  
Power Coefficient ◽  
Smart Power ◽  
Quadratic Polynomials ◽  
Operation Conditions ◽  
Operation Control ◽  
Lower Temperature ◽  
Grid Operation

The smart electricity meter (SEM) is an important part of smart power grid, and the accuracy of SEMs is the basis for power grid operation control and trade settlement between power supply and electricity consumption, but the evolution behaviors of metering error of SEMs under field operation conditions have not yet been identified. The SEMs were installed and operated on site, metering error data were collected under various temperature and current conditions. The influences of current, power coefficient, and temperature on metering error and consistency were analyzed separately with the help of quadratic polynomials, and then an integrated model elaborating the joint effects of multi-stress was developed based on a binary quadratic polynomial. We find that a lower temperature and a larger current result in a higher metering error of SEMs; however, the effects of current on metering error are determined by power coefficients. The results have reference value for remote metrological verification, error monitoring, and the optimization of the operation and maintenance scheme of SEMs.

scholarly journals Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2181
Author(s):  
Rafik Nafkha ◽  
Tomasz Ząbkowski ◽  
Krzysztof Gajowniczek
Keyword(s):  
Deep Learning ◽  
Power Grid ◽  
Hybrid Approach ◽  
Electricity Consumption ◽  
Maximum Power ◽  
Learning Approaches ◽  
Two Stage ◽  
Load Optimization ◽  
Optimal Capacity ◽  
Maximal Load

The electricity tariffs available to customers in Poland depend on the connection voltage level and contracted capacity, which reflect the customer demand profile. Therefore, before connecting to the power grid, each consumer declares the demand for maximum power. This amount, referred to as the contracted capacity, is used by the electricity provider to assign the proper connection type to the power grid, including the size of the security breaker. Maximum power is also the basis for calculating fixed charges for electricity consumption, which is controlled and metered through peak meters. If the peak demand exceeds the contracted capacity, a penalty charge is applied to the exceeded amount, which is up to ten times the basic rate. In this article, we present several solutions for entrepreneurs based on the implementation of two-stage and deep learning approaches to predict maximal load values and the moments of exceeding the contracted capacity in the short term, i.e., up to one month ahead. The forecast is further used to optimize the capacity volume to be contracted in the following month to minimize network charge for exceeding the contracted level. As confirmed experimentally with two datasets, the application of a multiple output forecast artificial neural network model and a genetic algorithm (two-stage approach) for load optimization delivers significant benefits to customers. As an alternative, the same benefit is delivered with a deep learning architecture (hybrid approach) to predict the maximal capacity demands and, simultaneously, to determine the optimal capacity contract.

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