Abstract
The operation and planning of distribution grids require the joint processing of measurements from different grid locations. Since measurement devices in low- and medium-voltage grids lack precise clock synchronization, it is important for data management platforms of distribution system operators to be able to account for the impact of nonideal clocks on measurement data. This paper formally introduces a metric termed Additive Alignment Error to capture the impact of misaligned averaging intervals of electrical measurements. A trace-driven approach for retrieval of this metric would be computationally costly for measurement devices, and therefore, it requires an online estimation procedure in the data collection platform. To overcome the need of transmission of high-resolution measurement data, this paper proposes and assesses an extension of a Markov-modulated process to model electrical traces, from which a closed-form matrix analytic formula for the Additive Alignment Error is derived. A trace-driven assessment confirms the accuracy of the model-based approach. In addition, the paper describes practical settings where the model can be utilized in data management platforms with significant reductions in computational demands on measurement devices.
Highlights
Utilizing electrical measurements for grid operation and planning is common practice in higher voltage layers, and it is slowly being adopted in distribution grids
In addition to the consideration of measurement value errors caused by current transformers and the measurement device itself, the joint processing of electrical measurements from different measurement points in the distribution grid requires a quantification of the impact of imperfect clocks
While typical clock deviation errors of the participating measurement devices are in the order of few seconds [3,4], they can be larger in the case of infrequent clock synchronization or slow and highly variable communication delays on the communication network between master clock and measurement devices such as frequently observed in Smart Meter communication networks based on power-line communications or lowthroughput Internet of Things communication technologies [5], which are highly dependent on wireless sensor networks (WSN)
Summary
Utilizing electrical measurements for grid operation and planning is common practice in higher voltage layers, and it is slowly being adopted in distribution grids. While the high-voltage grids deploy highly accurate and synchronized phasor measurement units [1], the operation and planning of medium- and low-voltage grids in practice need to rely on low-cost electrical measurements at transformer stations or junction boxes and increasingly on measurements from customer connection points, such as those provided by Smart Meters or by inverters that connect distributed energy resources. Such measurements of voltages, currents, and power are typically averaged over some logging interval of duration T, where T can range from a few seconds to tens of minutes [2]. 1. formally introduces a set of precise metrics to capture the behavior of the time alignment error (Section 3.3); 2. shows that this measurement error is strongly dependent on the measured quantity and on time of day (Sections 4.3 and 5.5), establishing that such error needs to be estimated online and cannot be replaced by a rule-of-thumb approximation; 3. shows the challenges for measurement device complexity in a straightforward online estimation approach; 4. introduces a model-based formula for Additive Alignment Error, assesses the accuracy of this model-based approach, and shows the benefits of applying the model-based online estimation in practical systems (Section 4.3)
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