Conservation Voltage Reduction Effects Research Articles

Theoretical and Experimental Verification of CVR Energy Savings for Refrigeration Loads

This paper presents analytical and experimental studies on Conservation Voltage Reduction (CVR) for Refrigeration Loads (RLs) driven by single-phase and three-phase induction motors. It is shown that the increase in motor efficiency is the factor that determines the regions where CVR works for RL loads. The results of this paper dispel the belief that CVR cannot save energy from RLs because the output energy must be constant to keep the same temperature. A criterion is developed to assess the effectiveness of CVR for RL loads. It is shown theoretically and experimentally that CVR energy savings come from the reduction of iron-core losses. Traditionally, the iron core losses are represented by shunt resistances ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_m</i> ) that are frequently neglected in the literature. However, they are essential for the proper evaluation of CVR. Induction machines of all ratings have a region where energy savings can be obtained. When operating near their rating smaller machines produce larger energy savings under reduced voltages than larger machines in proportion to their ratings. An additional benefit of CVR, when applied to motor loads, is that the power factor increases substantially.

CVR-Based Real-Time Power Fluctuation Smoothing Control for Distribution Systems With High Penetration of PV and Experimental Demonstration

This paper proposes a real-time power smoothing control strategy for distribution systems with high penetration of photovoltaic (PV) based on conservation voltage reduction (CVR) technology, which employs CVR effects of feeder loads to smooth net-load power fluctuations caused by PV intermittency. CVR technology is implemented in real-time based on feedback signals provided by phasor measurement unit, and feeder voltage is under closed-loop control within statutory limits to make load demand change with PV power. Meanwhile, an online CVR model identification application based on generalized recursive least squares and a power smoothing application based on PI-Smith controller are separately designed. CVR model identification application uses well-designed M-sequence voltage disturbance to online induce CVR effects, which can accurately identify dynamic and steady-state CVR effects without being affected by color-noises. Power smoothing application combines PI controller with Smith predictor to limit the fluctuation rate of net-load without being affected by practical time-delay. The parameters of controller are automatically tuned through the identified CVR model with small computational burden, which is beneficial to engineering applications. The proposed method has been demonstrated in real 10kV distribution system in China. Simulation and field results demonstrate that proposed method has significant potential for real-time power smoothing.

Fast Coscheduling of Discrete and Continuous Resources in Active Distribution Systems

This article proposes a novel fast method for flexibility scheduling and steady-state voltage control in smart distribution systems enabled with both conventional discrete controllable devices (DCDs), e.g., capacitor banks and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> -load tap changers, and continuous and faster control devices, e.g., distributed energy resources, renewable resources, and static voltage regulators. By approximating the active and reactive losses, a linear branch flow model is presented for the optimal power flow (OPF) problem in distribution systems. Accurate models are also proposed for DCDs which suit the proposed OPF formulation and require fewer binary variables compared to those available in the literature. Conservation voltage reduction (CVR) scheme is modeled in the proposed OPF methodology. The CVR effects on energy saving, peak-load reduction, line losses, and transformer losses are analyzed in the case studies. As shown in various case studies, the proposed method is fast enough for quasi-real-time applications and is efficacious in minimizing the system operation cost, holding an acceptable level of power quality based on the available standards and evading the conflict between control actions.

Online Assessment of Conservation Voltage Reduction Effects With Micro-perturbation

Online assessment of conservation voltage reduction (CVR) has great effects on the performance of CVR-based applications, especially for real-time CVR-based controls. The challenges of online CVR assessment mainly come from time-varying load composition and inevitable noise. Besides, the dynamic process resulting from motors further complicates this problem. This article proposes a micro-perturbation based load-to-voltage (LTV) process identification method to online assess the CVR effects. In particular, we construct a well-designed pseudo-random-binary-sequence voltage perturbation to excite the CVR process continually and fully. With the experimental data, the CVR factor and transfer function of LTV are estimated by the cross-correlation method, which can reflect both steady-state and dynamic CVR effects without being affected by noise. The proposed method is verified on a specific hardware-in-the-loop testbed under various cases. In addition, through the energy-saving and power smoothing applications, the impact of CVR assessment on performance of CVR-based applications is also analyzed, which further verify the validity and economic benefits of the proposed assessment method.

Time-Varying Stochastic Assessment of Conservation Voltage Reduction Based on Load Modeling

This paper presents a time-varying stochastic technique to assess conservation voltage reduction (CVR) effects based on load modeling. A time-varying exponential load model is developed to represent voltage dependences of loads. The recursive least square (RLS) method is applied to identify model parameters in a recursive way. CVR factors can be calculated using the identified model parameters. The time-varying stochastic model for CVR effects can then be constructed by the Kolmogorov-Smirnov (K-S) test. The proposed CVR assessment method is applied to one-year measurement data from a utility company. The calculated CVR factors are verified by a Euclidian distance-based comparison method. Stochastic models of CVR effects in each time window are constructed. Compared with previous efforts on assessing CVR effects, the proposed method does not require control groups or assumptions of linear relationships between the load and its impact factors. The probabilistic nature of CVR effects is also fully considered.

Review on Implementation and Assessment of Conservation Voltage Reduction

Conservation voltage reduction (CVR) is widely adopted by utilities for peak demand reduction and energy savings through reducing the voltage level of the electrical distribution system. This paper presents an in-depth review on implementing and assessing CVR. The methodologies to quantify CVR effects are categorized into comparison-based, regression-based, synthesis-based and simulation-based methods. The implementation strategies for voltage reduction are classified into open-loop and closed-loop methods. The impacts of emerging smart-grid technologies on CVR are also discussed. The paper can provide researchers and utility engineers with further insights into the state of the art, technical barriers and future research directions of CVR technologies.

Analysis of Conservation Voltage Reduction Effects Based on Multistage SVR and Stochastic Process

This paper aims to develop a novel method to evaluate Conservation Voltage Reduction (CVR) effects. A multistage Support Vector Regression (MSVR)-based model is proposed to estimate the load without voltage reduction during the CVR period. The first stage is to select a set of load profiles that are close to the profile under estimation by a Euclidian distance-based index; the second stage is to train the SVR prediction model using the pre-selected profiles; the third stage is to re-select the estimated profiles to minimize the impacts of estimation errors on CVR factor calculation. Compared with previous efforts to analyze the CVR outcome, this MSVR-based technique does not depend on selections of control groups or assumptions of any linear relationship between the load and its impact factors. In order to deal with the variability of CVR performances, a stochastic framework is proposed to assist utilities in selecting target feeders. The proposed method has been applied to evaluate CVR effects of practical voltage reduction tests and shown to be accurate and effective.