Hierarchical Look-Ahead Conservation Voltage Reduction Framework Considering Distributed Energy Resources and Demand Reduction

Mak, Davye; Choi, Dae-Hyun

doi:10.3390/en11123250

Cited by 9 publications

(7 citation statements)

References 37 publications

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“…The voltage magnitude at node i ∈ N PV ∪ N EVCS is expressed in (30). The controlled amount of reactive power injection or absorption is shown in (31) where Q q k represents the y-position of the six breaking points in the Q-V curve with 32)-( 34) are used to form the linear functions corresponding to the piecewise linear curves in (29). However, ( 30) is still a nonlinear constraint because it includes the terms of the multiplication of two variables, α q i,t,3 V q i,3 and α q i,t,4 V q i,4 .…”

Section: B Local Control Stagementioning

confidence: 99%

“…In [30], a three-stage VVO method was developed in which the OLTC and CBs are scheduled in 1 h period at the first stage, the smart inverters are scheduled in a 15 min period at the second stage, and the local voltage control based on the Q-V curve is executed by smart inverters in a second period at the third stage. In [31], a two-level conservation voltage reduction method was presented to achieve energy saving with a lower voltage profile in which the OLTC and CBs reduce the voltage profile at the global level, and the smart inverters of PV systems and ESSs help to maintain a lower voltage profile using the proposed Q-V curves at the local level. In [32], a power factor-based droop control curve for the smart inverter of a PV system was proposed to mitigate voltage violations and excessive tap operations of step voltage regulators with line drop compensation.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Volt-VAR Optimization Framework Considering Voltage Control of Smart Electric Vehicle Charging Stations Under Uncertainty

Prabawa

Choi²

2021

IEEE Access

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Smart electric vehicle charging stations (EVCSs) equipped with distributed energy resources (DERs), such as photovoltaic (PV) systems and energy storage systems (ESSs), are promising entities for maintaining voltage quality in power distribution networks through voltage regulation using the smart inverters of DERs. This study proposes a hierarchical Volt-VAR optimization (VVO) framework that reflects the voltage regulation capability of smart EVCSs, which consists of global and local voltage control stages. At the global stage, smart inverters of EVCSs cooperate with conventional voltage regulators, such as an on-load tap changer (OLTC) and capacitor banks (CBs)), and smart inverters of PV systems to minimize the total active power losses and voltage deviations along with the determination of optimal parameters for local droop control functions of the smart inverters. At the local stage, smart inverters of EVCSs and PV systems quickly mitigate local voltage violations using dynamically varying local droop control functions with their optimal parameters calculated from the global stage. Under uncertainties in PV generation outputs and driving patterns of electric vehicle users, the deterministic optimization-based VVO problem at the global stage is reformulated into the chance-constrained optimization-based VVO problem. A simulation study was performed in an IEEE 33-bus distribution system with an OLTC, CBs, PV systems, and smart EVCSs. The results demonstrate the effectiveness of the proposed framework in terms of total active power loss/voltage deviation, optimized local droop control function, and probability level of chance constraints. INDEX TERMSVolt-VAR optimization, local voltage control, smart inverter, electric vehicle charging station, chance-constrained optimization NOMENCLATURE The main notations used throughout this paper are summarized here. Hat symbols represent estimates of true parameter values. Other undefined symbols are explained in the text.

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Section: B Local Control Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical Volt-VAR Optimization Framework Considering Voltage Control of Smart Electric Vehicle Charging Stations Under Uncertainty

Prabawa

Choi²

2021

IEEE Access

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“…Next, we investigate the impact of voltage minimization on the proposed HEMS. To this end, we reformulate the proposed HEMS optimization problem with additional objective function J 3 in (44) and constraints (45), (46) [37] as follows:…”

Section: B Performance Assessment Of the Proposed Hemsmentioning

confidence: 99%

Smart Home Energy Management in Unbalanced Active Distribution Networks Considering Reactive Power Dispatch and Voltage Control

Mak

Choi

2019

IEEE Access

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This paper presents a new optimization algorithm for home energy management systems (HEMSs) in three-phase unbalanced low-voltage (LV) distribution networks. Compared with conventional HEMS optimization methods, which consider only the active power consumption scheduling for smart home appliances and distributed energy resources (DERs) (e.g., solar photovoltaic (PV) systems and energy storage systems (ESSs)), the novelty of the proposed approach is to consider: i) both active and reactive power consumption schedulings of home appliances and DERs, ii) realistic three-phase unbalanced LV distribution networks with voltage-dependent load models, and iii) voltage control using an on-load tap changer transformer and smart inverters of PV system and ESS at the households. The proposed HEMS optimization algorithm, which is formulated using mixed-integer linear programming, is tested in the modified CIGRE LV distribution network. Numerical examples demonstrate the performance of the proposed algorithm in terms of active/reactive power consumption, three-phase voltage magnitudes, and the total cost of electricity. INDEX TERMS Home energy management, smart household appliance, three-phase unbalanced distribution system, reactive power dispatch, voltage control, voltage dependent load.

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“…In order to remedy this issue, fast response devices such as Battery Energy Storage Systems (BESSs) and STATCOMs can be installed [20][21][22][23][24]. A piecewise droop control using a BESS for rapid changes in voltage profiles was presented in [25]. More recently, a reinforcement-learning-based management technique for BESSs was introduced in [26].…”

Section: Introductionmentioning

confidence: 99%

A Sensitivity Matrix Approach Using Two-Stage Optimization for Voltage Regulation of LV Networks with High PV Penetration

et al. 2021

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The occurrence of voltage violations is a major deterrent for absorbing more rooftop solar power into smart Low-Voltage Distribution Grids (LVDGs). Recent studies have focused on decentralized control methods to solve this problem due to the high computational time in performing load flows in centralized control techniques. To address this issue, a novel sensitivity matrix was developed to estimate the voltages of the network by replacing load flow simulations. In this paper, a Centralized Active, Reactive Power Management System (CARPMS) is proposed to optimally utilize the reactive power capability of smart Photovoltaic (PV) inverters with minimal active power curtailment to mitigate the voltage violation problem. The developed sensitivity matrix is able to reduce the time consumed by 55.1% compared to load flow simulations, enabling near-real-time control optimization. Given the large solution space of power systems, a novel two-stage optimization is proposed, where the solution space is narrowed down by a Feasible Region Search (FRS) step, followed by Particle Swarm Optimization (PSO). The failure of standalone PSO to converge to a feasible solution for 34% of the scenarios evaluated further validates the necessity of the two-stage optimization using FRS. The performance of the proposed methodology was analysed in comparison to the load flow method to demonstrate the accuracy and the capability of the optimization algorithm to mitigate voltage violations in near-real time. The deviations of the mean voltages of the proposed methodology from the load flow method were: 6.5×10−3 p.u for reactive power control using Q-injection, 1.02×10−2 p.u for reactive power control using Q-absorption, and 0 p.u for active power curtailment case.

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Hierarchical Look-Ahead Conservation Voltage Reduction Framework Considering Distributed Energy Resources and Demand Reduction

Cited by 9 publications

References 37 publications

Hierarchical Volt-VAR Optimization Framework Considering Voltage Control of Smart Electric Vehicle Charging Stations Under Uncertainty

Hierarchical Volt-VAR Optimization Framework Considering Voltage Control of Smart Electric Vehicle Charging Stations Under Uncertainty

Smart Home Energy Management in Unbalanced Active Distribution Networks Considering Reactive Power Dispatch and Voltage Control

A Sensitivity Matrix Approach Using Two-Stage Optimization for Voltage Regulation of LV Networks with High PV Penetration

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