Zonal Voltage Control Combined Day-Ahead Scheduling and Real-Time Control for Distribution Networks with High Proportion of PVs

Xiao, Chuanliang; Zhao, Bo; Ding, Ming; Li, Zhihao; Ge, Xiaohui

doi:10.3390/en10101464

Cited by 10 publications

(12 citation statements)

References 25 publications

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“…Theoretically, different methods can be applied for voltage management of Medium-Voltage (MV) distribution systems, but the most common methods are based on using on-load tap changer mechanism of the transformer, reactive power compensation and curtailment of DG active powers [8,9]. It is generally known that each of the above voltage control methods has its own advantages and drawbacks, and there is no single perfect voltage regulation method [10].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Voltage Control to Deal with Model Uncertainties in Distribution Networks

et al. 2020

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This paper addresses the voltage control problem in medium-voltage distribution networks. The objective is to cost-efficiently maintain the voltage profile within a safe range, in presence of uncertainties in both the future working conditions, as well as the physical parameters of the system. Indeed, the voltage profile depends not only on the fluctuating renewable-based power generation and load demand, but also on the physical parameters of the system components. In reality, the characteristics of loads, lines and transformers are subject to complex and dynamic dependencies, which are difficult to model. In such a context, the quality of the control strategy depends on the accuracy of the power flow representation, which requires to capture the non-linear behavior of the power network. Relying on the detailed analytical models (which are still subject to uncertainties) introduces a high computational power that does not comply with the real-time constraint of the voltage control task. To address this issue, while avoiding arbitrary modeling approximations, we leverage a deep reinforcement learning model to ensure an autonomous grid operational control. Outcomes show that the proposed model-free approach offers a promising alternative to find a compromise between calculation time, conservativeness and economic performance.

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Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Voltage Control to Deal with Model Uncertainties in Distribution Networks

et al. 2020

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“…Moreover, the impact of DGs on the partitioning solutions was not considered. In [17,18], a community detection algorithm with the network structure information and the impact of PVs taken into account was proposed. However, it could not restrict the size of the sub-networks, which might result in unreasonable sub-networks and more voltage regulating cost.…”

Section: Introductionmentioning

confidence: 99%

“…A novel Laplacian spectrum-based scheme was proposed in [20], bi-objective network dividing problems could be solved in the scheme, and the resultant Pareto front of the voltage management solution can be obtained. With the increasing penetration of PVs in ADNs, the ability to regulating voltage of PV inverters has been greatly developed, and the control functions of PV inverters have been greatly enriched [17,18]. A combined day-ahead scheduling and real-time control strategy for optimizing the reactive power of PV inverters in sub-networks was proposed in [17].…”

Section: Introductionmentioning

confidence: 99%

“…However, only the reactive power compensation of PV inverters was optimized, the active power curtailment of PV inverters was not taken into account. In [18], a particle swarm optimization algorithm was employed to optimize the reactive power and active power of the PV inverters in each sub-network. Hence, the node voltage could be regulated within the secure operating range.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Spatiotemporal Characteristics-Based Zonal Voltage Control for High Penetrated PVs in Active Distribution Networks

2020

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The penetration of photovoltaic (PV) outputs brings great challenges to optimal operation of active distribution networks (ADNs), especially leading to more serious overvoltage problems. This study proposes a zonal voltage control scheme based on multiple spatiotemporal characteristics for highly penetrated PVs in ADNs. In the spatial domain, a community detection algorithm using a reactive/ active power quality function was introduced to partition an ADN into sub-networks. In the time domain, short-term zonal scheduling (SZS) with 1 h granularity was drawn up based on a cluster. The objective was to minimize the supported reactive power and the curtailed active power in reactive and active power sub-networks. Additionally, a real-time zonal voltage control scheme (RZVC) with 1 min granularity was proposed to correct the SZS rapidly by choosing and controlling the key PV inverter to regulate the supported reactive power and the curtailed active power of the inverters to prevent the overvoltage in each sub-network. With the time domain cooperation, the proposed method could achieve economic control and avoid overvoltage caused by errors in the forecast data of the PVs. For the spatial domain, zonal scheduling and zonal voltage control were carried out in each cluster, and the short-term scheduling and voltage controlling problem of the ADN could then be decomposed into several sub-problems. This could simplify the optimization and control which can reduce the computing time. Finally, an actual 10kV, 103-node network in Zhejiang Province of China is employed to verify the effectiveness and feasibility of the proposed approach.

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“…To simplify the optimal control methods and shorten computing time, some multilevel dispatch is proposed. In [26], zonal voltage control combining day-ahead dispatch and real-time control for distribution networks with high proportion of PV power is researched. In [27], a bilevel voltage/Var optimization to coordinate smart inverters with voltage control devices is carried out.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Var Compensation Schemes in Unbalanced Distribution Systems

Huang

Wang

2019

Complexity

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Distributed rooftop photovoltaic (PV) generators prospered distributed generation (DG) in recent years. Certain randomness of rooftop PV connection may lead to significant PV power imbalance across three phases, especially in low-voltage distribution systems. Due to interphase line coupling, traditional Var compensation methods which typically have competent voltage regulation performance may become less effective in such PV imbalance scenarios. In this paper, the limitation of traditional Var compensation methods in voltage regulation with unbalanced PV power integration is demonstrated and comprehensively analyzed. After describing the voltage regulation challenge, based on the voltage sensitivity analysis, it is revealed that PV power unbalanced level together with equivalent mutual impedance among phase conductors has a significant impact on the effectiveness of traditional Var compensation methods on voltage regulation. On this basis, to improve the performance of voltage regulation methods, some suggestions are proposed for both current system operation and future distribution system planning. Numerical studies demonstrate the effectiveness of the proposed suggestions. Future rooftop PV integration in LV systems can benefit from this research.

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Zonal Voltage Control Combined Day-Ahead Scheduling and Real-Time Control for Distribution Networks with High Proportion of PVs

Cited by 10 publications

References 25 publications

Deep Reinforcement Learning-Based Voltage Control to Deal with Model Uncertainties in Distribution Networks

Deep Reinforcement Learning-Based Voltage Control to Deal with Model Uncertainties in Distribution Networks

Multiple Spatiotemporal Characteristics-Based Zonal Voltage Control for High Penetrated PVs in Active Distribution Networks

Investigation of Var Compensation Schemes in Unbalanced Distribution Systems

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