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In the context of sustainable development, electric vehicles (EVs) and renewable-based distributed generation (RDG) integration into the distribution networks (DNs) introduce various merits. They involve lowering harmful emissions, employing various types of energy sources, and encouraging the dependence of renewable energy. However, the two most challenging issues posing a grave danger to the technical operation of the DN would be the inappropriate integration of RDGs and EVs. Consequently, in order to guarantee safe operation, distribution system operator (DSO) is responsible for precisely identifying two crucial terms, distributed generation hosting capacity (DG-HC) and electric vehicles hosting capacity (EV-HC). Despite an increase in the amount of research on HC approaches, there is still a remarkable research gap in the discussion of models that effectively combine demand response (DR), smart inverters (SI) Volt/VAR control, DG-HC, and EV-HC targets simultaneously. This study offers a hierarchical bi-level optimization HC framework depends on both dynamic tariff-based DR and SI Volt/VAR control. In the lower layer, Participating customers’ load curves and EV aggregators’ charging demands are optimally adjusted, on a forecast basis, based on the proposed dynamic tariff. Nonetheless, the suggested multi-objective function—which includes DG-HC, EV-HC maximization, and loss minimization—is optimized by the DSO at the upper layer based on all of these optimal load curves. To further support the proposed objective function, DR is mixed with the optimal Volt/VAR controlling offered by SIs. In addition, the role of grid-connected EVs (GCEVs) on DG-HC increase is revealed with both uncoordinated and coordinated charging schemes. To check the proposed approach robustness, three types of loads are considered. Through comparison with three other optimization approaches, the effectiveness of the equilibrium optimizer (EO) is demonstrated when it is employed to solve the proposed optimization scheme. The suggested planning approach is applied on both the IEEE 33-bus test system, and a real DN with 59 buses in Cairo, Egypt. Several significant conclusions are validated by the obtained results. First, the DG-HC assessment differs significantly depending on whether the EV charging demand is considered or not. Considering the EV integration in IEEE 33-bus, the mean value of the optimal DG-HC increased by more than 133% during the day. Secondly, the implementation of the proposed dynamic-pricing DR program in the IEEE 33-bus DN significantly improved both the DG-HC and EV-HC, named as the combined DG-EV-HC, with improvements of around 34% and 27% for DG-HC and EV-HC respectively. Finally, in IEEE 33-bus, the combined DG-EV hosting capacity was improved by approximately 49.2% regarding DG-HC and 61.2% regarding EV-HC, using the proposed synergistic DR-Volt/VAR control enhancement technique. [ABSTRACT FROM AUTHOR]

In order to smooth the impact of distributed photovoltaic and load fluctuations on the distribution network, this paper proposes a dual time scale distribution network reactive voltage control method based on data-driven and source-load uncertainty. The proposed method can be divided into two stages, the first stage is centralized optimization, which performs long time scale scheduling of slow regulation devices in active distribution networks, and converts the original centralized optimization problem into a second-order cone mixed-integer planning problem after second-order cone and linearization theory. The second stage is based on data-driven distributed control, which is based on the information communication between the regional controllers to achieve short time scale optimization control of photovoltaic inverters, where the regional controllers are modeled based on deep neural networks. In order to make the model training more effective, the paper uses an improved generative adversarial network to generate a large number of source-load samples conforming to the real distribution for the training of the regional controllers. Through the analysis of the improved IEEE33 model, the optimal control of reactive voltage with dual time scales is achieved, which keeps the voltage within the safety range while significantly reducing the network loss, and the computation time of the regional controller based on the data-driven model is reduced by 96.52% compared to the centralized control, which makes the frequent control possible in the face of more complex power systems. [ABSTRACT FROM AUTHOR]

Power flow studies are essential for planning and operating microgrids (MGs). However, power flow is generally calculated separately for MGs and medium voltage (MV) systems, which tends to overlook some characteristics of the joint MG-MV system. In this context, the literature proposes methods to simulate MG and MV systems in a unique power flow structure. However, some Volt/Var controls must be considered when solving the MG power flow, which increases the power flow iterations. This paper proposes a method to include the Volt/Var strategy in the three-phase Newton–Raphson current injection (NRCI) power flow. The Volt/Var strategy is included in the Jacobian matrix and solved with each power flow iteration. The CIGRE benchmark MV and LV networks and the European LV test Feeder are used to validate the proposed methodology. The conventional Newton–Raphson is employed for the MV system, and the NRCI for the MG systems. Three cases are considered: the first evaluates the MG with the Volt/Var strategy; the second compares the implementation; and the last evaluates the global power flow with the Volt/Var. The results show that the proposed implementation can converge smoothly with few iterations. [ABSTRACT FROM AUTHOR]

Integrating photovoltaic (PV) and battery energy storage systems (BESS) in modern power distribution networks presents opportunities and challenges, particularly in maintaining voltage stability and optimizing energy resources. This systematic review and bibliometric analysis investigates the coordination of smart inverter-enabled distributed energy resources (DERs) for enhancing PV-BESS integration and ensuring voltage stability. The study synthesizes recent advancements in smart inverter technologies, which provide grid support functions such as Volt/VAr control, and their applications in DER coordination. A comprehensive review of the literature is conducted to identify prevailing trends, research gaps, and emerging techniques in the field. Bibliometric analysis is employed to quantify the research landscape, highlighting key publications, citations, publications per country, and collaborative networks. The findings reveal that smart inverters play a crucial role in mitigating voltage violations and improving the hosting capacity of PV systems in distribution networks. Furthermore, optimal inverter settings, strategic placement of PV-BESS, and advanced control algorithms are identified as critical factors for effective DER integration. The study concludes by proposing future research directions, including the exploration of smart inverter interactions with legacy grid management systems and the development of robust algorithms for dynamic and adaptive DER coordination. This review serves as a valuable resource for researchers and practitioners aiming to enhance the stability and efficiency of power distribution networks through advanced DER management strategies.

It is widely recognised that improving the visibility and controllability of distributed energy resources (DERs) within electricity distribution networks will have significant benefits, particularly for the management of low-voltage (LV) and medium-voltage (MV) networks. Much work within the electricity distribution industry is currently focused on improving the visibility of DERs on LV networks. From a control-theoretic perspective, this enables closing the loop between the DER and the control room and enables a shift towards utilising data-driven model-based control strategies for DERs. The result is a system-wide performance that is closer to the theoretical optimal. In the Australian context, several jurisdictions are trialling techniques such as dynamic operating envelopes to enhance DER hosting capacity, using IEEE 2030.5-based architectures, with the implementation of distributed energy resource management (DERMS) systems at the enterprise level still quite limited. While there is significant activity focused on DER behaviour and control techniques by way of inverter grid codes and standards, the core issue of interoperability with distribution management systems (DMSs), market operators or participants, electric vehicles (EVs) or other DERs is still a work in progress. Importantly, this is also an impediment to realising distributed architectures for DER control in the grid. The unique characteristics of Australian distribution networks highlights several challenging problems for DER control and management. The objective of this paper is to provide a broad overview of DER control and management strategies in the Australian context, with an application focus on DER control in distribution network management. [ABSTRACT FROM AUTHOR]

In response to the uncertainties and power quality degradation caused by the integration of numerous renewable energy resources (RESs) and the increasing adoption of plug-in electric vehicles (PEVs) in active distribution networks (ADNs), this paper proposes a three-timescale hierarchical volt/var control (VVC) model operating at 1 hour, 15 minutes, and 1 minute. The model aims to achieve effective coordination between centralized optimization and local control, considering the unique characteristics of different reactive power control devices. A three-stage optimization method is developed to solve the proposed three-timescale models; specifically, a three-step method is proposed to solve the one-hour look-ahead model for scheduling the tap position of the on-load tap changer (OLTC) and the number of switching groups for the shunt capacitors (SCs). For the 15-minute timescale, the voltage set-point of the inverter is optimized to minimize the voltage deviations of the pilot buses in typical scenarios, taking into account the uncertainties associated with RESs. For the 1-minute timescale, local measurement information is utilized, and a double-loop droop control method is adopted to track the voltage setting value provided by the 15-minute timescale model. This ensures the optimization of the system’s voltage level and the minimization of voltage fluctuations. The IEEE 33-bus and IEEE 123-bus distribution networks are employed to validate the proposed method and its ability to deal with uncertainties.

Distribution systems are evolving from traditional passive networks into, what is known as, Active Distribution Networks (ADNs). Unlike traditional distribution networks, ADNs are characterized by bi-directional power flow, the high penetration of DERs, storage capabilities and sophisticated control strategies. Multiple layers of communications, sensing and computation are being integrated into ADNs for monitoring, control and protection of a variety of components and critical operations. This enhanced dependency on information and communication technologies, however, increases the exposure of ADNs to cyber-attacks. Several papers have been published in recent years with a focus on cyber-physical security (CPS) of smart grids. However, the published survey papers primarily emphasize the transmission level of smart grid threats and challenges, with little focus on the ADNs. Given the rapid deployment of ADNs and the increasing cyber threats against power grids and critical infrastructures, we are motivated, in this article, to present a review and survey focused, instead, on the latest research advancements in the area of CPS for ADNs. This paper represents the first survey of timely research in the area of CPS of ADNs with a focus on ADN critical operations and components. The cyber-physical aspects of each critical operation/component are analyzed. In addition, the challenges and requirements of associated communication protocols and standards are presented. Cybersecurity of ADN devices and sensors including Phasor Measurement Units (PMUs), smart meters, advanced metering infrastructure and protection relays are discussed in detail. Moreover, a thorough study of ADNs application drivers and enablers including microgrids, Electric Vehicles (EVs), Internet-of-Things (IoT) and smart homes is conducted. Potential and existing solutions by industry are highlighted. Finally, survey outcomes and directions for future work are presented to highlight emerging avenues of research.

We consider the problem of decentralized control of reactive power provided by distributed energy resources for voltage support in the distribution grid. We assume that the reactance matrix of the grid is unknown and potentially time-varying. We present a decentralized adaptive controller in which the reactive power at each inverter is set using a potentially heterogeneous droop curve and analyze the stability and the steady-state error of the resulting system. The effectiveness of the controller is validated in simulations using a modified version of the IEEE 13-bus and a 8500-node test system.

The use of renewable energy sources is increasing day by day due to their economic and environmental benefits. However, improper penetration of renewable energy into power grids can lead to problems such as over-voltages and higher active power losses. Therefore, the voltage regulation problem in distribution networks is critical due to the increasing integration of renewable energy sources. On the other hand, an increase in renewable energy penetration leads to lower operational costs due to decreased energy purchases from the overhead grid. Therefore, it can be challenging for distribution system operators (DSOs) to decide the trade-off between more Photovoltaic (PV) integration for cost minimization or less penetration to minimize voltage deviation from a rated value. In this study, we formulated this trade-off as a novel multi-objective optimization framework, aiming to minimize operating costs and voltage deviations from a rated value in an unbalanced distribution grid. The proposed formulation is applied to the modified IEEE 34-bus unbalanced distribution network, where the $\varepsilon $ -constraint method is utilized for solving the resulting multi-objective optimization problem along with the Exterior Penalty Functions (EPF) method. The simulation results show that the proposed approach provides the DSO with a better view of decision-making in the optimal operation of the distribution networks.

This research paper addresses the limitations inherent in conventional volt-VAR control (VVC) mechanisms, such as on-load tap changers (OLTC) and voltage and capacitor banks (CBs), when applied to the implementation of efficient conservation voltage reduction (CVR) strategies within active distribution networks. In this paper, we present a cost-effective strategy that synergizes the rapid-response capabilities of PV smart inverters with traditional VVC mechanisms to enhance CVR operations. This study delves into the optimal coordination between photovoltaic smart inverters and conventional VVC systems for effective CVR operations. The investigation takes into account the implications of distribution network reconfiguration (DNR) on the overall operational costs, encompassing grid power procurement, emissions, losses, and switching expenses related to OLTC, CB, and remote controlled switches (RCS). To attain the optimal solution, we leverage the recent advancements of the white shark optimization (WSO) technique, meticulously validating its performance through extensive trials conducted on the 119-bus distribution system. The empirical results vividly showcase the substantial advantages of integrating VVC devices and smart inverters, particularly in scenarios characterized by fluctuating load conditions. Notably, the synergistic approach achieves a remarkable 6.51% reduction in energy consumption. Additionally, active and reactive power loss reductions of up to 33.67% and 28.55%, respectively, underscore the potency of the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed data-driven distributionally robust volt/var control (DDDR-VVC) approach which schedules on-load-tap changer (OLTC), capacitor banks (CBs) and Photovoltaic (PV) inverters coordinately. With integration of distributed optimization and data-driven distributionally robust optimization, the DDDR-VVC model is constructed to reduce power losses and maintain voltage within allowable range under uncertainty. To fully address the uncertainty impacts, construction of uncertain set is improved through that the probability distribution sets and trapezoidal fuzzy functions are developed to model PV supply and load demand respectively. An accelerated alternating optimization procedure which integrates the alternating direction method of multipliers and the column-and-constraint generation algorithm is proposed. This method improves the dual information update via Nesterov’s accelerated gradient method, thus solving the DDDR-VVC model at a high convergence rate. The effectiveness of proposed method is verified through numerical simulations using IEEE 123 bus test system.

Considering the integration of distributed energy resources (DER) such as distributed generation, demand response, and electric vehicles, day-ahead scheduling plays a significant role in the operation of active distribution systems. Therefore, this article proposes a comprehensive methodology for the short-term operational planning of a distribution company (DisCo), aiming to minimize the total daily operational cost. The proposed methodology integrates on-load tap changers, capacitor banks, and flexible loads participating in demand response (DR) to reduce losses and manage congestion and voltage violations, while considering the costs associated with the operation and use of controllable resources. Furthermore, to forecast PV output and load demand behind the meter at the MV/LV distribution transformer level, a short-term net load forecasting model using deep learning techniques has been incorporated. The proposed scheme is solved through an efficient two-stage strategy based on genetic algorithms and dynamic programming. Numerical results based on the modified IEEE 13-node distribution system and a typical 37-node Latin American system validate the effectiveness of the proposed methodology. The obtained results verify that, through the proposed methodology, the DisCo can effectively schedule its installations and DR to minimize the total operational cost while reducing losses and robustly managing voltage and congestion issues. [ABSTRACT FROM AUTHOR]

Large penetration of distributed generations into a distribution network can make a reverse power flow that causes overvoltage on the network. This paper studied a hybrid volt/var control algorithm between the on-load tap changer (OLTC) and the distributed generation (DG) to prevent overvoltage on the network. This algorithm is integrated in the central control program at the control center where all the measurement, control and monitoring data of the network are carried out. This algorithm is verified and evaluated through the simulation using the Matlab/Simulink software. The simulation results show the effectiveness of mitigating overvoltage of the hybrid volt/var control algorithm between the OLTC and the DG.

Abstract The fluctuation of photovoltaics (PVs) output may cause the voltage out‐of‐limit, while the PV inverters and hybrid distribution transformers (HDTs) bring flexible controllability to distribution networks (DNs). However, there are differences in response speeds between power electronic equipment and traditional discrete regulating equipment. Therefore, a network partitioning and hierarchical voltage regulation method using holomorphic embedding method (HEM) ‐based sensitivity is proposed. Firstly, the HEM‐based voltage sensitivity is derived, and the network partitioning method is proposed using the HEM‐based sensitivity matrix. Then, the hierarchical voltage regulation framework is constructed. The objects of the upper layer are the minimum network loss and voltage deviation, which centrally optimizes the operation status of discrete regulation equipment. The lower layer exploits the fast control ability of PV inverters and HDTs, so as to autonomously regulate the voltage of each partition. Finally, a practical 10 kV DN in China is used for case studies. The results demonstrate that the HEM‐based voltage sensitivity calculation method has advantages in computational efficiency and accuracy compared with the conventional method. Moreover, the proposed voltage regulation method can suppress the voltage fluctuation.

In coupled power distribution and transportation (CPT) system, a joint scheduling framework for mobile energy storage systems (MESSs) and Volt/VAR control (VVC) ensures reliable power distribution grid operations while supporting electric vehicle loads at electric vehicle charging stations (EVCSs). However, conventional model-based optimization methods for MESS scheduling and VVC may yield suboptimal solutions and greater computation times because of MESS operation and VVC in uncertain environment of CPT systems. To resolve this issue, this study proposes a model-free deep reinforcement learning (DRL) framework. In this framework, smart inverters of MESSs and solar photovoltaic (PV) systems cooperate to minimize the real power loss and mitigate the violations of both MESSs’ state of charge (SOC) and voltage in the power distribution network, while MESSs travel via the transportation network to satisfy EV loads at EVCSs. A MESS routing algorithm based on Dijkstra’s algorithm is developed to determine the optimal destinations of the MESSs. In addition, two safety modules are developed to ensure that neither SOC nor voltage violations occur by adjusting real and/or reactive power of MESSs and PV systems during the training process. The developed MESS routing algorithm and safety modules are integrated into the proposed DRL framework, wherein the DRL agent performs the desired MESS scheduling and VVC through safe exploration during the training procedure. The proposed approach is tested in coupled IEEE 33-bus power distribution and 15-node transportation systems and coupled IEEE 57-bus power distribution and 42-node transportation systems. Numerical examples demonstrate the advantages of the proposed approach in terms of training convergence, real power loss, and SOC/voltage violation.

Photovoltaic (PV) systems can reduce greenhouse gas emissions while providing rapid reactive power support to the electric grid. At the distribution grid level, the PV inverters are controlled to reduce the system’s active power loss and to address problems caused by the PV systems themselves. For example, the distribution grid may face overvoltages due to high PV generation during off-peak hours. In this paper, a reactive power control approach for PV inverters is proposed to control the injection/absorption of reactive power to reduce the active power loss of the system while solving the overvoltage problem. To achieve this, the proposed controller periodically dispatches the reactive power setpoints and applies a real-time volt/var algorithm. The proposed method uses probabilistic distributions to account for the uncertainties in PV generation and load demand. The controller is implemented at the lateral level which simplifies the required communication platform and reduces the computational cost. The real-time volt/var control coordinates the operation of the different inverters during overvoltage conditions so that the voltage rise is limited using as little reactive power as possible by the inverters. Accordingly, the active power loss due to reactive power flow in the system is reduced. Two distribution systems are simulated using Open Distribution System Simulator (OpenDSS) and used to evaluate the proposed controller and compare with two other methods. A daily time series simulation is performed to test different operating conditions. The simulation results show that the proposed controller is able to reduce the active power loss in general and solve the overvoltage problem with a lower reactive power requirement than the other volt/var methods.

The growing integration of distributed energy resources into distribution systems poses challenges for voltage regulation. Dynamic VAR Compensators (DVCs) are a new generation of power electronics-based Volt/VAR compensation devices designed to address voltage issues in distribution systems with a high penetration of renewable generation resources. Currently, the IEEE Std. 1547-based Volt/VAR Curve (VV-C) is widely used as the local control scheme for controlling a DVC. However, the effectiveness of this scheme is not well documented, and there is limited literature on alternative control and placement schemes that can maximize the effective use of a DVC. In this paper, we propose an optimal dispatch and control mechanism to enhance the conventional VV-C based localized DVC control. First, we establish a multi-objective optimization framework to identify the optimal dispatch strategy and suitable placement for the DVC. Next, we introduce two supervisory control strategies to determine the appropriate instances for adjusting the VV-C when the operating condition changes. The outlined scheme comprises two primary stages: time segmentation and VV-C fitting. Within this framework, each time segment aims to produce optimized Q-V trajectories. The proposed method is tested on a modified IEEE 123-bus test system using OpenDSS for a wide range of operating scenarios, including sunny and cloudy days. Simulation results demonstrate that the proposed scheme effectively reduces voltage variations compared to the standard VV-C specified in IEEE Std. 1547.

Increasing the penetration of renewable energy sources as distributed generation in modern power grids presents several challenges, including voltage level issues and protection system failures resulting from bidirectional power flow and changing network dynamics. Voltage limit violations could potentially damage the utility equipment and lead to power quality problems. Innovative Volt/VAr control methods, such as tap staggering, can be used to overcome these problems. Tap staggering utilizes circulating current between parallel transformers to provide reactive power absorption from the network. However, the absorption of reactive power by the primary substation using tap staggering poses a potential risk to the protection system, particularly the Directional Overcurrent protection scheme with Load Blinder. This could lead to failures or nuisance trips during normal load conditions. In this study, a real power system is modeled using real data in PSS CAPE with 120 scenarios generated through tap staggering application, all based on 24-hour demand/wind generation data. The Tap Staggering Macro and Adaptation Protection Macro were developed for the purpose of analyzing these scenarios. The study demonstrates that tap staggering effectively mitigates overvoltage issues on the transmission system by absorbing reactive power. Although there is an increase in active power losses when tap staggering level is increased on the parallel transformers, the power loss remains within reasonable limits. Despite its benefits, tap staggering has been found to affect the Directional Overcurrent with Load Blinder protection scheme, limiting power transfer generated through wind turbines in the distribution network. This results in changes to the protection scheme’s Directional Overcurrent Pickup, Load Blinder Resistance, and Load Blinder Alpha parameters, requiring adaptation in all scenarios. After adaptation, the protection system operates reliably, guaranteeing efficient and unrestricted transmission of distributed generation power to the grid.

With the explosive growth of distributed renewable energy connected to the power system, the application of AC–DC hybrid distribution network with power electronic transformer (PET) becomes more and more extensive. An equivalent loss model is formulated from the perspective of the internal topology of PET. Then, a novel PET power flow model is established to obtain the relationship between the three ports. On this basis, reactive power of PV nodes connecting PET and distributed renewable generation is modified by establishing an equivalent impedance matrix, thus the forward–backward sweep load flow calculation method is improved for power flow calculation and volt/var control of distribution network. Different from traditional reactive optimization methods, a multi-objective reactive power optimization model considering the PET and two types of photovoltaic converters is established in this paper, with the targets of minimizing voltage deviation and power loss simultaneously. Finally, the numerical simulation shows the accuracy, rationality, and feasibility of the proposed method.

The currently deployed volt-var infrastructure for voltage optimization in distribution networks would be unable to meet the stringent technological requirements of the electric power grid of the 21st century. If not handled correctly, conventional voltage control systems could inhibit the ubiquitous deployment of renewable energy resources into the future smart grid. For example, the incorporation of renewable energy sources and the new types of energy storage systems in the distribution system provides a threat for the seamless voltage control. In order to overcome such barriers, smart volt-var control methods are needed to be studied and implemented. However, achieving such goals requires a complete background of the contemporary strategies and developments of the volt-var technologies. So far, various techniques have been developed to accommodate the large penetration of renewable energy sources into the distribution networks. This paper provides a comprehensive review on the current technologies that enable distribution system operators to select appropriate strategies for the volt-var control in renewable-rich power grids. This review article investigates the emerging volt-var technologies for the distribution network along with their advantages and disadvantages. It also outlines some of the open research problems and future directions in this topic.

Electric distribution grids are seeing an increased penetration of photovoltaic (PV) generation. High PV generation exceeding the grid load demand results in a reverse active power flow in the grid, which raises the voltage level. This paper presents a reactive power controller to overcome the overvoltage problem in the distribution system. A sequentially coordinated and cooperative volt/var control technique is presented. The proposed controller aims to use as low reactive power as possible while mitigating the voltage issues. Accordingly, it reduces the active power loss associated with reactive power flow and reduces the probability for active power curtailment of the PV system. The controller is developed for each lateral and is replicated for all laterals. The lateral controller coordinates the operation of the smart PV inverters in a sequential manner. Cooperative control is proposed between the laterals' controllers as well and is engaged when the individual laterals' controllers are unable to solve their overvoltage issues. The performance of the proposed controller is evaluated by comparing it to two other volt/var controllers, and it demonstrates better performance in terms of reactive power requirement. To conduct the simulation study, a modified version of the unbalanced IEEE 13-bus system is utilized, which includes an additional 44 low-voltage bus. The study involves simulating 720 operating points across daily time series. The results indicate that the proposed controller effectively addresses overvoltage problems that occur during periods of high PV generation. [ABSTRACT FROM AUTHOR]

The fluctuation of photovoltaics (PVs) output may cause the voltage out‐of‐limit, while the PV inverters and hybrid distribution transformers (HDTs) bring flexible controllability to distribution networks (DNs). However, there are differences in response speeds between power electronic equipment and traditional discrete regulating equipment. Therefore, a network partitioning and hierarchical voltage regulation method using holomorphic embedding method (HEM) ‐based sensitivity is proposed. Firstly, the HEM‐based voltage sensitivity is derived, and the network partitioning method is proposed using the HEM‐based sensitivity matrix. Then, the hierarchical voltage regulation framework is constructed. The objects of the upper layer are the minimum network loss and voltage deviation, which centrally optimizes the operation status of discrete regulation equipment. The lower layer exploits the fast control ability of PV inverters and HDTs, so as to autonomously regulate the voltage of each partition. Finally, a practical 10 kV DN in China is used for case studies. The results demonstrate that the HEM‐based voltage sensitivity calculation method has advantages in computational efficiency and accuracy compared with the conventional method. Moreover, the proposed voltage regulation method can suppress the voltage fluctuation. [ABSTRACT FROM AUTHOR]

Intensive use of photovoltaic (PV) inverter in the volt/var control (VVC) methods in active distribution networks can impair inverter reliability. This article proposes a PV inverter reliability-constrained (PiReCon-) VVC method with a power smoothing scheme under uncertainties. First, considering impacts on inverter reliability, new reliability constraints are developed with a power smoothing factor to constrict inverter apparent power variation. Second, a new VVC optimization model is proposed with the reliability constraints, which minimizes power losses by optimizing both inverter var output and PV curtailment. Uncertainties of PV generation and loads are fully considered and addressed via a stochastic optimization method. Third, this article develops a penalty convex-concave programming method to effectively tackle nonconvexity of the proposed optimization model. The proposed PiReCon-VVC method is tested on a 33-bus distribution network, and simulation results verify its high efficiency in both minimizing the power losses and enhancing the PV inverter reliability. [ABSTRACT FROM AUTHOR]

In this paper, a model free volt/var control (VVC) algorithm is developed by using deep reinforcement learning (DRL). We transform the VVC problem of distribution networks into the network framework of PPO algorithm, in order to avoid directly solving a large-scale nonlinear optimization problem. We select photovoltaic inverters as agents to adjust system voltage in a distribution network, taking the reactive power output of inverters as action variables. An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment. OPENDSS is used to output system node voltage and network loss. This method realizes the goal of optimal VVC in distribution network. The IEEE 13-bus three phase unbalanced distribution system is used to verify the effectiveness of the proposed algorithm. Simulation results demonstrate that the proposed method has excellent performance in voltage and reactive power regulation of a distribution network. [ABSTRACT FROM AUTHOR]

• This methodology aims to regulate voltage and improve other aspects of power quality. • Energy companies have the necessary data readily available. • Defining the problem of mutual performance of inverters in voltage control. • Determining the best slope depending on the characteristics of the system. The high penetration of photovoltaic distributed generation is causing overvoltage in distribution networks due to the reverse active power flow. Proposals to mitigate this problem through Volt/Var control (VVC) by photovoltaic inverters are being discussed. However, the configuration of the appropriate Volt/Var curve depends on the topological factors of each system. In this context, this paper presents a practical methodology for calculating the reactive compensation of the local VVC. In addition, the impact of the slope of the curve on power quality and the problems of mutual actuation of inverters in the VVC are analyzed. The studies were conducted in quasistatic time series (QSTS) mode on the IEEE 33 bus system. The slope of the curve affects grid losses and the substation power factor, and can mitigate the negative effects of the mutual actuation of the inverters in the VVC. Based on this, guidelines were established to adapt the definition of the slope to the characteristics of each system. [ABSTRACT FROM AUTHOR]

Optimal Volt/VAR control (VVC) in distribution networks relies on an effective coordination between the conventional utility-owned mechanical devices and the smart residential photovoltaic (PV) inverters. Typically, a central controller carries out a periodic optimization and sends setpoints to the local controller of each device. However, instead of tracking centrally dispatched setpoints, smart PV inverters can cooperate on a much faster timescale to reach optimality within a PV inverter group. To accommodate such PV inverter groups in the VVC architecture, this paper proposes a bi-level optimization framework. The upper-level determines the setpoints of the mechanical devices to minimize the network active power losses, while the lower-level represents the coordinated actions that the inverters take for their own objectives. The interactions between these two levels are captured in the bi-level optimization, which is solved using the Karush-Kuhn-Tucker (KKT) conditions. This framework fully exploits the capabilities of the different types of voltage regulation devices and enables them to cooperatively optimize their goals. Case studies on typical distribution networks with field-recorded data demonstrate the effectiveness and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

As the penetration of power-electronics based smart inverters (SIs) is increasing in distribution grids, it adds computational challenges in solving dynamic models of large-scale distribution feeders. Voltage and reactive power (Volt/VAr), and voltage and active power (Volt/Watt) dynamics have been analyzed at slower time scales akin to the control of legacy grid devices. However, smart inverters, being power-electronics based devices, can provide dynamic active/reactive power support at a faster time scale, which necessitates Volt/VAr and Volt/Watt dynamics to be analyzed at a faster time scale. The existing dynamic models are overly detailed and computationally intractable for distribution feeders with a large number of inverters. In this context, this proposed work aims towards developing a computationally tractable, scalable, and accurate phasor-based model for dynamic Volt/VAr and Volt/Watt analyses of large distribution systems with high penetration of smart inverters. Case studies demonstrate that the proposed phasor-based model sufficiently captures the Volt/VAr and Volt/Watt dynamics, and is computationally faster by one order of magnitude compared to the average model and by two orders of magnitude compared to the detailed switching model. Case studies also demonstrate the efficacy and scalability of the proposed model in analyzing Volt/VAr and Volt/Watt dynamics of large-scale power networks with hundreds of SIs. [ABSTRACT FROM AUTHOR]

In this study, an optimal reactive power (Volt/VAr) control of smart inverters for photovoltaic (PV) and battery energy storage systems (BESSs) to improve the PV hosting capacity (PVHC) of distribution networks is proposed. The primary objective of the proposed method is to improve the PVHC of a distribution network by determining the optimal oversize, dispatch, and control setting of the Volt/VAr functions of the smart inverters for both PVs and BESSs. Concurrently, the optimal locations of the PVs and BESSs are determined. The problem is formulated as a multi-objective mixed-integer nonlinear optimization to maximize the PVHC and minimize the voltage deviation simultaneously. A bio-inspired metaheuristic optimization method, i.e., the slime mould algorithm (SMA), is employed to solve the optimization problem. To assess the efficacy of the proposed PVHC improvement method, extensive simulations are conducted on an IEEE 33-node system using MATLAB software. The simulation results verify that the proposed method improves the PVHC of the distribution network compared to different cases and the default Volt/VAr control settings of the smart inverters. Furthermore, the SMA optimization method provides superior performance in finding the optimal PVHC of a distribution network compared to the conventional metaheuristic optimization methods.

Photovoltaic Distributed Generation (PV-DG) produces some technical, commercial, and regulatory challenges in distribution systems. The most important technical challenge are the overvoltages produced by a high PV-DG penetration, which modifies the voltage profiles along the network and disturbs the operation of conventional voltage regulation devices. Extensive research has been carried out using various regulation devices under different control schemes to mitigate this impact. This paper presents a review of the literature dedicated to mitigate these overvoltage problems, proposing the classification and definition of regulation devices and control schemes used. [ABSTRACT FROM AUTHOR]

The emergence of distributed generation such as solar systems has introduced new challenges in distribution networks that are becoming more apparent with increasing penetration levels. The time mismatch between peak load and peak generation can make voltage levels in distribution networks swing towards extreme limits during a day. Distribution network service providers are struggling to cater for new distributed generation installations while ensuring that the quality of steady state supply voltage meets stipulated requirements. The segregation between medium and low voltage networks in control strategies may result in unnecessary or worse, opposing control actions leading to voltage issues and control on one side of the distribution network influencing the other. By developing an efficient volt/var control method, the steady state voltage level and voltage unbalance in a comprehensive distribution network can be controlled simultaneously. This paper analyses voltage issues and volt/var control in medium and low voltage networks as a whole, unabridged problem. This paper proposes a pragmatic and effective volt/var control method that addresses voltage regulation and voltage unbalance simultaneously using existing infrastructure. The proposed control method is implemented on three types of representative Australian distribution networks and results obtained demonstrate that the proposed volt/var control can simultaneously manage voltage level and voltage unbalance whilst reducing the number of tap change operations and maximizing solar penetration. [ABSTRACT FROM AUTHOR]

This paper presents the development and application of methods to estimate and reduction energy losses in active distribution system with the support of smart grids technologies. Technical and nontechnical losses are accurate estimated using energy balance and power flow analysis. In technical losses reduction perspective, a heuristic volt/var control (VVC) strategy is considered. The optimization technique employed in VVC allows reaching the optimized solution through a reduced combinatorial analysis and computational intensity, once explores only the calculus of potential solutions of the problem. The space of search is define according to the effectiveness of control equipment in technical loss minimization, being able to acts on the adjustment of conventional and electronics control devices. The methods consider real data of measurement and grid parameters given by Supervisory Control and Data Acquisition (SCADA) and Geographic Information Systems (GIS) and the historical billing of energy utility. Two real distribution feeder are used to assess the robustness of proposed methodologies. The control variable considered in the optimized process are based on available control devices on those feeders and comprise TAP position of voltage regulators, reactive power inject by the capacitor banks and power factor variation in smart inverters used in grid-connected distributed generation. The experimental analyzes conducted in this paper showed promisor results, detecting the occurrence of abnormal nontechnical losses and optimizing the technical losses value in relation to a traditional scheme control. [ABSTRACT FROM AUTHOR]

The recent technological innovations, related to advanced measurement and automation infrastructures, and even sophisticated computational intelligence mechanisms, create opportunities to improve the operational efficiency of electrical systems and power quality indicators within the smart grids context. Aiming to this purpose, distribution companies use supervision, monitoring, control, management and georeferencing systems and have invested in automation and modernization projects of their electrical grids. Thus, this paper presents an architecture deployment for advanced distribution automation (ADA) applications, which includes state estimator, Volt/VAr control and FLISR (fault location, isolation and service restoration) tools and an interoperability bus that integrates the corporate systems with the operational management. The developed applications were tested and applied for real networks, simulated in a computational environment and integrated with the Smart Grids NAPREI/USP Laboratory environment. The results also demonstrate the processes of integrating ADA applications into a commercial service bus, which communicates the company's legacy systems, and a new contribution with information for advances in the distribution automation area. [ABSTRACT FROM AUTHOR]

The penetration of photovoltaics (PVs) has been increasing in active distribution networks (ADN), which leads to severe voltage violation problems. PV inverters can provide fast and flexible reactive power support and are now allowed to participate in the voltage regulation process. This paper proposes a real-time combined central and local Volt/Var control (VVC) strategy to mitigate voltage violation problems while minimizing the network power loss. Based on the historical PV and load data, the load flow and optimal power flow are performed in the centralized controller (CC) to obtain multiple voltage and optimal power settings for each PV system. The local controller (LC) then generates voltage control curves with these optimal scatters. To improve the voltage control effect, a novel 3-Dimension voltage control curve is proposed considering both the measurements of node voltage and PV generation. Moreover, a data-driven deep convolution neural network is designed and trained to generate optimal local voltage control curves without prior knowledge of specific curve functions. The proposed approach is tested on the IEEE 33-bus distribution system and simulation results verify the effectiveness in voltage control compared with the optimal Q(V) and Q(P) method. [ABSTRACT FROM AUTHOR]

Inverter-based Volt/Var control (VVC) methods can effectively address issues of voltage deviations and constraint violations, as well as reduce network power losses in active distribution networks. Conventional VVC mainly relies on rule-based, mathematical, or heuristic methods. However, these methods can be inefficient or even infeasible for large-scale systems, especially when operational uncertainties are considered at both spatial and temporal scales. To this end, firstly, this paper formulates a multi-region coordinated VVC optimization problem to simultaneously minimize bus voltage deviations and network power losses. Furthermore, with the problem being formulated as a partially observable Markov Game, a multi-agent deep deterministic policy gradient (MADDPG) algorithm is modified to solve this problem. Each region, which is a sub-network with a control center, is modeled as an agent and learns to optimize inverter reactive power output setpoints through exploration in virtual environment and improved neural network training procedure. Spatial and temporal uncertainties of photovoltaic power generation and loads are modeled by stochastic programming as scenarios in the MADDPG algorithm. This algorithm can address the uncertainties to guarantee solution robustness. Thus, this paper eventually proposes a robust regionally coordinated VVC method, and numerical simulations indicate high computing efficiency and robust optimum. [ABSTRACT FROM AUTHOR]

We propose two novel two-stage Volt/Var control schemes based on the affinely adjustable robust counterpart (AARC) methodology, to mitigate the over-voltage issues caused by integration of photovoltaic panels into distribution systems. To cope with different grid code requirements, our first approach formulates the unused capacity of residential inverters to provide reactive power support based on the real power deviation, while the second approach formulates them based on voltage magnitude deviation. In the first stage of both schemes, we make central measurements throughout the network to determine a linear function, mapping the operating point deviations to the reactive power of inverters. In the second stage, the local controllers use the provided linear functions to determine the required reactive power to keep the voltages within the safe limits. Unlike similar approaches, voltage limit constraints are directly incorporated into the AARC problem, preventing the second stage controllers from unnecessarily use of reactive powers. We compare the performance of our schemes using a Monte-Carlo simulation with four other existing techniques on a real-world 27-bus and the IEEE 906-bus LV feeders. Our simulations show that our approaches decrease the real power loss, reactive power usage, and line congestion compared to the other Volt/Var control schemes. [ABSTRACT FROM AUTHOR]

The high penetration of intermittent renewable energy resources in active distribution networks (ADN) results in a great challenge for the conventional Volt-Var control (VVC). This article proposes a two-stage deep reinforcement learning (DRL)-based real-time VVC method to mitigate fast voltage violation while minimizing the network power loss. In the first stage, on-load tap changer (OLTC) and capacitor banks (CBs) are dispatched hourly based on the optimal power flow method. The optimization problem is formulated as a mixed-integer second-order cone programming (MISOCP) which can be effectively solved. In the second stage, the reactive power of photovoltaics (PVs) is regulated dynamically to mitigate fast voltage fluctuation based on the well-learned control strategy and local measurements. The real-time VVC problem is formulated and solved using a multi-agent deep deterministic policy gradient (MADDPG) method, which features offline centralized training and online decentralized application. Rather than using the critic network to evaluate the output of the actor-network, the gradient of the action-value function to action is derived analytically based on the voltage sensitivity method. The proposed approach is tested on the IEEE 33-bus distribution system and comparative simulation results show the enhanced control effect in mitigating voltage violations. [ABSTRACT FROM AUTHOR]

The increasing penetration of photovoltaic (PV) systems promotes utilization of PV inverters for volt/var control (VVC) in distribution networks. However, PV inverters are vulnerable and their reliability is one of the most critical concerns for sustainable PV energy utilization. To enhance PV inverter reliability, this paper proposes a PV inverter reliability-constrained VVC method of distribution networks under uncertainties. Firstly, a full reliability analysis procedure for PV inverters is developed to estimate the lifetimes of core devices inside inverters. Secondly, considering the impacts of VVC on PV inverter reliability, reliability constraints are proposed based on regulation of inverter apparent power and application of a PV curtailment scheme. Thirdly, a multi-objective PV inverter reliability-constrained VVC optimization model is proposed, which aims to minimize network power loss and PV curtailed power, simultaneously. Accordingly, the nonconvex model is relaxed by a second-order cone programming method, and the uncertainties such as PV power generation and loads are addressed by a scenario-based stochastic optimization method. The proposed VVC method is tested on the 33-bus and 69-bus distribution networks, and the simulation results verify high efficiency of the proposed method in energy loss reduction and inverter reliability enhancement. [ABSTRACT FROM AUTHOR]

The penetration of photovoltaics (PVs) and electric vehicles (EVs) is increasing in active distribution networks (ADN), which may lead to severe voltage violation problems. This paper proposes a two-stage real-time Volt/Var control method to mitigate fast voltage violations. In the first stage, hourly on-load tap changer (OLTC) and capacitor banks (CBs) are scheduled based on the optimal power flow method. The optimization problem is formulated as a mixed-integer second-order cone programming (MISOCP) which can be effectively solved. In the second stage, a data-driven network partition method is proposed to select critical bus and assess the voltage violation risk of each control area, followed by the intra-day dispatch of EVs. Based on the partition results, reactive power of PVs and EVs is controlled in real-time to mitigate voltage violations. Considering the suddenly active power drop of PVs, a rule-based control strategy coordinating PVs and CBs is proposed to improve the reactive power reserve in ADN. The proposed approach is tested on the IEEE 33-bus and 123-bus distribution networks and simulation results verify the effectiveness both in network partition and real-time voltage control. [ABSTRACT FROM AUTHOR]

This article proposes a novel optimal hybrid control framework to improve the voltage profile of highly unbalanced Distribution Grids (DGs) by coordinating the injection of reactive power from multiple off-board Electrical Vehicles (EVs) chargers. It exploits the time-synchronized measurements advantages, centralized control, and local controllers embedded into a hierarchical and scalable scheme, coordinated by the distribution system operator (DSO). The proposed framework is aimed to increase the modern electrical networks’ flexibility, reliability, and stability. It is powered by the Eigensystem Realization (ER)-based system identification technique, a Linear Quadratic Gaussian (LQG) controller, the Distribution-level Phasor Measurement Units (D-PMUs), and a new aggregator agent that handles the EV chargers power injection to precisely and timely regulate the dynamic voltage response in a sub-area or the entire DG. The robustness and feasibility of the proposal are demonstrated by employing simulated scenarios with unbalanced faults, latency in communications, and highly unbalanced loads conditions on the IEEE 13 and 123 nodes test feeders, compensating the voltage variations accurately in less than 205 ms. The promising outcome of this study suggests a novel application for an emerging measurement-based control system in the operation of modern active DGs. [ABSTRACT FROM AUTHOR]

This paper presents a fully distributed algorithm for the stochastic Volt/VAr control (VVC) problem in active distribution networks. Exact convexification of the VVC problem is achieved through the use of second-order cones on the continuous relaxation of the original optimization structure. The global optimum solution of the relaxed problem is obtained through the application of the alternating direction method of multipliers (ADMM). In order to minimize the effect of rounding off on the final solution, an adaptive threshold discretization technique is used. A two-stage control strategy is adopted where the discrete controllers, like load tap changers and switched capacitors, are dispatched at the beginning of the optimization interval and the continuous controllers, like distributed generation (DG) inverters, are adjusted in real time according to an optimized decision rule. The superiority of the proposed algorithm is demonstrated through numerical simulations on the UKGDS-95 and the IEEE-123 bus systems. [ABSTRACT FROM AUTHOR]

The operation of distribution systems benefits from an efficient voltage and reactive power control, which nowadays is generally achieved by local and independent actions performed by voltage regulators, on-load tap changer transformers, and capacitor banks. However, the modernization of distribution systems has been accompanied by the integration of a communication infrastructure and new controllable equipment allowing the development of innovative procedures to control the voltage and reactive power. In this context, an alternative is the implementation of an Integrated Volt/Var Control (IVVC), consisting of the integration of voltage and reactive power controls in a centralized structure. This work investigates a hierarchical Volt/Var control methodology that considers the use of both local and centralized controls in a coordinated manner aiming to optimize distribution system operation and overcome the disadvantages of using either of the approaches separately. In this methodology, the centralized control acts when the local control is not enough, overriding its commands. The methodology is tested in a distribution system with photovoltaic generators and considers the use of inverters' advanced functions into the hierarchical control. The results show that the proposed methodology allows improvements in the performance of the test system operation increasing its efficiency. [ABSTRACT FROM AUTHOR]

The penetration level of photovoltaic (PV) keeps increasing in modern distribution networks, which leads to various severe voltage limits violation problems. This article aims to aggregate and utilize the PV inverters for voltage regulation by a fully distributed two-level Volt/VAr control (VVC) scheme. In the lower-level VVC (real-time scale), the rooftop PV inverters are aggregated via consensus algorithms and then governed by droop controllers in medium-voltage networks. The droop controller adjusts the reactive power output of each PV aggregator in real-time from its dispatched value depending on the bus voltage variations. In the upper-level VVC (15-min timescale), the reactive power of PV aggregators is dispatched for power loss minimization, where control signals are set as base values for PV aggregators. This problem is formulated as second-order cone programming and solved by the alternating direction method of multipliers. The simulation results demonstrate the effectiveness of the proposed method in both short-term and long-term scenarios with different system scales. [ABSTRACT FROM AUTHOR]

Under the smart grid paradigm, distribution systems with large penetrations of photovoltaic-based power generation are called to optimize their operational resources to achieve a more efficient and reliable performance. In this context, this paper proposes a multiperiod mixed integer second order cone formulation to optimize distribution feeders operation. The model takes into account the feeder physical behavior; discrete control equipment (tap changers and capacitors banks) with a maximum allowable daily switching operation number; photovoltaic inverters operation; and the uncertain nature of solar energy and loads. A two–stage robust optimization framework is used to include the uncertainty into the model, where discrete and continuous control actions are assumed to be part of the first and second stage of this model, respectively. The conservativeness level of the robust model is controlled by an polyhedral uncertainty set whose vertexes are adaptively adjusted in a data–driven fashion in order to better capture complex spatiotemporal dependencies among uncertain parameters. Extensive computational experiments are performed utilizing modified versions of various IEEE test feeders. The performance of the proposed data–driven model is contrasted against traditional deterministic and robust budget–constrained models, using a rolling horizon out–of–sample evaluation methodology. When compared to the deterministic model, the data–driven approach yields a reduction in power losses of approximately 15% and a reduction up to 98% in hourly voltage violations. Results also suggests that the proposed approach exhibits better performance in terms of both average and conditional–value–at–risk metrics in comparison to budget–constrained models. [ABSTRACT FROM AUTHOR]

This paper investigates the voltage and reactive power control problem in low voltage grids with connected prosumers and bulk loads. The X(U) local control, which maintains the voltage at the feeders’ ends within a predefined band, and its combination with Q-Autarkic customer plants are the most effective and reliable strategies in grids with high prosumer share. However, these strategies may need adaptations to guarantee voltage limit compliance when bulk loads, such as electric vehicle parking garages and community-owned photovoltaic systems, are connected to the low voltage feeders. This paper extends the X(U) local control concept to involve bulk loads in Volt/var control and investigates the resulting load flows in different real low voltage grids. The results show that the extended control arrangement reliably removes all voltage limit violations by deteriorating the effectiveness of the original X(U) local control arrangement: reactive power flows and equipment loading within the low voltage grids are increased.