Smart Photovoltaic Inverters Research Articles

Evaluation of Voltage Unbalance Definitions for Voltage Control in Distribution Systems with Photovoltaic Penetration

AbstractThis paper examines the impact of different voltage unbalance definitions on voltage control in unbalanced distribution systems. Traditional voltage regulation methods for unbalanced systems rely on a single voltage unbalance definition with inconsistent adoptions. In such a situation, voltage control performance can vary between definitions, and improper choice leads to control failures. In this study, we perform numerical analysis and simulations to investigate the impact of different definitions. In the numerical analysis, two definitions quantifying negative and zero sequence components are used as reference definitions, and the theoretical error range of the remaining definitions is identified. The results are further verified by the simulations on the IEEE 123 node test system with phase‐by‐phase controlled three‐phase step voltage regulators and smart photovoltaic inverters. The control of the devices is optimized to minimize or limit a specific definition. The results show that the values of each definition at the same voltage condition can differ significantly, and inappropriate definitions may lead to significant uncertainty in the voltage control. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A SOCP-Based ACOPF for Operational Scheduling of Three-Phase Unbalanced Distribution Systems and Coordination of PV Smart Inverters

The proliferation of distributed energy resources (DERs) imposes new challenges to distribution system operation, e.g., power quality issues. To overcome these challenges and enhance system operation, it is critical to effectively utilize all available resources and accurately characterize unbalanced distribution networks in operational tools. This paper proposes a convex second-order-cone programming (SOCP)-based AC optimal power flow (ACOPF) model for three-phase unbalanced distribution networks, including smart inverters and Volt-VAr controller (VVC) devices. Reactive power-voltage (Q-V) characteristics of smart inverters of solar photovoltaic (PV) units are also modeled. Moreover, the settings of Q-V characteristics of VVC are co-optimized within the proposed ACOPF, considering the allowable range of the IEEE 1547-2018 standard. Furthermore, dynamic analyses are conducted to verify the stability of optimal settings of VVC. The proposed models are tested on an actual 1747-node primary distribution feeder in Arizona. The results illustrate the effectiveness of the proposed ACOPF for unbalanced systems in providing an optimal solution while capturing the non-linearity and non-convexity of ACOPF. By co-optimizing settings, system operation is improved due to the flexibility of adjusting reactive power output from PV units with VVC. The time-domain simulations show that the optimal settings cause no stability issue for the distribution system.

Multiple control strategies for smart photovoltaic inverter under network voltage fluctuations and islanded operation

This article proposes a central control system that communicates with both grid-tied and off-grid control systems to offer various control strategies for operating a smart photovoltaic (PV) inverter. The target is to connect two sets of PV panels and one set of battery storage unit to either a 440 V/60 Hz utility grid or to feed local loads at 380 V/50 Hz using a smart inverter. When the smart PV inverter is connected to the grid, on the one hand, it injects fixed and programmed active power into the grid under all operating conditions, both normal and critical conditions, and on the other hand, by reciprocal exchanging reactive power with the grid, it addresses balanced and unbalanced fluctuations of the grid voltage. Also, when the smart PV inverter is disconnected from the grid, it can operate as an islanded mode to feed time-varying three-phase loads. Nonlinear simulations of the time domain in MATLAB-Simulink software show that the set of control strategies offered for the smart PV inverter not only by controlling the active power leads to the sale of electricity to the grid, but also by controlling the reactive power problems on the grid side such as voltage drop as a result of single-line-to-ground (SLG) or line-to-line-to-ground (2LG) faults, balanced grid voltage fluctuations such as voltage sag-swell and unbalanced voltage conditions will be resolved. Also, variable three-phase loads are fed well in off-grid mode.

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. This paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

Overview of Intelligent Inverters and Associated Cybersecurity Issues for a Grid-Connected Solar Photovoltaic System

The major problem associated with the grid-connected solar photovoltaic (PV) system is the integration of the generated DC power into the AC grid and maintaining the stability of the system. With advancements in research on these PV inverters, artificial intelligence (AI)-based control models are replacing the existing linear methods. These smart PV systems are prone to a variety of attacks, ranging from physical attacks on the PV plants to data integrity attacks and communication-based attacks. This paper provides an overview of the cybersecurity issues with smart PV inverters, their impacts on the grids, and control methods that exist to detect and identify cyber-attacks on a smart PV grid system. An extensive bibliography is provided on grid-forming and grid-following inverters with a variety of control techniques like Proportional–Integral–Derivative (PID) control, fuzzy-based control, and their performances under different fault situations. Multi-level inverter design approaches with various intelligent control techniques to overcome switching failures and other system faults are reviewed. Moreover, some recommendations for future research on intelligent inverters for grid-connected PV systems are proposed. This work will benefit researchers, scientists, and engineers who are working in the field of intelligent inverters for the grid-connected PV system.

A bi-level optimization method for voltage control in distribution networks using batteries and smart inverters with high wind and photovoltaic penetrations

In electrical distribution systems, the voltage quality problem considers the most restrictive issue that hinders high photovoltaic (PV) and wind integration. Therefore, this study aims at providing a reliable control method for the voltage problem in distribution networks embedded with high PV and wind sources. The proposed voltage management strategy consists of two controlling stages. The first stage uses battery energy storage systems (BESS), while the second stage employs the smart PV inverters' reactive power injection capability. For BESS operation, a bi-level optimization method based on metaheuristic optimization algorithms (MOA) is developed to regulate the voltage levels by governing the batteries charging/discharging rates. The bi-level optimization problem aims to maximize PV and wind energy exploitation while improving the voltage profile. In this study, a detailed comparison is conducted between three MOA: Social Spider Optimization, Particle Swarm Optimization, and Cuckoo Search Optimization. In addition, considering the uncertainty associated with PV system generation, a PV generation forecast is embedded in the voltage control strategy to reinforce the decision-making process of BESS operation. The efficacy of the proposed method is deployed on a modified IEEE-34 bus test feeder with real-world load and PV and wind power generation data. Experimental results show the effectiveness of the proposed method and indicate that proper coordination between the BESS and smart PV inverters is beneficial for distribution system operations that seamlessly integrate PV and wind energy. Overall, the proposed voltage management strategy can be used to control the voltage to any desired range at different locations.

高低圧PV混在配電線のVolt-var制御による無効電力出力とSVR動作の適正化

The voltage fluctuations that occur locally in the distribution system caused by high penetration of photovoltaic (PV) power generation based on the carbon neutrality strategy have been regarded as one of the serious operational problems. Assuming that larger amount of PV power sources will be connected in the future distribution system, Volt-var control of PV smart inverters is expected. This will enable more flexible and effective voltage control support by provide custom var response. The authors have investigated the effective voltage control of coordination between tap operation of Step Voltage Regulator (SVR) and Volt-var control of PV smart inverters. In our previous studies, we proposed a Volt-var curve determination method for MW-large-scale PV smart inverters connected to medium-voltage to decrease SVR tap operations and minimize distribution losses. However, there are uncertainties such as PV outputs and loads of low-voltage consumers that cannot be completely and individually determined from the measurements of medium-voltage PV smart inverters. In this study, using a detailed distribution line model that reflects actual power measurement datasets of individual low-voltage customers, effects of reduction in distribution losses, reduction in the number of SVR tap operations, and changes in cumulative reactive power output sharing of PV inverters are evaluated through simulation studies to confirm the effectiveness of the proposed Volt-var setting method.

Multi-stage voltage control in high photovoltaic based distributed generation penetrated distribution system considering smart inverter reactive power capability

The intermittent nature of photovoltaic (PV) based distributed generation can cause voltage control issues. This research aims to investigate the impact of using the reactive power capability of PV smart inverters, which can function as distributed static compensators (DSTATCOMs) during non-feed-in hours, to address this problem. In other words, the suggested PV-DSTATCOM can be used to provide voltage control whenever there is a high demand placed on the system around the clock. This study presents a coordinated multi-stage voltage control (CMSVC) strategy that utilizes both PV-DSTATCOMs and traditional voltage control devices through a hybrid of local and centralized control algorithms. The goal is to minimize energy waste while maintaining a voltage that is within acceptable limits. To achieve the best results, an improved whale optimization algorithm has been proposed for optimal optimization. To test the proposed method, the IEEE 33 bus radial distribution system and IEEE 69 bus radial distribution system were evaluated. According to the findings, the solution offered in this research significantly reduces energy losses and voltage variations, demonstrating the effectiveness of the proposed method

Sequentially Coordinated and Cooperative Volt/Var Control of PV Inverters in Distribution Networks

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.

Smart inverter and battery storage controls to reduce financial loss due to overvoltage-induced PV curtailment in distribution feeders

The rapid growth of rooftop solar photovoltaic (PV) systems in low-voltage distribution networks has caused reverse power flow leading to voltage rise. As the voltage level increases, PV inverters first reduce the output power to regulate the voltage and may eventually shut down if the voltage level remains above the permissible limit. When this happens, the PV output power is curtailed, leading to financial loss. This paper examines two control strategies to reduce PV curtailment: (1) smart PV inverters and (2) residential battery storage system optimally sized to reduce the cost of household energy. Smart PV inverters can reduce the voltage level by absorbing reactive power at the expense of curtailing its active power output. Residential battery storage can reduce the voltage level by storing excess PV power instead of exporting it to the grid Using these two strategies, a systematic approach is presented to reduce PV curtailment and the associated financial loss. Three types of electricity tariffs are used, namely, electricity market pool price, and retail flat and time of use tariffs for household customers. An 11-kV distribution feeder in South Australia is analysed with the prevailing feeder and inverter voltage limits per Australian regulatory standards. The results demonstrate that installing 100% of PV systems with smart inverters and optimally sized battery storage can reduce the PV curtailment and the corresponding financial losses by 47%.

Impact of smart photovoltaic inverter control modes on medium‐voltage grid voltage and inverter lifetime: An experimental approach

AbstractThis study relies on an experimental approach, utilising real data from multiple photovoltaic (PV) sites located in the US Northeaster region, to inspect how different inverter reactive and active power settings impact gird voltage regulation and inverter life expectancy. These voltage regulation schemes come at a cost for the operator. Data from different solar sites with inverters running at different reactive and active power settings were analysed to compare operational trade‐offs. These trade‐offs range from production losses to shortening the lifetime of the inverters. Voltage versus reactive power plots were analysed to show production losses, while the thermal analysis was used to correlate with the inverter life expectancy.

Adaptive Control of Distributed Energy Resources for Distribution Grid Voltage Stability

Volt-VAR and Volt-Watt functionality in photovoltaic (PV) smart inverters provide mechanisms to ensure system voltage magnitudes and power factors remain within acceptable limits. However, these control functions can become unstable, introducing oscillations in system voltages when not appropriately configured or maliciously altered during a cyberattack. In the event that Volt-VAR and Volt-Watt control functions in a portion of PV smart inverters in a distribution grid are unstable, the proposed adaptation scheme utilizes the remaining and stably-behaving PV smart inverters and other Distributed Energy Resources to mitigate the effect of the instability. The adaptation mechanism is entirely decentralized, model-free, communication-free, and requires virtually no external configuration. We provide a derivation of the adaptive control approach and validate the algorithm in experiments on the IEEE 37 and 8500 node test feeders.

ADMM-Based Multi-Objective Control Scheme for Mitigating the Impact of High Penetration DER Integration in the Modern Distribution Systems

The high penetration of renewable energy sources in modern distribution networks poses challenges for grid voltage regulation. In this study, a multi-agent distributed voltage control strategy based on the proximal Jacobian alternating direction method of multipliers (PJ-ADMM) is proposed for distribution power systems with a high penetration of photovoltaic (PV) resources coordinated with battery energy storage systems (BESS). In this context, all active and reactive power outputs of the PV are locally optimized through smart inverters to improve the grid voltage with minimum power loss. Uncertainties associated with the solar energy generation and load demands are considered in the defined scenarios. This study consists of two phases. In the first phase, the voltage control problem is formulated as an optimization problem to regulate the voltages within an acceptable limit with fast convergence. In the second phase, a coordinated voltage control strategy for smart PV inverters and BESS is proposed to allocate the power capacity of the battery energy storage systems and the active power loss reduction. Finally, the proposed method is tested on modified IEEE 13-bus, 33-bus and 141-bus distribution systems using MATLAB/Simulink and MATPOWER. A comparison of the results of the voltage profiles with and without the control algorithm demonstrated the efficacy, robustness, and scalability of the distributed scheme for voltage improvement and optimal utilization of PV power under different scenarios.

Dynamic DNR and Solar PV Smart Inverter Control Scheme Using Heterogeneous Multi-Agent Deep Reinforcement Learning

The conventional volt-VAR control (VVC) in distribution systems has limitations in solving the overvoltage problem caused by massive solar photovoltaic (PV) deployment. As an alternative method, VVC using solar PV smart inverters (PVSIs) has come into the limelight, which can respond quickly and effectively to solve the overvoltage problem by absorbing reactive power. However, the network power loss, that is, the sum of line losses in the distribution network, increases with reactive power. Dynamic distribution network reconfiguration (DNR), which hourly controls the network topology by controlling sectionalizing and tie switches, can also solve the overvoltage problem and reduce network loss by changing the power flow in the network. In this study, to improve the voltage profile and minimize the network power loss, we propose a control scheme that integrates the dynamic DNR with volt-VAR control of PVSIs. The proposed control scheme is practically usable for three reasons: Primarily, the proposed scheme is based on a deep reinforcement learning (DRL) algorithm, which does not require accurate distribution system parameters. Furthermore, we propose the use of a heterogeneous multiagent DRL algorithm to control the switches centrally and PVSIs locally. Finally, a practical communication network in the distribution system is assumed. PVSIs only send their status to the central control center, and there is no communication between the PVSIs. A modified 33-bus distribution test feeder reflecting the system conditions of South Korea is used for the case study. The results of this case study demonstrates that the proposed control scheme effectively improves the voltage profile of the distribution system. In addition, the proposed scheme reduces the total power loss in the distribution system, which is the sum of the network power loss and curtailed energy, owing to the voltage violation of the solar PV output.

Multi-objective Stochastic Planning of Electric Vehicle Charging Stations in Unbalanced Distribution Networks Supported by Smart Photovoltaic Inverters

Due to the environmental concerns, getting deteriorated ongoing, it is becoming essential to encourage people to use sustainable energies. One of the most effective alternatives to mitigate pollution is to promote green mobility via electric vehicles. However, the lack of electric charging stations may decrease individuals’ satisfaction to use electric vehicles in daily life. To bridge the gaps, this paper aims to simultaneously allocate electric vehicle charging stations and smart photovoltaic inverters in distribution networks to optimize three important objective functions, including power loss, voltage deviation, and voltage unbalance factor. To solve such a multi-objective optimization problem, a novel hybrid fuzzy Pareto dominance concept with differential evolution algorithm is proposed. A scenario-based framework is also used to capture the uncertainties of the model comprising loads, PVs’ generation and the demand of electric vehicle charging stations. The effectiveness of the stochastic multi-objective approach is then examined and verified on an unbalanced 37-bus network under different case studies. Attained results illustrate that if smart photovoltaic inverters integrate into the network with charging stations, the network performance is significantly improved, such as keeping voltage unbalance factor under standard value accounting for two percent.

Bi-Level Volt/VAR Optimization in Distribution Networks With Smart PV Inverters

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.

PV Active Power Curtailment for Distribution Network Management

Two of the main operation issues in medium voltage (MV) feeders are the voltage and thermal limits and become more relevant when a significant amount of distributed generation is integrated. This paper proposes a control strategy based on PV active power curtailment for distribution network management in order to reduce voltage and thermal issues in MV feeders under high photovoltaic (PV) penetration in order to postpone or avoid feeder reinforcement costs, when usually this kind of analysis is carried out in low voltage feeders. The strategy estimates the hosting capacity (HC) of the feeders based on decreasing the active power generated by the PV systems as required. The proposed approach uses Monte-Carlo simulations to estimate the HC in the feeder. In order to get more realistic scenarios, stochastic simulations are used to determine the random location of the PV systems, and by using statistics, several PV capacities are proposed. Smart PV inverters are employed to reduce their active power output based on the derived control signals. As an example, Mexican standards are used to estimate the HC and to test the control strategy. The results show an increase of HC in the feeder and benefits as better voltage regulation and thermal feeder profile.

A benchmark model for low voltage distribution networks with PV systems and smart inverter control techniques

Unbalanced three-phase low-voltage distribution networks (LVDNs) modeling, optimization, and control are essential for enabling high photovoltaic (PV) penetration levels. Accordingly, a new case study is developed to show the gaps and challenges at different PV penetration levels in LVDNs. In this case study, the aim is to provide a better understanding of LVDNs’ behavior in order to support the development and validation of the models and tools. Therefore, a reduction model is proposed to decrease the simulation time by lowering the number of buses in the IEEE European LV Test Feeder, with a negligible error. In addition, an OpenDSS-Julia interface is developed to demonstrate the effects of different PV penetration levels on the inverters’ behavior, active power curtailment, and voltage level in LVDNs. Results are demonstrated concerning several limitations and challenges in using existing smart inverter control techniques, in terms of the inverters’ behavior, active power curtailment, and the voltage level. These limitations and challenges include over-voltage issues using the constant power factor technique, high active power curtailment using the volt–watt technique, and high current flows in the network assets and poor power factors using the volt–var technique. In addition, state-of-the-art system models have not taken-into-account the modeling of uncertainty effects on the performance of PV modules. Similarly, such models have largely ignored the internal and standby losses in the inverter models. These neglected issues may lead to under- or over-estimation of the impacts of PV systems on LVDNs and inaccurate estimations of the network’s ability to accommodate high PV penetration levels.

Online PV Smart Inverter Coordination using Deep Deterministic Policy Gradient

Fast and frequent solar power variations present new challenges to modern power grid operation with increasing adoption of photovoltaic (PV) energy. PV smart inverters (SIs) provide a fast-response method to regulate voltage by modulating active and/or reactive power at the connection point. In this paper, a deep reinforcement learning (DRL) based algorithm is proposed to coordinate multiple SIs. A reward scheme is designed to balance voltage regulation and SI reactive power utilization. The proposed DRL agent for voltage control learns its policy through massive offline simulations and adapts to load and solar variations. The DRL agent results are compared against autonomous Volt-Var control and optimal power flow (OPF) on the IEEE 37 bus feeder and IEEE 123 bus feeder for 8760 different scenarios. The results demonstrate that a properly trained DRL agent can intelligently coordinate different SIs to satisfy grid voltage limits despite large solar and load variations. The DRL agent achieves nearly the optimal performance of OPF by mitigating all voltage violations, while reducing PV production curtailment by 88% compared to the autonomous Volt-Var scheme. Contrary to OPF, the DRL agent can provide a coordination signal in milliseconds without load and solar forecasts and without explicit knowledge of the distribution network model.

Evaluation of Data-Enhanced Hierarchical Control for Distribution Feeders With High PV Penetration

Reliable and resilient grid operations with high penetration levels of distributed energy resources (DERs) can be achieved with improved situational awareness and seamless integration of DERs with utility enterprise controls. This paper presents the details of the development of a data-enhanced hierarchical control (DEHC) architecture and the results of its evaluation. The DEHC is a hybrid control framework that enables the efficient, reliable, and secure operation of distribution grids with extremely high penetrations of solar photovoltaic (PV) generation by seamlessly integrating centralized utility controls, distributed controls for DERs, and autonomous grid-edge controls. In the DEHC architecture, the advanced distribution management system (ADMS) controls the legacy devices (such as load tap changers and capacitor banks), the PV smart inverters are dispatched by real-time optimal power flow, and the grid-edge devices regulate local voltages in coordination with each other. The DEHC is demonstrated using a commercial ADMS platform, real utility distribution feeder models, and grid-edge devices. The performance of the DEHC architecture is evaluated using simulations and hardware-in-the-loop experiments with voltage regulation as the control objective. The results show that the DEHC enables high penetration levels of PV in distribution feeders by effectively managing system voltages through the synergistic operation of ADMS, distributed PV smart inverter controls, and secondary-level grid-edge device control.