Control Of Distribution Networks Research Articles

Fast Detection and Localization of Multiple Leaks in Water Distribution Network Jointly Driven by Simulation and Machine Learning

The leakage control in water distribution networks (WDNs) is of high concern in the water supply industry. One direct and effective way to reduce leakage is to adopt leakage detection and localization methods to guide water utilities to repair broken pipes in time. In order to achieve higher accuracy in the leakage detection process in WDN with multiple leaks, a novel multiple leak detection and localization framework (MLDLF) based on existing pressure and flow measurements is proposed. The MLDLF decomposed the problem into three substages: model calibration, leakage identification, and leakage localization. After using the calibrated hydraulic model to predict pressure values and estimate overall leakage flow in each area in the first stage, the data-driven methods, STLK, including the seasonal and trend decomposition using loess (STL decomposition) and the k-means clustering method, were performed in the identification stage to distinguish different leakage scenarios so as to determine the occurrence time of every leakage event. Finally, combined with the stepwise model–based fault diagnosis method, leakages were located gradually with high computational efficiency. A case study of applying MLDLF to the WDN of L-Town showed that 56.52% of the leakage events were successfully identified and located with the economic score reaching €264,873, indicating the robustness and good applicability of MLDLF in identifying and localizing all types of leaks under multiple leakage scenarios.

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.

Artificial Intelligence-Based Control and Coordination of Multiple PV Inverters for Reactive Power/Voltage Control of Power Distribution Networks

The integration of Renewable Energy Resources (RERs) into Power Distribution Networks (PDN) has great significance in addressing power deficiency, economics and environmental concerns. Photovoltaic (PV) technology is one of the most popular RERs, because it is simple to install and has a lot of potential. Moreover, the realization of net metering concepts further attracted consumers to benefit from PVs; however, due to ineffective coordination and control of multiple PV systems, power distribution networks face large voltage deviation. To highlight real-time control, decentralized and distributed control schemes are exploited. In the decentralized scheme, each zone (having multiple PVs) is considered an agent. These agents have zonal control and inter-zonal coordination among them. For the distributed scheme, each PV inverter is viewed as an agent. Each agent coordinates individually with other agents to control the reactive power of the system. Multi-agent actor-critic (MAAC) based framework is used for real-time coordination and control between agents. In the MAAC, an action is created by the actor network, and its value is evaluated by the critic network. The proposed scheme minimizes power losses while controlling the reactive power of PVs. The proposed scheme also maintains the voltage in a certain range of ±5%. MAAC framework is applied to the PV integrated IEEE-33 test bus system. Results are examined in light of seasonal variation in PV output and time-changing loads. The results clearly indicate that a controllable voltage ratio of 0.6850 and 0.6508 is achieved for the decentralized and distributed control schemes, respectively. As a result, voltage out of control ratio is reduced to 0.0275 for the decentralized scheme and 0.0523 for the distributed control scheme.

Coordinated voltage control in unbalanced distribution networks with two-stage distributionally robust chance-constrained receding horizon control

The integration of increasing share of renewable based distributed generation in distribution networks brings great challenges to voltage control. To address this issue, this paper presents a two-stage distributionally robust chance-constrained receding horizon control algorithm. In the proposed method, the distributionally robust chance-constrained reformulation of chance-constrained voltage control is derived, which is not only accurate, but also computationally efficient. Rather than perfect knowledge about the uncertainty associated with renewable generation, the proposed method only requires partial information of the underlying probability distribution. In addition, the mechanical voltage regulation devices and the DG inverters are controlled in two stages, considering their different characteristics in voltage control. By taking into account both the current and forecasted renewable generation, the proposed method utilizes receding horizon control to determine the control actions of voltage regulation devices. The effectiveness of the proposed method is demonstrated by case studies on unbalanced IEEE-123 bus system.

Effect of Capacitor Switching on Reactive Power Control in Electrical Distribution Network

In electrical distribution network, load varies over the day, with very low load from midnight to early morning and peak values occurring in the evening due to the load demand by the consumer. This variation in load demand leads to variation in reactive power because most loads are inductive especially in industries where three phase industrial motors are used. In addition, Furthermore, the variation in reactive power affects current flow through the lines, transformers, generators and cause power losses in distribution network. Hence, there is the need to ensure reliable energy distribution even in the presence of load and reactive power variations. This research paper applied shunt switching capacitor for reactive power control of electrical distribution network using 11 kV distribution network located at Monatan, Ibadan, Oyo State Nigeria as a case study. Hourly load data of the network were collected to determine the reactive power of the network for stability evaluation. Capacitor Switching Compensation was incorporated into distribution network using KCL algorithm as power flow to form Capacitor Switching Compensation Model (CSCM) to improve the reactive power of the distribution network and simulation was carried out in MATLAB environment. The results showed that reactive power of the distribution network was 3.1. The CSCM improved the reactive power to 52 %. The study showed that incorporating capacitor switching as a compensation technique enhances the stability of the distribution network. Keywords: Electrical Distribution Network, Reactive Power, Capacitor Switching Compensation Model, Power Loss, Load Data, MATLAB. DOI: 10.7176/CEIS/13-4-03 Publication date: August 31 st 2022

Local voltage control in distribution networks using PI control of active and reactive power

Overvoltage is becoming increasingly prevalent in distribution networks with high penetration of renewable distributed energy sources (DERs). Local control of converter-based resources is a flexible and scalable method to prevent this growing issue. Reactive power is used for voltage control in many local control schemes. However, the typical range of R/X ratios for distribution power lines indicates that mitigation of overvoltage often requires excessive amounts of reactive power. Complete reliance on reactive power thus limits the effectiveness of local control strategies. In this work we instead propose a method that combines enhanced power factor voltage control with upper voltage limit tracking using PI control. We develop a modelling framework and demonstrate the stability of the proposed method. We then simulate the nonlinear operation of two parallel PI controllers in a medium voltage test system.

Model-less robust voltage control in active distribution networks using sensitivity coefficients estimated from measurements

Measurement-rich power distribution networks may enable distribution system operators (DSOs) to adopt model-less and measurement-based monitoring and control of distributed energy resources (DERs) for mitigating grid issues such as over/under voltages and lines congestions. However, measurement-based monitoring and control applications may lead to inaccurate control decisions due to measurement errors. In particular, estimation models relying on regression-based schemes result in significant errors in the estimates (e.g., nodal voltages) especially for measurement devices with high Instrument Transformer (IT) classes. The consequences are detrimental to control performance since this may lead to infeasible decisions. This work proposes a model-less robust voltage control accounting for the uncertainties of measurement-based estimated voltage sensitivity coefficients. The coefficients and their uncertainties are obtained using a recursive least squares (RLS)-based online estimation, updated whenever new measurements are available. This formulation is applied to control distributed controllable photovoltaic (PV) generation in a distribution network to restrict the voltage within prescribed limits. The proposed scheme is validated by simulating a CIGRE low-voltage network interfacing multiple controllable PV plants.

Robust Data-Driven and Fully Distributed Volt/VAR Control for Active Distribution Networks With Multiple Virtual Power Plants

This paper proposes a data-driven and fully distributed volt/var control (VVC) method for active distribution networks (ADNs) with multiple virtual power plants (VPPs), which is model-free and robust against bad data in measurements. The proposed scheme combines the advantages of a robust regression based feedback optimization algorithm and a revised alternating direction multiplier method (ADMM). Each VPP continuously obtains local system feedback to correct control strategies, and exchanges only limited information with its neighbors. Furthermore, faster convergence is achieved by applying Nesterov’s accelerated gradient method to the dual update in ADMM. The effectiveness of the method is demonstrated via numerical simulations using several distribution systems.

Centralized Protection and Control System: Are We Ready for Deployment in the Chemical, Oil, and Gas Industry?

This article discusses the recent trends and customer experience with the deployment of centralized protection and control (CPC) systems mainly by utility segment customers and its feasibility in the chemical, oil, and gas (COG) segment. (See “Abbreviations Used in This Article” for a list of abbreviations used throughout.) CPC represents a new approach to protection and control in power distribution networks—centralizing all protection and control functionality in one single device on the substation level. Being International Electrotechnical Commission (IEC) 61850-compliant and ready for future upgrades with the evolving grid, it supports optimal asset management. To demonstrate the improved utilization of technologies for CPC systems, this article describes (i) a new centralized/hybrid protection and control (HPC) scheme for the medium-voltage/low-voltage (MV/LV) network, (ii) benefits to substantially improve the “ease of operation and maintenance” of industrial plants, and (iii) supplier and end customer perspective with the pros and cons of this new CPC system and its feasibility for deployment in COG industries.

Peer to Peer Flexibility Trading in the Voltage Control of Low Voltage Distribution Network

Most existing voltage optimization approaches of low voltage distribution networks (LVDNs) assume users follow the regulations unconditionally, which is not always true. To tackle this, we propose a real-time peer to peer flexibility trading scheme for LVDNs. Besides distributed optimization, our scheme offers a way to let users trade their flexibility, in case some users do not want to follow the system regulations at some point. The scheme consists of flexibility allocation and trading. Player compatibility equilibrium and Bernoulli trials are employed to approximate the Pareto optimum in flexibility allocation, while consensus and voltage reference are used to provide real-time offers for flexibility trading. The proposed scheme is computationally tractable and can work with limited communication in a decentralized manner. All the user data is used locally so that privacy is well protected. A network based on a real Belgian semiurban LVDN is employed to validate the proposed scheme with three different scenarios. Besides, the flexibility allocation is benchmarked with a centralized ACOPF algorithm. The case studies result clearly prove the effectiveness and robustness of the proposed scheme.

Coordinated Real-Time Voltage Control in Active Distribution Networks: An Incentive-Based Fairness Approach

This paper proposes a three-stage incentive-based fair voltage control strategy to mitigate fast voltage violations in distribution networks while ensuring the benefits of both distribution system operators (DSO) and photovoltaics (PV) customers. In the first stage, optimal power flow (OPF) is formulated as a mixed-integer second-order cone programming (MISOCP) and performed in the central controller (CC) to obtain day-ahead dispatch of on-load tap changers (OLTC) and capacitor banks (CBs) based on forecasting data of PV and load. In the second stage, two voltage control modes namely optimal voltage control mode (OVM) and fair voltage control mode (FVM) are first formulated. The incentive-based mode selection method is then developed to make a decision by comparing the total benefits in each mode. In the third stage, a multi-dimensional adaptive local voltage control strategy is proposed to sequentially regulate reactive power output and active power curtailment of PVs in real time. The control curve is designed with an adaptive voltage dead band to activate voltage regulation in time. The multi-dimensional characteristics also help track the optimal operating points with varying PV and load to further reduce network power losses. Moreover, the fairness is guaranteed by providing economic compensation for each PV from the DSO, which is estimated in the second stage and corrected in the third stage. The proposed approach is tested on the IEEE 33-bus distribution system and simulation results verify the effectiveness both in addressing voltage problems and ensuring the fairness of PVs in economic benefits.

MPC-Based Coordinated Voltage Control in Active Distribution Networks Incorporating CVR and DR

With massive integration of solar photovoltaics (PVs) in the distribution networks, the voltage regulation problem has become one of the serious concerns for distribution network operators. This article proposes a model predictive-based voltage control that optimally coordinates the reference voltage of distribution static synchronous compensator and on-load tap changer, and PV inverters’ active and reactive power set points to maintain network voltages within the operating limits. The two functionalities of the active distribution management system are demand response (DR) and conservation voltage reduction (CVR), which are further explored in the voltage control methodology to enhance energy efficiency of the distribution networks. The proposed methodology is implemented in a 33-bus distribution network to verify its effectiveness for different cases. Furthermore, simulation results demonstrate the benefits of CVR and DR on the proposed methodology.

Data-Driven Estimation of Voltage-to-Power Sensitivities Considering Their Mutual Dependency in Medium Voltage Distribution Networks

Voltage-to-power sensitivities play a key role in the control and operation of distribution networks. To estimate these sensitivities without network information, data-driven estimation methods have been studied. However, conventional methods do not consider mutual dependency (MD) of the sensitivities and thus the estimation can be inaccurate. Thus, this paper proposes a new data-driven estimation method of the sensitivities, which considers MD of the sensitivities in medium voltage distribution networks. In the proposed method, via MD analysis, the sensitivities are estimated by solving nonlinear least square problems and thus the accurate estimation can be achieved. The effectiveness of the proposed method is verified using the real-time platform.

Three-Stage Inverter-Based Peak Shaving and Volt-VAR Control in Active Distribution Networks Using Online Safe Deep Reinforcement Learning

This paper presents a three-stage inverter-based peak shaving and Volt-VAR control (VVC) framework in active distribution systems using the online safe deep reinforcement learning (DRL) method. The proposed framework aims to reduce the peak load, voltage violations, and real power loss by coordinating three stages with different control timescales. In the first stage, a day-ahead charging/discharging scheduling of energy storage systems (ESSs) with a 30 min resolution is performed via their inverters for peak shaving. In the second stage, the discharging power of ESSs is adjusted through measurements with a 1 min resolution to completely shave peak loads. A model-free DRL algorithm integrated with a safety module is also implemented in the second stage. Using this algorithm, the reactive powers of photovoltaic (PV) systems and ESSs are controlled by the DRL agent to reduce the voltage violation and real power loss, whereas no voltage violation occurs during the online training process. In the third stage, a proportional-integral controller with real-power compensation is integrated into inverters of PV systems and ESSs to rapidly mitigate local voltage violations with a 0.1 s resolution. The high efficiency and safety of the proposed method were validated on the IEEE 33-bus and IEEE 123-bus systems.

Evaluation of load forecast model performance in Croatian DSO

During the revitalization of the Remote Control Systems of four Distribution System Operators in Croatia: Elektra Zagreb, Elektroslavonija Osijek, Elektroprimorje Rijeka and Elektrodalmacija Split, the load forecasting subsystems were implemented as an integral part of the DMS system. Accurate electricity load forecasting presents an important challenge in managing supply and demand of electricity since it cannot be stored and has to be consumed immediately. Electricity consumption forecasting has an important role in the scheduling, capacity and operational planning of the distribution power system. Load forecasting of certain parts or the whole distribution network helps to improve distribution network planning, operation and control which also increases the safety level of the entire distribution system. Although many forecasting methods were developed, none can be generalized for all load patterns. Accurate results of electricity load models are essential to make important decisions in planning and controlling so it is important to keep models as accurate as possible regarding input variables such as historical loads and meteorological data. This article gives a description of the implemented load forecasting subsystems using an artificial neural network with a feedforward multilayer perceptron and backpropagation as a learning strategy. The emphasis is on the simple and systematic use of input and output data as well as on forecasting scenarios of specific measured points where hourly forecasted results for a week ahead are presented and compared for Croatian Distribution Centers.

Cluster Partition-Based Zonal Voltage Control for Distribution Networks With High Penetrated PVs

With the high penetrated photovoltaics (PVs) accessing distribution networks (DNs) in the future, the overvoltage of distribution network will be more serious, and the voltage control will be more complex. To solve this problem, a cluster partition-based zonal voltage control method for DN is proposed in this study. First, a cluster partition method using a comprehensive performance index is proposed: in terms of DN structure, a global density quality function index is introduced to measure the coupling degree of nodes in clusters. In terms of cluster function, the inconsistency coefficient index is presented to reflect the PV output characteristics, and a node membership function index is presented to limit the scale of the cluster. Then, the DN cluster partition is carried out under the fast Newman (FN) algorithm. Second, a second-order cone programming (SOCP) model of voltage control is established to maximize the PV consumption in each cluster. To achieve the coordinated optimization of clusters, an iterative optimization strategy among clusters is proposed. Finally, the effectiveness and rationality of the proposed method are verified in a 10 kV actual feeder system in Zhejiang Province, China.

Volt-VAR control for distribution networks with high penetration of DGs: An overview

Continuously improving the ability to accept distributed renewable energies is the trend of future grid development, and a large number of papers have been published in recent years to study the problem of Volt-VAR control (VVC) for distribution networks with high penetration of distributed generations. This paper summarizes the relevant modeling and solution methods for VVC problems, mainly including VVC based on multiple time scales, hierarchical partitioning, multi-stage and network reconstruction, in conjunction with the operational characteristics of distribution networks containing distributed renewable energies; meanwhile, it analyzes the advantages and disadvantages of traditional optimization methods, heuristic intelligent algorithms and random variable processing methods used to solve VVC problems, and then introduces the application of model-free deep reinforcement learning as a latest decision method in VVC of distribution networks. Most of the models and methods compiled in this article are from the research results of the last three years and have some reference value.

AoI research on PMU cloud side Cooperative System of active distribution network

Phasor measurement units (PMUs) are being widely installed in transmission networks. Many PMU based power system applications have been developed or are under development in academia and industry. For these applications, data status update is usually a key factor affecting its performance, especially for distribution network equipment information acquisition and control computing delay performance cannot accurately ensure the timely processing of services. Therefore, an index of information freshness is proposed—age of information(AoI). According to the model of intelligent computing system of distribution network equipment, the average peak information age of the system is calculated. Finally, the DQN method is adopted to minimize the peak information age. Simulation results show that the proposed service unloading optimization scheme based on DQN can effectively reduce the peak AoI of the system.

Distributed Network Control by Multi-agent System

Distributed Network Control by Multi-agent System

Deep Reinforcement Learning-Based Adaptive Voltage Control of Active Distribution Networks with Multi-terminal Soft Open Point

The integration of highly penetrated distributed generators (DGs) aggravates the rise of voltage violations in distribution networks. Connected by multi-terminal soft open points (M−SOPs), distribution networks gradually evolve into an interconnected flexible architecture with high controllability. Distribution networks with M−SOPs can exchange active power flexibly, and M−SOPs can provide local reactive power support to alleviate voltage violations. However, conventional model-based M−SOP optimization methods cannot regulate voltage profiles adaptively owing to the rapid fluctuations of DGs. In this paper, a data-driven voltage control method is proposed for M−SOPs using a deep deterministic policy gradient network (DDPG). First, the data-driven voltage control framework is proposed for M−SOPs based on DDPG. The M−SOP−based voltage control problem is reformatted as a Markov decision process (MDP) to construct the DDPG agent. Based on real-time measurement, the DDPG agent can adaptively regulate the M−SOP operation to address the frequent DG fluctuations. Then, a multi-dimensional and dynamic boundary action masking approach is proposed to address the complex coupling in the action space of M−SOPs. Finally, the effectiveness of the proposed method was verified using the IEEE 33-node system. The results show that the proposed method can adaptively alleviate the voltage fluctuations caused by rapid DG power variations.