Optimal Voltage Control Based on a Modified Line Drop Compensation Method in Distribution Systems

Information technology will play a major role in every country in their industries, monitoring and control of power generation and distribution systems, gathering of information regarding needs of rural people and online interaction to provide assistance in the areas of skills for management of their electric pump systems, distribution transformers and a host of other such applications. 50% of activities in all countries will be carried out through information technology. This is due to the current availability of low cost personal computers and the high level of applied software which is available. The present paper outlines the role of information technology in the monitoring and control of power distribution systems.

A new approach to optimal reactive power and voltage control problem in a radial distribution system is proposed in this paper. The optimal control problem is to find a proper dispatch schedule for shunt capacitor banks and on-load tap changer at substation and shunt capacitor banks on feeders such that the power loss is minimized and the voltage profile is improved. To reduce computation burden, the whole problem is decomposed into two sub-problems, namely sub-problem on substation level and sub-problem on feeder level. A simplified dynamic programming and a fuzzy logic control algorithm are used to deal with the two sub-problems respectively. Coordination of the two algorithms provides an appropriate solution to the whole control problem. The numerical results demonstrate that the proposed approach is effective and feasible.

The increasing adoption of electric vehicles and the electrification of heat and transportation is expected to challenge the capacity of UK electricity networks, necessitating costly reinforcements and additional spinning reserves to handle peak demand. To explore and address such challenges, the Demand Reduction through Voltage Control (DVRC) project investigates voltage manipulation techniques, such as transformer tap decrement, tap staggering, and transformer tripping to manage the demand and reactive power within a DNO network in the UK. To demonstrate the new solution, five distinct trials were defined for this project which evaluated the impact of these techniques on demand, reactive power, and system frequency. The trials were categorised based on the activation method and the evaluated metric. Activation method in turn was either triggered based on time or threshold for demand and system frequency. Voltage measurements were taken at the LV side of secondary substations, and they were found to be compliant with statutory voltage limits while the trials were in progress. The results of this project show that the activation methods explored can effectively reduce demand, enabling network operators to defer reinforcements without compromising on power quality for the customers.

Electric utilities have a significant role in creating a network of plug-in electric vehicle (PEV) parking lots (PLs) to promote the use of PEV in cities around the world. The location of PLs in distribution systems, as well as the consideration of vehicle to grid (V2G) capability and flexible charging control in the PLs for PEVs enhance the distribution system operation. This paper optimizes the location and capacity of PEV PLs with flexible charging profiles of PEVs to minimize the power distribution system losses and maximize reliability and the flowing of PEV traffic that can be charged or discharged through a candidate PEV PL. The daily trips data are considered to calculate the dwell time of PEVs in PLs and to determine the initial state of charge of PEVs at the arrival time to PLs. The proposed method and analysis are examined on the standard IEEE 33 load point network, considering various PEV penetration levels and different charger types in PLs.

This paper presents a multiobjective daily voltage and reactive power control (Volt/VAr) in radial distribution systems, including distributed generation units. The main purpose is to determine optimum dispatch schedules for on-load tap changer (OLTC) settings at substations, substation-switched capacitors, and feeder-switched capacitors based on the day-ahead load forecast. The objectives are selected to minimize the voltage deviation on the secondary bus of the main transformer, total electrical energy losses, the number of OLTCs, and capacitor operation and voltage fluctuations in distribution systems for the next day. Since this model is the weighted sum of individual objective functions, an analytic hierarchy process is adopted to determine the weights. In order to simplify the control actions for OLTC at substations, a time interval-based control strategy is used for decomposition of a daily load forecast into several sequential load levels. A binary ant colony optimization (BACO) method is used to solve the daily voltage and reactive control, which is a nonlinear mixed-integer problem. To illustrate the effectiveness of the proposed method, the Volt/VAr control is performed in IEEE 33-bus and 69-bus distribution systems and its performance is compared with the genetic, hybrid binary genetic, and particle swarm optimization algorithms. The simulation results verify that the BACO algorithm gives better performances than other algorithms.

Knowledge-based expert system for optimal reactive power control in distribution system

Recently, renewable energy such as wind turbine generator and photovoltaic system are introduced as distributed generators (DGs). Connection of a large amount of distributed generator causes voltage deviation beyond the statutory range in distribution system. Reactive power control of inverter interfaced with DG is one of the solutions against this problem. Additionally, reactive power control has a possibility to contribute for reduction of distribution loss. In this paper, we propose a voltage control method in distribution system by reactive power control of inverters interfaced with DGs. The proposed method has been developed with the aim of reduction of distribution loss and voltage regulation into statutory range without any telecommunications. In the proposed method, each interfaced inverter controls reactive power based on voltage control reference, which is calculated from local measurements. The calculation rule of control reference has been developed using optimal data, which consist of relations between ramdomly given inputs and corresponding optimal outputs calculated by an optimization technique. Simulations are conducted to show the effectiveness of the proposed method.

This paper presents an optimal voltage control algorithm developed to increase the efficiency of operation of power systems and minimizing the reaction time for compensating voltage deviations due to load fluctuations. Because of the many variables of influence in the optimal voltage control (e.g. line power flow limits, bus voltage limits, etc.), the mathematical formulation is a constrained optimization problem with continuous and discrete variables, known as Optimal Power Flow (OPF), which is non-linear and non-convex. We propose a methodology to convexify the OPF problem based on Semidefinite Programming (SDP), with a novel formulation to include discrete decision variables such as shunt elements and tap changers positions as discrete variables. Finally, the SDP formulation of the case of a DC network is included.

Network partition and distributed voltage coordination control strategy of active distribution network system considering photovoltaic uncertainty

This paper addresses an unequal interconnected multi-area multi-source power system whose frequency, voltage, and tie-line power are regulated in the conventional scenario. The proposed system consists of three areas with common thermal and wind plants. Besides these plants, Area-1, 2 and 3 have gas turbine, diesel and DSTS plant respectively. A maiden attempt was made to implement the cascaded Proportional-Integral-Derivative with Filter (PIDN)-Tilt-Integral-Derivative with Filter (TIDF) controller in both Automatic Load Frequency Control (ALFC) and Automatic Voltage Regulator (AVR) loops. A nature-inspired meta-heuristic algorithm called Harris Hawks Optimization (HHO) is successfully applied to the unified ALFC and AVR studies to optimize the controller parameters showing that PIDN-TIDF controller predominates in all time-domain indices. AVR impact proclaims a substantial increase in system dynamics. The system dynamics is studied with the impact of wind turbines and system nonlinearities. System performance is measured by Integral Squared Error (ISE) with 1% disturbance in Area-1.

Accurate estimates of network parameters are essential for modeling, monitoring, and control in power distribution systems. In this paper, we develop a physics-informed graphical learning algorithm to estimate network parameters of three-phase power distribution systems. Our proposed algorithm uses only readily available smart meter data to estimate the three-phase series resistance and reactance of the primary distribution line segments. We first develop a parametric physics-based model to replace the black-box deep neural networks in the conventional graphical neural network (GNN). Then we derive the gradient of the loss function with respect to the network parameters and use stochastic gradient descent (SGD) to estimate the physical parameters. Prior knowledge of network parameters is also considered to further improve the accuracy of estimation. Comprehensive numerical study results show that our proposed algorithm yields high accuracy and outperforms existing methods.

Volt-VAR control is critical to keeping distribution network voltages within allowable range, minimizing losses, and reducing wear and tear of voltage regulating devices. To deal with incomplete and inaccurate distribution network models, we propose a safe off-policy deep reinforcement learning algorithm to solve Volt-VAR control problems in a model-free manner. The Volt-VAR control problem is formulated as a constrained Markov decision process with discrete action space, and solved by our proposed constrained soft actor-critic algorithm. Our proposed reinforcement learning algorithm achieves scalability, sample efficiency, and constraint satisfaction by synergistically combining the merits of the maximum-entropy framework, the method of multiplier, a device-decoupled neural network structure, and an ordinal encoding scheme. Comprehensive numerical studies with the IEEE distribution test feeders show that our proposed algorithm outperforms the existing reinforcement learning algorithms and conventional optimization-based approaches on a large feeder.

The high penetration of distributed generation (DG) is changing the energy flows in the grid, as well as the energy losses and voltage profile of the system. To solve these problems, the impact of the connection of Renewable Energy Generators (REGs) on the grid is being studied. This work is intended to present the voltage variation of each node of the distribution grid before and after the insertion of a wind turbine or solar panel and the combination of both. The proposed method is self-adaptive regulation by combining settings in P/Q or P/V mode with a setpoint decision making block (setpoint adaptation block) based on fuzzy logic in order to maintain a stable and optimal voltage profile.The simulation results obtained using Matlab®/Simulink® clearly show the efficiency of the controllers used and their optimal control capacity.

Knowledge-based expert system for optimal reactive power control in distribution system

Voltage and Frequency control are the two fundamental control problems in power systems. Unlike frequency control, voltage control is complicated by the fact that reactive power can't travel far distances from its source of generation. Due to this distributed nature of reactive power, voltage control is usually performed in decentralized manner. Typically, voltage control problem is divided into a three-level hierarchical structure namely primary, secondary and tertiary voltage control.;The aim of this thesis is to present an optimal secondary voltage control by decomposing a large power system into small subsystems called voltage control areas (VCAs) using the fast community detection algorithm. Each VCA is self-sufficient in satisfying its reactive power demand. A load bus, called pilot point/bus, is selected in each VCA as a representative of the voltage profile of the whole area. Singular value decomposition of Fast Decoupled Load Flow (FDLF) Jacobian is used to optimally control the voltages of these pilot buses.;The presented approach is tested on two standard IEEE test power systems i.e. 9-Bus and 39-Bus systems. The computational time comparison of the fast community detection algorithm with another algorithm called original-GN algorithm is also presented. Through simulation results, it is shown that the presented optimal voltage control (Opt-VC) is a better approach compared to sensitivity based voltage control (Sen-VC).