Volt-var Control Research Articles

A new market-based approach for daily Volt/Var control of distribution systems in the presence of distributed energy resources using Benders decomposition algorithm

Distributed energy resources (DERs), if properly coordinated with other Volt/Var control (VVC) devices, could be incorporated in daily VVC problem. In this paper, a new 2-stage model is presented for daily VVC of distribution systems including DERs, taking into account environmental and economic aspects. First, in the day-ahead market, an initial environmental-economic dispatch is performed to minimize both the electrical energy costs and the gas emissions pertaining to generation units. The initial scheduling results determined by the market operator are delivered to the second stage, namely the daily optimal dispatches of VVC devices, to examine them from operational viewpoints and to yield the daily optimal dispatches of the VVC devices. The objective function of the second stage consists of the total cost of the following components: active power losses, adjustment of the initially scheduled active powers, and depreciation of switchable devices. In this paper, attention is directed towards 2 important types of DERs to be connected to the network: synchronous machine-based distributed generations and wind turbines. Because the active and reactive powers of DERs are coupled via the capability diagram, in this paper, this issue is addressed by considering the capability diagram of DERs in the proposed model. In particular, the Q-capability diagram of wind turbines should be analyzed taking the hourly wind speed fluctuations into consideration, so that delivery of a uniform reactive power can be ensured over the entire hour of operation. Due to the complexity of solving mixed integer nonlinear programming problems, an algorithm based on Benders decomposition is suggested to solve the proposed VVC model in the second stage. Finally, 2 standard distribution test networks are utilized to validate the effectiveness of the proposed method.

Real-Time Benefit Analysis and Industrial Implementation for Distribution System Automation and Control

Smart grid technologies are expected to increase system efficiency and reliability using advancement in automation, communication, computation, and optimization. Several control algorithms have been developed or enhanced for operation and control of smart distribution grid. In recent years, volt/var control (VVC) algorithms have been proposed for energy saving in distribution systems due to advancement provided by smart grid automation and control. VVC helps to lower distribution feeder voltage to decrease energy consumption. However, quantification of this impact is hard due to several external factors such as weather and end users' behavior. On the other hand, existing evaluation methods of VVC algorithms are either not suitable for long-term study or intrusive. This paper addresses these issues by proposing a new technique to estimate “distribution system demand without smart grid implementation” utilizing “data measured from system with smart grid implementation.” Interdependence of voltage and load is considered based on estimated load characteristic. Our key contribution is developing a method for quantifying the real-time energy saving of smart grid automation and control focused on VVC and implementing in an electric utility in eastern Washington, USA. The algorithm is developed using both commercial and open-source software.

Maintenance Scheduling of Volt-VAR Control Assets in Smart Distribution Networks Using Advanced Metering Infrastructure

This paper investigates a novel approach for maintenance scheduling of volt-VAR control components (VVCCs) of distribution networks with the aid of new generation of volt-VAR optimization (VVO) solutions called quasi-real-time VVO. The new quasi-real-time VVO technique optimizes distribution network using advanced metering infrastructure (AMI) data of each quasi-real-time stage. As this VVO performs automatically and online, it is necessary for VVCCs to undergo maintenance without disturbing VVO performance. Moreover, the lost benefits that could be gained by online VVO have to be minimized. Hence, this paper proposes an AMI-based VVO consisting of a VVO engine and a maintenance scheduling engine (MSE) that operate in tandem to optimize distribution network and find the optimal maintenance scheduling of different VVCCs. To test the accuracy and the applicability of the proposed solution, a 33-node distribution feeder is employed. Furthermore, five different maintenance scenarios are investigated to check the proposed VVO performance. The results prove that the integration of VVO with MSE could be a reliable approach that can solve maintenance scheduling of VVCCs without interrupting and/or resetting VVO.

A New Approach to Optimal Allocation of Reactive Power Ancillary Service in Distribution Systems in the Presence of Distributed Energy Resources

One of the most important Distribution System Operators (DSO) schemes addresses the Volt/Var control (VVC) problem. Developing a cost-based reactive power dispatch model for distribution systems, in which the reactive powers are appropriately priced, can motivate Distributed Energy Resources (DERs) to participate actively in VVC. In this paper, new reactive power cost models for DERs, including synchronous machine-based DGs and wind turbines (WTs), are formulated based on their capability curves. To address VVC in the context of competitive electricity markets in distribution systems, first, in a day-ahead active power market, the initial active power dispatch of generation units is estimated considering environmental and economic aspects. Based on the results of the initial active power dispatch, the proposed VVC model is executed to optimally allocate reactive power support among all providers. Another novelty of this paper lies in the pricing scheme that rewards transformers and capacitors for tap and step changing, respectively, while incorporating the reactive power dispatch model. A Benders decomposition algorithm is employed as a solution method to solve the proposed reactive power dispatch, which is a mixed integer non-linear programming (MINLP) problem. Finally, a typical 22-bus distribution network is used to verify the efficiency of the proposed method.

Economic-environmental active and reactive power scheduling of modern distribution systems in presence of wind generations: A distribution market-based approach

Distribution System Operator (DSO) is responsible for active and reactive power scheduling in a distribution system. DSO purchases its active and reactive power requirements from Distributed Energy Resources (DERs) as well as the wholesale electricity market. In this paper, a new economical/environmental operational scheduling method based on sequential day-ahead active and reactive power markets at distribution level is proposed to dispatch active and reactive powers in distribution systems with high penetration of DERs. In the proposed model, after day-ahead active power market was cleared the participants submit their reactive power bids and then the reactive power market will be settled. At distribution level, developing a Var market, in which DERs like synchronous machine-based Distributed Generation (DG) units and Wind Turbines (WTs) could offer their reactive power prices, DERs are motivated to actively participate in the Volt/VAr Control (VVC) problem. To achieve this purpose, based on the capability curves of considered DERs, innovative multi-component reactive power bidding structures for DERs are introduced. Moreover, the effect of reactive power market clearing on the active power scheduling is explicitly considered into the proposed model by rescheduling of active power by usage of energy-balance service bids. On the other hand, environmental concerns that arise from the operation of fossil fuel fired electric generators are included in the proposed model by employing CO2 emission penalty cost. The suggested reactive power market is cleared through a mixed-integer nonlinear optimization program. The effectiveness of the proposed scheduling model is investigated through a typical 22-bus distribution test system over a 24-h period.

Volt-VAR Multiobjective Optimization to Peak-Load Relief and Energy Efficiency in Distribution Networks

This paper addresses the integrated volt-var control for distribution network operation via multiobjective optimization. This paper seeks to explore the problem of energy savings and peak demand relief through the voltage reduction procedure. Currently, due to the emergence of the distribution smart grids, these procedures are gaining renewed interest and attention. The proposal presented here is for the operation phase, with a strategy based on an hourly load forecast for the next day/week, taking into account the active power intake reduction, and the voltage deviation. Therefore, the result is a set of nondominated optimal solutions, and then one may decide when, where, and how to apply them to meet different goals. The obtained solutions, for two typical distribution networks, describe relevant economic and technical benefits.

Smart distribution system volt/VAR control using distributed intelligence and wireless communication

This study presents a smart volt/VAR control (VVC) technique for smart distribution systems, which is designed to be integrated into a distributed real-time analysis and control framework implemented with the use of multiple processing units equipped with wireless communication transceivers. The distributed processing units collaborate to perform power flow analysis based on smart meter measurements for controlling and coordinating the switched capacitor banks and voltage-regulating transformers. The objective is to maintain acceptable voltage levels along the distribution feeder, minimise system losses, and limit the number of switching operations. GNU-Octave simulations are employed as a means of evaluating the performance of the proposed smart VVC technique with respect to loss reduction and number of switching operations. The network simulator ns-3 is used to simulate the distributed processing units that execute the proposed smart VVC technique. Worldwide interoperability for microwave access (WiMAX) and long-term evolution (LTE) communication networks are employed in the ns-3 simulations in order to provide data connectivity among the distributed processing units. The performance of the communication network is evaluated in terms of the execution time of the smart VVC technique, the average packet delay and the average packet delivery ratio.

Simulation of the Volt/Var Control in Distribution Feeders by Means of a Networked Multiagent System

The adoption of networked multiagent systems (MAS) has been recently proposed for the solution of the volt/var control (VVC) problem in distribution feeders. However, constraints and limitations due to the communication network, to the dynamic behavior of power system components and regulators, as well as to the measurement uncertainties of the adopted sensors need to be specifically analyzed for the design of the MAS. For this purpose, a cosimulation platform has been built by the integration of two simulation tools: 1) the electromagnetic transient program EMTP-rv; and 2) the communication network simulator Riverbed-OPNET Modeler. This paper presents the modeling and simulation of an asynchronous leaderless MAS-based approach that coordinates the reactive power outputs of a set of power compensators equipped with phasor measurement units via a shared band-limited packet-switched digital communication network. The effects of communication network latency and packet loss on the VVC performances are analyzed for two unbalanced IEEE Test feeders equipped with on-load tap changer transformers. The paper compares the results obtained by using a transmission control protocol (TCP) and a user datagram protocol (UDP), for different levels of background traffic (BT) and different packet discard ratios (PDRs) in the communication links.

Inverter-Less Hybrid Voltage/Var Control for Distribution Circuits With Photovoltaic Generators

This paper proposes a hybrid voltage/var control (VVC) architecture for distribution systems with a high PV penetration. The architecture consists of two control loops: coordinated normal control loop and uncoordinated transient cloud movement control loop. In the first loop, hourly dispatches are scheduled for on-load tap changer (OLTC), capacitor banks (CBs), and static var compensators (SVCs) based on forecasted load and PV power output so as to minimize power losses and voltage deviations. The second loop is triggered when large variations of PV power output caused by rapid cloud movement happen. All SVCs and CBs become self-controlled based on local voltage measurements with the single control objective to minimize voltage deviations. SVCs will operate firstly to flatten the voltage profile. If SVCs fail, CBs will switch to provide reactive power support. A time-adaptive delay is applied to each CB to avoid overcompensation. Case studies show the proposed method can optimize the system operation and is effective in voltage regulation with PV generators.

Implementation of non-intrusive energy saving estimation for Volt/VAr control of smart distribution system

There has been a growing interest among power distribution utilities to explore smart grid technologies to improve the operational efficiency and reliability. As electricity distribution grid is evolving to become “smart”, energy demand reduction is one of the goals for the distribution utilities. In order to obtain this goal, utilities need to commit significant financial resources. Therefore, it became important to assess the benefit of new technologies such as Volt/VAr control (VVC). To compute the energy savings due to VVC implementation, existing algorithms are intrusive, and generally require altering the distribution system control settings and operating points, which is undesirable for system operator. On the other hand, these may require large amount of historical data. In this paper, implementation of a new non-intrusive energy saving estimation algorithm has been presented for integrated Volt/VAr control by Avista Utilities in Northwest USA. Developed algorithm utilizes measurements from smart distribution system. Develop algorithm allows studying the energy saving in long term as it requires no change in control settings of actual distribution system. Satisfactory results have been obtained and validated against field data from experiments on real feeders by Avista Utilities.

Voltage regulation in LV grids by coordinated volt-var control strategies

The increasing penetration level of photovoltaic (PV) power generation in low voltage (LV) networks results in voltage rise issues, particularly at the end of the feeders. In order to mitigate this problem, several strategies, such as grid reinforcement, transformer tap change, demand-side management, active power curtailment, and reactive power optimization methods, show their contribution to voltage support, yet still limited. This paper proposes a coordinated volt-var control architecture between the LV distribution transformer and solar inverters to optimize the PV power penetration level in a representative LV network in Bornholm Island using a multi-objective genetic algorithm. The approach is to increase the reactive power contribution of the inverters closest to the transformer during overvoltage conditions. Two standard reactive power control concepts, cosφ(P) and Q(U), are simulated and compared in terms of network power losses and voltage level along the feeder. As a practical implementation, a reconfigurable hardware is used for developing a testing platform based on real-time measurements to regulate the reactive power level. The proposed testing platform has been developed within PVNET.dk project, which targets to study the approaches for large PV power integration into the network, without the need of reinforcement.

Fuzzy Inference System for Integrated Vvc In Isolated Power Systems

This paper presents a fuzzy inference system for integrated volt/var control (VVC) in distribution substations. The purpose is go forward to automation distribution applying conservation voltage reduction (CVR) in isolated power systems where control capabilities are limited. A fuzzy controller has been designed. Working as an on-line tool, it has been tested under real conditions and it has managed the operation during a whole day in a distribution substation. Within the limits of control capabilities of the system, the controller maintained successfully an acceptable voltage profile, power factor values over 0,98 and it has ostensibly improved the performance given by an optimal power flow based automation system. CVR savings during the test are evaluated and the aim to integrate it in the VVC is presented.

Induction Motor Starting in Islanded Microgrids

Motor loads require high amount of reactive power for a short period during their startup. The high reactive power drawn from the system causes voltage dips at startup time and potentially overvoltage after motor startup is over. The voltage dip and overvoltage may cause the relays to trip and the system to go unstable. This phenomenon is more challenging in weak distribution systems and isolated systems such as microgrids due to the limited inertia of the master generator. This paper presents a dynamic voltage controller that coordinates all the reactive power sources in the system to provide the necessary reactive power during motor startup. The presented Model Predictive Control (MPC) based dynamic Volt/Var Control (VVC) scheme considers the dynamics of the microgrid in the VVC formulation to overcome the voltage dip caused by motor startup. This method uses predictions of voltage behavior of the system based on a simplified system model and tries to eliminate the effect of motor startup by coordinating the reactive power sources in the system.

Scenario-Based Multiobjective Volt/Var Control in Distribution Networks Including Renewable Energy Sources

This paper proposes a stochastic multiobjective framework for daily volt/var control (VVC), including hydroturbine, fuel cell, wind turbine, and photovoltaic powerplants. The multiple objectives of the VVC problem to be minimized are the electrical energy losses, voltage deviations, total electrical energy costs, and total emissions of renewable energy sources and grid. For this purpose, the uncertainty related to hourly load, wind power, and solar irradiance forecasts are modeled in a scenario-based stochastic framework. A roulette wheel mechanism based on the probability distribution functions of these random variables is considered to generate the scenarios. Consequently, the stochastic multiobjective VVC (SMVVC) problem is converted to a series of equivalent deterministic scenarios. Furthermore, an Evolutionary Algorithm using the Modified Teaching-Learning-Algorithm (MTLA) is proposed to solve the SMVVC in the form of a mixed-integer nonlinear programming problem. In the proposed algorithm, a new mutation method is taken into account in order to enhance the global searching ability and mitigate the premature convergence to local minima. Finally, two distribution test feeders are considered as case studies to demonstrate the effectiveness of the proposed SMVVC.

Residential Demand Response model and impact on voltage profile and losses of an electric distribution network

This paper develops a model for Demand Response (DR) by utilizing consumer behavior modeling considering different scenarios and levels of consumer rationality. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices for different types of consumers. These price elasticity matrices (PEMs) are utilized to calculate the level of Demand Response for a given consumer considering a day-ahead real time pricing scenario. DR models are applied to the IEEE 8500-node test feeder which is a real world large radial distribution network. A comprehensive analysis has been performed on the effects of demand reduction and redistribution on system voltages and losses. Results show that considerable DR can boost in system voltage due for further demand curtailment through demand side management techniques like Volt/Var Control (VVC).

Quasi-parallel network applications in real-time distribution management system

In modern distribution management systems (DMSs), the real-time calculation of the network's state plays an important role for the operation of the network. High sophisticated algorithms for the estimation and calculation of the system's state like distribution system state estimation (DSSE), distribution system power and short circuit calculations (DSPF and SCC), voltage var control (VVC) and others must operate in a real-time environment, supplying subsequently running functions with reliable input data. Closed-loop requirements also increase the requirements concerning performance, availability and robustness. In this paper, an implementation of the sequence control of different DMS-network applications is presented. This proposed real-time scheduler takes advantage of CPU-cores inside of a modern computer system: by passing subtasks to different CPU-cores, the overall performance is significantly increased. The scheduling process is analysed in detail taking the nature of a distribution system into consideration. The presentation is finalised with a report about the gain of performance for the state estimation process.

In Situ Nanoscale In-Plane Deformation Studies of Ultrathin Polymeric Films During Tensile Deformation Using Atomic Force Microscopy and Digital Image Correlation Techniques

The distributed Volt/Var control (VVC) methods have been widely studied for active distribution networks(ADNs), which is based on perfect model and real-time P2P communication. However, the model is always incomplete with significant parameter errors and such P2P communication system is hard to maintain. In this paper, we propose an online multi-agent reinforcement learning and decentralized control framework (OLDC) for VVC. In this framework, the VVC problem is formulated as a constrained Markov game and we propose a novel multi-agent constrained soft actor-critic (MACSAC) reinforcement learning algorithm. MACSAC is used to train the control agents online, so the accurate ADN model is no longer needed. Then, the trained agents can realize decentralized optimal control using local measurements without real-time P2P communication. The OLDC with MACSAC has shown extraordinary flexibility, efficiency and robustness to various computing and communication conditions. Numerical simulations on IEEE test cases not only demonstrate that the proposed MACSAC outperforms the state-of-art learning algorithms, but also support the superiority of our OLDC framework in the online application.

Optimal Capacitor Switching in a Distribution System Using Functional Link Network

One of the most important control decision functions in a modern distribution automation system is the volt-var control. The objective of the volt-var control is to supply reactive power by optimally switching the capacitors installed in the distribution system so that the voltage drop and real power loss are minimum. Traditionally, this problem of optimal capacitor switching has been solved through various optimization techniques. However, as the time taken by these traditional optimization methods is quite significant, these methods may not be much suitable for on-line application. To reduce the time required to solve the optimal capacitor-switching problem, a Functional Link Network (FLN) based approach has been developed in this article. For a practical number of capacitors in the system it has been found that the FLN based technique is at least 100 times faster than the traditional optimization methods. Moreover, as the number of capacitors in the system increases, the effectiveness of the FLN over the traditional approach (in terms of the solution time) increases.

Artificial neural network-based optimal capacitor switching in a distribution system

Abstract One of the most important control decision functions in a modern distribution automation system is volt–var control. The objective of volt–var control is to supply controlled reactive power by switching optimally the switchable capacitors installed in the distribution system such that the voltage drop and real power loss is minimum. Traditionally, this problem of optimal capacitor switching has been solved through various optimization techniques. However, as the time taken by these traditional optimization methods are quite significant, these methods may not be much suitable for online application. To reduce the time required to solve the optimal capacitor switching problem, an artificial neural network (ANN)-based approach has been developed in this paper. It has been found that the ANN-based technique is at least a 100 times faster than the traditional optimization methods for a practical number of capacitors in the system. Moreover, as the number of capacitors in the system increases, the effectiveness of the ANN over the traditional approach (in terms of the solution time) increases.

Pilot project to estimate the centralized volt/VAr control effectiveness

This paper describes a volt/VAr control (VVC) project in which a centralized network-model based VVC was put in place to control a 13.8 kV distribution subsystem. The goal of the project is to estimate the effectiveness of VVC based on different objective functions, including an objective function for future market economy (revenue maximization). The technical issues which are critical to implementing the VVC are discussed. The effectiveness of VVC for energy conservation and revenue maximization is calculated as an effect due to the immediate (within the span of a few minutes) load to voltage response. This effect is compared with the actual effect calculated based on a fifteen minutes average archived telemetered data.