A smart volt-var optimization engine for energy distribution system

Volt/Var Optimization (VVO) function is a key element in operation of distribution networks and major part of advanced Distribution Management Systems (DMS). From planning prospective, VVO function can be used to optimize reactive power flow in distribution network to recommend the best operating condition for the control equipment in a predefined period of time in future (i.e. 24 hour). In fact VVO minimizes the total system loss for a forecasted set of load and computes the optimized setting for transformer on-load tap changers (OLTC), Voltage Regulators (VR), and Capacitor Banks (CB), while system voltage profile is maintained within its limits. In this paper we use a full mixed integer linear programming (MILP) model for solving VVO problem for a planning application. The objective of this paper is to develop a planning VVO engine which can calculate the most probable expected loss of the network for the next 24 hours, and can recommend the best expected operating condition for the control equipment. To model the uncertainty of load, an ARMA model is applied to create several forecasted load scenarios to feed them into the VVO engine (which is implemented in a commercial solver GAMS (General Algebraic Modeling System). The implemented models have been tested on a real distribution network in southern Sweden and results are presented.

Project Objectives The AEP Ohio gridSMART® Demonstration Project (Project) achieved the following objectives: • Built a secure, interoperable, and integrated smart grid infrastructure in northeast central Ohio that demonstrated the ability to maximize distribution system efficiency and reliability and consumer use of demand response programs that reduced energy consumption, peak demand, and fossil fuel emissions. • Actively attracted, educated, enlisted, and retained consumers in innovative business models that provided tools and information reducing consumption and peak demand. • Provided the U.S. Department of Energy (DOE) information to evaluate technologies and preferred smart grid business models to be extended nationally. Project Description Ohio Power Company (the surviving company of a merger with Columbus Southern Power Company), doing business as AEP Ohio (AEP Ohio), took a community-based approach and incorporated a full suite of advanced smart grid technologies for 110,000 consumers in an area selected for its concentration and diversity of distribution infrastructure and consumers. It was organized and aligned around: • Technology, implementation, and operations • Consumer and stakeholder acceptance • Data management and benefit assessment Combined, these functional areas served as the foundation of the Project to integrate commercially available products, innovative technologies, and new consumer products and services within a secure two-way communication network between the utility and consumers. The Project included Advanced Metering Infrastructure (AMI), Distribution Management System (DMS), Distribution Automation Circuit Reconfiguration (DACR), Volt VAR Optimization (VVO), and Consumer Programs (CP). These technologies were combined with two-way consumer communication and information sharing, demand response, dynamic pricing, and consumer products, such as plug-in electric vehicles and smart appliances. In addition, the Project incorporated comprehensive cyber security capabilities, interoperability, and a data assessment that, with grid simulation capabilities, made the demonstration results an adaptable, integrated solution for AEP Ohio and the nation.

This paper summarizes the results of a study on improvement in the Volt/ Var Optimization (VVO) capabilities due to Advanced Metering Infrastructure (AMI) in comparison with a currently operating advanced VVO application that does not use the AMI. The potential incremental VVO benefits attributable to AMI implementation are detailed based on a utility's prospect.

This paper presents a new class of false data injection attacks (FDIAs) on volt/VAR optimization (VVO), which may result in abnormal voltage conditions along the radial medium voltage (MV) distribution feeder with an on-load tap changer (OLTC), capacitor banks (CBs), solar photovoltaic (PV) systems, and smart meters. In comparison with existing FDIAs against voltage control that do not consider the VVO process, we propose a new attack strategy with which the adversary can maliciously change the distribution feeder voltage profile by misleading the VVO function through stealthily injecting false data into smart meter measurements that are used for the VVO. The proposed attack strategy is formulated as a bilevel optimization problem using mixed integer linear programming (MILP). Injected false load data that raise or lower the tap position of the OLTC are calculated at the upper level while the VVO process is guaranteed to correctly operate with false data at the lower level. The bilevel optimization problem is finally reformulated to a single-level optimization problem based on Karush–Kuhn–Tucker conditions of the lower level optimization problem. A simulation study is carried out in an IEEE 33-bus distribution system with an OLTC, CBs, PV systems, and smart meters, and our results demonstrate the feasibility and capability of the proposed attack approach in terms of voltage level, attack effort, and PV penetration rate.

This paper investigates a real-time communication platform for a Smart Grid adaptive Volt-VAR Optimization (VVO) engine. Novel VVO techniques receive inputs from Advanced Metering Infrastructure (AMI) to dynamically optimize distribution networks. As communication platform design and characteristics affect Smart Grid-based VVO performance in terms of control accuracy and response time, VVO ICT studies is essential for grid planners and/or power utilities. Hence, this paper primarily introduces a real-time co-simulated environment comprised of Smart Grid adaptive VVO engine, RTDS model and system communication platform using DNP3 protocol. This platform is built to test and asses the influence of different components included in Smart Grid monitoring and control system; namely the sensors, measurement units, communication infrastructure on the operation and control of VVO. Moreover, this paper uses a real-time platform to check the robustness of the monitoring and control applications for communication network considerations such as delays and packet loss. Next, this paper investigates how such a platform could look into communication issues while taking system requirements into consideration. A 33-node distribution feeder is employed to check system performance through communication parameters such as throughput and response time.

Volt/VAR Optimization (VVO) function is an important element in real time operation of distribution networks and major part of advanced Distribution Management Systems (DMS). From planning prospective, VVO function can be used to optimize reactive power flow in distribution network to recommend the best operating condition for the control equipment in a predefined period of time in future (i.e. 24 hour). The typical objective function of VVO functions are minimizing the total system loss for a certain system load level. VVO function computes the optimized setting for transformer on-load tap changers (OLTC), Voltage Regulators (VR), and Capacitor Banks, while system voltage profile is maintained within its limits. In this paper the objective is to develop a planning VVO engine which can calculate the most probable expected loss of the network for the next 24 hours, and can recommend the best expected operating condition for the control equipment. For the VVO algorithm a full mixed integer linear programming (MILP) model is used to solve the loss objective of VVO problem for a planning application. The load uncertainty is modeled by an ARMA model which can create any arbitrary number of forecasted load scenarios to be used by VVO engine (implemented in a commercial solver GAMS, “General Algebraic Modeling System”). The implemented models have been tested on a real distribution network in southern Sweden and the results are presented.

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.

Smart grid adaptive volt-VAR optimization: Challenges for sustainable future grids

This paper addresses the issues of VVO (Volt/VAr Optimization) such as loss minimization, acceptable voltage profiles and optimized number of switching operations. Basic function of the DMS (Distribution Management System) is to upgrade system intelligence so that it can make dynamic decisions and control the network in realtime. Distributed generators can cause the system to operate above and below the desired limits due to their variable nature. Therefore, devices like SC (Shunt Capacitors) and OLTC (On Load Tap Changers) are used in distribution system as control devices. Main focus of this paper is to inspect effects of DG (Distributed Generation) on switching states of control devices while considering Volt/VAr standards. An optimization search algorithm is employed to search the optimal solution considering the system constraints. The GA (Genetic Algorithm) is used for the optimization process of the system and the simulation is done in MATLAB using IEEE-30 bus system with DG under 24 hour changing load profiles. By setting up constraints of distribution system’s voltage limits, capacitor bank and OLTC, losses are minimized up to 50%. Merits of the proposed optimized method are demonstrated through simulation results .The result achieved from the proposed technique has proven to be beneficial for switching optimization of control devices under variant conditions of loads and distributed generation

Smart Grid functions such as Advanced Metering Infrastructure, Pervasive Control and Distribution Management Systems have brought numerous control and optimization opportunities for distribution networks through more accurate and reliable techniques. This paper presents a new predictive approach for Volt/VAr Optimization (VVO) of smart distribution systems using Neural Networks (NN) and Genetic Algorithm (GA). The proposed predictive algorithm is capable of predicting the load profile of target nodes a day ahead by employing the historical metrology data of Smart Meters, It can further perform a comprehensive VVO in order to minimize distribution network loss/operating costs and run Conservation Voltage Reduction (CVR) to conserve more energy. To test the merits of the proposed algorithm, British Columbia Institute of Technology north campus distribution grid is used as research case study.

The electricity industry has been developed through the introduction of the smart grid. This has brought about two-way communication to the grid and its components. The smart grid has managed to increase the efficiency and reliability of the traditional power grid over the years. A smart grid has a system that is used to measure and collect readings for power consumption reflection, and the system is known as the Advanced Metering Infrastructure. The advanced metering infrastructure has its components too which are the smart metre, metre control system, collector or concentrator and communication networks (wide area network, neighbourhood area network, and home area network). The communication networks in the advanced metering infrastructure have created a vulnerability to cyber-attacks over the years. The reliability of the power grid to consumers relies on the readings from the smart metre, and this brings about the need to secure the smart metre data. This article presents a review of key management methods in advanced metering infrastructure environments. The article begins with an overview of advanced metering infrastructure and then shows the relationship between the advanced metering infrastructure and the smart grid. The review then provides the security issues related to advanced metering infrastructure. Finally, the article provides existing works of key management methods in advanced metering infrastructure and future directions in securing advanced metering infrastructure and the smart grid.

Volt/Var Optimization (VVO) technology helps to ensure that power is delivered to customers at optimal power factor to minimize line loss and maintain acceptable voltage at all points along the distribution feeder. Meanwhile, applying Conservation Voltage Reduction (CVR) will lower the customer's peak demand to conserve more energy. Advanced Metering Infrastructure (AMI) realizes the voltage monitoring and management at customers' side. This paper presents an VVO approach associated with CVR using prime-dual interior point method (PDIPM) applied on a system model based with AMI data, to make customers' voltages achieve the lower portion of the accepted range and meanwhile minimize the sum of power loss and power demand. Compared to the widely used algorithm, i.e. Genetic Algorithm (GA), PDIPM has an obvious advantage at better operation regularity of capacitors and more energy saving. IEEE 33 bus test system, as well as a practical distribution network is adopted for the validation and comparison. The results show that PDIPM is stable and effective for VVO application in power industry.

Impact of EV penetration on Volt–VAR Optimization of distribution networks using real-time co-simulation monitoring platform

Demand Response with Volt/Var Optimization for unbalanced active distribution systems

Sensitivity analysis of volt-VAR optimization to data changes in distribution networks with distributed energy resources