Proceedings of the AAAI Workshop on Artificial Intelligence for Cities, Austin, USA, 2015. The Smart Grid allows users to monitor power usage through the use of Smart Meter technology. In principle, this information can be used to modify usage habits in a way that reduces consumer costs as well as greenhouse emissions. However, in an urban environment, many users are restricted by the same constaints: they work during the day, and they are home at night. This creates spikes in power cost at peak usage times, and it may also lead to increased emissions in scenarios where sustainable resources are limited. An individual user can avoid these spikes by using an electric car as a storage device; it can be charged at the cheapest times, and then discharged to the home at the most expensive times. While this idea is intuitively appealing, it turns out that the benefits vary greatly depending on the storage algorithm used. In this paper, we describe the Power Storage Simulator, a tool for experimenting with storage algorithms to improve the efficiency of vehicle to grid systems. We suggest that this tool is also useful for educating power consumers about load balancing on the Smart Grid through an engaging, visual simulation., Conference paper, Published.
This paper aims to investigate the impact of different Electric Vehicle (EV) penetration on quasi real-time Volt–VAR Optimization (VVO) of smart distribution networks. Recent VVO solutions enable capturing data from Advanced Metering Infrastructure (AMI) in quasi real-time to minimize distribution networks loss costs and perform Conservation Voltage Reduction (CVR) to save energy. The emergence of EVs throughout distribution feeder increases grid complexity and uncertainty levels that could affect AMI-based VVO objectives. Hence, this paper primarily introduces an AMI-based VVO engine, able to minimize grid loss and Volt–VAR control assets operating costs while maximizing CVR benefit. It then presents a real-time co-simulation platform comprised of the VVO engine, grid model in a real-time simulator and monitoring platform, communicating with each other through DNP.3 protocol, to test the precision and performance of AMI-based VVO in presence of different EV penetration levels. Accordingly, 33-node distribution feeder is studied through different EV penetration scenarios. The results show significant changes in AMI-based VVO performance especially in CVR sub-part of VVO according to EV model and type. Thus, this study could lead near future VVO solutions to gain higher levels of accuracy and efficiency considering smart microgrid components such as EV in their models., Article, Published. Received 27 November 2015, Revised 8 January 2016, Accepted 22 January 2016, Available online 16 February 2016.
Proceeding of IEEE ElectricalPower and Energy Conference (EPEC 2013), Aug. 2013, Halifax, Canada. Deployment of Smartgrid downstream features such as Smart Metering, pervasive control and Distributed Management Systems has brought great opportunities for distribution network planners to optimize the network in more precise methods. Moreover, the advent of Electric Vehicles (EVs) has brought more opportunities for grid optimization. Recent studies stipulate that EVs are able to inject reactive power into the grid by changing their inverter's operating mode. This paper primarily discusses a real-time adaptive Volt/VAr Optimization (VVO) engine, designed to minimize system apparent power losses, optimize voltage profiles, and reduce the operating costs of Switched Capacitor Banks of the grid. The paper goes on to study the impact of EVs on the distribution network VVO, taking into account different EV charging and penetration levels and checks the validity of the proposed algorithm by employing revised IEEE-37 Node Test Feeder in presence of various load types as a case study., Conference paper, Published.
Proceedings of IEEE Electrical Power And Energy Conference, London, Ontario, Oct. 2012. This paper proposes a multi-agent based control system for real-time and adaptive Volt/VAR Optimization (VVO) and Conservation Voltage Reduction (CVR) in Smart Substations. The design and implementation of the proposed distributed control system using agent technology is discussed in the paper. Furthermore, the architecture, tasks and limits of each Intelligent Agent (IA) as a component of a multi-agent system (MAS) have been explained. A number of control functions are simulated and the results are presented in the paper. The results obtained demonstrate the potential of MAS for improving the efficiency of the system., Conference paper, Published.
In recent years, Smart Grid technologies such as Advanced Metering, Pervasive Control, Automation and Distribution Management have created numerous control and optimization opportunities and challenges for smart distribution networks. Availability of Co-Gen loads and/or Electric Vehicles (EVs) enable these technologies to inject reactive power into the grid by changing their inverter’s operating mode without considerable impact on their active power operation. This feature has created considerable opportunity for distribution network planners to explore if EVs could be used in the distribution network as reliable VAR suppliers. It may be possible for network operators to employ some EVs as VAR suppliers for future distribution grids. This paper proposes an innovative Smart Grid-based Volt-VAR Optimization (VVO) engine, capable of minimizing system power loss cost as well as the operating cost of switched Capacitor Banks, while optimizing the system voltage using an improved Genetic Algorithm (GA) with two levels of mutation and two levels of crossover. The paper studies the impact of EVs with different charging and penetration levels on VVO in different operating scenarios. Furthermore, the paper demonstrates how a typical VVO engine could benefit from V2G’s reactive power support. In order to assess V2G impacts on VVO and test the applicability of the proposed VVO, revised IEEE-123 Node Test Feeder in presence of various load types is used as case study., Article, Published. Received 24 May 2014, Revised 23 July 2015, Accepted 29 July 2015, Available online 8 August 2015.
Proceedings from CIGRÉ Canada Conference, Montreal, Sept. 2012. In recent decade, smart microgrids have raised the feasibility and affordability of adaptive and real-time Volt/VAr optimization (VVO) and Conservation Voltage Reduction (CVR) implementations by their exclusive features such as using smart metering technologies and various types of dispersed generations. Smart distribution networks are presently capable of achieving higher degrees of efficiency and reliability through employing a new integrated Volt/VAr optimization system. For VVO application, two well-known approaches are recommended by different utilities and/or companies: Centralized VVO and Decentralized VVO. In centralized VVO, the processing system is placed in a central controller unit such as DMS in the so called “Utility Back Office”. The DMS uses relevant measurements taken from termination points (i.e. utility subscribers) supplied to it from either field collectors or directly from MDMS, to determine the best possible settings for field-bound VVO/CVR assets to achieve the desired optimization and conservation targets. These settings are then off-loaded to such assets through existing downstream pipes, such as SCADA network In contrast, decentralized VVO utilizes VVO/CVR engines which are located in the field and in close-proximity to the relevant assets to conserve voltage and energy according to local attributes of the distribution network. In this case, local measurements do not need to travel from the field to the back-office, and the new settings for VVO/CVR assets are determined locally, rather than from a centralized controller. Without having any preference between above mentioned VVO techniques, this paper studies an adaptive optimization engine for real-time VVO/CVR in smart microgrids based on Intelligent Agent technology. The optimization algorithm provides the best optimal solution for VVO/CVR problem at each real-time stage through minimizing system loss cost and improves system energy efficiency as well as voltage profile of the relevant distribution system. The algorithm may employ distributed generation sources to address the Volt/VAr optimization problem in real-time. Coordinated VVO/CVR requires real-time data analysis every 15 minutes. It utilizes a distributed command and control architecture to supply the VVO Engine (VVOE) with the required data, and secures real-time configuration from the VVO engine for the VVO control devices such as On-Load Tap Changers (OLTCs), Voltage Regulators (VRs) and Capacitor Banks (CBs). It also has the option of employing distributed generation (DG) as well as modelling load effects in VVO/CVR application. The algorithm minimizes the distribution network power loss cost at each time stage, checks the voltage deviation of distribution buses and distributed generation sources considering different types of constraints such as system power flow, distribution network power factor, system active and reactive power constraints and switching limitations of Volt/VAr control devices. The algorithm receives required real-time data from an intelligent agent. Then, it starts to solve the real-time VVO/CVR problem in order to find the best optimal configuration of the network in real-time. The paper uses British Columbia Institute of Technology (BCIT) distribution network as its case study in order to explore the effectiveness and the accuracy of the optimization engine. Moreover, the VVO/CVR optimization algorithm is implemented in different configurations; a) VVO/CVR confined to the substation and b) VVO/CVR optimization algorithm within the substation and along distribution feeders. The algorithm also checks the availability of DGs to assist VVO/CVR control functions and assesses the impact of new distributed sources such as: Flywheel Energy Storage System (FESS) on real-time VVO/CVR. For this reason, the algorithm classified DGs in a microgrid based on their impacts and instantiates them based on their application feasibility for real-time VVO/CVR., Conference paper, Published.
This paper aims to present a novel smart grid adaptive energy conservation and optimization engine for smart distribution networks. The optimization engine presented in this paper tries to minimize distribution network loss, improve voltage profile of the system and minimize the operating cost of reactive power injection by switchable shunt Capacitor Banks using Advanced Metering Infrastructure data. Moreover, it performs Conservation Voltage Reduction (CVR) and minimizes transformer loss. To accurately weight the optimization engine objective function sub-parts, Fuzzification technique is employed in this paper. Particle Swarm Optimization (PSO) is applied as Volt-VAR Optimization (VVO) algorithm. Substantial benefits of the proposed energy conservation and optimization engine include but not limited to: adequate accuracy and speed, comprehensive objective function, capability of using AMI data as inputs, and ability to determine weighting factors according to the cost of each objective sub-part. To precisely test the applicability of proposed engine, 33-node distribution feeder is used as case study. The result analysis shows that the proposed approach could lead distribution grids to achieve higher levels of optimization and efficiency compared with conventional techniques., Article, Published. Received 27 November 2015, Revised 13 April 2016, Accepted 16 April 2016, Available online 26 April 2016.
In recent years, smart grid technologies such as Distribution Management Systems (DMS) and Advanced Metering Infrastructure (AMI) have created remarkable opportunities for distribution grids in terms of operation, control and optimization. The advent of AMI has created considerable amount of data that can be used in optimization applications. Other smart grid functionalities could increase the performance of energy conservation and optimization solutions. As such, this paper aims to review the main requirements of two important smart grid adaptive energy conservation and optimization solutions called Volt-VAR Optimization and Conservation Voltage Reduction, in terms of control, measurement, communication and standards for grids., Article, Published. Received 13 May 2016, Revised 13 September 2016, Accepted 22 September 2016, Available online 3 October 2016.