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Sgsim: a Unified Smart Grid Simulator SGSim: A Unified Smart Grid Simulator Sung-Yong Son, Member, IEEE, Jaewoong Kim, Seung-Min Lee, Sungwon Park, Beom Jin Chung Department of Energy and Information Technology Gachon University Seongnam, Korea {xtra, violit, smthis, swpark, bjchung}@gachon.ac.kr Abstract—SGSim is a unified simulator to enable development simulate power and communication systems altogether [5]-[9]. and evaluation of new operation algorithms and technologies for Some others designed smart grid simulator with combining smart grids. SGSim provides full-scale distribution side virtual commercial software or hardware simulators [10][11]. A smart environment that smart grid is pursuing by combining power city simulator is developed as a multi-agent model with many flow and communication altogether. Essential elements such as households and EVs to identify the relationship among power customers, intelligent electronic devices, and renewable consuming entities [12]. Smart grid simulators are also used to resources, are designed as agent models and they interact and optimize energy purchase, distributed generator installation communicate with each other or with operation systems. In locations, or greenhouse gas emission [13][14]. addition, to consider the regional characteristics of renewable generations weather simulation is combined. It has a multi-agent Most researches, however, have limitations in their and multi-platform architecture for scalability and flexibility. scalability and simulating more complex situations. SGSim With the simulation system, unexpected impact hidden behind pursues more complex and large-scale smart grid simulation complex correlations and influence propagation for specific by adopting multi-platform architecture as well as multi-agent events can be easily monitored and evaluated. approaches. Index Terms—SGSim, smart grid, simulator, multi-agent, multi- II. SYSTEM ARCHITECTURE OF SGSIM platform SGSim simulates full-scale distribution-side smart grid environment including participating customer, distributed I. INTRODUCTION generator, and operation system models by unifying physical A simulator is a practical and essential tool in developing power grid and information systems. As shown in Figure 1, and evaluating new technologies in complex and future- SGSim first simulates usual life power consumption from the oriented environments such as smart grid. Conventional view point of power grid. It allows of adding new smart grid simulations for power grid have tended to focus on a single elements such as SP, ST, SR, SPG and SS which mean smart view point such as power flow analysis, fault analysis, and place, smart transportation, smart renewable, smart power grid communication analysis depending on their goals. Traditional and smart service, respectively. separate approaches, however, are not enough to observe and analyze the interacting effects of intelligent but complex smart SGSim creates customer-side load and power generation grid. profiles based on history or statistical data. They are connected to the corresponding distribution network and work Recently there have been some research efforts on smart as a single power system. Simultaneously, they are influenced grid simulators. The necessity of fast simulation and modeling by the distribution network with the supply voltage, power especially for distribution networks is proposed to identify quality, etc. Based on the amount of aggregated loads and issues in self-healing smart grid and to perform analysis of the locations in the grid, power flow occurs over distribution lines. system in advance. Issues in distribution system analysis for In SGSim, system devices installed in distribution lines are the smart grid are reviewed and key challenges are studied, emulated, and according to the given characteristics they and high penetration of DER, increased demand response, and generate the monitoring information. Since the generated data higher constraints of distributions networks are mentioned as can be sent to a distribution automation system in a standard key features for simulators [1][2]. Researches on smart grid protocol, the system recognizes the obtained information as simulators and the differences between smart grid and real ones. With the information, operators or automated traditional grid are reviewed and their requirements are also algorithms are able to control the devices in the smart grid. discussed [3][4]. The control changes the configuration and status of the grid Many approaches for smart grid simulation are based on and again affects customer side environment such as voltage, multi-agent model because it is good to implement complex power quality, etc. Each customer can reduce or shift its own objects and their relationships. They usually define customer loads depending on the prices signal and /or demand response side entities such as loads and generators as agents and try to This work is supported by the power generation & electricity delivery program of Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Knowledge Economics under the project 211101050011C. 978-1-4799-1303-9/13/$31.00 ©2013 IEEE signals. In the way, the simulator composes a unified smart phenomena that they want to observe. It also provides grid eco-system. interface for external systems to add user defined functions and algorithms. The following requirements are considered for SGSim: • Unified environment of power and communication B. Customer-Side Simulation Platforms systems The main function of the customer-side simulation • platforms is to generate customer side loads and power Sufficient performance of real-time simulation generations for a specific time. The platforms basically create • Replay or generation of accurate seamless data customer loads and powers generated by DERs. In the real throughout the entire simulation system world, very limited data can be obtained from a distribution network because there are a few sensors in the field even if • Standard and/or open interface with external systems they are fully automated and accurate enough. Furthermore, the collected data have problems in accuracy, time resolution • Scalability in simulation size and synchronization among sensors. Two main approaches are • User algorithm support used in SGSim: statistics-based creation and history-based refinement. The statistics-based creation is performed • Ability to accept future smart grid services considering the average load profiles and demographic and • Communications with standard protocols geographic characteristics of the region. The merit of this approach is that it is possible to know the exact load profile for each customer and has less constraint. The approach has As shown in Figure 2 SGSim is designed to have a multi- advantages in developing new operation algorithms. History- platform architecture to support scalability and flexibility in based refinement is similar to the first one in creating implementing realistic smart grid operation environment. customer’s data, but it is based on real field data obtained from grid. Since the data include errors and noise, it is A. SGSim Platform necessary to be refined with additional tools such as state estimators before using. Although this approach can be The SGSim platform is the core management module of considered more realistic, in actual not sufficient data can SGSim that unifies and coordinates the entire simulation attenuates the merit. AMI data can be used to enhance data systems. It has high performance data bus, SGSim data bus to mismatch problems. share synchronized data and a timekeeper to maintain simulation time and the size of time steps for simulation. The timekeeper supports dynamic time steps depending on Figure 1 The concept diagram of SGSim. Figure 2 The architecture of SGSim. modeled based on weather history data. The power generation amount over time for each PV module is determined by combining with a solar position calculation module. Other smart grid components such as electrical vehicles (EV), energy storage systems (ESS), etc. can also be added as agents or platforms and interact with each other. Since every customer and DER agents can react from the signal obtained from other systems by adding user defined algorithms such as demand response, ESS operation, EV charging, etc. can be easily implemented to each agent. Furthermore, by grouping and giving direct commands to them, more complex operation models such as load aggregators, VPPs, etc. can be implemented. C. Grid Mapper and Configuration Manager Figure 3 The real-time load generation platform interface. Loads and powers need to be connected to the appropriate location of the distribution network. The grid mapper allocates Figure 3 shows a customer-side load generation interface. these to the network in cooperation with the configuration Main load consuming entities such as home appliances are manager. The configuration manager handles distribution modeled as sub-agents to consist a single customer. Their network configurations and performs topology analysis individual action is designed to match with the statistical data according to the status of switches in the network. The of the region. Using the agent approach, the entities can be topology generally changes when the switch status change or controlled separately to implement demand response services fault occurs. with pre-set scenarios or user-defined algorithms. D. Power Grid Analysis Engine DER simulation handles distributed
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