SGSim: A Unified 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 , 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 & 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), 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 resources The power grid analysis engine performs power flow such as conventional generators, PV generators and wind analysis and fault current calculation for the given distribution turbines. Each DER is implemented as an agent model, and network. Currently it supports both single and three phase has information of installed location. Its operation depends on static power flow analysis for radial and loop networks. To its physical model. For example, the generation power of a PV make the analysis more realistic, it is designed to take less system can be modeled as a function of time, weather and than 3 seconds for 30,000 customer nodes in a single installed location. SGSim has a weather simulation platform simulation step for normal case. Fault calculation is activated for advanced function support. To consider the distribution by fault events and takes less time than real-system operation location effect of renewable generator, PV, clouds are time resolution. Therefore, it is so enough that conventional operation system cannot distinguish from signals obtained from real systems. The analyzed results are shared by entire systems by subscribing to the SGSim data bus.

E. Real-Time Data Sampler The real time data sampler extracts the corresponding data from the power grid analysis results for each field switch or sensor device in the distribution network.

F. IED emulators Intelligent electric device emulators are developed to establish distribution automation environment. Each emulator obtains measured data from the real-time data sampler with measurement errors and delays which are determined by Figure 3 Voltage drops in distribution network without DER. individual pre-set characteristics. Autonomous IED emulation agents carry out grid protection activities according to its set Figure 6 illustrates the impact of DER (PV) installation in value such as inverse time and over current limit values. The distribution line DL#3. It shows the effect of DER installation IED emulators generate appropriate event signals for further locations comparing two cases: at the front and rear of the applications. Both internal event signal and DNP 3.0 protocol distribution line. The farther DER installed location is, the based real field communication signal can be generated in larger voltage changes. By performing demand response for SGSim. By connecting the DNP based signal directly to a real customers or adopting energy storage system, the voltage drop distribution automation system, it is possible to use the system can be enhanced and well managed. as a part of smart grid simulation with SGSim.

G. Control and Management Systems The control and management systems include distribution automation systems (DAS), substation automation systems and EMS. Electricity market system can be also added if needed. Since currently SGSim focuses on distribution network, a distribution automation system is considered. SGSim supports virtual AMI that can communicate with customer agents interchanging metering and price information. The information is handled via a meter data management system (MDMS). These management systems can be substituted with the real operating system via external interfaces of the SGSim platform. CIM interfaces are supported for sharing data to synchronize the system Figure 4 Voltage changes in DL#1 with DER (PV). configurations.

III. CASE STUDY AND DISCUSSIONS Figure 7 shows voltage changes over time when clouds move from the rear part of the distribution line to front. PVs Three radial distribution lines that are named as DL #1, #2, are uniformly installed throughout DL#3. The voltage drops and #3 from the top as shown in Figure 4 are considered in the fast first and slowly comes back. SGSim can simulate these case study. The lines are about 35 km long with 10MW effects with a single unified platform. capacity and have light, middle and heavy load profiles for DL #1, #2, and #3, respectively. Therefore the voltage drop over the distribution line is larger in #3 than others as shown in Figure 5 without having DER.

Figure 5 Voltage change over time when clouds move from rear to front along distribution line DL#3 with PV penetration. Figure 2 A distribution network used for case study. With SGSim line faults can be made any location of distribution lines any time during simulation. Figure 8 shows a line fault case that occurred in the middle of DL #2. A recloser IV. CONCLUSIONS right in front of the fault location is closed, and customers past This paper proposed a full-scale unified simulator for to the recloser are experiencing outage which is illustrated in smart grid. The architecture of SGSim and its platforms are red color. Switches experiencing overcurrent generate fault explained. To obtain performance and scalability, SGSim is index signals to the distribution operation system for manual implemented with a multi-platform model. SGSim can handle or automated operation management. not only static power analysis but also fault analysis for smart grid distribution networks. It also cooperates with smart grid resources such as demand response, DERs, energy storage, and EVs that are implemented in agent-models separately. Especially, by adopting intelligent electric device emulators, SGSim plays the role of a real power grid, and generates measurement and control signal based on standard protocols. In this way, SGSim is connected to a conventional distribution automation system. It provides flexible and easy development and evaluation environments for smart grid by combining standard communication based electric device emulators and distribution system analysis into one.

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