Centre for smart grid analysis with real time simulation platform

Power systems have been undergoing radical changes in recent years, and their planning and operation will be surely undertaken according to the Smart Grid (SG) vision in the near future. The SG initiatives aim at introducing new technologies and services in power systems, to make the electrical networks more reliable, efficient, secure and environmentally-friendly. In particular, it is expected that communication technologies, computational intelligence and distributed energy sources will be widely used for the whole power system in an integrated fashion. In particular, nowadays, unprecedented challenges like as stringent regulations, environmental concerns, growing demand for high quality, reliable electricity and rising customer expectations are forcing utilities to rethink about electricity generation and delivery from the bottom up. Moreover, the availability of low cost computing and telecommunications technologies, new generation options, and scalable, modular automation systems push utilities to be dynamic, innovative and ambitious enough to take advantage of them. Driven by the dynamics of the new energy environment, leading utilities, technology vendors and government organizations have created a vision of the next generation of energy delivery systems: the Smart Grid. Operational changes of the grid, caused by restructuring of the electric utility industry and electricity storage technology advancements, have created an opportunity for storage systems to provide unique services to the evolving grid. Especially Battery Energy Storage Systems (BESSs), thanks to the large number and variety of services they can provide, are powerful tools for the solution of some challenges that future grids will face. This consideration makes BESSs critical components of the future grids. The BESS can be applied for different services into the different levels of power system chain to satisfy technical challenges and provide financial benefits. In the context of the application of BESSs in SGs, there are two main problems that need to be addressed in a way that exploits the BESS potential, that are linked to their operation and sizing. This thesis focuses on both these aspects, proposing new strategies that allow optimizing the BESS adoption. When dealing with BESSs, sizing and operation are strictly linked. The correct sizing of a BESS, in fact, needs to take into account its operation which in turn will be effected with the aim of optimizing the whole system where it is included. In the first part of this research study, advanced optimal operating strategies were proposed for BESSs by considering both the distribution system operator perspective and the end user. Thus, the proposed operating strategies were performed with the aim of (i) leveling the active power requested by the loads connected to a distribution system (distribution system operator service), (ii) reducing the electricity costs sustained by an end-use costumer that provides demand response (DR) (end user service) and (iii) scheduling a microgrid (µG) with DR resources such as Plug-in Electric Vehicles (PEVs) and Data Centers (DCs) (both the two section service). The proposed strategies also satisfied technical constraints of BESSs and other components of the µG. The second part of the thesis presented the optimal sizing of BESSs aimed at maximizing the benefits related to their use. In the thesis, the sizing, which is performed by considering the end user point of view with reference to both the industrial and residential customers, is effected by adopting both deterministic and probabilistic approaches. With reference to the deterministic approach, a simple and quick closed form procedure for the sizing of BESSs in residential and industrial applications was proposed. In case of probabilistic approach, the case of a BESS installed in an industrial facility was considered and the sizing was performed based on the decision theory. Technical improvements and economic benefits of optimal operation and optimal sizing of BESSs in SG are demonstrated by the obtained results which are reported in the numerical applications. More specifically, it was clearly determined that BESSs can offer technical supports into the distribution operator section of the grid in terms of load management and security challenges. Moreover optimal integration of BESSs into the grid was also appealing for end users thanks to valuable amounts of electricity bill cost reduction. Regarding the original contribution of the thesis, the following considerations can be done. With reference to the load leveling service, an innovative two-step procedure (day-ahead scheduling and very short time predictive control) was proposed which optimally controls a BESS connected to a distribution substation in order to perform load leveling. In case of DR, a proper control of the BESS was proposed in order to perform DR under different price schemes, such as Real Time Pricing (RTP) and Time of Use (TOU) without modifying the daily work cycle of the industrial loads. The control procedure allows achieving contemporaneously two important goals that are the reduction of the bill costs and the prolonging the battery's lifetime so further reducing the costs sustained by the customer. With reference to the scheduling of microgrids, the original contribution of the thesis is focused on the proposal of optimization strategies aimed at managing and coordinating, simultaneously, batteries on board of vehicles or equipping data centers' Uninterruptable Power Supply (UPS) and Distributed Generation (DG) units. Also comparisons among different single-objective based strategies are made in order to highlight the most convenient. With reference to the sizing based on deterministic approach, unlike the other relating literature, the innovative contribution is that the closed form procedure takes into account both the technical constraints of the battery and contractual agreements between the customer and the utility. Moreover, in the economical analysis performed for the sizing, which is applied with reference to both residential and small industrial customers and is based on actual TOU tariffs, a wide sensitivity analysis to consider different perspectives in terms of life span and future costs was performed. Some aspects that affect the profitability of the battery, such as technological limitations (e.g. the battery and converter efficiency), economic barriers (e.g. capital cost and the rate of change of the cost) and variation of the load profile along the years were deeply analyzed. In case of sizing based on probabilistic approach, the original contributions of the thesis are mainly referred to the proposal of a new method that uses a decision theory-based process to obtain the best sizing alternative considering the various uncertainties affecting the sizing procedure. The thesis is organized in three chapters which are dealing with integration of BESSs in SGs. The first chapter reports basic concepts and characteristics of BESSs, fundamental components and features of SGs and different services that BESSs can provide. The optimal operation strategies of BESS are considered in second chapter which includes their problem formulation, solving procedures and results. The third chapter deals with the optimal sizing problem of BESSs for which the problem formulation, solving procedures and results are reported. Finally, the conclusions are presented in the last part of thesis.

A power hardware-in-the-loop (PHIL) facility for grid-linked energy storage is evaluated in this research in terms of communication latency, and power system model performance. The PHIL consists of a 240 kVA 180 kWh Siemens SieStorage battery energy storage system (BESS), which via a dSPACE real-time platform is interfaced using TCP/UDP Ethernet to a real-time digital simulator (RTDS). The communication latency has been monitored at the dSPACE and RTDS side, and for the ramp signal used in the loop-back test the average latency was only 2.32 ms, and 99.8% of the transitions analysed had a 2 or 3 ms latency. The RTDS was then demonstrated to command the BESS, and the overall time delay in this test was 646 ms, of which, over 50% is due to the communication between the dSPACE and SieStorage system. The Ethernet comms latency is several orders of magnitude smaller than the BESS response time, and so this enables the PHIL to be used in future studies requiring a fast system response, such as frequency regulation, with a high accuracy. The RTDS was then used to command the operation of the BESS, and the measured real-time data from the BESS was used to impose changes on a basic power system model running in real-time on the RTDS.

Testing battery pack configurations for their performance under different dynamic load profiles for grid and electric vehicle applications is expensive. Lowering the cost to understand the response of packs and their control strategies is critical. Power hardware in the loop (PHIL) simulation is widely used in power engineering for system and component testing. A simulator computes the model output in real-time, and then controls a power source to generate a dynamic power output with the expected current and voltage response of the emulated battery pack. This emulated battery pack power output can be coupled to a physical load to perform testing.1 Small time steps are useful to observe the transient response of the emulated battery when there is a sudden change in the load. PHIL simulations can also be useful to study the transient response of controllers and inverters present in the battery management system by connecting these devices to the simulator. PHIL simulations can be used to test the model’s accuracy2, to conduct repeatability experiments and to test battery performance under extreme conditions without compromising on safety.3 It provides an efficient approach to designing and testing new configurations of batteries from data of a single cell without building their real prototypes. The computational speed required to solve the battery models chosen for PHIL simulations must be fast in order to achieve real-time implementation. Therefore, models chosen for these simulations tend to have less complexity. However, to provide a better understanding of a battery’s response under a specific load profile, it is necessary to use a model that that accurately represents the behavior of a real battery. Real-time simulations for physics-based battery models have been explored in the past.4 , 5 , 6 It is also essential to comprehend how different battery architectures and how aging affects the overall performance. In literature, equivalent circuit models that describe charge transfer and diffusion dynamics have been used as the main way to capture battery dynamics.7 , 8 We first show results generated using SIMULINK equivalent circuit models that are compiled to run fast on our Opal-RT real-time simulator. The Opal-RT simulator drives an AMETEK 40 kW power supply with feedback to the model, enabling an emulated battery pack response capable of 10ms dynamic control. We evaluate pack configurations with physical parameters that correspond to either LCO or NMC 18650 cells. Overall, this represents a 400V, 10kW pack. A programmable load is used to modulate the emulated battery pack and monitor its dynamic response. We further explore implementing single particle models with our PHIL simulations to help understand the internal dynamics of the cell captured by these models. Physics-based models have rarely been used for PHIL. Our results show that the PHIL emulation of the battery pack meets the expectations for the battery dynamics. However, we observe high frequency noise in the output signal of the emulated battery pack. Because this level of noise is not a trait present in real battery packs, we are exploring signal processing methods to filter and remove it, while retaining the essential dynamics of the emulated battery pack. References A. Viehweider, G. Lauss, and L. Felix, Simul. Model. Pract. Theory, 19, 1699–1708 (2011).Y. He, W. Liu, and B. J. Koch, J. Power Sources, 195, 2969–2974 (2010).H. Dai, X. Zhang, X. Wei, Z. Sun, J. Wang, and F. Hu, Electr. Power Energy Syst., 52, 174–184 (2013).P. W. C. Northrop, V. Ramadesigan, S. De, and V. R. Subramanian, J. Electrochem. Soc., 158, A1461 (2011).V. R. Subramanian, V. Boovaragavan, V. Ramadesigan, and M. Arabandi, J. Electrochem. Soc., 156, A260 (2009).P. W. C. Northrop, V. Suthar, Bharatkumar Ramadesigan, S. Santhanagopalan, R. D. Braatz, and V. R. Subramanian, J. Electrochem. Soc., 161, E3149 (2014).J. V. Barreras, C. Fleischer, A. E. Christensen, M. Swierczynski, E. Schaltz, S. J. Andreasen, and D. U. Sauer, IEEE Trans. Ind. Appl., 52, 5086–5099 (2016).H. Song, T. Kim, J. Jeong, B. Kim, D. Shin, B. Lee, and H. Heo, IET Power Electron., 6, 417–424 (2013).

The evolution from conventional to smart grids over the last decades has obliged the grid to be prone to challenges by means of secure and reliable operation. One of the most important facts about smart grids is the communication infrastructure, which is a key factor while maintaining the secure operation of smart grid systems. This paper will focus on the communication techniques used in smart grids and their applications. Communication techniques have a crucial importance to ensure the efficient, flexible, reliable, sustainable, secure and robust features of the smart grids. Long term benefits and challenges of the smart grid communication techniques are elaborated thoroughly. Quantitative and qualitative features of the communication techniques are classified. Existing communication techniques such as ZigBee, WLAN, cellular communication, Power Line Communication (PLC) are compared within the standards and protocols. With the extensive integration of distributed energy resource (DER)s to the smart grid, the communication network infrastructure used in these resources are investigated. Moreover, the future oriented techniques (FoT) are presented for consideration in smart grid applications.

Electrification of the transportation sector can play an essential role in curbing fossil fuel scarcity and oil shortages in the world. Electric vehicles (EVs) can significantly reduce CO2 emissions, improve urban pollution. Apart from these advantages of EVs, they may also pose challenges to the distribution grid. Increasing penetration of EVs puts an extra burden and leads to affect the grid severely. Load congestions, voltage drops/regulation, overloading are some of the issues that might incur in the grid because of improper charging of EVs. The uncertain nature of EV owners makes it very difficult to predict the charging pattern. So the grid operator may face a daunting task to avoid overloading if huge EVs are charged without following any smart charging management strategies. We propose a framework that allows EV users to follow a well-coordinated charging schedule and fulfill various objectives like load variance minimization and social welfare maximization for both electric utility and EV owners. A proof of concept of distributed resource allocation for EV charging is implemented using the output consensus approach. Microgrids are components of a smart power distribution grid that can operate as a decentralized/localized grid when disconnected from the main grid. A microgrid can enhance the grid resiliency and provide adequate power to a community or a region in case of grid failure caused by natural disasters or any other disruptions. We also propose the optimal scheduling model for Distributed Energy Resources (DERs) including an EV charging station and battery energy storage system (BESS) in a community microgrid. This model also minimizes the cost incurred in operating the DERs, charging station, BESS, and the grid load. Due to the two-way power flow in the smart grid because of the EVs and distributed energy resources (DERs) involvement, the distribution system operator (DSO) must provide control & protection to equipment and manage the reverse power flow without violating system constraints. Artificial Intelligence (AI) leads the path for a promising smart grid future and helps the DSO tackle such challenges. Given the large size of data flow in the smart grid due to phasor measurement units (PMUs) and smart sensors, AI-based techniques help the grid operator to manage and analyze such data effectively and improve grid resiliency. Machine learning can help the grid operator forecast the uncertainty in supply-demand because of the integration of renewable energy sources. For system planning and operation, the utility needs to perform the

Security issues are the major concerns in the deployment of the smart grid system. One proposal of Secure, Intuitive and Low-cost Device Authentication (SILDA) mechanism designed for the Home Area Networks (HANs) in smart grid has presented an authentication protocol which is resilient against insider attackers, man-in-the-middle (MITM) attacks and impersonation attacks. However, it encounters a problem with the key management and has been proved to be vulnerable to reply attacks and unknown key sharing attacks. In this paper, we propose an Enhanced Secure Device Authentication (ESDA) scheme for the HAN in smart grid. By our proposal, t he vulnerability of the SILDA protocol can be overcome. The Burrows-Abadi-Needham Logic (BAN Logic) has been used to provide a proof on the logical correctness of the proposed scheme.

As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs and increase reliability. In smart grid, control center networks have attracted a great number of attentions, because their security and dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of security, but few work focuses on the dependability analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the dependability of control center networks in smart grid. With the proposed SPNs model, we can measure the dependability from two metrics, i.e., the reliability and availability, through analyzing the transient probability. We also study a specific case to demonstrate the feasibility and efficiency of the proposed model in the dependability analysis of control center networks in smart grid.

This paper proposes a new configuration and novel reclosing procedure of a distribution system with a battery energy storage system (BESS) used as an uninterruptible power supply (UPS) in a smart grid. The proposed new configurations of the distribution systems are the installation of a circuit breaker (CB) on both sides of the distribution line, the replacement of the recloser with a CB and protective relay, and the requirement of a communication method. The proposed reclosing procedure performs the reclosing of the CB at the load side and then judges the fault clearance using the load current. If the fault is cleared, the synchronism checking between the main source and the BESS is performed. After completing this, the CB at the main source side is reclosed. The smart grid environment, including a new distribution system, BESS, and reclosing method are modeled with the Electromagnetic Transients Program (EMTP)/ATPDraw. To verify the proposed method, the various simulations according to the fault clearance time are performed and analyzed. The simulation results show that the BESS can be operated as a UPS and successful reclosing is possible.

In smart power grid, communication network is an integral fragment that communicate vital information to various stakeholders for efficient transmission of electricity. For autonomous functioning of power system, the sensor node connected to grid components enable information sharing for monitoring and control of the state of the grid. Also, the smart grid deployment scenarios require data communication to various grid nodes to be reliable, secure and standards compliant. In this paper, we have proposed a conceptual architecture for evaluating IoT deployment scenario and presenting power system simulation on real-time simulator and experimental evaluation of IPv6 based IETF 6TiSCH compliant grid nodes in smart grid scenario. The proposed architecture for simulation and experiments leverages Internet of Things (IoT) approach to address the smart grid requirement.

A project titled “Establishing the advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation,” was launched as Core Research for Evolutional Science and Technology (CREST) by the Japan Science and Technology Agency (JST). Intended to save as many lives as possible in future national crises involving earthquake and tsunami disasters, the project works on a disaster mitigation system of the big data era, based on cooperation of large-scale, high-resolution, real-time numerical simulations and assimilation of real-time observation data. The world’s most advanced specialists in disaster simulation, disaster management, mathematical science, and information science work together to create the world’s first analysis platform for real-time simulation and big data that effectively processes, analyzes, and assimilates data obtained through various observations. Based on quantitative data, the platform designs proactive measures and supports disaster operations immediately after disaster occurrence. The project was launched in 2014 and is working on the following issues at present.Sophistication and fusion of simulations and damage prediction models using observational big data: Development of a real-time simulation core system that predicts the time evolution of disaster effect by assimilating of location information, fire information, and building collapse information which are obtained from mobile terminals, satellite images, aerial images, and other new observation data in addition to sensing data obtained by the undersea high-density seismic observation network.Latent structure analysis and major disaster scenario creation based on a huge amount of simulation results: Development of an analysis and extraction method for the latent structure of a huge amount of disaster scenarios generated by simulation, and creation of severe scenarios with minimum “unexpectedness” by controlling disaster scenario explosion (an explosive increase in the number of predicted scenarios).Establishment of an earthquake and tsunami disaster mitigation big data analysis platform: Development of an earthquake and tsunami disaster mitigation big data analysis platform that realizes analyses of a huge number of disaster scenarios and increases in speed of data assimilation, and clarifies the requirements for operation of the platform as a disaster mitigation system.The project was launched in 2014 as a 5-year project. It consists of element technology development and system fusion, feasibility study as a next-generation disaster mitigation system (validation with/without introduction of the developed real-time simulation and big data analysis platform) in the affected areas of the Great East Japan Earthquake, and test operations in affected areas of the Tokyo metropolitan earthquake and the Nankai Trough earthquake.

The operation of smart power grids is supported by interconnected sensors and devices that provide real-time monitoring. Smart grids provide more secure and dependable electricity than traditional grids because of constant monitoring. This paper reviews the technology used in smart grids, specifically sensors and communication technologies. It also talks about the challenges that smart grids face, such as providing power to the network's sensors, islanding, cyber-security, and issues that occur when using distributed generators. Current solutions for sensing changes and communication of sensed data in smart grid networks are also discussed.

As an indispensable infrastructure for the future life, smart grid is being implemented to save energy, reduce costs, and increase reliability. In smart grid, control center networks have attracted a great deal of attention, because their security and dependability issues are critical to the entire smart grid. Several studies have been conducted in the field of smart grid security, but few work focuses on the dependability analysis of control center networks. In this paper, we adopt a concise mathematic tool, stochastic Petri nets (SPNs), to analyze the dependability of control center networks in smart grid. We present the general model of control center networks by considering different backup strategies of critical components. With the general SPNs model, we can measure the dependability from two metrics, i.e., the reliability and availability, through analyzing the transient and steady-state probabilities simultaneously. To avoid the state-space explosion problem in computing, the state-space explosion avoidance method is proposed as well. Finally, we study a specific case to demonstrate the feasibility and efficiency of the proposed model in the dependability analysis of control center networks in smart grid.

WSNs (Wireless Sensor Networks) have been considered one of the very promising technologies for the implementation of smart grid. And security of WSNs becomes a central concern. The security solutions for generic wireless sensor networks cannot be directly used in smart grid WSNs. In this paper, the applications of sensor networks in electric power systems are discussed and analyzed first. Then, the characteristics of smart grid WSNs are summarized. Threats and security requirements special for wireless sensor networks used in smart grid systems are presented. Based on these works, reference security architecture was proposed to guide the development and the design of the security solutions of wireless sensor networks in smart grid systems, considering the information security requirements of electric power systems. Moreover, open security issues needed solve to protect WSNs applied in smart grid, and research challenges are introduced.

For hybrid energy systems utilizing renewable energy sources such as the power grid of Phu Quy island - Binh Thuan province, the integration of battery energy storage system (BESS) could be seen as a practical solution with remarkable advantages including economic and technical preeminence. BESS contributes to increasing renewable energy penetration, enhancing system stability and reducing the dependence on fossil fuel generation. However, the integration of BESS into an existing hybrid power system also necessitates to conduct a quantitative analysis of the performance of different solutions. This study investigates operational data of Phu Quy power grid for one year, evaluates solar energy potential and proposes a proper BESS integration solution based on a careful quantitative analysis. The simulation results show that selection of larger BESS capacity increases penetration rate of renewable energy. Nevertheless, of all proposed BESS integrated scenarios, renewable energy penetration rate as well as diesel consumption do not show a major difference. Therefore, choosing BESS also needs to consider the load capacity and time required to start and interconnect standby diesel generators.

Guest Editorial: Situational awareness of integrated energy systems