Integrated fault detection algorithm for transmission, distribution, and microgrid networks

Future Smart Grids will need to integrate thousands of independently operated intelligent energy devices into one organizational unit. Distributed ledger technologies can provide the means for a business-driven consensus between supply and demand of electrical power. This approach is limited by the computing power of the participating devices. We describe a way to increase the scalability of this approach by a) aggregating power consumption and generation, thus reducing the computational load on the blockchain and by b) applying the concept of edge computing to increase reliability and reaction time. We propose a hierarchical clustering approach for smart grid power balancing, which aims to advance our understanding of architectures for future real-world smart grids.

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.

Grid inefficiency, grid instability, projected world energy consumption, decreasing use of fossil resources and reduction of the CO2 footprint, were the motivations to develop the distributed generation system (DGS) and the Smart grid. The future DGS and Smart grid worker will need a solid background at several disciplines such as engineering math and physics, electrical, electronic, power, control, information technology, and business & management. Motivated by the last facts several attempts have been proposed in colleges and universities to satisfy the current and future work force. Following this trend in this paper a new course in DG and Smart grid for an Electrical Engineering Technology Program at Mohawk College is presented. Fundamentals and practical recommendations based on inquiry-based model are also covered.

The development and study of Smart Grid technologies rely heavily on high-fidelity data from Phasor Measurement Units (PMUs). However, the scarcity of real-world PMU data due to privacy, security, and variability issues poses significant challenges to researchers, developers, and related industries. To address these challenges, this article introduces the bases for a digital metrology framework, focusing on a newly designed and developed synthetic PMU data generator, that is both metrologically accurate and easy to adapt to various grid configurations for data generation from point-on-wave (PoW) data. This initial phase for a Smart Grid research framework aligns with Open Science principles, ensuring that the generated data are Findable, Accessible, Interoperable, and Reusable (FAIR). By embracing these principles, the generated synthetic data not only facilitate collaboration for Smart Grid research but also ensure their easy integration into existing Smart Grid simulation environments. Additionally, the proposed digital metrology framework for Smart Grid research will provide a robust platform for simulating real-world scenarios, such as grid stability, fault detection, and optimization. Through this open science approach, future digital metrology frameworks can support the acceleration of research and development, overcoming current limitations, e.g., lack of significant amounts of real-world scenarios by PMU data. This article also presents an initial case study for situational awareness and control systems, demonstrating the potential for future Smart Grid research framework and its direct real-world impact. All research outcomes are provided to highlight future opportunities for reusability and collaborations by a novel approach for research on sensor network metrology.

A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid

The Phasor Measurement Unit(PMU) is used for accurate measurement of synchronous phasor of voltages and currents at separated points across the power grid and are widely installed at the transmission level. As the next point of setup in smart grid, PMU is necessary for components control, fault detection and control, energy management in distribution network. This paper first illustrates the architecture of PMU setup that has been fully designed by the authors and then describes the challenge of designing and implementing which integrates field measurements to deliver Global Positioning System (GPS) synchronized phasor data to the distribution networks. By using this platform, our efforts have provided the real-time data detected, power quality analysis and direction to future efforts to analyze data in cloud calculation, harmonic state estimation, etc.

An efficient dependable smart power grid relies on the secure real-time data collection and transmission service provided by a monitoring system. In such a system, the measuring units, such as phasor measurement units (PMUs) and smart meters (SMs), are critical. These measuring equipments function as sensors in the smart grid. Data exchanges between these sensors and the central controller are protected by various security protocols. These protocols usually contain computationally intensive cryptographic algorithms that cause heavy energy overhead to the sensor nodes. Since PMUs and SMs are mostly energy-constrained, the problem of how to ensure the secure communication with minimum energy cost becomes a critical issue for the functionality of the whole smart grid. In this article, we focus on the low power secure communication of the PMUs and SMs. We take two wireless sensor platforms as examples to experimentally investigate the approaches and principles of reconciling the two conflicting system requirements-communication security and low energy consumptions. The proposed methods are general ones and applicable to other energy-constrained yet security sensitive systems.

Optimal phasor measurement unit placement for power system observability using teaching–learning based optimization

In this article, we focus on smart power grids, which are critical in building smart city architectures, especially devices in Transmission Networks (TNs) and Distribution Networks (DN) which are becoming smarter, and therefore more dynamic. The security control of these networks requires the knowledge in real-time of their key parameters which are voltage and current magnitude, and angle differences in these networks. This identification is the main function of Phasor Measurement Units (PMUs) in TNs and Micro Phasor measurement units (PMUs) in DNs. These tools send and receive data automatically and with the advent of Global Positioning System (GPS) and the IEEE 1588 Precision Time Protocol (PTP) for time synchronization of these devices, they can be installed in any node in the grids, therefore, μPMUs and PMUs become inevitable IoT devices in power networks.

The utilization of phasor measurement unit (PMU) is highly recognized in modern power industry for its smart measurement capability. To implement a smart transmission network, PMU should be allocated at all buses but for large power systems, PMU allocation at all buses is a matter of huge investment and it should be conducted in certain number of steps. In this paper, an effective and practical multi-step PMU allocation strategy is proposed for IEEE 30-bus test system. In, the proposed strategy, the multi-step PMU allocation is framed in such a way that the important and preferred buses will be directly monitored during the initial and intermediate steps. In this paper, several strategies such as optimal PMU allocation (OPA) for full observability, important and critical bus preferences are taken into account. An efficient binary crow search algorithm (BCSA) is developed to solve the OPA problem and the developed algorithm is compared with other metaheuristic algorithms to show its efficiency.

A pervasive and extensive communication, obtainment and processing of the wireless communication network is evident as a significant development of the 5th generation network. The evolution, in reference to the current cellular technology is evolving the 5th generation to a novel smart grid. The 5th generation wireless network exhibits fundamental services for timely and crucial application for the smart grids. Currently, the electricity market is supplied with the 5th generation network as a new organization framework to enhance the manner in which the utility relates to the smart grids. This research work is purposed to present a thorough evaluation of the 5th generation network and its vision concerning the smart grids. The critical evaluation presented in this paper discusses the current wireless initiatives and the potential architectural transitions evident in the past few years. Moreover, to effectively comprehend the user-centered analyses of the smart grid systems, a thorough evaluation of the 5th generation architecture with the grids is vital. The present status of the 5th generation is the smart grid with various analyses for energy abundances as enumerated in this research paper. The focus presented in this paper focuses on future potential smart grids communication frameworks composed of future roadmaps and the problems that should be visional. The full research provides a thorough evaluation of the 5th generation network in accordance to future smart networks.

The pursuit of achieving total power network observability in smart grids using Phasor Measurement Units (PMUs) carries a significant promise of real-time Wide-Area Monitoring, Protection, and Control (WAMPAC). PMU applications eliminate periodical measurements, thereby increasing accuracy through a high sampling rate of the measured power systems quantities. The high costs of installation of PMUs for total power system observability presents a challenge in the implementation of PMUs. This is due to the expensive costs of PMU devices. This has led to a prominent optimal PMU placement (OPP) problem that researchers tirelessly aim to solve by ensuring a complete power network observability while using the least installed PMU devices possible. In this paper, a novel Binary Firefly Algorithm (BFA) based on the node degree centrality scores of each bus is proposed to minimize PMU installations. The BFA solves the OPP problem in consideration of the effect of Zero Injection Buses (ZIBs) under normal operation and single PMU outage (SPO). The robustness and efficiency of the proposed algorithm is tested on IEEE-approved test systems and visualized with a force-directed technique on an undirected power network graph. The proposed BFA yields the same but better optimal PMU numbers, obtained by existing meta-heuristic optimization techniques found in the literature for each of the IEEE test cases, as well as highlighting the cost–benefit of having a robust system against single PMU loss while considering the ZIB effect for an improved system measurement availability.

Power generation and transmission infrastructure is vulnerable to the interaction of various Distributed Generations (DG), which leads to the imbalance of power system operation, frequent voltage drops or spikes, and even power outages. This phenomenon not only wastes energy, but also affects grid security. The main reason is a delayed feedback of circuit failure and load changes, and the optimization of energy management system and path is an effective way to solve the above problems. In this paper, a method of multi-objective optimization based on ANFIS algorithm is proposed which can help to improve the demand response, energy storage and management of smart power grid, reduce the volatility of DGs, reducing electricity costs and improving energy efficiency. Firstly, based on the ANFIS algorithm, the distributed power generation control mode, inverter control, real-time electricity price calculation method, energy transfer and storage scheme are improved, and the optimization path of the energy management system is defined. Secondly, the advantages of ANFIS algorithm in response speed and running stability are verified by comparing with other algorithms. Finally, a distributed energy microgrid is constructed for simulation verification. The results show that :(1) ANFIS optimization algorithm has good adaptability in smart grid, and has advantages in large amount of data processing and information transmission; (2) The verification model based on ANFIS has strong elasticity and efficient response speed. The research results will help solve various problems in the smart grid, including establishing a clear energy management system path, maintaining the stable operation of the power system, providing users with more reasonable power plans and the lowest cost of electricity.

The impact of distributed generation (DG) on losses is of great interest in the smart grid research area. This paper analyses how DG locations, automatic voltage controls, generator types, and the operation modes of DGs impact the losses of distribution system. The topology of the grid is selected from the practical distribution network in Xiamen, which is one of the four pilot smart distribution grid cities in China. Shapley Value method is used to identify each DG's individual impact on these losses. Results show that the losses are reduced most significantly when DGs operate at the constant current mode and voltage control mode; followed by the power factor control mode. The induction machine bus could increase the losses. At last, according to the Shapley Values of the DGs, the individual impacts of DGs on losses are location-dependent. The method proposed in this paper has been employed in Xiamen smart grid.

The wide area measurements systems (WAMS) play a vital role in the operation of smart grids. The phasor measurement units (PMU) or synchrophasors are one of the principle components under WAMS. PMU in a smart grid converts power system signals into phasor from voltage and current which enhances the observability of the power system. A variety of operations is performed by the PMUs such as adaptive relaying, instability prediction, state estimation, improved control, fault and disturbance recording, transmission and generation modeling verification, wide area protection and detection of fault location. The PMUs can improve the performance of grid operations and monitoring. Thus, PMU optimization is very necessary to achieve the desired power system observability. The performance of the PMUs can be optimized using artificial intelligence (AI) technologies. The novice method of monitoring maximum power transfer using PMUs equipped with artificial neural networks has been discussed in this paper. In this paper, a two-bus system model is developed that can be generalized to multiple bus systems. The proposed method is novel, simple, feasible, and cost effective for smart grids.