With the rapid advancement of phasor measurement units (PMUs) technology, system operators in different level of power systems have access to new and abundant measurements. Taking into account these measurements in active distribution systems (ADNs), a new algorithm for short-circuit fault detection and identification based on state estimation (SE) is introduced in this paper. In this regard, as the first step, traditional SE process is revised to be compatible with fault conditions. Then, a fault location algorithm (FLA) based on the revised SE (RDSSE) is presented which attends to detect the location of fault after diagnosing faulted zone. For this purpose, current and voltage synchrophasors captured by PMUs as well as pre-fault SE results are used and according to calculated measurement residual indexes, the correct location of fault is diagnosed. The performance of RDSSE and SE based fault location method are tested by applying on an ADN, considering different fault scenarios in the network. The results proved that the proposed method is more accurate and reliable than traditional SE based methods in fault conditions and can precisely determine the real location of fault at lower SE execution times.

Introduction of renewable energy sources (RESs) and independently operated multi-microgrid (MMG) systems have led to new issues in the management of power systems. In this context, uncertainty associated with RESs as well as intense ramps inflicted on the network called system flexibility constraints have raised new challenges in power systems. The new condition necessitates the implementation of novel frameworks that enable local system operators to efficiently manage the available resources to cope with the flexibility-ramp constraints. Moreover, the new framework should facilitate energy management in a system with an MMG structure considering uncertainty of RESs. Consequently, in this article, we aim to provide a novel framework that composes of a two-level stochastic optimization procedure to optimize the energy management in an MMG, considering uncertainty of RESs as well as grid flexibility constraints. In the proposed scheme, resource scheduling in microgrids (MGs) is conducted in the first level by their control units, while the second-level procedure focuses on the coordination of MGs, considering flexibility constraints. Furthermore, interaction with gas grid as a potential flexible resource is optimized in the second-level procedure. Finally, the provided flexibility-oriented management scheme is implemented to schedule the local resources in a three-MG test system, considering flexibility constraints.

Due to the lack of data in active distribution networks, employing new accurate measurement devices like phasor measurement units (PMUs) and micro‐PMUs with a high reporting rate becomes an inevitable choice for the future vision of distribution systems. As a result, different algorithms have been presented to optimally place PMUs cost‐effectively based on the estimation errors of the distribution state estimation (DSE) results. However, any component failure in measurement devices or communication links between sending ends and monitoring system of the distribution management system can significantly affect the DSE results. In response, this study introduces the reliability of satisfying accuracy constraints (RSACs) as an important requirement in the DSE problem. This reliability index along with the estimation errors of the DSE results are then used as two performance indices for determining the optimal number and location (configuration) of PMUs. Finally, the performance of the proposed algorithm in comparison with traditional approaches in different topologies and operating conditions of two considered ADNs is evaluated. The results proved that employing the RSACs in the optimal PMU placement problems leads to a PMU configuration with the optimum RSACs and also the cost and latency of the communication system between all configurations which have the same measurement cost and satisfy accuracy constraints of DSE results.

Restructuring in power systems has resulted in the development of microgrids (MGs) as entities that could be operated in grid-connected or islanded modes while managing the operation of their systems. On the other hand, privatization and integration of independently operated distributed resources in energy systems have caused the introduction of multi-agent structures. In this regard, new operational management methodologies should be employed by the MG operator (MGO) to efficiently operate the system while addressing the distributed nature of multi-agent structures. Accordingly, this paper aims to provide a new algorithm to operate an islanded multi-agent MG utilizing the peer-to-peer (P2P) management concept; which copes with the distributed nature of the system. Consequently, each agent would independently schedule its respective local resources, while participating in the hourly P2P market scheme. Moreover, MGO manages the power transactions among the agents. Furthermore, different types of power generation resources are modeled in the proposed optimization scheme while scenario-based stochastic optimization, as well as the condition-value-at-risk index, are deployed to address the uncertainty and the operational risk associated with the operational optimization of renewable energies. Finally, the developed framework is implemented on a 10-bus-MG test system to investigate its effectiveness in the management of the system and also on a 33-bus-MG test system to study its scalability.

Due to low levels of observability and automation in active distribution networks (ADNs), the deployment of accurate measurement devices would be inevitable to increase the network observability. This work develops an optimal framework for phasor measurement unit (PMU) placement considering the accuracy of the distribution system state estimation (DSSE) process. In this regard, first, considering the significance of active power in supplying network loads, an active power sensitivity analysis is conducted in which the accuracy of active power injection at each bus is multiplied by its value of lost load. In this way, the accuracy of active power estimation could be transformed into a monetary value. Then, based on the determined sensitivity criterion, the optimal placement of PMUs has been performed with the aim of appropriate accuracy for the DSSE. On the other hand, the accuracy of the results obtained in the state estimation procedure can affect the estimation of line-loadings and lead to load shedding due to the low accuracy of estimated line-loadings. It is noteworthy that due to the high integration of distributed energy resources in distribution systems, ADNs would be prone to congestion issues. Therefore, in the next stage, this perspective is also used for determining the optimal number and location of PMUs in ADNs to improve their observability based on the operational conditions of the network. Finally, the developed algorithm is applied on the 77-bus-UK-test distribution network to investigate its effectiveness in improving the DSSE accuracy and mitigating interrupted loads. The numerical results indicate that implementing the proposed framework not only increases the DSSE accuracy but also decreases the cost of compensating the accuracy of active power by 7% compared to the typical network without PMUs. Further, the results show that the proposed model significantly mitigates the total curtailed loads due to the low accuracy of estimated line-loadings.

Delivery of the Smart Grid is a topic of considerable interest within the power industry in general, and the IEEE specifically. This paper presents the smart grid landscape as seen by the IEEE Dielectrics and Electrical Insulation Society (DEIS) Technical Committee on Smart Grids. We define the various facets of smart grid technology, and present an examination of the impacts on dielectrics within power assets. Based on the trajectory of current research in the field, we identify the implications for asset owners and operators at both the device level and the systems level. The paper concludes by identifying areas of dielectrics and insulation research required to fully realize the smart grid concept. The work of the DEIS is fundamental to achieving the goals of a more active, self-managing grid.