Demand response (DR) as a key integral part of the future smart grid is gaining a great and still growing focus of attention in nowadays electric power industries. However, many potential benefits of DR, although they have been envisioned to be significant, have not been yet thoroughly and quantitatively investigated. DR provides network operators with the opportunity to mitigate operational limit violations by load modification in place of load shedding when network reliability is jeopardised. This paper aims to comprehensively assess the potential impacts of DR on major attributes of service reliability in a residential distribution network. For doing so, firstly, load profiles for major residential appliances are extracted from grossly metered consumptions. Secondly, the flexibility associated with individual load profiles is estimated using the statistical data gathered through surveys and questionnaires. Thirdly, for every contingency, appliance level load profiles are modified based on their flexibilities such that the least possible interruption cost is realised. The obtained results are finally combined to calculate service reliability indices. The proposed framework is applied to Finnish distribution system and the obtained results demonstrate the efficiency and applicability of the proposed approach in real‐world systems.

Due to load growth, aging infrastructure, and a competitive environment, innovative solutions are required by electrical utilities to enhance the utilization of transformers which are cost intensive. This paper proposes a demand response optimization model based on transformer hottest-spot temperature. The optimization model quantifies the improvement of transformer utilization through DR. The proposed model is applied to a typical Finnish residential primary and secondary distribution transformers for case studies of load with and without DR. The results show that the loading on the transformers can be significantly increased without sacrificing the life of transformers. The gain in utilization depends on the DR capability of the load. Significant monetary benefits can be achieved with the deployment of the proposed model in a real system.

With anticipated proliferation of electric vehicles and distributed generations in near future, dynamic thermal rating (DTR) as a tool for unlocking network capacities, is becoming critical for distribution network operators. DTR is gaining a great and still growing focus of attention in today's power industries. However, potential benefits of DTR, although have been envisioned to be significant, have not yet been studied quantitatively. This paper intends to comprehensively assess the potential impacts of DTR on the performance of a realistic Finnish distribution network. For doing so, first a step-by-step procedure is devised. Then, weather data and loading information of circuits in the network are gathered by advanced metering infrastructure available in the network. Thereafter, the gathered data are fed to mathematical models provided by the IEEE Standard 738-2006 and component dynamic thermal models to give thermal rating of circuits. The ratings are then used in reliability analysis where likely system states are simulated. This paper is performed for several cases and the results are discussed.

This article investigates and analyzes the security aspects of 5G specifications from the perspective of IoT‐based smart grids. As the smart grid requires high‐speed and reliable communication to enable real‐time grid monitoring via Internet of Things (IoT) devices, 5G can be considered a catalyst to transform the current power grid infrastructure into a smart grid. Thus, an understanding of what 5G can bring in terms of cyber security in IoT‐based smart grids is important for design decisions and future risk analysis efforts. In this article, we explore a smart grid use case on automatic voltage control—a use case utilizing 5G as a wireless communication infrastructure with edge support. We identify the benefits 5G brings to several security aspects, and show how 5G security techniques are applicable to the smart grid, thus providing a foundation for future security analysis of 5G enabled smart grid systems. Future research should extend this work to additional smart grid use cases.

Demand response (DR) will play an essential role in smart grids by contributing to the operational flexibility requirement arising from the increased penetration of intermittent renewable generation. However, DR activation could be hampered in the absence of intelligent network management. Real-time thermal rating (RTTR) functions as a smart network management tool for unlocking the network capacities by allowing the network to safely operate during overload states. This paper offers an optimal residential DR approach integrated with RTTR to balance the hourly wind power production. The proposed framework is modeled from the perspective of an electrical aggregator that manages the population of heating, ventilation and air-conditioning (HVAC) loads for wind power balancing considering the RTTR of a distribution network. The model schedules the HVAC loads without deterioration of the customers' temperature preferences. To demonstrate the performance of the proposed approach, simulations are performed on a typical Finnish distribution network plan. The results demonstrate the considerable benefits that can be realized by coordinating the DR and RTTR in a distribution network for wind generation balancing.