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Microgrids are recently regarded as promising solutions to the delivery of sustainable, economically feasible and more reliable power supply in the energy transition. The Low-Voltage (LV) distribution networks across Europe are experiencing a notable increase in integrated microgrids. Coordinated control is crucial to enable the microgrids to ensure the grid reliability and security of supply. Additionally, coordinated control facilitates the capability of providing ancillary services to the upstream distribution network. Such functionalities require communications among the involved actors within the microgrid as well as their interactions with external stakeholders including distribution system operators (DSOs). Extensive research has been conducted to develop such communication systems and this paper serves as a thorough review of different approaches for information and communication technology (ICT) systems in microgrid settings. The review contains the architecture, requirements, available technologies, interoperability, reliability and security issues.

Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power system, and to help the customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using Deep Reinforcement Learning, a hybrid type of methods that combines Reinforcement Learning with Deep Learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and Deep Policy Gradient, both of them being extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly-dimensional database includes information about photovoltaic power generation, electric vehicles as well as buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide real-time feedback to consumers to encourage more efficient use of electricity.

In recent years there has been an increase in the use of plug-in electric vehicles (PEVs). Though it has many advantages, PEVs could stress the grid if their charging schedule is not managed properly. There are several issues to be addressed such as (i) Scheduling of PEV under different types of Tariffs (ii) Prediction of load profile and charging requirement. This paper presents three algorithms for scheduling PEV charging by an aggregator under different conditions. This paper has an aim to present the approaches for different possible cases to schedule PEVs charging. In order to achieve this ambition, first, a simple placement algorithm to schedule the PEVs is developed provided prices are predetermined. Then the scheduling problem is formulated as a single stage decision making problem in case prices are not known in advance and PEVS are independent atomic loads. However, it has been researched that PEVs are independent non-atomic loads. Finally, this paper modifies other cases and then the problem is formulated as a multistage decision making problem to determine the optimum schedule in more realistic scenario. In all cases, proposed algorithms are simulated for a building with an objective to schedule PEVs charging during working hours such that the total cost of energy is minimized and the requirements of user are also satisfied.

Recently, demand flexibility has been highlighted as a promising distributed resource from the customer side, especially from industrial customers like commercial buildings, capable of providing grid support services. However, the quantification of demand flexibility is a complex process that requires a methodology including the requirements of both the grid operators and the customers. This paper proposes a novel approach to quantify the available demand flexibility of individual buildings, while taking into account the underlying building energy physics. The proposed approach constructs on the operational flexibility concept from the power systems, and extends it to include a comfort domain, identifying different flexibility parameters with the aim of giving a better insight into the flexibility potential of commercial buildings. This method includes a development of building energy simulations to assess the effects of weather variations, construction types, and comfort constrains on demand flexibility. The proposed quantification method has been validated using 15 different office building models and two different climate zones, i.e., the Netherlands and Texas, US. The results presented in this paper suggest that buildings located in a hot climate could offer higher flexibility potential during shorter time ranges, while buildings in a cold climate could offer lower flexibility potential but during longer time ranges. Determining these differences could potentially facilitate the dispatch of flexible demand resources, to assess their real potential, and to schedule demand flexibility between stakeholders.

This work discusses historical and technical events in USA and Europe over the last few years that are aimed at modernizing the electric power grid. The US federal government has ratified the “Smart Grid Initiative” as the official policy for modernizing the electricity grid including unprecedented provisions for timely information and control options to consumers and deployment of “smart” technologies. European countries are unified in researching and developing related technologies through various structures supported by the European Union. This chapter presents the development of smart grids and an analysis of the methodologies, milestones and expected evolutions of grid technologies that will transform society in the near future.

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

Under the transition towards sustainable smart energy systems (SES), utilization of distributed intelligence has been gradually proposed along with the expansion of Information and Communication Technology (ICT) infrastructure and advanced control services. Distributed intelligence (DI)-based control and management solutions proved a perfect complement to the existing control structures to handle the SES’ uncertainty which is getting quite complex with different system layers and involved stakeholders. Advanced modelling and simulation techniques are crucial here to realize and enable the applications of DI to enhance grid reliability while optimize market operation. However, several challenges arise while modelling DI applications and integrating them in the simulation platform due to the complexity of the multi-disciplinary smart grids. As an activity of IEEE Task Force on Interfacing Techniques for Simulation Tools, this paper mainly reviews the interface issues between modelling and simulation of physical, ICT, and application layers, as well as business processes of the whole smart energy systems. By means of a conceptual framework for SES development, this paper aims to position most of DI-based control applications in specific research domain and elaborate on their interface with the whole SES context. Keywords Distributed intelligence; Smart grids; Smart energy systems; ICT; Uncertainty reduction