The tremendous complexity of modern distribution systems calls for alternative coordination architectures, supported by smart, self-adaptable, and, to a large degree, environment-agnostic algorithms. In this article, we discuss decentralized and distributed coordination architectures for the operation of active distribution grids aiming at effectively coping with their complexity. We present relevant methods and algorithms under the framework of multiagent systems (MASs) and decentralized decision-making associated with handling different parts of the optimal grid operation. The decision-making models are based on distributed optimization algorithms using consensus/gossip models, bioinspired algorithms from the field of population dynamics, and a method for decomposing the power-flow model. The developed techniques aim at matching production with demand in microgrids, settling the short-term energy imbalances at the distribution grid level, mitigating voltage deviations, and resolving distribution grid congestions in real-time operation. The algorithms are implemented as MAS-based software platforms, able to aggregate diverse DG units and flexible loads. Results are provided from the theoretical simulation-based models and demonstrations of the operational techniques in actual pilot sites. The applied implementations have been performed in a smart grid pilot site, for which MAS platforms have been developed and tested.

As one of the most important infrastructures, the energy system covers electricity, heating, cooling, natural gas, oil, coal, hydrogen, etc. Energy security, energy equity, and environmental sustainability are the well-known energy trilemma. The energy system is the largest source of carbon emissions; therefore, the goal for carbon neutrality puts forward very high requirements. Renewable energy will account for the majority of generation in the future, while the current energy system is not ready.

The last decade the continuous integration of Distributed Energy Resources (DER) along distribution networks, follows an uncoordinated fashion posing manifold technical challenges for the operation of the grid. However, DER could be actively incorporated in the operation of distribution networks providing certain flexibility. Such DER flexibility is generally associated with temporal shifting of energy (i.e. for consumption or injection). This work assesses the active management of multiple DER in a coordinated manner in Low Voltage (LV) distribution networks. The flexible use of DER is hereby regarded for the provision of support to the LV grid, mainly for voltage regulation and phase balancing, line congestions management as well as to ensure rated power for the transformer of the secondary substation. A control framework is proposed for the management of DER flexibilities, which relies on a three phase multi-period optimal power flow. A study based on a real -IEEE benchmark- LV distribution network is presented to demonstrate and quantify the importance of active management of DER such as battery storage system, electric vehicles (with vehicle to grid operation) and microgeneration.

Power systems have typically been designed to be reliable to expected, low-impact high-frequency outages. In contrast, extreme events, driven for instance by extreme weather and natural disasters, happen with low-probability, but can have a high impact. The need for power systems, possibly the most critical infrastructures in the world, to become resilient to such events is becoming compelling. However, there is still little clarity as to this relatively new concept. On these premises, this paper provides an introduction to the fundamental concepts of power systems resilience and to the use of hardening and smart operational strategies to improve it. More specifically, first the resilience trapezoid is introduced as visual tool to reflect the behavior of a power system during a catastrophic event. Building on this, the key resilience features that a power system should boast are then defined, along with a discussion on different possible hardening and smart, operational resilience enhancement strategies. Further, the so-called ΦΛEΠ resilience assessment framework is presented, which includes a set of resilience metrics capable of modeling and quantifying the resilience performance of a power system subject to catastrophic events. A case study application with a 29-bus test version of the Great Britain transmission network is carried out to investigate the impacts of extreme windstorms. The effects of different hardening and smart resilience enhancement strategies are also explored, thus demonstrating the practicality of the different concepts presented.

This paper proposes the online reconfiguration of active distribution networks. The control of the active/reactive output power of distributed generation (DG) units combined with the control of remote controlled switches are employed in order to minimize DG curtailment, alleviate lines congestion, and mitigate voltage rise issues due to DG integration. Convex relaxations of the ac power flow equations and mixed integer linear disjunctive formulations are adopted to the optimization model in order to obtain fast and optimal solutions using standard branch and bound solvers. The computation burden of the optimization procedure is drastically reduced by exploiting the assessment of switching actions, which is performed using multiple load/generation scenarios. The effectiveness of the proposed optimization model is verified using different distribution test systems.

The incorporation of distributed generation (DG) under the virtual power plant (VPP) paradigm allows the coherent central control, and the coordinated market integration of several and widely dispersed electric power sources. That way, VPPs can participate in frequency control by regulating in a coordinated manner their output power for the sake of system stability. This study analyzes a decision tree (DT)-based methodology that dispatches a requested reduction of active power within a VPP (among the sources it consists of), so as to support the mitigation of over-frequency. The presented control con-cept finds its application concerning grid support in case of over-frequency, and is also meant as a response to related reports and studies asking for the increased contribution of DG to overcome such phenomena.

The provision of ancillary services by generators connected to distribution networks has been addressed in several international and national standards and guidelines in the last few years. Several testing procedures have been defined, however comprehensive tests for certain requirements are still missing. This paper addresses this gap by introducing testing procedures for specific requirements, but also for promising functionalities that are not yet thoroughly defined in standards. The status of current testing procedures concerning ancillary services is discussed. Several advanced open-loop testing procedures are proposed, that have not been sufficiently defined in current standards-guidelines. The procedures concern steady-state voltage support (such as Q(U) control) and frequency control (such as droop control and provision of inertia). In addition, procedures for Power-Hardware-in the Loop testing are proposed that show the added-value of this promising approach compared to conventional testing.

The challenges in achieving higher integration of Distributed Energy Resources (DER) in electricity grids require advances and novel approaches in simulation and testing. An approach that is increasingly gaining interest is Hardware-in-the-Loop (HIL) simulation and especially Power-Hardware-in-the-Loop (PHIL), where power equipment is connected to a simulated system. This paper firstly discusses interfacing issues of PHIL simulation providing a theoretical basis. A detailed description of the design and development phase of a PHIL environment for DER devices is provided. The development of the Power Interface, stability analysis and design of appropriate protection schemes are presented, aiming at assisting the development of other PHIL environments. As a first experiment, a voltage divider circuit is implemented in the PHIL approach. Subsequently, hardware PV panels and a PV inverter are connected at a simulated simple distribution grid and it is demonstrated that the voltage of the common-node follows the variation of the solar irradiation.

More than 70% of the total worldwide electricity-generating wind turbines (17500 MW total) has been installed in Europe. Of these, 3500 MW were installed in 2000 alone, continuing an impressive pace of development over the last several years. At the same time, the European wind manufacturing industry is booming with two-thirds of the world market share. These developments are attributed to the effective support of the European Union and of the national European states, following their commitment to reduce greenhouse gas (GHG) emissions. Wind power is now seen as a clean, cost-effective alternative to other forms of conventional electricity production with clear benefits to the environment and to the economy, as a whole. In this review paper the current state and prospects of wind power in Europe are described, with special emphasis on the three countries leading wind power development in Europe, i.e., Germany, Denmark, and Spain. The investment models applied in Europe play a decisive role in this development. Their differences and effects are shown and the role of renewables in reducing CO/sub 2/ (the primary GHG) emissions is highlighted. Finally, current trends and prospects of the European Wind Turbine technology are discussed.

The paper presents an effective method to reduce the iron losses of wound core distribution transformers based on a combined neural network/genetic algorithm approach. The originality of the work presented is that it tackles the iron loss reduction problem during the transformer production phase, while previous works concentrated on the design phase. More specifically, neural networks effectively use measurements taken at the first stages of core construction in order to predict the iron losses of the assembled transformers, while genetic algorithms are used to improve the grouping process of the individual cores by reducing iron losses of assembled transformers. The proposed method has been tested on a transformer manufacturing industry. The results demonstrate the feasibility and practicality of this approach. Significant reduction of transformer iron losses is observed in comparison to the current practice leading to important economic savings for the transformer manufacturer.