This research introduces an innovative method for selecting renewable energy sources tailored to specific contexts, integrating the Multicriteria Decision-Making (MCDM) approach with considerations for resilience constraints and Sustainable Development Goals (SDGs). The aim is to analyze current climatic conditions, identify the primary drivers of power outages, and develop strategies to mitigate their effects. Additionally, the research offers tools for energy managers and decision-makers to select appropriate renewable energy sources based on geographically-based criteria. The criteria are divided into quantitative and qualitative types and are ranked using a cost-benefit approach. The Analytic Hierarchy Process (AHP) is employed to calculate the criteria weights, ensuring decision accuracy and compatibility. Furthermore, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method ranks alternative renewable energy options, aligning with the overarching SDGs. The study explores criteria associated with photovoltaic (PV) and wind energy, considering over 50 criteria for each renewable energy source. This comprehensive study is supported by more than 50 experts from various countries and case studies, validating the criteria. This pioneering technique represents a novel approach in the renewable energy sector, marking the first research to incorporate such a broad range of criteria for selecting renewable energy sources based on specific conditions. Consequently, this research provides a robust framework for identifying and evaluating renewable energy options while accounting for resilience limitations and SDGs, employing methodologies that ensure an accurate and systematic approach, thereby significantly contributing to the long-term advancement of renewable energy.

This paper proposes distributed, event-triggered, power-sharing strategies to solve the economic power dispatch (EPD) problem for virtual energy storage systems (VESS) composed of smart homes in microgrids. Existing literature on this subject requires continuous or periodic communication. In this paper we propose a dynamic, event-triggered, information delivery strategy, combined with a weighted, distributed optimization algorithm. By inheriting economic parameters, the proposed algorithm guarantees power balance at each iteration, while power capacity constraints are guaranteed by deriving a virtual energy price for each smart home. These techniques enable the proposed resilient, distributed algorithm to solve EPD problem online with reduced communications. Theoretical analyses demonstrate the convergence and optimality of the proposed algorithm, which also excludes Zeno triggering behavior. Numerical analyses and simulations demonstrate the validity and effectiveness of the proposed strategies.

False data injection attacks (FDIA) in power systems involve intentionally injecting fabricated data to compromise system integrity, reliability, and functionality. These attacks have devastating consequences for power systems, particularly when they have the potential for sequential outages. To defend against these types of attacks, the implementation of an effective defense mechanism is crucial. The defense system should have advanced security measures, like encrypted phasor measurement units (PMU), to protect the integrity and reliability of the power system infrastructure from FDIA-caused outages that happen one after the other. In order to protect against sequential outages brought on by load redistribution attacks (LRA), this paper proposes a PMU placement optimization problem. In this study, we conduct an analysis of the susceptibility of the IEEE 30 bus standard system to LRA, resulting in sequential outages. Additionally, we explore the optimal placement of PMU as a means of improving the system's defense against such vulnerabilities.

The increase of green house gases (GHGs) emissions and depletion in fossil fuels have urged an extensive use of renewable energy resources (RESs) and energy storage systems (ESSs) in short route ferries and yachts. In current times, most of the smaller seaports are not equipped with the cold ironing facility, hence, when fossil fuel based SMGs are berthed at the port their auxiliary engines are kept on, which pollutes the seaport. In order to address this issue, RES and ESS equipped SMGs can be interconnected via DC bus to formulate a sea port microgrid. The Dc bus interfaced sea port microgrid can be used for providing mobile cold ironing facility to the nearby ships that are integrated with fossil fuels to minimize the emissions, vibrations, and noise pollution. Generally, power sharing among several mcicrogrids is achieved through a communication based control strategies that adds up complexity and the cost. This study, alternatively, proposes an adaptive droop based control algorithm, which is applied to DC SMGs for communication less power-sharing among hybrid energy resources (Battery, Photovoltaic and Fuel Cell). The proposed scheme further helps to charge the battery banks that are below the threshold limits by using the RES integrated in several SMGs.

A VICINITY platform-based load scheduling method by considering the smart parking and household smart appliances for renewable energy resources integrated residential microgrids is proposed in this paper. The proposed method can shift the local loads and publish the vacant number of parking slot with a real-time electric vehicle charging price to VICINITY platform according to the renewable energy generation and the usage of the parking spaces to reduce energy cost and increase resident's revenue. The experimental platform-based real-life cross domain lab testing verifies the effectiveness of the proposed control approach.

The papers in this special section offer a collection of new and original papers that cover different aspects from architecture, scheme design, control strategy, optimization, application and deployment of energy Internet systems.

Increasing power electronics controlled distributed generation resources and active loads provide a future distributed system with more controllability as well as challenges to synthesize heterogeneous devices. From system engineering's perspective, control and operation of the future distribution system need more insight into the system architecture of the grid. In this paper, in light of the start-of-the-art control strategies for microgrids which rely on power electronics systems, a grid architecture model for future distribution system is proposed based on microgrid clusters. Both the physical and cyber structures for this architecture are described. Two illustrative examples are presented to explain different control methods that can be adopted in this model to harmonize different devices. This architecture can be used to guide the system design for the smart distribution system.

Smart meters are the cornerstone in the new conception of the electrical network or Smart Grid (SG), providing detailed information about users' energy consumption and allowing the suppliers to remotely collect data for billing. Nevertheless, their features are not only useful for the energy suppliers, but they can also play a big role in the control of the Microgrid since the recorded power and energy profiles can be integrated in energy management systems (EMS). In addition, basic power quality (PQ) disturbances can be detected and reported by some advanced metering systems. Thus, this paper will expose an example of Smart Meters integration in a Microgrid scenario, which is the Intelligent Microgrid Lab of Aalborg Univeristy (AAU). To do this, first the installation available in the Microgrid Lab will be introduced. Then, three different test scenarios and their respective results will be presented, regarding the capabilities of this system and the advantages of integrating the Smart Meters information in the Microgrid control.