A proportional-integral (PI) controller in parallel with a nonlinear cubic controller is proposed in order to achieve the objective of addressing both the issues of small-signal and large-signal stability, with the latter also being referred to as transient stability. Within the scope of application and of practical relevance is the enhancement of the region of attraction for which equilibrium states are found for an electric vehicle (EV) charging station while considering constraints. As a main contribution, a methodology for the design of the cubic controller to expand the constrained region of attraction (CROA) through sum-of-squares (SOS) programming is formulated, implemented, and validated. The developed SOS program incorporates the construction of Lyapunov functions, which are employed to estimate the CROA. The optimal coefficient of the cubic controller is obtained by estimating the largest CROA. The integration of the cubic controller enhances the robustness of the EV charging station against large disturbances, while the performance under minor disturbances is dealt with by the accompanying PI controller. As a result, the proposed PI-cubic voltage controller enhances the stability across wide operating ranges including fast charging and in the presence of constant power loads. In general, application also includes DC microgrid stability. Time-domain simulations conducted in Matlab validate the made claims. During the considered outage of local power generation on the DC side of the charging station and microgrid, transient stability was only maintained with the proposed controller, and an overcurrent situation was avoided. The PI-cubic controller is shown to be effective in enhancing robustness.

This article introduces a sequential Monte-Carlo simulation method (MCS) based on flow networks to represent decentralized power systems and evaluate the reliability and availability of their topologies. It can be used to provide a comparison basis during the system design phase. The method can be employed to simulate a variety of types of decentralized local power systems. It is able to simulate bidirectional, radial, and meshed systems with both repairable and nonrepairable elements. Examples of elements that can be simulated include power sources, loads, and energy-storage units. The determination of system success is performed using a minimum-cost-maximum-flow graph algorithm that calculates the power delivered to the system loads given the state of the system components. If the loads are being properly supplied, the system is deemed functional. The simulation can accommodate additional sets of rules for system success if needed. The method can generate a variety of indices for system availability and reliability based on the system topology and the parameters of its individual components. Three example systems are presented and simulated to showcase the method's capabilities.

In accordance with European plans, dynamic controls are developed to allow for system-wide integration of 100 % renewable energy sources (RESs) interfaced through power electronic converters while maintaining security. At the heart of the development is the concept of the grid-forming resource (GFR) which brings together both the technologies of renewable energy resource and grid-forming converter. Extending on an extensive review, a grid-forming converter control and protection scheme is presented to ensure operation under small-and large-signal transients as needed for overall system security. The scheme is shown to be practical in offering inertial response emulation and frequency control based on droop characteristics to maintain power balances rapidly, to control voltages, as well as to support transient stability enhancement under grid faults. The integration with the control of wind energy conversion systems (WECSs) creates a grid-forming wind park as the prototype of the GFR. Feedforward signals exchanged between grid and resource-side controls enhance fast overall controllability. The principal claims are substantiated based on a scenario developed in the European project initiative MIGRATE with a focus on the Irish power transmission system. The model comprises more than 2000 individual WECSs grouped into wind parks, where ten wind parks are GFRs. The transient behavior of this scenario comprising 100 % converter-interfaced generation is shown to be superior compared with a counterpart case comprising synchronous machinery. The results validate the fact that GFRs with their proposed controls are expected to be key elements in creating a renewable and secure electric power system.

The increasing penetration of renewable energy sources and electric vehicles (EVs) poses a significant challenge for the power grid operator in terms of increasing peak load and power quality reduction. Moreover, there is a growing demand for fast charging services in smart grids. Addressing the growing demand from fast charging services is challenging. To overcome this challenge, in this article, we propose a new computational architecture combining energy trading and demand responses based on cloud computing for managing virtual power plants (VPPs) in smart grids. In the proposed system, EVs can be charged at high charging rates without affecting the operation of the power grid by purchasing energy through the energy trading platform in the cloud. In addition, users with storage devices can sell energy surplus to the market. On the one hand, the energy trading platform can be regarded as an internal market of the VPP that aims to maximize its revenue. The interest of the EV owners, on the other hand, is to minimize the cost for charging. Therefore, we model the interactions between the EV owners and the VPP as a non-cooperative game. To search for the Nash equilibrium (NE) of the game, we design an algorithm and then analyze its computational complexity and communication overhead. We utilize real data from the California Independent System Operator (CAISO) to evaluate the performance of the proposed algorithm. Our results illustrate that the users with only storage devices can obtain nearly$200\%$200%higher revenue on average by participating in the proposed internal market. Moreover, users with only EVs can reduce their charging costs by nearly$50\%$50%in average. Users with both EVs and storage devices can reduce the charging costs even further by approximately$120\%$120%where the users get profit by utilizing the internal market.

Wind power is a clean and widely deployed alternative to reducing our dependence on fossil fuel power generation. Under this trend, more turbines will be installed in wind farms. However, the inspection of the turbines in an offshore wind farm is a challenging task because of the harsh environment (e.g., rough sea, strong wind, and so on) that leads to high risk for workers who need to work at considerable height. Also, inspecting increasing number of turbines requires long man hours. In this regard, unmanned aerial vehicles (UAVs) can play an important role for automated inspection of the turbines for the operator, thus reducing the inspection time, man hours, and correspondingly the risk for the workers. In this case, the optimal number of UAVs enough to inspect all turbines in the wind farm is a crucial parameter. In addition, finding the optimal path for the UAVs' routes for inspection is also important and is equally challenging. In this article, we formulate a placement optimization problem to minimize the number of UAVs in the wind farm and a routing optimization problem to minimize the inspection time. Wind has an impact on the flying range and the flying speed of UAVs, which is taken into account for both problems. The formulated problems are NP-hard. We therefore design heuristic algorithms to find solutions to both problems, and then analyze the complexity of the proposed algorithms. The data of the Walney wind farm are then utilized to evaluate the performance of the proposed algorithms. Simulation results clearly show that the proposed methods can obtain the optimal routing path for UAVs during the inspection.

Recent developments in renewable energy and information technology (IT) fields made it easier to set up power systems at a smaller scale. This proved to be a turning point for developing first-access electricity systems for the underserved locations around the world. However, there are planning and operation challenges due to lack of past data on such places. Deployment of Internet-of-Things (IoT) devices and proliferation of smart infrastructures with additional sensors will lead to tremendous opportunities for gathering very useful data. For different stakeholders to access and manage these data, trusted and standardized mechanisms need to be in place. Storing proper data in a well-structured common format allows for collaborative research across disciplines, large-scale analytics, and sharing of algorithms and methodologies, in addition to improved customer service. Data standardization plays a more vital role in the context of electricity access in the underdeveloped countries, where there is no past data on generation or consumption as in utility grids. Data collected in a standard structure, being it for a short period of time, facilitate learning from the past experiences, monitoring the current projects, and delivering better results in the future endeavors. It will result in ways to better assist consumers and help the industry operate more efficiently by sharing data with different stakeholders. It can also enhance competition, thus making electricity accessible faster and to more people. The focus of this article is data standardization for first-access electricity systems, in general, and renewable energy-based microgrids, in particular. Different data sources and ways that the corresponding data can be exploited, technological and capacity constraints for storage of data, political and governance implications, as well as data security and privacy issues, are examined. This article is relevant to different stakeholders, such as investors, public utilities, nongovernmental organizations (NGOs), and communities. Using the data standardization approach developed here, it is possible to create a much-needed first-access electricity system database. This will provide an important resource for project developers and energy companies to assess the potential of a certain unelectrified site, estimating its demand growth in time and establishing universal control systems that can seamlessly communicate with different components.