This paper analyzes the requirements for the reporting rate of the live data source to support the operation of Digital Twins (DTs) of Distributed Energy Resources (DERs) for real-time power systems monitoring applications. The visibility of distribution networks is currently limited due to the lack of sufficient measurement and communication infrastructures. With the rapid increase of DERs, it becomes increasingly important to improve the visibility of the distribution networks to ensure the critical system operating constrains are continuously met. DTs are virtual replicas of physical systems, and with certain live measurement data, they can be used to accurately represent the real-time dynamics of the physical entities. The features of DTs could therefore be applied to increase the visibility of network and potentially support real-time decisions making. This paper presents the investigation of the impact of data reporting rate on DT accuracy, based on which, the paper presents a method that could be used to quantify the minimum requirements for data reporting rate to adequately support the DT operation, which provides valuable learning for specifying measurement devices and communication networks to enable DTs-based solutions.

This article presents a novel approach for realizing coordinated control of Distributed Energy Resources (DERs) based on cloud-hosted and edge-hosted digital twins (DTs) of DERs. DERs are playing an increasingly important role in supporting the frequency regulation of power systems with massive integration of renewable resources. However, due to the significant differences in DERs’ capability and characteristics, individual and un-coordinated responses from DERs could lead to a less effective overall response with undesirable traits, e.g., slow response, severe overshoots, etc. Therefore, the coordination of DERs is critical to ensure the desirable aggregated overall response. A major shortcoming of conventional centralized or distributed approaches is their significant reliance on real-time communications. This article addresses the challenges by the application of DTs that can be hosted in the cloud for the centralized control approach and the edge for the distributed approach to minimize the need for real-time communications, while being able to achieve the overall coordination among DERs. The proposed DT-based coordinated control is validated using a realistic real-time simulation test setup, and the results demonstrate that the DT-based coordinated control can significantly improve the aggregated DERs’ response, thus offering effective support to the grid during contingency events.

Grid-forming converter establishes a stable and controllable voltage at its output terminal without requiring external angle reference, which enables the GFC to be a candidate for providing black start services. However, this attribute poses significant challenges to the conventional power hardware-in-the-loop (PHIL) simulation, which incorporates the physical power converter by regulating its voltage angle to be synchronized with that of an interfacing power amplifier mimicking the real-time emulated power grid. The lack of voltage synchronization at the coupling point of GFC and interfacing power amplifier leads to instability. To address this challenge, the current-type interfacing method with compensation and scaling scheme is proposed to interface a GFC with soft black-start capability into a PHIL setup. Analytical assessment and experimental results involving interfacing a 90 kVA power converter implemented with grid-forming control are presented to verify the methodology.

An autonomous eco-robotic Surface Vessel (ASV) is designed to operate in extreme weather conditions with an autonomy of several days or months. This research work aims to present a process for on-board power management between the vessel’s power sources, while maximizing the use of Renewable Energy Sources (RES) and taking into consideration onboard sensor, navigation and control and data transfer power requirements. A detailed architecture for a DC network integrating Photovoltaic (PV) panels, Fuel Cells (FCs), hydro generator and energy storage systems is developed. An efficient and flexible Energy Management System (EMS) is developed for managing power sources and maximising endurance using only clean energy. To assess the performance of EMS in meeting the energy demands of the Ocean drone’s equipment and propulsion systems, a simulation-based analysis is carried out for realistic missions and scenarios. The developed EMS strategy intends to harvest the energy from PV and hydrogenerator while maintaining the Battery Energy Storage Systems (BESS) as charged as possible. The developed power supply system architecture and EMS can jointly accommodate the need for efficient and long-lasting operation of the vessel with CO2-emission free energy sources.

Power hardware-in-the-loop (PHIL) simulation leverages the real-time emulation of a large-scale complex power system, while also enabling the in-depth investigation of novel actual power components and their interactions with the emulated power grid. The dynamics and non-ideal characteristics (e.g., time delay, non-unity gain, and limited bandwidth) of the power interface result in stability and accuracy issues within the PHIL closed-loop simulations. In this paper, a compensation method is proposed to compensate for the non-ideal power interface by maximizing its bandwidth, maintaining its unity-gain characteristic, and compensating for its phase-shift over the frequencies of interest. The accuracy of power signals synchronization and the transparency of power transfer within the PHIL configuration are assessed by employing the error metrics. In conjunction with the frequency-domain stability analysis and the time-domain simulations, a case study is made to validate the proposed compensation method.

Internet-of-Things concepts are evolving the power systems to the Energy Internet paradigm. Microgrids (MGs), as the basic element in an Energy Internet, are expected to be controlled in a cooperative and flexible manner. This article proposes a novel distributed control scheme for multiagent systems (MASs) governed MGs in future Energy Internet. The control objectives are frequency/voltage restoration and proportional power sharing. The proposed control scheme considers both intra- and inter-MASs interactions, which offers group plug-and-play capability of distributed generators. The stability and communication delay issues in the control framework are analysed. A multisite implementation framework is presented to explain the agent architecture as well as data exchange in local area networks and the cloud server. Then a cyber hardware-in-the-loop experiment is conducted to validate the proposed control method with multisite implementation. The experimental results prove the effectiveness and application potentials of the proposed approach.

Power hardware-in-the-loop (PHIL) is a state-of-the-art simulation technique that combines real-time digital simulation and hardware experiments into a closed-loop testing environment. The transportation delay or communication latency impacts the stability and accuracy of PHIL simulations. In this paper, for the purpose of synchronizing the PHIL out-put signal and promoting both the stability and accuracy of PHIL simulation, a hybrid compensation scheme is proposed to compensate for the time delay in the PHIL configuration. A model-based compensator is implemented to shift the time delay out of the PHIL closed-loop to enhance PHIL stability. A time delay compensation model and its equivalent inverse model are employed in the PHIL closed-loop to compensate for the time delay. A phase lead compensator and digital linear-phase frequency sampling filter (FSF) are candidate compensation models to compensate for the time delay and reshape the phase curve on a harmonic-by-harmonic basis. Simulations are made to validate the effectiveness of the compensation scheme.

Optimization problems such as resource allocation, job-shop scheduling, equipment utilization and process scheduling occur in a broad range of processing industries. This paper presents modeling, simulation and optimization of a port facility such that effective operational management is obtained. A GA base approach has been integrated with the port system model to optimize its operation. A case study of bulk material port handling systems is considered.

The classical generation scheduling problem defines on/off decisions (commitment) and dispatch level of all available generators in a power system for each scheduling period. In recent years researchers have focused on developing new approaches to solve nonclassical generation scheduling problems in the newly deregulated and decentralized electricity market place. In this paper a GA-based approach has been developed for a system operator to schedule generation in a market akin to that operating in England and Wales. A generation scheduling problem has been formulated and solved using available trading information at the time of dispatch. The solution is updated after information is obtained in a rolling fashion. The approach is tested for two IEEE network-based problems, and achieves comparable results with a branch and bound technique in reasonable CPU time.

This paper details the optimization of bulk material port handling systems through the use of an evolutionary based approach. The effective management of port systems requires effective solutions for the design, operation and maintenance of these facilities. This has to be achieved through a reduction in financial costs, and an increase in the utilization of equipment and other resources. The operation of a port system is complex and difficult to model mathematically. Consequently, through the explicit characterization of port components, a port modeling tool was developed that permits the generic construction of port simulation models. A genetic algorithm based approach was developed to provide an optimization capability to the port simulation tool. Two case studies based on real world port systems are presented and the results are discussed. A significant improvement is demonstrated in both the operational and economic performance as a result of the GA/model interaction.

This paper proposes the application of a hybrid genetic algorithm (GA) for scheduling storage tanks. The proposed approach integrates GAs and heuristic rule-based techniques, decomposing the complex mixed-integer optimization problem into integer and real-number subproblems. The GA string considers the integer problem and the heuristic approach solves the real-number problems within the GA framework. The algorithm is demonstrated for three test scenarios of a water treatment facility at a port and has been found to be robust and to give a significantly better schedule than those generated using a random search and a heuristic-based approach.

Proposes the application of a genetic algorithm (GA) and simulated annealing (SA) based hybrid approach for the scheduling of generator maintenance in power systems using an integer representation. The adapted approach uses the probabilistic acceptance criterion of simulated annealing within the genetic algorithm framework. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the solution technique are discussed. The results in this paper demonstrate that the technique is more effective than approaches based solely on genetic algorithms or solely on simulated annealing. It therefore proves to be a valid approach for the solution of generator maintenance scheduling problems.

This paper proposes the application of a genetic algorithm based methodology for the scheduling of storage tanks. The proposed approach is an integration of GA and heuristic rule-based techniques, which decomposes the complex mixed integer optimisation problem into integer and real number subproblems. The GA string considers the integer problem, and the heuristic approach solves the real number problems within the GA framework. The algorithm is demonstrated for a test problem related to a water treatment facility at a port, and has been found to give a significantly better schedule than those generated using a heuristic-based approach.