Islands, due to their geographical characteristics, face unique challenges in energy production, distribution, and storage, particularly with respect to integrating renewable energy and reducing reliance on fossil fuels. Hydrogen, in this context, is gaining attention as a potential energy carrier capable of facilitating renewable integration, offering energy storage solutions, and aiding in the reduction of emissions in maritime transport. This paper proposes a two-stage sequential optimization model for the scheduling of Hydrogen Carrier Vessel (HCV) and Island Hybrid Energy System Network. The objective is to efficiently coordinate energy production, storage, and hydrogen distribution while minimizing operational costs under uncertainty in renewable energy generation and demand. The first stage focuses on scheduling energy systems. A scenario-based stochastic optimization approach is applied to determine optimal schedules of shore-side and island hybrid energy systems. In the second stage, based on the first stage's results, the optimal dispatch and routing of HCV are determined. The HCV scheduling ensures all islands receive hydrogen within their specified time windows while minimizing transportation costs. A 24-hour case study demonstrates the effectiveness of the proposed model, the proposed optimization reduced the required peak hydrogen reserves by 11.58% compared with a conservative uncoordinated baseline, while avoiding the reliability violations observed under deterministic scheduling. This research provides a viable framework for incorporating green hydrogen production, storage, and distribution into island energy systems, supporting decarbonization initiatives in maritime transport and island communities.

Unbalance situation usually occurs in a three-phase four-wire power system due to the unbalanced loads across its different phases. In this case, the unbalance compensation is necessary for the stable operation of power generation units. In order to restore balance, in this paper, a functional and flexible unbalance compensation system has been used in the AC microgrid which operates at constant frequency and contains one grid-forming generator (inverter) and three grid-supporting inverters. The unbalance compensation works with energy fed back to the grid in order to reduce energy waste. For this purpose, three separate rectifiers each of which is followed by a medium-frequency transformer isolated DC/DC converter are controlled to transfer different levels of power. The outputs of the three DC/DC converters are joined together to be the input for the subsequent DC/AC inverter which can output balanced power back to the grid. In order to increase the transient stability, a LCCL filter has been used for each rectifier.

This paper proposes a three-in-one generation system with a target to reduce the usage of iron, copper and aluminum. The same machine system is used to harness water potential energy, wind tower-based energy, solar energy and gravity storage-based generation. By doing so, its service time across a year is greatly increased. It could potentially alleviate pressure from overuse of metals when solving energy crisis. Multiple units of such a generation system could be used to construct large-scale DC microgrids which are integral forming components in large-scale autonomous AC microgrids. Such a system is also suitable for grid-connected operation for harnessing water and wind energy and for gravity storage when there is extra power from the grid. This paper further presents results from the modeling of an AC microgrid formed by three-phase inverters, each of which is composed of three single-phase multistage inverters. The proposed three-in-one generations together with the multistage high-voltage inverters pave the way for building large-scale AC microgrids.

The wide-spread integration of renewable energy has increased inverter-based generation, weakening grid strength and inertia, bringing challenges to power system stability. To address these issues, virtual synchronous generator (VSG) control has emerged as a modern grid-forming (GFM) technology that can autonomously provide active and reactive power to support grid frequency and voltage. However, when a VSG inverter operates under strong grid conditions, power oscillations may occur under a fixed parameter setting, making the grid difficult to reach a steady state. Under such circumstances, traditional stability analysis based on the rate of change of frequency (RoCoF) is insufficient, advanced analysis methods based on the changes in system impedance and further improvements are required. This paper proposed an accurate small-signal model of the VSG inverter along with an impedance identification method for grid impedance. Additionally, an improved VSG control adaptive to grid impedance is proposed based on the knowledge of small-signal model and system impedance. All proposed methods have been tested on a single-machine-infinite-bus (SMIB) system and the stability results demonstrate their effectiveness under both weak and strong grid scenarios.

In the three-phase four-wire power system, when the voltages and currents contain not only positive-sequence components but also negative-sequence and/or zero-sequence components, the unbalance situation will occur and bring many electrical problems. In this paper, an unbalance compensation technique using power electronic converters with solid-state transformer has been developed. Its effectiveness has been validated using the modelling of Matlab/Simulink by integrating it with the power system to solve the unbalance issue. Furthermore a new circuit topology for high-voltage high-power unbalance compensation has been proposed.

The increasing demand for sustainable transportation solutions has positioned hydrogen fuel cell vehicles (FCVs) as a viable alternative to conventional internal combustion engine vehicles and battery electric vehicles. FCVs utilize green hydrogen as a clean energy source, offering key advantages such as zero greenhouse gas emissions, extended driving ranges, and rapid refueling capabilities. However, the integration of FCVs into vehicle routing problems (VRPs) remains insufficiently explored, particularly concerning their unique hybrid energy systems. This study introduces a novel Fuel Cell Vehicle Routing Problem (FVRP) model that accounts for the dynamic interactions between hydrogen fuel cells and the auxiliary battery, thereby ensuring precise energy management. A case study is presented to establish benchmark results for the proposed FVRP model under the given parameter setup. To the best of our knowledge, this research is the first to examine FCV energy behavior within the context of VRPs, contributing to the advancement of hydrogen-powered mobility and the optimization of FCV operations within sustainable logistics frameworks.

The successful integration of renewable energy resources into the power grid hinges on the development of energy storage technologies that are both cost-effective and reliable. These storage technologies, capable of storing energy for durations longer than 10 hours, play a crucial role in mitigating the variability inherent in wind and solar-dominant power systems. To shed light on this matter, a transparent, least-cost macro energy model with user-defined constraints has been utilized for a case study of California. The model addresses all included technologies, solving for both hourly dispatch and installed capacities. Real-world historical demand and hourly weather data have been utilized to do this analysis. A novel approach has been introduced to assess the significance of long-duration energy storage technologies (LDS) in terms of their energy and power capacity. This method explores the contributions of pumped hydropower storage (PHS), compressed air energy storage (CAES), and power-to-gas-to-power (PGP) storage toward minimizing the overall balance of system cost. Historical electricity demand, hourly weather data, and current technology costs are used to investigate high-level implications for California’s power system options. Increasing the storage capacity of each technology from 1 to 10 hours results in 29.6%, 14.4%, and 7.5% cost reduction for PHS, CAES, and PGP cases respectively. However, in studied simulations, maximum availability (maximum) of pumped hydropower storage reduces the balance of system costs by 72.3% followed by CAES (60.6%) and PGP (48.6%) and suggests that pumped hydropower storage in combination with CAES/PGP could play an important role in California’s electricity system, provided that suitable sites can be identified and constructed at reasonable costs.

The weather-dependent uncertainty of wind and solar power generation presents a challenge to the balancing of power generation and demand in highly renewable electricity systems. Battery energy storage can provide flexibility to firm up the variability of renewables and to respond to the increased load demand under decarbonization scenarios. This paper explores how the battery energy storage capacity requirement for compressed-air energy storage (CAES) will grow as the load demand increases. Here we used an idealized lowest-cost optimization model to study the response of highly renewable electricity systems to the increasing load demand of California under deep decarbonization. Results show that providing bulk CAES to the zero-emission power system offers substantial benefits, but it cannot fully compensate for the 100% variability of highly renewable power systems. The capacity requirement of CAES increases by <= 33.3% with a 1.5 times increase in the load demand and by <= 50% with a two-times increase in the load demand. In this analysis, a zero-emission electricity system operating at current costs becomes more cost-effective when there is firm power generation. The least competitive nuclear option plays this role and reduces system costs by 16.4%, curtails the annual main node by 36.8%, and decreases the CAES capacity requirements by <= 80.7% in the case of a double-load demand. While CAES has potential in addressing renewable variability, its widespread deployment is constrained by geographical, societal, and economic factors. Therefore, if California is aiming for an energy system that is reliant on wind and solar power, then an additional dispatchable power source other than CAES or similar load flexibility is necessary. To fully harness the benefits of bulk CAES, the development and implementation of cost-effective approaches are crucial in significantly reducing system costs. An idealized lowest-cost optimization model explores the response of increasing load demand of California under deep decarbonization. Results show that providing bulk compressed-air energy storage to the zero-emission power system offers substantial benefits, but it cannot fully compensate for the 100% variability of highly renewable power systems. Graphical Abstract

Load flow analysis can produce accurate voltage profile and power generation from each distributed generator in large-scale microgrids. Nevertheless, its application is limited to balanced or nearly balanced condition. For unbalanced operation, it is still indispensable to use modelling approach to examine their steady-state response, transient response, and stability under both balanced and unbalanced conditions. In this paper, the equivalent model of the PV panel-based generation is introduced to replace the detailed circuits for studying more complex microgrids in the modelling. To duplicate the functions of using multiple Texas Instrument 28377s or 28379x for controlling each inverter, multi-rate modelling in Simulink has been conducted. Moreover, a new formulation for the load flow analysis and voltage profile calculation is given, based on which the analytical solutions have been reached. It is found that the results from the analytical approach are very close to those from the multi-rate modelling. This validates the effectiveness of two different approaches. Furthermore, the influence of shunt capacitor in the LCL filter and software filters has been investigated.

With the advancement of power electronics technology and the growing demand of renewable energy harness, solid-state transformer (SST), as an emerging transformer technology, has garnered significant attention. This paper proposes a control strategy based on state space averaging method (SSAM) for a DC/AC converter formed by two parts, one being mediumfrequency transformer isolated DC/DC converters while the other being single-phase multistage DC-AC inverter. The first part in such a SST is controlled by comparison of reversed triangular waveforms with the same reference signal to produce two pairs of switching signals. For the second part in the SST, the same circuit topology is controlled by two different methods to make it work as a current-controlled voltage source inverter or a voltage-controlled voltage source inverter. MATLAB was utilized to validate the proposed theory. The modeling results demonstrate that the proposed control method effectively enhances the stability of these structures while ensuring balanced output voltage across each of multistage in the single-phase inverter.

In recent years, the power outages caused by catastrophic weather events have become an imperative issue in power system research. Mutual impacts of pre-and post-event operation, uncertainties during system recovery, as well as binary decision variables are still challenging. To address these issues, this paper proposes an adaptive robust load restoration method for active distribution networks which coordinates network reconfiguration, mobile energy storage systems (MESSs) and repair crews (RCs). In the pre-event stage, pre-positioning of MESSs and proactive network reconfiguration are conducted to enhance system survivability. In the post-event stage, dynamic network reconfiguration is coordinated with scheduling MESSs and RCs, to restore all loads under uncertainties. A mixed integer second-order conic programming model which also contains practical voltage dependent loads is formulated. Further, a two-stage robust optimization method is applied to guarantee solution robustness against multiple uncertainties including fault locations. To optimize the binary recourse variables in the post-event stage, a feasibility pump based solution algorithm is developed. Numerical simulations conducted on a 33-bus system demonstrate effectiveness of the proposed load restoration method, high efficiency of the solution algorithm and high solution robustness.

Bulk energy storage can play an important role in the decarbonization of renewable-dominant electricity system. It can offer a solution to the grid balancing problem caused by the variability in the output of renewable power generation. This paper explores the requirement for compressed air energy storage (CAES) capacity as the penetration of renewable energy increases to compensate for the variability of wind and solar. A case study for California using parsimonious macro energy model with real-world historical demand and hourly weather data has been utilized to do this analysis. In the least-cost model, with no excess wind and solar power generation, required CAES capacity is 3.71TWh in 100% decarbonized scenario. If a strict rule of net-zero curtailment was in place, the storage capacity required would be 3.2% higher (3.83TWh) with 9.8% increased cost of electricity. However, the required CAES capacity decreases with the excess solar and wind power generation.In case of 2 times increase in the wind and solar potential the required CAES capacity with strict zero curtailment would be 19.2% less (3.10TWh) and the corresponding cost would decrease up to 29.7%. The study also revealed that the type of renewable energy mix (wind, solar or both) has strong effect on the required energy storage capacity for deep decarbonization. The presented results demonstrate that excess wind and solar power generation can be used to significantly reduce the required storage needs for a fully renewable power system at reduced cost.In California, building of a large battery energy storage (up to 3 TWh) is limited by societal, geographical, and economic constraints. This study suggests that in addition to the battery energy storage California may need to look for other dispatchable power sources (or the equivalent in load flexibility) for 100% wind and solar based fully decarbonized power system.

Inter-connected large-scale AC microgrids, each of which operates at several to tens of MWs, will play pivotal role in future power system. This paper keeps on advocating to adopt constant frequency operated microgrids. Several reliability and protection issues have been discussed. This includes 1) temporary disintegration of a large-scale microgrid into several independent parts due to a fault, each of which is supervised by one grid-forming generator; re-synchronization of different parts to form a complete large-scale microgrid after the fault is cleared; 2) resilience study of a large-scale microgrid system with double transmission lines under a fault condition. By adopting proper control and fault management method, the system can continue to operate smoothly even though fault occurs.

An energy system with desalination and hydrogen production and storage is a promising option for remote areas with shorelines, e.g., Middle East, to jointly manage electricity, desalinated water and hydrogen resources. Thus, a hybrid renewable energy system considering seawater reverse osmosis desalination, proton exchange membrane electrolyzer stacks, and also proton exchange membrane fuel cell, is proposed. This work focuses on the optimal operation problem of the system. It is established in a multi-objective optimization manner, with consideration of minimizing system total cost, power transmission and renewable energy curtailment. The problem is solved with Non-dominated Sorting Genetic Algorithm-III, a meta-heuristic method dedicated to multi-objective optimization. Results show through the solving of the optimization problem the optimal energy management strategy can be obtained, and in the studied scenario, the system can avoid 38–42% of carbon dioxide emission compared to conventional electricity generation and gray hydrogen production measures. The operational benefit of fuel cell is also verified. Compared to existing works, this work maintains the flexibility and cleanness of green hydrogen production components, consider more aspect in operation and can be solved with limited computational resources and obtain a satisfying result.

To harmonize the operation of renewable and conventional generation, an approach of regionalized microgrid is proposed. Regionalization has been adopted for islanded microgrid in which the distribution system has been sectionalized into conventional generation-based regions (CGRs) operating with droop control and renewable generation-based regions (RGRs) operating with constant frequency de-coupled PQ control method. Back-to-back converters have been installed between the regions for bi-directional power exchange. In this paper, a novel load flow algorithm has been proposed for a regionalized microgrid in an islanded mode that aims to solve the power flow problem for both types of the regions. The algorithm considers the uncertainty of renewable distributed generators (RDGs) and loads. The approach focuses on the frequency regulation in both types of regions. Case studies have been carried out for IEEE 15, 33 and 69-bus distribution systems by converting them into region-alized microgrids (RMG). Results have demonstrated the strength of proposed load flow approach with a fast convergence speed.

To increase power level in an autonomous microgrid, higher voltage is necessary. In this paper, detailed Matlab/Simulink modeling of a microgrid operated at medium-voltage level and at constant frequency has been conducted. Modified boost converter and medium-frequency transformer isolated DC/DC converter are adopted for both solar energy and wind energy harnessing in order for them to be connected with the microgrid. This paper further adopted a differential evolution (DE)-based method to carry out load flow analysis to work out the voltage at each bus. Results obtained from the DE method are nearly the same at some nodes or buses as those from Matlab/Simulink platform-based time-domain fixed-step modeling, while at other nodes they are very close to each other. Then DE method can be used to carry out load flow analysis in more complex microgrids with more nodes to overcome the limited modelling capability of Matlab/Simulink and other tools. Moreover, this new research effort also paves the way for the stability analysis of large-scale microgrids.

In recent years, the power outages caused by catastrophic weather events have become the main concern. Although loads can be effectively restored by network reconfiguration and line repair, topology variation has a significant impact on bus voltages in active distribution networks (ADNs). Additionally, temporal variations and intermittency of renewable power outputs also bring challenges to voltage control. To address these issues, this paper proposes a data-driven voltage/VAR control (VVC) method using PV-associated inverters for the optimal recovery process of active distribution networks (ADNs). Mathematical methods are widely used to solve the VVC problem, which may be inefficient or infeasible when uncertainties and binary recourse variables are involved. Thus, this paper coordinates a mathematical method and a deep reinforcement learning (DRL) method to solve a local VVC problem using inverters responding to uncertain PV power output and different network topologies. Routing and scheduling of repair crews are optimized by a mathematical method, and the DRL agent learns to determine the reactive power output of inverters in terms of repair procedures and uncertainties. A case study conducted on a 33-bus system demonstrates the effectiveness of the proposed method on load restoration and VVC.

Large-scale autonomous microgrids have potential application values as they can increase renewable energy penetration level without compromising the stability of the existing large power systems. Before their widespread implementation, critical issues like stability analysis etc need to be solved. This paper analyses the stability in an autonomous microgrid operated at constant frequency with the consideration of reactive power balance. Difference equations of reactive power for the grid-forming generator are constructed separately from those for the grid-supporting and grid-feeding generators while the difference equations of the real power for all the generators are the same. For the voltage source inverter with its current controlled by proportional resonant controller, at the fundamental frequency, its output current is disentangled from its terminal voltage and is controlled to trace its reference accurately, namely . Therefore, each inverter can be modelled as an equivalent current source and the equivalent circuits for d-component and q-component can be separated from each other. Then, the nodal equations in matrix form for the microgrid system can be established readily. With these, the system level state-space equations are built to study the distribution of eigenvalues. By choosing proper coefficients for real power and reactive power reference generations and controller’s parameters, one can make all the eigenvalues falling in the left-hand-side of the complex plane. Therefore, the system is stable. Such a research paves the way for systematically searching good sets of coefficients and controller’s parameters which make system operate safely away from unstable region with necessary margin.