G. M. Shafiullah

Hydrogen-enabled renewable microgrids offer a promising pathway for long-duration energy storage and the decarbonization of remote off-grid power systems. However, their techno-economic and operational performance depends on the optimization formulation, coordinated component sizing, storage dispatch, reliability constraints, and the trade-off between diesel-assisted and fully renewable pathways. Hence, this study develops a reliability-aware optimization framework for a remote Australian off-grid microgrid integrating photovoltaic, wind, battery, electrolyzer, hydrogen tank, fuel cell, with or without diesel backup. Five hydrogen-integrated configurations are initially investigated using HOMER Pro, after which two representative pathways, diesel-assisted (PV/WT/DG/BT/EL/FC/HT) and diesel-free (PV/WT/BT/EL/FC/HT) are selected for detailed optimization using constraint-based particle swarm optimization (CPSO) and NSGA-II under a unified hierarchical energy management strategy. Reliability is enforced through a loss-of-power-supply-probability (LPSP) constraint. For the diesel-assisted pathway, NSGA-II achieves a net present cost (NPC) of AUD 8.07 million, levelized cost of energy (LCOE) of AUD 0.241/kWh, levelized cost of hydrogen (LCOH) of AUD 7.543/kg, renewable fraction of 97.6%, autonomy of 120.2 h, and loss-of-power-supply probability (LPSP) of 1.2%. The diesel-free pathway achieves an NPC of AUD 7.76 million, LCOE of AUD 0.233/kWh, autonomy of 126 h, and LPSP of 1.705%, while producing zero direct operational emissions. Sensitivity analysis indicates that the discount rate and project lifetime dominate LCOE variation, while electrolyzer efficiency and hydrogen storage cost primarily influence LCOH. The framework demonstrates how coordinated electricity-hydrogen-electricity can support reliable and sustainable energy conversion in isolated renewable energy systems.

The increasing integration of renewable energy sources (RES) and the widespread adoption of electric vehicles (EVs) have significantly expanded distribution networks in recent years, leading to challenges such as increased power losses and reduced transmission reliability. To address these issues, optimizing distribution network topology through reconfiguration is crucial for enhancing voltage stability and improving overall system performance. This paper presents an optimized distribution network reconfiguration (DNR) model that incorporates RES and EVs, focusing on the optimal placement of bus tie switches and reactive power regulation using the Reverse-Multiverse Learning Archimedes Algorithm (RMLAA). The main goals are to minimize power losses and voltage deviations across buses, while optimizing reconfiguration and reactive power from RES (wind and PV) and EVs, with RMLAA proposed as the decision variable. The RMLAA integrates Reverse-learning and Multiverse-directing strategies to enhance optimization precision and computational efficiency in multi-objective optimization. It focuses on minimizing reactive and active power losses while improving voltage stability. To assess its effectiveness, the RMLAA is compared with widely used algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO), using the CEC’17 benchmark test suite. Applied to the IEEE 33-bus and 69-bus systems, the RMLAA achieves remarkable reductions in active power losses of 70.35% and 69.08%, and in reactive power losses of 47.65% and 26.01%, respectively. Additionally, voltage stability improves by 15.2%, and computational efficiency increases by up to 22.3% compared to conventional methods. The study further demonstrates the effectiveness of the proposed algorithm through a comparative analysis with existing algorithms on the IEEE 33-bus and 69-bus systems. These findings confirm that the proposed RMLAA-based DNR framework is an effective and robust approach for improving efficiency, voltage stability, and computational performance in modern distribution networks with RES and EV integration.

Precisely assessing electric vehicle hosting capacity (EVHC) is critical for ensuring the secure integration of EVs and optimizing the use of distribution network resources. Although optimization-based methods such as Particle Swarm Optimization (PSO) can identify a high theoretical HC under steady-state voltage constraints, these static formulations fail to capture short-term dynamics such as photovoltaic (PV) intermittency and uncoordinated EV arrivals. As a result, the hosting capacity that can actually be used in practice is often reduced to a much lower capacity to keep the system operating safely. This study compares optimization-based and simulation-based HC assessments and introduces a Weighted Average Power Estimator (WAPE)-based dynamic control framework to preserve the higher HC identified by optimization under real-world conditions. Case studies on a modified IEEE 13-bus system show PV drops of 90% during a 4-s cloud event. Studies also demonstrate that a sudden clustering of multiple EVs would significantly lower effective HC. With WAPE control, the system maintains stable operation at full HC, holding the bus voltage within an acceptable range (400–430 V) during the two events, representing a 2–3% voltage improvement. In addition, WAPE allows the EV to continue charging at a lower rate during disturbances, reducing the total charging time by almost 10% compared with completely stopping the charging process. Overall, the proposed WAPE substantially improves the usable and sustainable HC of distribution networks, ensuring reliable EV integration under dynamic and uncertain operating conditions.

Murdoch University (MU) is committed to achieving net-zero Scope 1 and Scope 2 emissions by 2030. MU’s security, transport, and logistics fleet currently relies on petrol and diesel vehicles, with typical emission factors of approximately 3.2 kg CO₂-e per litre of petrol and 3.7 kg CO₂-e per litre of diesel. The fleet contributes an estimated 250–280 tCO₂-e annually, representing around 13% of MU’s Scope 1 emissions. This study presents a techno-economic evaluation of producing green hydrogen locally (on campus) for use as a direct fuel to transition MU’s fleet to hydrogen fuel cell electric vehicles (FCEVs). Two renewable electricity (RE) supply configurations were assessed for hydrogen production: a grid-connected solar PV-only option and a stand-alone hybrid solar–wind option. The results indicate that the hybrid configuration offers the most favourable feasibility and fuel-cost performance; however, the outcome is sensitive to the sell-back price for excess electricity exported to the MU campus and/or the grid. The hydrogen production, storage, and dispensing infrastructure was modelled using both (i) a discrete-component balance-of-plant (BoP) approach and (ii) a pre-engineered compact hydrogen fuelling solution, exemplified by a Simple Fuel Unit (SFU), to assess plausible upfront costs. The levelised cost of hydrogen (LCOH) was benchmarked against petrol and diesel on an equivalent cost per 100 km range basis. Under suitable excess RE sell-back arrangements, locally produced green hydrogen can deliver zero-emissions refuelling and reduce fleet fuel costs by up to 79%. The analysis also finds that FCEV leasing costs are typically 25–40% higher than comparable internal-combustion vehicles. Nevertheless, when fuel-cost savings are combined with internalised benefits, such as emissions reduction, reputational value, and potential carbon-pricing exposure, FCEV leasing can become cost-effective for MU.t Body]

The rapid proliferation of Electric Vehicles (EVs) offers a promising pathway toward reducing greenhouse gas emissions and fostering a sustainable environment. However, the large-scale integration of EVs presents significant challenges to distribution networks, potentially increasing stress on grid infrastructure. To address these challenges, this study proposes the integration of a Virtual Power Plant (VPP) framework within EV charging stations as a novel approach to facilitate dynamic power management. The proposed framework integrates electric vehicle (EV) scheduling, battery energy storage (BES) charging, and vehicle-to-grid (V2G) support, while dynamically monitoring energy generation and consumption. This approach aims to enhance voltage regulation and minimize both EV charging durations and waiting periods. A modified IEEE 13-bus test network, equipped with six strategically placed EV charging stations, has been employed to evaluate the performance of the proposed model. Simulation results indicate that the proposed VPP-based method enables dynamic power coordination through EV scheduling, significantly improving the voltage stability margin of the distribution system and efficiently reduces charging times for EV users.

Regional distribution networks (RDNs) frequently encounter challenges related to peak load demands, such as increased system operational costs, grid instability, transmission and distribution line losses, and decreased system efficiency. Thus, this study specifically examines the practice of peak shaving for RDN by employing a battery energy storage system (BESS) in order to decrease overall operational expenses and improve system performance. A centralized peak shaving technique is developed using an economic size of BESS to provide optimal peak shaving performance. Real load data is utilized in this work to eliminate the forecasting errors which is collected from a real RDN located in Australia. The developed technique is applied to real RDN furnished with real data to undertake a variety of simulation tests aimed at assessing peak shaving performance. A comparison study is used to demonstrate the success of the improved peak shaving operation by comparing it with previous strategies. The result reveals that the developed method with an economic size of BESS can generate substantial electricity savings and economic benefits, with a net revenue over investment 2.2 times higher.

Hydrogen-based Power Systems (H2PSs) are gaining accelerating momentum globally to reduce energy costs and dependency on fossil fuels. A H2PS typically comprises three main parts: hydrogen production, storage, and power generation, called packages. A review of the literature and Original Equipment Manufacturers (OEM) datasheets reveals that no single manufacturer supplies all H2PS components, posing significant challenges in system design, parts integration, and safety assurance. Additionally, both the literature and H2PS projects’ database highlight a gap in a systematic hydrogen equipment and auxiliary sub-systems technology selection process, and how this selection affects the overall H2PS Balance of Plant (BoP). This study addresses that gap by providing a guideline for available technology options and their impact on the H2PS-BoP. The analysis compares packages and auxiliary sub-system technologies to support informed engineering decisions regarding technology and equipment selection. The study finds that each package’s technology influences the selection criteria of the other packages and the associated BoP requirements. Furthermore, the choice of technologies across packages significantly affects overall system integrity and BoP. These interdependencies are illustrated using a cause-and-effect matrix. The study’s significance lies in establishing a structured guideline for engineering design and operations, enhancing the accuracy of feasibility studies, and accelerating the global implementation of H2PS.