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Battery energy storage systems can be integrated with photovoltaic (PV)-diesel microgrids as an enabling technology to increase the penetration of PV systems and aid microgrid stability by smoothing out the power fluctuations of the PV systems. This paper focuses on this topic and aims to derive correlations between the optimal capacity of the smoothing batteries and variabilities in the daily solar irradiance. To this end, the two most commonly used moving average and ramp rate control techniques are employed on a real solar irradiance dataset with a 1-min resolution for a full calendar year across 11 sites in Australia. The paper then presents the developed empirical model, based on linear regressions, to estimate the batteries' optimal capacity without requiring detailed simulation studies, which are useful for practitioners at the early stages of a project's feasibility evaluation. The performance of the developed technique is validated through numerical simulation studies in MATLAB®. The study demonstrates that the empirical model provided reasonably accurate estimates when using the moving average smoothing technique but had limited accuracy under the ramp rate control technique. [ABSTRACT FROM AUTHOR]

One of the most reliable and prominent renewable energy resource which addresses the global energy demand is the Solar photovoltaics (PV) systems. Usage of the PV system substantially reduces the carbon footprint of the energy generation process leading towards an environmentally friendly alternative. The substantial rise in the adaptation of the distributed energy resources likes solar PV and batteries into conventional electric power grids, has increased the complexity of the energy management problem. Many Researchers across the world are working towards developing accurate forecasting models to predict the solar PV system’s generation capacity in advance to effectively manage the supply and demand. In this research, a modified support vector regression (SVR) based solar irradiance forecasting model with a minute wise time horizon is proposed, developed, and tested using the data obtained from Bureau of Meteorology (BOM), Australia. The performance of the proposed approach is then validated using the benchmark statistical models like, the persistence algorithm, autoregression model, moving average model, a hybrid autoregressive moving average model and an autoregressive integrated moving average model. Metrics like root mean square error (RMSE), the mean absolute error (MAE) and mean bias error (MBE) are used to validate the performance of the proposed approach. Results indicated that the Modified SVR model reduces the forecasting error and the computational complexity substantially and outperforms the other conventional approaches. The increased performance of the forecasting method will assist in developing an efficient energy management system for future electricity grids. [ABSTRACT FROM AUTHOR]

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A substation-level grid emissions intensity model of the Australian NEM is developed. • Model maps emissions intensity from generators to consumption points by flow-tracing. • The underlying power flow model is validated with operational data from the network. • Regional areas have lower emissions electricity than urban areas. • Proximity to renewables impacts local grid emissions intensity. Reducing greenhouse gas emissions in the energy industry is broadly acknowledged as important with numerous countries implementing targets to reduce emissions. Understanding the carbon emissions intensity of electricity grids is an important step in this process, and higher spatial and temporal granularity estimates are critical for delivering actionable insights. Existing emissions intensity models are limited in terms of their real-time availability and, in particular, their spatial granularity. Here we outline a method for estimating emissions intensity of large-scale interconnected electricity networks in real-time at substation-level resolution. This is realized by integrating nation-wide high-resolution electricity generation and demand data, regional population data, a detailed model of the electricity grid, and power flow simulation and flow tracing models. Results for Australia's National Electricity Market reveal that most capital city locations have higher emissions intensity than neighbouring regional areas, though the variation within a city can also be substantial. In general, emissions intensity is higher in areas closer to, or directly fed by emissions intensive coal and gas generators. These results have important implications for the design of energy efficiency incentives and demand response strategies, in the rollout of distributed renewable generation, and in carbon accounting. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) have become a driving force for the decentralized energy industry, due to their efficient management and control of distributed energy resources. Most of the operation strategies for VPPs are designed based on the day-ahead forecasts. However, the prediction errors of the renewable energy sources (RES) and loads in the power dispatch schedule can lead to a suboptimal operation. In this article, an adaptive and predictive energy management strategy for a real-time optimal operation of VPPs is proposed based on the model predictive control technique with a feedback correction (FC) to compensate for the prediction error. This strategy has two parts: 1) receding-horizon optimization (RHO), and 2) FC. In the first part, a hybrid prediction algorithm based on the integration of the time-series model and the Kalman filter is used to forecast the output powers of RES and the loads. Based on the prediction, the RHO model schedules the operation following the latest forecast information. In the second part, the receding schedule is adjusted based on the fast-rolling gray model's ultrashort-term error prediction. The FC is applied to minimize the adjustments for compensating the prediction error. The proposed strategy is implemented on a VPP in a real electricity distribution system in New South Wales, Australia. The simulation results demonstrate the effectiveness of the proposed strategy with a better tracking of the actual available resources and a minimal mismatch between demand and supply. [ABSTRACT FROM AUTHOR]

Unlike a land-based standalone microgrid, a shipboard microgrid of an all-electric ship (AES) needs to shut down generators during berthing at the port for exanimation and maintenance. Therefore, the cost of onshore power plays an important role in an economic operation for AESs. In order to fully exploit its potential, a two-stage joint scheduling model is proposed to optimally coordinate the power generation and voyage scheduling of an AES. Different from previous studies that only consider the operation cost of the ship itself, a novel coordinated framework is developed in this article to address the shore-side electricity price variations on the ship navigation route. A deep learning-based forecasting method is utilized to predict the electricity price in various harbors for ship operators. Then, a hybrid optimization algorithm is designed to solve the proposed multiobjective joint scheduling problem. A navigation route in Australia is adopted for case studies and simulation results demonstrate the high energy utilization efficiency of the proposed algorithm and the necessity of on-shore power influence on the AES voyage. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an interlink inverter control method for providing a constant tie‐line smoothing service in a grid‐connected residential microgrid (MG) to mitigate the fluctuating nature of renewable power generation and load demand. A grid power controller is designed for an MG to keep a constant grid power on typical days of the year by maintaining the charging/discharging of the battery. To achieve this objective, the MG controller sends the reference to the interlink inverter controller based on load dynamics, average PV generation and battery capacity. Moreover, a state‐space model is developed from the dynamic model of the MG system, and an eigenvalue‐based stability analysis is performed for different system parameters. The proposed control method is verified through extensive case studies by using real irradiance and typical residential customer load data in Queensland, Australia. In addition, a comparison with a dynamic energy management‐based method that aims to achieve the same objective is presented to demonstrate the performance of the proposed control method. Results show that the proposed method provides a constant tie‐line power for a grid‐connected residential MG for typical days of the year. [ABSTRACT FROM AUTHOR]

Many modern industries are equipped with onsite renewable generation and are normally connected to the grid. A battery energy storage system (BESS) can complement the intermittency of the available onsite renewable generation. The combination of the BESS and the renewable generation can operate as a microgrid. If the microgrid is properly sized and managed, it is possible to reduce the electricity bill to have a huge saving in the electricity cost. This article proposes an energy management system for such an industrial microgrids. The decisions to charge and discharge the BESS in the proposed energy management are usually constrained by the size of the energy storage. The proposed energy management strategy aims to optimize the operation of the industrial microgrids subject to the scalability of the BESS under uncertainties. The proposed optimization involves two stages. In the first stage of optimization, it determines the optimum size of the energy storage taking into account the cost of the BESS, and in the second stage, it minimizes the cost of the microgrid operation based on the decision made in the first stage. This proposed two-stage energy management strategy is formulated as a single-stage linear program that incorporates stochastic scenarios for addressing uncertainties. In addition, the proposed strategy also considers the various operating limits of the energy storage, such as the efficiency and the charging and the discharging rates, and considers the fading effect of the batteries of the BESS. The proposed strategy is then validated using two typical datasets from two different industrial units in New South Wales, Australia. The simulation results show that the proposed strategy effectively calculates the optimum size of the BESS and reduces the operational cost. [ABSTRACT FROM AUTHOR]

This study aims to assess the techno-economic viability of a grid-connected microgrid for providing low-cost, reliable energy supply in regional parts of Australia. In this research, a regional community with solar and wind energy potential is examined as a case study. The study evaluates five different system configurations with and without energy storage based on the available resources. The results indicate that the most reliable and cost-effective system is the grid-connected microgrid with solar, wind, diesel generator, and battery. The energy cost for this system is calculated as $0.152/kWh, which is 7.11% less expensive than the base scenario that solely utilizes grid power. The results also demonstrate that the proposed system can achieve a reduction in operating costs by 7.37%. Additionally, this microgrid configuration may potentially save 174.647 metric tonnes of CO 2 annually. Finally, sensitivity analysis is performed to identify the critical sensitive parameters. The results reveal that variations in solar radiation, wind speed, real interest rate, and battery storage lifetime have a significant impact on the design of microgrids. The study findings would help microgrid planners, analysts, and policymakers to identify the latest design constraints and formulate appropriate strategies for renewable-based community power systems. • A multi-energy community microgrid is proposed for Australian perspective. • The potential of accessible renewables in the NSW South Coast region is evaluated. • The high capital expenditure of renewables is investigated and compared with the grid. • Sensitive factors affecting the economic viability of microgrids are identified. • Cost comparability of the proposed microgrid is investigated with similar studies. [ABSTRACT FROM AUTHOR]

Off-grid groundwater cotton irrigators using traditional technologies have had few options to reduce water extraction costs. New options of blending of Alternating Current and Direct Current through an all-in-one soft start converter/controller offer opportunities to lower costs, diversify fuel sources and continuous off-grid power in remote locations. Diesel energy can be replaced by renewable fuel sources through installing an electric submersible pump powered by solar photovoltaic in combination with a diesel generator. An investment analysis using a discounted cashflow over 25 years showed a 23% internal rate of return and payback over 5 years. Benefits of installing a renewable fuel source included lower greenhouse gas emissions per bale of cotton produced and less labour to monitor and service traditional diesel irrigation plant. Emissions calculations reveal a reduction of 26%, or 35 kg CO 2 e per cotton lint bale, and an abatement of 2665 t/CO 2 e for the life of the project. The cost of emissions abatement under the Renewable Energy Target to displace almost one million litres of diesel was found to be $31 t CO 2 e. • A microgrid solution reducing energy costs and emissions is analysed for an irrigation pump in NSW, Australia. • The microgrid includes photovoltaic panels and a diesel generator used to power a submersible pump 24 h a day. • The analysis indicates a 5-year payback and 23 per cent internal rate of return for the investment. • Emissions are reduced by 26 per cent (per 227 kg cotton lint bale) equating to 2665 t/CO 2 e abatement over the project life. [ABSTRACT FROM AUTHOR]

Copyright of Regional Studies is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Simulating biomass usage in Australian energy transitions and 100% renewable grid. • High spatio-temporal grid configuration featuring disaggregated biomass feedstocks. • Bioelectricity generation share reaches ∼9%-12% at carbon prices above 30$/t. • Biomass plants operate in gap-filling mode can better facilitate load balancing. • Biomass can reduce system capacity and cost by 32% and 21% in 100% renewable grid. Responding to the global crises - Covid19 and climate change - governments around the world are formulating green recovery plans to stimulate economic growth, boost clean energy technologies and cut emissions. Potential transition pathways for low carbon energy systems, however, remain as open questions. Generally, the simulation of biomass in the grid models is limited in their tempo-spatial resolution, transition pathways description, and/or biomass feedstock supply representation. This study aims to provide spatio-temporal highly resolved grid configurations featuring disaggregated biomass feedstocks, to assess Australia's potential energy transition pathways and 100% renewable electricity supply scenarios under various biomass bidding strategies and cost assumptions. We find that, as carbon prices increase, bioelectricity will prove to be a cost-effective flexible option compared to other low-carbon (such as CSP) and fossil-based flexible options (e.g. coal and gas), with its generation share reaching ∼9%-12% at higher carbon price scenarios. Biomass power plants can be well suited for operating in gap-filling mode to provide flexible power generation and to facilitate grid stability and load balancing. In light of the high biomass resource potential in Australia, keeping bioelectricity in the generation mix is beneficial for reducing system capacity and cost by 32% and 21%, respectively, under a future renewable-dominated Australian grid system. [ABSTRACT FROM AUTHOR]

• Empirical analysis of a solar PV and battery storage system in apartment building. • Shared energy microgrid achieving 75% self-sufficiency using PV and battery. • Shared system may be more effective than separate supply connections. • Analysis of apartment's energy consumption and shared energy microgrid performance. • Enabling Australian apartment buildings to access PV and battery storage. This study presents the energy performance of a three unit apartment building in Perth, Western Australia equipped with a shared energy microgrid. Although there has been a dramatic growth of residential rooftop solar PV across Australia, apartment buildings and their occupants are rarely able to access the benefits associated with onsite renewable energy generation and consumption. To address this, an apartment building in Perth was fitted with a PV and battery energy storage system, with metering architecture. The microgrid configuration enabled the sharing of energy between the apartment units. A one year dataset (December 2017-December 2018) obtained from onsite pulse meters was analysed. Load profiles were assessed and grid minimisation was evaluated through self-sufficiency metric. The three unit apartment showed a 22% reduction in average yearly energy consumption against the benchmark. The findings demonstrated an overall 75% dependency of the microgrid on renewables; and suggest that a shared energy microgrid may be more effective than separate supply connections. [ABSTRACT FROM AUTHOR]

Challenging frequency control issues, such as the reliability and security of the power system, arise when increasing penetration levels of inverter-interfaced generation are imposed. As a result of the displacement of convention generation in favour of renewable energy sources, the reduction of frequency response capabilities can be seen. A promising method of overcoming the aforementioned challenges is to utilise Battery Energy Storage Systems (BESS), which provides frequency support by injecting instantaneous power to the grid and back up the conventional generation systems. However, large battery systems increase the cost while inadequate battery capacities result in poor performance. This paper, therefore, proposes an approach for finding the optimum BESS size for an islanded microgrid power system. The determination of the optimum BESS size is based on an existing case study, under which the most severe contingencies of generation loss and load loss have been accounted for, as well as different levels of penetration of renewable energy sources. As a result of using meta-heuristic optimization (Grey Wolf Optimization), the constraint optimization problem has been identified as BESS sizing. Through the use of real-time simulation DIgSILENT PowerFactory software, estimated BESS size can be applied to a standalone microgrid to test the frequency of support capabilities. The simulation has made it apparent that through the selection of the optimum BESS size, the system frequency response is not only mitigated, but improved. • Proposing a novel optimization model for determining the optimal size of the battery devices of standalone microgrids. • Propose an intelligent approach to mitigate the frequency response of standalone microgrids based on hybridization sources. • Combining the advantages of the BESS controller and the meta-heuristic Grey Wolf Optimizer (GWO) algorithm. • Validate the system performance based on a real case study under different scenarios. [ABSTRACT FROM AUTHOR]

Growing global consensus around the pressing need for decarbonisation to avert uncertain climate futures has accelerated the transition away from fossil fuels, towards widespread experimentation with distributed energy systems and practices. An investigation of four different microgrid-related experiments in Victoria, Australia revealed a number of commonalities and differences in relation to the drivers and challenges that supported and constrained these innovative projects. Despite differences in the types of microgrid projects examined, the case studies presented similar internal challenges, particularly in relation to organisational and individual risk aversion. External challenges arising from the broader regime were also present, particularly in relation to securing regulatory approvals, limitations in workforce capacity, and lack of industry acceptance. The empirical data suggests that the majority of challenges identified speak to the socio-institutional context rather than technological feasibility. Drawing on niche management framings, seven critical strategies for policy development aimed at s hielding, nurturing and empowering future microgrid projects to move beyond experimentation and achieve more widespread decarbonisation are presented. • Microgrid trials across Australia are challenging conventional modes of energy production, distribution and consumption. • Need for greater orchestration and knowledge sharing across disjointed experiments. • Seven process-related factors revealed relating to expediting future replication. [ABSTRACT FROM AUTHOR]