Ali Arefi

As the load and some photovoltaic (PV) resources of microgrids (MGs) bring uncertainty to the system, they potentially affect the solution of conventional optimal MG planning. The scenario-based models do not guarantee the provision of continuous power in the possible worst-case scenario. This paper therefore develops a robust framework to address the uncertainty caused by load and PV generation. The operational constraints are also considered where the MG is connected to the grid and participates in the day-ahead market. Considering an energy storage system (ESS) and its sizing in the MG planning problem makes the determination of the worst-case scenario more challenging. A bi-level optimization framework is used to address this issue. One level tries to find the worst case through those variables directly being affected by uncertainties. The other level attempts to optimize the solutions to the MG planning problem. The Column-and-Constraint Generation algorithm is utilized to solve the proposed two-stage problem. The non-linearity involved in the model is transformed into linear equivalents to reduce complexity. It has been applied to a real case in Western Australia, and the results are discussed for both grid-connected and islanded modes by incorporating the effect of a PV tracker system in the planning problem.

Modern home energy management systems (HEMSs) increasingly employ consensus-based algorithms for distributed energy resource (DER) planning. However, these cyber-physical systems face emerging security vulnerabilities that traditional detection methods cannot adequately address. Despite extensive research on consensus-based energy management and cyberattack detection independently, significant limitations persist in their integration, particularly in residential networks with bidirectional electric vehicles (EVs) and vehicle-to-grid (V2G) capabilities. This article addresses this research gap by developing a comprehensive framework that integrates consensus-based home energy management optimization with an advanced false data injection attack (FDIA) detection methodology. Our primary innovation is the enhanced fast go decomposition (EFGD) technique. EFGD employs adaptive parameter selection mechanisms that dynamically determine matrix rank and detection thresholds based on statistical properties of power exchange data. This enables more accurate separation of legitimate system behavior from malicious data injections compared to fixed-parameter approaches. Simulation results on the future renewable electric energy delivery and management (FREEDM) microgrid system with three distributed energy storage devices (DESDs), a wind turbine, a photovoltaic panel, a bidirectional EV, and residential loads over 24-h operation cycles demonstrate superior performance. The consensus-based optimization effectively coordinates multiple energy resources, while the integrated security framework successfully detects attacks that cause 0.52% daily operational cost increases without compromising distributed management efficiency. The EFGD method achieves 99.96% detection accuracy with only 0.02% false positive rate and 98.7% precision. This approach addresses critical vulnerabilities in residential energy systems with V2G integration while maintaining practical implementation feasibility.

Although the integration of renewable energy sources (RES), battery storage systems, vehicle-to-grid (V2G) technologies, and power-to-gas (P2G) have been introduced for expediting the net-zero target achievements, however they introduce significant challenges related to resource management, operational coordination, system stability, and so on. Virtual power plants (VPPs) have emerged as effective aggregators of the aforementioned distributed resources to facilitate coordinated operations within evolving power systems. However, understanding the cutting-edge advancements in VPP development requires a comprehensive review and critical comparison of existing research in this field. This article therefore presents a comprehensive review associated with the recent technologies that enable VPPs to strategically participate in future electricity markets. This study particularly examines key topics such as demand response programming, uncertainty management, multi-level VPP interactions across the power system, V2G integration, P2G applications, ancillary services, and multi-energy market participation. The existing methodologies, benefits, limitations, and applications are thus analyzed across different market contexts, offering a thorough comparative assessment. This utter review not only presents valuable insights for researchers and industry stakeholders to grasp the role of VPPs and their transformative impacts on modern energy markets but also enhances their strategic integration into future power systems.

This paper proposes a novel method to tackle the growing problem of system instability in microgrids, which is brought on by the widespread adoption of renewable energy sources (RESs) and distributed generators (DGs). Connecting RESs and DGs to microgrids through power electronic interfaces leads to a decrease and fluctuation in the system's inertia. This reduction in inertia leads to increased uncertainty and instability in the system, necessitating either load shedding or the curtailment of renewable generation, which are undesirable for both utilities and customers. To address these challenges, this paper provides a novel approach for enhancing inertia in microgrids to avoid any instability or load shedding. This approach introduces a multi‐objective optimization algorithm based on the non‐dominated sorting genetic algorithm II (NSGA‐II), implemented in a co‐simulation platform combining MATLAB and DIgSILENT PowerFactory. The algorithm simultaneously determines the minimum required capacity of battery energy storage systems (BESSs) acting as virtual inertia and their optimum droop coefficients to stabilize the grid and prevent load shedding. The proposed approach is formulated within the context of a multi‐objective optimization algorithm, by utilizing the NSGA‐II in an integrated DIgSILENT and MATLAB framework. The simulation results show the positive results of the proposed approach in stabilizing the microgrid of Broome city and avoiding any load shedding. The method is validated under three critical scenarios: a 0.97 MW step load increase, a cloud event reducing PV output by 49%, and a synchronous generator outage. Results show that the optimized BESS configuration successfully maintains frequency stability and avoids any load shedding. The minimum sizing of BESSs and their optimum droop coefficient are obtained for different scenarios including step load change, cloud event, and synchronous generator outage. Compared to conventional approaches, the proposed method significantly reduces the required BESS capacity while ensuring compliance with frequency nadir and RoCoF constraints. This approach provides a cost‐effective and scalable decision‐making tool for microgrid operators to enhance system resilience and customer satisfaction. The outcome of this research is a critical decision‐making tool for the microgrid owner to cost‐effectively decide the virtual inertia sizing and their parameters for stabilization of microgrid and to improve customer satisfaction by avoiding load shedding. This contribution bridges the gap between academic research and field‐level implementation, offering a scalable and adaptable strategy for future renewable‐rich microgrids.

Optimizing greenhouse envelopes to balance energy efficiency, economic feasibility, and crop lighting requirements is a growing priority for sustainable agriculture. This study developed a multi-objective optimization framework integrating passive daytime radiative cooling (PDRC) materials and semi-transparent photovoltaics (STPV) to enhance greenhouse performance under varying daily light integral (DLI) constraints. Using coupled energy simulation, daylight analysis, and cost modeling, we evaluated material configurations across various representative DLI thresholds, reflecting different crop requirements. Also, the study presents quantitative assessment of PDRC’s contribution to cooling demand reduction (CDR). An improved equilibrium optimizer (IEO) algorithm was employed to solve the multi-objective problem. Results revealed two distinct energy benefit modes. In passive-dominant regimes (DLI = 10), PDRC coatings accounted for over 97 % of total net energy savings (7649 kWh), enabling the lowest-cost configuration ($6,800). In contrast, active-dominant regimes (DLI = 30) favored STPV deployment, achieving up to 16,290 kWh net energy with higher transparency and electrical efficiency. The results reveal that increased PDRC’s reflectivity from 0.75 to 0.89 resulted in CDR gains of over 640 kWh annually in a representative configuration. This study provides a decision-support framework for designing climate-responsive, energy-efficient greenhouses, emphasizing the critical role of material selection and spatial allocation in achieving sustainability goals.
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Effective energy management is crucial in greenhouse farming to ensure efficient operations and optimal crop growth. This study investigates the energy autonomy—defined as the ratio of on-site energy generation to the total energy demand—of greenhouses equipped with semi-transparent photovoltaic (STPV) systems under two scenarios: with and without a Battery Energy Storage System (BESS). STPV systems are beneficial because they generate energy while still allowing enough light to pass through for healthy plant development. Seasonal variations in energy autonomy during summer and winter were analyzed. Results show that incorporating BESS significantly reduces reliance on grid electricity, with energy autonomy improving from 43.43% to 24.17% in summer and 81.36% to 69.45% in winter. The system’s performance was highly sensitive to the transmittance rate of STPV panels and the minimum Daily Light Integral (DLI) required for crops. These findings highlight the potential of BESS to enhance energy independence and promote sustainable agricultural practices. The study provides insights into optimizing renewable energy systems in greenhouses, emphasizing practical implications for scalability and economic feasibility.

Precise water demand forecasting (WDF) is crucial for sustainable irrigation and resource efficiency in urban greenhouse systems. This study introduces a cutting-edge hybrid deep learning approach designed for short-term WDF, while also considering the energy nexus between water, energy, and environmental factors. The model integrates the least squares generative adversarial network (LSGAN) for data pre-processing and noise reduction, convolutional neural networks (CNN) for feature selection, and bidirectional long short-term memory (BiLSTM) for time-series state modeling, and named as LSGAN-CBiLSTM. Using real-world data from the Wageningen Research Centre in Bleiswijk, Netherlands, the model significantly outperformed benchmark approaches, achieving an R-value of 99.57 % with minimal forecasting errors. The model demonstrated exceptional stability, minimal bias, and strong handling of environmental variability, improving short-term WDF accuracy, optimizing water management in urban agriculture, enhancing sustainable irrigation, and addressing the energy nexus for efficient resource use. [Display Omitted]

Due to the intermittent and uncertain nature of emerging renewable energy sources in the modern power grid, the level of dispatchable power sources has been reduced. The contemporary power system is attempting to address this by investing in energy storage within the context of standalone microgrids (SMGs), which can operate in an island mode and off-grid. While renewable-rich SMGs can facilitate a higher level of renewable energy penetration, they also have more reliability issues compared to conventional power systems due to the intermittency of renewables. When an SMG system needs to be upgraded for reliability improvement, the cost of that reliability improvement should be divided among diverse customer sectors. In this research, we present four distinct approaches along with comprehensive simulation outcomes to address the problem of allocating reliability costs. The central issue in this study revolves around determining whether all consumers should bear an equal share of the reliability improvement costs or if these expenses should be distributed among them differently. When an SMG system requires an upgrade to enhance its reliability, it becomes imperative to allocate the associated costs among various customer sectors as equitably as possible. In our investigation, we model an SMG through a simulation experiment, involving nine distinct customer sectors, and utilize their hourly demand profiles for an entire year. We explore how to distribute the total investment cost of reliability improvement to each customer sector using four distinct methods. The first two methods consider the annual and seasonal peak demands in each industry. The third approach involves an analysis of Loss of Load (LOL) events and determining the hourly load requirements for each sector during these events. In the fourth approach, we employ the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) technique. The annual peak demand approach resulted in the educational sector bearing the highest proportion of the reliability improvement cost, accounting for 21.90% of the total burden. Similarly, the seasonal peak demand approach identified the educational sector as the most significant contributor, though with a reduced share of 15.44%. The normalized average demand during Loss of Load (LOL) events also indicated the same sector as the highest contributor, with 12.34% of the total cost. Lastly, the TOPSIS-based approach assigned a 15.24% reliability cost burden to the educational sector. Although all four approaches consistently identify the educational sector as the most critical in terms of its impact on system reliability, they yield different cost allocations due to variations in the methodology and weighting of demand characteristics. The underlying reasons for these differences, along with the practical implications and applicability of each method, are comprehensively discussed in this research paper. Based on our case study findings, we conclude that the education sector, which contributes more to LOL events, should bear the highest amount of the Cost of Reliability Improvement (CRI), while the hotel and catering sector’s share should be the lowest percentage. This highlights the necessity for varying reliability improvement costs for different consumer sectors.

Solar photovoltaic (PV) and wind power generation are key contributors to the integration of renewable energy into modern power systems. The intermittent and variable nature of these renewables has a substantial impact on the power system’s reliability. In time-series simulation studies, inaccuracies in solar irradiation and wind speed parameters can lead to unreliable evaluations of system reliability, ultimately resulting in flawed decision making regarding the investment and operation of energy systems. This paper investigates the reliability deviation due to modeling uncertainties in a 100% renewable-based system. This study employs two methods to assess and contrast the reliability of a standalone microgrid (SMG) system in order to achieve this goal: (i) random uncertainty within a selected confidence interval and (ii) splitting the cumulative distribution function (CDF) into five regions of equal probability. In this study, an SMG system is modeled, and loss of load probability (LOLP) is evaluated in both approaches. Six different sensitivity analysis studies, including annual load demand growth, are performed. The results from the simulations demonstrate that the suggested methods can estimate the reliability of a microgrid powered by renewable energy sources, as well as its probability of reaching certain levels of reliability.

The growing power quality issues stemming from the higher presence of converter-based appliances in the distribution network increases the harmonic risks. Given the importance of tackling these issues to deliver high-quality power to customers, this investigation provides a promising approach focused on effectively designating soft open points (SOPs) and tie switches in the distribution networks. The objectives of this investigation include the reduction of total harmonic distortion (THD), power loss per various harmonic frequencies and the number of SOPs. To evaluate the performance of the proposed approach, the study is carried out on two well-known networks, 33-bus, and 69-bus. Through these analyses, it was shown how effectively THD, power loss, and voltage profiles are improved when SOPs and tie switches are allocated together. The outcomes of thorough analyses provide credence to the assertion that the suggested approach is very promising for mitigating power quality issues by handling uncertainties in loads by Monte Carlo Simulation (MCS). In more detail, the obtained solutions assure that the suggested method has superior power quality across the networks by maintaining the THD levels within the standard margins. Additionally, the developed model offers advantages including reduced power loss and enhanced voltage profiles, which increase the overall network efficiency, while reducing the total investment cost by reducing the number of SOPs and power loss in power distribution networks.