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Renewable energy sources (RES) are currently being substantially integrated into the power grid because of the recent liberalization of the energy market and associated benefits to both the environment and the economy. A significant amount of renewable energy sources are stochastic and weather dependent, being integrated into the power system that causes a mismatch between the generation of power and the load demand, which creates major issues like variations in frequency and power transmission between the regions. So it is necessary to maintain the frequency and tie-line power variations within a defined range. This is known as automatic generation control (AGC) or load frequency control (LFC). This paper uses brown bear optimization algorithm (BOA) to tune the PID and cascaded PI-PDN controllers for LFC of the renewable integrated two-area power system. The ITAE objective function is utilized to obtain optimal gains of the PID controller. The performance of LFC with BOA-tuned PID controller is compared with other optimization techniques named grey wolf optimization technique (GWO) and particle swarm optimization (PSO) on a RES integrated two-area power system under various scenarios. Further, the effectiveness of the BOA is verified by comparing performance of the PID and cascaded PI-PDN controllers. According to simulation results, BOA offers superior performance in comparison with the GWO and PSO technique.

Agrivoltaics (APV) mitigates land-use competition between photovoltaic installations and agricultural activities, thereby supporting multifaceted policy objectives in energy transition and sustainability. The availability of organic residuals from agrifood practices may also open the way to their energy valorization. This paper examines a small-scale farm in the Basilicata Region, southern Italy, to investigate the potential installation of an APV plant or a combined APV and bioenergy system to meet the electrical needs of the existing processing machinery. A dynamic numerical analysis is performed over an annual cycle to properly size the storage system under three distinct APV configurations. The panel shadowing effects on the underlying crops are quantified by evaluating the reduction in incident solar irradiance during daylight and the consequent agricultural yield differentials over the life period of each crop. The integration of APV and a biomass-powered cogenerator is then considered to explore the possible off-grid farm operation. In the sole APV case, the single-axis tracking configuration achieves the highest performance, with 45.83% self-consumption, a land equivalent ratio (LER) of 1.7, and a payback period of 2.77 years. For APV and bioenergy, integration with a 20 kW cogeneration unit achieves over 99% grid independence by utilizing a 97.57 kWh storage system. The CO2 emission reduction is 49.6% for APV alone and 100% with biomass integration.

•Dataset of energy demand for terrace houses including self-sufficiency indexes.•Archetypes are representative of 3 climatic zones and ground thermal conductivities.•Archetypes’ characterization supports energy policies on ground source heat pumps.•The methodology aims to be replicable supporting district scale retrofit strategies.•Integrated data collection will create the description of the whole building stock. According to the recent policies regarding energy use in buildings and the need of retrofit strategies, the aim of this work is to support policies concerning the installation of ground source heat exchangers in urban and historical areas, raising the awareness on the potential energy saving achievable with optimal sizing and limited impact on the urban environment. Archetypes have been developed distinguishing among existing and historic buildings, focusing on single-family terrace houses, which are the typical residential buildings in European historic centres. A methodology for the optimal sizing of ground source heat pumps, eventually considering dual-source system or air system has been developed combining simulations of a photovoltaic system to estimate the self-sufficiency and the self-consumption for five orientations of the building. Extreme results have been obtained for warm climates, with negligible heating energy demand and possibly free cooling systems rather than traditional cooling systems needed in wintertime. Penalty temperature was acceptable despite unbalanced energy demands. With proper inclination, photovoltaic systems could provide up to 40% of self-sufficiency share also in northern climates. An energy - economic analysis was carried out obtaining a variety of cases representing a general overview of the European building stock and the potential benefits achievable in terms of renewable energy share, energy savings and economic investments needed to be extended to simulations at urban scale. [Display omitted]

This paper proposes a reduced sensor-based nonlinear maximum power point tracking (MPPT) controller for grid-integrated photovoltaic (PV) systems operating under rapidly changing climatic conditions. Unlike conventional approaches that require costly irradiance sensors, the proposed method employs a mathematical irradiance estimation model and a radial basis function neural network to generate optimal reference voltages, which are then enforced by a backstepping nonlinear controller. This two-stage design enables fast and robust MPPT while maintaining DC-link stability and grid power quality. The controller was validated on a 100 kW MATLAB/Simulink-based grid-tied PV system with a DC–DC boost converter and inverter. Under step changes in irradiance, the system tracked the new MPP in as little as 7 ms, while restoring DC-link stability (500 V) within 42 ms. Under continuously varying conditions, it maintained synchronization with the grid and achieved a total harmonic distortion (THD) below 0.1%. Comparative results against Perturb & Observe (P&O), Improved Differential Evolution (IDE), and Particle Swarm Optimization (PSO) demonstrated that the proposed method achieved the highest PV-side power yield (80.41 kW vs. 79.71 kW for P&O, 73.44 kW for IDE, and 59.34 kW for PSO), the highest grid-side active power delivery (78.69 kW vs. 77.98 kW, 72.14 kW, and 58.29 kW respectively), and the lowest integral absolute error of DC-link voltage (IAE = 10.6156). These results confirm that the controller provides faster convergence, improved voltage regulation, and superior grid stability compared to state-of-the-art MPPT methods, making it a promising solution for real-world deployment in large-scale PV systems.

Frequency stability in renewable-integrated power systems faces critical challenges from load fluctuations, solar/wind intermittency, and nonlinear dynamics. Conventional Proportional-Integral-Derivative (PID) controllers exhibit poor disturbance rejection during concurrent solar irradiance drops and load steps, while optimization algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Support Vector Machine (SVM) controllers suffer from premature convergence or computational inefficiency. To address this, we propose a hybrid Simulated Annealing-Quadratic Interpolation Optimizer (hsa-QIO)-tuned filtered PID (PID-F) controller for robust load frequency control in two-area grids with wind, photovoltaic (PV), and thermal generation. The hsa-QIO synergizes global exploration (quadratic interpolation) and probabilistic refinement (simulated annealing), overcoming solution space stagnation. Validated through MATLAB/Simulink simulations under dynamic solar irradiance fluctuations and random load perturbations (0.1-0.4 pu), our approach achieves: 55.7% reduction in Integral Time-weighted Absolute Error (ITAE) versus QIO; 25% lower overshoot (0.9% vs. ANN-PID's 1.2%) and 50% faster settling (0.1 s vs. GA-PID's 0.2 s) in Area 1; superior tie-line regulation (0.3% overshoot/0.4% undershoot vs. SVM's 0.5% and PSO-PID's 0.8%); and statistical robustness with 98.8% lower ITAE deviation (σ = 0.010578) across 30 runs. This computationally efficient solution enables stringent frequency stability (± 0.2 Hz) in high-renewable penetration grids.

This research presents the best power management of flexible-renewable integrated energy systems (FRIESs) with smart distribution networks (SDNs) by taking nonlinear load harmonic compensation into account. A deterministic model that optimizes for three distinct goals serves as the foundation for the proposed system. The goal is to minimize the combined impact of the network’s operational costs, energy losses, and voltage harmonic distortion, taking into account their respective weights. In the FRIES framework, the goal function serves as a constraint on the operation of flexible and renewable sources, as well as the AC optimum harmonic power flow model. The suggested design is first formulated using nonlinear programming, and it is then approximated to a linear model in order to quickly arrive at the one and only optimum solution to the issue by different solvers. Furthermore, there is inherent uncertainty in the design of this work about the output power of renewable sources, load demand, energy consumption of mobile storage devices, and energy costs. Adaptive robust optimization has been applied to develop solutions that effectively address these uncertainties. Ultimately, the results show that, even with the aforementioned uncertainties, the SDN operation is resilient up to a maximum prediction error of 45%. Furthermore, if the distribution substation power factor is maintained at or above 0.9, the worst-case implemented design may enhance the network’s energy losses, voltage profile, and harmonic status by 8.2%, 43.5%, and 51.2%, respectively, as compared to load flow studies.

Rural electrification is necessary for both the country’s development and the well-being of the villagers. The current study investigates the feasibility of providing electricity to off-grid villages in the Indian state of Odisha by utilizing renewable energy resources that are currently available in the study area. However, due to the intermittent nature of renewable energy sources, it is highly improbable to ensure a continuous electricity supply to the off-grid areas. To ensure a reliable electricity supply to the off-grid areas, three battery technologies have been incorporated to find the most suitable battery system for the study area. In addition, we evaluated various demand side management (DSM) techniques and assessed which would be the most suitable for our study area. To assess the efficiency of the off-grid system, we applied different metaheuristic algorithms, and the results showed great promise. Based on our findings, it is clear that energy-conservation-based DSM is the ideal option for the study area. From all the algorithms tested, the salp swarm algorithm demonstrated the best performance for the current study.

A significant portion of the Indian population lives in villages, some of which are located in grid-disconnected remote areas. The supply of electricity to these villages is not feasible or cost-effective, but an autonomous integrated hybrid renewable energy system (IHRES) could be a viable alternative. Hence, this study proposed using available renewable energy resources in the study area to provide electricity and freshwater access for five un-electrified grid-disconnected villages in the Odisha state of India. This study concentrated on three different kinds of battery technologies such as lithium-ion (Li-Ion), nickel-iron (Ni-Fe), and lead-acid (LA) along with a diesel generator to maintain an uninterrupted power supply. Six different configurations with two dispatch strategies such as load following (LF) and cycle charging (CC) were modelled using nine metaheuristic algorithms to achieve an optimally configured IHRES in the MATLAB© environment. Initially, these six configurations with LF and CC strategies were evaluated with the load demands of a low-efficiency appliance usage-based scenario, i.e., without demand-side management (DSM). Later, the optimal configuration obtained from the low-efficiency appliance usage-based scenario was further evaluated with LF and CC strategies using the load demands of medium and high-efficiency appliance usage-based scenarios, i.e., with DSM. The results showed that the Ni-Fe battery-based IHRES with LF strategy using the high-efficiency appliance usage-based scenario had a lower life cycle cost of USD 522,945 as compared to other battery-based IHRESs with LF and CC strategies, as well as other efficiency-based scenarios. As compared to the other algorithms used in the study, the suggested Salp Swarm Algorithm demonstrated its fast convergence and robustness effectiveness in determining the global best optimum values. Finally, the sensitivity analysis was performed for the proposed configuration using variable input parameters such as biomass collection rate, interest rate, and diesel prices. The interest rate fluctuations were found to have a substantial impact on the system’s performance.

Stable operating conditions in electrolyzers are crucial for preserving system durability, ensuring highly pure hydrogen production, and enabling the sustainable utilization of surplus renewable electricity. However, in active distribution networks, the output uncertainty of distributed energy resources, such as renewable energy sources (RES) on the generation side and load demand side, can lead to voltage fluctuations that threaten the operational stability of electrolyzers and limit their contribution to a low-carbon energy transition. This paper proposes a novel framework for optimal electrolyzer placement, tailored to their operational requirements and to the planning of sustainable renewable-integrated distribution systems. First, probabilistic scenario generation is carried out for RES and load to capture the characteristics of their inherent uncertainties. Second, based on these scenarios, continuous power-flow-based P–V (power–voltage) curve analysis is conducted to evaluate voltage stability and identify the loadability and load margin for each bus. Finally, the optimal siting of electrolyzers is determined by analyzing the load margins obtained from the voltage stability assessment and deriving a probabilistic electrolyzer hosting capacity. A case study under various uncertainty scenarios examines how applying this method influences the ability to maintain acceptable voltage levels at each bus in the grid. The results indicate that the method can significantly improve the likelihood of stable electrolyzer operation, support the reliable integration of green hydrogen production into distribution networks, and contribute to the sustainable planning of other voltage-sensitive equipment.

The insufficient flexibility of the hybrid renewable integrated energy system (HRIES) causes renewable power curtailment and weak operational performance. The regulation potential of flexible buildings is an effective method for handling this problem. This paper builds a regulation model of flexible heat load according to the dynamic heat characteristics and heat comfort elastic interval of the buildings, as well as a regulation model of the flexible electrical load based on its transferability, resectability, and rigidity. An operation optimization model, which incorporates flexible regulation of multiple loads and a variable load of devices, is then developed. A case study is presented to analyze the regulation and synergy mechanisms of different types of loads. Its results show a saturation effect between heat and electrical loads in increasing renewable energy consumption and a synergistic effect in decreasing the operating cost. This synergy can reduce the operating cost by 0.73%. Furthermore, the operating cost can be reduced by 15.13% and the curtailment rate of renewable energy can be decreased by 12.08% when the flexible electrical and heat loads are integrated into the operation optimization of HRIES.