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Due to the limited availability of fossil fuels and environmental concerns over greenhouse gas emissions, micro-grid systems (MGSs) have become crucial for integrating a high penetration of renewables. Nonetheless, the complexity of the operation of MGS increases in the actual operating process due to the irregular nature of renewables, electrical loads (load demands), and uncertain market prices. The core aim of this paper is to suggest a bi-objective optimization model for managing energy efficiently in a grid-connected MG while considering the unsteadiness of electrical load demands, market prices, and renewable output powers. Also, the suggested model tries to improve the MG’s scheduling while considering the wind turbine, photovoltaic, loads, and market pricing uncertainty. MG operators can develop strategies that balance cost savings with risk management and ensure long-term sustainability by considering the uncertainty of the different generation/demand resources and markets. The suggested optimization model includes two distinct objective functions – minimizing the 24-hour horizon’s total projected cost of the grid-connected MG and reducing MG emissions during the considered period. Further, a simultaneous reduction of the overall operating cost and the pollutant emission MG over 24 h is presented. The general algebraic modeling system (GAMS) has been used to solve the presented multi-objective problem. The findings clearly demonstrate the efficacy of the suggested energy management (EM) methodology for improving the techno-economic-environmental performance of grid-connected MGs under uncertain situations.

Introduction: At the present time, much attention has been focused on new types of solar cells, called perovskite solar cells. They are highly efficient devices with more than 25% power conversion efficiency. However, perovskite solar cell performance has not yet been fully explored. Objectives: We aimed to mathematically investigate the analytical modeling of current–voltage curves of planar heterojunction perovskite solar cells using Perovich Special Trans Function Theory (STFT). Furthermore, we proposed novel analytical closed-form solutions for short-circuit current and open-circuit voltage of these cells in terms of STFT. We evaluated the safety for laying the theoretical foundation by comparing the accuracy of the proposed expressions by the known methods. Methods: A novel hybrid metaheuristic algorithm, called particle swarm optimization (PSO) – evaporation rate water cycle algorithm (ERWCA), is proposed to determine equivalent circuit parameters of the perovskite solar cell. A novel objective function is introduced for estimating the parameters for that purpose too. Results: It was shown that STFT is very applicable and efficient for representing current–voltage expressions of perovskite solar cells. STFT provides a more accurate solution and requires fewer order members than the solutions provided by the conventional Taylor series. Based on these expressions and numerical calculations, it is verified that the characteristic values ​​of variables (short-circuit current, no-load voltage, efficiency, and fill factor) were not accurately calculated in the literature. Also, parameters of equivalent circuits of these cells were not accurately estimated. The equivalent circuit parameters were determined using the algorithm proposed in this work, which fit the verified values ​​of characteristic quantities much better than the literature. Conclusion: This work lays the foundation for developing the planar-structured perovskite solar cell models, in which the proposed estimation method and expressions are highly effective and provide excellent results.

Incorporating renewable energy sources (RESs) into the grid influences the system’s power quality (PQ). These sources are unreliable because of their intermittence. To analyze the influence of RESs like PV and wind farm systems on voltage quality and harmonic analysis, a Dynamic Voltage Restorer (DVR) series compensator is one of the most effective alternatives presently offered. In this work, the DVR is employed to enhance the PQ issues related to the RESs (e.g., voltage sag, swell, flickering, and harmonics). The DVR is controlled by the PI controller with the employment of the gorilla troops algorithm (GTA) to get the gain values of the presented controller under a variety of PQ issues. Two operating modes are addressed – the off-grid mode and the on-grid mode with considerable PV, wind, and nonlinear loads. Moreover, two comparative study evaluations of different optimization strategies and an additional controller are provided to accomplish the desired result and power of the offered DVR. The MATLAB/Simulink platform is used in this study to validate the suggested strategy’s efficiency in various situations. The findings show that the suggested control technique performs well in terms of voltage quality response and harmonic rejection when subjected to grid voltage distortions.

Concentrator PhotoVoltaics (CPV) may generate considerable heat as a byproduct in addition to power. Using that heat as concentrated solar power (CSP) to thermally desalinate salt water would allow for value addition in arid regions by supplementing produced electricity with fresh water. This paper discusses the primary trade-off between the net electricity amount and the net distilled water amount, both of which are desirable products in a hybrid concentrated photovoltaic and thermal system (CPV-T). While higher temperatures promote freshwater generation, they also cause a quick fall in cell efficiency, reducing the quantity of power generated and causing cell deterioration. The CPV-T receiver, which is partially covered with cells and then supplementary heated in a cell-free portion, is modeled and established. Furthermore, simple-stage and multistage desalination unit models are presented, linked to the receiver, and examined to cope with the reduced temperature limits of this receiver. The results demonstrate a CPV-T receiver's ability to generate both electrical power and freshwater, while also demonstrating that the cell temperature constraint can become quite important beyond 50% of the receiver surface coverage. These first order results establish the system's validity and potential and will aid the system designer in the initial system design.

Atmospheric pollutants, mainly produced by thermal power plants compel to utilize green energy sources such as renewable energy sources and hydroelectric plants in a power system. But due to blinking behavior of sources of renewable energy and due to very high rate of outages, it has a detrimental consequence on overall grid. Demand side management (DSM) programs decrease cost and improve power system security. This study proposes non-dominated sorting genetic algorithm-II (NSGA-II) to solve multi-objective scheduling of generation for fixed head hydro-thermal system integrating pumped hydro energy storage and sources of renewable energy taking into consideration the outage and uncertainty in presence of DSM. Numerical results of the test system attained using the proposed technique were compared with strength pareto evolutionary algorithm 2 (SPEA 2).

For electric power companies, the economic utilization of existing fossil fuel is currently a primary issue because of the diminishing supply of fossil fuel. Thermal power plants have fuel limitations and contractual restrictions that must be adhered to. As a result, the scenario-based fuel-constrained short-term hydrothermal scheduling problem with renewable energy sources is presented in this paper. The elephant clan optimization (ECO) approach is offered for short-term hydro-thermal scheduling (STHTS) with thermal generators, cascaded hydro, solar PV plants, wind turbine generators (WTG), and pumped storage hydro (PSH) with and without demand side management (DSM) for various scenarios. On a typical test system, the suggested approach is shown to be successful. An analysis of the typical test system’s numerical results is compared to those produced via the self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf algorithms (GWO). The comparison shows that the suggested ECO is capable of providing a better solution.

Multi-terminal high voltage DC transmission currently represents a leading technology in long-distance power transmission systems. Among the main technical challenges facing such technology, DC fault isolation, permitting different grounding schemes, providing interoperability, and high DC voltage stepping between different HVDC networks, and allowing high-speed power reversal without power interruption especially when connecting the pre-existing voltage source converters (VSC) and line commutated converters (LCC)-based HVDC networks. This paper introduces a new modular multilevel converter (MMC) based front-to-front DC-DC converter to interconnect two different types (LCC/VSC) of HVDC networks. The proposed topology comprises a voltage source MMC (VS-MMC) and a current source MMC (CS-MMC), while both are coupled via an AC link including the isolating transformer. The proposed topology can successfully provide an uninterruptible bi-directional power flow, high DC voltage stepping with a DC fault blocking capability, and low number of semiconductors due to the usage of only half-bridge SMs. The system design is provided with a detailed mathematical analysis. Furthermore, two active power control methodologies are proposed and compared. The first control technique is simpler and entails lower passive elements, while the second technique ensures a zero reactive power over the full range of active power flow. Furthermore, Losses analysis and comparison are provided between the two proposed control techniques. Finally, Control-Hardware-in-the-Loop (CHiL) test validation is employed to confirm the validity of the proposed system under healthy as well as different fault scenarios.

Power grid pattern is expected to evolve from generator-based power systems towards converter-based systems in the forthcoming decades. Therefore, grid-forming converters will be pertinent to interconnected power grids in pursuance of enhancement their resilience against disturbances. This paper introduces a new efficient damping control method for grid-forming converters that provides a smooth power modulation and an efficient damping response against frequency and voltage deviations. First, an averaged state-space representation for a grid forming application in dq synchronization frame is derived. Based on this model, a new hybrid damping controller, including the concept of state feedback control and PI control, is proposed to address the main issues in existing controllers. The state feedback controller is optimally designed using a linear-quadratic regulator (LQR) approach to optimize the system performance. Moreover, the PI controller is optimally designed using the pattern search algorithm. The proposed damping control method integrates optimally between the control loops through a mapping matrix to rapidly synchronize with the grid and efficiently damp the oscillations. Simulations are carried out to prove the proposed method robustness. Finally, a comparative study using controller hardware-in-the-loop (CHiL) is employed against conventional system to validate the proposed damping method.

Biomass is one of the key components for bioenergy generation in recent epoch as traditional resources and depleting swiftly. Going through this fact, the present study aims to appraise the feasibility of Holy Thistle (HT) to produce energy and valuable organic chemicals through pyrolysis. The HT was pyrolyzed at four heating rates including, 10, 20, 30, and 40 °C min−1 to perform thermo kinetic modeling and estimate thermodynamics parameters to establish the pyrolysis reaction process. The pyrolysis process of HT illustrated about 85–88 % of thermal degradation achieved through three different stages from 30 to 800 °C. The major degradation stage was observed from 170 to 450 °C with 55% to 60 % product formation from mainly cellulose and hemicelluloses components. Moreover, the average activation energy and pre-exponential factors demonstrated to be 183–184 kJ mol−1 and 1010 min−1 to 1025 min−1, respectively. The pyrolytic products exhibited agreement to the cleaner production of bio-gas, char and bio-oil which is evident for the potential and productivity of the feed combination.

Human civilization uses vast quantities of water and electricity. Natural fresh water is in short supply in many regions, and the shortfall is increasingly filled through energy-intensive desalination. Electricity still largely comes from burning fossil fuels, resulting in CO2 emissions. Concentrator photovoltaics (CPV) can provide not only electricity but also heat from cooling of the CPV cells, providing additional energy for thermally driven processes such as desalination. This paper evaluates a CPV receiver cooling arrangement for linear CPV systems which maximizes electricity production for a given CPV cell area while supplying heat byproduct boosted to a temperature higher than the temperature of the fluid that cools the CPV cells, increasing the range of processes to which the heat can be applied.

There are three standard equivalent circuit models of solar cells in the literature—single-diode, double-diode, and triple-diode models. In this paper, first, a modified version of the single diode model, called the Improved Single Diode Model (ISDM), is presented. This modification is realized by adding resistance in series with the diode to enable better power loss dissipation representation. Second, the mathematical expression for the current–voltage relation of this circuit is derived in terms of Lambert’s W function and solved by using the special trans function theory. Third, a novel hybrid algorithm for solar cell parameters estimation is proposed. The proposed algorithm, called SA-MRFO, is used for the parameter estimation of the standard single diode and improved single diode models. The proposed model’s accuracy and the proposed algorithm’s efficiency are tested on a standard RTC France solar cell and SOLAREX module MSX 60. Furthermore, the experimental verification of the proposed circuit and the proposed solar cell parameter estimation algorithm on a solar laboratory module is also realized. Based on all the results obtained, it is shown that the proposed circuit significantly improves current–voltage solar cell representation in comparison with the standard single diode model and many results in the literature on the double diode and triple diode models. Additionally, it is shown that the proposed algorithm is effective and outperforms many literature algorithms in terms of accuracy and convergence speed.

Parameters of the solar cell equivalent circuit models have a significant role in assessing the solar cells’ performance and tracking operational variations. In this regard, estimating solar cell parameters is a difficult task because cells have nonlinear current-voltage characteristics. Thus, a fast and accurate optimization algorithm is usually required to solve this engineering problem effectively. This paper proposes two hybrid variants of honey badger algorithm (HBA) and artificial gorilla troops optimizer (GTO) to estimate solar cell parameters. The proposed algorithms minimize the root mean square error (RMSE) between measurement and simulation results. In the first variant, GTO is used to determine the initial population of HBA, while in the second variant, HBA is used to determine the initial population of GTO. These variants can efficiently improve convergence characteristics. The proposed optimization algorithms are applied for parameter estimation of different equivalent circuit models of solar cells and various photovoltaic (PV) modules. Namely, the proposed algorithms test three solar cell equivalent models: single-diode, double-diode, and triple-diode equivalent circuit models. Different photovoltaic modules are investigated, such as the RadioTechnique Compelec (RTC) France solar cell, Solarex’s Multicrystalline 60 watts solar module (MSX 60), and the Photowatt, France solar panel (Photo-watt PWP 201). In addition, the applicability of the proposed optimization algorithms is verified using obtained results from a commercial solar module called Shell Monocrystalline PV module (SM55) with different irradiation and temperature levels. The good results of the proposed algorithms show that they can efficiently improve convergence speed and the accuracy of the obtained results than other algorithms used for parameter estimation of PV equivalent circuit models in the literature, particularly in terms of the values of the RMSE and statistical tests. In addition, the parameters estimated by the proposed methods fit the simulation data perfectly at different irradiance and temperature levels for the commercial PV module.

This paper proposes an enhanced modular multilevel converter as an alternative to the conventional half-bridge modular multilevel converter that employs a reduced number of medium-voltage cells, with the aim of improving waveforms quality in its AC and DC sides. Each enhanced modular multilevel converter arm consists of high-voltage and low-voltage chain-links. The enhanced modular multilevel converter uses the high-voltage chain-links based on medium-voltage half-bridge cells to synthesize the fundamental voltage using nearest level modulation. Although the low-voltage chain-links filter out the voltage harmonics from the voltage generated by the high-voltage chain-links, which are rough and stepped approximations of the fundamental voltage, the enhanced modular multilevel converter uses the nested multilevel concept to dramatically increase the number of voltage levels per phase compared to half-bridge modular multilevel converter. The aforementioned improvements are achieved at the cost of a small increase in semiconductor losses. Detailed simulations conducted in EMPT-RV and experimental results confirm the validity of the proposed converter.