Sivkumar Mishra

The rapid proliferation of renewable energy sources (RESs) has significantly reduced system inertia, thereby intensifying stability challenges in modern power grids. To address these issues, this study proposes a comprehensive approach to improve the grid stability concerning RESs and load disturbances. The methodology integrates controlled energy storage systems, including ultra-capacitors (UC), superconducting magnetic energy storage (SMES), and battery storage, alongside a robust frequency regulation management system (FRMS). Central to this strategy is the implementation of a novel controller which combines a constant with proportional–integral–derivative (PID) and modified fractional-order (MFO) control, forming 1+MFOPID controller. The controller parameters are optimized using a novel formulation of an improved objective function that incorporates both frequency and time domain characteristics to achieve superior performance. The efficacy of the proposed controller is validated by comparing its performance with conventional PID and fractional-order PID controllers. System stability is further analyzed using eigenvector analysis. Additionally, this study evaluates the performance of various energy storage systems and their individual contributions to frequency regulation, with a particular emphasis on the synergistic benefits of battery storage in conjunction with other storages. Finally, sensitivity analysis is conducted to assess the impact of parameter uncertainties in the system design, reinforcing the robustness of the proposed approach.

Electricity is the best technological advancement ever. Power is now used for everything in our culture because of how far it has come. Quiddity of life would not be possible without it. We know that the sole free hotspot for the PV module in our environmental factors is the sun. The PV cell changes sun-powered energy into electrical energy when the sun radiates on it. At the point when you produce power with the sunlight-based chargers, no ozone-harming substance emanations are placed into the climate. Since, the sun creates more energy than any manmade process at any point requires so as a result, in this article we will look at a variety of ways as well as a successful MPP strategy with high efficacy. It encompasses incremental conductance, perturbs & observe, and fuzzy logic approaches. A boost (DC-DC) converter ameliorates the likeness between a solar array and storage or power grid. In many solar-producing systems, DC/DC converters assist in surveilling the utmost power point by acting as a bridge betwixt load and solar panel. The intent of achieving the highest possible electricity can be done by calibrating the load to compare the current and voltage of a photovoltaic cell.

Solar photovoltaic (PV) power, for its multiple benefits, has adhered to prominent consideration in the electrical energy generation region. The double-stage triple-phase grid-connected solar PV (SPV) system is utilized to enhance the power quality by employing a lymphoblastoid cell lines LCL filter. In this method, a DC-DC converter and DC-AC converter make a feasible juncture of the PV systems to the electrical interface. For converting boosted DC into AC, A 3-phase DC-AC converter is used, which is supplied into the grid. A 3-phase voltage converter is employed in place of an inverter for interfacing amid the voltage generated by the PV system and the grid possessing an AC transmission line. An maximum power point tracking (MPPT) application is used in this proposal to amplify the effectiveness of the PV array in face of any unsteady climatic circumstances. Hence, the highest energy could be secured out of the solar PV array and interfaced with the grid. Enhancing power quality by employing an LCL filter is quantified by FFT analysis in MATLAB. The advised proposal has attained a very low total harmonic distortion (THD), proving its efficacy. Also, the outcomes ascertain the applications of the proposed system and extend future advances of renewable energy with a great power quality improvement.

This paper develops a maximum power point tracking (MPPT) approach for a microgrid-linked solar photovoltaic (PV) system based on the concept of a perturb and observe (P&O) approach. It is an iterative algorithm that requires a starting point to trace the maximum power point tracking. This study proposes the P&O MPPT algorithm for a 3-ɸ grid-connected PV system. In this work, the PV system is modeled as a DC grid-connected PV system, and the MPPT algorithm is applied to that model. An MPPT application is used in this proposal to amplify the effectiveness of the PV array in face of any unsteady climatic circumstances. Hence, the highest energy could be secured out of the solar PV array and interfaced with the grid. The voltage source converter will empower defining of boundaries and greatest degrees of force from sunlight-based chargers in an efficient way. A few outcomes like the current, voltage and result power for each different mix have been noted.

This paper presents an improved equilibrium optimizer (IEO) based method for dynamic hourly network reconfiguration (NR) with optimal allocation of dispatchable and non-dispatchable renewable distributed generator (DG) units in radial power distribution networks. In this work, solar and wind-based DGs are considered as non-dispatchable DG, while biomass DGs are considered as dispatchable DG. The output of biomass DGs is adjusted according to the 24-hour load demand curve and that of solar and wind DG follow their hourly power generation. For this purpose, a multi-objective function is developed with consideration of hourly power loss minimization and profit maximization without violating the system’s operational constraints. The viability and efficacy of the proposed method are tested on IEEE 33-bus and 69-bus distribution systems for five distinct scenarios of network reconfiguration and renewable DG allocation. The analysis shows that the simultaneous NR and DG have a more substantial impact than considering the cases separately.

Distribution static compensators (D-STATCOMs) can enhance the technical performance of the power distribution network by providing rapid and continuous reactive power support to the connected bus. Accurate modeling and efficient utilization of D-STATCOMs can maximize their utility. In this regard, this article offers a novel current-injection-based D-STATCOM model under the power control mode of operation for the reactive power compensation of the power distribution network. The versatility of the proposed D-STATCOM model is demonstrated by combining it with two of the most established distribution load flow techniques, viz., the forward–backward sweep load flow and the BIBC–BCBV-matrix-based direct load flow. Further, the allocation of the proposed D-STATCOM model is carried out under a multiobjective mathematical formulation consisting of various technical and economic indices such as the active power loss reduction index, voltage variation minimization index, voltage stability improvement index and annual expenditure index. A novel parameter-free metaheuristic algorithm, namely a student-psychology-based optimization algorithm, is proposed to determine the optimal assignment of the different number of D-STATCOM units under the multiobjective framework. The proposed allocation scheme is implemented on a standard 33-bus test system and on a practical 51-bus rural distribution feeder. The obtained results demonstrate that the proposed D-STATCOM model can be efficiently integrated into the distribution load flow algorithms. The student-psychology-based optimization algorithm is found to be robust and efficient in solving the optimal allocation of D-STATCOMs as it yields minimum power loss compared to other established approaches for 33-bus PDNs. Further, the economic analysis carried out in this work can guide network operators in deciding on the number of D-STATCOMs to be augmented depending on the investment costs and the resulting savings.

In a quest to solve the multi-objective optimal planning problem using a simple parameter-free metaheuristic algorithm, this paper establishes the recently proposed student psychology-based optimization (SPBO) algorithm as the most promising one, comparing it with the other two popular nonparametric metaheuristic optimization algorithms, i.e., the symbiotic organisms search (SOS) and Harris hawk optimization (HHO). A novel multi-objective framework (with suitable weights) is proposed with a real power loss minimization index, bus voltage variation minimization index, system voltage stability maximization index, and system annual cost minimization index to cover various technical, economic, and environmental aspects. The performances of these three algorithms are compared extensively for simultaneous allocation of multitype distributed generations (DGs) and D-STACOM in 33-bus and 118-bus test systems considering eight different cases. The detailed analysis also includes the statistical analysis of the results obtained using the three algorithms applied to the two test distribution systems.