The increased popularity of small-scale DER has replaced the well-established concept of conventional generating plants around the world. In the present energy scenario, a significant share of energy production now comes from the grid integrated DERs installed at various consumer premises. These DERs are being renewable-based generates only intermittent power, which in turn makes the scheduling of electrical dispatch a tough task. The Virtual Power Plant (VPP) is a potential solution to this challenge, which coordinates and aggregates the DERs generation into a single controllable profile. In this paper, a modified PSO-based multi-objective optimization is proposed for the VPP scheduling in distribution network applications such as energy cost minimization, peak shaving, and reliability improvement. For feasibility analysis of the VPP, a case study of state power utility is taken, which includes a 90 bus industrial feeder with grid integrated PVs as DER. The optimized results are computed in both grid-connected and autonomous mode reveal that the operating cost, peak demand, and EENS are declined by 31.70%, 23.59%, and 62.30% respectively. The overall results obtained are compared by the results obtained from other well-established optimization techniques and it is found that the proposed technique is comparatively more cost-effective than others.

Maximum power point tracking System (MPPT) is so important in PV systems to increase the efficiency of solar cells. So many methods are proposed to generate the maximum voltage from PV modules under different weather conditions. This paper proposed an intelligent method for maximum power point tracking using the P & O algorithm. The model contains a PV module connected to DC to DC boost converter. The PV System is tested under disturbance in the solar irradiation and temperature level. The simulation results show the maximum power tracker could track the maximum power accurately and successfully in all conditions tested. Comparison of various working parameters such as: tracking efficiency and response time of the system shows that the proposed method gives higher efficiency and better performance than the conventional perturbation and observation method. The voltage, current, and power of the Module can be measured through the P&O Method. The Fuzzy logic based Mppt controller is proposed in this method to increase the voltage Pv module. The proposed method used the fuzzy logic-based controlled (FLC) to initiate the control command to the output buck-boost converter as there is a change in the voltage and current across the PV panel. The modeling of the FLC-based MPPT controller is done for the PV module with the help of MATLAB/SIMULINK.

Recent trends in the photovoltaic (PV) technology industry are moving towards utilizing bifacial PV panels. Unlike traditional PV panels, bifacial PV panels can yield energy from both sides of the panel. Manufacturers specify that bifacial PV panels can harness up to 30% more energy than traditional PV panels. Hence, bifacial PV panels are becoming a common approach at low solar irradiance conditions to yield more energy. However, a bifacial PV panel increases PV inverter loading. The PV inverter is the most unreliable component in the entire PV system. This results in a negative impact on PV system reliability and cost. Hence, it is necessary to anticipate the inverter’s reliability when used in bifacial PV panels. This paper analyzes the reliability, i.e., lifetime, of PV inverters, considering both monofacial and bifacial PV panels for the analysis. Results showed that the increase in bifacial energy yield could significantly affect PV inverter reliability performance, especially in locations where the average mission profile is relatively high.

To improve the competiveness in the energy market, it is necessary that the wind power plants provide guaranteed power generation, although, it is not possible to forecast power availability from wind power plant accurately. This paper presents a stochastic model and solution technique for the combined operation of wind and pumped storage power plants to improve the power availability and increasing the profit considering uncertainties of wind power generation. In this model, uncertainties in wind data have been forecasted for grid connected day-ahead market using Weibull distribution model. The imbalances in the forecasted wind data and the market demand have been reduced by operating the pumped storage power plant. In this stochastic mixed integer problem, pumped storage plant can take the supply either from the grid or from the wind power plant for the pumping operation to store the energy in order to utilize this energy during peak hours for increasing the overall revenue. The reliability of the pumped storage is improved by replacing the conventional unit with the adjustable speed type pumped storage unit. In order to prove the optimality of the solution, two case studies were considered. In case study – I, scheduling is provided by operating the conventional pumped storage unit, whereas in case study – II, adjustable speed pumped storage unit has been used. It has been found that the adjustable speed pumped storage unit has further reduced the imbalance between generated power and demand. The complete approach has been formulated and implemented using AMPL software.

PyPSA is a Python-based open-source tool used for load flow analysis in the power system. PyPSA (Python for load flow analysis) is a leading tool, which is convenient for simulation and optimization of the power system network. It is a free software which regulates parametric valuation along with multi-temporal analysis in the power system. The Modern power system is the integration of various devices like Generators, Transformers, Bus bar, lines Protecting devices and load connected through it. Load flow analysis is used to determine the different types of parameters in the power system i.e. Voltage Magnitude, Phase angle, Power injected, power output and losses through the power system etc. Various kind of devices is utilised in modern power system analysis. Conventionally many techniques are adopted for power System analysis i.e. Mathematical modelling of the system and then calculation. These techniques consume very much time for the calculation. But, due to the advantage of Science and Technology, different types of new methods are developed. PyPSA is one of them. These methods are accurate, fast and reliable for power system analysis. In this study, PyPSA tool is used to analyse the IEEE 12 bus system for determining the voltage magnitude across each bus.