Load Current Power Research Articles

Hybrid Renewable Power Generation for Modeling and Controlling the Battery Storage Photovoltaic System

A major portion of the global energy demand was likely to be fulfilled by an extensive supply of renewable power. Renewable energy outputs, on the other hand, are changeable due to the dynamic nature of their sources. The integration of these variable sources of power into current power grids is proving difficult for electrical power system operators all around the world. The fundamental issue with renewable energy systems is that, due to the stochastic nature of renewable power, electricity production varies from period to period. Recent research and development on renewable technologies can ensure the islands’ long-term electricity supply. Renewable energy sources, on the other hand, are limited by their unpredictable nature and significant reliance on weather conditions. To offset this disadvantage, several renewable energy sources and converters must be joined. To balance the power generation and load power, a hybrid renewable power generation for standalone application is proposed. The solar plant model is made up of a 170 W photovoltaic (PV) panel connected in series, and conversion of energy is done using the maximum power point tracking (MPPT) algorithm, which regulates a buck-boost converter modulation. The MPPT method used in the converter’s control step is based on perturb and observe (P&O) and enhanced with a PI controller. The bidirectional buck-boost DC-DC converters (BBDC) are utilized to preserve a DC-link voltage stable. This is also storing additional hybrid energy in a large battery and is distributed to the system load; then there is a shortage of hybrid power. The load current power is regulated in terms of the frequency and enables it to be achieved using three vector control technique voltage source inverters (VSI). The results were offered to demonstrate a hybrid performance of this organization.

A Solar Photovoltaic Performance and Financial Modeling Solution for Grid-Connected Homes in Zambia

Zambia is today 90% hydropower dependent, but this may change because Zambia and the World at large are today facing a changing climate that affects the ecosystem, rain patterns, and spurs drought which reduces the production of hydropower. The current power deficit experienced in Zambia points to a need to deploy a renewable energy generation-mix strategy. This study conducts a solar photovoltaic performance and financial analysis for grid-connected homes in Zambia to investigate the role of solar energy as an enabler for energy security in Zambia using the National Renewable Energy Laboratory (NREL) System Advisor Model (SAM) simulation method. It further reviews the available solar irradiance, modeling a detailed grid-connected photovoltaic system using locally available products for a single owner in a power purchase agreement (PPA) with the Zambia Electricity Company Limited (ZESCO). This model would alleviate the current power load shedding experienced by the residential sector, of up to 22 hours of no electricity out of 24 hours in a day. Alongside the technical performance model and an unfavorable business climate in Zambia, a financial model is also developed to help assess project feasibility and financial viability. A 1 kW solar PV system was modeled at an installation cost of US$1.27 per watt on a short-term basis of 5 years and found that the project is feasible with a 28.52% IRR achieved in 3 years and a 69% performance ratio and a debt service coverage ratio (DSCR) of 5.12 by the end of the project life, thereby indicating capability to turn around Zambia’s energy poverty to meet the UN SDG 7.

Using an adaptive self-tuning approach to forecast power loads

To overcome the difficulty in forecasting power loads precisely, an adaptive self-tuning approach is proposed. Using an RBF neural network as a predictor and building an evaluator to evaluate the output of the predictor, then, according to the evaluator's judgment, the predictor adjusts its structure and weight by using critical self-learning mechanics. The predictor can keep the same pattern as the current power loads state and power loads can be forecasted precisely. The simulation of practical date indicated that this method is effective.