Due to the continual fusion reaction, the sun generates tremendous energy. This solar energy is freely available and can be extracted by installing a large-scale solar power plant. Therefore, such PV solar plants are key contributors to cutting the energy deficit in remote areas. This study focused on predicting a 10-year performance analysis of a large-scale solar power plant by using 1 year of real-time data from the Quaid-e-Azam Solar Park (QASP) situated in Bahawalpur, Pakistan. For the purpose of prediction, the ARIMA model was developed using Python, which is one of the best tools in machine learning. Since ARIMA is a statistical technique for prediction, by using the developed model through Python, we predicted the values of the performance ratio (PR), production amount (MWh), and plan of array (POA) of the solar plant for the next 10 years using 1 year of real-time data. This machine learning prediction technique is very effective and efficient, compared with other traditional prediction and forecasting techniques, for estimating the performance of the solar power plant and the status of the solar power plant in the long-term future. The forecasting/prediction results acquired from the process show that power production during the next ten years increases to approximately 400 MW and that POA will grow from 6.8 to 7.8 W/m². However, a decline occurred in the performance ratio, which decreased from 76.7% to 73%. Based on these results, the ARIMA model for predicting solar power generation is effective and can be utilized for any solar power plant.

The installation of 3 × 50 MW (150 MW DC) large utility scale solar power plant is ground based using ventilated polycrystalline module technology with fixed tilt angle of 28° in a 750-acre land, and the site is located about 115 km northeast of Karachi, Pakistan, near the town of ThanoBula Khan, Nooriabad, Sindh. This plant will be connected to the utility distribution system through a national grid of 220 kV outgoing double-loop transmission line. The 3 × 50 MW solar PV will be one of the largest tied grid-connected power projects as the site is receiving a rich average solar radiation of 158.7 kW/h/m²/month and an annual average temperature of about of 27°C. The analysis highlights the preliminary design of the case project such as feasibility study and PV solar design aspects and is based on a simulation study of energy yield assessment which has all been illustrated. The annual energy production and energy yield assessment values of the plant are computed using the PVSYST software. The assumptions and results of energy losses, annual performance ratio (PR) 74.73%, annual capacity factor 17.7%, and annual energy production of the plant at 232,518 MWh/year are recorded accordingly. Bear in mind that reference recorded data indicates a good agreement over the performance of the proposed PV power plant.

This paper presents an industrial approach to assess the performance of large-scale solar plants (LSSPs) has been developed using a novel performance ratio (PR) formula model based on energy production. The PR is an important quality indicator used for the LSSP projects with a nominal installed capacity of 20 MWdc upward. When solar power plants are put into operation on the national grid, the PR can be used as a performance indicator to ensure the energy guarantee and the plant’s efficiency required to guarantee a level of performance. Then, PR gives the real face of the plant’s efficiency. This is not an easy task because it has no constant value over the years due to the effective incident global irradiation (GlobInc) value always not being available. For this purpose, the proposed PR formula model is used to compute the correct performance guarantee of the grid-connected 100MWdc-LSSP in Pakistan’s Quaid-e-Azam Solar Park (QASP). The main purpose of this study is to incorporate a PR formula based on energy production evaluation to achieve the energy guarantee (excess energy, bonus energy, or energy shortfall) in LSSPs. The 1-year on-site plant data is utilized to investigate the plant’s performance energy inputs such as production, irradiance, PR, and the excess energy. Based on the actual annual energy output and irradiance of the plant, the following results are obtained: Actual PR is achieved at a higher level than the target benchmark, so no shortfall or liquefied damage (LD) is imposed due to excess energy. Thus, 100MWdc-LSSP’s power generation is found to be 5% higher than expected by using the theoretical calculations of PR. In FY (2018–2019), the system fed 0.34% higher PR, with an actual PR of 76.58% higher than the target PR of 76.24%, and extra energy of 710.72MWh produced, as well as bonus energy of 142,143.62 USD. The majority of LSSP projects are determined to be successfully operational and generate a significant amount of power, but they fail to meet the contractual requirements for energy guarantee because they use the incorrect PR formula and also fail to provide the desired energy to stakeholders owing to a poor theoretical assessment of the PR criteria based on energy production. As a result, solar power plants confront a great energy shortfall for many years to come.

Access to solar energy is a prerequisite to remedy CO₂ and improve the standard of human living. Green solar energy is only an immediate solution to add its zero emission profile and provide carbon footprint reduction benefits. This energy does not emit greenhouse gases which means it is a renewable free source of energy when producing electricity. The purpose of this paper is to investigate the accurate annul energy production data reducing uncertainty in solar energy estimates. This work investigated the solar energy assessment taking into account a detailed solar resource and energy production assessment for a 3 × 50 MW PV project with uncertainty analysis. The authors defined a total uncertainty of energy production which is estimated at 9.5% for one year and 8.9% for ten years as well as future variability of 3.4% for one year and 1.1% for ten years. The annual power degradation and expected energy production over the plant lifespan at dissimilar 99%, 90%, 75%, 60%, 50%, and 25% probability of surplus are also observed in this paper.

Traditional energy supply infrastructures are on the brink of facing a major transformation due to energy security concerns, environment pollution, renewable energy intermittency and fossil fuel scarcity. A hybrid energy system constitutes the integration of different energy carriers like electricity, heat and fuel which play a vital role in addressing the above challenges. Various technological options like combined heat and power, heat pumps, electrolysers and energy storages ease out multiple carrier integration in an energy hub to increase system flexibility and efficiency. This work models the hybrid energy system of China for the year 2030 by using EnergyPLAN. Atmosphere decarbonization is achieved by replacing conventional coal and natural gas boilers with alternative individual heating sources like hydrogen operated micro combined heat and power natural gas micro combined heat and power and heat pumps. Moreover, rockbed storage as well as single and double penstock pumped hydro storages are added in the proposed system in order to cope with the stochastic nature of intermittent renewable energy such as wind and solar photovoltaic. The technical simulation strategy is employed to analyze the optimal combination of energy producing components by determining annual costs, fuel consumption and CO₂ emissions. The results substantiate that a heat pump and double penstock pumped hydro storage addition to the individual heating and electricity network not only proves to be an economically viable option but also reduces fuel consumption and emissions.

The accuracy of energy management system for renewable microgrid, either grid-connected or isolated, is heavily dependent on the forecasting precision such as wind, solar, and load. In this paper, an improved fuzzy prediction horizon forecasting method is developed to address the issue of intermittence and uncertainty problem related to renewable generation and load forecast. In the first phase, a Takagi-Sugeno type fuzzy system is trained with many evolutionary optimization algorithms and established coverage grade indicator to check the accuracy of interval forecast. Secondly, a wind, solar, and load forecaster is developed for renewable microgrid test bed which is located in Beijing, China. One day and one step ahead results for the proposed forecaster are expressed with lowest RMSE and training time. In order to check the efficiency of the proposed method, a comparison is carried out with the existing models. The fuzzy interval-based model for the microgrid test bed will help to formulate the energy management problem with more accuracy and robustness.