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Solar power generation (SPG) is essentially dependent on spatial and meteorological characteristics which makes the planning and operation of power systems difficult. To promote the integration of solar power into electric power grid, accurate prediction of geographically distributed SPG is needed. In this paper, we present a combined method for day-ahead SPG prediction of multi-region photovoltaic (PV) plants. First, automatic machine learning (AML) is applied to generate the most suitable ensemble prediction model with optimal parameters and then an improved genetic algorithm (GA) is implemented which processes the candidate features by assigning appropriate operators. To achieve more accurate forecast results as well as mine the interpretable relationship between SPG and related weather or PV system factors, the SPG physical model is taken into account. The method performance is evaluated by the real SPG data along with meteorological variables of multi-region PV plants in Hokkaido from 2016 to 2018. Results indicate that the combined method provides acceptable accuracy and outperforms several baselines and other methods used for comparison. • AML is introduced to establish the optimal combination of different forecast models. • The raw data of multi-region PV plants are reconstructed through suitable operators. • SPG mechanism model is applied to generate suitable candidate operators. • GA algorithm is developed to identify optimal operator set for different PV plant. [ABSTRACT FROM AUTHOR]

As a fundamental parameter of the electric grid, obtaining spatial electric load distribution is the premise and basis for numerous studies. As a public, world-wide, and spatialized dataset, NPP/VIIRS night-light satellite image has been long used for socio-economic information estimation, including electric consumption, while little attention has been given to the electric load estimation. Additionally, most of the previous studies were performed at a large spatial scale, which could not reflect the electric information inner a city. Therefore, this paper proposes a method to estimate electric load density at a township-level spatial scale based on NPP/VIIRS night-light satellite data. Firstly, we reveal the different fitting relationships between EC (Electric Consumption)-NLS (Night-Light Sum) and EL (Electric Load)-NLI (Night-Light Intensity). Then, we validated the spatial-scale's influence on the estimation accuracy by experiment via generating a series of simulated datasets. After working out the super-resolution night-light image with the SRCNN (Super-Resolution Convolutional Neural Network) algorithm, we established a finer spatial estimation model. By taking a monthly data of Shanghai as a case study, we validate the model we established. The result shows that estimating electric load at township-level based on night-light satellite data is feasible, and the SRCNN algorithm can improve the performance. • Electric load estimation is conducted based on night-light satellite data. • Fitting relationships between electric Load and night-light intensity is revealed. • The spatial-scale's influence on the estimation model fitting is validated. • A finer electric load estimation method based on the SRCNN algorithm is conducted. [ABSTRACT FROM AUTHOR]

Compressed air energy storage (CAES), a technology that stores energy in the form of compressed air at times of excess supply and releases it to meet the higher demand in peak load periods, has been considered for numerous applications, most notably to support the electric grid for load leveling applications. Nonetheless, one of the downsides of CAES is the large energy losses incurred in the form of waste compression heat. Considering this issue, a novel CAES system integrating two separate compression process in sequence was proposed in this study. The near-isothermal compression pressurized the air from adiabatic pre-compression could enhance the thermodynamic performance of liquid piston and eliminate the generation of compression heat fundamentally. Through setting up a systematic model, the thermodynamic performance of the system was investigated in detail and the most outstanding advantage characterized with the efficient near-isothermal compressed air process within the liquid piston due to (after) the pre-compression was explored. For the comprehensive utilization of system energy, the intercooler, compressor, and expander were found to be the main contributors to exergy destruction and accounted for 25.8%, 17.3%, and 15.7% of the total exergy destruction, respectively. The significant effects of the liquid piston on thermal management led to the improvement in thermal performance. Moreover, with an optimum inlet pressure of liquid piston, the round-trip efficiency and exergy efficiency were achieved to be 62.6% and 55.4%, respectively, in actual operating conditions. • A novel isothermal compressed air energy storage system was proposed. • Combination of adiabatic pre-compression and near-isothermal compression was employed. • Dynamic mathematical model of the proposed system was built for performance analyzing. • Comprehensive performance of the proposed system performance was evaluated. [ABSTRACT FROM AUTHOR]