• Energy Research

In the low-carbon era, photovoltaic power generation has emerged as a pivotal focal point. The inherent volatility of photovoltaic power generation poses a substantial challenge to the stability of the power grid, making accurate prediction imperative. Based on the integration of a backpropagation (BP) neural network and a genetic algorithm (GA), a prediction model was developed that contained two sub-models: no-rain and no-snow scenarios, and rain and snow scenarios. Through correlation analysis, the primary meteorological factors were identified which were subsequently utilized as inputs alongside historical power generation data. In the sub-model dedicated to rain and snow scenarios, variables such as rainfall and snowfall amounts were incorporated as additional input parameters. The hourly photovoltaic power generation output was served as the model’s output. The results indicated that the proposed model effectively ensured accurate forecasts. During no-rain and no-snow weather conditions, the prediction error metrics showcased superior performance: the mean absolute percentage error (MAPE) consistently remained below 13%, meeting the stringent requirement of the power grid’s tolerance level below 20%. Moreover, the normalized root mean square error (NRMSE) ranged between 6% and 9%, while the coefficient of determination (R2) exceeded 0.9. These underscored the remarkable prediction accuracy achieved by the model. Under rainy and snowy weather conditions, although MAPE slightly increased to the range of 14% to 20% compared to that of scenarios without rain and snow, it still adhered to the stringent requirement. NRMSE varied between 4.5% and 8%, and R2 remained consistently above 0.9, indicative of satisfactory model performance even in adverse weather conditions. The successful application of the proposed model in predicting hourly photovoltaic power generation output during winter in Henan Province bears significant practical implications for the advancement and integration of renewable energy technologies.

Abstract A novel concentrating photothermal reactor with the ultrafast heating rate at around 1800 °C/min was developed to study the effects of CO2 and H2O on coal pyrolysis for oxy-fuel combustion. The mass loss rate with the ultrafast heating rate increased by several decuples than that with the slow heating rate. Coal pyrolysis process with the ultrafast heating rate was promoted by CO2 within 50% concentration but inhibited for a further higher concentration (70%). The addition of H2O into 30% CO2 exhibited a continuous positive role in coal pyrolysis with the ultrafast heating rate. Different from the satisfied prediction of chemical reaction models with the slow heating rates, diffusion models were of higher correlation coefficients for the ultrafast-heating pyrolysis process. The correction factors for CO2 and H2O were determined accordingly, and the modified three-dimensional diffusion mechanism model based on Jander equation (D3 model) well described the ultrafast-heating coal pyrolysis process.

Pyrolysis of walnut shells to biochar at 700 °C using N2gas was compared to one-step activation using steam/KOH under four different heating rates (10–40 °C/min). Steam biochars developed better surface area property of 342–556 m2/g compared to others (19–135 m2/g) over the same heating rates; meanwhile, increasing heating rate caused functional groups alteration and was distinct on the surface area property as about six-fold and two-fold difference from the lower to higher rates were recorded in neutral char and the steam/KOH activated biochars respectively. Increasing heating rate affected the neutral and steam biochars’ Raman intensity ratio whereas trivial influence was recorded on KOH-biochars.