Search

To reduce the source-load uncertainty and carbon emission levels of the power system, this study proposes a novel low-carbon economic stochastic optimization scheduling model. Firstly, to partially compensate for the instability of wind power (WP) and photovoltaic power (PV) generation, enhance energy efficiency, and reduce system carbon emissions, the introduction of concentrating solar power (CSP) as one of the power sources on the generation side. Secondly, to optimize load characteristics, a peak–valley time period division method based on fuzzy theory and an active demand response model for electric load using the Logistic fuzzy function are introduced on the load side. Thirdly, taking into account the role of the carbon trading market, a ladder-type carbon trading mechanism (LCTM) is introduced to control carbon emissions and reduce overall economic costs. Finally, considering WP-PV output fluctuations, the degree of matching between power output and load, WP-PV utilization, system economic efficiency, and carbon trading factors, with the objective of maximizing the tracking of the load curve and minimizing comprehensive system economic costs, a multi-objective stochastic optimized scheduling model for power systems is established. A multi-objective Runge–Kutta algorithm based on a population-based parallel search mechanism (PSMORUN) is proposed to solve the active demand response model and the low-carbon stochastic optimization scheduling model. Additionally, constraint repair techniques are employed to handle the complex constraints of the models. To verify the effectiveness of the proposed model, a 10-generator power system, including a WP farm, a PV plant, and a CSP plant, is used as a test case for simulation experiments and compared with other multi-objective scheduling models. The experimental results show that the proposed stochastic optimal scheduling model has higher safety, better economy, and lower carbon emissions. • A novel low-carbon economic stochastic optimization scheduling model is proposed. • A novel multi-objective Runge–Kutta solution algorithm is proposed. • A novel multi-objective forecasting model for wind and photovoltaic power is proposed. [ABSTRACT FROM AUTHOR]

Integrated demand response (IDR) is an integral part of the park-level integrated energy system (PIES). Most existing IDR strategies depend heavily on explicit forecasts of future uncertainties. Due to the randomness of wind power generation and load demands, these approaches are limited by forecasting response time or accuracy. A two-stage demand response strategy for multiple scenarios based on deviation compensation is proposed to consider prediction uncertainties arising in distributed wind power generation and multi-energy load. In the day-ahead stage, considering the impact of energy prices on the economy of PIES operation, it is proposed to divide PIES operation scenarios based on energy prices. It is also proposed to develop a day-ahead response plan considering the demand response capability of the load side. The real-time stage considers the uncertainty of wind power and load forecasting as well as the real-time response speed of the strategy, and proposes to use deviation compensation to change some day-ahead plans in order to solve the uncertainty of prediction. Finally, considering the synergy between energy storage and demand response strategies, the above demand response strategy is embedded into multi-objective Harris Hawk optimization (MOHHO) to work out the energy storage capacity. The simulation results show that the strategy considers multiple operational scenarios, is more economical, and only changes a portion of the day-ahead plan in the real-time phase, so it has faster real-time response speed. • A multi-scenario multi-level demand response strategy for multiple operation scenarios in the day-ahead stage is proposed to provide reference for control commands. A real-time multi-level demand response strategy based on deviation compensation is proposed to improve the real-time response speed. [ABSTRACT FROM AUTHOR]

In recent years, the application of wind power forecasting in power systems has gained widespread recognition. However, most studies have only focused on improving forecasting accuracy and few have considered the stability of forecasting results. To solve these two issues simultaneously, this study proposed a novel hybrid wind power forecasting model. First, two types of Laguerre polynomials are used to construct the hybrid Laguerre neural network. Then, a multi-objective Runge–Kutta algorithm is proposed to optimize the weights of the neural network while enhancing the accuracy and stability of the forecasting. Finally, ensemble learning is introduced to further improve the forecasting capability of the model. To verify the effectiveness of the proposed hybrid forecasting model, a large number of comprehensive experiments are carried out using the wind power data of a wind farm in Xinjiang, China. The experimental results show that the proposed hybrid forecasting model had higher forecasting accuracy and better stability than other forecasting models. • A new multi-objective Runge–Kutta algorithm (MORUN) is proposed. • Test MORUN performance on MOP and ZDT test suites. • A novel hybrid wind power forecasting model is proposed. • Extensive experiments are conducted to verify the efficacy of the forecasting model. [ABSTRACT FROM AUTHOR]