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As the information technology develops, educational resource recommendation system has become an indispensable part of the education field. At the same time, people’s demand for personalised educational resources is also growing; therefore, how to accurately predict user demand and provide personalised recommendations has become an important issue. In this study, a fuzzy predictive control model on the grounds of neural network is proposed to optimise the design of educational resource recommendation in the context of information technology. After experimental testing, the model recommendation fit reached 95.16 and 92.91% on the two test datasets, which are significantly higher than the control model. The average F1 values of the proposed model also reached 95.21 and 88.77%, which are higher than the control model. In other control experiments, the proposed model of this study also has a better performance. The relevant outcomes showcase that the predictive performance and recommendation effect of the model can be further enhanced by improving the structure of the neural network and the parameter optimisation method. Meanwhile, the proposed model has high performance in information overload and personalised demand, which offers a useful reference for the optimal design of educational resource recommendation system.

The modular multilevel converter (MMC) has the advantages of high efficiency, low harmonic, modular design, and easy cascade, which has been widely used in the field of high voltage and large capacity energy conversion. In order to improve the dynamic response speed of the MMC-HVDC based on the modular multi-level converter, a novel method which combines a model predictive control (MPC) of MMC-HVDC system with improved sub-module voltage balanced control strategy is proposed. The method, which utilises the prediction model, feedback correction and rolling optimisation to obtain the optimal voltage control, overcomes the difficulties in the traditional way of setting PI parameters of the internal loop current controller and the outer loop controller and tackles the problem of low dynamic response. Finally, a 21-level MMC-HVDC system simulation model is built on PSCAD-EMTDC software platform. The simulation results indicate the effectiveness and feasibility of the control strategy.

• A joint operation system of wind turbine and battery storage system is constructed. • Demand response programme is utilised to optimal dispatch the energy. • Two-stage rolling optimisation with multiple time resolutions is proposed. • Mutation-based artificial bee colony (MABC) algorithm is applied and compared. Considering increasingly scale of renewable energy resources penetration in modern power system, especially the wind power generation, this paper constructs a joint operation system which combined the wind turbine with the battery energy storage system (BESS) in a highly wind power penetrated microgrid, in order to weaken the impact of wind power uncertainty. Simultaneously, the demand response (DR) is optimally incorporated into system operation as flexible resources to adjust the imbalance of supply-demand in the microgrid. The battery investment capital and operation maintenance cost are also modeled as components of the optimisation. A novel two-stage energy generation schedule market rolling optimisation framework, which consists of the day-ahead energy generation schedule market and intraday energy generation reschedule market, is presented to pursue the best energy profits for the microgrid. A mutation-based artificial bee colony (MABC) algorithm is designed for the day-ahead market optimisation due to the nonlinear of battery operation model in this stage, while without considering the dispatch of battery, the intraday market rolling optimisation is solved as a mixed integer programming. Simulations are carried out on a small-scale microgrid with high penetration of wind power, the results show that the constructed joint operation system can effectively reduce the effect of wind power uncertainty and the proposed two-stage energy generation schedule market rolling optimisation framework can adjust the generation schedule along with the updated wind power predicted data, and thus increase the total energy profits for the joint operation system prominently. [ABSTRACT FROM AUTHOR]

The modular multilevel converter (MMC) has the advantages of high efficiency, low harmonic, modular design, and easy cascade, which has been widely used in the field of high voltage and large capacity energy conversion. In order to improve the dynamic response speed of the MMC-HVDC based on the modular multi-level converter, a novel method which combines a model predictive control (MPC) of MMC-HVDC system with improved sub-module voltage balanced control strategy is proposed. The method, which utilises the prediction model, feedback correction and rolling optimisation to obtain the optimal voltage control, overcomes the difficulties in the traditional way of setting PI parameters of the internal loop current controller and the outer loop controller and tackles the problem of low dynamic response. Finally, a 21-level MMC-HVDC system simulation model is built on PSCAD-EMTDC software platform. The simulation results indicate the effectiveness and feasibility of the control strategy. [ABSTRACT FROM AUTHOR]

Daily gas demand in the UK is variable. This is partly due to weather patterns and the changing nature of electricity markets, where intermittent wind energy levels lead to variations in the demand for gas needed to produce electricity. This uncertainty makes it difficult for traders in the market to analyse the market. As a result, there is an increasing need for models of the UK natural gas market that include stochastic demand. In this paper, a Rolling Optimisation Model (ROM) of the UK natural gas market is introduced. It takes as an input stochastically generated scenarios of demand. The outputs of ROM are the flows of gas, i.e., how the different sources of supply meet demand, as well as how gas flows in to and out of gas storage facilities. The outputs also include the daily System Average Price of gas in the UK. The model was found to fit reasonably well to historic data (from the UK National Grid) for the years starting on the 1st of April for both 2010 and 2011. These results allow ROM to be used to predict future flows and prices of gas and to investigate various stress-test scenarios in the UK natural gas market. [ABSTRACT FROM AUTHOR]