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The coordinated control systems (CCS) in ultra-supercritical thermal power unit, like many other industrial systems, is a complex multivariable system with severe nonlinearity, strong multivariable coupling and uncertainties. In order to meet the requirements of operational stability, economy. etc in ultra-supercritical unit, it is necessary to establish its accurate mathematical model and further design the advanced controller. Against this background, a new fuzzy neural network modeling method is proposed in this paper. First of all, the incremental model is considered separately to improve the rationality of the local linear model structure. Then, the parameters in antecedent part is initialized by a kernel k -means++ algorithm, in which Xie-Beni index is used to optimize the number of fuzzy rules. Finally, supervised adaptive gradient descent algorithm and artificial immune particle swarm optimization algorithm work in stages to complete the training of the consequent part parameters. The proposed modeling method in this paper is applied to a 1000 MW unit in China and shows satisfactory accuracy. In the established model, the MSE of power output, main steam pressure and separator outlet steam temperature are 0.0099, 1.21E-4, 0.0023, respectively. Both numerical and graphical simulation results confirm the effectiveness of the presented fuzzy neural network in modeling. [ABSTRACT FROM AUTHOR]

In 2003, China implemented market-oriented reforms to its electric power industry, aimed at improving the generation efficiency of its thermal power plants. In this paper, we use the polynomial functions, PLS (partial least squares) algorithm, and generation efficiency data from 1993 to 2012 to evaluate the effect of these reforms. Empirical analysis shows that the reforms caused a sudden down shift of 0.142 kW h per kg SCE (standard coal equivalent) to the “natural” generation efficiency curve of the thermal power industry, resulting in 555.8 million tons of SCE of wasted fossil energy during 2003–2012. This was mainly due to the non-implementation of electricity price bidding. To improve the generation efficiency of the thermal power plants, market competition should be further introduced into China's electric power industry as a matter of urgency. The major policy adjustment directions include: a) Electricity price bidding should be promoted by sub-region according to the unified trading rules designed by the central government; b) Over-the-counter transaction should be permitted; as well as c) Dynamic incentive mechanisms for renewable energy development should be established. [ABSTRACT FROM AUTHOR]

Cascade hydropower stations have good regulation and storage capacity and they can be used as a regulatory and compensatory "medium" to compensate for the instability of wind–photovoltaic power generation. This paper presents a short-term multi-objective coordinated dispatching model based on wind–photovoltaic–hydro heterogeneous energy hybrid power generation. The multi-objectives of the model included accepting new energy for power generation to the greatest possible extent, tracing the load curve of the grid with minimum differences, and minimizing the discarded water volume of the cascade reservoirs. To reduce the complexity of the problem, the model was decomposed into two-stage optimization model and solved by using the firefly algorithm. This model was applied to a national-level wind–photovoltaic–hydro complementary power generation base in China. The research results verified the validity of the model and showed that it was feasible to compensate for the wind–photovoltaic power output fluctuation by the cascade hydropower stations and to supply power to the grid by bundling these three power sources. During this process, the cascade hydropower stations certainly made some sacrifices and increased the amount of discarded water. The new operational strategy proposed in this paper can promote the low-carbon power dispatching and optimization of the energy structure. • A framework for a heterogeneous energy complementary power system was proposed. • A new grid–source coordinated dispatching model was developed. • A two-stage decoupled optimization model was built for a multi-objective solution. • The developed framework and the method were assessed in Yalong River in China. [ABSTRACT FROM AUTHOR]

The objective and accurate prediction of energy consumption can supply an important reference and advance indicator for government to implement economic policies and energy development strategy. On account of the complexity and uncertainty of the energy system, this paper establishes a novel flexible grey multivariable model by introducing a power exponential term, a linear correct term and a random disturbance term. The novel model has the advantages in capturing the dynamic characteristics of the energy system, also it can be compatible with eight existing grey models when some parameters are assigned certain values. Additionally, to further promote the prediction performance of the novel model, the grey wolf optimizer is employed to determine the power indexes of the model. To demonstrate its performance, the proposed model is utilized to predict the energy consumption of three major provinces in China, and the fitting and prediction results of the novel model are compared with those provided by diversified competing models. The results illustrated that the novel model is superior to other competing models, offering more accurate and better performance. Finally, based on the results, several proposals for energy development are put forward for decision-makers. • The flexible multivariable grey model can adapt to more energy system structures. • The novel model can be flexibly compatible with the eight existing grey models. • A specific solution algorithm for the novel model's power indexes is discussed. • The novel model can be easily used to energy system with higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an artificial neural network (ANN) model is developed for estimating monthly mean daily global solar radiation of 8 typical cities in China. The feed-forward back-propagation algorithm is applied in this analysis. The results of the ANN model and other empirical regression models have been compared with measured data on the basis of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE). It is found that the solar radiation estimations by ANN are in good agreement with the measured values and are superior to those of other available empirical models. In addition, ANN model is tested to predict the same components for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou stations over the same period. Data for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou are not used in the training of the networks. Results obtained indicate that the ANN model can successfully be used for the estimation of monthly mean daily global solar radiation for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou. These results testify the generalization capability of the ANN model and its ability to produce accurate estimates in China. [Copyright &y& Elsevier]

To guarantee the heat demand during winter, most combined heat and power (CHP) units in the integrated energy system (IES) usually work under following heat load (FTL) mode, and the renewable energy accommodation is limited. With the development of Power Internet of Things (PIoT), the information exchange in IES become more frequent. Through flexible interaction between different networks in IES, the accommodation capacity of renewable energy can increase significantly. Therefore, this paper focus on the optimization of IES under the background of PIoT. Firstly, based on the influence of PIoT on IES, a novel integrated demand response (DR) way and the model of the critical components in IES are established. Secondly, a Bi-level economic dispatching method for regional IES is developed, considering the cyber-physical infrastructure of PIoT and IES. The upper level of the dispatching method is used to optimize the overall IES operation; the lower level is to optimize the output of demand-side facilities and integrated DR. Thirdly, with adaptive particle swarm optimization (APSO) algorithm, the solution method for the Bi-level dispatch is established. Finally, the feasibility and effectiveness of the proposed method are verified in a standard IES and a real system in northern China. • Dispatching method of integrated energy system (IES) under Power Internet of Things (PIoT) background is proposed. • The proposed method can utilize the flexibility of the demand side under PIoT background. • Bi-level optimization model is used for the proposed method. • The proposed method can facilitate the accommodation of renewable energy and reduce the operation cost of IES. [ABSTRACT FROM AUTHOR]