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This article analyzes the technical difficulties in full-section backfill mining and briefly introduces the technical principle and advantages of backfilling combined with caving fully mechanized mining (BCCFM). To reveal the strata behavior law of the BCCFM workface, this work establishes a three-dimensional numerical model and designs a simulation method by dynamically updating the modulus parameter of the filling body. By the analysis of numerical simulation, the following conclusions about strata behavior of the BCCFM workface were drawn. (1) The strata behavior of the BCCFM workface shows significant nonsymmetrical characteristics, and the pressure in the caving section is higher than that in the backfilling section. φ has the greatest influence on the backfilling section and the least influence on the caving section. C has a significant influence on the range of abutment pressure in the backfilling section. (2) There exits the transition area with strong mine pressure of the BCCFM workface. φ and C have significant effect on the degree of pressure concentration but little effect on the influence range of strong mine pressure in the transition area. (3) Under different conditions, the influence range of strong mine pressure is all less than 6 m. This article puts forward a control strategy of mine pressure in the transition area, which is appropriately improving the strength of the transition hydraulic support within the influence range (6 m) in the transition area according to the pressure concentration coefficient. The field measurement value of Ji15-31010 workface was consistent with numerical simulation, which verifies the reliability of control strategy of the BCCFM workface.

Coal mining with partial gangue backfilling (CMPGB) method has the advantages of both high filling efficiency and excellent workface capacity, which breaks through the technical bottleneck of full-section backfilling mining. In order to reveal the fracture mechanism and characteristics in CMPGB workface, this paper presents a comparative analysis of the filling ratio of different filling patterns in CMPGB. A local composite elastic foundation mechanical model of basic roof in CMPGB was established using thin elastic plate theory. Then, Galerkin’s semi-analytic solution process was designed according to local composite elastic foundation characteristics. A deflection equation of the basic roof was derived, and a critical condition of the basic roof breakage was given. Based on engineering calculation example of Ji15-31010 workface of Pingdingshan No. 12 Coal Mine, the following conclusions are drawn. (1) At the basic roof of caving section, tensile-shear failure occurred in workface, cutoff position, and transition section, while compressive-shear failure occurred in the central part of the goaf. The basic roof showed a typical local “C–X” failure characteristic. (2) The value of first caving span decreased from 32.7 to 31.4 m as the elastic foundation coefficient of backfilling body increased from 70 × 106 to 120 × 106 N/m3, with a decreasing amplitude of only 4.1%. The increase of density of backfilling body only changed the support structure of backfilling section and had an insignificant effect on the first caving span. (3) The value of the first caving span decreased from 59.1 to 21.68 m as the length of caving section increased from 40 to 140 m, indicating that the first caving span was mainly influenced by the length of caving section. The measured value of the first caving span of Ji15-31010 CMPGB workface was 29.8 m, which was close to the theoretical value of mechanical model.

Machine-type communication plays a critical role in the reliable operation of the smart grid. To overcome the problem of spectrum scarcity, the massive number of machine-type devices (MTDs) are allowed to reuse the spectrum resource allocated to the cellular users (CUs) opportunistically. However, the complicated interference caused by channel reusing and the energy efficiency (EE) issues pose new challenges on resource allocation. In this paper, we investigate the problem of EE maximization for MTDs via jointly optimizing channel selection and power control. A two-stage optimization algorithm is proposed based on many-to-one matching with externalities. In the first stage, each MTD is temporally allocated to predefined transmission power, and then the channel selection optimization problem is solved by matching with externalities to derive the matching relationship between MTDs and resource blocks (RBs). In the second stage, the power control optimization problem is solved by the nonlinear fractional programming algorithm based on the established matching relationship obtained in the first stage. The simulation results demonstrate that the proposed algorithm outperforms the other two heuristic algorithms distinctly.

Social Evolutionary Programming algorithm based on a Social Cognitive Model is proposed in this paper. The uphill and downhill spinning reserves are introduced to cope with the power imbalances caused by wind fluctuation on the premise of full utilization of wind energy. The proposed algorithm has an advantage in the convergent stability and computational efficiency. Since climbing rates constraints have impacts on both start-up & shutdown schedules of generating units and the economic dispatch based on a fixed schedule, two different ways are put forward to calculate UC problem containing climbing rates constraints. Simulation results of a 10-unit system show that such a Social Evolutionary Programming algorithm can effectively cope with the change of reserve requirement due to the grid-connection of wind farm, and meet the multiple requirements of UC and dispatching.

More and more distributed generation have accessed to the power distribution network, which brings great risks to the operation security. In this paper, according to the characteristics of the distribution network, the three-phase risk control model is established based on many controllable equipment in active distribution network, and then the original nonlinear non-convex programming problem is converted into a convex feasible domain by the second-order cone relaxation technique. Finally, the improved IEEE34 nodes case study is illustrated to verify feasibility and validity of the proposed method.

This paper analyses the stochastic dependence between random variables in the distribution network containing a variety of distributed generations, and obtains the nonlinear correlation properties between different random variables of wind power generation, photovoltaic generation and solar-wind hybrid power system. A joint probability distribution model based on the Copula theory is presented to transform the joint probability density into marginal probability density functions and Copula function, and two-stage method is used to analyse and choose the Copula function. The process combining the Copulas theory and Monte Carlo simulation based on Latin hypercube sampling is proposed to analyse the influence of correlated random variables on the node voltage and three-phase unbalance degree in distribution network. The case study demonstrates that the established model can properly describe the joint probability characteristics of different distributed generations, and can comprehensively reflect the impact of randomness of the distributed generations on distribution network.

This paper describes a transaction pricing and evaluating method for maximizing profits in a hydro-thermal power system. The proposed method is based on the Nash bargaining game for power flow analysis in which evety transaction and its price are determine by a fair way. The Proposed method considers the important affect of short term system scheduling, such as unit Commitment and hydro-thermal schedule. The transaction losses are also considered. Test examples and a real scale system result present the merits of the proposed method.

With the proposed of smart grid, the user's role in power system is being re-recognized. In order to realize user's role, many countries are implementing smart metering and demand response (DR). The investment of this system is so huge and the benefits of all parties participating in DR are not clear, all parties are not active to participate in it. In this paper, the problems of nonDR are listed. Then the basic premises of implementing DR are given. On this basis, the effects of DR are calculated quantitatively and analyzed qualitatively. Nowadays, Air-conditioning is one of main reasons which caused peak load. This peak load increases more cost to consumers, power companies and the whole society. According to the mathematical model of air-conditioning between temperature and power, the consumer saving cost of adjusting air-conditioning is calculated. At the same time, the benefits of power companies and society are analyzed qualitative. According to Europe and US pilot projects, the effects of DR are given. The results demonstrate that several million tons/year of coal are saved and thousands tons of CO 2 and SO 2 are reduced to emission. Finally, the barrier of implementation DR is analyzed. There will need opened electricity market, time-of-use or real-time price, advanced metering infrastructure (AMI), advanced communication and control system. Through the benefits analysis of all parties, we hope to promote the every aspect to invest in DR and speed up the process of DR and smart grid.

This paper presents an optimization approach for real-time imbalance market supported and operated by the regional transmission organization (RTO) or alliance regional transmission organization (ARTO). The revised simplex-based linear programming is used for determining the market clearing prices. The imbalance market is performed every 5 minutes cyclically for three future 5-minute intervals. A study case of 3 areas, 8 generation and 2 load participants has been tested and satisfactory results are illustrated.

In this paper, through analyzing the characteristics of WPT and transient signal, a method of extracting and separating transient components based on wavelet packet is presented. During the process of seeking for the best tree, a new rule that is more appropriate than the least entropy rule for transient signal is used. So the best decomposition tree can be found, namely, the different transient components can be extracted which are very important for power quality analysis, pre-alarm of the fault, the reason analysis of the fault and treatment of the fault or abnormality of equipment. Using the method and the WPT best tree method based on the least entropy rule to decompose the same simulated transient signal, the results show that the proposed method is effective.

Short-term load forecasting, which forecasts the electric load of the future day or week, is basis not only for power generation and operation, but also for power market contract. Short-term load forecasting has become an essential part of modern control centers. In the past three decades, many forecasting models have been presented. This paper proposes a new model, which divides the electric load into two parts: the load scaled curve and the day maximal load and minimal load. The load scaled curve is forecasted using five artificial neuron networks. The day maximal load and minimal load are forecasted using fuzzy logic. In this model, weather condition, seasonal index, Test shows the proposed method in this paper can improve the forecasting accuracy.

In this paper, unit commitment (UC) using the genetic algorithm (GA) with different implementation techniques, such as different sampling space methods, different selection schemes, different fitness value scaling methods and different crossover/mutation rates, is developed and implemented in 10 units system and 110 units system. This paper thoroughly studies the effects on convergent time, convergent generation number and convergent value of GA based on different implementation techniques, which indicates that the algorithm is effective in a certain degree, and reveals that different implementation techniques have different effects on convergent time, convergent generation number, and convergent value of GA-based UC. A good foundation is settled for further practicable study of GA-based UC.

A novel generation reliability model of wind power plants and the corresponding calculating method are given, which takes into account the wake effects of wind turbine generators and the wind correlation among wind power plants. Some useful concepts such as the efficiency matrix, equivalent power curve, etc. are defined. The model cannot only be used in power system probabilistic production simulation, but also in stochastic power flow analysis. Power system probabilistic production simulation is utilized to evaluate grid connected wind power plants. The method of calculating fuel savings, pollutant emission reduction, capacity credit and avoided cost of wind power plants is described in detail.

This paper describes a brokerage model for multi-period generation schedule in the interconnected power system based on the nonlinear formulations. The security constraints and transmission losses are considered in this model. We use nonlinear programming to get the most profitable value according to the power flow in the transmission net and the marginal cost offered by clients. The transaction schedules for a 6 bus system with a pump-station and a 30 bus system are calculated to demonstrate the effect of the proposed model.