Taking the high pressure elevation stopping problem of a pressure machine hydraulic system as an example and based on analysis, research and measuring, this paper demonstrates the main reasons causing problem of over length time when the status change is made from system unloading into high pressure overflow of the pilot relief valve by remote control Given the conditions that the structure of the pilot relief valve unchanged,a solution is proposed to solve the mentioned problem by modification of the return system and the practice has been proved of realatively good effect
In this study, a superiority–inferiority-based minimax-regret analysis (SI-MRA) model is developed for supporting the energy management systems (EMS) planning under uncertainty. In SI-MRA model, techniques of fuzzy mathematical programming (FMP) with the superiority and inferiority measures and minimax regret analysis (MMR) are incorporated within a general framework. The SI-MRA improves upon conventional FMP methods by directly reflecting the relationships among fuzzy coefficients in both the objective function and constraints with a high computational efficiency. It can not only address uncertainties expressed as fuzzy sets in both of the objective function and system constraints but also can adopt a list of scenarios to reflect the uncertainties of random variables without making assumptions on their possibilistic distributions. The developed SI-MRA model is applied to a case study of long-term EMS planning, where fuzziness and randomness exist in the costs for electricity generation and demand. A number of scenarios associated with various alternatives and outcomes under different electricity demand levels are examined. The results can help decision makers identify an optimal strategy of planning electricity generation and capacity expansion based on a minimax regret level under uncertainty.
System planning of energy resources management is an effective way for supporting socio-economic development and enhancing eco-environmental sustainability. In this study, an inexact coupled coal and power management model with ecological restoration and pollutants mitigation was developed to analyze the impacts of growing coal use and electricity production on eco-environmental quality by integrating a complete set of ecological and environmental constraints. The model can not only effectively handle the uncertainties and complexities of the coupled coal and power management systems, but can also facilitate a dynamic analysis of capacity expansion, facility installation, energy resource inventory, coal blending and environmental regulation changes within a multi-period and multi-option context. The developed model was applied to a long-term coupled coal and power management system planning problem to support the regional eco-environmental sustainability in north China. The interval solutions associated with different risk levels of the constraint violations were obtained, which could be used to formulate decision alternative options. The results generated could also aid decision makers in identifying desirable strategies under various social-economic, environmental and system-reliability constraints with the highest system reliability and the lowest system cost and ecological environment impact. In addition, the tradeoffs between system costs and constraint-violation risks could also be tackled.
In this study, an inexact mixed-integer fuzzy robust linear programming model for coupled management of coal and power with consideration of CO2 emissions mitigation system planning (IMIFLP-CCPM) was developed under uncertainty. This model could reach into the closed relationship and interactive characteristics of China's coal production, electric power generation, and CO2 emissions in coupled coal and power management system and thus explore the applicability of the decarburization facilities and mechanism incorporated in the system through scenario analysis. Based on the integration of interval linear programming, fuzzy robust linear programming, and mixed-integer linear programming, the IMIFLP-CCPM could effectively incorporate and handle uncertainties presented in terms of interval values and fuzzy sets. Also, dynamic analysis of capacity expansion, facility improvement, and inventory planning within a multi-period and multi-option context could be facilitated in this model. The developed IMIFLP-CCPM was applied to a long-term coupled coal and power management with CO2 reduction systems in Subei region, Northeast China. One base scenario and four CO2 reduction scenarios were presented and analyzed to examine the optimal coal-flow allocation patterns and carbon mitigation schemes for the studied system when forced to comply with a given CO2 emission limit. The results indicated that the IMIFLP-CCPM model could provide in-depth analysis of tradeoffs between system costs, energy security, and CO2 emission reduction, thus helping investigate interactive relationships among multiple economical, environmental, and energy structural targets within the study system. Moreover, the attempt of planning coupled coal and power management with CO2 mitigation under uncertainty would provide an effective reference to cope with the dilemma of energy development and CO2 mitigation under the climate change situation in China.
Abstract Macro‐scale distributed hydrologic modeling advanced hydro‐system understandings and scientized relevant human activities. However, it faces challenges of hydrometeorological heterogeneities, parametric interactions, data uncertainty, computational expensiveness, and other complexities, especially over cold regions with intense climatic changes. As an effort to address them, a multifactorial principal‐monotonicity inference (MFPMI) method is developed through integrating, extending, or improving climate classification, hydrologic modeling, and sensitivity analysis. MFPMI is applied to an undammed macro‐scale high‐latitude cold‐region watershed, Athabasca River Basin (ARB) in Canada. MFPMI mitigates the underestimation of climatic impacts on streamflows in process‐based models, hydrologic classification, large‐scale hydroclimatic data deconstruction, parametric‐interaction neglection, and climatic homogenization; its superiority is particularly evident for highly heterogeneous climates. Dominant climatic impacts on ARB streamflows of various regimes increase from tributaries to the mainstem and decrease from up‐ through down‐ to mid‐stream catchments, possibly due to the offset effects of non‐climatic factors (e.g., vegetation and soil). The impacts also decline with streamflow magnitudes, and vary with seasons, spatial scales and lead months rather than temporal resolutions. Streamflow magnitudes, catchments and metrics differ compositions of the climate conditions explaining cross‐scale uppermost discharge variations. In spite of this, streamflow increases with temperature and precipitation, and headwater climates play part of dominant roles in forcing discharges throughout ARB. Accuracy metrics differentiate parameters and accordingly structures of MFPMI models, and hydroclimatic data uncertainty is high for high flows, fine temporal scales or low climatic impacts, increasing uncertainty in hydrologic simulations and climatic‐impact estimates. This study helps advance modeling and understandings of macro‐scale cold‐region hydrology.
Abstract There are various types of power sources in the cascade water-wind-solar-storage clean energy base around the basin. The joint operation conditions are complex. It not only requires the base to meet the needs of the receiving-end power system as a whole but also needs to implement the optimal operation of water-storage joint compensation to solve the problem of new energy consumption and optimal allocation of resources. This paper proposes an optimization method for the operation simulation of multi-energy complementation of water, wind, photovoltaic, and energy storage based on a heuristic feasible solution strategy. It efficiently solves the joint-operation optimization model that considers multiple constraints and aims at optimizing the system’s power generation. By deriving the feasible corridors of complex constraints, it takes into account the universality, practicability, and high efficiency of the algorithm solution, providing strong technical support for the operation simulation of the current typical multi-energy complementary power generation systems of cascade HPPs, wind farms, photovoltaic plants, and energy-storage facilities in river basins.
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