Explore the vast range of titles available.

【Background】 Combined use of surface water and groundwater for irrigation is a common practice in the lower reaches of the Yellow River Basin to control groundwater table at a desirable depth. In this paper, we investigate the optimal timing and quantity of irrigation using groundwater, based on crop water consumption and crop yields in the region. 【Method】 The analysis was based on modelling. A dissipative hydrological model was used to simulate water balance influenced by irrigation using different combinations of surface water and groundwater. The results were combined with the Jensen crop water production function to analyze the variation in the yields of winter wheat and summer corn with irrigation. 【Result】 ① Significant water deficits were identified in early April, mid- to late-May, early June, late July, and early August, which negatively impacted crop yields. Supplementary irrigation using groundwater in mid-October did not have a noticeable effect on crop yield, and the difference in crop yields between irrigations using surface and groundwater and irrigation using only surface water was minimal. ② The relative yield of winter wheat during the jointing stage and the relative yield of summer maize were sensitive to supplementary irrigation using groundwater, underscoring the importance of timing in irrigation using groundwater. ③ Supplementary irrigation of 40 mm using groundwater during the jointing stage of winter wheat notably increased its grain yield, with the optimal ratio of surface water to groundwater for irrigation being 5.25∶1. Irrigating 60 mm with groundwater for summer maize during the heading stage also boosted yield, with the optimal ratio of surface water to groundwater for irrigation being 4.4∶1. 【Conclusion】 Optimizing the timing and quantity of groundwater for irrigation is critical for improving crop yields in the lower reaches of the Yellow River basin. In our experimental study, irrigating 40 mm of groundwater for winter wheat during the jointing stage and 60 mm for summer maize during the heading stage can significantly improve their yields.

Current research on electricity‐gas–integrated energy systems (EG‐IESs) often overlooks power quality issues prevalent in power systems. Voltage sags, critical and frequent power quality disturbance, significantly affect the EG‐IES due to sensitive coupling devices. To minimize economic losses from voltage sags in the EG‐IES, this study introduces an optimal configuration methodology for EG‐IES coupling devices, considering fault propagation within both electrical and gas subsystems. Initially, the impact of voltage sags on the bidirectional interaction of the EG‐IES is analyzed, with a focus on the influence of coupling devices. Subsequently, tolerance characteristic curves for compressors and gas turbines are presented, and a system economic loss model, based on the tolerance curves of coupling devices, is developed. An objective function is then formulated to minimize economic losses, incorporating a coupling device cost model, and solved using an enhanced particle swarm optimization algorithm to determine the optimal configuration of coupling devices. The efficacy and applicability of the proposed method are validated using an EG‐IES model comprising the IEEE 14‐bus system and an 11‐node gas network. The results indicate that the proposed optimal configuration method for EG‐IES coupling devices, implemented during the planning phase, effectively reduces losses caused by voltage sags in the EG‐IES while accounting for equipment installation costs.

Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031.

The optimal configuration of the rated capacity, rated power and daily output power is an important prerequisite for energy storage systems to participate in peak regulation on the grid side. Economic benefits are the main reason driving investment in energy storage systems. In this paper, the relationship between the economic indicators of an energy storage system and its configuration is first analyzed, and the optimization objective function is formulated. Then, according to the objective limitations of the energy storage system configuration and operation, the constraints are formulated. A set of typical parameters is selected, and the CPLEX (IBM ILOG CPLEX Optimization Studio) solver is used in MATLAB to solve the optimal configuration results. Several sets of optimization results are obtained by taking different subjective coefficient β values, and the economic and social benefits of the optimization results are analyzed. When the economic benefits of the energy storage system are more important, the value of β needs to be smaller, such as a value of 1000. Conversely, when the peak-regulation effect is more important, the value of β should be larger. Finally, configuration results of the control groups are given, and the effect of optimizing the calculation for improving economic benefits is verified.

Aiming at the economic problems of industrial users with wind power, photovoltaic, and small hydropower resources in clean energy consumption and trading with superior power grids, this paper proposes a distributed pumped storage capacity optimization configuration method considering clean energy systems. First, considering the maximization of the investment benefit of distributed pumped storage as the upper goal, a configuration scheme of the installed capacity is formulated. Second, under the two-part electricity price mechanism, combined with the basin hydraulic coupling relationship model, the operation strategy optimization of distributed pumped storage power stations and small hydropower stations is carried out with the minimum operation cost of the clean energy system as the lower optimization objective. Finally, the bi-level optimization model is solved by combining the alternating direction multiplier method and CPLEX solver. This study demonstrates that distributed pumped storage implementation enhances seasonal operational performance, improving clean energy utilization while reducing industrial electricity costs. A post-implementation analysis revealed monthly operating cost reductions of 2.36, 1.72, and 2.13 million RMB for wet, dry, and normal periods, respectively. Coordinated dispatch strategies significantly decreased hydropower station water wastage by 82,000, 28,000, and 52,000 cubic meters during corresponding periods, confirming simultaneous economic and resource efficiency improvements.

The extensive research and application of integrated energy systems (IES) coupled with renewable energy sources have played a pivotal role in alleviating the problems of fossil energy shortage and promoting sustainability to a certain extent. However, the uncertainty of photovoltaic (PV) and wind power in IES increases the difficulty of maintaining stable system operation, posing a challenge to long-term sustainability. In addition, the capacity configuration of each device in IES and the operation strategy under different conditions will also significantly impact the operation cost and expected results of the system, influencing its overall sustainability. To address the above problems, this paper establishes an optimization model based on linear programming to optimize the equipment capacity and operation strategy of IES coupled with PV and wind power with the minimum total annual cost as the objective function, thereby promoting economic sustainability. Moreover, an integrated assessment framework, including economic, energy efficiency, and environmental aspects, is constructed to provide a comprehensive assessment of the operation of IES, ensuring a holistic view of sustainability. Finally, taking the IES of an industrial park in Xi’an, China, as the specific case, sensitivity analysis is used to explore the impact of a variety of critical parameters on the equipment capacity and operating strategy. Additionally, the Monte Carlo method is used to explore the impact of source-load uncertainty on the performance of the IES. The results show that the facilitating or constraining relationship between renewable energy access and the cascading utilization of combined heat and power generation (CHP) energy depends on the relative magnitude of the user load thermoelectric ratio to the prime mover thermoelectric ratio. To cope with the negative impact of source-load uncertainty on the stable operation of the IES, the capacities of the electric chiller and absorption chiller should be increased by 4.0% and 5.8%, respectively. It is worth noting that the increase in the penetration rate of renewable energy has not changed the system’s dependence on the grid.

Water rights trading plays an important role in the market mechanism to optimize the allocation of water resources. This study takes Luxian county of Sichuan province as the research area. Based on the prediction of water supply and demand, this study aims to achieve minimum water shortage and maximum economic benefits for regional water distribution, and introduces a water-saving reward and water price punishment mechanism to construct a two-layer collaborative regulation model of water rights trading for water users. The self-improved elite strategy and cogenetic algorithm (NSGA II-S) are used to solve the optimization model, and the optimal allocation of water resources and water rights trading in different towns in the planning year (2025 and 2030) under different flat and dry scenarios is studied. The results show that there would be an obvious problem in the uneven distribution of water resources between supply and demand in 2025 and 2030. The overall water shortage rates in the flat and dry scenario areas in 2025 are 13.71% and 31.99%, respectively, and the overall water shortage rates in the flat and dry scenario areas in 2030 are 11.55% and 31.94%, respectively. Water rights trading can increase the economic benefit value, with the economic benefit increasing by an average of CNY 614 million in all scenarios, an average increase of 8.68%. The research results could be helpful in alleviating the contradiction between the supply and demand of regional water resources and provide a theoretical basis for optimizing water resource allocation by means of water rights trading in the region.

As the growing demand for energy as well as the strengthening of environmental awareness, photovoltaic power generation, as a clean and renewable energy source, has gradually attracted people's attention and attention. To facilitate the dispatching and planning of power system, this study uses historical data and meteorological data to build a photovoltaic power generation prediction and configuration optimization model on the ground of Gaussian process regression and improved particle swarm optimization algorithm. The simulation results show that the regression prediction curve of the Gaussian process regression prediction model is the closest to the real curve, and the prediction curve is stable and not easily disturbed by noise data. The Root-mean-square deviation and the average absolute proportional error of the model are small, and the disparity in the predicted value and the true value of the model is small; The integration of multi factor data has improved the accuracy of prediction data, and the regression prediction effect is good. The improved Particle swarm optimization algorithm could continuously enhance in the search for the optimal solution, and the Rate of convergence is fast. The Pareto solution can provide different solutions suitable for photovoltaic power generation optimization. Reasonable optimization configuration can effectively reduce active power line loss and voltage deviation, with the maximum reduction values reaching 132kW and 0.028, respectively. The research and design of predictive models and optimized configuration models can promote the formation of smart grids.

Urban land spatial optimization is one of the important issues in urban planning and land resource management. As the speed advancement of urbanization and the continuous increase of population, the rational use of land resources has become the key to sustainable urban development. Based on this, the study adopts the optimization goals of maximizing gross domestic product (GDP), reducing aerosol optical thickness and non-point source pollution (NPSP) load, and reducing land use change costs and incongruity. Three constraints are set simultaneously, including minimum construction land, water body, and cultivated land area. In addition, a fast non dominated sorting genetic algorithm (NSGA2) with elite strategy is used to address it. The outcomes denoted that the iterative distance of the proposed algorithm on the Bin and Cohen functions was only 0.048%, which was 0.522% lower than that of the NSGA2. Meanwhile, the reverse iteration distance value of this algorithm was only 4.14%, which was 22.76% lower than the adaptive weighted genetic algorithm. In addition, the algorithm’s Spacing value was only 4.28%, and the hypervolume index value was as high as 78.66%. This indicated that the research method had a good optimization effect on the optimal allocation (OA) of land space in urban agglomerations, providing scientific decision-making support for sustainable urban development.

The present study is based on the application of an interval two-stage stochastic programming (ITSP) model in the Yinma River Basin. A robust method based on interval two-stage robust (ITSR) optimization is introduced to construct an optimization model of water resource distribution in order to solve the problems of water shortage in low-income and high-income areas caused by the unreasonable distribution of water resources. The model would help in reducing the system risk in the Yinma River Basin caused by an excessive pursuit of economic benefits. The model simulations show that the amount of water required for the water resource distribution is significantly reduced after balancing the risks and the water resource distribution of the water use departments is reduced by up to 20%. In addition, the situation of water scarcity of various water use departments shows a decreasing trend. There is no scarcity of water use in Panshi, Yongji, Shuangyang and Jiutai areas. The water shortage of water use departments in other areas is reduced by up to 97%. The allocation of reused water to ecological and environmental departments with higher water demand further solved the water shortage problem in low-income departments in the interval-two-stage planning model. In this study, after the introduction of the robust optimization method in the Yinma River Basin, the stability of the water resources distribution system is significantly improved. In addition, the risk of water use system in the interval-two-stage stochastic model can be avoided.