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The park-level integrated energy system characterized by electricity heat cooling storage is regarded as a viable solution to energy and environmental crises due to its cleanliness and high efficiency. However, decision-makers encounter challenges in picking the optimum construction scheme. The paper introduces the matter element extension into the investment decision-making of the park-level integrated energy system. To begin, the paper analyzes the composition of the evaluation index system from the basic composition of the park-level integrated energy system, and establishes a standard system for comprehensive performance analysis, including economic, environmental, energy, security, and intelligence. Second, the subjective weight is calculated using the fuzzy analytic hierarchy process, the objective weight is calculated using the entropy weight. The matter element extension is used to evaluate the alternatives' performance. Finally, an industrial park in Jiangsu Province of China is selected as a case to verify. The results show that good comprehensive performance can be obtained by using photovoltaic, wind turbines, electric energy storage, ice storage, and heat pumps. The decision-making results will not be affected by the change in subjective and objective weight and the change in index weight within 20 %, but changes in matter-element classification would. • Intelligent performance criterion is introduced. • The evaluation system includes economy, environment, energy, security, intelligence. • A decision-making framework for the park-level integrated energy system is established. • A variety of sensitivity analyses were performed. • Using renewable energy and energy storage can obtain good comprehensive performance. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative integration of geothermal and solar energies to assess the production of power, cooling, and freshwater. These products are achieved through a modified absorption refrigeration cycle, a modified organic Rankine cycle, and humidification-dehumidification subsystems driven by a flash-binary configuration. The performance of the setup is evaluated using both thermodynamic and economic approaches. Additionally, three multi-objective optimizations are applied to determine a suitable optimal state. The approaches reveal net power, cooling, and freshwater production rates of 807.3 kW, 1345 kW, and 7.96 kg/s, respectively. The designed setup requires a total capital investment cost of $2,490,310 and has a payback period of 5.76 years in the base mode. The study highlights that the temperature difference of superheating significantly impacts the total capital investment cost and exergy efficiency, while the cooling production is mainly influenced by the evaporator temperature. The final optimal state achieved an exergy efficiency of 15.98 % and a net present value of $1,999,864. Overall, this innovative hybrid geothermal-solar setup demonstrates a viable solution for sustainable energy production, combining power generation, cooling, and freshwater production in a single integrated system. The findings support the system's potential for real-world applications, particularly in regions with suitable geothermal and solar resources. • Designing a novel multi-generation driven by solar and geothermal energies. • Considering the energy, exergy, and economic analyses for the performance evaluation. • Producing 807.7 kW net power, 1592 kW cooling, and 8.8 kg/s freshwater. • Achieving 15.98 % of exergy efficiency with a payback period of 5.36 years. [ABSTRACT FROM AUTHOR]

Industrial steam heating is a significant aspect of energy consumption in China, playing a crucial role in industrial energy efficiency. Historically, demand response research has predominantly focused on the electricity sector, with limited emphasis on steam supply. This study explores a novel Time-of-Use pricing approach for steam heating systems, anchored in the demand response theory of price incentives. It develops a peak-valley segmentation method by considering both the load differences between supply and demand and the thermal storage capacity of the pipeline system. Additionally, the paper delves into user response characteristics to formulate an optimized Time-of-Use pricing model for steam heating systems. Implemented in a Jiangsu-based case study, the model demonstrates significant efficiency improvements: it reduces the peak-valley load difference by up to 68.3% and cuts steam purchasing costs by ¥135,000. Furthermore, the alignment between the users' total steam load curve and the heat source's steam supply is enhanced by 18.8%, illustrating the model's effectiveness in balancing demand and supply. • Cluster analysis to extract steam user load characteristics. • Divide peak/valley times based on network storage and user traits. • Create a multi-objective Time-of-Use model for steam pricing optimization. • Carry out a case validation in an actual industrial park in Jiangsu. [ABSTRACT FROM AUTHOR]

Industry has always been an important driving force to promote social and economic development, and the development of industry is inseparable from energy consumption. In the process of modern production, more and more modern advanced equipment is put into use, and the main power source of these equipment is electricity. However, the production of electricity is limited by conditions. Therefore, the main purpose of this paper is to simulate and forecast the industrial power consumption of Jiangsu Province through the nonlinear transformation of time variables, so that the industrial enterprises in Jiangsu can reasonably arrange the next power demand and ensure the smooth progress of industrial activities. The final research results show that the time series regression prediction model proposed in this paper can effectively simulate and predict the results of industrial power consumption, with an accuracy of 1.02 %. • Consider fewer factors, which saves time and cost and can obtain higher prediction accuracy. • Only consider the impact of the current time, without considering the impact of the lag time. • Taking Jiangsu, an industrial province as an example, can better guide other provinces. [ABSTRACT FROM AUTHOR]

To efficiently utilize renewable energy sources primarily consisting of wind and solar power, large-scale compressed air energy storage (CAES) can be implemented using underground salt caverns. In this paper, the abundant wind and solar energy resources and the geological conditions of the three major salt mines in Jiangsu Province are introduced. A geomechanical model is established based on the Huai'an rock salt formation. The simulation results show that as the internal air pressure (IAP) increases, the safety factor increases, and the displacement, plastic zone, volume shrinkage, and equivalent strain decrease. Based on the above results, the minimum IAPs of caverns AN 13 and AN 21 are recommended to be 10–12 MPa. According to estimates, to store 15% of wind and solar power in Jiangsu Province through CAES by 2050, a 9894.0 × 104 m3 salt cavern volume is needed, of which at least 77.8 million m3 can be provided by salt mining enterprises. The cost of reconstructing an underground salt cavern is 12.1 billion yuan, while the investment in surface equipment is 746.7 billion yuan. We provide an economical and feasible solution for achieving carbon neutrality in Jiangsu Province in this study. • A CAES salt cavern is utilized to achieve a 100% renewable energy power system in Jiangsu Province. • The non-salt interlayers significantly reinforce the stability of CAES salt caverns. • The minimum internal air pressure of the CAES salt cavern is recommended to be 10–12 MPa in Huai'an. • A preliminary evaluation of the investment cost of CAES salt caverns is analyzed. [ABSTRACT FROM AUTHOR]

In this paper, GM(1,N r) model is established to improve the traditional GM(1,N) model from three aspects: (1) transforming the original sequence to satisfy the modeling conditions with particle swarm optimization algorithm; (2) introducing grey incidence analysis to obtain the grey incidence ranking and carrying out stepwise test for significant variables to determine the number of variables; and (3) predicting the related factor sequence through the improved GM(1,1) model. Empirical analysis shows that the proposed GM(1,N r) model has remarkable good prediction performance compared with the traditional grey forecasting model. It is also demonstrated that the extraction of influencing factors can significantly improve the prediction effectiveness, especially when pursuing the best fitting effect on small sample data. The findings indicate that the electricity consumptions of Jiangsu Province in the next several years will be at a high level and keep rising, with a predicted value of 9712.48 billion kilowatt-hours in 2030. The findings can help the government and energy related institutions to develop management policies on energy demand, and the proposed model can also be extended for the application in other regions. • Propose a new model to improve the traditional GM(1,N) model. • Adopt a new method to find the best value to satisfy the modeling conditions. • Use the grey incidence analysis with stepwise examination to identify factors. • Combine the improved GM(1,1) model to improve the performance of GM(1,N). • Apply this new model to analysis electricity consumption in Jiangsu Province. [ABSTRACT FROM AUTHOR]

Promoting the utilization of clean energy has been identified as one potential solution to addressing environmental pollution and achieving sustainable development in many countries around the world. Evaluating clean energy alternatives includes a requirement to balance multiple conflict criteria, including technology, environment, economy and society, all of which are incommensurate and interdependent. Traditional MCDM (multi-criteria decision making) methods, such as the weighted average method, often fail to aggregate such criteria consistently. In this paper, an improved MCDM method based on fuzzy measure and integral is developed and applied to evaluate four primary clean energy options for Jiangsu Province, China. The results confirm that the preferred clean energy option for Jiangsu is solar photovoltaic, followed by wind, biomass and finally nuclear. A sensitivity analysis is also conducted to evaluate the values of clean energy resources for Jiangsu. The ordered weighted average method is also applied to compare the method mentioned above in our empirical study. The results show that the improved MCDM method provides higher discrimination between alternative clean energy alternatives. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the influence of different regulation strategies on wind energy integration into the existing energy system of Jiangsu. The ability of wind integration is defined in terms of the ability to avoid excess electricity production, to conserve primary energy consumption and to reduce CO2 emissions in the system. Firstly, a reference model of Jiangsu''s energy system is built using the energy system analysis tool EnergyPLAN based on the year 2009. The model results are then compared to actual values from 2009 to validate their accuracy. Based on the reference model, different regulations of Jiangsu''s energy system are compared and analyzed in the range of a wind input from 0% to 42% of the total electricity demand. It is concluded that operating power plants in a flexible way facilitates the promotion of more intermittent wind integration. [Copyright &y& Elsevier]

In this paper, the gasification conditions, the gasifier structure, the measuring system and the gasification rationale of a pilot project experiment of underground coal gasification (UCG) in the Liuzhuang Colliery, Tangshan, are illustrated. The technique of two-phase underground coal gasification is proposed. The detection of the moving speed and the length of the gasification working face is made using radon probing technology. An analysis of the experiment results indicates that the output of air gas is 3000 m3/h with a heating value of about 4.18 MJ/m3, while the output of water gas is 2000 m3/h with a heating value of over 11.00 MJ/m3, of which H2 content is above 40% with a maximum of 71.68%. The cyclical time of two-phase underground gasification is 16 h, with 8 h for each phase. This prolongs the time when the high-heating value gas is produced. The moving speed of the gasification working face in two alternative gasifiers is identified, i.e. 0.204 and 0.487 m/d, respectively. The success of the pilot project experiment of the underground gasification reveals the strides that have been made toward the commercialization of the UCG in China. It also further justifies the reasonability and feasibility of the new technology of long channel, big section, two-phase underground gasification. A conclusion is also drawn that the technology of the pilot project experiment can be popularized in old and discarded coal mines. [Copyright &y& Elsevier]

Traditional stability analysis for salt cavern as UGS overlooks the thermal effects induced by cycling loading and cannot predict the potential risk. In this study viscoelastic WIPP model is employed to describe the temperature-dependent creep behavior of rock salt. A thermal-mechanical simulation framework is established to couple the effect of mechanical stress and thermal stress. A coupling thermal-mechanical numerical model implemented by FLAC3D in combination with the WIPP creep model is proposed. A field case, Cavern L, in Jintan, Jiangsu Province of China is demonstrated in this paper to explore the thermal response to gas injection-and-withdrawal process. The results show that there is tensile stress concentration region at the cavern roof. The dynamic process of thermal damage evolution and block falls on cavern roof is also investigated. The disintegration of rock salt mass at cavern roof occurs after the most severe gas depressurization period, and the retain of low pressure after gas withdrawal aggravate the thermal damage. It indicates that the thermal-induced tensile stresses are critical for salt cavern stability assessment. The feasibility and accuracy of the proposed thermal-mechanical modeling is verified by the field case, and the simulation results of thermal damage are coincided with the sonar results. This analysis confirmed the influence of thermal-mechanical effects on salt cavern wall. The approach used in this study provides an improved method for the stability evaluation and optimal operation parameters for salt caverns. • A coupled thermo-mechanical model is established for stability analysis of cavern wall. • The thermal-mechanical effect on salt cavern during cycling loading is investigated by thermal-mechanical coupling model. • The thermal damage on salt cavern roof occurs after the most severe gas withdrawal period. • The retain time of low pressure operation after gas withdrawal aggravates the thermal damage, resulting in collapse. [ABSTRACT FROM AUTHOR]

The remarkable prediction performance of electricity consumption has always assumed particular importance for electric power utility planning and economic development. On account of the complexity and uncertainty of the electricity system, this paper establishes a self-adaptive grey fractional weighted model to predict Jiangsu's electricity consumption, which efficiently enhances the prediction quality of electricity consumption. This newly constructed grey model introduces the fractional weighted coefficients to design a novel initial condition. Compared with the old one in the conventional grey models, the newly optimized initial condition has a flexible structure, which has advantages in capturing the dynamic characteristics of the electricity consumption observations. In addition, to further promote the forecasting precision, the adjustable fractional weighted coefficients and corresponding time parameter of the initial condition are estimated by utilizing the Particle Swarm Algorithm (PSO). Furthermore, five competing models are employed to forecast Jiangsu's electricity consumption in China, which certifies the validity of the established model. Experimental results illustrate that the newly designed model has significant advantages over other five competing models. According to the forecasted results, electricity consumption in Jiangsu Province is expected to reach 6778 billion kilowatt-hours in 2020, while the growth rate will fall down by 1.11%. Therefore, several proposals are made for decision-makers. • A self-adaptive fractional weighted grey model is developed for electricity demand forecast. • The novel initial condition having dynamic fractional order weighted coefficients is proposed. • The PSO algorithm is applied to estimate the weighted coefficients and time parameter. • Jiangsu's electricity consumption is predicted from 2018 to 2020. [ABSTRACT FROM AUTHOR]