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The objective of this paper is to introduce the resource reserves, utilization status, power generation potentials, problems and outlooks of development of low-medium hydrothermal geothermal resource in China, especially in Tianjin. Tianjin is one of the earliest and biggest cities in China to utilize the geothermal resource, and the geothermal resource of Tianjin is featured as low-medium hydrothermal geothermal resources. Data of utilization of geothermal resource in Tianjin is taken in this paper by years from 2001 to 2014, and by utilization patterns which including space heating, domestic hot water, bathing and agriculture. A case study of the geothermal power generation in Tianjin is proposed and analyzed to reveal the feasibility of power generation by the methods of optimization and grey relational analysis. Besides, a 500 kW grade geothermal power plant will be built in Tianjin in 2016. At last, Outlooks are proposed to achieve the sustainable exploitation and development of low-medium temperature hydrothermal geothermal resource in the view of underground and above-ground, such as strengthening the injection, monitoring, and exploitation of geothermal resources; enhancing hydrothermal geothermal power generation and absorption refrigeration technologies, and exploring the potential applications of multiple energy system based on the principle of temperature matching. [ABSTRACT FROM AUTHOR]

Considering the huge power consumption, rapid response and the short-term heat reserving capacity of the air conditioning load in the building's energy system, the air conditioning load and its system can be equivalent to the virtual energy storage device for the power grid. Therefore, to obtain a high matching building renewable energy system, a virtual energy storage system of the air conditioning load, accompanied by a storage battery, was built in the paper. Then, operating strategies for the virtual energy storage of the air conditioning load and storage battery were designed. Further, to quantize the contribution of the virtual energy storage to the improvement of matching characteristics, two indicators of the demand side and supply side in the energy system were adopted, including on-site energy fraction (OEFr) and on-site energy matching (OEMr). Lastly, matching characteristic research of the building's renewable energy system based on virtual energy storage of the air conditioning load was established and analyzed by TRNSYS and MATLAB in Tianjin, China. The results revealed that a better matching characteristic performance of the building's renewable energy systems driven by virtual energy storage was obtained. In the condition set out in the paper, compared with that without virtual energy storage, the average values of OEFr and OEMr after virtual energy storage were 0.66 and 0.77, which increased by 8.19% and 8.45% respectively. Simultaneously, the battery operation performance in the building renewable system was improved when the virtual energy storage was working. The times of charge and discharge cycles decreased after virtual energy storage, and the depth of discharge of the battery reduced by 23.37% on a specific day. [ABSTRACT FROM AUTHOR]

• Quantifying the response behavior of heating users by Weber–Fechner law. • Constructing the typical response behavior model of heating users using K-means. • Simulating user participation in demand response via an evolutionary game model. • Using deep Q network to build a dynamic subsidy price generation framework. • The dynamic subsidy price generation framework can benefit all parties. Applications of electric heating, which can improve carbon emission reduction and renewable energy utilization, have brought new challenges to the safe operation of energy systems around the world. Regenerative electric heating with load aggregators and demand response is an effective means to mitigate the wind curtailment and grid operational risks caused by electric heating. However, there is still a lack of models related to demand response, which results in participants not being able to obtain maximum benefits through dynamic subsidy prices. This study uses the Weber–Fechner law and a clustering algorithm to construct quantitative response characteristics models. The deep Q network was used to build a dynamic subsidy price generation framework for load aggregators. Through simulation analysis based on the evolutionary game model of a project in a rural area in Tianjin, China, the following conclusions were drawn: compared with the benchmark model, regenerative electric heating users can save up to 8.7% of costs, power grid companies can save 56.6% of their investment, and wind power plants can increase wind power consumption by 17.6%. The framework proposed in this study considers user behavior quantification of demand response participants and the differences among users. Therefore, the framework can provide a more reasonable, applicable, and intelligent system for regenerative electric heating. [ABSTRACT FROM AUTHOR]