## 5 Conclusions  
With the opening of the Chinese electricity market, the park-integrated energy system (PIES) appeared as an energy service provider. Under this new energy market mode, some new problems need to be addressed urgently: (1) How could we formulate the energy selling price so that the consumers spend less money and the PIES obtains more benefits? (2) How could we quantify the effects of DR on the PIES configuration? (3) How could we quantify the impact of the installed capacity on the energy selling prices? To resolve these problems, a P-M model was proposed in the planning and design stages of the PIES. The P-M model consisted of two sub-models: the C-P sub-model and the S-P sub-model. In the C-P sub-model, DR and energy configuration (types, capacities, and strategies of equipment) were both considered. Using the C-P sub-model, the cost price of cooling, heating, and electricity were obtained under 12 different load profiles. Then, the function relationship between the energy cost price and TR was established through regression analysis. In the S-P sub-model, two pricing schemes were given. One is that prices remain constant throughout the entire year, which is suitable for cooling and heating. The other is the two-stage time-of-use pricing scheme, which is suitable for electricity. The hourly electrical price was obtained by using the S-P sub-model, which was beneficial to both PIES and consumers.  
In numerical studies, an RDR zone was found, where the DR of consumers did not bring a reduction in the electricity bill, and from the perspective of the long-term benefit, the DR of consumers would disappear. The results indicate that the PIES sharing its profits with consumers (in the benefit-sharing zone) is the ultimate choice for PIES and consumers; that is, point B is the optimal solution for the long-term situation. From the perspective of P-M model optimization, it can be considered that point A is the optimal solution for the short-term scenario. This study can provide the owners of PIES with a suggestion that win-win is the optimal choice in energy pricing and a method for determining the optimal energy selling price. Furthermore, it would be compelling to focus on the free competitive energy market involving multiple PIESs in future research. However, the biggest obstacle of model application in the real world is that when the user group reaches a certain size and the information flow is unobstructed between users, the users will only respond to the benefits exceeding a certain value.  
Figure 14: Shifted load of point A compared with original load.  
In addition, the limitations in practical applications also include the accurate prediction of demand load and accurate quantification of demand response. However, these are not the focus of this study. The paper only provides a pricing tool of cooling, heating, and electricity in PIES based on the existing load forecasting and demand response research.  
Conceptualization, Z.L. and H.Y.; methodology, Z.L. and H.Y.; software, C.L.; validation, Z.L., and R.L.; investigation, Z.L., and C.L.; resources, H.Y.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, H.Y., C.L., and R.L.; visualization, R.L.; supervision, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China (No.2018 YFC0704600), Special Fund for Fundamental Scientific Research Business Fees of Central Universities (22120180199) 'Urban New District Energy System and Planning and Design Optimization Technology' and Shanghai Natural Science Foundation (19 ZR1460500).  
The authors declare no conflict of interest.