###### Abstract
The former is governed by the relationship between electricity suppliers and consumers, with consideration of the DR. For the latter, the price is the link between the upper-level problem (function of the large power grid) and the lower-level problem (function of the park-integrated energy system (PIES) operator), which means that the price is often the decision variable in the upper-level problem and the parameter in the lower-level problem [12]. Ref. [13] established a pricing model between a large number of consumers that have self-generating equipment (PV (photovoltaic power generation) or wind power) and public equipment, which purchases electricity from consumers, based on the Stackelberg Game. The results indicate that using the new pricing model not only improves the total utility [14] of consumers but also reduces the total electricity costs of public equipment. In [15], the DR of consumers and the pricing method of the grid are considered as a bilevel optimization problem, where the grid is the upper-level leader, and the consumer is the lower-level passive receiver. [PERSON] et al. [16] proposed a novel method to optimize the behavior of household appliances towards retail electricity price. The Home Energy Management System (HEMS) determines the operational strategies of appliances according to retail price based on a heuristic Forward-Backward Algorithm (F-BA). The results show that the method can relieve congestion in weak lines. [PERSON] et al. [17] constructed the electricity-heat coordinated retail market framework to achieve the coordinated settlement of electric and heating load, managing the distributed energy generation and consumption through coordinated control method. The results show that the method can optimize the resource allocation of demand-side energy stations and the energy use of customers by economic means. For the grid price, there are many electricity price forecasting models. [PERSON] et al. [18] developed a novel outlier-robust hybrid model for forecasting grid prices considering the negative impact of outliers on power price modeling, which combines a basic forecasting engine called the outlier-robust extreme learning machine model and three new algorithms. The experimental verification shows that the hybrid model is superior to other models and can provide a reliable electricity price prediction method for power market management. [PERSON] et al. (2019) established a new adaptive hybrid model based on variational mode decomposition (VMD), self-adaptive particle swarm optimization (SAPSO), seasonal autoregressive integrated moving average (SARIMA), and deep belief network (DBN) for short-term power price forecasting. The results indicate that the model can significantly improve the accuracy and stability of the prediction. In addition, there are some electricity price model studies in macro level. [PERSON] et al. (2018) established an unrestricted vector autoregressive model to study the impact of fossil fuel prices on different electricity prices in Mexico. The research analyzes the influencing factors of electricity price from a macro perspective, and the results show that fossil fuel prices have a significant positive impact on electricity prices, and alternative electricity price can increase the robustness of the analysis. However, none of the current studies quantifies the initial investment reduction and the energy efficiency improvement which is caused by the reduction in the configuration capacity due to load leveling and peak shaving.