## 1 Introduction  
In recent years, due to the ever-increasing electricity demand and environmental problems, renewable energy sources (RESs), such as wind and solar power, have been quickly developed and have become an essential part of the electricity system. Many governments have provided incentives to increase investments in renewable energy plants, for example, allowing these sources to participate in the electricity market or provide regulation reserve service [1, 2]. However, integrating RESs into the electricity market faces several challenges. The authors of [3, 4, 5, 6] show that most electricity markets, such as PJM and AESO, require the participating resources to be at least 100 kW of capacity. Meanwhile, the rated capacity of RESs is typically small, especially rooftop solar systems in which sizing can be less than 10 kW. Another disadvantage of RESs is the stochastic nature of wind speed and solar radiation, meaning RESs maximum available power output can not be predicted with high accuracy. As a result, it is difficult to ensure that RESs can meet the registered schedule in the electricity market. To overcome these issues, the concept of the virtual power plant (VPP) has been developed. A VPP aggregates the capacity of different types of RESs, flexible loads, energy storage systems (ESS), and acts as a single participant in the wholesale market [7, 8, 9, 10]. A VPP can play two roles in the market: a supplier or a consumer, depending on the sizing of RESs and ESS in comparison to local demand. This paradigm shift allows small-scale resources to participate in the market and makes them more profitable [9, 11]. This is because the operation of RESs, controllable demand, and ESS are coordinated; consequently, the available power output of each RES can be fully utilized while the surplus or lack of power due to predictive errors can also be compensated. By using advanced information and communication technology and control systems, a VPP can even manage and control many resources that may be dispersed in different points in a grid [11]. Research by [PERSON] et al. [11] shows that a VPP can be classified into two operational levels, depending on the economic or technical viewpoint: the Commercial VPP (CVPP) and the Technical VPP (TVPP). The CVPP bids in the wholesale market, which decides each RES scheduling to maximize profits. It can be seen that the CVPP only focuses on economic issues and does not consider the network constraints. Meanwhile, the TVPP's task is to receive the operating parameters of all resources from the CVPP, perform system management, and provide transmission balancing services. The TVPP should take the network diagram and transmission congestions into account in its operational planning. With the integration of a large amount of RES, ESS, and loads, a VPP has the ability to participate in many different power markets such as the day-ahead (DA) market, intraday (ID) market, balancing capacity (BC) market, or balancing energy (BE) market. [PERSON] and [PERSON] [12] show that many studies focus on developing a VPP's operating model to achieve optimal coordination between components in the VPP, thereby maximizing VPP profits. References [13; 14; 15] focus on the optimal schedule of a VPP in a two-settlement electricity market, including DA and ID markets. Meanwhile, studies [8; 16; 17] consider the role of a VPP in the reserve market. In these papers, the day-ahead market is cleared at noon, and a VPP should propose its bid for each hour of the following day before the end of the trading period. The prediction errors of demand and RESs power output will cause power imbalances in the real-time operation. A VPP can access the BE market as a regulator when the balancing price is cleared at least one hour before operation time. Another study [18] proposes a new market bid format that allows a VPP to act as either an active or passive regulator in the BE market. The result shows that this market model allows a VPP to gain higher profits.