The coupling between multiple energy sources such as electricity, gas, and heat is strengthened in an integrated energy system (IES), and this, in turn, improves the operational flexibility of the IES. As an upper-level energy supply system, an IES can play a role as virtual energy storage, which can provide regulating power to smooth out the volatility from large-scale renewable energy generation. The establishment of an aggregating virtual energy storage model for IESs has become an important issue. Under this background, a multi-objective optimization-based adjustable capacity evaluation method is proposed in this paper. Firstly, the mathematical model of an IES considering the coupling of multiple kinds of energy forms is proposed. Then, an aggregating model considering demand response and economic constraints is established to demonstrate the adjustable capacity of the IES. In addition, multi-objective optimization is used to identify parameters in the proposed model, and the normal boundary intersection (NBI) method is used to solve the problem. Finally, a simulation example is provided to verify the effectiveness and feasibility of the proposed method. The external energy demand boundary of the IES can be modeled as virtual energy storage, and the coupling relations of electricity and gas can be presented. Case studies demonstrate that economic constraints narrow the adjustable capacity of the IES while the demand response extends it.

Integrated energy systems (IESs) strengthen electricity–gas–heat multi-energy coupling and reduce wind and light abandonment. For grids with superior distribution, IESs are similar to virtual energy storage systems and are able to realize efficient interaction with the grid by synergizing the operating status of the internal equipment and improving the security, economy, and flexibility of the grid’s operation. However, the internal equipment coupling of an IES is complex, and determining how to evaluate its adjustable capacity range (that is, the upper and lower boundaries of its external energy demand) considering the uncertainty and volatility of wind power and photovoltaic output is a problem to be solved. To solve this problem, this paper presents a chance-constrained evaluation method for the adjustable capacity of IESs. Firstly, mathematical models and operational constraints of each device within the IES are established. Secondly, based on the mathematical model of chance-constrained planning, an adjustable capacity range assessment model considering the uncertainty of wind and photovoltaic output is established. Finally, the MATLAB/Yalmip/Gurobi solver is used for the optimization solution, and the adjustable capacity range interval of the constructed IES model is solved using an arithmetic example to analyze and verify the correctness and validity of the method and to study the influencing factors of its adjustable range.

With the deepening of the power system reform, an increasing number of microgrids are being integrated into the distribution network. In traditional centralized optimization algorithms, the optimal power flow model of the distribution network and the optimal scheduling model of microgrid clusters are directly coupled and solved simultaneously. This process involves extensive information exchange between the upper distribution network system and the lower microgrid clusters, which not only increases the communication burden but also prolongs computation time and raises computational complexity. Moreover, it requires excessive information sharing, making it difficult to achieve limited information exchange between the upper and lower systems. In this paper, an optimization model and solution method based on the analytical target cascading approach are proposed. First, a typical microgrid model is constructed. On this basis, a collaborative optimization model for the active distribution network (ADN) and microgrid clusters is established. The distribution network and the microgrid clusters are treated as a unified entity of interest, with their interconnection power represented as virtual generators and virtual loads to achieve decoupling. Finally, simulations based on the IEEE-33 node standard system are conducted. Compared with the centralized algorithm, the effectiveness of the analytical target cascading method in coordinating the distribution network and microgrid clusters is verified. The proposed approach reduces computational complexity and enables optimized operation with limited information exchange.