**1. Introduction**

In recent years, the sustainable development of energy has received extensive attention. Regional integrated energy systems (RIESs) are expected to become an important way to improve the energy structure and achieve sustainable energy development [1]. However, the intermittency and volatility of renewable energy have brought certain challenges to the stable operation of RIESs [2,3]. With the rapid development of energy storage technology, the development of RIESs with hybrid energy storage has become the main way to solve the volatility of renewable energy and alleviate the contradiction between supply and demand [4,5].

To explore the performance of the integrated energy system with hybrid energy storage, the studies shown in Table 1 have conducted in-depth research on RIESs from aspects of system structure, operation strategy, and optimization model. Different from the

**Citation:** Jin, B.; Liu, Z.; Liao, Y. Exploring the Impact of Regional Integrated Energy Systems Performance by Energy Storage Devices Based on a Bi-Level Dynamic Optimization Model. *Energies* **2023**, *16*, 2629. https://doi.org/10.3390/ en16062629

Academic Editors: Luis Hernández-Callejo, Jesús Armando Aguilar Jiménez and Carlos Meza Benavides

Received: 10 February 2023 Revised: 2 March 2023 Accepted: 8 March 2023 Published: 10 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

traditional energy system, the RIES involves the deep coupling of multiple heterogeneous energy sources. The modeling idea of the Energy Hub (EH) proposed by Geidl et al. [6] was used to describe the relationship between energy conversion and conservation. However, this modeling idea is not conducive to the RIES's extension and matrix representation in the model. To improve the model portability, an EH modeling method based on graph theory was proposed [7]. Based on this modeling idea, Ma et al. [8] adopted the static equipment model to establish the coupling system optimization model with the total system cost as the optimization objective. User-side energy saving and load management are also one of the main ways to reduce the total cost of RIESs [9–11]. To make full use of the flexibility of loads, Liu et al. [12] established a coupling system optimization model considering the comprehensive demand response. At the same time, this model is also used to explore the impact of energy storage devices on the design and operation of a RIES [13]. The static system optimization model cannot reflect the off-design characteristics of the equipment. To address the issue, a dynamic system optimization model, considering the off-design characteristics of the equipment, was established [14]. On this basis, Mansouri et al. [15] established a dynamic multi-objective optimization model and used it to optimize the design of a RIES with power-gas (P-G) technology. The results showed that the gas storage device could effectively improve the utilization of renewable energy. Unfortunately, none of the above optimization models realizes the decoupling of design and operation.

To achieve the decoupling of design and operation, Mago et al. proposed the following electric load (FEL), following thermal load (FTL), and following hybrid electric-heating load (FHL) strategies according to the role of the combined heating and power (CHP) unit in RIESs [16,17]. Based on the above strategies, Kang et al. [18] explored the operational performance of a RIES under different loads. Wang et al. [19] investigated the impact of energy storage characteristics on the system optimization results based on the system optimization model with the total system cost as the optimization objective. To take the economic, energy-saving, and environmental performance of systems into account, a weighted multiobjective optimization model was established for the optimal design of RIESs [20]. Based on the weighted multi-objective optimization model, Zeng et al. [21,22] used the static and dynamic equipment models to optimize the coupling systems of CCHP and ground source heat pump(GSHP), respectively. However, the value of weight is often subjective. Thus, a multi-objective optimization model with the optimization objectives of cost-saving rate, primary energy saving rate, and CO2 emission reduction rate was proposed for the optimal design of a RIES [23]. Zhai et al. [24,25] used this model to explore the impact of building types on the operational performance of RIESs. Different types of building loads have certain complementary characteristics. For this reason, Li et al. [26] explored the impact of loads' complementary characteristics on optimization results and operational performance of a RIES. The research showed that the complementary characteristics of loads could reduce the capacity of energy storage devices to a certain extent. In addition, the equipment model and operation strategy also have a certain impact on the optimization results of RIESs. Therefore, Deng et al. [27] established a dynamic multi-objective optimization model based on the dynamic equipment model. Han et al. [28] used a dynamic multi-objective optimization model to optimize the design of a RIES with hybrid, compressed air energy storage. To improve the operational performance of RIESs, an improved FEL strategy was used in the optimal design of a RIES [29]. Compared with the traditional FEL strategy, the improved operation strategy could effectively reduce the energy consumption, operating cost, and CO2 emission of the RIES. At the same time, the adaptive operation strategy, based on user load, was proposed successively to improve the operational performance of RIESs [30]. However, the relatively fixed operation strategy could not realize the flexible scheduling of RIESs. Hence, Luo et al. [31] adopted the decision tree method to formulate the operation strategy of RIESs. Nonetheless, machine learning greatly relies on building historical load data. To avoid this problem and achieve the flexible scheduling of RIESs, a bi-level optimization model was proposed where the upper-level optimization model is used to determine the optimal configuration of systems, and the lower-level optimization

model is used to realize the flexible scheduling of systems [32]. Based on the bi-level optimization model, Luo et al. [33] optimized the standalone renewable energy system with the total system cost as the optimization objective. Energy storage devices can improve the penetration rate of renewable energy. Li et al. [34] used the bi-level optimization model to optimize the design of an electricity-hydrogen RIES. Ma et al. [35] used this model to explore the impact of shared energy storage on the renewable energy utilization rate and operating cost of RIESs. Although the bi-level optimization model has been widely used in the optimization design and operation analysis of the integrated energy system with energy storage devices, few studies have systematically explored the effects of different energy storage devices on the optimal design and operational performance of the system by the bi-level dynamic optimization model.


**Table 1.** Literature review of the integrated energy system with energy storage.

Different energy storage devices can realize the time-series transfer of different energies. To explore the impact of energy storage devices on the design and operation of RIESs, this paper optimizes three RIESs with different energy storage devices and compares their operational performance according to a public building load in Changsha. The main contributions of this paper are as follows: (1) A bi-level dynamic optimization model is established based on the dynamic equipment model; (2) Three RIESs with different energy storage devices are optimally designed; (3) According to the optimization results, the operational performance of three RIESs with different energy storage devices is compared. The remainder of this study is organized as follows: Part II is the introduction and equipment modeling of RIESs with different energy storage devices; Part III is the establishment of the bi-level system optimization model; Part IV presents the impact of different energy storage

devices on the optimal design and operational performance of the integrated energy system based on the case results; and the conclusions of this work are drawn in Part V.
