Energy crisis and environmental protection issues are receiving more attention worldwide. Many countries are focusing on the development of sustainable renewable energy resources. China is in the stage of energy transformation, facing the challenge of carbon neutrality target by 2060. The strategy of energy revolution has emerged, which paves the way for low-carbon industrial development. In addition, in the process of energy structure transformation, flexible resources are important to achieve low-carbon advancement in power systems.

The trend of clean power integration is irreversible. The uncertainties brought by large-scale integration of renewable energy resources pose a higher challenge to the secure and stable operation of power systems (Shan et al., 2018; Cheng et al., 2021). On the one hand, customers are guided to stagger power consumption and optimize the load structure through reasonable demand response (DR) (Shu et al., 2017). On the other hand, high-quality ES systems should be selected to match the generation dispatch of the power system (Shan et al., 2018; Zhang et al., 2018). Generally, DR in power systems refers to the electricity customer behavior of changing their electricity consumption activities according to market regulation signals (Hobbs et al., 1993; Federal Energy Regulatory Commission, 2007; Zhang et al., 2008). The DR in the electricity market can be classified into price-based response (PDR) and incentive-based response (IDR) according to the response mode. In addition, the electricity customers are guided to respond to the power system dispatch in long-term and short-term time scales (Wang et al., 2021). As an effective method for optimal dispatch of power systems, DR has been proposed in a large number of countries, helping change the electricity consumption pattern. It supports not only the grid’s peak-shaving and valley-filling but also renewable power consuming (Gao et al., 2014; Aghaei and Alizadeh, 2013) IDR can combine with PDR to improve the reliability and flexibility of DR and make DR dispatched precisely in real time (Xu et al., 2019). Zhang et al. (2021) propose an optimal DR dispatch model considering supply–demand balance and security constraints; the imbalance pressure caused by renewable energy is alleviated. In the study by Hong-Tao et al. (2018), Chen establishes a wind–solar power consumption model, and it verifies the effectiveness of DR on reducing the curtailment of wind and solar power. In the study by Gao et al. (2019), Gao characterizes the uncertainty of DR participation by considering the risk attitudes. It shows that the introducing DR can improve the adequacy of generation systems including wind power. In the study by Li et al. (2021), Li develops an optimal DR dispatch strategy for DR dispatch coordinated with the load aggregator to achieve joint optimization of entity benefits. Furthermore, the application of automatic DR in smart grids can greatly enhance the security of power system operation (Taorong et al., 2020). DR can be applied in frequency modulation to balance the active power (Zhu et al., 2021) and ensure sufficient voltage balancing control capacity (Tan and Shaaban, 2020).

Similar to DR, energy storage (ES) also has the function of flexible regulation. The renewable power curtailment can be reduced by introducing ES into the system. Thus, the ES configuration strategy is regarded as an effective approach to enhance the friendliness of wind and solar power generation (Zhang et al., 2022). It is also helpful for the stability and economic efficiency of power systems (Ani, 2021). Dorahaki developed an optimal VPP dispatch model which contains distributed wind power and ES devices. The ES device can smooth the fluctuations caused by wind power in the study by Dorahaki et al. (2020). In addition, the joint operation of wind power and ES can relieve the contradiction of renewable energy supply and reprogram the tariff profit (Zhang J. et al., 2020). The storage duration, capacity, and charging/discharging frequency of ES are investigated in the study by Hargreaves and Jones, (2020) to make it suitable for renewable energy systems. The combination of DR and ES is more beneficial to promote the optimal operation of the power systems (Wang et al., 2016; He et al., 2021). The adverse impacts of wind power uncertainties on power system stability can be solved by introducing DR and ES. Su integrates DR to a hybrid Wind-PV-ES system, achieving a goal of zero-curtailment of renewable power based on the correlation analysis (Su et al., 2020). In the study by Firouzmakan et al. (2019), DR and ES are considered in comprehensive stochastic energy management system containing micro-CHP units and renewable energy to implement resource complementarity and improve integral revenue. A multi-energy microgrid with wind–solar power generation considering DR and ES is constructed to provide a reasonable plan for multiple energy applications (Shen et al., 2022). In conclusion, the integration of DR and ES offers additional sources of flexibility in the system (Mimica et al., 2022).

Carbon emission trading is an effective way to promote global emission reduction through the market mechanism. In the study by Lou et al. (2017), Lou includes carbon emission trading cost in the objective function based on the concept of low-carbon economy. It optimizes the power generation dispatch under the random charging/discharging behavior of EVs and effectively reduces the system’s carbon emission. At the same time, the focus on carbon emission stimulates the demand for EVs, which helps take the lead in achieving the goal of “carbon peak” and “carbon neutrality” (Nie et al., 2022). Melgar-Dominguez et al. (2020) demonstrates that implementing a carbon emission trading scheme can make reduction in costs of the supplied energy and purchase of emission allowances. By incorporating the cost of carbon emission trading into a multi-energy complementary system, the environmental factors and system operating characteristics can be fully considered. Thus, wind and photovoltaic power curtailment and load shedding are reduced while minimizing system operating costs (Zhu et al., 2019). In the study by Zhang W. et al. (2020), Zhang quantitively evaluates the operation efficiency of different carbon emission trading systems to determine whether they are profitable to the economy and environment. Flexible resources such as DR and ES can cooperate with renewable energy to optimize power system dispatch and promote renewable power consumption. In addition, the import of the carbon emission trading market model can quantify the impacts of the dispatch strategy on carbon emission.

The existing literature has examined the response characteristics of DR and ES from various perspectives, showing the enhancing functions of DR and ES on carbon emission reduction. However, the integrated utilization of flexible resources still needs to be further explored, especially during the low-carbon transition period of power systems. Therefore, we propose a low-carbon dispatch strategy that combines carbon emission index and flexible DR and ES resources in this study. The strategy that realizes the reasonable coupling of conventional thermal power units, flexible resources, and carbon trading can effectively reduce wind power curtailment and quantitatively evaluate the reduction of carbon emission.

The remainder of this study is organized as follows. First, Section 2 establishes the models of DR and ES based on their behavioral characteristics. Subsequently, Section 3 proposes a low-carbon dispatch strategy through the combination of carbon market with the flexible DR resources and ES models. Section 4 demonstrates the effectiveness of the proposed strategy with simulation results. Finally, Section 5 draws the conclusion of this work.

To verify the effectiveness of our proposed low-carbon dispatch strategy, a modified IEEE30-bus test system is selected and shown in Figure 1. It has six thermal units and 41 lines. The system is assembled with three DR aggregators (named as D1, D2, and D3 in Figure 1) containing different DR resources at Bus 5, 7, and 21, respectively. D1 and D2 each contain three IDRs, and D3 contains one PDR resource. A wind farm with a total capacity of 45 MW is located at Bus 28. An ES module with a capacity of 500 MW·h is located at Bus 15. The relevant parameters of thermal power units, DR resources, ES device, system load forecast, and wind power forecast output are given in Supplementary Material S1. The model proposed in this study is solved by GAMS on a 16-core CPU/16G RAM PC. To quantitively access the impact of flexible DR and ES resources on the wind power consumption and carbon reduction, three scenarios are designed as follows.

In this article, we propose a low-carbon dispatch strategy for power systems considering flexible DR and ES. First, the models of flexible DR resources and ES is established based on their behavior characteristics. Second, by combining the carbon market model with the flexible DR resources and ES model, the low-carbon dispatch strategy is proposed. Finally, the effectiveness of the proposed strategy is verified with simulations.

From the presented work, general conclusions can be drawn as follows:

1) The cooperation of DR and ES has a remarkable impact on the power system dispatch. The combined operation mode of DR and ES effectively promotes peak-shaving and valley-filling.

In the future work, we plan to combine the uncertainties derived from power and load with our framework and make the proposed model appropriate for short-time scale dispatch environment.