With the accelerated construction of new types of power systems, the proportion of integrated renewable energy sources has increased rapidly. Achieving an accurate, efficient, and practical power balance analysis under strong uncertainty of the source and load is a basic theme in the dispatching, controlling, and planning of new power systems. Hence, the shortcomings of current research in the description and calculation of the balance state are reviewed by summarizing the research objects, analytical models and methods, evaluation indices, and applications of power and quantity balance analyses. Based on the two key scientific issues of “electric power and energy balance state description under strong randomness and strategy” and “electric power and energy balance analysis and calculation under the centralized regulation-coordination autonomous balancing mechanism,” a research theoretical system for electric power and energy balance is proposed. First, this system covers “balance boundary generation-balance analysis and calculation-balance state assessment- balance early warning-balance capacity improvement.” Then, research ideas and technical schemes are proposed, including random balanced scenario generation, multi-period coordination decoupling balanced analysis, balanced state evaluation index system design, multi-spatial-temporal hierarchical balanced early warning, and balanced cooperation mechanism construction. Finally, some key technologies are worthy of consideration in further research on electric power and energy balance analyses in new types of power systems.

The development of low-carbon power generation technologies and effective participation in carbon quota market trading are the keys to promoting low-carbon transformation of electric power, and with the help of flexible resources such as energy storage and demand response, the prosumers have stronger balancing capabilities in electric energy production and consumption. A two-layer optimal scheduling model of the power system considering carbon demand response and carbon trading is proposed based on the carbon flow theory. The node carbon potential of the distribution network is used as one of the price factors for the demand response of producers and consumers, and the optimal operation of the distribution network is realized through the interactive transmission of supply/consumption information as well as electricity price and carbon price information between the upper distribution network and the lower producers and consumers. The lower tier producers and consumers trade carbon allowances based on the operation plan and determine the revenue through the Nash bargaining method. Case simulations verify the effectiveness of the model proposed in this paper in controlling the carbon emissions of the system and promoting the enthusiasm of demand response.

To determine the energy supply potential of distributed energy resources, this study focuses on the dispatching optimization of combined heat and power virtual power plants (VPP). First, combined heat and power (CHP) units and various distributed energy sources were integrated into a combined heat and power virtual power plant. Carbon capture and power-to-gas devices were also introduced to realize the recycling utilization of CO₂. Further, carbon and hydrogen storage devices are added to decouple the carbon capture and electro-gas conversion processes. Then, through un-certainty scenario generation and conditional value-at-risk (CVaR) theory, the risks in virtual power plant real-time dispatching are qualified. Finally, with operating cost, carbon emissions, and operating risk as the objectives, a multi-objective stochastic optimization model of a virtual power plant is constructed and solved using a subjective and objective integrated weighting method. The example results demonstrate that the proposed method can promote the consumption of wind and photovoltaic power, besides reduce the carbon emissions of power plants.

With the background of the new power system, studying the multi-microgrid (MMG) low-carbon scheduling strategy that considers the uncertainty of wind power is conducive to reducing the impact of the uncertainty of wind power on the low-carbon and economic dispatching strategy. Therefore, considering the carbon trading mechanism, an MMG adjustable robust low-carbon economic scheduling model is proposed, which employs meteorological clustering and grouping generation to improve the uncertainty set of wind power. First, the wind power output generation model based on meteorological feature clustering is established by using minimum redundancy and maximum correlation feature selection technology, the CURE algorithm, and a conditional generative adversarial network. Using the fuzzy set of the probability distribution, the uncertainty set of the wind power output under different meteorological types is constructed. Second, to realize the low-carbon economic operation of the microgrid, the stepped carbon trading mechanism is introduced into the microgrid. Meanwhile, considering the impact of wind power uncertainty, a two-stage adjustable robust optimization model based on improved wind power uncertainty is established. Third, the ADMM nested C&CG algorithm is employed to solve the MMG dispatch model. Finally, examples are analyzed to verify that the proposed model can increase the accuracy of wind power uncertainty description and improve the economic and low-carbon performance of microgrid operation.

Load scenario generation is the basis of studying energy measurement, operation scheduling and other fields, which is of great significance. Due to difficulty of data collection and multi-energy coupling of integrated energy system, it is still a big challenge to generate load data with diversity. A novel multi-load scenario generation method based on generative adversarial network (GAN) is proposed in this paper. Firstly, the Wasserstein generative adversarial network model with gradient penalty optimization is established to overcome the misconvergence and mode collapse caused by high randomness of load. Secondly, on the basis of the recurrent neural network with deep long-term and short-term memory, the generator and discriminator in the GAN are constructed to be more suitable for load data generation of complex integrated energy system. The result shows that the scenarios generated by proposed model achieves better results in probability distribution, curve signature features and correlation in cooling, heating and power load than original GAN and Monte Carlo method. The model can generate realistic load scenarios with diversity in different modes.

Limited by the resource endowment of the power system, the bottleneck of renewable energy absorption capacity has always restricted the development of renewable energy. In recent years, integrated energy systems have been widely used owing to their multi-energy coupling property. They can transfer the surplus power of the power system to other energy subsystems such as gas/heat/cold for consumption, providing a new idea for the consumption of renewable energy. However, owing to the complex multi-energy coupling, large scale, high dimension, and strong non-convex nonlinearity of the integrated energy system, accurately evaluating its renewable energy absorption capacity is difficult. In this study, considering the physical characteristics of energy subsystems such as electricity/gas/heat/cold, a renewable energy absorption model of the integrated energy system was developed to evaluate the limit of the system’s renewable energy absorption capacity under the multi-energy coupling effect. Then, a convex relaxation and approximation method was developed for transforming the original mixed-integer nonlinear programming problem into a mixed-integer conic programming problem, for solving the model. Finally, on the basis of the improved IEEE-39 node power system and the Belgium 20 node natural gas system, a numerical example of an electricity/gas/hydrogen/hydrothermal/wind/optical coupling integrated energy system was constructed. The model is used to demonstrate the enhancement of renewable energy integration through multi-energy coupling and to thoroughly investigate the variations in the capabilities of different nodes to accommodate renewable energy.

Focusing on the goal of carbon peak and carbon neutrality, the low-carbonization of the whole link of energy power system (i.e., source-network-load-storage) faces new requirements and challenges. The high proportion of renewable energy generation has become an inevitable trend. Considering the impact of the uncertainty of renewable energy generation on the safe and stable operation of the power system, the use of virtual power plant (VPP) with multi-energy complementary characteristics is a favourable way to solve this problem. Therefore, an optimal scheduling strategy of multi-energy complementary VPP was proposed. Firstly, taking into account the coupling relationship between multiple energy sources, a VPP operation mechanism that takes into account the entire source-grid-load-storage chain was constructed. Secondly, according to the proposed operation mechanism, a multi-energy complementary optimal dispatching model with low carbon economy as the goal was proposed to promote the consumption of renewable energy by coordinating the dispatching of various types of devices. Finally, the effectiveness of the proposed strategy was verified by simulating and analyzing a reference case of a multi-energy complementary VPP including renewable energy generation in a region.