In order to improve clean energy consumption and flexible operation of multi energy coupling systems, while reducing the impact of source and load uncertainties on the system, this paper constructs a wind-photovoltaic-electric-heat-hydrogen integrated energy system (WPEHH-IES) including concentrating solar power, and proposes a two-stage robust optimization method with adjustable robustness for spatial polyhedral uncertainty sets. Based on the column-and-constraint generation (C&CG) algorithm, a two-stage three-layer min-max-min robust optimization model is decomposed into a one-layer min main problem and two-layer max-min subproblem. The structure of the two-layer subproblem is simplified by using the strong duality principle, and the optimal solution of the original problem is obtained by alternating the subproblem and the main problem. Finally, the effectiveness of the constructed model and solving algorithm, as well as the flexibility of the output of the multi energy coupling system, were verified through example analysis.

In order to reduce the influence of uncertainty on both sides of the source and load on the security and economy of the integrated energy system (IES), and to improve the flexibility and stability of IES in the face of uncertainties, various strategies for energy storage participation in smoothing out the uncertainty fluctuations are proposed, and a robust model is established for the day-ahead and real-time two-stage cooperative optimization under multiple uncertainties. A robust adjustable factor is added to the model to comprehensively evaluate the system economy and robustness. In the day-ahead phase, a pre-dispatch plan is determined based on the predicted power of new energy and load to realize the power balance at the minimum operating cost. In the real-time phase, the adjustment power of the secondary flexible adjustment equipment is determined according to the new energy output and the actual simulated power of the load to realize power rebalancing at minimum cost. The case study shows that the real-time adjustment of power supply side and energy storage side can better play the synergistic adjustment function of IES to deal with uncertainty; the introduction of robust adjustable factor to portray the uncertainty better balances the economy and security of system operation.

Against the backdrop of the "dual carbon" target, the power sector has become an important part of carbon reduction. Virtual power plants (VPP) can further improve their overall efficiency by integrating and aggregating distributed resources to participate in the carbon market. However, the uncertainty of distributed new energy output poses many challenges for their operation and management. Therefore, on the basis of using the scenario generation and scenario reduction method based on Latin hypercubic sampling to deal with the uncertainty problem of wind power and photovoltaic output of distributed energy, the VPP, which aggregates multiple units and takes into account the user-side demand response, participates in electric energy market as well as the carbon market as a whole, and the optimal scheduling model with the minimum total cost of the VPP is constructed, which is finally solved by using the improved gray wolf optimization algorithm. Through comparative analysis of different scenarios, it can be concluded that the existence of carbon market and demand response enhances the consumption of clean energy such as wind power and photovoltaic, and reduces greenhouse gas emissions, and reduces the operating cost of the VPPs, and takes into account its economy and environmental protection.

New energy storage is pivotal for smoothing fluctuations in renewable energy generation and adapting to dynamic changes in load, and it is a crucial support for future power systems. With policies allowing new energy storage as an independent participant in the power market, studying its technical and economic feasible region during market transactions becomes crucial to influence the widespread application and realization of commercial value in power systems. This paper establishes a trading model for new energy storage participating in electricity markets, outlines economic calculation methods for its participation in the electricity energy and peak shaving auxiliary service markets. Focusing on trading power, it characterizes the technical and economic feasible region of new energy storage in the electricity energy and auxiliary service markets, and studies the impact of factors like charge-discharge power and electricity prices. Through Matlab simulation analysis, the proposed method can effectively enhance situational awareness for new energy storage transactions, and provide decision-making support for market investors.