In this paper, a collaborative power dispatching system (CPDS) was developed to maximize the profit of a regional biomass power system consisting of an independent power grid. A power generating, dispatching and carbon emissions trading system (CETS) could be engaged in joint strategic planning and operational execution. The principal of CPDS is interactive planning of generating units in power generation and carbon emissions trading. An efficient carbon emissions trading plan for a CPDS would lead to optimized power generation levels under available power production capacities and carbon emissions. In a case study, four generator policies are proposed by considering basic CETSs to comparatively analyze the function of each generator in the CPDS. Results of four scenarios are compared, showing that biomass energy could replace thermal units to a certain extent, the carbon emissions and coal consumption of the CPDS would lie at a lower level, and a pumped storage unit could adjust the load fluctuations. The results of a carbon trading analysis show that the CETS has no significant impact on the CPDS, but along with the increase in trading price or the decrease in the free quota, the economic interests of power plants will be reduced accordingly. This may lead to carrying out low-carbon projects and reducing carbon emissions. Therefore, it is imperative to reduce carbon emissions by replacing power units with high energy consumption, and improve the consumption capacity of renewable energy.

To achieve the carbon reduction target, the Chinese government not only requires a quota allocation scheme in line with the current development situation, but also needs to minimize the economic expenditure in the emission reduction work. Therefore, this study integrates the multi-index method and zero-sum gains–data envelopment analysis model to obtain a fair and efficient multi-criteria quota allocation scheme. To ensure the effectiveness of the scheme, the fairness and cost effect of the scheme are quantitatively tested. In addition, regional cooperation factors are introduced into the research framework to providing feasible practical measures. The results show that: (1) After optimization, the eastern region has the largest quota increment, accounting for 45% of the country’s quota. (2) The multi-criteria quota allocation scheme after secondary allocation is a more ideal scheme. The quota scheme not only meets the requirements of fairness and efficiency, but also has lower abatement costs. (3) Regional cooperation should be encouraged by China, especially the eastern region, which can alleviate the reducing emission pressure through cooperation. Although the western region needs to undertake additional emission reduction tasks, it can improve the utilization rate of local renewable resources and be conducive to long-term economic development. These results will provide a reference for China to achieve its carbon reduction targets.

The Chinese government has launched a guideline for carbon emissions up to the peak (CEUP) in the 2030 target. The electric power sector has to make its own contributions to the national CO² emissions mitigation target. In this study, a patron–client interactive optimized (PCIO) model is proposed to investigate the regional energy–water–carbon nexus optimization under the policy background of the CEUP target. Inner Mongolia, the largest energy base in China, which is also facing the prominent contradiction including the energy production and serious environmental problems, is chosen as a case study. Multiple uncertainties, including the fuel price uncertainty and output of the wind and solar power, are considered to make the optimization process more realistic. Results show that coal-fired power will gradually be substituted by the gas, wind, and solar power in Inner Mongolia to reach the CEUP target. The CO₂ capture and storage technology and air-cooling systems will play important roles, especially under the strict water policy scenario. However, the achievement of the CEUP and water-saving target will be at the expense of high system costs. The PCIO model makes it possible for the decision-maker to make flexible strategies to balance the CEUP target and saving system costs. The results have demonstrated the validity of the PCIO model in addressing the hierarchical programming problems.

Studies on the energy–water–CO₂ synergetic relationship is an effective way to help achieve the peak CO₂ emission target and carbon neutral goal in global countries. One of the most valid way is to adjust through the electric power structure transformation. In this study, a mixed-integer linear resource planning model is proposed to investigate the energy–water–CO₂ synergetic optimization relationship, concerning the uncertainties in the fuel price and power demand prediction process. Coupled with multiple CO₂ emissions and water policy scenarios, Beijing, the capital city of China, is chosen as a case study. Results indicate that the demand-side management (DSM) level and the stricter environmental constraints can effectively push Beijing’s power supply system in a much cleaner direction. The energy–water–CO₂ relationship will reach a better balance under stricter environmental constraints and higher DSM level. However, the achievement of the energy–water–CO₂ synergetic optimization will be at an expense of high system cost. Decision makers should adjust their strategies flexibly based on the practical planning situations.