China Energy’s National Institute of Clean-and-Low-Carbon Energy (NICE) is developing a Power Plant Smart Management (PPSM) platform that employs digital-twin technology to undertake techno-economic modelling analysis on China Energy’s existing coal-fired power-plant units and explore cost-effective solutions to improve those plant units’ thermal efficiencies and operating performance. This paper presents a case study of PPSM on a 320-MWe coal-fired thermal power-plant unit, demonstrating how the digital-twin technology was employed to explore and analyse optimization solutions. Various optimization solutions and their cost-effectiveness were assessed using the digital-twin-modelling analysis; the results indicated the optimization solutions are expected to improve the plant unit’s operating efficiency and reduce its current electricity-generation coal consumption by up to 3.5 g/kWh standard coal equivalent (sce), worth annual fuel-cost savings of approximately 4 million RMB for a single unit or 8 million RMB for the two identical 320-MWe units that the power plant currently operates. The digital twin was also employed to assess the power-plant unit’s operating economics during both summer and winter. In summer, when the unit operates in electricity-generation-only mode, the unit’s operating thermal efficiency could drop by up to 6% points following the grid demand of load changes from 100% maximum continuous rating (MCR) down to 30%MCR, resulting in an ~ 45 RMB/MWh increase of electricity-generation cost. In winter, when the unit operates in combined heat and power (CHP) cogeneration mode, for the same boiler load, the CHP operation increases the plant unit’s operating profit with increasing district-heating duty, although the relative profit gain from the CHP cogeneration could start to decrease when the district-heating steam-extraction flow increases to a certain point that varies depending on the market prices of heat and electricity, while the fuel cost was found to be equivalent to ~50% of the unit’s total CHP income cogenerated from its electricity and district heat outputs.

In municipal solid waste (MSW) management, many impact factors, such as waste generation rate, treatment capacity, diversion goal, and disposal cost appear uncertain. These uncertainties can result in difficulties in the long-term planning of MSW management activities. A critical issue that decision makers should mitigate is how to address these uncertainties due to a lack of knowledge founded on an incomplete characterization, understanding or measurement of MSW systems. In this study, an inexact twostage waste management (ITWM) model is developed for planning long-term MSW management in the City of Changchun, China. The ITWM model incorporates the techniques of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) within an integer programming framework, such that uncertainties expressed as both intervals and probabilities can be reflected; it can also analyze different policy scenarios that are associated with different economic penalty levels. Two cases related to different waste management policies are examined, generating varied levels of waste-management cost and system-failure risk. The results obtained are valuable for addressing issues of waste diversion and capacity expansion with a minimized system cost. They also suggest that the developed model be meaningful for real-world planning problems and the practicality of this approach can be extended to other environmental planning applications containing significant sources of uncertainty.