A case study of digital-twin-modelling analysis on power-plant-performance optimizations

Abstract

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.

Virtual Power Plant Optimization in Smart Grids: A Narrative Review

“…Digital twins of energy assets 13 [9][10][11][12][13][14][15][16][17][18][19][20][21] Energy forecasting 14 [1,[22][23][24][25][26][27][28][29][30][31][32][33][34] Optimization and coordination 18 [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] VPP applications in smart grids Energy services delivery 31 [4,5,22,35,38, Local energy autonomy 21 [5,…”

“…A user's DT profile seamlessly incorporates their flexibility profiles, representing the selected flexibility assets, and can increase the "smartness level" of buildings which will lead to increased participation in energy flexibility services to be delivered by the VPP [17]. The modeling techniques' focus is not just on the average dynamics but also on modeling uncertainty, i.e., a statistical description of stochastic behavior, as it has a severe impact on any decision-making logic on VPP optimization [18]. In holistic terms, the DT can consider the consumer's profile in terms of behavior and incentive mechanisms of VPPs along with their community aggregation association and flexibility assets reflecting various commodities (electricity, biogas, heat) [19].…”

Shaping future low-carbon energy and transportation systems: Digital technologies and applications

“…General Electric Co. GE used DT to optimize the whole process of wind farm development, operation, and maintenance to improve the plant's profitability and sustainability [171] . Siemens created an asset and operation management system as a DT model for Fingrid, Electrical Verkko Information System, which connects a network model and measurement database automatically via an interface [172] . Additionally, Xu et al suggested that DT technology can help search and analyze costeffective approaches to power plant smart management and presented a specific example of a 320-MWe coal-fired generating system [173] .…”

Future era of techno-economic analysis: Insights from review

“…Although DT and CPS can offer numerous advantageous features to the economic performance of a system, the cost associated with the implementation of these data-driven technologies is often overlooked. For example, numerous past studies that have conducted TEA on DT (Xu et al, 2019;Gerogiorgis and Castro-Rodriguez, 2021;Koulouris et al, 2021) and CPS (Zhao et al, 2019) incorporated only processes, taking into account the cost associated with production processes (i.e., equipment cost, contractor's fee, etc. ), while ignoring the cost required to set-up these smart technologies.…”