Coordinated computing energy optimization of data center micro 2027 Applied

With the rapid advancement of cloud computing, big data and artificial intelligence technologies, data center microgrids (DCMGs) have become crucial infrastructures supporting the digital economy. In this context, a key challenge is how to fully exploit their multi-dimensional scheduling potential in the peer-to-peer (P2P) electricity market to enhance operational economics and demand-side flexibility. Therefore, a coordinated computing-energy optimization method of DCMGs in a joint kilowatt (kW) and negawatt (nW) P2P market is proposed. First, a coordinated computing-energy management optimization model for DCMGs is established, explicitly modeling the computing load scheduling and cooling systems. Then, a joint kW-nW P2P market mechanism is developed to coordinate the endogenously determined DCMG flexibility across electricity and demand-side flexibility transactions. On this basis, an RHC-based dynamic clearing algorithm is introduced by embedding iterative double-sided-auction clearing within outer rolling optimization, thereby coupling market decisions with operational states and improving adaptability to source-load uncertainty. Case studies show that the proposed DCMG model reduces net energy demand by 6.9% – 36.6% and energy costs by 8.6% – 41.6% compared with simplified models. Relative to alternative market configurations, the joint kW-nW market reduces total operating cost by 4.9% – 7.7%. Compared with static clearing, the proposed dynamic algorithm improves performance significantly.