[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85411-en":3,"doc-seo-85411-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85411,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1782109480056885918",8,"Research & Report","Unlocking Innate Computing Abilities in Electric Grids","Electric power grids are engineered energy systems whose forward electrical dynamics transform input signals through high-dimensional, memory-bearing processes. This work shows that such transformations, arising intrinsically from circuit elements, power flows, and network topologies, can be harnessed for computation without changing grid architectures. Structured data are encoded into operational setpoints of power electronic converters, and an affine transformation example on a DC grid demonstrates reproducible input-output mappings. The framework positions energy networks as sustainable computational substrate alongside normal power delivery.","arXiv :2505 . 10382v2 [ ee ss . SY] 11 Jul 2026  \nUnlocking Innate Computing Abilities in Electric Grids  \nYUBO SONG and SUBHAM SAHOO  \nDepartment of Energy, Aalborg University, Denmark  \nElectric power grids are engineered energy systems whose forward electrical responses embody high-dimensional and memorybearing transformations of input signals. In this work, we reveal that these transformations—inherent in electric circuit elements, power flows and network topologies—can be conveniently harnessed for computation without modifying physical grid architectures. By encoding structured input data into the operational setpoints of power electronic converters inside grids, we demonstrate how forward grid dynamics are interpreted into physical representations comprising system variables—by showcasing through an affine transformation example implemented on a direct-current (DC) grid—which justifies the capability of grids performing information processing tasks concurrently alongside normal power flows. Our work not only underscores the computation capability intrinsic to grid physics, but also opens a new perspective on how energy networks can function as sustainable computational substrate. This positions them as flexible assets where several computing tasks from data centers can be sustainably outsourced.  \nKey Words: Smart Energy Systems | Electric Grids | Physical Computing | System Network Responses | Power Electronics | Sustainable Infrastructures  \nIntroduction  \nOver the years, digital computations in data centers have pushed electric power grids to their tipping limits due to insufficient energy availability. Albeit the advances in algorithms, specialized hardware (processors) and edgecomputing architectures have improved the energy efficiency, the aggregated energy consumptions associated with computation in scaled engineered systems remain spanning in the order ofterawatt-hours (TWhs) annually from electric grid infrastructure [1] . This fact has escalated to a point where challenges emerge in electric grid operation, energy storage management and de-carbonization trajectories [2]—highlighting the need of new physical computations in the environment that enable learning using existing physical signals, such as electricity, light, and material properties [3] .  \nFrom a physical standpoint, computation is not tied to digital algorithms and in silico infrastructures. At its core, computation amounts to structured transformation of input signals by physical systems into reproducible responses through deterministic dynamics—including both instantaneous gains and spatial/temporal signal propagation—subject to physical laws [4] . This underlies a broad class of computing paradigms, ranging from biological neural systems that inspire neuromorphic schemes [5] and artificial dynamical systems that form the basis of physical reservoir computing networks [6, 7] . Exploiting such mechanisms offers a promising pathway towards computation with reduced energy overhead from specialized digital architectures by aligning the signal transformation with the naturally occurring physical processes.  \nElectric power grids are one of the scaled and complex engineered dynamic systems, where its behavior is governed by electric circuit laws, power flow constraints and network topologies [8, 9], giving rise to high-dimensional signal transformation capabilities both spatially and temporally. Perturbations introduced into the network—episodic changes in power generations, loads or control references—will propagate through the physical network and control paths, producing predictable system responses and naturally exhibiting the same intuition as digital processors. This is further underscored in modern electric grids, particularly dominated by power electronics, where:  \ne-mails: {yuboso, [sssa}](sssa}@energy.aau.dk)[@energy.aau.dk](sssa}@energy.aau.dk)  \n2 Yubo Song and Subham Sahoo  \n(1) Power electronic converters interfacing renewable energy sou","cbCaipr2Ka9ulLgp","https://ap.wps.com/l/cbCaipr2Ka9ulLgp","pdf",951554,1,13,"English","en",105,"# Introduction\n## Motivation: Grid power limits from digital computation\n## Computation as physical structured signal transformation\n## Grid dynamics and programmability via power electronics\n## Proposed framework and computational demonstration","[{\"question\":\"How does the document propose electric grids can perform computation?\",\"answer\":\"It proposes encoding structured input data into the reference signals or operational setpoints of power electronic converters, so that forward grid dynamics produce system responses representing the computed outputs.\"},{\"question\":\"What physical mechanisms in the grid enable computation according to the paper?\",\"answer\":\"The paper highlights transformations inherent in circuit elements, power flows, and network topologies, including spatial/temporal propagation and intrinsic memory from temporal coupling, all governed by physical laws and constraints.\"},{\"question\":\"What is the demonstrated example used to validate the idea?\",\"answer\":\"The paper demonstrates an elementary affine transformation task for images using a five-converter direct-current (DC) microgrid, showing an input-output mapping determined by topology, electrical parameters, and 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does the document propose electric grids can perform computation?","Question",{"text":75,"@type":76},"It proposes encoding structured input data into the reference signals or operational setpoints of power electronic converters, so that forward grid dynamics produce system responses representing the computed outputs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What physical mechanisms in the grid enable computation according to the paper?",{"text":80,"@type":76},"The paper highlights transformations inherent in circuit elements, power flows, and network topologies, including spatial/temporal propagation and intrinsic memory from temporal coupling, all governed by physical laws and constraints.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the demonstrated example used to validate the idea?",{"text":84,"@type":76},"The paper demonstrates an elementary affine transformation task for images using a five-converter direct-current (DC) microgrid, showing an input-output 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