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Bash, C., Patel, C., Shah, A., & Sharma, R. (2011). Demand response in data centers through energy-efficient scheduling. In *Proceedings of the 48th ACM/EDAC/IEEE Design Automation Conference (DAC)* (pp. 620–625). IEEE. This paper frames data centers as flexible electrical loads capable of responding to grid conditions, which supports grids with variable wind and solar generation. It proposes workload and power-management strategies that shift or reduce demand in response to utility signals. Simplified assumptions limit its treatment of rebound effects, market barriers, and renewable-generation uncertainty. Gao, P. X., Curtis, A. R., Wong, B., & Keshav, S. (2012). It’s not easy being green. In *Proceedings of the ACM SIGCOMM 2012 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication* (pp. 211–222). Association for Computing Machinery. https://doi.org/10.1145/2342356.2342398 This study clarifies when geographical load balancing can increase renewable-energy use and reduce emissions in large-scale computing infrastructures. It evaluates optimization models incorporating workload placement, latency, electricity prices, renewable availability, and carbon using real-world traces. Its conclusions depend on grid, pricing, and carbon assumptions, while deployment may be constrained by data locality, regulation, and workload mobility. Goiri, Í., Katsak, W., Le, K., Nguyen, T. D., & Bianchini, R. (2013). Parasol and GreenSwitch: Managing datacenters powered by renewable energy. In *Proceedings of the eighteenth international conference on architectural support for programming languages and operating systems* (pp. 51–64). Association for Computing Machinery. https://doi.org/10.1145/2451116.2451123 This work presents an integrated system for studying and controlling data centers powered by on-site solar generation, batteries, and the grid. It combines the Parasol micro–data center with GreenSwitch, an optimization-based contro