Self-Sufficient Participation in Cloud-Based Demand Response

Sehloff, David; Marathe, Maitreyee; Manur, Ashray; Venkataramanan, G.

doi:10.1109/tcc.2021.3118212

Cited by 6 publications

(1 citation statement)

References 41 publications

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“…A further study conducted in [27] also utilized a cloud-edge strategy to provide demand response using deep reinforcement learning, where cloud computing services are used for the deep learning activities, while the authors in [28] demonstrate a virtual power plant (VPP) energy trading platform deployment on the cloud. The authors of [29] propose a cloud-based scheme for control and optimization during contingencies, with the participation of demand side response, while presenting a comprehensive list of research-based demand response formulations in optimization problems. The solution comprises an IoT-based cloud platform with multiple cloud nodes in charge of the various tasks, including a DC power flow.…”

Section: Introductionmentioning

confidence: 99%

Multi-Vector Power Island Operation Utilizing Demand Side Response Based on a Cloud- Based Optimization Strategy (CbOS)

Patsidis,

Dyśko,

Booth

et al. 2024

IEEE Access

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Intentional islanding is one of the potential strategies to mitigate risks related to total blackouts by partitioning the network into multiple power islands. This paper focuses on developing a cloud-based strategy for managing the post-islanding power islands operation considering the coupling of the electrified heating vector. At the core, a novel multi-vector cloud-based optimization strategy (CbOS) is utilized to harness the hidden flexibility of heating, ventilation and air-conditioning (HVAC) systems, resulting in reduced load shedding required to balance the power island and decreased operational costs. To maintain the sustainability of the power island, CbOS is further integrated with an additional objective of optimizing a voltage stability index and costs. The architecture upon which CbOS is built, provides the means to deploy the required software tools and its operation is tested in a generalizable power island under representative cases studies with respect to the level of controllability that CbOS is expected to have among the fleet of energy assets. The results reveal that when all energy assets are operated under CbOS, a substantial cost reduction up to 55.6% can be achieved by utilizing the flexibility stemming from the HVAC systems. Concurrently, voltage stability profiles are improved for the lines under stress.INDEX TERMS Cloud-based optimization, demand side response, electrified heating, intentional islanding, multi-vector power island NOMENCLATURE

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Section: Introductionmentioning

confidence: 99%