A Hierarchical Demand Response Framework for Data Center Power Cost Optimization under Real-World Electricity Pricing

Wang, Cheng; Urgaonkar, Bhuvan; Wang, Qian; Kesidis, George

doi:10.1109/mascots.2014.45

Cited by 24 publications

(14 citation statements)

References 28 publications

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“…The peak-based charging model has been considered in the cost minimization problem for data centers in [8], [25] and for content delivery network in [26]. In the microgrid scenario, distributed energy storage scheduling [27], demand response [28]- [30], HVAC controlling for buildings [31], and climate control for storage systems [32] are also studied with peak-charging taken into consideration, assuming prediction of future input with certain level accuracy.…”

Section: Related Workmentioning

confidence: 99%

Peak-Aware Online Economic Dispatching for Microgrids

Zhang

Hajiesmaili

Cai

et al. 2018

IEEE Trans. Smart Grid

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By employing local renewable energy sources and power generation units while connected to the central grid, microgrid can usher in great benefits in terms of cost efficiency, power reliability, and environmental awareness. Economic dispatching is a central problem in microgrid operation, which aims at effectively scheduling various energy sources to minimize the operating cost while satisfying the electricity demand. Designing intelligent economic dispatching strategies for microgrids, however, is drastically different from that for conventional central grids, due to two unique challenges. First, the demand and renewable generation uncertainty emphasizes the need for online algorithms. Second, the widely-adopted peakbased pricing scheme brings out the need for new peak-aware strategy design. In this paper, we tackle these critical challenges and devise peak-aware online economic dispatching algorithms. We prove that our deterministic and randomized algorithms achieve the best possible competitive ratios 2−β and e/(e−1+β) in the fast responding generator scenario, where β ∈ [0, 1] is the ratio between the minimum grid spot price and the localgeneration price. By extensive empirical evaluations using realworld traces, we show that our online algorithms achieve near offline-optimal performance. In a representative scenario, our algorithm achieves 17.5% and 9.24% cost reduction as compared to the case without local generation units and the case using peak-oblivious algorithms, respectively. NOTATIONSThis section lists the main notations used in this paper. e(t) The net electricity demand at time t in KWh. u(t) The amount of energy generated by local generators at time t in KWh. v(t) The amount of energy purchased from electricity grid at time t in KWh. p e (t) The spot price of the electricity from grid at time t in $/KWh. p min e Minimum spot price of the electricity from grid, min t p e (t). p gThe unit cost of the electricity by local generators in $/KWh. β Ratio between p min e and p g .

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Section: Related Workmentioning

confidence: 99%

Peak-Aware Online Economic Dispatching for Microgrids

Zhang

Hajiesmaili

Cai

et al. 2018

IEEE Trans. Smart Grid

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“…In contrast, our work tackles the problem using local generators to shave the peak. The cost minimization with the same pricing mechanism taken into account is also studied for data centers in [19,21], for EV charging in [23], and for content delivery in [6]. For fast-responding generator scenario, the economic dispatching problem we study in this paper can be considered as a generalization of the classic Bahncard problem [9].…”

Section: Related Workmentioning

confidence: 99%

Peak-Aware Online Economic Dispatching for Microgrids

Zhang

Hajiesmaili

Chen

2015

Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems

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By employing local renewable energy sources and power generation units while connected to the central grid, microgrid can usher in great benefits in terms of cost efficiency, power reliability, and environmental awareness. Economic dispatching is a central problem in microgrid operation, which aims at effectively scheduling various energy sources to minimize the operating cost while satisfying the electricity demand. Designing intelligent economic dispatching strategies for microgrids, however, is drastically different from that for conventional central grids, due to two unique challenges. First, the erratic renewable energy emphasizes the need for online algorithms. Second, the widely-adopted peak-based pricing scheme brings out the need for new peakaware strategy design. In this paper, we tackle these critical challenges and devise peak-aware online economic dispatching algorithms. For microgrids with fast-responding generators, we prove that our deterministic and randomized algorithms achieve the best possible competitive ratios 2 − β and e/(e − 1 + β), respectively, where β ∈ [0, 1] is the ratio between the minimum grid spot price and the localgeneration price. Our results characterize the fundamental price of uncertainty of the problem. For microgrids with slow-responding generators, we first show that a large competitive ratio is inevitable. Then we leverage limited prediction of electricity demand and renewable generation to improve the competitiveness of the algorithms. By extensive empirical evaluations using real-world traces, we show that our online algorithms achieve near offline-optimal performance. In a representative scenario, our algorithm achieves 23% and 11% cost reduction as compared to the case without local generation units and the case using peak-oblivious algorithms, respectively.

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“…Mishra et al (2003) explored demand-responsive electricity price enabled by the emerging smart grid technology, and proposed an efficient online algorithm called CNDC, to control the number of active servers for minimizing the data center operational cost while satisfying carbon neutrality. Wang et al (2014) proposed a general framework for optimizing data center electric utility bills under real-world pricing schemes, using the many control knobs that exist for modulating the power consumption of IT equipment.…”

Section: Related Workmentioning

confidence: 99%