Intelligent Dynamic Pricing Scheme for Demand Response in Brazil Considering the Integration of Renewable Energy Sources

Vilar, Diego Branches; Affonso, Carolina M.

doi:10.3390/en14164839

Cited by 3 publications

(1 citation statement)

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“…Electricity price forecasting can be done in several ways, including artificial neural networks and regression analysis. However, earlier works like [57,58] have already employed the ARIMA model with good results. It is important to remember that the value of call option V is calculated by factoring in the anticipated power price.…”

Section: Methodsmentioning

confidence: 99%

Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

et al. 2022

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It is becoming increasingly difficult to properly control the power consumption of widely dispersed data centers. Energy consumption is high because of the need to run these data centers (DCs) that handle incoming user requests. The rising cost of electricity at the data center is a contemporary problem for cloud service providers (CSPs). Recent studies show that geo-distributed data centers may share the load and save money using variable power prices and pricing derivatives in the wholesale electricity market. In this study, we evaluate the problem of reducing energy expenditures in geographically dispersed data centers while accounting for variable system dynamics, power price fluctuations, and renewable energy sources. We present a renewable energy-based load balancing employing an option pricing (RLB-Option) online algorithm based on a greedy approach for interactive task allocation to reduce energy costs. The basic idea of RLB-Option is to process incoming user requests using available renewable energy sources. In contrast, in the case of unprocessed user requests, the workload will be processed using brown energy or call option contract at each timeslot. We formulate the energy cost minimization in geo-distributed DCs as an optimization problem considering geographical load balancing, renewable energy, and an option pricing contract from the derivative market while satisfying the set of constraints. We prove that the RLB-Option can reduce the energy cost of the DCs close to that of the optimal offline algorithm with future information. Compared to standard workload allocation methods, RLB-Option shows considerable cost savings in experimental evaluations based on real-world data.

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Section: Methodsmentioning

confidence: 99%