Abstract
In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.
Highlights
The energy demand of data centers (DCs) is rapidly growing, and studies have shown that, in 2014, worldwide, they consumed 194 TWh of electricity
In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs
Few studies have approached the efficient integration of DCs with the local energy grids via direct participation in DR programs; most of them focused on increasing the utilization of on-site renewable energy to take advantage of low energy prices [9,10]
Summary
The energy demand of data centers (DCs) is rapidly growing, and studies have shown that, in 2014, worldwide, they consumed 194 TWh of electricity (about 1% of the global electricity demand) These numbers are expected to increase to 3% by 2025 [1,2]. To achieve a significant impact on load, flexibility schemes will require the dispatching of set-points to a greater number of assets and during a broader timeframe for 24 hours of a day This inherently requires the introduction of engagement strategies for new types of energy customers (such as DCs) and the overtaking of technological barriers such as the accurate forecasting of demand, generation, and flexibility. To participate in DR programs, the DCs must accurately forecast their demand and potential flexibility and optimally manage their operation to follow a DR signal provided by the DSO directly or by a flexibility aggregator [11]. These valueTshaereretshteonfuthseedpbaypetrheisFslterxuicbtiulirteydAagsgfroelgloawtosr:tSoeicstsiuoena2fplerxeisbeinlittsysotardteerofasthaeDarRt saipgpnraolainchteesrmons oenf ethrgeyDpCreedniecrtigoyndreelmataenddtoprDoRfilpertohgartammuss, tSebcetiaocncu3rdateeslcyrifboellsotwheedeninsetmheblnee-bxtasdeadyptoregdeicttrioewn amrdodedel, ofothr eDrwCiesneebregiyngdeamt rainskd oafndpeennaelrtgyychflaerxgibesil.itDy,uSreincgtiothne4ospheorwatsiopnraelddicatyio,nthreesseulsttseopns caamn ebdeiuremp-esactaelde cDoCnstiedsetbriendg, wa 4h-ihle-aSheecatidontim ceofnracmlued.es the paper and presents future work
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