Optimization of energy performance with renewable energy project sizing using multiple objective functions

Ogedengbe, Emmanuel O.; Aderoju, Peter; Nkwaze, Darlington; Aruwajoye, John; Shitta, Manasseh B.

doi:10.1016/j.egyr.2019.07.005

Cited by 17 publications

(3 citation statements)

References 20 publications

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“…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. 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]. To participate in DR programs, the DCs must accurately forecast their demand and potential flexibility and also optimally manage their operation to follow a DR signal provided by the DSO directly or by a flexibility aggregator [11].…”

Section: Introductionmentioning

confidence: 99%

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

et al. 2020

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“…At present, computer technology has been applied in many fields to obtain optimal solutions, such as microgrid, solar energy, and renewable energy. [24][25][26][27][28][29][30][31][32] Many scholars have also proposed T A B L E 1 Improvement of process equipment or technology of the oil pipeline system Frequency control technology The output power of the motor is effectively matched with the pipe flow, which reduces energy consumption…”

Section: Introductionmentioning

confidence: 99%

“…With the continuous development of mathematics and computer science, the continuous updating of artificial intelligence technology and intelligent optimization algorithms makes the process of solving many engineering problems simple, and the solution is better. At present, computer technology has been applied in many fields to obtain optimal solutions, such as microgrid, solar energy, and renewable energy 24‐32 . Many scholars have also proposed different solutions in the hot oil pipeline system, as shown in Table 2.…”

Section: Introductionmentioning

confidence: 99%