Demonstrating a generic four-step approach for applying flexibility for congestion management in daily operation

Fonteijn, Rik; Nguyen, Phuong H.; Morren, Johan; Slootweg, J.G.

doi:10.1016/j.segan.2020.100378

Cited by 19 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the local support, the local distribution network takes the advantage of the flexible demands. Mitigation of power congestion [59,60], voltage support [61], power loss minimization [62], and power quality improvement [63] are the key local support. Volt-Var energy management approach is addressed in distribution systems to improve voltage profile and Var injection in demand sectors [34].…”

Section: Demand Flexibilitymentioning

confidence: 99%

Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors

Golmohamadi

2022

Sustainability

View full text Add to dashboard Cite

In recent years, environmental concerns about climate change and global warming have encouraged countries to increase investment in renewable energies. As the penetration of renewable power goes up, the intermittency of the power system increases. To counterbalance the power fluctuations, demand-side flexibility is a workable solution. This paper reviews the flexibility potentials of demand sectors, including residential, industrial, commercial, and agricultural, to facilitate the integration of renewables into power systems. In the residential sector, home energy management systems and heat pumps exhibit great flexibility potential. The former can unlock the flexibility of household devices, e.g., wet appliances and lighting systems. The latter integrates the joint heat–power flexibility of heating systems into power grids. In the industrial sector, heavy industries, e.g., cement manufacturing plants, metal smelting, and oil refinery plants, are surveyed. It is discussed how energy-intensive plants can provide flexibility for energy systems. In the commercial sector, supermarket refrigerators, hotels/restaurants, and commercial parking lots of electric vehicles are pointed out. Large-scale parking lots of electric vehicles can be considered as great electrical storage not only to provide flexibility for the upstream network but also to supply the local commercial sector, e.g., shopping stores. In the agricultural sector, irrigation pumps, on-farm solar sites, and variable-frequency-drive water pumps are shown as flexible demands. The flexibility potentials of livestock farms are also surveyed.

show abstract

Section: Demand Flexibilitymentioning

confidence: 99%

Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors

Golmohamadi

2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…Local market platforms also require advanced technical capabilities from DSOs, such as demand forecasting with high spatial granularity [124] and LV state estimators to monitor the grid in (near) real-time to activate flexibility [125].…”

Section: Market-basedmentioning

confidence: 99%

Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services

Venegas

Petit

Pérez

2021

Renewable and Sustainable Energy Reviews

101

View full text Add to dashboard Cite

“…Machine learning (ML) models such as neural networks (NNs) have successfully been employed to provide electric load forecasts in the last 30 years [6]. However, there has been a lack of studies that seek to satisfy the two-fold requirement, as only a few publications (e.g., [7]- [10]) have developed forecast models with a resolution of a few minutes.…”

Section: Introductionmentioning

confidence: 99%

Effect of Short-term and High-resolution Load Forecasting Errors on Microgrid Operation Costs

Antoniadou-Plytaria

Eriksson

Johansson

et al. 2022

2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

View full text Add to dashboard Cite

The aim of this paper is to evaluate the effect of the load forecasting errors to the operation costs of a gridconnected microgrid. To this end, a microgrid energy scheduling optimization model was tested with deterministic and stochastic formulations under two solution approaches i.e., day-ahead and rolling horizon optimization. In total, twelve simulation test cases were designed receiving as input the forecasts provided by one of the three implemented machine learning models: linear regression, artificial neural network with backpropagation, and long short-term memory. Simulation results of the weekly operation of a real residential building (HSB Living Lab) showed no significant differences among the costs of the test cases for a daily mean absolute percentage forecast error of about 12%. These results suggest that operators of similar microgrid systems could use simplifying approaches, such as day-ahead deterministic optimization, and forecasts of similar, non-negligible accuracy without substantially affecting the microgrid's total cost as compared to the ideal case of perfect forecast. Improving the accuracy would mainly reduce the microgrid's peak power cost as shown by its 20.2% increase in comparison to the ideal case.

show abstract

Demonstrating a generic four-step approach for applying flexibility for congestion management in daily operation

Cited by 19 publications

References 14 publications

Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors

Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors

Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services

Effect of Short-term and High-resolution Load Forecasting Errors on Microgrid Operation Costs

Contact Info

Product

Resources

About