Decentralized Participation of Flexible Demand in Electricity Markets—Part II: Application With Electric Vehicles and Heat Pump Systems
By D. Papadaskalopoulos, Goran Strbac, Pierluigi Mancarella, Marko Aunedi, Vladimir Stanojevic. IEEE Transactions on Power Systems, 2013
161 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/e46a74ee94ce8a8aa2deebc8f08d9614fedbebb8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Thomas Morstyn
- Malcolm Duncan McCulloch
- Goran Strbac
- Alexander Teytelboym
- Joseph Mutale
- Sereen Z. Althaher
- Pierluigi Mancarella
- Qiuwei Wu
In Electrical and Electronic Engineering
- Thomas Morstyn
- Malcolm Duncan McCulloch
- Sarah Darby
- Pierluigi Mancarella
- Joseph Mutale
- Sereen Z. Althaher
- Goran Strbac
- Marko Aunedi
Citations in the corpus
- Automated Demand Response From Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints (2015, methodology): “, 30 miles average), arrival times (average 18:00), and departure times (08:00) [29], [30], [32].”
- Bilateral Contract Networks for Peer-to-Peer Energy Trading (2019, background): “This could be used for several different types of flexible loads including electric hot water tanks and electric vehicle charging [32].”
- Whole-Systems Assessment of the Value of Energy Storage in Low-Carbon Electricity Systems (2014, methodology): “Flexibility parameters associated with various forms of DSR are obtained using detailed bottom-up modelling of different types of flexible demand, as described in [24] and [25] for EVs and heat pumps (HPs).”
- The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial (2017, background): “[20] take a sample of building types from the UK building stock in different regions, derive heat and electricity demand profiles from the dynamic simulation tool Energy Plus, and add a certain number of these together according to different heat pump uptake scenarios (10–30% penetration).”
- Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants (2018, methodology): “Pricing uniformly applied to multiple prosumers runs the risk of causing them to shift their loads to the same low price periods and creating new, possibly worse, demand peaks 43 .”