Economical evaluations of reactive power supply as an ancillary service offered by distributors

Ippolito, Mariano Giuseppe; Massaro, Fabio; Pecoraro, G.; Sanseverino, Eleonora Riva

doi:10.1109/pes.2006.1709428

Cited by 4 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The flexible energy amounted respectively to 30%, 30% and 50% of the total of the energy for the three considered clusters respectively of type (1), (2) and (3); the commercial cluster's user and the office cluster's user have similar trends of load consumption therefore only the trend of energy required by the commercial cluster's users were represented and referred to as tertiary users. (3.21) In order to let the RTP curves be quite smooth, the difference between the energy price for consecutive hours was set to be less than a given amount: ðp max À p min Þ=4.…”

Section: Case Studymentioning

confidence: 99%

Real-time pricing for aggregates energy resources in the Italian energy market

Favuzza

Galioto

Ippolito

et al. 2015

Energy

Self Cite

View full text Add to dashboard Cite

Section: Case Studymentioning

confidence: 99%

Real-time pricing for aggregates energy resources in the Italian energy market

Favuzza

Galioto

Ippolito

et al. 2015

Energy

Self Cite

View full text Add to dashboard Cite

An algorithm for simulating end-user behaviour in a real time pricing market

Pecoraro

Favuzza

Ippolito

et al. 2015

2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

View full text Add to dashboard Cite

The energy market has changed radically over the last decade, mainly due to an increased penetration of renewable energies. Now the end users have directly access to the energy market and can actively take part to the electricity market. Electricity customers can indeed modify their behavior through Demand Response, namely by means of pricing strategies that support a change in the end-users habits. This can be accomplished through a 'loads aggregator', a third party that collects the requests and signals for Active Demand-based services coming from the markets and the different actors of energy market. This paper describes a simulation framework to generate the simulated optimal behavior of the electrical users. Based on a given real-time pricing curves the simulator evaluates the power consumption hour by hour if the user behaves according to what the price curve and thus the loads aggregator suggests. A case study shows the advantages/disadvantages of the proposed pricing approach and the limits of the simulator

show abstract