A framework for demand bidding to achieve demand response objectives by EVs charging and heating loads

Abstract

Energy markets which are based on fixed day-ahead energy prices could face bottlenecks due to the simultaneous activation of flexible loads in the system and hence causing power imbalances and possibly also causing congestion in the distribution network. In order to mitigate these problems a suitable scheme is required to reschedule the flexible loads based on incentives for the end users for changing their demand from their day-ahead optimized demand pattern. This paper proposes a three-layer architecture of intelligent agents for scheduling of flexible loads which implies a demand bidding strategy. A real case study is considered from Nordpool spot prices for four different seasons of the year to show case the rationality of the study. Two flexible loads, Electric Vehicles (EVs) and Storage Space Heating (SSH) are considered in this scenario, while other loads are assumed non-flexible. Analysis of energy cost based on flexibility indices of loads is studied, which shows that flexibility of loads not only supports the power network capacity in congestion situation but it also benefits the consumers with cost effective use of energy. From aggregator’s perspective, the system resources are utilized optimally hence reducing the need for running extra generators and the spikes at the cheap price hours are mitigated. From the results, it is noted that the proposed bidding scheme has benefited the households up to 60% of the energy cost saving by participating in the demand response. The number of times each household wins a bid depends on the flexibility of loads and in this particular simulation scenario most of the households are able to win the bids 2 times in a day.