Optimal supply and demand bidding strategy for an aggregator of small prosumers. (1st March 2018)

• Record Type: Journal Article
• Authors: Iria, José; Soares, Filipe; Matos, Manuel
• Is Part Of: Applied energy. Volume 213(2018)
• Page Start: 658
• Page End: 669
• Publication Date: 2018-03-01
• Subjects: Aggregator -- Electricity markets -- Model predictive control -- Prosumers -- Two-stage stochastic optimization; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.09.002
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Participation of an aggregator of small prosumers in the energy market. The aggregator exploits the flexibility of prosumers' appliances, in order to reduce market net costs. A two-stage stochastic optimization model to define demand and supply bids for the day-ahead energy market is presented. A model predictive control method to set the operation of flexible loads in real-time is presented. A case study of 1000 small prosumers from the Iberian market is used to compare the proposed methods to other benchmarks. Abstract: This paper addresses the problem faced by an aggregator of small prosumers, when participating in the energy market. The aggregator exploits the flexibility of prosumers' appliances, in order to reduce its market net costs. Two optimization procedures are proposed. A two-stage stochastic optimization model to support the aggregator in the definition of demand and supply bids. The aim is to minimize the net cost of the aggregator buying and selling energy at day-ahead and real-time market stages. Scenario-based stochastic programing is used to deal with the uncertainty of electricity demand, end-users' behavior, outdoor temperature and renewable generation. The second optimization is a model predictive control method to set the operation of flexible loads in real-time. A case study of 1000 small prosumers from the Iberian market is used to compare four day-ahead bidding strategies and two real-time control strategies, as well as the performance of combined day-ahead and real-time strategies. The numerical results show that the proposed strategies allow the aggregator to reduce the net cost by 14% compared to a benchmark typically used by retailers (inflexible strategy).