Implementing flexible demand: Real-time price vs. market integration. (15th April 2018)

• Record Type: Journal Article
• Authors: Kühnlenz, Florian; Nardelli, Pedro H.J.; Karhinen, Santtu; Svento, Rauli
• Is Part Of: Energy. Volume 149(2018)
• Page Start: 550
• Page End: 565
• Publication Date: 2018-04-15
• Subjects: Real-time pricing -- Day-ahead market -- Balancing market -- Market design -- Demand response -- Smart grid; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.02.024
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper proposes an agent-based model that combines both spot and balancing electricity markets. From this model, we develop a multi-agent simulation to study the integration of the consumers' flexibility into the system. Our study identifies the conditions that real-time prices may lead to higher electricity costs, which in turn contradicts the usual claim that such a pricing scheme reduces cost. We show that such undesirable behavior is in fact systemic. Due to the existing structure of the wholesale market, the predicted demand that is used in the formation of the price is never realized since the flexible users will change their demand according to such established price. As the demand is never correctly predicted, the volume traded through the balancing markets increases, leading to higher overall costs. In this case, the system can sustain, and even benefit from, a small number of flexible users, but this solution can never upscale without increasing the total costs. To avoid this problem, we implement the so-called "exclusive groups". Our results illustrate the importance of rethinking the current practices so that flexibility can be successfully integrated considering scenarios with and without intermittent renewable sources. Highlights: Demonstrates benefits of market integration of flexible demand. Identifies systemic issues of using day-ahead price as end-user real-time price. Introduces an open-source model for coupled day-ahead and balancing market.