Abstract
Active participation of end users in energy markets is identified as one of the major challenges in the energy transition context. One option to bridge the gap between customers and the market is aggregators of smart homes or buildings. This paper presents an optimization model from the standpoint of an aggregator of residential prosumers who have PV panels, electric water heaters, and batteries installed at home level. This aggregator participates in the day-ahead energy market to minimize operation costs by controlling the settings of flexible devices. Given that energy prices, PV production, and demand have uncertain behavior, appropriate models should be used to include these effects. In the present work, Adjustable Robust Optimization (ARO) is used to include uncertainty in the optimization model, and a comparative study of modifications to this formulation is carried out to determine its potential and limitations. The comparative analysis is performed from the point of view of average cost and risk, after performing Monte Carlo simulation. Simulations show the advantages of using an ARO framework when compared to deterministic approaches and also allow us to conclude about the advantages of using the proposed alternative formulation to find more attractive solutions for an aggregator.
Highlights
The energy transition requires a series of efforts by industry, governments, and citizens in general.One of the main challenges of reaching decarbonization goals is the transformation of electrical systems.In an attempt to achieve this, the number of large renewable power plants has increased in recent years
The present paper builds on the work presented by the authors in [30], but significantly improves, expands, and differs from the mentioned paper in several aspects: (1) We present a comparative analysis of robust optimization and a hybrid stochastic/robust approach
Following robust optimization to account for uncertainty of energy prices and after obtaining the reduced scenarios and corresponding probabilities with the methodology explained in the previous subsection, a stochastic optimization problem can be formulated
Summary
The energy transition requires a series of efforts by industry, governments, and citizens in general. Energy storage systems and demand side management play an important role to support decisions made by the multiple actors in the system. The presence of microgrids (MG) in lower layers of the distribution grid, in the form of aggregated smart building/homes or energy communities, makes it possible for demand side management and energy storage systems to support operational decisions and to increase/decrease profit/cost when market rules permit trading of flexibility services on wholesale, ancillary, or local markets. European authorities have highlighted the importance of promoting consumer participation in energy markets by creating the necessary marketplaces or by removing market barriers to enable the participation of local energy communities [2] This stimulation of consumers in order to put them at the center of the energy market can be done from individual participation standpoints or by aggregated mechanisms. New mathematical optimization models need to account for uncertainty, which brings additional complexity to the decision-making process
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