Enabling Active/Passive Electricity Trading in Dual-Price Balancing Markets

Abstract

In electricity markets with a dual-pricing scheme for balancing energy, controllable production units typically participate in the balancing market as "active" actors by offering regulating energy to the system, while renewable stochastic units are treated as "passive" participants that create imbalances and are subject to less competitive prices. Against this background, we propose an innovative market framework whereby the participant in the balancing market is allowed to act as an active agent (i.e., a provider of regulating energy) in some trading intervals and as a passive agent (i.e., a user of regulating energy) in some others. To illustrate and evaluate the proposed market framework, we consider the case of a virtual power plant (VPP) that trades in a two-settlement electricity market composed of a day-ahead and a dual-price balancing market. We formulate the optimal market offering problem of the VPP as a threestage stochastic program, where uncertainty is in the day-ahead electricity prices, balancing prices and the power output from the renewable units. Computational experiments show that the VPP expected revenues can increase substantially compared to an active-only or passive-only participation, and in the paper we discuss how the variability of the stochastic sources affects the balancing market participation choice.

Optimal bidding functions for renewable energies in sequential electricity markets

“…The upper level is a stochastic program that models the integrated trading, itself, while the lower-level problems are the dispatching problems on the day-ahead and intraday market. Sequential bidding in a day-ahead and a balancing market is considered in, e.g., Boomsma et al (2014); Kumbartzky et al (2017); Kongelf et al (2019); Mazzi et al (2019). Most authors restrict themselves to a small number of markets.…”

“…This is because the simultaneous price and volume decisions result in nonlinear and non-concave decision problems. Therefore, most authors decide on either the prices p i or the volumes x i , while the counterpart is given by parameters (e.g., Morales et al (2010); Löhndorf et al (2013); Boomsma et al (2014); Guerrero-Mestre et al (2016); Mazzi et al (2019); Wozabal and Rameseder (2020)). This reduces the computational burden but leads to sub-optimal decisions.…”

Design of the Smart Grid Architecture According to Fractal Principles and the Basics of Corresponding Market Structure

“…It defines three control modules: Local, regional and enterprise, requiring a tremendous amount of data to be exchanged between its modules [16]. Market researches focus on details of how they function, such as the development of different participation models in the balancing market [17] or the provision of secure market products [18], but not on the harmonization of the market structure with the power grid architecture.…”

Optimal Reserve and Energy Scheduling for a Virtual Power Plant Considering Reserve Activation Probability

“…A VPP can access the BE market as a regulator when the balancing price is cleared at least one hour before operation time. Another study [18] proposes a new market bid format that allows a VPP to act as either an active or passive regulator in the BE market. The result shows that this market model allows a VPP to gain higher profits.…”