In future energy systems, peaks in the daily electricity generation and consumption are expected to increase. The “smart grid” concept aims to maintain high levels of efficiency in the energy system by establishing distributed intelligence. Software agents (operating on devices with unknown computational capabilities) can implement dynamic and autonomous decision making about energy usage and g...

In uniform price, sealed-bid, day-ahead electricity auctions, the market price is set at the intersection between aggregate demand and supply functions constructed by a market operator. Each day, just one agent the marginal generator owns the market-clearing plant. Moreover, day-ahead auctions are embedded in multi-segment systems, wherein diverse protocols coexist and change over time. This co...

This paper presents an environmentally constrained economic dispatch algorithm in a hydrothermal system within the framework of a competitive and deregulated electricity market. The optimization problem of one firm in a competitive market is described, whose objective function can be defined as its profit maximization, and we consider that the thermal plants are constrained to technical and env...

A new formulation of the optimization problem implementing European market rules for non-convex day-ahead electricity markets is presented, that avoids the use of complementarity constraints to express market equilibrium conditions, and also avoids the introduction of auxiliary binary variables to linearise these constraints. Instead, we rely on strong duality theory for linear or convex quadra...

This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price variation. The benchmark is the bivariate VaR modeling approach proposed by Ahoniemi et a...

This paper presents a methodology based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) for the preparation of optimal bidding strategies corresponding unit commitment by Generation companies (Gencos) in order to gain maximum profits in a day-ahead electricity market. In a competitive electricity market with limited number of suppliers, Gencos are facing an oligopoly market rather than a ...

This paper proposes a bi-level stochastic optimization problem (a Stackelberg game) to generate optimal bids for a profit maximizing hydropower producer and presents a mathematical approach to solve it. The first level represents the strategically acting hydropower producer also called a Stackelberg leader, while the second level represents the transmission system operator (TSO) also called a S...

Motivated by the increasing integration among electricity markets, in this paper we propose three different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of t...

The integration of renewable generation poses operational and economic challenges for the electricity grid. For the core problem of power balance, the legacy paradigm of tailoring supply to follow random demand may be inappropriate under deep penetration of uncertain and intermittent renewable generation. In this situation, there is an emerging consensus that the alternative approach of control...