• Energy Research

Capítulos en libros In this paper we compare two cutting-edge timeperiod aggregation methodologies for power system models that consider both renewables and storage technologies: the chronological time-period clustering; and, the enhanced representative period approach. Such methodologies are used in order to reduce the computational burden of highly complex optimization models while not compromising the quality of the results. With this paper, we identify which method works best, and under which conditions, in order to reproduce the outcomes of the hourly benchmark model. info:eu-repo/semantics/publishedVersion

This paper provides an overview of the Ecogrid EU project, which is a large-scale demonstration project on the Danish island Bornholm. It provides Europe a fast track evolution towards smart grid dissemination and deployment in the distribution network. Objective of Ecogrid EU is to illustrate that modern information and communication technology (ICT) and innovative market solutions can enable the operation of a distribution power system with more than 50% renewable energy sources (RES). This will be a major contribution to the European 20-20-20 goals. Furthermore, the proposed Ecogrid EU market will offer the transmission system operator (TSO) additional balancing resources and ancillary services by facilitating the participation of small-scale distributed energy resources (DERs) and small end-consumers into the existing electricity markets. The majority of the 2000 participating residential customers will be equipped with demand response devices with smart controllers and smart meters, allowing them to respond to real-time prices based on their pre-programmed demand-response preferences. © 2012 IEEE.

In this paper, we introduce four new indices of market power in transmission-constrained electricity markets that are based on economic principles together with faithful representation of the effects of Kirchhoff's laws.

This letter proposes a new merit order for the dispatch of stochastic production in forward markets (e.g., day-ahead markets). The proposed merit order considers not only the marginal cost of the stochastic generating unit, which is often very low or zero, but also the projected cost of balancing its energy deviations during the real-time operation of the power system. We show, through an illustrative example, that the proposed merit order leads to increased market efficiency as the penetration of stochastic generation in the electricity market grows.

Electricity plays an important role in the future energy framework around the world. The foreseen high penetration of renewable energy resources and electric vehicles (EV) will change the way of understanding and operating power systems. Consequently, significant investment in network infrastructure needs to be made in order to cope with this tremendous change in an efficient and effective manner. Longrun incremental cost (LRIC) pricing method is recognized as an economically efficient approach for pricing network charges, which provides forward-looking information for future investment cost. LRIC evaluation is usually conducted on the basis that demand is passive and uncontrollable. The impact of demand flexibility on LRIC has not been comprehensively studied. In this paper, the effect of dynamic electricity tariff and flexible demand on LRIC and network investment decisions is deeply analyzed and discussed. A modified test system (RBTS) illustrates the proposed method.

To reduce the computational burden of capacity expansion models, power system operations are commonly accounted for in these models using representative time periods of the planning horizon such as hours, days, or weeks. However, the validity of these time-period aggregation approaches to determine the capacity expansion plan of future power systems is arguable, as they fail to capture properly the mid-terms dynamics of renewable power generation and to model accurately the operation of electricity storage. In this paper, we propose a new time-period clustering method that overcomes the aforementioned drawbacks by maintaining the chronology of the input time series throughout the whole planning horizon. Thus, the proposed method can correctly assess the economic value of combining renewable power generation with interday storage devices. Numerical results from a test case based on the European electricity network show that our method provides more efficient capacity expansion plans than existing methods while requiring similar computational needs.

This paper analyzes the impact of production forecast errors on the expansion planning of a power system and investigates the influence of market design to facilitate the integration of renewable generation. For this purpose, we propose a stochastic programming modeling framework to determine the expansion plan that minimizes system-wide investment and operating costs, while ensuring a given share of renewable generation in the electricity supply. Unlike existing ones, this framework includes both a day-ahead and a balancing market so as to capture the impact of both production forecasts and the associated prediction errors. Within this framework, we consider two paradigmatic market designs that essentially differ in whether the day-ahead generation schedule and the subsequent balancing re-dispatch are co-optimized or not. The main features and results of the model set-ups are discussed using an illustrative four-node example and a more realistic 24-node case study.

The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and black-box tools that have been left in its wake, it is sometimes difficult to perceive a glimmer of Occam's razor principle. In this letter, we use the unit commitment problem (UCP), an NP-hard mathematical program that is fundamental to power system operations, to show that simplicity must guide any strategy to solve it, in particular those that are based on learning from past UCP instances. To this end, we apply a naive algorithm to produce candidate solutions to the UCP and show, using a variety of realistically sized power systems, that we are able to find optimal or quasi-optimal solutions with remarkable speedups. Our claim is thus that any sophistication of the learning method must be backed up with a statistically significant improvement of the results in this letter.