• 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

Artículos en revistas This paper proposes a model to carry out analysis of storage facilities operation including a transmission network. The model represents short-term storage operation in an approxi-mated way that reduces computational requirements, which makes it suitable for medium and long-term operational planning in power systems with a high level of renewable energy penetra-tion. In the proposed model, we cluster hourly data using the so-called system-states framework developed in previous work. Within this framework, non-consecutive similar time periods are grouped, while chronological information is represented by a tran-sition matrix among states. We extend the system-state framework from a single-bus system to a transmission network. We define and analyze two alternative sets of representative variables for clustering hours to obtain system states when the transmission network is considered. This extension of the system states framework allows us to evaluate the impact of transmission congestions in medium- and long-term planning models in a rea-sonable computation time. A case study shows that the proposed model is 235 times faster than an hourly approach, used as benchmark, whereas the overall system cost is approximated with less than 2% error. The overall charging/discharging trends are similar enough to those of the hourly model, being hydro storage better approximated than fast-ramping batteries. Besides, for the analyzed case study, it is shown how congestion in the transmission network in fact improves the accuracy of the proposed approach. info:eu-repo/semantics/publishedVersion

Artículos en revistas This paper introduces a bilevel model that represents the behavior of a firm which, faced with the need to build new plants, has to choose between a number of alternative technologies. The main contribution of the model is that the market representation (lower level) is made using a conjectured-price-variation approach. This formulation is able to represent different degrees of oligopoly in the market, which is a more realistic hypothesis than the perfect competition or Cournot oligopoly considerations. In the objective function of the upper level, the firm s net present value is maximised, considering that the company is in an incumbent position and knows the new capacity that will be built by the others. A study case is analyzed to show the outputs corresponding to four different market behaviors: perfect competition, two intermediate oligopolistic market situations represented by different values for the conjectured variations, and Cournot oligopoly as an extreme oligopolistic situation. The results show that if a company is taken to be the only investing company at the investment level, the higher the degree of oligopoly in the market, the greater the investment and the greater the use of peak technologies. info:eu-repo/semantics/publishedVersion

Artículos en revistas This paper proposes a bilevel model to assist a generation company in making its long-term generation capacity investment decisions considering uncertainty regarding the investments of the other generation companies. The bilevel formulation allows for the uncoupling of investment and generation decisions, as investment decisions of the single investing generation company are taken in the upper level with the objective to maximize expected profits, and generation decisions by all companies are considered in the lower level. The lower level represents the oligopolistic market equilibrium via a conjecturedprice response formulation, which can capture various degrees of strategic market behavior like perfect competition, the Cournot oligopoly and intermediate cases. The bilevel model is formulated as an MPEC, replacing the lower level by its KKT conditions, and transformed into a MILP. Results from a study case are presented and discussed. info:eu-repo/semantics/publishedVersion

This editorial summarizes the papers selected for publication in the Special Issue on Climate Change Mitigation and Adaptation in Power and Energy Systems (CMAP). After a rigorous review of 86 submitted manuscripts, 23 papers were accepted for publication. These accepted papers cover various aspects of climate change mitigation and adaptation and are classified as follows: boosting renewable energy efficiency (two papers), climate resilience strategies (four papers), decarbonization strategies (four papers), renewable energy integration (five papers), policies, incentives, and science communications (four papers), and the role of energy markets (four papers). The Guest Editorial Board is optimistic that this Special Issue will serve as a rich resource, offering invaluable insights to propel future research and advancements in climate change mitigation and adaptation.

Artículos en revistas Due to computational constraints, power system planning models are typically unable to incorporate full annual temporal resolution. In order to represent the increased variability induced by large amounts of renewables, two methods are investigated to reduce the time dimension: the integral approach (using typical hours based on demand and renewable output) and the representative days method (using typical days to capture annual variability). {These two approaches are tested with a benchmark implementation that incorporates full time representation in order identify their suitability for assessing power systems with high renewable penetration. The integral method predicts renewable capacities within a 10% error margin, this paper's main performance metric, using just 32 time steps, while the representative days approach needs 160-200 time steps before providing similarly accurate renewable capacity estimates. Since the integral method generally cannot handle variation management, such as trade and storage, without enhancing the state-space representation, it may be more applicable to one-node models, while the representative days method is suitable for multi-regional models. In order to assess power systems with increasing renewable policy targets, models should be designed to handle at least the 160 time steps needed to provide results that do not systematically overestimate the renewable capacity share. info:eu-repo/semantics/publishedVersion

Artículos en revistas In this paper we propose a DC OPF framework for storage portfolio optimization in transmission constrained power networks. In particular, this model is designed to investigate two problems: (i) optimizing storage operation and allocation over a network given a fixed technology portfolio and (ii) optimizing the storage portfolio (i.e., the size, technology and network allocation of these resources). We demonstrate this framework using case studies based on the IEEE 14 bus test system with four different storage technologies. Our results show that although certain technologies are generally classified as being suitable for either power or energy services, many technologies can add value to the system by performing both fast-time scale regulation (power) and load-shifting (energy) services. These results suggest that limiting the type of service that a certain technology is compensated for may result in inefficiencies at the system level and undervaluation of storage. info:eu-repo/semantics/publishedVersion

One of the fundamental problems of using optimization models that use different time series as data input, is the trade-off between model accuracy and computational tractability. To overcome the computational intractability of these full optimization models, the dimension of input data and model size is commonly reduced through time series aggregation (TSA) methods. However, traditional TSA methods often apply a one-size-fits-all approach based on the common belief that the clusters that best approximate the input data also lead to the aggregated model that best approximates the full model, while the metric that really matters - the resulting output error in optimization results - is not well addressed. In this paper, we plan to challenge this belief and show that output-error based TSA methods with theoretical underpinnings have unprecedented potential of computational efficiency and accuracy. time series aggregation, clustering, optimization, modeling