Market Power Assessment and Mitigation in Hydrothermal Systems

The objective of this work is to investigate market power issues in bid- based hydrothermal scheduling. Initially, market power is simulated with a single stage Nash-Cournot equilibrium model. Market power assessment for multiple stages is then carried through a stochastic dynamic programming scheme. The decision in each stage and state is the equilibrium of a multi-agent game. Thereafter, mitigation measures, specially bilateral contracts, are investigated. Case studies with data taken from the Brazilian system are presented and discussed.Game theory, Hydroelectric-thermal power generation, Power generation economics

Generation Scheduling in Power Systems with Hydro Electric Plants

Medium-term generation scheduling is an important component of power systems operation and management. The traditional problem statement aimed at reducing the total production cost can hardly correspond to the market environment. The paper considers specific features of the problem statement for a wholesale electricity market environment. An approach is suggested to solve the problem on the basis of bi-level optimization models. Such models take into account possible distortion of economic and technical parameters of generating units. The proposed technique obtains equilibrium of the generation company's interests to simulate the competitive behavior under the oligopoly electricity market. A mathematical statement of the problem supposes the application of a dynamic programming method. An algorithm for the stochastic dynamic programming application is developed. A numerical example is presented to demonstrate the applicability of the method and algorithm. The efficiency of the proposed approach is shown in comparison with the traditional generation scheduling technique

Power market models for the clean energy transition: State of the art and future research needs

As power systems around the world are rapidly evolving to achieve decarbonization objectives, it is crucial that power system planners and operators use appropriate models and tools to analyze and address the associated challenges. This paper provides a detailed overview of the properties of power market models in the context of the clean energy transition. We review common power market model methodologies, their readiness for low- and zero‑carbon grids, and new power market trends. Based on the review, we suggest model improvements and new designs to increase modeling capabilities for future grids. The paper highlights key modeling concepts related to power system flexibility, with a particular focus on hydropower and energy storage, as well as the representation of grid services, price formation, temporal structure, and the importance of uncertainty. We find that a changing resource mix, market restructuring, and growing price uncertainty require more precise modeling techniques to adequately capture the new technology constraints and the dynamics of future power markets. In particular, models must adequately represent resource opportunity costs, multi-horizon flexibility, and energy storage capabilities across the full range of grid services. Moreover, at the system level, it is increasingly important to consider sub-hourly time resolution, enhanced uncertainty representation, and introduce co-optimization for dual market clearing of energy and grid services. Likewise, models should capture interdependencies between multiple energy carriers and demand sectors.publishedVersio

Combined Operational Planning of Natural Gas and Electric Power Systems: State of the Art

The growing installation and utilization of natural gas fired power plants (NGFPPs) over the last two decades has lead to increasing interactions between electricity and natural gas (NG) sectors. From 1990 to 2005, the worldwide share of NGFPPs in the power generation mix has almost doubled, from around 10% to nearly 19%; reaching in 2007, for instance, the 54% in Argentina, the 42% in Italy, the 40% in USA, and the 32% in UK (IEA, 2007; IEA, 2009a). The installation of NGFPPs has been driven by technical, economic and environmental reasons. The high thermal efficiency of combined-cycle gas turbine (CCGT) power plants and combined heat and power (CHP) units, their relatively low investment costs, short construction lead time and the prevailing low natural gas prices until 2004 have made NGFPPs more attractive than traditional coal, oil and nuclear power plants, particularly in liberalized electricity markets. Additionally, burning NG has a smaller environmental footprint and a lower carbon emission than any other fossil fuel. Under the light of all conditions previously described, there is a strong and rising interdependency between NG and electricity sectors. In this context, it is essential to include NG system models in electric power systems operation and planning. On the other hand, NG system operation and planning require, as input data, the NG demands of each NGFPPs, which accurately values can only be obtained from the electric power systems dispatch. Therefore, several approaches that address the integrated modeling of electric power and NG systems have been presented. These new approaches contrast with the current models in which both systems are considered in a decoupled manner.Fil: Rubio Barros, Ricardo German. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Ojeda Esteybar, Diego Mauricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Año, Osvaldo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Vargas, Alberto. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; Argentin

Measuring the impact of market coupling on the Italian electricity market using ELFO++

This paper evaluates the impact on the Italian electricity market of replacing the current explicit auction mechanism with market coupling. Maximizing the use of the cross-border interconnection capacity, market coupling increases the level of market integration and facilitates the access to low-cost generation by consumers located in high-cost generation countries. Thus, it is expected that a high-priced area such as Italy could greatly benefit from the introduction of this mechanism. In this paper, the welfare benefits are estimated under alternative market scenarios for 2012, employing the optimal dispatch model ELFO++. The results of the simulations suggest that the improvement in social surplus is likely to be significant, especially when market fundamentals are tight.Market coupling; market integration; Italian day-ahead electricity market.

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions