Transmission Benefits of Co-Locating Concentrating Solar Power and Wind

In some areas of the U.S. transmission constraints are a limiting factor in deploying new wind and concentrating solar power (CSP) plants. Texas is an example of one such location, where the best wind and solar resources are in the western part of the state, while major demand centers are in the east. The low capacity factor of wind is a compounding factor, increasing the relative cost of new transmission per unit of energy actually delivered. A possible method of increasing the utilization of new transmission is to co-locate both wind and concentrating solar power with thermal energy storage. In this work we examine the benefits and limits of using the dispatachability of thermal storage to increase the capacity factor of new transmission developed to access high quality solar and wind resources in remote locations

Capacity Value of Concentrating Solar Power Plants

This study estimates the capacity value of a concentrating solar power (CSP) plant at a variety of locations within the western United States. This is done by optimizing the operation of the CSP plant and by using the effective load carrying capability (ELCC) metric, which is a standard reliability-based capacity value estimation technique. Although the ELCC metric is the most accurate estimation technique, we show that a simpler capacity-factor-based approximation method can closely estimate the ELCC value. Without storage, the capacity value of CSP plants varies widely depending on the year and solar multiple. The average capacity value of plants evaluated ranged from 45%?90% with a solar multiple range of 1.0-1.5. When introducing thermal energy storage (TES), the capacity value of the CSP plant is more difficult to estimate since one must account for energy in storage. We apply a capacity-factor-based technique under two different market settings: an energy-only market and an energy and capacity market. Our results show that adding TES to a CSP plant can increase its capacity value significantly at all of the locations. Adding a single hour of TES significantly increases the capacity value above the no-TES case, and with four hours of storage or more, the average capacity value at all locations exceeds 90%

Comparison of Capacity Value Methods for Photovoltaics in the Western United States

This report compares different capacity value estimation techniques applied to solar photovoltaics (PV). It compares more robust data and computationally intense reliability-based capacity valuation techniques to simpler approximation techniques at 14 different locations in the western United States. The capacity values at these locations are computed while holding the underlying power system characteristics fixed. This allows the effect of differences in solar availability patterns on the capacity value of PV to be directly ascertained, without differences in the power system confounding the results. Finally, it examines the effects of different PV configurations, including varying the orientation of a fixed-axis system and installing single- and double-axis tracking systems, on the capacity value. The capacity value estimations are done over an eight-year running from 1998 to 2005, and both long-term average capacity values and interannual capacity value differences (due to interannual differences in solar resource availability) are estimated. Overall, under the assumptions used in the analysis, we find that some approximation techniques can yield similar results to reliability-based methods such as effective load carrying capability

Multi-agent Electricity Markets and Smart Grids Simulation with Connection to Real Physical Resources

The increasing penetration of distributed energy sources, mainly based on renewable generation, calls for an urgent emergence of novel advanced methods to deal with the associated problems. The consensus behind smart grids (SGs) as one of the most promising solutions for the massive integration of renewable energy sources in power systems has led to the development of several prototypes that aim at testing and validating SG methodologies. The urgent need to accommodate such resources require alternative solutions. This chapter presents a multi-agent based SG simulation platform connected to physical resources, so that realistic scenarios can be simulated. The SG simulator is also connected to the Multi-Agent Simulator of Competitive Electricity Markets, which provides a solid framework for the simulation of electricity markets. The cooperation between the two simulation platforms provides huge studying opportunities under different perspectives, resulting in an important contribution to the fields of transactive energy, electricity markets, and SGs. A case study is presented, showing the potentialities for interaction between players of the two ecosystems: a SG operator, which manages the internal resources of a SG, is able to participate in electricity market negotiations to trade the necessary amounts of power to fulfill the needs of SG consumers.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N. 641794 (project DREAM-GO). It has also received FEDER Funds through the COMPETE program and National Funds through FCT under the project UID/EEA/00760/2013. The authors gratefully acknowledge the valuable contribution of Bruno Canizes, Daniel Paiva, Gabriel Santos and Marco Silva to the work presented in the chapter.info:eu-repo/semantics/publishedVersio

Analysing the Impact of Rationality on the Italian Electricity Market

International audienceWe analyze the behavior of the Italian electricity market with an agent-based model. In particular, we are interested in testing the assumption that the market participants are fully rational in the economical sense. To this aim, we extend a previous model by considering a wider class of cases. After checking that the new model is a correct generalization of the existing model, we compare three optimization methods to implement the agents rationality and we verify that the model exhibits a very good fit to the real data. This leads us to conclude that our model can be used to predict the behavior of this market

Nord Pool Ontology to Enhance Electricity Markets Simulation in MASCEM

This paper proposes the use of ontologies to enable information and knowledge exchange, to test different electricity market models and to allow players from different systems to interact in common market environments. Multi-agent based software is particularly well fitted to analyse dynamic and adaptive systems with complex interactions among its constituents, such as the complex and dynamic electricity markets. The main drivers are the markets’ restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. An ontology to represent the concepts related to the Nord Pool Elspot market is proposed. It is validated through a case study considering the simulation of Elspot market. Results show that heterogeneous agents are able to effectively participate in the simulation by using the proposed ontologies to support their communications with the Nord Pool market operator.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.info:eu-repo/semantics/publishedVersio