Browsing by Author "Olivares, Daniel"

Now showing 1 - 6 of 6
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    A low-complexity decision model for home energy management systems
    (2021) Salgado, Marcelo; Negrete-Pincetic, Matias; Lorca, Alvaro; Olivares, Daniel
    A low-complexity decision model for a Home Energy Management System is proposed to follow demand trajectory sets received from a Demand Side Response aggregator. This model is designed to reduce its computational complexity and being solved by low performance processors using available Single-Board Computers as a proof of concept. To decrease the computational complexity is proposed a two-stage model, where the first stage evaluates the hourly appliance scheduling using a relaxed set of restrictions, and the second stage evaluates a reduced set of appliances in a intra-hourly interval with a detailed characterization of the scheduled appliance properties. Simulations results show the effectiveness of the proposed algorithm to follow trajectories for different sets of home appliances and operational conditions. For the studied cases, the model presents deviations in the demand for the 3.2% of the cases in the first-stage and a 12% for the second-stage model. Results show that the proposed model can schedule available appliances according to the demand aggregator requirements in a limited solving time with diverse hardware.
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    A robust decision-support method based on optimization and simulation for wildfire resilience in highly renewable power systems
    (2021) Tapia, Tomas; Lorca, Alvaro; Olivares, Daniel; Negrete-Pincetic, Matias; Lamadrid L, Alberto J.
    Wildfires can pose a major threat to the secure operation of power networks. Chile, California, and Australia have suffered from recent wildfires that have induced considerable power supply cuts. Further, as power systems move to a significant integration of variable renewable energy sources, successfully managing the impact of wildfires on the power supply can become even more challenging due to the joint uncertainty in wildfire trajectories and the power injections from wind and solar farms. Motivated by this, this paper develops a practical decision-support approach that concatenates a stochastic wildfire simulation method with an attacker-defender model that aims to find a worst-case realization for (i) transmission line and generator contingencies, out of those that can potentially be affected by a given wildfire scenario, and for (ii) wind and solar power trajectories, based on a max-min structure where the inner min problem represents a best adaptive response on generator dispatch actions. Further, this paper proposes an evaluation framework to assess the power supply security of various power system topology configurations, under the assumption of limited transmission switching capabilities, and based on the simulation of several wildfire evolution scenarios. Extensive computational experiments are carried out on two representations of the Chilean power network with up to 278 buses, showing the practical effectiveness of the proposed approach for enhancing wildfire resilience in highly renewable power systems.
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    Optimization-based analysis of decarbonization pathways and flexibility requirements in highly renewable power systems
    (2021) Verastegui, Felipe; Lorca, Alvaro; Olivares, Daniel; Negrete-Pincetic, Matias
    Several countries are adopting plans to reduce the contaminant emissions from the energy sector through renewable energy integration and restrictions on fossil fuel generation. This process poses important computational and methodological challenges on expansion planning modeling due to the operational details needed for a proper analysis. In this context, this paper develops a planning model including an effective representation of the operational aspects of the system to understand the key role of flexible resources under strong decarbonization processes in highly renewable power systems. A case study is developed for the Chilean power system, which is currently undergoing an ambitious coal phaseout process, including the analysis of a scenario that leads to a completely renewable generation mix. The results show that highly renewable generation mixes are feasible, but rely on an effective balance of the key flexibility attributes of the system including ramping, storage, and transmission capacities. Further, such balance allows for faster decarbonization goals to remain in a similar cost range, through the deployment of flexible capacity in earlier stages of the planning horizon. (c) 2021 Elsevier Ltd. All rights reserved.
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    The diverse impacts of COVID-19 on electricity demand: The case of Chile
    (2022) Sanchez-Lopez, Miguel; Moreno, Rodrigo; Alvarado, Diego; Suazo-Martinez, Carlos; Negrete-Pincetic, Matias; Olivares, Daniel; Sepulveda, Carlos; Otarola, Hector; Basso, Leonardo J.
    This paper analyzes the impacts of the first wave of COVID-19 (March 2020 -September 2020) on the electricity demand of different types of consumers in Chile, including residential, commercial, and industrial demand. We leverage data from 230 thousand smart meters of residential and commercial consumers in 32 communes of Santiago (the capital city of Chile), which allows us to investigate the evolution of their demands with an hourly temporal resolution. Additionally, we use demand data of large industrial consumers provided by the Chilean system operator to study the impact of the pandemic on different economic sectors. This paper demonstrates that the COVID-19 pandemic, and the associated containment measures, have featured a drastically different impact on the various types of consumers in Chile. In particular, we show that the demand of residential consumers has increased throughout the first wave, even when we isolate the effects of the pandemic from those related to weather. Furthermore, we study how these effects change in different communes of Santiago, contrasting our findings with the socio-economic levels of the population. In effect, we find different demand response patterns depending on the socio-economic background of consumers. We also show that commercial demand has significantly declined due to the containment measures implemented and that the hospitality and construction economic sectors have been the most affected in the country.
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    The impact of short-term pricing on flexible generation investments in electricity markets
    (2021) Villalobos, Cristian; Negrete-Pincetic, Matias; Figueroa, Nicolas; Lorca, Alvaro; Olivares, Daniel
    The massive growth in the integration of variable renewable energy sources is producing several challenges in the operation of power systems and its associated markets. In this context, flexibility has become a critical attribute to allow the system to react to changes in generation or demand levels. Thus, it is critical for market signals at both short and long term scales to include flexibility features, to align agents' incentives with systemic flexibility requirements. In this paper, different pricing schemes for short-term markets are studied, based on various relaxations of the unit commitment problem, including convex-hull approximations, with the aim of representing operational flexibility requirements in a more explicit way. Extensive simulations illustrate the performance of the proposed schemes, as compared to conventional ones, in terms of the capability of the system to properly incentivize flexibility attributes, resulting in better agents' cost recovery and more variable renewable energy utilization. The results show that short-term pricing schemes considered improve the long-term signals for flexible investments but additional changes to market design are still required. Thus, there is a need to revisit historical practices for pricing rules by incorporating additional flexibility-related attributes into them. Several alternatives are discussed and policy recommendations based on these considerations are provided.
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    The value of aggregators in local electricity markets: A game theory based comparative analysis
    (2021) Rodriguez, Rafael; Negrete-Pincetic, Matias; Figueroa, Nicolas; Lorca, Alvaro; Olivares, Daniel
    Demand aggregators are expected to have a key role in future electricity systems. More specifically, aggregators can facilitate the harnessing of consumers' flexibility. This paper focuses on understanding the value of the aggregator in terms of aggregation of both flexibility and information. We consider the aggregation of flexibility as the ability to exercise a direct control over loads, while the aggregation of information refers to knowledge of the flexibility characteristics of the consumers. Several game theory formulations are used to model the interaction between the energy provider, consumers and the aggregator, each with a different information structure. We develop a potential game to obtain the Nash equilibrium of the non-cooperative game with complete information and we analyze the system dynamics of consumers using the adaptive expectations method in an incomplete information scenario. Several key insights about the value of aggregators are found. In particular, the value of the aggregator is mainly related to the aggregation of information rather than flexibility, and flexibility is valuable only when it can be coordinated. In this sense, prices are not enough to guarantee an effective coordination. (C) 2021 Elsevier Ltd. All rights reserved.