Browsing by Author "Negrete-Pincetic, Matias"

Now showing 1 - 12 of 12
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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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    Coal Phase-Out and Carbon Tax Analysis with Long-Term Planning Models: A Case Study for the Chilean Electric Power System
    (2024) Castillo, Patricio; Aguad, Matias; Lorca, Alvaro; Cordova, Samuel; Negrete-Pincetic, Matias
    Large CO2 emissions constitute a significant problem today due to their effect on climate change, and the need to design appropriate energy policies to mitigate their consequences and reduce emissions requires a detailed analysis of one of the main sources of such emissions: the electricity system. Thus, this paper presents a study on the effects of energy policies on decarbonization by comparing the detailed phase-out of coal-fired power plants across a range of cases with the implementation of a carbon tax to meet Nationally Determined Contributions (NDCs). The case study focuses on the Chilean electricity system, using a long-term generation and transmission expansion planning model (GTEP) that incorporates a wide range of generation technologies. The study examines the long-term effects of these policies, including costs, investments, and CO2 emissions, as well as their impact on consumer prices reflected in the marginal costs of the system. The transmission system modeling covers various regions of Chile and significant projections for renewable energy sources. It evaluates three economic scenarios based on generation technology costs, fuel prices, and electricity demand under four different closure schemes and fourteen different carbon tax levels. The results indicate that implementing a carbon tax can be more cost-effective for the system than the implementation of a phase-out schedule for coal plants, taking the form of reduced CO2 emission and overall system costs, with an optimal carbon tax value of 37 USD/tCO(2). Additionally, the study reveals significant effects on consumer prices, showing that a carbon tax as an energy policy leads to lower prices compared to a phase-out scheme.
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    Data-driven optimization for seismic-resilient power network planning *
    (2024) Oneto, Alfredo; Lorca, Alvaro; Ferrario, Elisa; Poulos, Alan; De La Llera, Juan Carlos; Negrete-Pincetic, Matias
    Many regions of the planet are exposed to seismic hazards that can cause devastating consequences on power systems. Due to these systems' crucial role, the evaluation and planning for their safe and reliable operation are paramount. This paper develops a novel data -driven optimization framework to assess the power network's seismic resilience and plan cost-effective investments for its enhancement. Under a robust optimization scheme, an earthquake attacker-defender model finds the worst -case realization of random earthquake network contingencies within an uncertainty set defined with a large number of scenarios generated by state-of-the-art engineering methods. Moreover, data -driven stochastic -robust optimization is employed in a two -stage seismic -resilient power network planning model, leveraging multiple seismic sources' distributional information. Transmission line expansions and siting and sizing of battery energy storage systems are decided in the first stage, while the second stage decides operational variables. Experiments on a 281 -node Chilean power system provide insights for seismic -resilient planning and demonstrate the efficiency of the proposed approach.
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    Demand-side energy flexibility estimation for day-ahead models
    (2023) Salgado-Bravo, Marcelo; Negrete-Pincetic, Matias; Kiprakis, Aristides
    A flexibility estimation model is proposed to evaluate the immediate aggregate flexibility response in a day -ahead scheme. The proposed model schedules a set of appliances , calculates the aggregated flexibility according to the energy and flexibility prices. The temporal granularity of the problem can be adjusted to evaluate the flexibility for the next 15 min or 4 h, using an alternative flexibility scenario approach to evaluate immediate flexibility available. The alternative flexibility scenario is a limited number of time step used to evaluate whether the estimated flexibility is available for a defined time window in each time step. New flexibility constraints were introduced to evaluate the flexibility and the rebound effect for air conditioners, pool pumps , electric water heater, according to the alternative flexibility scenario. The model was tested under different energy price schemes and flexibility requirement durations to observe how the price signals influenced the flexibility offered. The results show how a model that considers extended flexibility, even if it does not consider the total duration requirement, can offer a more accurate flexibility response than a flexibility estimation without an extended flexibility response.
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    Flexible load management using flexibility bands
    (2022) Saavedra, Aldo; Negrete-Pincetic, Matias; Rodriguez, Rafael; Salgado, Marcelo; Lorca, Alvaro
    The large integration of variable renewable energy sources brings new challenges to system operations due to their volatile nature. In this context, demand response programs appear as an important alternative to match the instantaneous supply and demand of energy by changing the electric use of end-users. The traditional load control schemes of demand response are the direct and indirect control. While the direct schemes assure coordination and fail in scalability, the indirect schemes assure scalability, but they could fail in coordination. To face some of the challenges, this paper defines a framework and mathematical formulation for the interactions between a demand aggregator and its end-users, based on flexibility bands. It allows the aggregator to communicate and control a higher number of end-users at the same time that the end-users can make local decisions regarding their consumption. The flexibility band is a control signal, composed of an upper and a lower consumption bound, so that end consumers can consume electricity in the way they want as long as they stay within these bounds. The band features can be linked to contractual arrangements allowing the creation of new products for electricity markets. The simulations consider an aggregator interacting with 10000 end-users. The end-users present different types of flexible loads, such as thermal, deferrable, and non interruptible loads. The results indicate that this scheme is capable of achieving scalability and coordination, and it has the potential to provide new services such as adjusting the flexibility bands after the first response from end-users.
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    Multistage adaptive robust optimization for the hydrothermal scheduling problem
    (PERGAMON-ELSEVIER SCIENCE LTD, 2023) Favereau, Marcel; Lorca, Alvaro; Negrete-Pincetic, Matias
    The current water scarcity faced by many countries increases the need to consider an appropriate representation of future hydro inflows in power system operation and planning models. Hydrothermal scheduling is the problem that seeks to use the water stored in reservoirs throughout time in order to find an optimal dispatch policy between hydro and thermal power plants. Due to both the inherent randomness of water inflows and the intertemporal decision process, this problem has been typically approached through multistage stochastic optimization, minimizing the total expected operational cost over the entire planning horizon. However, this approach has some practical disadvantages. Among the main ones we highlight (i) the complexity of balancing the statistical representativeness of the stochastic processes and the computational efficiency of the optimization model; (ii) the need to employ computationally intensive decomposition methods for its solvability; and (iii) the need to carry out network simplifications to tackle tractability issues arising in large networks. As an alternative, we propose a multistage adaptive robust optimization model for the hydrothermal scheduling problem. Robust optimization is useful to prevent the previous disadvantages because it does not make any distributional assumption and it works with the so-called uncertainty sets instead of carrying out sampling processes. In particular, we propose an efficient formulation based on linear decision rules and vector autoregressive models to represent the uncertainty in hydro inflows. Our experiments, based on the Chilean electric power system with hundreds of hydro nodes and connections, show the proposed model's efficiency for large-scale systems and provide insights into the adequate balance between cost-effectiveness and reliability that robust optimization models guarantee.
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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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    Robust streamflow forecasting: a Student's t-mixture vector autoregressive model
    (SPRINGER, 2022) Favereau, Marcel; Lorca, Alvaro; Negrete-Pincetic, Matias; Vicuña, Sebastian
    Accurate streamflow forecasting is one of the main challenges in the management of reservoirs, where autoregressive models have been commonly used. Typically, the noise of these models is considered Gaussian. However, this assumption can overestimate the presence of outliers, generally presented in water inflow real-world data. Motivated by this, we propose a novel streamflow forecasting method by modeling the noise of a vector autoregressive model as a multivariate Student's t-mixture based on the use of the variational expectation-maximization algorithm. The proposed model is able to capture the trend, seasonality, and spatio-temporal correlations of hydro inflows, along with both asymmetry and multimodal features of the vector autoregressive process' residuals. Based on 12 of the main inflows of the Chilean hydroelectric network, our experiments show the proposed model's efficiency and improvements for forecasting medium to long-term inflows over a classical vector autoregressive model. Results show that the expected forecasts are improved with the proposed model and the predictive distributions present tighter intervals based on standard and state-of-the-art metrics.
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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.