Cordova, S., Canizares, C., Lorca, A., & Olivares, D. E. (2021). An Energy Management System With Short-Term Fluctuation Reserves and Battery Degradation for Isolated Microgrids. IEEE Trans. Smart Grid, 12(6), 4668–4680.
Abstract: Due to the low-inertia and significant renewable generation variability in isolated microgrids, short time-scale fluctuations in the order of seconds can have a large impact on a microgrid's frequency regulation performance. In this context, the present paper presents a mathematical model for an Energy Management System (EMS) that takes into account the operational impact of the short-term fluctuations stemming from renewable generation rapid changes, and the role that renewable curtailment and batteries, including their degradation, can play to counter-balance these variations. Computational experiments on the real Kasabonika Lake First Nation microgrid and CIGRE benchmark test system show the operational benefits of the proposed EMS, highlighting the need to properly model short-term fluctuations and battery degradation in EMS for isolated microgrids with significant renewable integration.
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Cordova, S., Canizares, C. A., Lorca, A., & Olivares, D. E. (2022). Frequency-Constrained Energy Management System for Isolated Microgrids. IEEE Trans. Smart Grid, 13(5), 3394–3407.
Abstract: Second-to-second power imbalances stemming from renewable generation can have a large impact on the frequency regulation performance of isolated microgrids, as these are characterized by low inertia and, more commonly nowadays, significant renewable energy penetration. Thus, the present paper develops a novel frequency-constrained Energy Management System (EMS) that takes into account the impact of short-term power fluctuations on the microgrid's operation and frequency regulation performance. The proposed EMS model is based on accurate linear equations describing frequency deviation, rate-of-change-of-frequency, and regulation provision in daily microgrid operations. Dynamic simulations on a realistic CIGRE benchmark test system show the economic and reliability benefits of the presented EMS model, highlighting the need of incorporating fast power fluctuations and their impact on frequency dynamics in EMSs for sustainable isolated microgrids.
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Cordova, S., Canizares, C. A., Lorca, A., & Olivares, D. E. (2023). Aggregate Modeling of Thermostatically Controlled Loads for Microgrid Energy Management Systems. IEEE Trans. Smart Grid, 14(6), 4169–4181.
Abstract: Second-to-second renewable power fluctuations can severely hinder the frequency regulation performance of modern isolated microgrids, as these typically have a low inertia and significant renewable energy integration. In this context, the present paper studies the coordinated control of Thermostatically Controlled Loads (TCLs) for managing short-term power imbalances, and their integration in microgrid operations through the use of aggregate TCL models. In particular, two computationally efficient and accurate aggregate TCL models are developed: a virtual battery model representing the aggregate flexibility of TCLs considering solar irradiance heat gains and wall/floor heat transfers, and a frequency transient model representing the aggregate dynamics of a TCL collection considering communication delays and the presence of model uncertainty and time-variability. The proposed aggregate TCL models are then used to design a practical Energy Management System (EMS) integrating TCL flexibility, and study the impact of TCL integration on microgrid operation and frequency control. Computational experiments using detailed frequency transient and thermal dynamic models are presented, demonstrating the accuracy of the proposed aggregate TCL models, as well as the economic and reliability benefits resulting from using these aggregate models to integrate TCLs in microgrid operations.
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Salgado, M., Negrete-Pincetic, M., Lorca, A., & Olivares, D. (2021). A Low-complexity Home Energy Management System for Electricity Demand Side Aggregators. Appl. Energy, 2021(294), 116985.
Abstract: 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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Salgado, M., Negrete-Pincetic, M., Lorca, A., & Olivares, D. (2021). A low-complexity decision model for home energy management systems. Appl. Energy, 294, 116985.
Abstract: 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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