IEEE Transactions on Sustainable Energy

Journal: IEEE Transactions on Sustainable Energy

Abbreviation

IEEE Trans. Sustain. Energy

Publisher

IEEE

ISSN

1949-3029
1949-3037

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Publications 1 - 10 of 17

Additional Capacity Value from Synergy of Variable Renewable Energy and Energy Storage
Byers, Conleigh; Botterud, Audun (2020)
IEEE Transactions on Sustainable Energy
On Decisive Storage Parameters for Minimizing Energy Supply Costs in Multicarrier Energy Systems
Adamek, Franziska; Arnold, Michèle; Andersson, Göran (2014)
IEEE Transactions on Sustainable Energy
Energy Storage Sizing Taking into Account Forecast Uncertainties and Receding Horizon Operation
Baker, Kyri; Hug, Gabriela; Li, Xin (2017)
IEEE Transactions on Sustainable Energy
On Power Control of Grid-Forming Converters: Modeling, Controllability, and Full-State Feedback Design
Chen, Meng; Zhou, Dao; Tayyebi Khameneh, Ali; et al. (2024)
IEEE Transactions on Sustainable Energy
The popular single-input single-output control structures and classic design methods (e.g., root locus analysis) for the power control of grid-forming converters have limitations in applying to different line characteristics and providing favorable performance. This paper studies the grid-forming converter power loops from the perspective of multi-input multi-output systems. First, the error dynamics associated with power control loops (error-based state-space model) are derived while taking into account the natural dynamical coupling terms of the power converter models. Thereafter, the controllability Gramian of the grid-forming converter power loops is studied. Last, a full-state feedback control design using only the local measurements is applied. By this way, the eigenvalues of the system can be arbitrarily placed in the timescale of power loops based on predefined time-domain specifications. A step-by-step construction and design procedure of the power control of grid-forming converters is also given. The analysis and proposed method are verified by experimental results and system-level simulation comparisons in Matlab/Simulink.
Coordination of distributed reactive power sources for voltage support of transmission networks
Valverde, Gustavo; Shchetinin, Dmitry; Hug-Glanzmann, Gabriela (2019)
IEEE Transactions on Sustainable Energy
Assessing Maximal Capacity of Grid-Following Converters With Grid Strength Constraints
Yuan, Hui; Xin, Huanhai; Wu, Di; et al. (2022)
IEEE Transactions on Sustainable Energy
The increasing penetration of renewable resources via power-electronic converters is turning the modern power grid into a multi-converter system (MCS). In an MCS, most renewable resources currently use grid-following converters (GFLCs) for grid synchronization. The increasing integration of renewable resources via GFLCs can cause small-signal stability problems, especially under weak grid conditions. One way to prevent the stability issue is limiting the capacity of GFLCs in an MCS. Thus, it is important to assess the maximal capacity of GFLCs in the MCS while considering small signal stability constraints (SSSCs). This assessment is challenging due to 1) the complexity of assessing the small signal stability resulting from the complicated interaction between the power network and a large number of GFLCs, especially for a heterogeneous GFLCs with unknown inner parameters; 2) the difficulty of finding the optimal solution to the relevant nonlinear optimization problem for maximal capacity assessment. To address these challenges, this paper proposes a semi-definite programming (SDP)-based method to assess the maximal capacity of GFLCs with SSSCs. In the proposed method, we first formulate the SSSCs based on the generalized short-circuit ratio (gSCR), which is a grid strength metric that can significantly reduce the complexity of quantifying small signal stability in an MCS. Then, we convert the formulated gSCR-based nonlinear optimization problem into an SDP that can conveniently find the optimal solution to the maximal capacity of GFLCs and their optimal allocations in the MCS. The efficacy of the proposed method is demonstrated on a 39-bus test system and a practical wind power system.
LQR-Based Adaptive Virtual Synchronous Machine for Power Systems With High Inverter Penetration
Markovic, Uros; Chu, Zhongda; Aristidou, Petros; et al. (2019)
IEEE Transactions on Sustainable Energy
Interpretable Probabilistic Forecasting of Imbalances in Renewable-Dominated Electricity Systems
Toubeau, Jean-François; Bottieau, Jérémie; Wang, Yi; et al. (2022)
IEEE Transactions on Sustainable Energy
High penetration of renewable energy such as wind power and photovoltaic (PV) requires large amounts of flexibility to balance their inherent variability. Making an accurate prediction of the future power system imbalance is an efficient approach to reduce these balancing costs. However, the imbalance is affected not only by renewables but also by complex market dynamics and technology constraints, for which the dependence structure is unknown. Therefore, this paper introduces a new architecture of sequence-to-sequence recurrent neural networks to efficiently process time-based information in an interpretable fashion. To that end, the selection of relevant variables is internalized into the model, which provides insights on the relative importance of individual inputs, while bypassing the cumbersome need for data preprocessing. Then, the model is further enriched with an attention mechanism that is tailored to focus on the relevant contextual information, which is useful to better understand the underlying dynamics such as seasonal patterns. Outcomes show that adding modules to generate explainable forecasts makes the model more efficient and robust, thus leading to enhanced performance.
Online Ensemble Approach for Probabilistic Wind Power Forecasting
Von Krannichfeldt, Leandro; Wang, Yi; Zufferey, Thierry; et al. (2021)
IEEE Transactions on Sustainable Energy
Probabilistic wind power forecasting is an important input in the decision-making process in future electric power grids with large penetrations of renewable generation. Traditional probabilistic wind power forecasting models are trained offline and are then used to make predictions online. However, this strategy cannot make full use of the most recent information during the prediction process. In addition, ensemble learning is recognized as an effective approach for further improving forecasting performance by combining multiple forecasting models. This paper studies an online ensemble approach for probabilistic wind power forecasting by taking full advantage of the most recent information and leveraging the strengths of multiple forecasting models. The online ensemble approach is first formulated as an online convex optimization model. On this basis, a quantile passive-aggressive regression model is proposed to solve the online convex optimization model. Case studies and comparisons with other online learning methods are conducted on an open wind power data set from Belgium. Results show that the proposed method outperforms competing methods in terms of pinball loss and Winkler score with high reliability.
A Multi-Time Scale Co-Optimization Method for Sizing of Energy Storage and Fast-Ramping Generation
Kargarian, Amin; Hug, Gabriela; Mohammadi, Javad (2016)
IEEE Transactions on Sustainable Energy

Publications 1 - 10 of 17

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