Energy Management Systems for Multi-Microgrid Networks Under Uncertainties
Bibliographic record
Abstract
Environmental concerns have motivated a gradual transformation of power systems in recent years, mainly focused on replacing fossil fuel-based energy sources with Renewable Energy Sources (RESs) such as solar and wind energy. However, due to their variable nature, the large-scale integration of RESs poses several technical challenges for the safe and efficient operation of evolving power systems. The adoption of microgrids (MGs) has increased as a viable option to effectively integrate RESs into existing grids and reduce the dependency on conventional, centralized power stations, as well as enhancing the electrical supply resiliency. Furthermore, MGs can provide sustainable energy to remote areas in which a connection to the main power grid is not possible. In this context, the Energy Management System (EMS) of the MG, which is responsible for determining its optimal operation, is an important part of MG control. However, the variability of electricity demand and RESs within an MG complicates the adequate dispatch of the MG resources to maintain supply-demand balance. Hence, uncertainties inherent to an MG must be taken into account, which is one of the main topics of this thesis. \n \nThe coordinated operation of multiple MGs as a multi-microgrid (MMG) system has recently attracted attention due to the potential benefits that originate from a coordinated operation, as opposed to the individual and independent operation of each MG. The collective operation enables the possibility of power exchanges among MGs and the main grid, which can mitigate the unpredictability of RESs, as well as reduce the operational costs by taking advantage of the heterogeneity of load and generation profiles in each MG. Furthermore, differences in generation costs and grid buying/selling prices can incentivize power exchanges and ensure the maximum utilization of RESs. Therefore, it is important to design EMSs that adequately consider the collective operation of a set of MGs while taking uncertainties into account, which is the primary focus of this thesis. \n \nIn the first part of this thesis, a centralized MMG EMS model is proposed, which is formulated as a cost minimization problem that considers the operation of all MGs and their interactions among each other and the main grid as a single system. The model includes detailed operational constraints of thermal generation units and Energy Storage Systems (ESSs), as well as power capacity limits at the Point of Common Coupling (PCC) of each MG. A decomposition procedure based on Lagrangian relaxation is then applied, with the goal of separating the complete problem into subproblems corresponding to each MG, which can be solved independently with minimal information exchange through a subgradient-based distributed optimization algorithm. Demand and solar irradiance data from a realistic Active Distribution Network (ADN) in São Paulo, Brazil, are then used to design a system to test and validate the proposed models. The simulation results show that the distributed algorithm converges to the optimal or a near-optimal solution of the centralized model, making the proposed approach a viable alternative for the implementation of a distributed MMG EMS. Furthermore, the advantages of an MMG system are demonstrated by showing that the operational costs of the system are significantly reduced when MGs are able to exchange power among each other and with the main grid, compared to their costs in individual operation. \n \nIn the second part of this thesis, the proposed centralized MMG EMS model is reformulated using an Affine Arithmetic (AA) optimization framework to consider uncertainties associated with electricity demand and renewable generation. First, the uncertainties are characterized by their affine forms, which are then used to redefine the variables, objective function, and constraints of the original model in the AA domain. Then, the linearization procedure of the absolute values introduced by the AA operators is explained in detail. The proposed AA model is validated through comparisons with the deterministic and Monte Carlo Simulation (MCS) solutions. The test system used in the aforementioned MMG distributed dispatch approach is utilized to show that the AA model is robust under a range of possible realizations of the uncertain parameters, and can be solved with lower computational burden and in shorter execution times with respect to an MCS approach, while considering the same range of uncertainties, which is one the main advantages of the proposed AA model. Furthermore, it is demonstrated that the affine forms of the solution variables can be used to find a dispatch for different realizations of demand and renewable generation, with no need to repeatedly solve the optimization problem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".