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1. Mitigation of limitation imposed on hosting capacity in low voltage networks by their distribution transformer loading and degradation considerations

   https://doi.org/10.1049/esi2.12143  

   Ame‐Oko, Agaba; Lavrova, Olga (March 2024, IET Energy Systems Integration)

   Abstract A distribution transformer's thermal operating conditions can impose a limitation on the Hosting Capacity (HC) of an electrical distribution feeder for PV interconnections in the feeder's low‐voltage network. This is undesirable as it curtails PV interconnection of both residential and commercial customers in the secondary networks at a time when there are record numbers of interconnection requests by utilities' customers. The authors analyse the limitations on HC due to transformer loading and degradation considerations. Then, the paper proposes a battery energy storage system (BESS) dispatch strategy that will mitigate the limitation on distribution feeder HC by distribution transformers. Three scenarios of HC were simulated for a test network—HC evaluation without restrictions by the distribution transformer (scenario 1), HC evaluation with restrictions by the distribution transformer (scenario 2), and HC evaluation without restriction by the distribution transformer, and with the implementation of the proposed BESS mitigation strategy (scenario 3). Simulation results show that transformer lifetime is depleted to about 6% of expected lifetime for unrestricted HC in scenario 1. Curtailing the HC by 32% in scenario 2 improves the lifetime to 149% of expected lifetime. Implementing the proposed BESS in scenario 3 improves the transformer lifetime to 127% and increases the HC by 62% above the curtailed value in scenario 2, and by 10% above the original HC in scenario 1. The BESS strategy implementation produced cost savings of 49% and 27% of the transformer cost in scenarios 2 and 3, respectively, due to deferred transformer replacement. Conversely, there is a 1600% replacement cost incurred in scenario 1, which underscores the need for a mitigation strategy. The proposed BESS strategy does not only improve the HC of a distribution feeder but also increases a distribution transformer's lifetime leading to replacement cost savings. 

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2. Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids

   https://doi.org/10.1109/TSG.2022.3196943  

   Wu, Jiaqi; Yuan, Jingyi; Weng, Yang; Ayyanar, Raja (January 2023, IEEE Transactions on Smart Grid)

   The widespread use of distributed energy sources (DERs) raises significant challenges for power system design, planning, and operation, leading to wide adaptation of tools on hosting capacity analysis (HCA). Traditional HCA methods conduct extensive power flow analysis. Due to the computation burden, these time-consuming methods fail to provide online hosting capacity (HC) in large distribution systems. To solve the problem, we first propose a deep learning-based problem formulation for HCA, which conducts offline training and determines HC in real time. The used learning model, long short-term memory (LSTM), implements historical time-series data to capture periodical patterns in distribution systems. However, directly applying LSTMs suffers from low accuracy due to the lack of consideration on spatial information, where location information like feeder topology is critical in nodal HCA. Therefore, we modify the forget gate function to dual forget gates, to capture the spatial correlation within the grid. Such a design turns the LSTM into the Spatial-Temporal LSTM (ST-LSTM). Moreover, as voltage violations are the most vital constraints in HCA, we design a voltage sensitivity gate to increase accuracy further. The results of LSTMs and ST-LSTMs on feeders, such as IEEE 34-, 123-bus feeders, and utility feeders, validate our designs. 

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3. Reactive Power Optimization for Flat Voltage Profiles in Distribution Networks

   https://doi.org/10.1109/NAPS46351.2019.9000354  

   Svenda, Vanja G.; Vu, Mai H.; Stankovic, Alex M. (October 2019, 2019 North American Power symposium)

   This paper considers achieving flat voltage profiles in a distribution network based on reactive power optimization (RPO) through voltage regulation devices (VRD). These devices include capacitor banks, load-tap-changing and regulating transformers, whose statuses can only assume pre-determined integer value levels, making this a non-convex problem. Two RPO-based algorithms are proposed, which can be applied to any initial states, node priority, topology and load model types. The first algorithm focuses on finding a practical solution by ensuring the VRD constraints are observed at each step. The second one focuses on finding the globally optimal solution by applying a convex relaxation technique and solving the resulting problem with the barrier interior point method. Here, the gradients are computed numerically, thus requiring no analytical functions of voltages in terms of VRDs. Numerical results and their analysis are examined on two test networks: 1) single feeder; and 2) network with laterals. 

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4. Market mechanism to enable grid-aware dispatch of Aggregators in radial distribution networks

   Nazir, Nawaf; Almassalkhi, Mads (July 2022, 11TH BULK POWER SYSTEMS DYNAMICS AND CONTROL SYMPOSIUM)

   This paper presents a market-based optimization framework wherein Aggregators can compete for nodal capacity across a distribution feeder and guarantee that allocated flexible capacity cannot cause overloads or congestion. This mechanism, thus, allows Aggregators with allocated capacity to pursue a number of services at the whole-sale market level to maximize revenue of flexible resources. Based on Aggregator bids of capacity (MW) and network access price ($/MW), the distribution system operator (DSO) formulates an optimization problem that prioritizes capacity to the different Aggregators across the network while implicitly considering AC network constraints. This grid-aware allocation is obtained by incorporating a con- vex inner approximation into the optimization framework that prioritizes hosting capacity to different Aggregators. We adapt concepts from transmission-level capacity market clearing, utility demand charges, and Internet-like bandwidth allocation rules to distribution system operations by incorporating nodal voltage and transformer constraints into the optimization framework. Simulation based results on IEEE distribution networks showcase the effectiveness of the approach. 

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5. Towards Optimal Kron-based Reduction Of Networks (Opti-KRON) for the Electric Power Grid

   https://doi.org/10.1109/CDC51059.2022.9992730  

   Chevalier, Samuel; Almassalkhi, Mads R. (December 2022, 2022 IEEE 61st Conference on Decision and Control)

   For fast timescales or long prediction horizons, the AC optimal power flow (OPF) problem becomes a computational challenge for large-scale, realistic AC networks. To overcome this challenge, this paper presents a novel network reduction methodology that leverages an efficient mixed-integer linear programming (MILP) formulation of a Kron-based reduction that is optimal in the sense that it balances the degree of the reduction with resulting modeling errors in the reduced network. The method takes as inputs the full AC network and a pre-computed library of AC load flow data and uses the graph Laplacian to constraint nodal reductions to only be feasible for neighbors of non-reduced nodes. This results in a highly effective MILP formulation which is embedded within an iterative scheme to successively improve the Kron-based network reduction until convergence. The resulting optimal network reduction is, thus, grounded in the physics of the full network. The accuracy of the network reduction methodology is then explored for a 100+ node medium-voltage radial distribution feeder example across a wide range of operating conditions. It is finally shown that a network reduction of 25-85% can be achieved within seconds and with worst-case voltage magnitude deviation errors within any super node cluster of less than 0.01pu. These results illustrate that the proposed optimization-based approach to Kron reduction of networks is viable for larger networks and suitable for use within various power system applications. 

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