Probabilistic Hosting Capacity Analysis via Bayesian Optimization

Geng, Xinbo; Tong, Lang; Bhattacharya, Anirban; Mallick, Bani K.; Xie, Le

doi:10.48550/arxiv.2011.05193

Cited by 2 publications

(9 citation statements)

References 21 publications

(43 reference statements)

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“…For example, the hosting capacity analysis in [19] takes the linearization route. We adopt a second-order cone programming (SOCP)-based relaxation of the set, similar to that in [8], and consider the alternate to (10d) for each ω in…”

Section: Cvar-sensitive Hca Problemsmentioning

confidence: 99%

“…The statistics of network constraint violation across scenarios impact the choice of the optimal installation profile. While our approach shares many parallels with the BayesOpt framework with probabilistic constraints in [8], there are important differences. To explain the difference, consider O C with probabilistic constraint enforcement, i.e., replace…”

Section: Cvar-sensitive Hca Problemsmentioning

confidence: 99%

“…The lack of convexity in the left-hand-side of (28) makes it challenging to find natural directions to update ψ. The Bayesian optimization framework in [8] tackles this challenge through a combination of two steps. First, the constraint in (28) is included through a penalty in the objective function to construct an unconstrained optimization problem.…”

Section: Cvar-sensitive Hca Problemsmentioning

confidence: 99%

“…Instead, one must account for the statistics of operating conditions, both observed in historical data and those obtained with reasonable projections of load characteristics into the future. Several recent works have indeed proposed to consider said statistics in HCA through stochastic programming; see [8] for a survey.…”

Section: Introductionmentioning

confidence: 99%

“…HCA methods that use scenario-based optimization typically entail the solution of a large number of power flow problems to compute networkrelevant variables such as voltages and power flows in each scenario. Then, the statistics of possible network constraint violations with these voltages/power flows are processed, possibly through a risk measure, e.g., the probability of constraint violations in [8]. See Figure 1 for an illustration of the work-flow of stochastic HCA methods.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Conditional Value at Risk-Sensitive Solar Hosting Capacity Analysis in Distribution Networks

Madavan¹,

Dahlin²,

Bose³

et al. 2022

Preprint

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Solar hosting capacity analysis (HCA) assesses the ability of a distribution network to host distributed solar generation without seriously violating distribution network constraints. In this paper, we consider risk-sensitive HCA that limits the risk of network constraint violations with a collection of scenarios of solar irradiance and nodal power demands, where risk is modeled via the conditional value at risk (CVaR) measure. First, we consider the question of maximizing aggregate installed solar capacities, subject to risk constraints and solve it as a secondorder cone program (SOCP) with a standard conic relaxation of the feasible set with power flow equations. Second, we design an incremental algorithm to decide whether a configuration of solar installations has acceptable risk of constraint violations, modeled via CVaR. The algorithm circumvents explicit risk computation by incrementally constructing inner and outer polyhedral approximations of the set of acceptable solar installation configurations from prior such tests conducted. Our numerical examples study the impact of risk parameters, the number of scenarios and the scalability of our framework.

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Section: Cvar-sensitive Hca Problemsmentioning

confidence: 99%

Section: Cvar-sensitive Hca Problemsmentioning

confidence: 99%

Section: Cvar-sensitive Hca Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Conditional Value at Risk-Sensitive Solar Hosting Capacity Analysis in Distribution Networks

Madavan¹,

Dahlin²,

Bose³

et al. 2022

Preprint

Self Cite

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

Yuan

Weng

2023

IEEE Trans. Smart Grid

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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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Probabilistic Hosting Capacity Analysis via Bayesian Optimization

Cited by 2 publications

References 21 publications

Conditional Value at Risk-Sensitive Solar Hosting Capacity Analysis in Distribution Networks

Conditional Value at Risk-Sensitive Solar Hosting Capacity Analysis in Distribution Networks

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

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