Investment Optimization to Improve Power Distribution System Reliability Metrics

Pierre, Brian J.; Arguello, Bryan

doi:10.1109/pesgm.2018.8586489

Cited by 5 publications

(7 citation statements)

References 8 publications

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“…A MILP for optimal distribution system routing and siting/sizing of distribution substations is presented in [24]- [25]. In [26] a MILP is formulated to select power distribution system small-scale investments that improve reliability. The DP algorithm proposed in this paper efficiently solves that MILP model to improve scalability.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…The DP technique in this paper leverages the fact that the MILP investment model presented in [26] has a nonlinear objective and only a knapsack constraint. The classic 0-1 knapsack DP algorithm [27]- [28] is generalized so that it can exactly solve the investment model.…”

Section: Contributions To Grid Reliability Researchmentioning

confidence: 99%

“…Constraints 3, 5, and (6) are nonlinear. However they are easily linearized through classic techniques as described in [26].…”

Section: A Nonlinear Knapsack Problemmentioning

confidence: 99%

“…Without loss of generality, the MILP model can further be developed as a deterministic model [26]. For computational experiments shown in section IV, the model is coded in the Python-based mathematical programming language Pyomo [29]- [30] and solved using the CPLEX solver.…”

Section: A Nonlinear Knapsack Problemmentioning

confidence: 99%

“…These models are then employed to generate synthetic future outage scenarios where each scenario represents a possible year of outages. The synthetic scenarios are based on the probability density functions of the actual historical outage data [26]. For example, in Fig.…”

Section: B Data Considerations and Scenario Generationmentioning

confidence: 99%

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Dynamic Programming Method to Optimally Select Power Distribution System Reliability Upgrades

Raja

Arguello

Pierre

2021

IEEE Open J. Power Energy

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This paper presents a novel dynamic programming (DP) technique for the determination of optimal investment decisions to improve power distribution system reliability metrics. This model is designed to select the optimal small-scale investments to protect an electrical distribution system from disruptions. The objective is to minimize distribution system reliability metrics: System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI). The primary input to this optimization model is years of recent utility historical outage data. The DP optimization technique is compared and validated against an equivalent mixed integer linear program (MILP). Through testing on synthetic and real datasets, both approaches are verified to yield equally optimal solutions. Efficiency profiles of each approach indicate that the DP algorithm is more efficient when considering wide budget ranges or a larger outage history, while the MILP model more efficiently handles larger distribution systems. The model is tested with utility data from a distribution system operator in the U.S. Results demonstrate a significant improvement in SAIDI and SAIFI metrics with the optimal small-scale investments.

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Section: B Literature Reviewmentioning

confidence: 99%

Section: Contributions To Grid Reliability Researchmentioning

confidence: 99%

“…Constraints 3, 5, and (6) are nonlinear. However they are easily linearized through classic techniques as described in [26].…”

Section: A Nonlinear Knapsack Problemmentioning

confidence: 99%

Section: A Nonlinear Knapsack Problemmentioning

confidence: 99%

Section: B Data Considerations and Scenario Generationmentioning

confidence: 99%