Application of Association Rule Mining in offshore HVAC transmission topology optimization

Autor: Stephen Hardy, Dirk Van Hertem, Hakan Ergun
Přispěvatelé: Ergun, Hakan, Van Hertem, Dirk
Rok vydání: 2022
Předmět:
Zdroj: Electric Power Systems Research. 211:108358
ISSN: 0378-7796
DOI: 10.1016/j.epsr.2022.108358
Popis: This work develops a hybrid optimization method for determining optimal radial transmission topologies for the connection of offshore wind farms combining Association Rule Mining (ARM) and a greedy algorithm. The method is capable of optimally placing offshore substations and accounts for Capital Expenditures (CAPEX), Corrective Maintenance (CM), losses and Expected Energy Not Transmitted (EENT). The stochastic nature of wind is also considered. First, an inequality based apriori algorithm is applied to a randomly generated population of Offshore Wind Power Plant (OWPP) pairs within a specified search domain. This way, a set of simple constraints is obtained reducing the effective combinatorial search space. A verified optimal greedy algorithm is then applied to efficiently search the reduced search space for the lowest cost radial topology to connect offshore wind. The hybrid approach is shown to introduce minimal error given a sufficient sample population while greatly extending the feasible problem size of the greedy search algorithm. ispartof: PSCC 2022 ispartof: PSCC 2022 location:Oporto Portugal date:27 Jun - 1 Jul 2022 status: accepted
Databáze: OpenAIRE