A probabilistic method to quantify the capacity value of load transfer

When a primary substation reaches its capacity limit reinforcement is required, usually via additional circuits. Load transfer constitutes an alternative solution to this problem, as it can provide substantial capacity support at little, or even zero, capital expenditure. This paper provides a probabilistic method which quantifies the capacity value of load transfer using the Effective Load Carrying Capability methodology within a Sequential Monte Carlo Simulation framework. Load transfer is mathematically formulated as a mixed-integer second-order cone programming problem, which can be efficiently solved using commercial solvers. The proposed methodology is applied to a realistically sized distribution network considering three different redundancy levels, namely N-1, N-0.75, and N-0.5. The results show a maximum capacity value of 25% and 37% of the base case demand for manual and remote control load transfer, respectively, for the N-0.5 case with 4.21 MWh/year. The results also show that the capacity value of load transfer is significantly higher if the initial level of reliability of the network is lower, indicating that the network operator is prepared to accept a higher level of risk.