IX. CONCLUSIONIn order to reflect the cost of network security in networkcharging, this paper, for the first time, incorporates nodalunreliability tolerance and the supply component’s reliabilityinto a distribution network charging model. It works byreflecting how a component’s failure rate and mean time torepair would affect its ability to deliver energy to customers,and how customers’ tolerance to supply interruption undercontingencies will impact the component’s time to reinforce.Based on the analysis in the paper, the following observationscan be obtained: The proposed model overcomes two disadvantages of theexisting pricing models: i) relying on deterministic criteriato reflect the cost of network security; and ii) unable torespect the nodal unreliability tolerance. It works byincorporating both factors into assessing the impact of anodal perturbation on assets’ investment horizons. The newmodel can better reflect the actual network planningpractice and the stochastic features of network failures. Component reliability, allowed nodal load loss and thefailure duration are the three major factors influencingnodal reliability. The new model reduces the nodal chargesby considering them: more reliable components and largerunreliability tolerance lead to smaller charges, and viceversa. The new model maintains the merits of the original LRICmodel of being able to produce locational and costreflective charges to influence prospective users’ behaviors to maximize the utilization of the existing networks,particularly those with higher reliability level and shorter
time to repair.
One problem with the new model is that it would need
substantial computational effort to analyze network
contingencies for large-scale systems. We ran the proposed
model on a practical EHV distribution network in the UK,
comprising 1,898 busbars: it took the original LRIC method
approximately 30 minutes to calculate charges for all load
busbars but the proposed approach about 27 hours.
Although it is a 50 fold increase in time, network charges
are calculated on an annual basis and thus such an increase
in running time is affordable by network operators to better
reflect their investment decisions. Besides, the advance in
computational techniques can benefit the application of the
proposed method.
Future research needs to investigate the correlation between
nodal charges, the costs of different reliability improvement
strategies, and how customers might respond to the locationl
charges so as to find the economic equilibrium for both
network operators and users. Additionally, the benefits in
network investment deferral brought about by renewable
generators are not considered in distribution network pricing in
the UK currently, due to their output intermittency. Future
work can also be conducted to examine how it would be more
cost-effective to charge them, respecting the impact that they
not only can bring forward network reinforcement but also
might defer the needed network investment
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