Abstract
This paper applies the principles of distribution locational marginal pricing (DLMP) to unbalanced three-phase distribution networks. We first propose a linear model for AC optimal power flow derived through a series of approximation and reformulation techniques. Then a scenario-generation algorithm is proposed to properly model the uncertain parameters in the linear model. Through a proposed No U-Turn sampler (NUTS) based algorithm, probability density functions (PDFs) of DLMPs are calculated. These PDFs provide statistical information about the locational and temporal price risks. By means of applications to two IEEE unbalanced test networks, the numerical results show promising performance for the proposed linear model and the NUTS-based algorithm in creating PDFs of DLMPs. DLMP price densities will be increasingly useful as distribution system operators seek flexible, low risk solutions from embedded generators and aggregators of distributed energy resources.
Highlights
This paper applies the principles of distribution locational marginal pricing (DLMP) to unbalanced three-phase distribution networks
In this paper we provide such an approach based upon DLMPs, demonstrated on two case-studies to be scalable for real applications
Whilst various approaches can handle this nonlinearity, we have focused on developing a linear program (LP) approximation
Summary
NE of the most remarkable changes in the operations of the power system supply chain has been in the distribution networks. P r(a ≤ X ≤ b) = a fX (x)dx = 0.05 These PDFs provide information regarding the risks of high or low prices in different parts of distribution network, being useful, we would argue, not just to the risk-averse procurement of services by the DSO, and to the asset owners of flexibility services in terms of their operational revenue risks. Some researchers such as [8] have gone beyond point estimates of the mean nodal prices to include variance as a risk measure, but this is clearly inadequate to properly estimate the tail probability risks in non-Normal distributions. Overall, such probabilistic analysis through PDFs provides a more complete assessment of TUDN risks for both operational and investment decisions
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