Abstract
The uncertainty of distribution network operation is increasing with the integration of large-scale renewable distributed generation (DG) units. To reduce the conservativeness of traditional robust optimization (RO) solutions, a data-driven robust optimal approach, which incorporates the superiority of both stochastic and robust approaches, is employed to solve the dispatch model in this paper. Firstly, a deterministic optimal dispatching model is established with the minimum total operation cost of distribution network; secondly, a two-stage distributed robust dispatching model is constructed based on the historical data of renewable-generators output available. The first stage of the model aims at finding optimal values under the basic prediction scenario. In the second stage, the uncertain probability distribution confidence sets with norm-1 and norm-∞ constraints are integrated to find the optimal solution under the worst probability distribution. The model is solved by column-and-constraint generation (CCG) algorithm. Numerical simulation on the IEEE 33-bus system has been performed. Comparisons with the traditional stochastic and robust approaches demonstrate the effectiveness of the proposal.
Highlights
With the increasing number of the distributed generation (DG), the traditional distribution network is going to be gradually transformed into the active distribution network (ADN), which is capable of coordinating DGs, energy storage systems (ESS), demand-side response to keep the distribution network operate in security and economy [1]
This paper proposes a two-stage robust optimization model for the economic dispatch of ADNs based on data-driven
The proposed two-stage data-driven optimization model generally can be solved by the standard column-andconstraint generation method (C&CG) [15], which is implemented in a master-sub-problem framework: subproblem (SP) aims to find the critical scenario of the uncertain set for a given first-stage decision variable that provides an upper bound; new variables and constraints are added to the master problem (MP) to obtain a lower bound
Summary
With the increasing number of the DGs, the traditional distribution network is going to be gradually transformed into the active distribution network (ADN), which is capable of coordinating DGs, energy storage systems (ESS), demand-side response to keep the distribution network operate in security and economy [1]. A large number of DG historical data can be used to construct a series of possible probability distribution, which can be constrained by the sets of norm-1 and norm-∞ This method is known as data-driven optimization [8,9], which neither require probabilistic distribution assumption nor need dualization, and it is less computational burden. This approach is adopted to solve the unit commitment problem [10,11], but has not been employed for power dispatch of distribution network to our best knowledge. (2) In the second stage optimal model, norm-1 and norm-∞are integrated together to construct the confidence set of uncertain probability distribution
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