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Register a new userFully Distributed DC Optimal Power Flow Based on Distributed Economic Dispatch and Distributed State Estimation
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Added by
Qiao Li
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Optimal power flow (OPF) is an important technique for power systems to achieve optimal operation while satisfying multiple constraints. The traditional OPF are mostly centralized methods which are executed in the centralized control center. This paper introduces a totally Distributed DC Optimal Power Flow (DDCOPF) method for future power systems which have more and more distributed generators. The proposed method is based on the Distributed Economic Dispatch (DED) method and the Distributed State Estimation (DSE) method. In this proposed scheme, the DED method is used to achieve the optimal power dispatch with the lowest cost, and the DSE method provides power flow information of the power system to the proposed DDCOPF algorithm. In the proposed method, the Auto-Regressive (AR) model is used to predict the load variation so that the proposed algorithm can prevent overflow. In addition, a method called constraint algorithm is developed to correct the results of DED with the proposed correction algorithm and penalty term so that the constraints for the power system will not be violated. Different from existing research, the proposed method is completely distributed without need for any centralized facility.
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The trend in the electric power system is to move towards increased amounts of distributed resources which suggests a transition from the current highly centralized to a more distributed control structure. In this paper, we propose a method which enables a fully distributed solution of the DC Optimal Power Flow problem (DC-OPF), i.e. the generation settings which minimize cost while supplying the load and ensuring that all line flows are below their limits are determined in a distributed fashion. The approach consists of a distributed procedure that aims at solving the first order optimality conditions in which individual bus optimization variables are iteratively updated through simple local computations and information is exchanged with neighboring entities. In particular, the update for a specific bus consists of a term which takes into account the coupling between the neighboring Lagrange multiplier variables and a local innovation term that enforces the demand/supply balance. The buses exchange information on the current update of their multipliers and the bus angle with their neighboring buses. An analytical proof is given that the proposed method converges to the optimal solution of the DC-OPF. Also, the performance is evaluated using the IEEE Reliability Test System as a test case.
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