A new iterative mixed integer linear programming based real time energy efficient management of AC-DC distribution networks

In the smart grid era, AC -DC distribution networks are evolving for integrating massive amounts of distributed energy resources, especially battery associated solar power generation units, into distribution networks in most energy efficient ways. Performing real time energy management for such networks is challenging due to presence of different power electronic converters, grid -edge devices and voltage controlling equipment. Existing studies either focused on developing offline day ahead methods, or shortsighted greedy strategies by neglecting the offline beneficial attributes in real time optimization frameworks. On the contrary, this article proposes a novel real time AC -DC distribution network energy management portfolio by merging load control and conservation voltage reduction techniques to achieve energy efficient operation of the network by regulating the operation of the controllable resources. Initially, the overall optimization problem is developed as a mixed integer non -convex programming by modelling the offline benefits as time coupled stochastic expressions in real time optimization platform. The non -convex expressions are later simplified by adopting different linearization techniques and the entire energy management framework is revised as a simple mixed integer linear programming (MILP). The original problem is decoupled for each time step by simplifying the time coupled expressions using virtual queues and Lyapunov functions. Additionally, a new iterative MILP algorithm is proposed to solve the revised energy management problem with less computation complexity and time. Unlike the previous solution methods, the proposed algorithm initializes the variables by their median values and handles the infeasibilities by adding tangential cutting plane constraints and penalty variable minimization objective. Demonstrating on 33 -bus ACDC distribution network, the superiority of the proposed real time energy management process and iterative MILP algorithm is established after comparing with standard energy management portfolios and existing solution methods. (c) 2017 Elsevier Inc. All rights reserved.