• Energy Research

The coordinated alternating current optimal power flow (ACOPF) for coupled transmission-distribution grids has become crucial to handle problems related to high penetration of renewable energy sources (RESs). However, obtaining all system details and solving ACOPF centrally is not feasible because of privacy concerns. Intermittent RESs and uncontrollable loads can swiftly change the operating condition of the power grid. Existing decentralized optimization methods can seldom track the optimal solutions of time-varying ACOPFs. Here, we propose an online decentralized optimization method to track the time-varying ACOPF of coupled transmission-distribution grids. First, the time-varying ACOPF problem is converted to a dynamic system based on Karush-Kuhn-Tucker conditions from the control perspective. Second, a prediction term denoted by the partial derivative with respect to time is developed to improve the tracking accuracy of the dynamic system. Third, a decentralized implementation for solving the dynamic system is designed based on only a few information exchanges with respect to boundary variables. Moreover, the proposed algorithm can be used to directly address nonlinear power flow equations without relying on convex relaxations or linearization techniques. Numerical test results reveal the effectiveness and fast-tracking performance of the proposed algorithm. 18 pages with 15 figures

The coordinated alternating current optimal power flow (ACOPF) for coupled transmission-distribution grids has become crucial to handle problems related to high penetration of renewable energy sources (RESs). However, obtaining all system details and solving ACOPF centrally is not feasible because of privacy concerns. Intermittent RESs and uncontrollable loads can swiftly change the operating condition of the power grid. Existing decentralized optimization methods can seldom track the optimal solutions of time-varying ACOPFs. Here, we propose an online decentralized optimization method to track the time-varying ACOPF of coupled transmission-distribution grids. First, the time-varying ACOPF problem is converted to a dynamic system based on Karush-Kuhn-Tucker conditions from the control perspective. Second, a prediction term denoted by the partial derivative with respect to time is developed to improve the tracking accuracy of the dynamic system. Third, a decentralized implementation for solving the dynamic system is designed based on only a few information exchanges with respect to boundary variables. Moreover, the proposed algorithm can be used to directly address nonlinear power flow equations without relying on convex relaxations or linearization techniques. Numerical test results reveal the effectiveness and fast-tracking performance of the proposed algorithm. 18 pages with 15 figures

This study focuses on the real‐time operation of a microgrid (MG). A novel approximate dynamic programming based spatiotemporal decomposition approach is developed to incorporate efficient management of distributed energy storage systems into MG real‐time operation while considering uncertainties in renewable generation. The original dynamic energy management problem is decomposed into single‐period and single‐unit sub‐problems, and the value functions are used to describe the interaction among the sub‐problems. A two‐stage procedure is further designed for the real‐time decisions of those sub‐problems. In the first stage, empirical data is utilised offline to approximate the value functions. Then in the second stage, each sub‐problem can make immediate and independent decision in both temporal and spatial dimensions to mitigate adverse effects of intermittent renewable generation in a MG. No central operator intervention is required, and the near optimal decisions can be obtained at a very fast speed. Case studies based on a six‐bus MG and an actual island MG are conducted to demonstrate the effectiveness of the proposed algorithm.

This study focuses on the real‐time operation of a microgrid (MG). A novel approximate dynamic programming based spatiotemporal decomposition approach is developed to incorporate efficient management of distributed energy storage systems into MG real‐time operation while considering uncertainties in renewable generation. The original dynamic energy management problem is decomposed into single‐period and single‐unit sub‐problems, and the value functions are used to describe the interaction among the sub‐problems. A two‐stage procedure is further designed for the real‐time decisions of those sub‐problems. In the first stage, empirical data is utilised offline to approximate the value functions. Then in the second stage, each sub‐problem can make immediate and independent decision in both temporal and spatial dimensions to mitigate adverse effects of intermittent renewable generation in a MG. No central operator intervention is required, and the near optimal decisions can be obtained at a very fast speed. Case studies based on a six‐bus MG and an actual island MG are conducted to demonstrate the effectiveness of the proposed algorithm.

With the continuously expanding installed capacity of the power system, the ever-increasing short-circuit current hinders its secure operation and further development. Installing fault current limiters to suppress short-circuit current is very effective but expensive. Thus, transmission line switching and unit commitment are considered in operation to reduce the investment cost. This paper establishes the optimal configuration model of fault current limiters considering transmission line switching and unit commitment, which is a complex Mixed-Integer Linear Programming (MILP) problem. To accelerate the solution of the MILP problem, a heuristic decomposition algorithm was proposed based on Analytical Target Cascading (ATC) and a Large Neighborhood Search (LNS) algorithm, which has two stages. In the first stage, the feasible solution is constructed based on the framework of ATC and combines the idea of LNS in ATC to improve the efficiency of constructing feasible solutions. In the second stage, LNS is used to improve the feasible solution based on the decomposed sub-problem. Finally, simulations were conducted on an IEEE 118-bus and 186-branch power system and a real 727-bus and 861-branch power system. The numerical results show that for these IEEE and real systems, compared with GUROBI, the proposed algorithm is 125.7 and 10.6 times faster with only 0.04% and 0.003% higher total cost, respectively; compared with ATC, it is slightly slower with 0.2% and 0.3% lower total cost, respectively.

With the continuously expanding installed capacity of the power system, the ever-increasing short-circuit current hinders its secure operation and further development. Installing fault current limiters to suppress short-circuit current is very effective but expensive. Thus, transmission line switching and unit commitment are considered in operation to reduce the investment cost. This paper establishes the optimal configuration model of fault current limiters considering transmission line switching and unit commitment, which is a complex Mixed-Integer Linear Programming (MILP) problem. To accelerate the solution of the MILP problem, a heuristic decomposition algorithm was proposed based on Analytical Target Cascading (ATC) and a Large Neighborhood Search (LNS) algorithm, which has two stages. In the first stage, the feasible solution is constructed based on the framework of ATC and combines the idea of LNS in ATC to improve the efficiency of constructing feasible solutions. In the second stage, LNS is used to improve the feasible solution based on the decomposed sub-problem. Finally, simulations were conducted on an IEEE 118-bus and 186-branch power system and a real 727-bus and 861-branch power system. The numerical results show that for these IEEE and real systems, compared with GUROBI, the proposed algorithm is 125.7 and 10.6 times faster with only 0.04% and 0.003% higher total cost, respectively; compared with ATC, it is slightly slower with 0.2% and 0.3% lower total cost, respectively.