Big-M based MILP method for SCUC considering allowable wind power output interval and its adjustable conservativeness

In contrast to most existing works on robust unit commitment(UC),this study proposes a novel big-M-based mixed-integer linear programming(MILP)method to solve security-constrained UC problems considering the allowable wind power output interval and its adjustable conservativeness.The wind power accommodation capability is usually limited by spinning reserve requirements and transmission line capacity in power systems with large-scale wind power integration.Therefore,by employing the big-M method and adding auxiliary 0-1 binary variables to describe the allowable wind power output interval,a bilinear programming problem meeting the security constraints of system operation is presented.Furthermore,an adjustable confidence level was introduced into the proposed robust optimization model to decrease the level of conservatism of the robust solutions.This can establish a trade-off between economy and security.To develop an MILP problem that can be solved by commercial solvers such as CPLEX,the big-M method is utilized again to represent the bilinear formulation as a series of linear inequality constraints and approximately address the nonlinear formulation caused by the adjustable conservativeness.Simulation studies on a modified IEEE 26-generator reliability test system connected to wind farms were performed to confirm the effectiveness and advantages of the proposed method.

A Data-driven Method for Fast AC Optimal Power Flow Solutions via Deep Reinforcement Learning

With the increasing penetration of renewable energy,power grid operators are observing both fast and large fluctuations in power and voltage profiles on a daily basis.Fast and accurate control actions derived in real time are vital to ensure system security and economics.To this end,solving alternating current(AC)optimal power flow(OPF)with operational constraints remains an important yet challenging optimization problem for secure and economic operation of the power grid.This paper adopts a novel method to derive fast OPF solutions using state-of-the-art deep reinforcement learning(DRL)algorithm,which can greatly assist power grid operators in making rapid and effective decisions.The presented method adopts imitation learning to generate initial weights for the neural network(NN),and a proximal policy optimization algorithm to train and test stable and robust artificial intelligence(AI)agents.Training and testing procedures are conducted on the IEEE 14-bus and the Illinois 200-bus systems.The results show the effectiveness of the method with significant potential for assisting power grid operators in real-time operations.

Allocation of a Variable Series Reactor Considering AC Constraints and Contingencies

The Variable Series Reactors(VSRs)can efficiently control the power flow through the adjustment of the line reactance.When they are appropriately allocated in the power network,the transmission congestion and generation cost can be reduced.This paper proposes a planning model to optimally allocate VSRs considering AC constraints and multi-scenarios including base case and contingencies.The planning model is originally a non-convex large scale mixed integer nonlinear program(MINLP),which is generally intractable.The proposed Benders approach decomposes the MINLP model into a mixed integer linear program(MILP)master problem and a number of nonlinear subproblems.Numerical case studies based on IEEE 118-bus demonstrate the high performance of the proposed approach.

Online coherence identification using dynamic time warping for controlled islanding

Controlled islanding is considered to be the last countermeasure to prevent a system-wide blackout in case of cascading failures.It splits the system into self-sustained islands to maintain transient stability at the expense of possible loss of load.Generator coherence identification is critical to controlled islanding scheme as it helps identify the optimal cut-set to maintain the transient stability of the post-islanding systems.This paper presents a novel approach for online generator coherency identification using phasor measurement unit(PMU) data and dynamic time warping(DTW).Results from the coherence identification are used to further cluster non-generator buses using spectral clustering with the objective of minimizing power flow disruptions.The proposed approach is validated and compared to existing methods on the IEEE39-bus system and WECC 179-bus system, through which its advantages are demonstrated.