Graph Computing Based Distributed Parallel Power Flow for AC/DC Systems with Improved Initial Estimate

The sequential method is easy to integrate with existing large-scale alternating current(AC)power flow solvers and is therefore a common approach for solving the power flow of AC/direct current(DC)hybrid systems.In this paper,a highperformance graph computing based distributed parallel implementation of the sequential method with an improved initial estimate approach for hybrid AC/DC systems is developed.The proposed approach is capable of speeding up the entire computation process without compromising the accuracy of result.First,the AC/DC network is intuitively represented by a graph and stored in a graph database(GDB)to expedite data processing.Considering the interconnection of AC grids via high-voltage direct current(HVDC)links,the network is subsequently partitioned into independent areas which are naturally fit for distributed power flow analysis.For each area,the fast-decoupled power flow(FDPF)is employed with node-based parallel computing(NPC)and hierarchical parallel computing(HPC)to quickly identify system states.Furthermore,to reduce the alternate iterations in the sequential method,a new decoupled approach is utilized to achieve a good initial estimate for the Newton-Raphson method.With the improved initial estimate,the sequential method can converge in fewer iterations.Consequently,the proposed approach allows for significant reduction in computing time and is able to meet the requirement of the real-time analysis platform for power system.The performance is verified on standard IEEE 300-bus system,extended large-scale systems,and a practical 11119-bus system in China.

Data-driven and Model-based Hybrid Reinforcement Learning to Reduce Stress on Power Systems Branches

This work proposes a reinforcement learning(RL)approach to tackle the control problem of branch overload relief in large power systems.Accordingly,a control agent is trained to change generators'real power output in order to relieve the stressed branches.For large power systems,this control problem becomes one whose decision space(i.e.,the action space)is both highly-dimensioned and continuous.This makes it extremely difficult to have successful training for RL-based agents.To improve the effectiveness,a data-driven and model-based hybrid approach is proposed to optimize the control by combining RL-agent actions and generator shifting factor-driven actions.Accordingly,with the proposed approach the RL-agent successfully trains on large power systems.The proposed design is tested on both the IEEE 118-bus testing system and a 2749-bus real system.The obtained results show that the proposed hybrid approach outperforms the data-driven training approach.

Graph Computing Based Security Constrained Unit Commitment in Hydro-thermal Power Systems Incorporating Pumped Hydro Storage

This paper proposes a graph computing based mixed integer programming(MIP)framework for solving the security constrained unit commitment(SCUC)problem in hydro-thermal power systems incorporating pumped hydro storage(PHS).The proposed graph computing-based MIP framework considers the economic operations of thermal units,cascade hydropower stations and PHS stations,as well as their technical impacts towards the network security.First,the hydro-thermal power system data and unit information are stored in a graph structure with nodes and edges,which enables nodal and hierarchical parallel computing for the unit commitment(UC)solution calculation and network security analysis.A MIP model is then formulated to solve the SCUC problem with the mathematical models of thermal units,cascade hydropower stations and PHS stations.In addition,two optimization approaches including convex hull reformulation(CHR)and special ordered set(SOS)methods are introduced for speeding up the MIP calculation procedure.To ensure the system stability under the derived UC solution,a parallelized graph power flow(PGPF)algorithm is proposed for the hydro-thermal power system network security analysis.Finally,case studies of the IEEE 118-bus system and a practical 2749-bus hydro-thermal power system are introduced to demonstrate the feasibility and validity of the proposed graph computing-based MIP framework.

Autonomous Charging of Electric Vehicle Fleets to Enhance Renewable Generation Dispatchability

A total of 19%of generation capacity in California is offered by PV units and over some months,more than 10%of this energy is curtailed.In this research,a novel approach to reducing renewable generation curtailment and increasing system flexibility by means of electric vehicles'charging coordination is presented.The presented problem is a sequential decision making process,and is solved by a fitted Q-iteration algorithm which unlike other reinforcement learning methods,needs fewer episodes of learning.Three case studies are presented to validate the effectiveness of the proposed approach.These cases include aggregator load following,ramp service and utilization of non-deterministic PV generation.The results suggest that through this framework,EVs successfully learn how to adjust their charging schedule in stochastic scenarios where their trip times,as well as solar power generation are unknown beforehand.

A convex relaxation approach for power flow problem

A solution to the power flow problem is imperative for many power system applications and several iterative approaches are employed to achieve this objective.However,the chance of finding a solution is dependent on the choice of the initial point because of the nonconvex feasibility region of this problem.In this paper,a non-iterative approach that leverages a convexified relaxed power flow problem is employed to verify the existence of a feasible solution.To ensure the scalability of the proposed convex relaxation,the problem is formulated as a sparse semi-definite programming problem.The variables associated with each maximal clique within the network form several positive semidefinite matrices.Perturbation and network reconfiguration schemes are employed to improve the tightness of the proposed convex relaxation in order to validate the existence of a feasible solution for the original non-convex problem.Multiple case studies including an ill-conditioned power flow problem are examined to show the effectiveness of the proposed approach to find a feasible solution.