Robust N−k Security-constrained Optimal Power Flow Incorporating Preventive and Corrective Generation Dispatch to Improve Power System Reliability

As extreme weather events have become more frequent in recent years,improving the resilience and reliability of power systems has become an important area of concern.In this paper,a robust preventive-corrective security-constrained optimal power flow(RO-PCSCOPF)model is proposed to improve power system reliability under N−k outages.Both the short-term emergency limit(STL)and the long-term operating limit(LTL)of the post-contingency power flow on the branch are considered.Compared with the existing robust corrective SCOPF model that only considers STL or LTL,the proposed ROPCSCOPF model can achieve a more reliable generation dispatch solution.In addition,this paper also summarizes and compares the solution methods for solving the N−k SCOPF problem.The computational efficiency of the classical Benders decomposition(BD)method,robust optimization(RO)method,and line outage distribution factor(LODF)method are investigated on the IEEE 24-bus Reliability Test System and 118-bus system.Simulation results show that the BD method has the worst computation performance.The RO method and the LODF method have comparable performance.However,the LODF method can only be used for the preventive SCOPF and not for the corrective SCOPF.The RO method can be used for both.

Modeling,Oscillation Analysis and Distributed Stabilization Control of Autonomous PV-based Microgrids

Driven by rising energy demand and the goal of carbon neutrality,renewable energy generations(REGs),especially photovoltaic(PV)generations,are widely used in the urban power energy systems.While the intelligent control of microgrids(MG)brings economic and efficient operation,its potential stability problem cannot be ignored.To date,most of the research on modeling,analyzing and enhancing the stability of MG usually assume the DC-link as an ideal voltage source.However,this practice of ignoring the dynamics of DC-link may omit the latent oscillation phenomena of autonomous PV-based MG.First,this paper establishes a complete dynamic model of autonomous PV-based MG including PV panels and DC-link.Different from previous conclusions of idealizing DC-link dynamics,participation factor analysis finds the potential impact of DClink dynamics on system dynamic performance,and different influence factors including critical control parameters and nonlinear V-I output characteristic of PV array are considered to further reveal oscillation mechanisms.Second,based on the average consensus algorithm,a distributed stabilization controller with strong robustness is proposed to enhance stability of the PVbased MG,which does not affect the steady-state performance of the system.Finally,the correctness of all theoretical analysis and the effectiveness of the proposed controller are verified by time domain simulation and hardware-in-loop tests.

Electric Vehicle Charging Scheduling Strategy for Supporting Load Flattening Under Uncertain Electric Vehicle Departures

The scheduled electric vehicle(EV)charging flexibility has great potential in supporting the operation of power systems,yet achieving such benefits is challenged by the uncertain and user-dependent nature of EV charging behavior.Existing research primarily focuses on modeling the uncertain EV arrival and battery status yet rarely discusses the uncertainty in EV departure.In this paper,we investigate the EV charging scheduling strategy to support load flattening at the distribution level of the utility grid under uncertain EV departures.A holistic methodology is proposed to formulate the unexpected trip uncertainty and mitigate its negative impacts.To ensure computational efficiency when large EV fleets are involved,a distributed solution framework is developed based on the alternating direction method of multipliers(ADMM)algorithm.The numerical results reveal that unexpected trips can severely damage user convenience in terms of EV energy content.It is further confirmed that by applying the proposed methodology,the resultant critical and sub-critical user convenience losses due to scheduled charging are reduced significantly by 83.5%and 70.5%,respectively,whereas the load flattening performance is merely sacrificed by 17%.

Bi-level Optimal Planning of Voltage Regulator in Distribution Systems Considering Maximization of Incentive-based Photovoltaic Energy Integration

This paper focuses on optimal voltage regulator(VR)planning to maximize the photovoltaic(PV)energy integration in distribution grids.To describe the amount of dynamic PV energy that can be integrated into the power system,the concept of PV accommodation capability(PVAC)is introduced and modeled with optimization.Our proposed planning model is formulated as a Benders decomposition based bi-level stochastic optimization problem.In the upper-level problem,VR planning decisions and PVAC are determined via mixed integer linear programming(MILP)before considering uncertainty.Then in the lower-level problem,the feasibility of first-level results is checked by critical network constraints(e.g.voltage magnitude constraints and line capacity constraints)under uncertainties considered by time-varying loads and PV generations.In this paper,these uncertainties are represented in the form of operational scenarios,which are generated by the Gaussian copula theory and reduced by a well-studied backward-reduction algorithm.The modified IEEE 33-node distribution grid is utilized to verify the effectiveness of the proposed model.The results demonstrate that a PV energy integration can be significantly enhanced after optimal voltage regulator planning.

A retroactive approach to microgrid real-time scheduling in quest of perfect dispatch solution

As an emerging paradigm in distributed power systems,microgrids provide promising solutions to local renewable energy generation and load demand satisfaction.However,the intermittency of renewables and temporal uncertainty in electrical load create great challenges to energy scheduling,especially for small-scale microgrids.Instead of deploying stochastic models to cope with such challenges,this paper presents a retroactive approach to real-time energy scheduling,which is prediction-independent and computationally efficient.Extensive case studies were conducted using 3-year-long real-life system data,and the results of simulations show that the cost difference between the proposed retroactive approach and perfect dispatch is less than 11%on average,which suggests better performance than model predictive control with the cost difference at 30%compared to the perfect dispatch.