Shengwei Mei

An Improved OPA Model and Blackout Risk Assessment

The ORNL-PSerc-Alaska (OPA) model is a blackout model proposed by researchers at Oak Ridge National Laboratory (ORNL), Power System Engineering Research Center of Wisconsin University (PSerc), and Alaska University (Alaska). Although the OPA model is a landmark study, it has two limitations. First, there is a significant difference between simulation and practice in transmission line outage and update; and second, the simulation of cascading failure and the probability distribution of blackout size are in general not accurate enough. Hence, an improved OPA model is proposed in this paper to address these limitations. The proposed model contains two layers of iteration. The inner iteration describes the fast dynamics of the system and considers the influence of power flow, dispatching, automation, relay protection, and so on. The outer iteration describes the slow overall system evolution and is concerned with the update of the power grid, operation modes and planning. Such a model can be applied to practical large-scale systems. Furthermore, based on the Value at Risk (VaR) and Conditional Value at Risk (CVaR), two new complementary blackout risk indices are defined, which reveal critical characteristics of blackouts and are used to evaluate security levels of power systems. The effectiveness of the improved OPA model is verified by the simulations concerning the Northeast Power Grid of China.

Energy Pricing and Dispatch for Smart Grid Retailers Under Demand Response and Market Price Uncertainty

This paper proposes a two-stage two-level model for the energy pricing and dispatch problem faced by a smart grid retailer who plays the role of an intermediary agent between a wholesale energy market and end consumers. Demand response of consumers with respect to the retail price is characterized by a Stackelberg game in the first stage, thus the first stage has two levels. A risk-aversive energy dispatch accounting for market price uncertainty is modeled by a linear robust optimization with objective uncertainty in the second stage. The proposed model is transformed to a mixed integer linear program (MILP) by jointly using the Karush-Kuhn-Tucker (KKT) condition, the disjunctive constraints, and the duality theory. We propose a heuristic method to select the parameter in disjunctive constraints based on the interpretation of Lagrange multipliers. Moreover, we suggest solving an additional linear program (LP) to acquire a possible enhanced bidding strategy that guarantees a Pareto improvement on the retailers profit over the entire uncertainty set. Case studies demonstrate the proposed model and method is valid.

A Study of Self-Organized Criticality of Power System Under Cascading Failures Based on AC-OPF With Voltage Stability Margin

From the perspective of self-organized criticality, this paper develops a novel model with AC-OPF and AC grid upgrade to study the cascading failures and blackouts in power systems, which overcomes some shortcomings of existing blackout models. The proposed model contains two types of dynamics, one is fast dynamics which simulates the serial blackouts in power systems, the other is slow dynamics which reflects the tendency of the power systems time evolution. This model also has voltage stability analysis function and can reveal critical characteristics from reactive power and voltage viewpoint. Simulation results of the IEEE 118-bus system with this model show that the fast dynamics can capture the cascading process and the criticality property in micro scale. Besides, the macro scale of self-organized criticality of power systems can be revealed from the mean value of fractional overloads and the ratio of total load demand to the total network transfer capability. Furthermore, the voltage stability criticality status could be detected from the eigenvalue with the smallest magnitude through reactive power and voltage relevant modal analysis.

2012 Special Issue: A boundedness result for the direct heuristic dynamic programming

Approximate/adaptive dynamic programming (ADP) has been studied extensively in recent years for its potential scalability to solve large state and control space problems, including those involving continuous states and continuous controls. The applicability of ADP algorithms, especially the adaptive critic designs has been demonstrated in several case studies. Direct heuristic dynamic programming (direct HDP) is one of the ADP algorithms inspired by the adaptive critic designs. It has been shown applicable to industrial scale, realistic and complex control problems. In this paper, we provide a uniformly ultimately boundedness (UUB) result for the direct HDP learning controller under mild and intuitive conditions. By using a Lyapunov approach we show that the estimation errors of the learning parameters or the weights in the action and critic networks remain UUB. This result provides a useful controller convergence guarantee for the first time for the direct HDP design.

The nonlinear internal control of STATCOM: theory and application

In this paper, a two-level control structure of Static Synchronous Var Compensator (STATCOM), which consists of internal and external control system, is described. The control targets of two levels and relations between them are also stated. The suggested configuration of control systems can benefit the systemic design for STATCOM controller. H∞ control approach, which considers imprecise parameters and external disturbances, is applied to design the internal controller of STATCOM. With the proposed internal control, the model of STATCOM can be simplified to be a first-order inertia block, which can significantly simplify the design of system control. After that, some improvements and optimizations based on the dynamical analysis of STATCOM have been developed to achieve better dynamical performance and higher tracking accuracy. Simulations are carried out to verify the performance of the proposed controller comparing with that of the conventional PI controller. The results show that the proposed controller has faster dynamic response, higher accuracy of tracking the reference, and stronger robustness to parameters variation and external disturbances.

Robust Energy and Reserve Dispatch Under Variable Renewable Generation

Global warming and environmental pollution concerns have promoted dramatic integrations of renewable energy sources all over the world. Associated with benefits of environmental conservation, essentially uncertain and variable characteristics of such energy resources significantly challenge the operation of power systems. In order to implement reliable and economical operations, a robust energy and reserve dispatch (RERD) model is proposed in this paper, in which the operating decisions are divided into pre-dispatch and re-dispatch. A robust feasibility constraint set is imposed on pre-dispatch variables, such that operation constraints can be recovered by adjusting re-dispatch after wind generation realizes. The model is extended to more general dispatch decision making problems involving uncertainties in the framework of adjustable robust optimization. By revealing the convexity of the robust feasibility constraint set, a comprehensive mixed integer linear programming based oracle is presented to verify the robust feasibility of pre-dispatch decisions. A cutting plane algorithm is established to solve associated optimization problems. The proposed model and method are applied to a five-bus system as well as a realistic provincial power grid in China. Numeric experiments demonstrate that the proposed methodology is effective and efficient.

Game Approaches for Hybrid Power System Planning

Game theory is introduced in this paper to model the planning of a grid-connected hybrid power system comprised of wind turbines, photovoltaic panels, and storage batteries. Both noncooperative and cooperative game-theoretic models (four scenarios in this case) are built by taking wind turbines, photovoltaic panels, and storage batteries as players and their life cycle income as payoffs. Furthermore, the existence of the Nash equilibriums is proved by analyzing the concavity of the payoffs. An iterative solving algorithm is also proposed to obtain the Nash equilibriums of the game model. Then, simulation based on a fictitious hybrid power system illustrates the feasibility of the proposed model. The comparison among all the Nash equilibriums under different coalition forms indicates that the cooperation can bring out more payoffs and competition will lead to the decrease of payoff. A simulation with multiobjective optimization method is also carried out for comparison. Finally, the uncertainties of wind speed, sunlight and load demand are considered to verify the stability of the Nash equilibriums, and sensitivities to some key parameters are also studied on the fictitious hybrid power system.

An Interaction Model for Simulation and Mitigation of Cascading Failures

In this paper, the interactions between component failures are quantified and the interaction matrix and interaction network are obtained. The quantified interactions can capture the general propagation patterns of the cascades from utilities or simulation, thus helping to better understand how cascading failures propagate and to identify key links and key components that are crucial for cascading failure propagation. By utilizing these interactions a high-level probabilistic model called interaction model is proposed to study the influence of interactions on cascading failure risk and to support online decision-making. It is much more time-efficient to first quantify the interactions between component failures with fewer original cascades from a more detailed cascading failure model and then perform the interaction model simulation than it is to directly simulate a large number of cascades with a more detailed model. Interaction-based mitigation measures are suggested to mitigate cascading failure risk by weakening key links, which can be achieved in real systems by wide-area protection such as blocking of some specific protective relays. The proposed interaction quantifying method and interaction model are validated with line outage data generated by the AC OPA cascading simulations on the IEEE 118-bus system.

Power System Dynamic Security Region and Its Approximations

Dynamic security region (DSR) for security assessment and preventive control is defined in terms of variables that are under the control of the dispatcher prior to a fault. The boundary of DSR can be expressed using the function describing the stable manifold of the controlling unstable equilibrium point. The function is shown to be the solution of a partial differential equation. Quadratic and linear approximations to the function characterizing the boundary of DSR are derived. Numerical tests of the approximations are conducted. Potential applications of the method are discussed

Experimental evaluation of nonlinear robust control for SMES to improve the transient stability of power systems

This paper presents a new approach and corresponding experiments for the nonlinear robust control of a superconducting magnetic energy storage (SMES) unit to improve the transient stability of power systems. Based on the result of SMES prototype experience, a new dynamic model with disturbances of SMES is adopted, and transferred to the per unit system for simplifying the dynamic analysis and controller design. Then, feedback linearization scheme and linear H/sub /spl infin// control theory are applied to design a novel SMES nonlinear robust controller in a one-machine infinite bus (OMIB) power system. In order to confirm such positive effects of the proposed control strategy, experiments are carried on a laboratory setup of SMES comparing that with a conventional proportional-integral (PI) controller. The results of the experiments demonstrate that the proposed nonlinear robust controller has more excellent performance to improve the transient stability of power systems than that of conventional PI controllers.