Pre-disaster transmission maintenance scheduling considering network topology optimization

Several devastating experiences with extreme natural disasters demonstrate that improving power system resilience is becoming increasingly important. This paper proposes a pre-disaster transmission maintenance scheduling considering network topology optimization to ensure the power system economics before disasters and power system resilience during disasters. The transmission line fragility is distinguished and considered in the proposed optimization model to determine the maintenance scheduling of defective lines that minimizes load shedding during disasters. The proposed model is established as a tri-level optimization problem that is further reformulated to a bi-level problem utilizing duality theory. The column-and-constraint generation (C&CG) algorithm is employed to solve the equivalent robust optimization problem. Finally, the proposed model and its solution algorithm are implemented on the modified IEEE RTS-79 system. The significant cost savings and increased resilience illustrate the effectiveness of the proposed model

Reliability-Constrained Economic Dispatch with Analytical Formulation of Operational Risk Evaluation

Operational reliability and the decision-making process of economic dispatch (ED) are closely related and important for power system operation. Consideration of reliability indices and reliability constraints together in the operation problem is very challenging due to the problem size and tight reliability constraints. In this paper, a comprehensive reliability-constrained economic dispatch model with analytical formulation of operational risk evaluation (RCED-AF) is proposed to tackle the operational risk problem of power systems. An operational reliability evaluation model considering the ED decision is designed to accurately assess the system behavior. A computation scheme is also developed to achieve efficient update of risk indices for each ED decision by approximating the reliability evaluation procedure with an analytical polynomial function. The RCED-AF model can be constructed with decision-dependent reliability constraints expressed by the sparse polynomial chaos expansion. Case studies demonstrate that the proposed RCED-AF model is effective and accurate in the optimization of the reliability and the cost for day-ahead economic dispatch

Coordinating Multiple Resources for Optimal Postdisaster Operation of Interdependent Electric Power and Natural Gas Distribution Systems

Electric power and natural gas systems are not separated but rather are increasingly connected physically and functionally interdependent due to the continuing development of natural gas-fired generation and gas industry electrification. Such interdependency makes these two systems interact with each other when responding to disasters. The aggravated risk of cascading failures across the two systems has been exposed in recent energy crises, highlighting the significance of preparing these interdependent systems against disasters and helping their impacted services quickly recover. This promotes us to treat power and gas systems as one whole to fully capture their interactive behaviors. In this paper, we focus on the interdependent electric power and natural gas distribution systems (IENDS) and propose a "supply - demand - repair" strategy to comprehensively help the IENDS tide over the emergency periods after disasters by coordinating mobile or stationary emergency resources for various uses. Specifically, 1) on the supply side, the fuel supply issue of different types of generators for emergency use is considered and the fuel delivery process among different fuel facilities is mathematically formulated; 2) on the demand side, a zonewise method is proposed for integrated dispatch of power and gas demand responses; and 3) in the repair process, a varying efficiency related to the repair units at work is introduced to accurately model repairs. The proposed strategy is formulated into a mixed-integer second-order cone programming model to obtain a globally optimal decision of deploying all of those resources in a coordinated and organized manner. A series of case studies based on test systems are conducted to validate the effectiveness of the proposed strategy.Comment: 31 pages, 9 figures, submitted to Applied Energ

Dynamic Cascading Failure Model for Blackout Risk Assessment of Power System With Renewable Energy

To assess the blackout risk of power system with high penetration of renewable, the existing cascading failure models need to be improved for capturing the dynamics and relays of renewable generation. In this paper, a dynamic model of cascading failure considering the utility-scale and distributed renewable energy is proposed. With the solution of dynamic equations for power system, the logics of relays are simulated for components such as transmission lines, conventional generators and renewable generations. The failure interactions among sources, networks, and loads are analyzed more comprehensively. In the proposed model, to capture the impact of renewable energy on the system dynamics, the dynamic equations for the utility-scale renewables are constructed with the second generic generation model of WECC (Western Electricity Coordinating Council), and the interactions among distributed renewables and the transmission system are considered in the amount of net load at buses. And to capture the tolerance of renewables for disturbances, the simulation logic is constructed for the voltage relays and frequency relays of utility-scale renewables and the anti-islanding relay of distributed renewables. The presented model is verified on the IEEE 39-bus system. The results show that renewable energy has a significant influence on the cascading failure risk

Optimization and Analysis of a Hybrid Energy Storage System in a Small-Scale Standalone Microgrid for Remote Area Power Supply (RAPS)

The analysis and application of hybrid energy storage systems (HESSs) in small-scale standalone microgrids for remote area power supply (RAPS) has received extensive attention. This application mode has its own characteristics which must be considered but have not been considered in the existing research. To reflect the common satisfaction of load demands and maximize the utilization of renewable energy in a standalone microgrid, a new index named effective rate of energy storage system (ESS) is proposed. To reflect the true work state of supercapacitor ESS (SC-ESS), the second-level data of field measurements is used in calculation and analysis. To further enhance the operational performance of the HESS, a coordinated control strategy based on state cooperation is adopted. To get a more reasonable and more credible HESS optimization model, the comparison of existing models and proposed model with different considerations on cost and life is provided. In addition, a comparative analysis of technical and economic characteristics improvements is presented for different ESS application schemes in practical projects

Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques

This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as the learning samples in the forecasting process and selected by a fuzzy clustering technique according to the degree of similarity of the input samples considering the periodic characteristics of the load. PSO is applied to optimize the model parameters. The complicated nonlinear relationships between the factors influencing the load and the load forecasting can be regressed using the SVR. The practical load data from a city in Chongqing was used to illustrate the proposed method, and the results indicate that the proposed method can obtain higher accuracy compared with the traditional method, and is effective for forecasting the short-term load of power systems

Incorporating public feedback in service restoration for electric distribution networks

Abstract Power outages in urban area carry heavy social and economic costs. Although social cost, especially public sentiment, is concerned by engineers and managers, it has been only qualitatively investigated without a rigorous model in the state‐of‐the‐art research and practice of service restoration (SR) for a long time. To fill this gap, this paper investigates a hybrid model which takes public sentiment into consideration by quantifying public sentiment triggered by power outage. Furthermore, conventional SR method focused on the optimization model with ideal conditions, which leaves a large room for improvement in complex environment. To improve the robustness of the model, the authors propose a reinforcement learning framework to analyze emergency management process without prior rules. At each time step, the optimal decision can be made automatically by a learned model. The numerical simulations with modified IEEE 33‐bus and IEEE 123‐bus systems demonstrate the effectiveness of the proposed method