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Fundamentals Of Engineering Thermodynamics

Understanding potential behaviors of attackers is of paramount importance for improving the cybersecurity of power systems. However, the attack behaviors in existing studies are often modeled statically on a single snapshot, which neglects the reality of a dynamically time-evolving power system. Accordingly, a dynamic cyber-attack model with local network information is proposed to characterize the typical data injection attack with the integration of potential dynamic behaviors of an attacker. The proposed model collaboratively alters the meter measurement in a stealthy way to illegally contaminate the system state, thus posing severe threats to cyber physical power systems. We then develop a novel anomaly detection countermeasure from the perspective of state estimation to effectively recognize the dynamic injection attack. In this countermeasure, an interval state forecasting method is proposed to approximate the possible largest variation bounds of each state variable based on a worst-case analysis considering the forecasting uncertainties of renewable energy sources, electric loads, and network parameter perturbations. In addition, the kernel quantile regression is introduced and implemented to formulate the uncertainties in renewable energy and electric load forecast as a series of confidence intervals. When any state variable falls outside its preforecasted intervals, the proposed countermeasure detects the anomaly and sets an alarm condition indicating the possibility of data contamination. Finally, the results from our extensive studies on several IEEE standard test systems have been presented to demonstrate the feasibility of the dynamic attack and the effectiveness of the detection countermeasure.

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Dynamic Data Injection Attack Detection of Cyber Physical Power Systems With Uncertainties

Abstract New energy integration and flexible demand response make smart grid operation scenarios complex and changeable, which bring challenges to network planning. If every possible scenario is considered, the solution to the planning can become extremely time-consuming and difficult. This paper introduces statistical machine learning (SML) techniques to carry out multi-scenario based probabilistic power flow calculations and describes their application to the stochastic planning of distribution networks. The proposed SML includes linear regression, probability distribution, Markov chain, isoprobabilistic transformation, maximum likelihood estimator, stochastic response surface and center point method. Based on the above SML model, capricious weather, photovoltaic power generation, thermal load, power flow and uncertainty programming are simulated. Taking a 33-bus distribution system as an example, this paper compares the stochastic planning model based on SML with the traditional models published in the literature. The results verify that the proposed model greatly improves planning performance while meeting accuracy requirements. The case study also considers a realistic power distribution system operating under stressed conditions. 

/pdf/statistical-machine-learning-model-for-capacitor-planning-df5hdg2s.pdf

Statistical machine learning model for capacitor planning considering uncertainties in photovoltaic power

Distribution system state estimation, which provides critical information for system monitoring and control, is being challenged by multiple sources of uncertainties such as random meter errors, stochastic power output of distributed generation (DG), and imprecise network parameters. This paper originally proposes a general interval state estimation (ISE) model to simultaneously formulate these uncertainties in unbalanced distribution systems by interval arithmetic. Moreover, this model can accommodate partially available measurements of DG outputs and inaccurate line parameters. Furthermore, a modified Krawczyk-operator algorithm is proposed to solve the general ISE model efficiently, and effectively provides the upper and lower bounds of state variables under coordinated impacts of these uncertainties. The proposed algorithm is tested on unbalanced IEEE 13-bus and 123-bus systems. Comparison with various methods including Monte Carlo simulation indicates that the proposed algorithm is many orders of magnitude faster and encloses tighter boundaries of state variables.

Interval State Estimation With Uncertainty of Distributed Generation and Line Parameters in Unbalanced Distribution Systems

Uncertainties associated with the increasing penetration of renewable power generation (RPG) in microgrids have introduced numerous challenges to their effective energy management. This paper proposes a novel risk-based uncertainty set optimization method for the energy management of typical hybrid AC/DC microgrids, where RPG outputs are considered as the major uncertainties. The underlying risks of RPG curtailment and load shedding are formulated in detail based on the probabilistic distribution of forecasted RPG values, and are further considered in the objective functions. The proposed model is solved using a piecewise linearization method combined with the quadratic Newton-Gregory interpolating polynomial technique to linearize the variable integration limit terms of the underlying risks, while the Chebyshev consistent linear approximation method is proposed to approximate the non-linear terms of the bi-directional converter conversion efficiency. Finally, the proposed model is reformulated as a mixed integer linear programming problem, and effectively solved using a high-performance solver. The proposed method is applied in simulations of an actual hybrid AC/DC microgrid system in China to demonstrate its effectiveness, good applicability, and robustness in comparison to standard robust optimization methods. The impact of RPG prediction accuracy and electric vehicle battery loss cost on the obtained solutions are further analyzed and discussed.

Risk-Based Uncertainty Set Optimization Method for Energy Management of Hybrid AC/DC Microgrids With Uncertain Renewable Generation

Interval power flow (IPF) is a special kind of uncertain power flow problem whose demand load and active power generation are regarded as interval uncertainties. A novel optimizing-scenarios method (OSM) is envisaged in this paper to solve this problem to obtain more accurate results. The OSM includes two kinds of approaches, namely the minimum and maximum programming models, in which the interval uncertainties are regarded as variables with varying bounds, and the objective function under study is set to determine these unknown variables. By solving these nonlinear programming models through the interior point method, the changing variables of the IPF problems are determined. Performance of the proposed approach is compared with that of previously established methods, including the affine arithmetic-based method as well as Monte Carlo simulation method, and the overall simulation results demonstrate the effectiveness and robustness of the proposed OSM.

An Interval Power Flow Analysis Through Optimizing-Scenarios Method

The fundamental theory of energy networks in different energy forms is established following an in-depth analysis of the nature of energy for comprehensive energy utilization. Based on the physical theories of analytical mechanics, thermodynamics, heat transfer, fluid mechanics (fluid network), electromagnetic field theory (electric network), the generalized equations of energy transfer and transformation are established. Then the generalized lumped element models are derived, which include the generalized resistance, capacitor and inductance. To establish the equations of energy networks, the Kirchhoff’s Law in electric networks is extended to energy networks, which is called the Generalized Kirchhoff’s Law. The equations are finally unified into a complete energy network equation system. A numerical example is given to testify the effectiveness of energy network theory.

Energy Network Theory for Modeling and Analysis of Integrated Energy Systems

In order to protect environment and improve utilization efficiency of energy, district electricity and heating system has been well developed with the supportive policy. However, the coupling between electrical system and heating system brings challenges to the existed theories for energy system analysis. Energy network theory is proposed after an in-depth analysis of the nature of energy. The analysis of transfer process of electric energy, pressure energy and heat energy is proposed following rigorous mathematical derivation and analysis. Based on the energy network theory, the commonalities and characteristics of energy subnets are studied. Then, the energy network equations of district electricity and heating system are established. These equations are used for the modeling of a typical district electricity and heating system. A numerical case is analyzed by comparison between theoretical results and actual parameters, showing the validity of the energy network theory.

The Application of Energy Network Theory in the Analysis of District Electricity and Heating System

The fundamental theory of energy networks in different energy forms is established following an in-depth analysis of the nature of energy for comprehensive energy utilization. The definition of an energy network is given. Combining the generalized balance equation of energy in space and the Pfaffian equation, the generalized transfer equations of energy in lines (pipes) are proposed. The energy variation laws in the transfer processes are investigated. To establish the equations of energy networks, the Kirchhoff's Law in electric networks is extended to energy networks, which is called the Generalized Kirchhoff"s Law. According to the linear phenomenological law, the generalized equivalent energy transfer equations with lumped parameters are derived in terms of the characteristic equations of energy transfer in lines(pipes).The equations are finally unified into a complete energy network equation system and its solvability is further discussed. Experiments are carried out on a combined cooling, heating and power(CCHP) system in engineering, the energy network theory proposed in this paper is used to model and analyze this system. By comparing the theoretical results obtained by our modeling approach and the data measured in experiments, the energy equations are validated.

Ming Qiu

Papers

An Interval Power Flow Analysis Through Optimizing-Scenarios Method

Energy Network Theory for Modeling and Analysis of Integrated Energy Systems

The Application of Energy Network Theory in the Analysis of District Electricity and Heating System

Energy network: towards an interconnected energy infrastructure for the future