1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue To date, substantial works have been conducted on smart meter data analytics To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management We also review the techniques and methodologies adopted or developed to address each application In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security

By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed in this paper for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming. The model is transformed into a readily solvable mixed integer linear programming formulation in general algebraic modeling system (GAMS) via a proposed discretized step transformation approach and finally solved by applying the CPLEX solver. By properly setting the confidence levels of the spinning reserve probability constraints, the MG operation can achieve a tradeoff between reliability and economy. The test results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab MG test system reveal that the proposal significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.

Optimal Scheduling of an Isolated Microgrid With Battery Storage Considering Load and Renewable Generation Uncertainties

The power flow equations model the relationship between voltage phasors and power injections at nodes (buses) in an electric power system and are fundamental in their analysis and operation. A wealth of literature exists on these equations, many providing different representations of them. This monograph provides the first comprehensive survey of representations in the context of optimization. To achieve this the power flow representations surveyed are categorized as either relaxations or approximations. The techniques described in here form the basis of running an optimally efficient modern day power system. A Survey of Relaxations and Approximations of the Power Flow Equations is a must-read for all students and researchers working on the cutting edge of electric power systems.

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A Survey of Relaxations and Approximations of the Power Flow Equations

Multi-energy systems (MESs) contribute to increasing energy utilization efficiency and renewable energy accommodation by coupling multiple energy sectors. The preferable characteristic of MESs raises the need for optimizing the configuration of MESs across multiple energy sectors at the planning stage. Based on the energy hub (EH) model, this research presents a two-stage mixed-integer linear programming approach for district level MES planning considering distributed renewable energy integration. The approach models an MES as a directed acyclic graph with multiple layers. The proposed EH configuration planning procedure includes two stages: 1) optimizing what equipment (e.g., energy converters, distributed renewable energy sources and storages) should be invested in for each layer and 2) optimizing the connection relationships between the invested equipment in each two adjacent layers. The proposed approach is able to optimize both the equipment selection and the MES configuration. It can be applied to both expansion planning and initial planning of MESs from scratch. An illustrative example of planning a typical MES is given. A sensitivity analysis is performed to show the impacts of load profiles, energy prices and equipment parameters on the optimal MES configuration. A case study based on the MES in Beijing’s new subsidiary administrative center is conducted using the proposed approach.

Optimal Configuration Planning of Multi-Energy Systems Considering Distributed Renewable Energy

Linearized power flow models are of great interest in power system studies such as contingency analyses and reliability assessments, especially for large-scale systems. One of the most popular models—the classical DC power flow model—is widely used and praised for its state independence, robustness, and computational efficiency. Despite its advantages, however, the DC power flow model fails to consider reactive power or bus voltage magnitude. This paper closes this gap by proposing a decoupled linearized power flow (DLPF) model with respect to voltage magnitude and phase angle. The model is state independent but is distinguished by its high accuracy in voltage magnitude. Moreover, this paper presents an in-depth analysis of the DLPF model with the purpose of accelerating its computation speed, leading to the fast DLPF (FDLPF) model. The approximation that is applied to obtain the FDLPF model from the DLPF model is justified by a theoretical derivation and numerical tests. The proposed methods are provably accurate and robust for several cases, including radial distribution systems, meshed large-scale transmission systems and ill-conditioned systems. Finally, expressions for sensitivity with regard to MW flow and bus voltage are provided as a potential application.

A State-Independent Linear Power Flow Model With Accurate Estimation of Voltage Magnitude

Multiple energy systems (MESs) bring together the electric power, heat, natural gas, and other systems to improve the overall efficiency of the energy system. An energy hub (EH) models an MES as a device with multiple ports using a matrix coupling the inputs and outputs. This paper proposes a standardized matrix modeling method based on the concept of EH to build the coupling matrix automatically. The components and the structure of MES are first defined using graph theory. Then, the matrices describing the topology of the MES and the characteristics of the energy converters are developed. On this basis, the energy flow equations are formulated. Gaussian elimination can then be applied to obtain the coupling matrix and analyze the degree of freedom of the EH. A standard data structure for basic information on the MES is proposed to facilitate computerized modeling. Further, extension modeling of energy storage and demand response is also discussed. Finally, a case study of a modified tri-generation system is conducted to illustrate the proposed method.

Standardized Matrix Modeling of Multiple Energy Systems

Smart meters provide large amounts of data and the value of this data is getting increased attention because a better understanding of the characteristics of consumers helps utilities and retailers implement more effective demand response programs and more personalized services. This paper investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-demographic information about the consumers.

Deep Learning-Based Socio-Demographic Information Identification From Smart Meter Data

A new challenge has arisen in power generation scheduling recently, as the rapid increase in the number of gas-fired units has made power systems more vulnerable to failures in natural gas networks. The large-scale integration of wind power further exacerbates the problem because gas-fired units are usually scheduled to catch up wind power uncertainty and thus lead to great variations in the state of gas network. To meet this challenge, it is necessary to commit and dispatch the gas-fired units considering both wind uncertainty and natural gas network security. However, the dynamic characteristics of gas flow are remarkably slower than those of power flow, which should be appropriately modeled to explore its benefits for power system operation. Because directly applying partial differential equations overly complicates the already complicated generation scheduling problem, we address this problem by proposing an approximated transient matrix-form gas flow model. A two-stage robust generation scheduling model is then proposed considering the dynamic security constraints of gas networks and the wind power uncertainty. Moreover, we successfully avoid the nonlinearity of gas flow constraints by developing a new solution methodology. Finally, an illustrative case is presented to demonstrate the effect of gas network dynamics in generation scheduling.

Jingwei Yang

Papers

Optimal Configuration Planning of Multi-Energy Systems Considering Distributed Renewable Energy

A State-Independent Linear Power Flow Model With Accurate Estimation of Voltage Magnitude

Standardized Matrix Modeling of Multiple Energy Systems

Deep Learning-Based Socio-Demographic Information Identification From Smart Meter Data

Effect of Natural Gas Flow Dynamics in Robust Generation Scheduling Under Wind Uncertainty