Smart Grid Era Research Articles

Integrated Cyber-physical Contingency Analysis and Risk Estimates

Introduction China’s power grid is now entering the new era of smart grid with the “power-information-business” interconnected stage， where the cybersecurity in the information system and the operational security in the power system are closely coupled， which， however， would cause severe security problem of the cyber-physical system（CPS）. Method This paper surveyed on the cybersecurity issue of CPS and summarizes the corresponding risk estimation and management framework.Then the paper introduced the background and current status of cybersecurity， categorized the state-of-the-art methods， risk estimation framework， modeling insights， etc. This paper also fundamentally investigated both the advantages and disadvantages of each model and evaluated its development potentials. Result This study would help enterprises to screen and identify cybersecurity events and explore the correlation and dependency within the events， which may help researchers to exploit new interests. Conclusion This work induces investors to optimize their resources and budget allocations， which would also guide for security engineers to proceed with cyber risk estimates and to prepare contingency plans.

Learning With End-Users in Distribution Grids: Topology and Parameter Estimation

Efficient operation of distribution grids in the smart-grid era is hindered by the limited presence of real-time nodal and line meters. In particular, this prevents the easy estimation of grid topology and associated line parameters that are necessary for control and optimization efforts in the grid. This article studies the problems of topology and parameter estimation in radial balanced distribution grids where measurements are restricted to only the leaf nodes and all intermediate nodes are unobserved/hidden. To this end, we propose two exact learning algorithms that use balanced voltage and injection measured only at the end users. The first algorithm requires time-stamped voltage samples, statistics of nodal power injections, and permissible line impedances to recover the true topology. The second and improved algorithm requires only time-stamped voltage and complex power samples to recover both the true topology and impedances without any additional input (e.g., number of grid nodes, statistics of injections at hidden nodes, and permissible line impedances). We prove the correctness of both learning algorithms for grids where unobserved buses/nodes have a degree greater than three and discuss extensions to regimes where that assumption doesn't hold. Further, we present computational and, more important, the sample complexity of our proposed algorithm for joint topology and impedance estimation. We illustrate the performance of the designed algorithms through numerical experiments on the IEEE and custom power distribution models.

The role of IT in energy systems: the digital revolution as part of the problem or part of the solution

The architectural design of our energy systems dates back to a time without information technology (IT). Over time, IT was applied where it increased efficiency and safety. About 12 years ago, the Smart Grid era began. In the meantime, we talk about digitalization. Electrical energy systems require embedded systems, Internet of Things, computation clusters and data analytics. However, IT also has another role in the energy system, namely that of a substantial consumer. Crypto currencies and data centers are on the rise. We analyze impacts on energy demand and discuss risks and chances of this development.

Power System Hardware in the Loop (PSHIL): A Holistic Testing Approach for Smart Grid Technologies

The smart-grid era is characterized by a progressive penetration of distributed energy resources into the power systems. To ensure the safe operation of the system, it is necessary to evaluate the interactions that those devices and their associated control algorithms have between themselves and the pre-existing network. In this regard, Hardware-in-the-Loop (HIL) testing approaches are a necessary step before integrating new devices into the actual network. However, HIL is a device-oriented testing approach with some limitations, particularly considering the possible impact that the device under test may have in the power system. This paper proposes the Power System Hardware-in-the-Loop (PSHIL) concept, which widens the focus from a device- to a system-oriented testing approach. Under this perspective, it is possible to evaluate holistically the impact of a given technology over the power system, considering all of its power and control components. This paper describes in detail the PSHIL architecture and its main hardware and software components. Three application examples, using the infrastructure available in the electrical engineering laboratory of the University of Sevilla, are included, remarking the new possibilities and benefits of using PSHIL with respect to previous approaches.

Hybrid PSO-TS Based Distribution System Expansion Planning for System Performance Improvement Considering Energy Management

Energy management (EM) has become very attractive in smart grid era for both industry and academia. EM in a power distribution system (PDS) can reduce the system losses, operational costs and emissions, and increases system’s reliability, voltage stability and efficiency. This article expands the PDS using different distributed energy resources (DERs) and observes their impacts on EM success, operational costs, system losses, and emissions. Moreover, the impacts of integrating reactive sources (RSs), energy storage systems (ESSs), and electric vehicles (EVs) on the system’s operational performance is investigated. An index is defined to measure the energy management success rate (EMSR) and several case studies are created for investigating variety of system expansion options individually and collectively. Furthermore, a new reliability index, namely reliability index success rate (RISR), is defined and sensitivity analysis are presented to investigate the DERs impacts on system’s voltage controllability and the defined reliability index. A new hybrid PSO-TS optimization algorithm is developed, and the well-known PG&E 69-bus distribution network is selected for the simulations. Several case studies presented in the paper provide a clear understanding of the impacts of integrating different resources on the EM as well as other operational aspects of a PDS. The results reveal that the system expansion using DERs with different capacities along with EM strategies could have significant impacts on PDS performance depending on the DERs types’ and capacities. Due to their availability and low operational costs, gas turbine further improves the performance indices comparing to the renewables that have uncertain availability and to diesel generators with their high operational costs.

Application and Research of Power Information Communication Technology in Smart Grid

Under the background of intelligent power grid, the rational application of information and communication technology can fully improve the quality of construction and management within the system. In the era of smart grid, workers in related fields need to more clearly define the application characteristics of information and communication technologies, rationally plan the application of information and communication technologies in new energy fields, power transmission and distribution, and power consumption, and improve information and communication technologies. The application efficiency optimizes the modernization and development level of information and communication technology.

A Control-Based Method to Meet TSO and DSO Ancillary Services Needs by Flexible End-Users

This paper presents a new methodology to exploit consumers’ flexibility for the provision of ancillary services (AS) in the smart grid era. The proposed framework offers a control-based approach that adopts price signalsas the economic driver to modulate consumers’ response. In this framework, various system operators broadcast price signals independently to fulfil their AS requirements. Appropriate flexibility estimators are developed from the transmission system operator (TSO) and distribution system operator (DSO) perspectives for price generation. An artificial neural network (ANN) controller is used for the TSO to infer the price-consumption reaction from pools of consumers in its territory. A proportional-integral (PI) controller is preferred to represent the consumers’ price-response and generate time-varying electricity prices at the DSO level for voltage management. A multi-timescale simulation model is built in MATLAB to assess the proposed methodology in different operational conditions. Numerical analyses show the applicability of the proposed method for the provision of AS from consumers at different levels of the grid and the interaction between TSO and DSOs through the proposed framework.

Managing Distributed Energy Resources (DERs) Through Virtual Power Plant Technology (VPP): A Stochastic Information-Gap Decision Theory (IGDT) Approach

As the number of distributed energy resources (DERs) increases, so would their influences on the power systems. Toward managing the intermittent nature of DERs, and providing market participation capability, the concept of virtual power plant (VPP) has been developed in the smart grid era. This paper proposes a novel offering strategy method for a VPP consisting of wind power generation along with storage capacity and flexible demand. In the proposed method, two different sources of uncertainty are addressed: stochastic method, applied in dealing with market price uncertainty, and information-gap decision theory (IGDT), incorporated in the offering model counting for wind power production uncertainty. This method enables us to formulate the offering strategy problem as a stochastic IGDT-based optimization. Within the framework of the proposed approach, we explore different operating facets that can affect the solution methodology and how the offering curve is constructed. The theoretical strategy is thoroughly demonstrated and then validated through numerical studies.

System reconfiguration in microgrids

Microgrids are emerging as an important technology in the smart grid era. They are particularly important from the perspective of integrating new renewable generation into the distribution side as well as ensuring system reliability. To operate the microgrid in an optimal and efficient way, one of the suggested technologies is the reconfiguration of the microgrid, which uses sectionalizing and tie switches already present in the system. This paper reviews the work done in microgrid reconfiguration so far, categorizing it on the basis of the several objective functions used, constraints considered, optimization techniques used, and methods of incorporating uncertainty inherent in renewable generation. The paper seeks to serve researchers and practitioning engineers with the state-of-the-art in the area and the research gaps and future challenges involved in microgrid reconfiguration are also highlighted.

Short Term Probabilistic Load Forecasting With Integrated Methods

In smart grid era, electric load is becoming more stochastic and less predictable in short horizons with more intermittent energy and competitive electricity market transactions. As a result, short-term probabilistic load forecasting (STPLF) is becoming essential for energy utilities because it helps quantify the risks of decision-making for power systems operation. Currently, probabilistic load forecasts (PLF) are commonly produced from three single components, namely input, model and output. Nevertheless, whether integrating two components to represent dual uncertainties of electric load is practical and able to improve STPLF attracts little regards. To address this issue, this paper proposes three integrated methods by pairwise combination of single representative component, i.e. uniform-biased temperature scenarios (UBTS), quantile regression (QR) and logarithmic residual empirical simulation (LRES). Case study on real utility data demonstrates the superiority of the integrated methods and excavates the relationship between predictive model class and specific integrated method.

Day-Ahead Hierarchical Probabilistic Load Forecasting With Linear Quantile Regression and Empirical Copulas

In the smart grid era, high granular data play an important role in providing an enormous amount of information for industry and commerce, both temporally and spatially. With massive data, a hierarchical structure can be constructed, containing load series at diverse levels. With the fluctuation and uncertainty of power supply and demand increasing rapidly, hierarchical probabilistic load forecasting is necessary for a hierarchy formed by power system network, which can provide comprehensive information on electricity consumption at different levels. System operators or power market participants can make coherent decisions based on coherent forecasting. The challenge for hierarchical probabilistic load forecasting is how to produce probabilistically coherent forecasts. In order to simplify the prediction procedure and improve the prediction accuracy, an effective approach that could generate probabilistically coherent forecasts for a hierarchy is introduced in this paper. The proposed methodology has three major achievements: 1) a naive multiple linear regression model is proposed for bottom-level series; 2) a novel approach of combining quantile regression and empirical copulas is proposed to estimate the joint distribution of random variables; 3) to improve the prediction accuracy, a weighted correction method based on constrained quantile regression is introduced to adjust predictive distributions at the bottom level. In case of studies, the effectiveness of our proposed method is verified by using two public datasets. Compared with four benchmarks, evaluation results show that the proposed approach makes better performance.

Detection of Energy Theft in Overhead Low-Voltage Power Grids – The Hook Style Energy Theft in the Smart Grid Era

This paper investigates the possibility of detecting the hook style energy theft in the overhead low-voltage (OV LV) power grids when the smart grid conveniences are available. On the basis of the broadband over power lines (BPL) technology and the proposed method of the detection of the hook style energy theft (HS-DET method), a plethora of different scenarios concerning the hook style energy theft is considered so that the performance of HS-DET method can be assessed. The impact of OV LV BPL topologies, hook characteristics and measurement differences on the performance of HS-DET method is mainly assessed through appropriate metrics, such as derivative metrics of percent error sum (PES). Finally, appropriate contour plots against the hook style energy theft are proposed revealing the efficiency of HS-DET method against any relevant threat in any conditions. Citiation: Lazaropoulos, A. G. (2019). Detection of Energy Theft in Overhead Low-Voltage Power Grids – The Hook Style Energy Theft in the Smart Grid Era. Trends in Renewable Energy, 5(1), 12-46. DOI: 10.17737/tre.2019.5.1.0081

The Impact of Renewable Energy for Occupational Health in the Smart Grid Era

The Impact of Renewable Energy for Occupational Health in the Smart Grid Era

Guest Editorial Special Section on Industrial and Commercial Demand Response

The eleven papers in this special section focus on the industrial and commercial potential of demand response (DR). Customers from this non-residential market base have great potential in providing flexibility for power systems through diverse demand response (DR) programs. Intelligent energy management can be carried out with DR in industrial and commercial facilities, especially if onsite control, information, and communication technologies are available, enabling also the inherent automation capabilities of heating, ventilation, and air conditioning systems. In the dawn of the Smart Grid era, with increasing distributed generation and the conversion of traditionally passive consumers to newly active energy players in the market, DR is being effectively considered for outage management and network reinforcement deferral.

Big Data Mining of Users’ Energy Consumption Patterns in the Wireless Smart Grid

A growing number of utility companies are starting to use cellular wireless networks to transmit data in the smart grid. Consequently, millions of users' daily energy consumption data are sent by wireless smart meters. However, the broadcast transfer manner of wireless communication makes it naturally vulnerable to cyber attacks. Since smart meter readings can easily be leaked, users' energy patterns could be inferred. Hence, users' privacy at home is under serious threat. This article begins by introducing the existing work on stealing data from wireless communication networks. Then three types of big data mining schemes for analyzing stolen data are represented. Finally, we discuss several ongoing defense strategies in the era of the wireless smart grid.

Ancillary Services 4.0: A Top-to-Bottom Control-Based Approach for Solving Ancillary Services Problems in Smart Grids

Power systems are experiencing a large amount of renewable generation with highly stochastic and partly unpredictable characteristics. This change in energy production implies significant consequences related to the provision of ancillary services (AS). Current markets dedicated to the provision of AS are not able to benefit from the flexible energy resources. They also cannot cope with the new level of stochasticity, non-linearity, and dynamics of generation and flexibility. To overcome such issues and exploit the potential of flexibility resources, a new strategy is required. In this paper, by capitalizing on flexibility resources’ potential, AS 4.0 approach is proposed, which offers a comprehensive solution for the AS provision in the smart grid era.

Digitization Era for Electric Utilities: A Novel Business Model Through an Inter-Disciplinary S/W Platform and Open Research Challenges

The digitization era for electric utilities is tightly coupled with the development of inter-disciplinary software (S/W) platforms aiming at establishing much more efficient communication pathways with their clientele and other smart grid market stakeholders. This paper presents the SOCIALENERGY S/W platform, which is funded by the EU H2020 work programme. SOCIALENERGY is a user engagement, social networking, gamification, and business management platform aiming at evolving energy markets’ operation and educating virtual energy communities. The proposed business model is targeted on electric utilities’ customer segment. The proposed system is modular by design incorporating several subsystems from various disciplines, such as ICT, energy efficiency, behavioral economics, education, and gamification. The proposed system facilitates the easy, rich, and deep communication among individual energy consumers, virtual energy communities, utilities, and other less direct stakeholders (such as electric appliance retailers and building renovators). Finally, the paper provides important research/innovation insights and challenges to be addressed towards the proposed Green Social Response Network concept and the exploitation of SOCIALENERGY system as part of more complex systems for the 2030 smart grid era and beyond.

Deferral of Power Distribution Reinforcement by Using Battery Energy Storage System

This paper focuses on the applications of the battery energy storage system (BESS) to be installed in distribution systems for deferment of the distribution reinforcement investment in the short-term power distribution planning. The distribution system in this paper refers to the cost and standard criteria of Provincial Electricity Authority (PEA). The existing system is 22-kV voltage level includes 115/22 kV substation, the 45-km. distribution feeder, 1 photovoltaic plant and 8 load points with mixed customer types. The technical problem often occurred in 22-kV distribution feeders is the power demand exceeds the line thermal limit in short time (1-4 hour per day) every day. Following PEA’s standard and criteria, distribution network owners (DNO) has 2 choices to solve this problem; taking the risk of fault or reinforce feeders. Nowadays, energy storage technologies play the interested role in smart grid era. It illustrates from the analysis that BESS can solve this problem and postpone the time of system reinforcement such as construction of the new distribution line with worthy investment. Power flow of the study system is calculated by DIgSILENT PowerFactory software. The best alternative is chosen based on the maximum net present value (NPV).

Dynamic Resource Allocation for Smart-Grid Powered MIMO Downlink Transmissions

Benefiting from technological advances in the smart grid era, next-generation multi-input multi-output (MIMO) communication systems are expected to be powered by renewable energy sources (RES) integrated in the distribution grid, thus realizing the vision of “green communications.” However, penetration of renewables introduces variabilities in the traditional power system, making RES benefits achievable only after appropriately mitigating their inherently high variability, which challenges existing resource allocation strategies. Aligned with this goal, an infinite time-horizon resource allocation problem is formulated to maximize the time-average MIMO downlink throughput, subject to a time-average energy cost budget. By using the advanced time decoupling technique, a novel stochastic subgradient-based online control approach is developed for the resultant smart-grid powered communication system. It is established analytically that even without a priori knowledge of the independently and identically distributed (i.i.d.) processes involved such as channel coefficients, renewables, and electricity prices, the proposed online control algorithm is still able to yield a feasible and asymptotically optimal solution. Numerical results further demonstrate that the proposed algorithm also works well in non-i.i.d. scenarios, where the underlying randomness is highly correlated over time.

Multi-Objective Hierarchical Control of Unbalanced Distribution Networks to Accommodate More Renewable Connections in the Smart Grid Era

Distribution networks, which are already complex large scale systems with diverse loads, are becoming more complicated in the smart grid era due to the increasing connections of distributed generators (e.g., solar and wind) and smart devices (e.g., electric vehicles and storage batteries). Although highly automated and integrated control systems have been proposed, systems with coverage from the zone substation to the low-voltage feeders practically do not exist. Based on the natural voltage decomposition of distribution networks and authors’ previous research on distribution system control, this paper proposes an efficient multi-objective hierarchical optimization strategy that can fully exploit the capabilities of both traditional and emerging devices, expand network load capacity and accommodate more renewable connections. Detailed simulations are performed on a real Australian distribution network over 24 hours to verify the feasibility, effectiveness and superiority of the proposed hierarchical optimization and some future work is also discussed.