Smart Grid Environment Research Articles

Multi-temporal robust expansion planning of distribution grids considering uncertainties and curtailment of RES

This study focuses on the rising complexity of network expansion planning due to different sources of uncertainty and the transformation into a smart grid environment. Both have to be considered in the process of determining the optimal multi-annual expansion plan. Owing to long life-spans and high investment cost of the grid assets, robust investment decisions avoiding sunk-cost are crucial. Thus, a stochastic, multi-step optimisation programme has to be solved. This study presents a new hybrid-heuristic optimisation approach to derive optimal multi-annual investment decisions considering the uncertainty of the renewable energy sources (RES) development and flexibility options on the example of an RES curtailment.

Multiple Perspective-Cuts Outer Approximation Method for Risk-Averse Operational Planning of Regional Energy Service Providers

In the smart grid and future energy internet environment, a regional energy service provider (RESP) may be able to integrate multiple energy resources such as generator units, demand response, electrical vehicle charging/swapping stations, and carbon emission trading to participate in the market. By imploring a well-known portfolio optimization theory conditional value-at-risk to tackle electricity price uncertainty, this paper formulates the risk-averse day-ahead operational planning for such a RESP as a mixed-integer quadratically constrained programming (MIQCP), named as RA-RESP. A global optimization method, named as multiple perspective-cuts outer approximation method (MPC-OAM) is proposed to solve this model efficiently. A remarkable stronger and tighter mixed integer linear programing master problem is designed to accelerate the convergence of the proposed method. Comprehensive simulation results show that, compared with existing day-ahead planning models, the RA-RESP is a good compromise between profit-based models and cost-based ones. The proposed MPC-OAM can solve complicated RA-RESP problem efficiently, and compared with state-of-the-art solution techniques, the MPC-OAM outperforms in both computing speed and solution quality, especially for scenario which includes more nonlinear factors.

A new fuzzy framework for the optimal placement of phasor measurement units under normal and abnormal conditions

Abstract This paper presents a new procedure for finding the optimal placement of the phasor measurement units (PMUs) in modern power grids to achieve full network observability under normal operating conditions, and also abnormal operating conditions such as a single line or PMU outage, while considering the availability of PMU measuring channels. The proposed modeling framework is implemented using the fuzzy binary linear programming (FBLP) technique. Linear fuzzy models are proposed for the objective function and constraints alike. The proposed procedure is applied to five benchmark systems such as the IEEE 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus. The numerical results demonstrate that the proposed framework is capable of finding a fine-tuned optimal solution with a simple model and acceptable solution characteristics compared with early works in the literature. Besides, four evaluation indices are introduced to assure the various criteria under study such as the observability depth, measurement redundancy, and robustness of the method under contingencies. The results show that full network observability can be met under normal conditions using 20% PMUs penetration; however, under contingencies, approximately 50% PMUs penetration is required. The novelty of the proposed procedure has proven the capability of the proposed linear fuzzy models to find better optimal number of PMUs with lower number of channels compared to other algorithms under various operating conditions. Hence, the proposed work represents a potential tool to monitor power systems, and it will help the operators in a smart grid environment.

A Comprehensive Model With Fast Solver for Optimal Energy Scheduling in RTP Environment

In the smart grid environment, demand side management makes it possible to encourage consumers participating more actively via proper pricing schemes. In this paper, we analyze previous real-time pricing models and then provide a comprehensive model. A new criterion for designing real-time pricing models is proposed, which can guarantee that the optimal solution obtained from centralized algorithms is the same as that from the distributed algorithms. Furthermore, a fast distributed dual gradient algorithm is proposed to achieve the optimal solution. Compared with the widely used distributed dual sub-gradient algorithm, theoretically, the proposed one does not only accelerate the convergence rate, but also overcome the possible non-convergence during iteration process, which is a demerit in the traditional method. This new solver is of great importance in the application of dynamic pricing mechanism due to real-time requirement. More specifically, this new algorithm can largely reduce the information exchange between the energy provider and its customers, making the proposed method more practical. The simulations also validate its effectiveness and efficiency in solving real-time pricing problems.

Cost Analysis for a Hybrid Advanced Metering Infrastructure in Korea

Advanced metering infrastructure (AMI) refers to the electricity service infrastructure between electricity consumers and suppliers and is technically essential for the realization of a smart grid environment. To implement AMI, various communications technologies are being used based on the application environment according to the utility. However, using a single communications method can give rise to attenuation in the downtown underground distribution line section or cause higher supply costs due to decreased density in the range from farming to fishing areas. A hybrid AMI is one solution to this problem. According to an economic analysis of previous AMI deployment, the cost to install a communications network accounts on average for 45% of the total cost. Since the installation cost of a communications network is influenced by the density of the installation environment, a hybrid AMI, which allows the configuration of a flexible network using both wired and wireless communications, can be a good alternative, both technically and financially. This study conducted a simulation based on density of the installation environment and configuration of the communications network to analyze the economic effect of installing a hybrid AMI communications network. It assumed that a hybrid AMI was deployed in an overhead distribution line in a low-density area. The simulation outcomes were compared and analyzed against the power line communication (PLC)-only AMI method. The results showed that the hybrid AMI method had a 10% communications network cost reduction effect compared to the PLC-only AMI method. In addition, the analysis indicated that there was a maximum 19% cost reduction effect in communications network installation depending on the method of network installation, suggesting that the hybrid AMI was economically more effective than the PLC-only AMI method.

Cyber attack models for smart grid environments

Abstract Smart grids utilize communication technologies that make them vulnerable to cyber attacks. Because the power grid is a critical infrastructure, it is a tempting target for sophisticated and well-equipped attackers. Cyber attacks are usually based on Malicious Software (malware) that must communicate with a controlling entity over the network to coordinate and propagate. In this paper we investigate communication and spreading of malware in smart grids, proposing a comprehensive, generic model for cyber attack life-cycles, and addressing the specific characteristics of smart grid environments. The generic model includes the building blocks for all major known malware types as well as different propagation methods, access vectors, scanning techniques, control structures, attack methods, triggers, and cleanup mechanisms. Supported by an extensive review of earlier work, we examine the techniques of many different existing malware types with respect to their potential impacts on smart grids, and then discuss countermeasures. Toward this end, we analyze and evaluate a variety of types of malware –well-known but persistent malware, malware featuring outstanding or innovative concepts, as well as very recent malware –with respect to metrics that are fundamental to the generic model. We then introduce three novel superclasses of malware that are particularly suited for smart grid attacks, and evaluate their methods and impacts. Our model provides a basis for the detection of malware communication and extrapolates from existing technologies in order to predict future malware types. The smart grid specific malware types thus extrapolated provide insight into new threats and help utility companies to prepare defenses for future attacks.

Energy efficient and QoS-aware routing protocol for wireless sensor network-based smart grid applications in the context of industry 4.0

Recently, there have been great advances in internet of things (IoT) and wireless sensor networks (WSNs) leading to the fourth industrial revolution in power grid, namely, Smart Grid Industry 4.0 (SGI 4.0). In the Smart Grid Industry 4.0 framework, the WSNs have the potential to improve power grid efficiency by cable replacement, deployment flexibility, and cost reduction. However, the smart grid (SG) environment that the WSNs operate in is very challenging because of equipment noise, dust, heat, electromagnetic interference, multipath effects and fading, which make it difficult for current WSNs to provide reliable communication. For SGI 4.0 to come true, a WSN-based highly reliable communication infrastructure is essential for successful operation of the next-generation electricity power grids. To address this need, in this paper a novel dynamic clustering based energy efficient and quality-of-service (QoS)-aware routing protocol (called EQRP), which is inspired by the real behavior of the bird mating optimization (BMO), has been proposed. The proposed distributed scheme improves network reliability significantly and reduces excessive packets retransmissions for WSN-based SG applications. Performance results show that the proposed protocol has successfully reduced the end-to-end delay and has improved packet delivery ratio, memory utilization, residual energy, and throughput.

A quality of service–aware preemptive tidal flow queuing model for wireless multimedia sensor networks in the smart grid environment

The smart grid incorporates a two-way communication system between customers and the utility for advanced monitoring and intelligent control of supply and demand. Wireless multimedia sensor network can be treated as an organic supplement and a peripheral network in this two-way communication system. However, the challenging smart grid environment makes it difficult to achieve a high quality of service in wireless multimedia sensor network. This article proposes a prioritization mechanism that considers the heterogeneous characteristics of smart grid traffic. Specifically, an innovative channel allocation and traffic scheduling scheme, named the preemptive tidal flow queuing model, is presented. This scheme achieves differentiated services for diverse communication data when the wireless multimedia sensor network accesses the core network and ensures the performance for high-priority data at the expense of the performance for low-priority data. Simulation analyses show that the performance for high-priority messages can be reliably guaranteed and that the preemptive tidal flow queuing model satisfies the requirements for a wireless multimedia sensor network operating in the smart grid environment. This article offers three main contributions: the development of a prioritization mechanism specifically for a wireless multimedia sensor network in the smart grid environment, the proposal of the preemptive tidal flow queuing model, and the presentation of formulas and simulations to verify the performance of the preemptive tidal flow queuing model.

Adaptive Dynamic Programming-Based Optimal Control Scheme for Energy Storage Systems With Solar Renewable Energy

In this paper, a novel optimal energy storage control scheme is investigated in smart grid environments with solar renewable energy. Based on the idea of adaptive dynamic programming (ADP), a self-learning algorithm is constructed to obtain the iterative control law sequence of the battery. Based on the data of the real-time electricity price (electricity rate in brief), the load demand (load in brief), and the solar renewable energy (solar energy in brief), the optimal performance index function, which minimizes the total electricity cost and simultaneously extends the battery's lifetime, is established. A new analysis method of the iterative ADP algorithm is developed to guarantee the convergence of the iterative value function to the optimum under iterative control law sequence for any time index in a period. Numerical results and comparisons are presented to illustrate the effectiveness of the developed algorithm.

Lightweight and efficient privacy-preserving data aggregation approach for the Smart Grid

Over the last few years, we have seen the emergence of a wide range of Smart Grid architectures, technologies, and applications made possible by the significant improvements in hardware, software, and networking technologies. One of the challenges that has emerged in the Smart Grid environment is the privacy of Smart Grid users. Although several privacy-preserving techniques have been proposed recently for the Smart Grid environment, many of them suffer from high computation and communication costs, different types of attacks, and the use of complex key management schemes. To address these drawbacks, we propose an efficient, lightweight privacy-preserving data aggregation approach that makes use of symmetric homomorphic encryption and Diffie–Hellman (DH) or Elliptic Curve Diffie–Hellman (ECDH) key exchange methods. In contrast to previously proposed privacy-preserving schemes for the Smart Grid, we demonstrate the superiority of our proposed approach in terms of its low transmission and message overheads, and resiliency against a wide range of session key attacks, and ability to maintain data integrity against unauthorized modification or data forgery and to ensure authenticity of smart meters’ data.

Performance Analysis of ZigBee in Beacon Enabled and Beacon-less Network for Smart Grid Environments

Background: Wireless sensor networks play very important role in the realization of Smart Grids having low cost and multi-functional sensor nodes to monitor the critical parameters of smart grid components. Its various applications are home automation, real time pricing, power outages, load control and security monitoring. It contains different harsh environments like 500 kV outdoor substation, main power control room and underground transformer vaults which are very difficult to access and monitor for a human being during an operational power grid. The communication in it constitutes of different type of traffic distinguished by exclusive attributes in terms of the data profile and latency requirement. Keywords: Wireless sensor network (WSN), IEEE 802.15.4, beacon, ZigBee, smart grid, QualNet 6.1.

A self-learning algorithm for estimating solar heat gain and temperature changes in a single-Family residence

To develop effective control optimization strategies for managing residential electricity consumption in a smart grid environment, predictive algorithms are needed that are simple to implement, minimize custom configuration, and provide sufficient accuracy to enable meaningful control decisions. This paper presents a self‐learning algorithm for predicting indoor temperature in a single-family residence using a first-order lumped capacitance (1R1C) model that can be used to evaluate the consequences of energy saving or load shifting strategies on thermal comfort. The algorithm is formulated in such a way that key design details such as window size/configuration, thermal insulation, and airtightness (all major factors that affect heat loss and solar heat gain) are combined into effective parameters that can be learned from observation. This approach eliminates the need for a custom configuration for each residence.The model was validated using experimental data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility. It was found that with a simple temperature decay test to determine a thermal time constant and a seven-day sliding window of training data to account for seasonal variations in other parameters, the algorithm can reliably predict indoor temperatures for a 24h period using a solar irradiance forecast, an outdoor air temperature forecast, and heat pump output. The average root mean square temperature prediction error was found to be 2.2%.

Probabilistic Multiobjective Transmission Expansion Planning Incorporating Demand Response Resources and Large-Scale Distant Wind Farms

Transmission expansion planning (TEP) is a critical issue, particularly coping with the new challenges of smart grids. Under the smart grid environment, demand response resources (DRRs) are contemplated as virtual power plants in energy policy decisions. DRRs affect the controllability of power systems, ranging from short-term to long-term scheduling. In this paper, a nonlinear economic model of responsive loads is presented based on price elasticity of demand and customers benefit function. In order to investigate the impacts of DRRs on TEP, a probabilistic multiobjective TEP incorporating demand response programs (DRPs) is introduced (PMO-TEP $^\mathrm{DRPs}$ ). IC, congestion costs, risk costs, and total incentive costs for participating in DRPs are considered different objectives of PMO-TEP $^\mathrm{DRPs}$ . Here, a probabilistic analysis technique, the so-called two-point estimation method, is applied to handle the uncertainty of wind-farm generation in the TEP problem. Furthermore, the maximum achievable potential and realistic achievable potential for participating customers in DRPs are derived. Due to the problem's nonconvex formulation, a nondominated sorting genetic algorithm II is utilized to evaluate PMO-TEP $^\mathrm{DRPs}$ . Several analyses are carried out on the IEEE Reliability Test System and Iran 400-kV transmission grid to confirm the capability of the proposed framework.

Demand Response Resources' Allocation in Security-Constrained Preventive Maintenance Scheduling via MODM Method

Security-constrained preventive maintenance (PM) scheduling is addressed as a crucial issue particularly coping with new challenges of smart grids. Under the smart grid environment, demand response resources (DRRs) are considered as virtual power plants in energy policy decisions, which affect the controllability of the power system, ranging from short term to long term. In this paper, an economic model of responsive loads is represented based upon price elasticity of demand and customers' benefit function. In order to investigate the impacts of DRRs on PM scheduling, a multiobjective security-constrained PM scheduling incorporating demand response programs (DRPs) is introduced. Total system expenditures, including operation and maintenance costs, reserve cost, and total incentive due to participating in DRPs, are contemplated as the first objective; and the second one is generated emissions over the scheduling time horizon. Utilizing the lexicographic method as one of the multiobjective decision-making techniques, the maintenance scheme, commitment status, energy and reserve scheduling are determined along the scheduling horizon. Furthermore, the actual potential for customers' participation in DRPs, optimum locations, and multifarious optimal incentive in per bus are also derived. Several analyses are carried out on the IEEE-RTS to confirm the capability of the proposed framework.

Decentralized Local Pricing Improving Network Usage in a Smart-Grid Environment Under Limited Information

With a smart grid environment, flexible load devices and provided local price incentives a more efficient grid usage may be achieved in the future. Bidirectional communication, smart devices and shiftable loads as electric vehicles and heat pumps have the potential to be coordinated with local supply when suitable incentives are provided. This can bring relief especially for distribution grid areas where infeeds from fluctuating renewable energy sources increase. This paper presents a decentralized local pricing mechanism, aiming at local prices that reflect the current load situation. That is in case of congestion a local price, deviating from the wholesale market price, is determined. With an iterative search algorithm suitable prices can be computed without gathering full-fledged bidding data. Simultaneously self-reinforcing effects are avoided. Further on this concept can be implemented rather easily precisely where and when required so that only areas with grid congestion are affected.

Research on Power Market Development Problems in the Smart Grid Environment

The relationship between smart grid and the development of electric power market is a very wide range of aspects and there are many unpredictable factors. From the nature and characteristics of the smart grid, the article has analyzed the influence and role in promoting the smart grid development of electricity market, and the smart grid as the carrier of the electric power market has been discussed.

Requirements, Design Challenges, and Review of Routing and MAC Protocols for CR-Based Smart Grid Systems

Cognitive radio technology can facilitate communication in smart grid applications through dynamic spectrum access. However, traditional routing and MAC protocols adopted for cognitive radio networks may not be beneficial in CR-based smart grid environments due to large data sizes and variable link quality among different functional blocks of smart grids. The interference and fading in wireless links necessitate efficient routing for reliable low-latency data delivery of smart grid applications. This low-latency data delivery must be achieved while protecting the legitimate primary users. Besides efficient routing, MAC layer protocols should be enhanced to achieve successful data delivery with simultaneous spectrum sensing and duty cycling for energy-efficient operation. In this article, we evaluate the requirements and key design challenges for routing and MAC protocols in the CR-based smart grid. We also provide a review of research carried out to date for routing and MAC protocols for the CR-based smart grid.

Classification of Power Quality Disturbances at Transmission System using Support Vector Machines

<p>Power Quality has become one of the important issues in modern smart grid environment. Smart grid generally utilizes computational intelligence method from the generation of electricity to electricity distribution to the customers. This is done for the safety, reliability, tenacity and efficiency of the system. The classification of power disturbances has become a major topic in maintaining power quality. These disturbances occur due to faults, natural causes, load switching, energizing transformer, starting large motor, as well as utilization of power electronic devices. The key issue is about maintaining the continuous supply of electricity to the end-users without any problem. If a problem occurs, it might increase the production cost significantly especially to large-scale industries. In this paper, S-transform is used to extract distinctive features of real data from transmission system, and Support Vector Machine was utilized to classify four types PQ disturbances namely, voltage sag, interruption, transient and normal voltage. Results obtained indicate that performance of the One Against One classifier produces high accuracy using k-fold cross validation and RBF kernel.</p>

Approach for smart meter load profiling in Monte Carlo simulation applications

Smart grids introduce new technological elements into power systems which take prevalent challenges to a new level by shaping parameters of power systems towards a complex regime of uncertainties. Rapid proliferation of advanced metering infrastructure (AMI) and integration of renewable energy sources in smart grids increase system-wide complexities. This study proposes an innovative approach to classify the energy consumptions of smart meter customers with typical profiles by processing with multi-layered clustering of energy consumption data of smart consumers extracted from the AMI. There are two stages for the approach of which the first stage analyses the data for intra-cluster similarity of energy consumption patterns and in case the patterns do not have a high intra-cluster similarity, they are fed back for re-clustering with multi-layered clustering process until the clearly identifiable energy patterns with high intra-cluster similarity is obtained. The second stage linearises the complex energy patterns using interpolant and curve fitting techniques until stabilised profiles are obtained. This study also proposes a methodology for smart meter load modelling for Monte Carlo (MC) simulation applications to reduce the computing time compared with traditional alternatives. This study validates the robustness of the approach and provides the corroboration of the method for MC simulation applications in a smart grid environment.

Increasing the Resilience of Cyber Physical Systems in Smart Grid Environments using Dynamic Cells

Resilience is an important system property that relies on the ability of a system to automatically recover from a degraded state so as to continue providing its services. Resilient systems have the means of detecting faults and failures with the added capability of automatically restoring their normal operations. Mastering resilience in the domain of Cyber-Physical Systems is challenging due to the interdependence of hybrid hardware and software components, along with physical limitations, laws, regulations and standards, among others. In order to overcome these challenges, this paper presents a modeling approach, based on the concept of Dynamic Cells, tailored to the management of Smart Grids. Additionally, a heuristic algorithm that works on top of the proposed modeling approach, to find resilient configurations, has been defined and implemented. More specifically, the model supports a flexible representation of Smart Grids and the algorithm is able to manage, at different abstraction levels, the resource consumption of individual grid elements on the presence of failures and faults. Finally, the proposal is evaluated in a test scenario where the effectiveness of such approach, when dealing with complex scenarios where adequate solutions are difficult to find, is shown.