Future Smart Grid Research Articles

Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids

This paper proposes a three-state energy model, which contains three states, i.e., generator, power load and closed states. Besides, this paper proposes an expandable deep learning for the real-time economic generation dispatch and control of three-state energies based future smart grids. Although three-state energies will interconnected into or disconnected from future smart grids with varying topology, the numbers of inputs and outputs of the proposed expandable deep learning can be expanded dynamically with the varying topology of future smart grids. Since expandable deep learning based real-time economic generation dispatch and controller can simultaneously provide multiple generation commands for future smart grids with varying topology, the framework of conventional generation dispatch and control can be replaced by the real-time economic generation dispatch and control framework. Compared with 216 combined conventional generation dispatch and control algorithms under a 118-bus power system with 54 three-state energies and a 13659-bus power system with a total of 4092 three-state energies in varying topology, the expandable deep learning obtains the highest control performance. Simulation results verify the effectiveness and feasibility of the proposed expandable deep learning for the real-time economic generation dispatch and control of three-state energies based future smart grids with varying number of three-state energies and varying topology.

Comparison of matrix converter stabilisation techniques based on the damping resistor and digital filter approaches for bidirectional power flow control

The matrix converter is becoming a mature technology with its specific advantages and limitations and can be effectively used as interface link in the future smart grids. In this context, the stability of the converter under different power flow conditions is highly important and needs more clarification. The input inductor-capacitor filter can significantly impact the stability of the converter when the output is tightly regulated, especially in bidirectional power flow control applications where a low-impedance source is connected to the converter output. This study investigates the matrix converter stability for bidirectional power flow control, considering the input filter and other parameters of the system. A detailed analysis of two commonly used active and passive stabilisation techniques known as digital filter and damping resistor approaches is presented, and a combination of these two methods is suggested as a better choice. The converter stability region for the proposed technique is determined by using the small-signal model of the converter. The converter performance for the methods is compared in terms of the efficiency, stability, transients, and quality of the input and output currents. Numerical simulations and experimental tests are conducted on a prototype direct matrix converter to validate the proposed method.

Schedulable capacity forecasting for electric vehicles based on big data analysis

Fast and accurate forecasting of schedulable capacity of electric vehicles (EVs) plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems. Traditional methods are insufficient to deal with large-scale actual schedulable capacity data. This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales. The time scale of these data analysis comprises the real time of one minute, ultra-short-term of one hour and one-day-ahead scale of 24 hours. The predicted results for different time scales can be used for various ancillary services. The proposed algorithm is validated using operation data of 521 EVs in the field. The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm, the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.

Building energy flexibility: a sensitivity analysis and key performance indicator comparison

Buildings are a key active element of the future Smart Grids with large shares of renewable energy, as they can provide flexible energy usage to help balancing power production intermittence. There is currently no consensus yet on how to quantify building energy flexibility. The various KPIs found in literature can be classified into 4 main categories: load shifting ability, power adjustment, energy efficiency and cost efficiency. Most of them use a reference scenario. Moreover, the envelope performance appears to be the most important parameter with regards to all aspects of building energy flexibility when using indoor temperature set point modulation.

Fast Linear Power Flow Algorithm for the Study of Steady-State Performance of DC Grid

As the penetration of renewable resources and direct current (dc) nature loads increase, dc grids are getting more attraction in the future smart grid architecture. However, how to obtain the steady-state performance of a dc grid under different disturbances fast is still a challenge. To solve this problem, this study aims to derive a fast linear power flow (LPF) model instead of nonlinear algorithms for a dc grid. After analyzing the control characteristics of different converters, a generalized control equation for each dc bus is derived. Then, on the basis of the generalized control and network equations, an LPF algorithm is proposed for the strict requirement of the dc voltage deviation. Error analysis is performed on the proposed LPF algorithm, and a correction method for the LPF algorithm is developed. Case studies based on Cigre B4 DC grid test system are conducted to validate the effectiveness of the proposed LPF. Compared with the Newton–Raphson algorithm, the proposed LPF not only improves the calculating speed greatly but also preserves the accuracy of the power flow of the dc grid.

Integration of 5G Technologies in Smart Grid Communication-A Short Survey

Smart grid is an intelligent power distribution system that employs dual communication between the energy devices and the substation. Dual communication helps to overseer the internet access points, energy meters, and power demand of the entire grid. Deployment of advanced communication and control technologies makes smart grid system efficient for energy availability and low-cost maintenance. Appropriate algorithms are analyzed first for the convenient grid to have proper routing and security with a high-level of power transmission and distribution. Information and Communication Technology plays a significant role in monitoring, demand response, and control of the energy distribution. This paper presents a broad review of communication and network technologies with regard to Internet of Things, Machine to Machine Communication, and Cognitive radio terminologies which comprises 5G technology. Networks suitable for future smart-grid are compared with respect to standard protocols, data rate, throughput, delay, security, and routing. Approaches adopted for the smart-grid system has been commended based on the performance and the parameters observed. ©2019. CBIORE-IJRED. All rights reserved

Pattern simulation and analysis of generalized load profile coupling with active load and renewable energy power

Due to the coupling of renewable energy (RE) generation and demand response, the load pattern changed greatly. The coupled loads are called generalized loads (GL). The simulation and analysis of GL pattern are necessary for the planning and operation of the future smart grid. However, because the influence factors of the GL model are very complex, there is no mathematical analytical model that accurately describes the linkage response between the active load and RE. In this paper, a mathematical model for formulating GL profile pattern is proposed. First, three linkage models of active load and renewable energy are constructed, including transferable load, interruptible load, and uncontrollable load. Based on three models, three kinds of active loads are generated. Second, the primary GL data is formed by adding base loads and active loads. However, the primary GL patterns are chaos and unrecognized. For solving this problem, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is applied to extract typical GL pattern. Then, the features of extracted GL patterns are analyzed. Furthermore, the sensitivity analysis of influent factors on GL is done, including the influences of RE permeability, active load ratio, and composition ration of the active load. The results show that the pattern of GL, which is influenced by RE and active load, is very different from the traditional load. The study of GL pattern is essential and can provide a reference for the prediction, planning, and operation of the smart grid.

QoS-aware traffic scheduling framework in cognitive radio based smart grids using multi-objective optimization of latency and throughput

To meet the diverse QoS requirements of future Smart Grid (SG) communication network, efficient traffic scheduling and optimization techniques to prioritize data from various SG applications are required. Recently, focus has only been on the latency and criticality based priority-aware policies for same channel bandwidth. Traffic scheduling and subsequent channel allocation are required to support both the differential throughput and latency requirements simultaneously for channels having different bandwidths and SNRs. In this paper, a QoS-aware framework for data traffic scheduling in cognitive radio based SG communication network is proposed. The channels available to smart grid Communication Nodes (SCNs) are categorized as low and high bandwidths. For each bandwidth, all SG applications are categorized into several priority-classes comprised of latency and throughput sub-classes. For both channel bandwidths, the scheduler maintains and updates two sets of priority queues based on weights associated with each class and its data type. The complete scheduling framework is formulated as a multi-objective optimization problem. The overall objective function is the weighted sum of individual utility functions of latency and throughput. A novel usage of Adam optimizer is proposed to minimize the latency and maximize the throughput by obtaining optimal system cost, resulting in optimal decision policy. Simulation results show that the proposed algorithm achieves desired QoS requirements in the presence of heavy PU traffic, whereas in case of no priority assignment, QoS requirements of lower priority applications are met by compromising the QoS of higher priority data.

Impact of Current and Power Measurements on Distribution System State Estimation Uncertainty

The effective management of future smart grids is strictly related to the accurate knowledge of the network operating conditions via distribution system state estimation (DSSE). To achieve this target, the measurement infrastructure of the distribution systems needs a substantial upgrade. However, obvious economic limitations prevent a massive deployment of measurement instruments on the field in short times. As a consequence, ad hoc meter placement techniques have to be applied to define location and type of a minimum set of measurements required to obtain the desired accuracy performance. This paper presents a mathematical analysis showing the impact of current and power measurements on the accuracy of DSSE results. The goal is to provide the analytical tools to identify the best placement for additional current or power measurements when applying an incremental deployment of meters in the distribution system. The analysis has a general validity but has also a clear impact on practical situations when the monitoring systems are upgraded starting from an existing infrastructure. Tests on a 95-bus sample grid are presented in order to validate the found mathematical results and to highlight the associated benefits in a meter placement perspective.

Energy load forecasting model based on deep neural networks for smart grids

In recent, smart grid has emerged as a promising technology to facilitate the future electric power grid and to balance between supply and demand. However, the intermittent nature of distributed energy resources causes dynamic uncertainties and nonlinearity in the smart grid environment. This may result in a large stress on power grid and has a big influence on energy planning, especially the generation and distribution. Therefore, energy load forecasting plays an important role in facilitating the operation of the future smart grid. Using the traditional statistical and machine learning approach there exists a significant forecasting error and high degree of overfitting. In this paper, we propose an energy load forecasting (ELF) model based on deep neural network architectures to manage the energy consumption in smart grids. First we investigate the applicability of two deep neural network architectures: deep feed-forward neural network (deep-FNN) and deep recurrent neural network (deep-RNN). To evaluate the models with low error, we simulate both architectures with multi size training set. Further, various activation functions and different combinations of hidden layer architectures are also tested. The simulation results are compared in terms of mean absolute percentage error. The results show that the proposed ELF model has attained better generalization and outperform the existing load forecasting models based on the shallow neural network, ensemble tree bagger and generalized linear regression.

Impedance Modeling and DC Bus Voltage Stability Assessment of a Solid-State-Transformer-Enabled Hybrid AC–DC Grid Considering Bidirectional Power Flow

The solid-state-transformer (SST) is a promising technology in the future smart grid. Compared to the line-frequency transformer, the SST has more control flexibilities and the plug and play ability, which allow it to interconnect a hybrid ac–dc multiterminal grid for reliable and flexible power distribution. The dc bus voltage stability of an SST-enabled hybrid ac–dc multiterminal grid is a critical issue due to the dynamic interactions of power converters, which has been investigated in this article. The output impedance modeling method of the dual active bridge (DAB) dc/dc converters considering both the control schemes and bidirectional power flow effect is proposed. Depending on the control strategy and power flow direction, the DAB converters have three different output impedance characteristics at the low-frequency range, which are capacitive, inductive, and resistive. It is found that the dynamic interaction between the inductive impedance and the capacitive impedance of DAB converters could cause instability issues when the system damping ratio is low. A hardware-in-the-loop-based impedance measurement testbed is used to extract the DAB converters’ output impedance, which validates the theoretical derivation. The real-time simulation results are also provided to demonstrate the dc bus voltage instability.

Interval State Estimation for Low-Voltage Distribution Systems Based on Smart Meter Data

The real-time monitoring and control of distribution systems are an essential step toward the future smart grid. This paper presents an interval state estimation model for low-voltage (LV) systems based on smart meter data provided by electrical customers. The formulation is based on a two-level approach, constituted of a nodal processor and a system state estimator. The nodal processor filters the smart meter data and provides a smaller amount of clean processed data to the upper level system state estimator. The system state estimator, based on the extreme value theory, provides the maximum and minimum values of the system states. To obtain narrow interval images, a scaling parameter is used to consider the fact that the errors of downstream smart meters are rarely at the maximum values simultaneously. The presented approach allows removing the assumption on the particular probability distribution of the smart meter measurement errors. Simulations are carried out on a sample LV network to show the characteristics and performance of the proposed approach.

Transactive Real-Time Electric Vehicle Charging Management for Commercial Buildings With PV On-Site Generation

In the future smart grids, it is important for prosumers to manage uncertainties from distributed renewable energy sources such as photovoltaic (PV) generation. As a type of distributed energy resource, electrical vehicles (EVs) are regarded as a promising solution to the problem. In this paper, a transactive real-time EV charging management scheme is proposed for the building energy management system (BEMS) of commercial buildings with PV on-site generation and EV charging services. Instead of direct EV charging control, the proposed EV charging management scheme applies a transactive energy concept-based approach to address real-time EV charging management. With the proposed scheme, the BEMS can schedule its net electricity exchange with the external grid under the uncertainties of PV generation and EV parking and maximize its profit in the real-time operation. Meanwhile, the EV owners need not provide the BEMS with further private information (such as future driving plans) but only their real-time charging requirements and preference setting of the response to the BEMS’s pricing signal in the proposed scheme. As such, the BEMS as a charging service provider only requires the minimal necessary information from EV owners. The EV owners’ charging requirements, preference setting of the response curves and their required reimbursements for the response are respected by the real-time charging management and their contributions to the demand response are reimbursed by the BEMS. Case studies with real world driving data from the Danish National Travel Survey were carried out to verify the proposed framework.

Artificial Neural Networks for Volt/VAR Control of DER Inverters at the Grid Edge

With increased grid penetration of distributed energy resources (DERs), volt/VAR control (VVC) is becoming increasingly complex and challenging in distribution systems. Conventional VVC approaches in distribution networks reside typically on the primary feeder. However, these approaches are ineffective for handling the voltage variability caused by DERs and load dynamics on the secondary side, termed as the grid edge. With more and more deployments of DERs at the grid edge, advanced VVC technologies through controlling DER inverters are perceived to be the key enabling technologies for distributed voltage control to meet the future smart grid requirements. Nevertheless, a great challenge is that DER inverters would fight each other when operated autonomously in participating grid voltage control using the conventional control methods. This paper proposes a novel VVC strategy based on an artificial neural network (ANN) control. The proposed approach is able to properly handle DER inverter constraints in achieving VVC objectives at the grid edge and overcomes the challenges of conventional DER inverter control techniques. The behaviors of the proposed method are evaluated via simulation and hardware experiments, which demonstrate great advantages of the ANN-based VVC for controlling DER inverters at the grid edge.

Demand side management of small scale loads in a smart grid using glow-worm swarm optimization technique

Demand Side Management (DSM) is one of the most important parts of future smart power grid. With the rise in global energy awareness, smart grids enhance the potency and peak levelling of power systems. DSM is the controlling scheme in such grids and it aims to optimize several characteristics of load demand. This smart grids comprises energy storage (battery) and distributed solar photovoltaic generation storage. In this proposed methodology the combination of Glow-worm Swarm Optimization (GSO) and Support Vector Machine (SVM) is used for decision making process in battery storage to reduce the electricity tariff. GSO is a powerful technique to obtain near optimal solution which is used for this load rescheduling problem for a sample test system to minimize the cost of end user. Especially, the electricity expenditures of the end user can be reduced by responding to pricing which changes with different hours of a day. Then optimized range of the battery's energy storage is extracted from the GSO. Here, the SVM is trained based on the optimized data from the GSO. This combination is used for finding the amount of energy is transferred in/out the battery which aims the minimal electricity bill value. The electricity tariff of the proposed methodology of Average gosc is 2.27 for residential load, while considering it is less when compared to the existing method of 2.3 at the consumed load of 8.2 kWh/day. The proposed GSO-SVM method reduces 11.2% of energy cost which helps decision makers to take best demand-side actions for balancing the stability.

A Magnetic Core Permeance Model for Inductive Power Harvesting

A current transformer (CT) is a preferred device to harvest power in the high-voltage utility grid to power monitoring equipment. Widespread CT use is foreseen in future smart grid deployment. The most common CT use involves a laminated transformer iron split core installation around a high-voltage cable with a particular number of turns to power a load or burden. Less common is a gapless core installation to improve reliability and prevent CT degradation by avoiding gap corrosion. Even laminated high resistivity cores still suffer from eddy current loss. The goal is to design a core able to deliver the desired power at optimum number of turns, minimized dimensions, and minimal loss. This article focuses on the core permeance as a complex parameter, on core size, losses or efficiency, load impedance optimization, flux level, power factor performance, and the relationship between these parameters. A similar size split core shows reduced loss compared to a gapless core. Lossy cores can still operate relatively efficiently at low flux levels indicating that the load current should preferably approach the short circuit current. Notwithstanding nanocrystalline and ferrite material advantages, silicon-doped laminated iron remains the preferred choice for 50/60 Hz inductive power harvesting.

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

The market for electric vehicles (EVs) has grown with each year, and EVs are considered to be a proper solution for the mitigation of urban pollution. So far, not much attention has been devoted to the use of EVs for public transportation, such as taxis and buses. However, a massive introduction of electric taxis (ETs) and electric buses (EBs) could generate issues in the grid. The challenges are different from those of private EVs, as their required load is much higher and the related time constraints must be considered with much more attention. These issues have begun to be studied within the last few years. This paper presents a review of the different approaches that have been proposed by various authors, to mitigate the impact of EBs and ETs on the future smart grid. Furthermore, some projects with regard to the integration of ETs and EBs around the world are presented. Some guidelines for future works are also proposed.

Security attacks on smart grid scheduling and their defences: a game-theoretic approach

The introduction of advanced communication infrastructure into the power grid raises a plethora of new opportunities to tackle climate change. This paper is concerned with the security of energy management systems which are expected to be implemented in the future smart grid. The existence of a novel class of false data injection attacks that are based on modifying forecasted demand data is demonstrated, and the impact of the attacks on a typical system’s parameters is identified, using a simulated scenario. Monitoring strategies that the utility company may employ in order to detect the attacks are proposed, and a game-theoretic approach is used to support the utility company’s decision-making process for the allocation of their defence resources. Informed by these findings, a generic security game is devised and solved, revealing the existence of several Nash equilibrium strategies. The practical outcomes of these results for the utility company are discussed in detail, and a proposal is made, suggesting how the generic model may be applied to other scenarios.

Control of a population of battery energy storage systems for frequency response

The control of multiple battery energy storage systems (BESSs) to provide frequency response will be a challenge in future smart grids. This paper proposes a hierarchical control of BESSs with two decision layers: the aggregator layer and the BESS control layer. The aggregator layer receives the states of charge (SoC) of BESSs and sends a command signal to enable/disable the BESS control layer. The BESS controller was developed to enable the BESSs to respond from the highest to lowest SoC when the frequency drops, and from lowest to highest when it rises. Hence, the BESS’s response is prioritised to reduce the impact on the power system and end-users during the service. The BESS controller works independently when a failure occurs in the communication with the aggregator. The dynamic behaviour of the population of the controllable BESSs was modelled based on a Markov chain. The model demonstrates the value of aggregation of BESSs for providing frequency response and evaluates the effective capacity of the service. The model was demonstrated on the 14-machine South-East Australian power system with a 14.5 GW load. 254 MW of responsive capacity of aggregated batteries was effective in reducing the system frequency deviation below 0.2 Hz following a sequence of disturbances.

A New Approach on Prioritization of the Circuit Breakers for Installation of Online Monitoring Systems

Employing online monitoring (OLM) of circuit breakers (CBs) is increasing continuously due to its compatibility with future smart grids. The proper and reliable operation of CBs directly affects the resiliency, reliability, and continuity of power systems. Since there is a large number of CBs in power systems, installation of OLM systems to all CBs is not economically justifiable. Consequently, an effective prioritization framework to find critical CBs to be monitored online is essential. This paper provides a sensible correlation between the failure rate and the deterioration process of the CBs through the combination of the discrete Markov model and signal processing of diagnostic features. The results have been employed to assess the condition of the CBs equipped with OLM, to set a proper maintenance activity, and to predict the subsequent condition. To prove its feasibility, the proposed approach is applied to a real utility substation using the Monte Carlo method based on real monitoring data. The CBs have been prioritized from both reliability and financial viewpoints. In addition, sensitivity analyses results interestingly indicate that the old CBs are not the optimistic choices all the time due to the dependency of the problem to the important parameters such as deterioration process, failure rates, operation frequency, and the location of the CBs in the power system. The proposed approach enables us to find the optimum choice in such a complex environment.