Grid Paradigm Research Articles

Development of a Power Conversion System for a Wave Energy Converter connected to the Electrical Network

Abstract Global efforts are focused on promoting a sustainable energy supply by increasing the use of renewable energies and decreasing reliance on conventional sources. Among renewable sources, Wave energy is a promising one, but it is still widely untapped worldwide. In the current energy context, it is crucial to synchronize the characteristics of renewable sources with the needs of the existing electrical network which is undergoing a deep change towards the smart grid paradigm. Power electronic converters, as interface devices, play a fundamental role in facilitating the transfer of renewable energy to the electrical network. For this reason, this study focuses on the design and optimization of an electrical power conversion system for a wave energy converter (WEC) coupled with a hydraulic power take-off. This electrical power conversion system allows the WEC to connect to the electrical network and deliver a smooth output electrical power. Indeed, thanks to a particular control system, the conversion from mechanical power to electrical power is achieved with losses of less than 5% in regular sea state conditions

Blockchain-enabled framework for transactive home energy management with cloud energy storage under uncertainties

In modern paradigm of smart grid, prosumers can trade energy peer-to-peer in a transactive energy market to reduce energy costs and improve energy efficiency. In addition, cloud energy storage (CES) is a type of shared energy storage systems with high economic efficiency that can provide energy storage services for prosumers and become an active player in energy trading. However, transactive energy implementation in power systems has several challenges such as data privacy and security. In existing researches, energy trading mechanisms rely on central authorities, where security and privacy cannot be maintained. Therefore, this paper presents a decentralized stochastic optimization model of transactive energy management based on blockchain in the presence of CES. In this framework, the smart contract based on Alternating Direction Method of Multipliers technique is deployed on the Ganache and Ethereum blockchains, and the YALMIP toolbox along with CPLEX solver handle decentralized optimization in the MATLAB software. The numerical results demonstrate that not only the capacity of distributed generations is fully utilized, but also total costs of smart homes are reduced by more than 27%, and the total grid’s delivery power to smart homes is decreased by more than 24% compared to the inflexible scheduling.

From Time-Series to Hybrid Models: Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems.

Graph‐based solution for smart grid real‐time operation and control

AbstractUnder the envisioned smart grid paradigm, there is an increasing demand for a fast, accurate, and efficient power flow solution for distribution system operation and control. Various solution techniques have been proposed, each with its own unique formulation, solution methodology, advantages, and drawbacks. Motivated by challenges associated with the integration of renewable distributed energy resources and electric vehicles into distribution systems and further by the speed and convergence limitations of existing tools, this paper presents a novel graph‐based power flow solution for smart grid's real‐time operation and control, named Flow‐AugmentationPF algorithm. The proposed method formulates a power flow problem as a network‐flow problem and solves it by using a maximum‐flow algorithm, inspired by the push‐relabel max‐flow technique. The performance of the proposed algorithm is tested and validated using several benchmark networks of different sizes, topologies, and parameters and compared against the most commonly used solution techniques and commercial software packages, namely PSS/E and PSCAD. The proposed formulation is simple, accurate, fast, yet computationally efficient, as it is based on matrix‐vector multiplication, and is also scalable, considering the formulation works as a graph‐based method, which, inherently, allows for parallel computation for added computational speed.

Detecting Cyber Attacks on Renewable Distributed Generation for Future Smart Electric Meters to Prevent Electricity Theft

Electricity theft represents a pressing problem that has brought enormous fiscal losses to electric mileage companies worldwide. In the United States alone, $ 6 billion of electricity is stolen annually. Traditionally, electricity theft is committed in the consumption sphere via physical attacks that include line tapping or cadence tampering. With the advanced metering structure (AMI), smart measures are installed at the guests’ demesne and regularly report the guests’ consumption for monitoring and billing purposes. In this environment, vicious guests can launch cyber-attacks on smart measures to manipulate the readings in a way that reduces their electricity bill. Second, the smart grid paradigm enables guests to install renewable-grounded distributed generation (DG) units at their demesne to induce energy and send it back to the grid driver and hence make a profit.

Techno-economic evaluation of meshed distribution network planning with load growth and expansion with multiple assets across multiple planning horizons

The smart grid paradigm has ushered in an era where modern distribution systems are expected to be both robust and interconnected in topology. This paper presents a techno-economic-based sustainable planning (TESP) strategy, which can be used as a planning framework for linked distribution systems, seeking to discover a realistic solution among competing criteria of diverse genres. In this comparative analysis-based study, three voltage stability assessment indices—VSA_A, VSA_B, and VSA_W—and a loss minimization condition (LMC)-based framework are used in the initial stage to achieve optimal distributed generation (DG)-based asset optimization for siting, followed by sizing. The respective techniques are evaluated across two variants of multiple load growth horizons spread across 10 years. The suggested TESP technique is tested on two variants of a mesh-configured microgrid (MCMG) with varied load growth scenarios. One variant considers a 65-bus MG with a fixed load growth of 2.7% across two load growth horizons. The other variant considers a 75-bus MG with varied load growth across four load growth horizons, encapsulating an expansion-based planning perspective. The numerical results of the suggested TESP approach in a comparative study demonstrate its effectiveness, and it can be used by researchers and planning engineers as a planning framework for interconnected distribution tools across multiple planning horizons. The proposed study would contribute to enhancing the robustness and interconnectivity of smart grid distribution systems. This dual focus could lead to more cost-effective and reliable power distribution systems.

Corrections to “Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi Agent-Based Optimization in Smart Grid Paradigm”

Corrections to “Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi Agent-Based Optimization in Smart Grid Paradigm”

Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi-Agent-Based Optimization in Smart Grid Paradigm

Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi-Agent-Based Optimization in Smart Grid Paradigm

A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning

In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operation. Although statistical methods were primarily used because ELF is a time series problem, deep learning (DL)-based forecasting approaches are more commonly employed and successful in achieving precise predictions. However, these DL-based techniques, known as black box models, lack interpretability. When interpreting the DL model, employing explainable artificial intelligence (XAI) yields significant advantages by extracting meaningful information from the DL model outputs and the causal relationships among various factors. On the contrary, precise load forecasting necessitates employing feature engineering to identify pertinent input features and determine optimal time lags. This research study strives to accomplish a mid-term forecast of ELF study load utilizing aggregated electrical load consumption data, while considering the aforementioned critical aspects. A hybrid framework for feature selection and extraction is proposed for electric load forecasting. Technical term abbreviations are explained upon first use. The feature selection phase employs a combination of filter, Pearson correlation (PC), embedded random forest regressor (RFR) and decision tree regressor (DTR) methods to determine the correlation and significance of each feature. In the feature extraction phase, we utilized a wrapper-based technique called recursive feature elimination cross-validation (RFECV) to eliminate redundant features. Multi-step-ahead time series forecasting is conducted utilizing three distinct long-short term memory (LSTM) models: basic LSTM, bi-directional LSTM (Bi-LSTM) and attention-based LSTM models to accurately predict electrical load consumption thirty days in advance. Through numerous studies, a reduction in forecasting errors of nearly 50% has been attained. Additionally, the local interpretable model-agnostic explanations (LIME) methodology, which is an explainable artificial intelligence (XAI) technique, is utilized for explaining the mid-term ELF model. As far as the authors are aware, XAI has not yet been implemented in mid-term aggregated energy forecasting studies utilizing the ELF method. Quantitative and detailed evaluations have been conducted, with the experimental results indicating that this comprehensive approach is entirely successful in forecasting multivariate mid-term loads.

Electric vehicles embedded virtual power plants dispatch mechanism design considering charging efficiencies

The increasingly popular electric vehicles (EVs) are changing the control paradigm of the power grid due to their uncoordinated charging behaviors. However, if well coordinated, smart homes, workplaces, and other locations that support EV charging could provide the grid with the urgently required flexibility via virtual power plants (VPP). In this paper, we develop the EV charging schedule model by capturing the unwillingness of EV drivers to alter their initial charging behaviors, referred to as the discomfort function. Predictability and the value of charging time, which represent the electricity consumption stability and the time value of EV drivers, characterize the discomfort function. Rather than existing works capturing discomfort by a direct simple parameter, such a computable data-driven quantification of discomfort enables us to customize an efficient VPP dispatch mechanism for EVs. In addition, to deal with the unknown charging efficiencies of EVs, we apply chance constraints only with the knowledge about mean and standard deviation of charging efficiencies, rather than their specific distribution. Using the concept of conditional value-at-risk (CVaR), we build an effective algorithm to solve the practical non-convex VPP dispatch model considering charging efficiencies. The effectiveness of our proposed models and associated algorithms are validated by numerical studies.

Quality of the information flow management at stochastic energy consumption conditions

Modern energy systems are rapidly changing and becoming increasingly complex. This process is facilitated by the growing demand for distributed energy resources, renewable energy sources, and distributed generation. This makes the energy system highly stochastic. An integral part of the concept of smart grids is the production of energy from distributed energy sources. Modernization of the energy network with the help of intelligent technologies allows us to fully take into account the peculiarities of energy consumption. The smart grid paradigm envisions flexible energy demand and storage to cope with the variability of renewable energy sources. A necessary condition for the implementation of demand response is an improved infrastructure, especially information and communication technologies. The paper presents the results of research on the quality of information flow management under conditions of stochastic energy consumption by maintaining a stationary queue of information transmission through the network and preventing overloading of the controlled network segment. The stages of monitoring and assessment of the network state, as well as the methodology of identification of network parameters are presented. According to the results of the analysis of the system of key network parameters and the specifics of their application for managing the quality of network service, it has been established that using the statistical approach one can distinguish between the key parameters of the network. The wireless network has been researched, which allows us to build a quality of service management system by regulating and shaping traffic. It is shown that when using a polling system with feedback on the speed of buffer filling to organize requests in the network, it is possible to maintain a stationary queue with a non-stationary flow of requests. Keywords: wireless network, information delay, stochastic energy consumption.

DETECTION OF ELECTRICITY THEFT CYBER-ATTACKS IN RENEWABLE DISTRIBUTED GENERATION FOR FUTURE IOT-BASED SMART ELECTRIC METERS

Electricity theft represents a pressing problem that has brought enormous financial losses to electric utility companies worldwide. In the United States alone, $6 billion worth of electricity is stolen annually. Traditionally, electricity theft is committed in the consumption domain via physical attacks that includes line tapping or meter tampering. The smart grid paradigm opens the door to new forms of electricity theft attacks. First, electricity theft can be committed in a cyber manner. With the advanced metering infrastructure (AMI), smart meters are installed at the customers’ premises and regularly report the customers’ consumption for monitoring and billing purposes. In this context, malicious customers can launch cyber-attacks on the smart meters to manipulate the readings in a way that reduces their electricity bill. Second, the smart grid paradigm enables customers to install renewable-based distributed generation (DG) units at their premises to generate energy and sell it back to the grid operator and hence make a profit.
 Therefore, this project evaluating performance of various deep learning algorithms such as deep feed forward neural network (DNN), recurrent neural network with gated recurrent unit (RNN-GRU) and convolutional neural network (CNN) for electricity cyber-attack detection. Now-a-days in advance countries solar plates are used to generate electricity and these users can sale excess energy to other needy users and they will be maintained two different meters which will record consumption and production details. While producing some malicious users may tamper smart meter to get more bill which can be collected from electricity renewable distributed energy. This attack may cause huge losses to agencies. To detect such attack, this project is employing deep learning models which can detect all possible alterations to predict theft.

Cosine Windows in Interpolated DFT-based Method for an Accurate High-Frequency Distortion Assessment in Power Systems

The transformation of electrical networks in the context of the new smart grid paradigm unavoidably involves new challenges regarding Power Quality (PQ) disturbances not only for customers but also for all the other involved stakeholders. Among PQ disturbances, waveform distortions have recently gained growing interest due to the massive presence of new technologies in distributed energy resources, in modern loads and in advanced smart metering systems. The presence of these devices determines arduous electromagnetic compatibility problems since the current and voltage waveform distortions in smart grids are characterized by spectral components above the traditional 2 kHz frequency limit, in a range extended up to 150 kHz. In this paper, an interpolated DFT-based (IDFT) method, recently proposed in the relevant literature in the field of signal processing, is properly extended for an accurate and fast assessment of power system waveform distortions in the frequency range from 2 to 150 kHz. Since DFT-based methods can suffer well-known spectral leakage problems, in this paper the IDFT is applied using cosine windows that minimize interference conditions among spectral components and maximise the estimation accuracy of the spectral component amplitude, phase angle and frequency. An optimal number of cosine window terms is also searched to improve the spectral analysis of high-frequency power system waveforms. Numerical applications on synthetic test signals and measured waveforms are carried out to quantify the accuracy and computational efforts of the proposed approach and to select the cosine window terms that better optimize the waveform distortion assessment.

A Novel SW Tool for the Evaluation of Expected Benefits of V2H Charging Devices Utilization in V2B Building Contexts

Energy systems need a complete decarbonization within the next 20–30 years, calling for the introduction of CO2-free renewable energy sources (RES). All final uses must face this challenge, now finally including the transportation sector which should mostly be electrified. This option could constitute both a challenge and an opportunity for the electric grid. In fact, connection to the grid of all electric vehicles (EVs) together with their electricity storage systems (ESSs) could reduce issues due to the nonprogrammable use of RES in electricity production; to this aim, sufficiently smart bi-directional vehicle-to-grid technologies (V2G) have to be designed and widely installed. Parallelly, electric grid capabilities must become fully bidirectional in all nodes, both physically and in terms of ICT capabilities (so-called smart grid paradigm). In the meanwhile, some of those V2G technologies may already be locally implemented in individual home contexts. Following previous research activity about the identification of potential users of the most promising V2H technologies and on the evaluation of their expected benefits in terms of local renewable energy auto-consumption and/or local consumption auto-feeding performance, the author aims his attention to the numerical evaluation of the further benefits obtainable through the combined utilization of a number of V2H technologies all acting on the same “building” energy node; this approach is normally referred to in the literature as a vehicle-to-building (V2B) application. The SW tool which was developed to this aim is fully physically consistent, scalable, modular, open-source, and user-friendly, and it can be distributed under request to other research groups. In the simulations performed, V2H devices all used the same controlling approach, but offered their services to a “building” energy community, defined by the instantaneous sum of the energy behaviors of all the individual users. The simulation results show that building environments make it possible to intersect energy fluxes far beyond single user expectation, leading to very energy grid performances. In particular, renewable energy auto-consumption ratios become higher than 50%, and almost all local electric final uses may be fed through grid-connected vehicular ESSs (100% home auto-feeding ratio). This limits building–grid interactions to much more predictable residual ESS charging phases, as well as the sale of PV panel overproduction. The performance obtainable through the simulated V2B approach proved to be much higher than that obtainable through the same V2H technologies acting on single individual grids (which were estimated in a previous study by the same research group), ranging from 25% to 69% in terms of PV auto-consumption ratios (with higher values only obtainable for “nocturnal workers”, living in their home mostly during the daytime); moreover, a poor performance was recorded in terms of local consumption auto-feeding, ranging from 27% to 81% (with higher values only obtainable for those users mostly inhabiting their home during the night-time).

False data injection attack in smart grid cyber physical system: Issues, challenges, and future direction

Smart grid integrates the physical power system infrastructure with internet-of-things-based digital communication networks that work together for grid stability, sustainability, and reliability. A significant number of smart devices converge in cyber-physical systems to make the smart grid more competitive and efficient in addressing the energy challenges and vulnerabilities in power system confidentiality, integrity, and availability in smart grid cyber-physical security systems. False data injection attacks are the most malicious threats in the smart grid paradigm and have been widely applied recently. Last few years, several detection algorithms for identifying the false data injection attack have been developed. Addressing these issues, this paper reports a false data injection attack and threat mathematical model, impacting the on-grid system, economy, and society. The classification of false data injection attack detection algorithms and mathematical models are mainly presented. Finally, issues and challenges are identified from existing research and recommended for future research direction.

Manta ray foraging optimization algorithm‐based load frequency control for hybrid modern power systems

AbstractThe regulation of the frequency and line power flow in interconnected power networks is considered to be a key aspect of load frequency control (LFC). This article broaches a modern power network composed of three interconnected control areas including traditional generation units taking into account non‐linearities, also renewable energy sources (RESs) and energy storage (ES) units are involved in the power grid paradigm. Two forms of RESs are included in the analysis, which are photovoltaic (PV) and wind power plants. In addition, the study framework involves three types of ES units, which are batteries of plug‐in electric vehicles (PEVs), flywheel energy storage system (FESS) and capacitive energy storage system (CESS). In this analysis, LFC is accomplished by the use of proportional‐integral‐derivative (PID) controllers in the system control loops. A recent optimization algorithm called Manta Ray Foraging optimization (MRFO) is employed to obtain the optimal gain configuration of the controllers. Real site measurements are imported to the RESs involved in the study aiming to examine the proposed control scheme under realistic conditions. Compared with other rival algorithms, the effectiveness of the MRFO‐based PID controller is validated. Simulation results confirm the efficacy of the proposed control scheme. The findings also ensure the role of ES units in optimizing the time‐domain responses. The main contributions of this paper are applying a new metaheuristic optimization algorithm to solve the LFC problem and introducing a new criteria for judging the system performance in compliance with the harmonic spectrum of the responses in the frequency domain. The results of the simulation are retrieved through a MATLAB model.

Multi-objective optimization of IoT-based green building energy system using binary metaheuristic algorithms

Green building energy optimization systems (GBEOS) are of great interest to academics and industry professionals. One of the main challenges of GBEOS is optimizing energy consumption while maintaining people's comfort level. This paper proposes a metaheuristic-based GBEOS for optimizing energy consumption within a smart green building operating under a smart grid paradigm while considering the local environment and energy demand behaviors. In this regard, two complementary control procedures are developed: the supply-part optimizer (SPO) and the demand-part optimizer (DPO). By combining SPO and DPO, six binary multi-objective optimization algorithms, including Xtreme gradient boosting (XGBoost), multiverse optimizer (MVO), moth flame optimization (MFO), shuffled frog leaping algorithm (SFLA), moth flame optimization, imperialist competitive algorithm (ICA), and chimp optimization algorithm (ChOA) are developed to simultaneously minimizes costs and maximizes client comfort satisfaction (CS). First of all, considering the importance of convergence speed, seven unimodal, six multimodal, and twenty complex benchmark optimization functions are used to evaluate the proposed approach's convergence behavior. Afterward, an efficient weighting mechanism is used to handle the cost-comfort trade-off, allowing the user to discover a reasonable trade-off between monetary expense and comfort level. Then, in order to determine the influence of the pricing scheme's nature on the potential savings achieved through EMS, three individual models named real-time pricing (RTP), time-of-utilize pricing (TUP), and fixed-pricing plan (FPP) are proposed. The results show that ChOA presents the best result in terms of convergence, with 19 out of 33 test functions; also, XGBoost has the second-best performance. In addition, results show that suggested metaheuristic algorithms significantly increase PV energy penetration for self-consumption and lower power costs while keeping an optimal balance between CS and economical costs. Moreover, using dynamic pricing systems, i.e., RTP or TUP, results in great user price savings than FPP. To sum up, ChOA is found to be the best algorithm in this challenge, followed by XGBoost in second place.

A Methodology for maturity quantification and evaluation of R&I needs of the smart grid

Power Grids of the future, embracing the integrated approach with high penetration of renewables and emerging technologies with advanced operational functionalities, is of paramount importance for meeting the European Union (EU) targets of sustainability and energy transition. As such, an analysis approach for assessing progress in developing major high level use cases of the smart grid is needed to be well established and adopted. This way not only the next steps of development shall be better defined but also the needs of the system will be better identified. This paper provides a solid methodology for quantification of the maturity level of the high-level use cases of the smart grid paradigm as they are defined in the European Technology and Innovation Platform Smart Networks for Energy Transition (ETIP SNET) roadmap. The first step is the evaluation of the advancement of the technologies, then the level of the maturity of the high-level use cases that will support the integrated grid of the future and lastly the smart grid system readiness as a whole. Through this evaluation, progress made so far can be evaluated, the needs for future research can be identified and the funding of the European Commission (EC) will have a quantified direction to go. This methodology is part of the EIRIE platform of the EC hosted by Joint Research Centre (JRC) and is validated through the extensive data base of European Horizon2020 projects.

Performance Evaluation and Cyberattack Mitigation in a Blockchain-Enabled Peer-to-Peer Energy Trading Framework

With the electric power grid experiencing a rapid shift to the smart grid paradigm over a deregulated energy market, Internet of Things (IoT)-based solutions are gaining prominence, and innovative peer-to-peer (P2P) energy trading at a micro level is being deployed. Such advancement, however, leaves traditional security models vulnerable and paves the path for blockchain, a distributed ledger technology (DLT), with its decentralized, open, and transparency characteristics as a viable alternative. However, due to deregulation in energy trading markets, most of the prototype resilience regarding cybersecurity attack, performance and scalability of transaction broadcasting, and its direct impact on overall performances and attacks are required to be supported, which becomes a performance bottleneck with existing blockchain solutions such as Hyperledger, Ethereum, and so on. In this paper, we design a novel permissioned Corda framework for P2P energy trading peers that not only mitigates a new class of cyberattacks, i.e., delay trading (or discard), but also disseminates the transactions in a optimized propagation time, resulting in a fair transaction distribution. Sharing transactions in a permissioned R3 Corda blockchain framework is handled by the Advanced Message Queuing Protocol (AMQP) and transport layer security (TLS). The unique contribution of this paper lies in the use of an optimized CPU and JVM heap memory scenario analysis with P2P metric in addition to a far more realistic multihosted testbed for the performance analysis. The average latencies measured are 22 ms and 51 ms for sending and receiving messages. We compare the throughput by varying different types of flow such as energy request, request + pay, transfer, multiple notary, sender, receiver, and single notary. In the proposed framework, request is an energy asset that is based on payment state and contract in the P2P energy trading module, so in request flow, only one node with no notary appears on the vault of the node.Energy request + pay flow interaction deals with two nodes, such as producer and consumer, to deal with request and transfer of asset ownership with the help of a notary. Request + repeated pay flow request, on node A and repeatedly transfers a fraction of energy asset state to another node, B, through a notary.

Modelling the Intensity of Electric Vehicle Arrivals at Charging Points

With Electric Vehicles’ (EV) market adoption surging in recent years, the smart grid paradigm requires accurate forecasts of EV arrivals at charging points. One efficient way to model these arrivals is to use Point Processes. This study introduces an additive model using both spline and wavelet effects for fitting the intensity of a non-homogeneous Poisson process applied to EV arrivals at charging points. The key contribution of this work is a novel estimation procedure inspired from backfitting which is illustrated by a case study on real-world EV arrivals at charging points. The results obtained show that this approach can help better capturing EV arrival peaks.