Grid Energy Management System Research Articles

Dual grid energy management strategy for electric vehicles in hybrid microgrid utilizing matrix pencil method

Abstract This study introduces a cutting-edge Matrix Pencil-based Dual Grid Energy Management System (MP-DEMS) aimed at seamlessly integrating electric vehicles (EVs) into power grids while enhancing power quality (PQ) and reliability (PR). Operating within an AC-DC hybrid microgrid (HMG) framework, the MP-DEMS reduces the necessity for additional conversion devices, simplifying the integration of EVs and batteries. The core of the proposed DEMS comprises three key modules: the Wind Energy Management Module (WEMS), Smart Storage and EV Power Coordination (SS-EVPC), and Coordinated Power Conversion Control (CPCC). These modules work together to optimize energy usage, leverage renewable sources effectively, and manage EV charging/discharging schedules to minimize grid impact. An innovative aspect of the MP-DEMS is its use of Matrix Pencil-based techniques, offering several advantages over traditional Discrete Fourier Transform (DFT) methods. These advantages include swift dynamic response, resilience to voltage variations, and precise voltage phase estimation. Software simulations and Hardware-in-the-Loop (HIL-402) testing validate the efficacy of the MP-DEMS approach, showcasing enhanced power transfer capabilities, improved harmonic performance, and superior voltage/frequency regulation within EV-HMS applications. Overall, the MP-DEMS presents a promising solution for advancing the integration of EVs into microgrid systems, contributing to a more sustainable and reliable energy future.

Analytical review study of the Grid connected Micro grid Energy Management System

As an environmentally friendly method of distributing energy production, the integration of photovoltaic systems into micro grids has drawn in significant focus on. Our goal in doing is to examine features regarding micro grid that is linked to the power grid, with a focus on photovoltaic energy management in particular. Finding the optimal micro grid capacity for the solar system seeks to increase energy efficiency, decrease dependence on main grid, and promote an utilization of green power. The outlined optimization approach evaluates the micro grid’s dynamic interactions using state-of-the-art modelling and simulation tools. These components include photovoltaic panels, energy storage systems, alongside the main grid. The refinement method takes into account crucial factors including patterns of load demand, costs of the grid electricity, and variations in solar irradiation. Finding a happy medium between increasing the amount of power generated by renewable sources and decreasing overall energy costs is the objective. That study takes a multi-scenario approach to determining how various micro grid sizes affect overall system efficiency. Using scenario-based simulations and techno-economic criteria, the appropriate size of the photovoltaic system was determined. Factors like payback time, ROI, and system reliability are taken into account here. The study’s findings provide light on grid-connected micro grids, particularly in regards to photovoltaic energy management, which is crucial for their planning and implementation. In order to make educated decisions towards more robust and ecologically friendly power systems, stakeholders, lawmakers, and decision-makers can use the optimal micro grid size as a benchmark for future renewable power projects. This paper reviews the relevant literature and proposes a division and performance strategy based on its findings. By classifying energy management into three groups according to grid connection, configuration, and control method, this article provides a description of the performance, application, advantages, and disadvantages of algorithms that may be used as a reference for selecting an appropriate algorithm. Also included is a comparison table for the control strategies that were used to regulate a micro grid system that is connected to the grid.

Grid-connected lithium-ion battery energy storage system towards sustainable energy: A patent landscape analysis and technology updates

Presently, as the world advances rapidly towards achieving net-zero emissions, lithium-ion battery (LIB) energy storage systems (ESS) have emerged as a critical component in the transition away from fossil fuel-based energy generation, offering immense potential in achieving a sustainable environment. This study conducts an in-depth analysis of grid-connected LIB ESS patents published from 1998 to 2022, aiming to comprehend essential developments and trends in the technology landscape while identifying key players and inventors. Employing extensive keyword searches within the Lens database, 95 patent documents were extracted along with the bibliometric data. The research highlights two prominent factors in the field of grid-connected LIB ESS patents. Firstly, a detailed patent bibliometric analysis including patent growth trends, keyword analysis, patent distribution over jurisdiction, and subject categories was presented. Moreover, the research identifies key owners, inventors and hierarchical jurisdiction in patent document publishing and evaluates the possibilities for future research sustainability. Secondly, detailed updates regarding technological advancements that are driving sustainable grid-connected LIB ESS development in various sectors such as; gird connected ESS, power conversion system (PCS), energy management system (EMS) and transportation sector are provided. The study concluded that the patents related to grid-connected ESS, minimizing voltage and frequency regulation to achieve grid stability and EMS of LIB are the key trending topics in the field of grid-connected LIB ESS. The comprehensive analysis not only illuminates crucial trends but also provides valuable guidance to future researchers and industry stakeholders, facilitating the path towards sustainable grid-connected LIB ESS and paving the way for a more environmentally conscious energy landscape.

Short-term electric load forecasting using an EMD-BI-LSTM approach for smart grid energy management system

Electricity is an essential resource for human production and survival. Accurately predicting electrical load consumption can help power supply companies make informed decisions, such as peak load shifting, to maintain a reliable power supply and reduce CO2 emissions. However, forecasting electricity consumption is challenging due to the nonlinear and nonstationary time series data that is correlated with climate change. To address this challenge, this paper proposes an electricity forecasting method based on empirical mode decomposition (EMD) and bidirectional LSTM. EMD is a solid and robust instrument for time–frequency analysis and signal preprocessing, which separates the time series into components at different resolutions. The proposed model predicts the future 24 h with a resolution of 15 min by creating many stationary component sequences from the original stochastic electricity usage time series data (IMFs). To predict each Intrinsic Mode Function, a hybrid model BI-LSTM is employed. The results of each component's forecast are then merged to give the overall forecast. Two comparative studies are conducted to justify the choice of the signal processing method and the prediction algorithm. The proposed model demonstrates a minimal MAPE of 0.28% and a better R2 close to 1 of 0.84 compared to other papers.

Blockchain-Driven Real-Time Incentive Approach for Energy Management System

In the current era, the skyrocketing demand for energy necessitates a powerful mechanism to mitigate the supply–demand gap in intelligent energy infrastructure, i.e., the smart grid. To handle this issue, an intelligent and secure energy management system (EMS) could benefit end-consumers participating in the Demand–Response (DR) program. Therefore, in this paper, we proposed a real-time and secure incentive-based EMS for smart grid, i.e., RI-EMS approach using Reinforcement Learning (RL) and blockchain technology. In the RI-EMS approach, we proposed a novel reward mechanism for better convergence of the RL-based model using a Q-learning approach based on the greedy policy that guides the RL-agent for faster convergence. Then, the proposed RI-EMS approach designed a real-time incentive mechanism to minimize energy consumption in peak hours and reduce end-consumers’ energy bills to provide incentives to the end-consumers. Experimental results show that the proposed RI-EMS approach induces end-consumer participation and increases customer profitabilities compared to existing approaches considering the different performance evaluation metrics such as energy consumption for end-consumers, energy consumption reduction, and total cost comparison to end-consumers. Furthermore, blockchain-based results are simulated and analyzed with the help of deployed smart contracts in a Remix Integrated Development Environment (IDE) with the parameters such as transaction efficiency and data storage cost.

Energy Management System for Smart Grids: Tests in Cyber-Physical Mode

The objective of this work regards the laboratory assessment of the energy management system (EMS) for a smart grid, to be applied to the Eigerøy island (Norway) inside the H2020 ROBINSON project. The smart grid is based on the integration of industrial needs (a steam boiler fueled by LNG) with renewable sources and waste recycling (internal production of syngas and biogas). The mentioned EMS, developed to minimize energy generation costs, includes an optimization tool and a Model Predictive Control (MPC) software for the calculation of the activation and the set-point values of the prime movers. Moreover, a special scheduling approach was proposed for the electrolyzers connected to a hydrogen storage pressure vessel. In this work the EMS was tested in cyber-physical mode in the Innovative Energy Systems (IES) laboratory of the University of Genoa. In details, the tests were performed coupling component models with real hardware (microturbine and solar panels) available in the laboratory. The obtained optimization performance was highlighted on the basis of a comparison with a standard management of the smart grid.

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning.

Synchrophasor Data Analytics: Transmission Line Parameters Online Estimation for Energy Management

Transmission line parameters are fundamental parameters of power system models that influence the safety, stability, and economy of power utility grids. Transmission line parameter estimation is a basic and necessary function and module of a power grid energy management system. However, traditional offline parameter estimation methods cannot cope well with parameter uncertainties caused by transmission line aging, environmental factors, and modern power grid developments. The widespread deployment of phasor measurement units (PMUs) in power networks has paved the way for accurate real-time-measurement-based transmission line parameter tracking. This article proposes PMU-based parameter estimation models considering series and shunt compensators. An online parameter estimation method is developed based on recursive least squares with a variable forgetting factor, which can adapt to various parameter mutations and has strong robustness to outlier measurements. This article also analyzes the sensitivity of parameters with respect to synchrophasor data. The simulation results under different scenarios verify the effectiveness, robustness, and adaptivity of the proposed method, which exhibits significant advantages over other existing parameter estimation methods.

Smart soft open point to synergically improve the energy efficiencies of the interconnected electrical railways with the low voltage grids

This paper presents a novel smart soft open point (sSOP) that interconnects railway electrification system to the local low voltage (LV) distribution grid to provide additional flexibility and controllability for both networks. The proposed sSOP builds on the concept of soft open points, adding an energy storage system and embedding a rail and grid (R + G) energy management system to enable the energy transfer between the two networks at different average power levels. The smartness of the proposed soft open point is realised in the integration of an energy storage system to act as an energy buffer between these two networks of different dynamics. The paper demonstrates that the proposed sSOP improves the braking efficiency of trains and increase the local use of renewable energy through employing an integrated simulation platform between a railway and smart grid simulators that takes into account the trains' timetables as well as the daily variation of the grid loads and renewable energy generation. The results show that an appropriate design and control of the sSOP enables the recuperation of most of the braking energy available from the railway and that energy is enough to mainly supply the local power distribution grid, thereby effectively implementing the concept of smart grids.

A multi-timescale smart grid energy management system based on adaptive dynamic programming and Multi-NN Fusion prediction method

The complexity of power grids, the intermittent renewable energy generation and the uncertainty of load consumption bring great challenges to modern energy management systems (EMSs). To solve the energy optimization problem in the time-varying smart grid, this paper proposes a multi-timescale EMS based on the adaptive dynamic programming (ADP) algorithm and multi-neural-network fusion (MNNF) prediction technology. In detail, according to different power consumption characteristics, this paper uses fuzzy C-means (FCM) clustering algorithm to classify power users into industrial users, commercial users and residential users. Based on the classification results, an MNNF prediction method is proposed that can integrate different influencing factors to predict load consumption and renewable energy generation. Then a multi-timescale ADP optimization algorithm is proposed to maximize the utilization of renewable energy on daily, intra-day and real-time (i.e., three timescales) of energy behavior. The convergence of the multi-timescale ADP algorithm is proved mathematically when the initial value is a random semi-positive definite function. Finally, the proposed ADP with MNNF energy management system is verified on a hardware-in-the-loop (HIL) platform.

Emerging Trends in Smart Grid Energy Management Systems

The rising penetration of renewable energy sources into the power system has necessitated the transformation of the conventional power system fromgeneration, transmission and distribution' to ‘Localized generation, on-site storage and distribution integrated with multi-site communication and transmission'. The change in load type from incandescent lamps and induction motors to digital (electronic) systems requires robust control algorithms to ensure better power quality, mitigate system cost and manage energy effectively. This paper attempts to overview the evolution of the power system from its inception to date and presents the emerging trends for cost and energy-efficient smart grid systems. The benefits of smart-grids and various approaches by different countries to reduce carbon emissions and promote intelligent power systems have been analyzed. The technical advancements in the conventional power system architecture at the communication level and power level have been discussed in detail, along with the microgrid types and deployed energy management strategies. In the last section, a summary of emerging trends in the nested / multi-microgrid systems collated from various projects going on across the globe and their implications on cost and battery reductions for widespread deployment is presented.

An Improved Energy Management for Pv/Battery Hybrid System Using interleaved Boost Converter

This project proposes the integration of photo voltaic array and battery in a micro grid and management of energy happening in the grid. The regulation of battery depending on the requisite of load i.e., battery hold, charge and discharge actions using a bi-directional DC-DC converter has been proposed. The usage of a simple boost converter and interleaved boost converter with MPPT control, two-level inverter and three-level NPC inverter has been compared for various situations like PV delivering supply to the load and charging battery, only PV system delivering supply to the load, only battery delivering supply to the load and PV-battery both delivering supply to the load. Output factors like power, voltage and current are analyzed and plotted for all the situations. The proposed micro grid energy management system is simulated under MATLAB/Simulink environment.

Neighborhood Energy Modeling and Monitoring: A Case Study

Cities and nations worldwide are pledging to energy and carbon neutral objectives that imply a huge contribution from buildings. High-performance targets, either zero energy or zero carbon, are typically difficult to be reached by single buildings, but groups of properly-managed buildings might reach these ambitious goals. For this purpose we need tools and experiences to model, monitor, manage and optimize buildings and their neighborhood-level systems. The paper describes the activities pursued for the deployment of an advanced energy management system for a multi-carrier energy grid of an existing neighborhood in the area of Milan. The activities included: (i) development of a detailed monitoring plan, (ii) deployment of the monitoring plan, (iii) development of a virtual model of the neighborhood and simulation of the energy performance. Comparisons against early-stage energy monitoring data proved promising and the generation system showed high efficiency (EER equal to 5.84), to be further exploited.

Dynamic Optimization Method of Transmission Line Parameters Based on Grey Support Vector Regression

Aiming at the problem of insufficient accuracy and timeliness of transmission line parameters in the grid energy management system (EMS) parameter library, a dynamic optimization method of transmission line parameters based on grey support vector regression is proposed. Firstly, the influence of operating conditions and meteorological factors on the changes of parameters is analyzed. Based on this, the correlation quantification method of transmission line parameters is designed based on Pearson coefficient, and the influence coefficient value is obtained. Then, with the influence coefficient as the constraint condition, a method for selecting strong influence characteristics of line parameters based on improved Elastic Net is proposed. Finally, based on the grey prediction theory, a grey support vector regression (GM-SVR) parameter optimization model is constructed to realize the dynamic optimization of line parameter values under the power grid operation state. The effectiveness and feasibility of the proposed method is verified through the commissioning of the reactance parameters of the actual local loop network transmission line.

Multi-Level Energy Management Systems Toward a Smarter Grid: A Review

Home Energy Management Systems (HEMSs) may not be able to solve network issues, especially in the presence of high penetration level of Electric Vehicles (EVs) and decentral renewable energy. To solve the problem, Grid Energy Management Systems (GEMSs) were introduced. However, because of the contradictory nature of the main objectives of HEMS which are economical oriented on end-users, e.g., cost minimization, and GEMS which are technical oriented on system operators, e.g., maximization of system stability and power quality cannot be satisfied simultaneously. Hence, a multi-level energy management system seems to be necessary to improve the techno-economic performance of the distribution system while satisfying end-users, electricity retailers, and the system operator. Because of the significance of the subject, this paper presents the state-of-the-art regarding different energy management systems at home, aggregator, and network levels. The advantages and disadvantages of each system are discussed and compared, considering their main elements such as objective functions, constraints, optimization algorithms, communication protocols, and impact of EVs. The challenges and limitations in hierarchical energy management are explained. Finally, some future research directions are suggested to improve the multi-level energy management system.

An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid

Electricity demand is increasing, as a result of increasing consumers in the electricity market. By growing smart technologies such as smart grid and smart energy management systems, customers were given a chance to actively participate in demand response programs (DRPs), and reduce their electricity bills as a result. This study overviews the DRPs and their practices, along with home energy management systems (HEMS) and load management techniques. The paper provides brief literature on HEMS technologies and challenges. The paper is organized in a way to provide some technical information about DRPs and HEMS to help the reader understand different concepts about the smart grid, and be able to compare the essential concerns about the smart grid. The article includes a brief discussion about DRPs and their importance for the future of energy management systems. It is followed by brief literature about smart grids and HEMS, and a home energy management system strategy is also discussed in detail. The literature shows that storage devices have a huge impact on the efficiency and performance of energy management system strategies.

Energy management system for smart grid: An overview and key issues

Energy management system for smart grid: An overview and key issues

Micro Grid Operational Issues And Challenges In Smart Grid Scenario While Heterogeneous Micro Level Generations Are Predominant

Micro Grid operations are more dependent on and vulnerable to intermittent renewable energy sources (RES) integration along with other emerging trends like Plug in Electric Vehicles (PEVs) and advanced Energy Storage Systems (ESSs). With the advent of Smart Grid technologies, the micro grid operations are becoming more realistic and promising without much delays and inaccuracies in control actions. In this paper, we mainly focused on Micro Grid Energy Management System (EMS), synchronization, V and f control. A comprehensive review has been carried out to list out the current issues and challenges in a Smart Grid technology aided Micro Grid. The major issues in Micro Grid are mainly concerned with integration of intermittent distribution generation and while running in island mode of operation, the issues like V and f correction are more critical. Micro grid community helps in power sharing and stability cooperation among all micro grids, but at the same it should be capable of identifying when to isolate in case of any micro grid blackout. The role of power electronic converters in controlling the grid parameters while interfacing DGs are discussed through available literature.

Electric Vehicle Charge–Discharge Management for Utilization of Photovoltaic by Coordination Between Home and Grid Energy Management Systems

This paper proposes an electric vehicle (EV) charge-discharge management framework for the effective utilization of photovoltaic (PV) output through coordination based on information exchange between home energy management system (HEMS) and grid energy management system (GEMS). In our proposed framework, the HEMS determines an EV charge-discharge plan for reducing the residential operation cost and PV curtailment without disturbing EV usage for driving, on the basis of voltage constraint information in the grid provided by the GEMS and forecasted power profiles. Then, the HEMS controls the EV charge-discharge according to the determined plan and real-time monitored data, which is utilized for mitigating the negative effect caused by forecast errors of power profiles. The proposed framework was evaluated on the basis of the Japanese distribution system simulation model. The simulation results show the effectiveness of our proposed framework from the viewpoint of reduction of the residential operation cost and PV curtailment.

IEC 61850-Based Communication Modeling of EV Charge-Discharge Management for Maximum PV Generation

Photovoltaic (PV) modules are the most common distributed energy resource utilized at household level, owing to the subsidies and payback schemes. Proliferation of residential PV reduces the bills for its owner and supports the grid by local generation when needed. However, since they are located at distribution networks, too much local generation causes over-voltage issues. The traditional solution is to curtail the PV generation when the voltage limit is reached. This limits the benefits of the residential PV systems, makes expected payback period longer, and reduces the amount of captured renewable energy. Alternative approaches that make use of the available storage, such as electric vehicles (EVs), to avoid over-voltage issues in the network and maximize the PV generation, are required. In this paper, a charge–discharge management scheme is shown, where the estimated PV generation as well as the EV location and its state of charge are utilized to calculate the expected PV curtailment due to the over-voltage issues. Based on this information, EV battery is utilized to capture the energy that would, otherwise, be curtailed. This scheme requires the exchange of data and information between different entities, such as irradiation sensors, households, and grid energy management system. Considering the variety of devices included in this communication network, a standardized approach is vital for its successful implementation. Therefore, IEC61850-based communication models are developed for the house energy management system, PV, EV, and grid energy management system. Furthermore, communication message flows have been developed, and their performances have been investigated using different communication technologies.