Grid Resources Research Articles

Resource Optimization for Grid-Connected Smart Green Townhouses Using Deep Hybrid Machine Learning

This paper examines Connected Smart Green Townhouses (CSGTs) as a modern residential building model in Burnaby, British Columbia (BC). This model incorporates a wide range of sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. The CSGTs operate in grid-connected mode to balance on-site renewables with grid resources to improve efficiency, cost-effectiveness, and sustainability. Real datasets are used to optimize resource consumption, including electricity, gas, and water. Renewable Energy Sources (RESs), such as PV systems, are integrated with smart grid technology. This creates an effective framework for managing energy consumption. The accuracy, efficiency, emissions, and cost are metrics used to evaluate CSGT performance. CSGTs with one to four bedrooms are investigated considering water systems and party walls. A deep Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. In particular, the Mean Absolute Percentage Error (MAPE) is below 5%, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are within acceptable levels, and R2 is consistently above 0.85. The proposed model outperforms other models such as Linear Regression (LR), CNN, LSTM, Random Forest (RF), and Gradient Boosting (GB) for all bedroom configurations.

A Novel Synchronization Strategy for Distributed Energy Resources in Weak Grids Using Remote Strong Grid Sensing

This paper proposes a novel strategy for the current injection-based control of distributed energy resources connected to weak grids via a voltage source converter. The current injection controller is no longer synchronized with the point of common coupling but with the strong grid point voltage. The strong grid synchronization control strategy improves the output dynamics of the voltage source converter and recovery after faults in weak grids. The phase difference between the voltage source converter and the strong grid voltages caused by the long power lines does not affect the power control. Furthermore, a time delay-compensation method is proposed which tolerates the communication time delay introduced by the transmission of the synchronization signal from the strong grid point. The performance of the proposed control strategy is verified in detail using MATLAB 2023b simulations and real-time digital simulations on a medium voltage model and also validated in an experiment on a low-voltage grid-feeding inverter setup.

Demand Flexibility: A Review of Quantification Methods, Models, and Required Data

As renewable energy is increasingly integrated into our electricity supply, it becomes more challenging to ensure reliability and security due to the intermittent nature of these resources. With the electrification of buildings and technological advancements across various aspects of their operations, the building sector is expected to play a key role in reducing emissions while supporting the needs of the grid. Buildings and the loads they house can provide grid resources via demand flexibility, shifting, and shedding electric load, as necessary. This key resource has received increased attention from researchers, building operators, electric utilities, policymakers, and system operators as a tool to improve power grid reliability and reduce system costs. Before increasing reliance on demand flexibility, however, a better understanding of its availability is needed to inform planning efforts. This paper includes a review of the literature on current methods and data used to model the available flexibility of power delivered to customers. This review also summarizes how demand flexibility is defined and quantified to help inform future studies in this field. The results of this review illustrate the diversity found within this field of research and the innovation that researchers are employing to solve this complex problem.

Reliability of Regression Based Hybrid Machine Learning Models for the Prediction of Solar Photovoltaics Power Generation

Solar Photovoltaics (PVs) have become widespread as micro-distributed electric power generators in urban residential and commercial areas due to their affordability and minimal maintenance requirements. Despite these advantages, PV generation is intermittent, necessitating the implementation of robust predictive algorithms to capture power generation trends effectively. Accurate PV generation prediction is crucial for balancing energy demand and supply. Precise predictions help utilities use other grid resources efficiently, enhance market participation, and make informed planning decisions. Therefore, ensuring the precise performance of predictive models is imperative in the power generation industry. However, the performance of the predictive models typically fluctuates over time, necessitating a reliability assessment of their performance. This paper develops a hybrid machine learning model by integrating the support vector machine (SVM), random forest (RF), and gradient boosting machine (GBM) with multivariate adaptive regression spline (MARS) to predict the hourly PV power generation for 3-day and 7-day periods in three urban areas of North Dakota. The performance of the models has been recorded over a year, and a probability distribution model is proposed to demonstrate the hybrid algorithm’s reliability and effectiveness. Results show the proposed prediction model is more reliable for shorter prediction periods. The hybrid-MARS models significantly improve prediction reliability compared to the stand-alone MARS model. Among these models, MARS-SVM exhibits the highest reliability and consistently better accuracy than other hybrid models.

Oscillation Mechanism and Suppression of Variable-Speed Pumped Storage Unit with Full-Size Converter Based on the Measured Single-Input and Single-Output Impedances

As the penetration rate of clean energy gradually increases, the demand for flexible regulation resources in the power grid is increasing accordingly. The variable-speed pumped storage unit with a full-size converter (FSC-VSPSU) can provide fast and flexible regulation resources for the power grid, which assists in the stable operation of the clean-energy-dominated power systems. Thus, its application is gradually becoming widespread. FSC-VSPSU relies on the power electronic converter for grid connection. When the impedance mismatch between FSC-VSPSU and the power grid occurs, the grid-connected system will experience oscillations, which seriously threatens its safe and stable operation. Aiming at this, this article firstly relies on the SISO impedance measurement and an equivalent impedance analysis to obtain the stability analysis criterion. Furthermore, applying the impedance matching analysis with the Bode diagram, the influence rules of the parameters of the grid-connected FSC-VSPSU on oscillation are obtained. In addition, with the summarized oscillation analysis results, this study delves into an exploration of pertinent strategies for oscillation suppression. Finally, a grid-connected FSC-VSPSU simulation model is built in MATLAB/SIMULINK, and the simulation results verify the correctness of the oscillation analysis results and the effectiveness of the proposed oscillation suppression strategy.

Empowering microgrids for sustainable transportation electrification: A comprehensive methodology for resource adequacy and grid resilience

Transportation electrification has emerged as a pivotal solution to mitigate carbon emissions from the transportation sector. However, this shift towards electric vehicles (EVs) presents significant challenges for municipalities and utilities, particularly in ensuring the electrical grid's capacity to meet the increased demand. In this sense, this work introduces a novel methodology to address these challenges by 1) quantifying the impact of EVs on the hosting capacity (HC) of residential microgrids (MGs), and 2) proposing a novel control strategy to meet grid resource adequacy requirements under high levels of transportation electrification. For this, first, a simplified approach to quantify the overall demand in residential MGs, accounting for the influence of EVs and typical demand profiles based on real-world data from municipalities and utility systems is developed. Second, a new frequency controller is introduced, utilizing Distributed Phasor Measurement Units (D-PMU) and mobile energy sources (MES) to ensure compliance with resource adequacy requirements under high levels of EVs penetration. The proposed controller takes advantage of the low latency and high-resolution capabilities of D-PMU technology to enable the harnessing of MES, preventing critical frequency nadir events, and improving the overall system resource adequacy performance under both transient and steady-state analysis. Real-world data from Seattle, WA, USA, is used in the developed case studies, and comparative analysis between traditional SCADA, state-of-the-art D-PMU-based controller, and the proposed controller is presented. The obtained results indicate that significant improvements are achieved by the proposed controller, empowering utilities to ensure reliable operations amidst transportation electrification challenges imposed on resource adequacy.

Electricity user behavior analysis and marketing strategy based on internet of things and big data

This paper examines power user behavior and the design of marketing strategies, using a case study of Smart Community A. We explore how advanced analytical models are used to enhance energy efficiency and user services. First, we apply spectral clustering to refine user segmentation and identify distinct electricity consumption patterns among different groups. Then, the Hidden Markov Model (HMM) analyzes user behavior, uncovering shifts in consumption habits and enabling personalized service offerings. Next, the ARIMA model predicts electricity consumption trends, guiding grid scheduling and resource allocation. Based on these analyses, we develop targeted marketing strategies, such as dynamic pricing and energy-saving incentives, which boost user engagement and reduce energy usage. Through an IoT and big data-driven interactive marketing platform, we enhance user experience and foster a culture of energy conservation. Finally, a feedback mechanism ensures continuous improvement and maximizes the effectiveness of the marketing strategies.

Interruption Cost Estimation for Value-Based Reliability Investment in Emerging Smart Grid Resources

Growing uncertainty in supply and demand in power systems causes significant challenges in maintaining supply reliability at affordable costs. Power grids are expected to undergo substantial transformations to address these challenges with upgrades and integration of emerging smart technologies that require significant investment costs. A value-based reliability assessment of these grid technologies is necessary to justify the worth of these investments. A key parameter required in such an assessment is the cost of power interruptions originating from transmission system failures. The interruption cost data available in published reports and past surveys relate to generation inadequacy since generation facilities comprise the most capital-intensive investment of an electric utility. Customer interruptions due to a lack of generation mainly occur due to generation failures during the peak demand period, whereas interruptions due to transmission component failures can occur at other periods with specific probabilities. This paper presents a methodology to estimate the cost of outages originating from transmission asset failures, which proposes a sector period model for each customer sector to obtain associated demand-normalized interruption costs. The proposed method can also be used to decide investment in grid resiliency enhancement against extreme weather that mainly impacts the grid network facilities.

Attention-Based Load Forecasting with Bidirectional Finetuning

Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based model with a bidirectional reading of time-series data. By incorporating both forward and backward temporal dependencies, the model gains a more comprehensive understanding of consumption patterns, leading to improved performance. We present a mathematical framework supporting this approach, demonstrating its potential to reduce forecasting errors and improve robustness. Experimental results on real-world load datasets indicate that our bidirectional model outperforms state-of-the-art conventional unidirectional models, providing a more reliable tool for short and medium-term load forecasting. This research highlights the importance of bidirectional context in time-series forecasting and its practical implications for grid stability, economic efficiency, and resource planning.

Regulation techniques and applications of distributed load resources in urban power grids based on internet of things

The power consumption of urban power systems is increasing rapidly with two typical trends: the first one is that the daily peak-valley difference of loads is more significant, and the power supply is tight during peak hours, which threatens the system’s safe and stable operation; the second one is that the load energy efficiency in urban power systems is not high, which is the primary source of carbon emission in the power industry. Therefore, reducing the peak power and improving the system’s energy efficiency are urgent tasks for enhancing the system’s security and achieving the carbon emission goals. The rapid development of the Internet of Things (IoTs) ushers new opportunities for regulating demand-side loads. By analyzing the technical characteristics of load control based on IoTs, this paper investigates the modeling methods of load resources. On this basis, different control and optimization methods of load resources are analyzed and compared thoroughly. Besides, considering that load control is not only related to technical methods but also impacted by incentive strategies, the load control mechanisms under the mature and immature market environments are analyzed. Finally, the research gap and prospect of load regulation are proposed.

Tools for development of the Russian electric power industry in the current realities

Electric power industry is one of the most important sectors of Russian economy that satisfies the needs of the country’s population, companies of various areas of economy in electric and thermal energy and supplies it for the CIS and Non-CIS countries. Due to this, it is significant to study the tools for development of Russian electric energy industry in the current realities, and this is the basis of the article. The purpose of the article is to study the modern tools for development of electric energy industry and work out the recommendations on their improvement. The following aspects are considered as the main tools for development of electric energy industry: interconnection of electric grid resources, carbon neutrality, legislative acts, digital technologies, financial instruments aimed at attracting investment, accumulation systems (portable sources of electricity), electric transport, and green energy. The methodological basis of the research is the analysis of scientific publications and Internet resources, synthesis and generalization of the data acquired. The authors of the study have analyzed the most significant tools that can be used to develop the electric energy industry in the current situation, and worked out the recommendations on improving the effectiveness of their implementation. To provide a full-fledged solution of the tasks set in the article the authors have analyzed the dynamics of the electric energy industry, identified the basic principles of its development, the peculiar features of their implementation, worked out the opportunities and recommendations for their improvement. According to the results of the study it has been suggested to establish a new institute – a specialized international innovation centre of electric energy industry which will ensure attraction of investment, formation of effective cooperation with friendly countries and development of green energy industry. The authors have identified the tools providing for the development of electric energy industry in Russia, worked out recommendations on their improvement, suggested a new institute of financing and development of electric energy industry. The results of the study in hand can be used in the activity of the companies involved in electric energy industry and further research on the topic.

Improving the performance of grid‐connected inverters during asymmetrical faults and unbalanced grid voltages

AbstractThe increasing penetration of the distributed energy resources (DER) in the power grid, which, while having significant advantages, also pose significant challenges. The behaviors of DERs differ from those of synchronous generators, particularly in abnormal conditions. For this reason, the power grid enforces grid codes to ensure that DERs perform properly in different conditions. For instance, short circuit faults and unbalanced grid voltage are severe transient events that inverters need to be able to pass through without disconnecting from the grid. Furthermore, the inverters are required to support the grid voltage by regulating the active and reactive power injections. This article proposes a voltage support control scheme to support grid voltage during asymmetrical voltage drop by utilizing an optimization problem. In this optimization problem, the active and reactive powers injected into the grid will be obtained optimally by considering constraints such as instantaneous active and reactive power oscillation magnitudes and peak current limitation. To aid in this purpose, the corresponding mathematical formulations such as instantaneous active and reactive power oscillation magnitudes will be obtained by using the currents and voltages in stationary reference frame. The proposed scheme will be verified by simulating it in MATLAB/Simulink under three different scenarios and tested on a real‐time experimental Opal‐RT platform.

Deep learning-based evaluation of photovoltaic power generation

Photovoltaic (PV) power generation has emerged as a rapidly growing renewable energy source. However, the PV system output’s intermittent and weather-dependent nature poses challenges when integrating with the power grid. These challenges manifest as critical issues, including voltage fluctuations, harmonic distortion, and current deviation, making it difficult to accurately predict grid conditions. Moreover, the spatial variability caused by PV system intermittency further complicates the situation. To address these challenges and ensure efficient grid integration, this paper proposes a comprehensive approach encompassing deep learning-based state prediction of PV power output. The paper introduces the utilization of a long short-term memory (LSTM) model, a type of deep learning architecture, for learning patterns from historical PV power generation data and weather forecasts. The LSTM model enables accurate predictions for effective grid management by capturing long-term dependencies in PV power generation data. Real-world PV power generation data was employed to evaluate the proposed approach. The results demonstrated the significant improvement in PV power generation prediction accuracy achieved by the LSTM model compared to traditional methods. The proposed approach offers a promising solution for addressing the challenges associated with PV system integration into the power grid. It enables enhanced grid planning, resource allocation, and protection measures to accommodate the increasing penetration of solar energy harnessed through PV systems and related power electronics interfaces.

The monitoring and analysis of protective devices in renewable energy resource grid by computer simulation technology

Nowadays, computer simulation technology is very important to promote the economic development of renewable energy, but simulation technology cannot effectively identify grid protection devices, so it needs to be identified with the help of optimization algorithms. This study focuses on the inspection and evaluation of protective equipment in renewable energy grids, and constructs a micro-power unit control algorithm to identify the retention status in renewable energy grids. First, the static and dynamic conditions will be distinguished, and the differences between the preservation devices will be explored by computer data identification through micropowerThe relationship structure between computer simulation technology and renewable energy is established, and the reliability of the relationship structure is calculated, so as to provide a reference for the identification and application of protective devices. The results show that the micropower algorithm can provide effective data for computer simulation technology, better identify protective devices, improve the degree of data fusion by about 10~20%, increase the recognition rate of abnormal data by about 10%, and reduce the amount of computer simulation data. Therefore, the integration of micropower algorithm and computer simulation technology can more accurately simulate close to the real environment, which can not only improve the accuracy of device detection, but also evaluate and optimize the detection effect, and provide key practical support for renewable energy grid protection.

A Digital Twin Framework for Simulating Distributed Energy Resources in Distribution Grids

As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework’s ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade.

Cluster partition-based two-layer expansion planning of grid–resource–storage for distribution networks

In order to realize the optimal planning of grid–resource–storage for distribution networks (DNs) with high penetrated distributed photovoltaics (PVs), a cluster partition-based two-layer expansion planning for DNs is proposed. First, a comprehensive cluster partition index-based cluster partition method is proposed, which involves the indexes such as electrical distance, power balance of the cluster, and cluster size. Second, a cluster partition-based two-layer expansion planning model is proposed. In the upper layer, a line planning model for clusters is established to carry out the planning of cluster connection lines. In the lower layer, a robust source-storage planning model is established with the uncertainty of PVs and loads, and then, the optimal location and capacity of PVs and energy storages (ESs) can be obtained. In addition, the uncertainty regulation parameter is utilized to control the range of uncertainty sets, which can reduce the conservatism of the optimization. Finally, the proposed method is carried out in a real DN in China, which can effectively improve the economy of DN planning.

Regulatory Implications for considering flexible resources in network expansion planning: main lessons from regional cases in the H2020 FlexPlan project

The FlexPlan Horizon2020 project aimed at establishing a new grid planning methodology which considers the opportunity to introduce new storage and flexibility resources in electricity transmission and distribution grids as an alternative to building new grid elements, in accordance with the intentions of the European Commission regulatory package "Clean Energy for all Europeans". FlexPlan creates a new innovative grid planning tool which is intended to go beyond the state of the art of planning methodologies by including the following innovative features: assessment of best planning strategy by analysing in one shot a high number of candidate expansion options provided by a pre-processor tool, simultaneous mid- and long-term planning assessment over three grid years (2030-2040-2050), incorporation of full range of cost benefit analysis criteria into the target function, integrated transmission distribution planning, embedded environmental analysis (air quality, carbon footprint, landscape constraints), probabilistic contingency methodologies in replacement of the traditional N-1 criterion, application of numerical decomposition techniques to reduce calculation efforts and analysis of variability of yearly RES and load time series through a Monte Carlo process. Six regional cases covering nearly the whole European continent are developed in order to cast a view on grid planning in Europe till 2050. The final step in FlexPlan was formulating guidelines for regulators and planning offices of system operators, by indicating to what extent system flexibility can contribute to reduce overall system costs (operational and investment) yet maintaining current system security levels and which regulatory provisions could foster such process. This paper focuses on the regulatory issues, which were uncovered during the initiation phase and of the project and refined in throughout six regional cases. In order to substantiate this, the paper explains in brief the developed and applied FlexPlan methodology and its testing in six regional cases.

Advancing Distance Relaying Reliability: S Transform Solution for Renewable Energy-Infused Smart Grid

Distance relaying is a crucial component in safeguarding transmission lines globally. However, the presence of Sub-Synchronous Resonance (SSR) introduces challenges, as oscillations in voltage and current magnitudes adversely affect the performance of conventional distance relays. Detecting faults accurately during these oscillations poses a significant challenge for traditional relaying mechanisms. This paper addresses the issue of SSR-induced maloperations in distance relays, particularly in the context of the increasing penetration of renewable energy resources in smart grids. The integration of wind power into the grid generates sub-synchronous oscillations, leading to protection issues. In response, a novel solution leveraging the S transform, a modern signal processing technique, is proposed to enhance the robustness of distance relaying under such challenging conditions. The developed algorithm is implemented in MATLAB and rigorously tested on the First Benchmark Model of sub-synchronous resonance. Simulation of the model is conducted in the PSCAD simulation software, and the results are thoroughly validated in MATLAB. The study demonstrates the efficacy of the S transform-based approach in accurately identifying faults and mitigating maloperations caused by SSR, thereby improving the reliability of distance relaying in smart grids with high renewable energy integration.

The design of an improved index system for frequency control in China Southern Power Grid

Introduction: In order to dispatch frequency regulation resources in regional power grids efficiently and promote the development of spot markets, China Southern Power Grid (CSG) established the unified frequency regulation control area. However, the existing regional control performance standards (CPS) for evaluating the performance of frequency control in bulk power systems is no longer suitable for the unified frequency control mode.Method: This paper proposed an innovative frequency control performance standard, named tertiary control performance standards (TCPS) and used Gaussian mixture model (GMM) and folded normal distribution (FND) to describe the distributions of frequency deviations and power deviations of tie line.Result and Discussion: Based on these probability models, the parameters of the proposed TCPS can be determined optimized. Finally, case studies were carried out with the practical data from CSG and indicated that the parameters of proposed TCPS index could be calculated and improved the control performance for the real time power balance of the regional power grid with spot markets.

A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power

AC-DC hybrid distribution grids realize power transmission through tie lines. Accurately characterizing the power exchange capacity between regional grids while ensuring safe grid operation is the basis for the coordinated scheduling of resources in interconnected distribution grids. However, most of the current AC/DC hybrid models are linear, and it is challenging to ensure the accuracy criteria of the obtained feasible regions. In this paper, a two-stage multi-segment boundary approximation method is proposed to characterize the feasible region of hybrid distribution grid tie line operation. Information such as security operation constraints are mapped to the feasible region of the boundary tie line to accurately characterize the transmission exchange capacity of the tie line. To avoid the limitations of linear models, the method uses a nonlinear model to iteratively search for boundary points of the feasible region. This ensures high accuracy in approximating the real feasible region shape and capacity limitations. A convolutional neural network (CNN) is then utilized to map the given boundary and cost information to obtain an estimated equivalent operating cost function for the contact line, overcoming the inability of previous methods to capture nonlinear cost relationships. This provides the necessary cost information in a data-driven manner for the economic dispatch of hybrid AC-DC distribution networks. Numerical tests demonstrate the effectiveness of the method in improving coordination accuracy while preserving regional grid privacy. The key innovations are nonlinear modeling of the feasible domain of the contact line and nonlinear cost fitting for high-accuracy dispatch.