Grid Resources Research Articles

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.

Large-scale deep reinforcement learning method for energy management of power supply units considering regulation mileage payment

To improve automatic generation control (AGC) performance and reduce the wastage of regulation resources in interconnected grids including high-proportion renewable energy, a multi-area integrated AGC (MAI-AGC) framework is proposed to solve the coordination problem of secondary frequency regulation between different areas. In addition, a cocktail exploration multi-agent deep deterministic policy gradient (CE-MADDPG) algorithm is proposed as the framework algorithm. In this algorithm, the controller and power distributor of an area are combined into a single agent which can directly output the power generation command of different units. Moreover, the cocktail exploration strategy as well as various other techniques are introduced to improve the robustness of the framework. Through centralized training and decentralized execution, the proposed method can nonlinearly and adaptively derive the optimal coordinated control strategies for multiple agents and is verified on the two-area LFC model of southwest China and the four-area LFC model of the China Southern Grid (CSG).

Distributed Energy Resources Management System (DERMS) and Its Coordination with Transmission System: A Review and Co-Simulation

Ever-increasing penetration of distributed energy resources (DERs) in the power grids, alongside their numerous benefits, brings new challenges that call for enhanced solutions in the field of control and management of power grids. The majority of the available research have considered either distribution or transmission grids in their studies. In this paper, a comprehensive review of the effects of DERs on the distribution and transmission grids is performed. The focus of this paper is on hierarchical management methods in order to categorize different approaches and highlight the gaps. Moreover, a review is conducted in the field of the newly introduced distributed energy resources management system (DERMS) concept. A DERMS can facilitate the hierarchical energy management procedure due to its functionalities and broad capabilities. Hence, its implementation in energy management and its impact on the power grid will be assessed with the aid of a co-simulation platform that considers both transmission and distribution grids.

Computer multimedia aided design and hand-drawn effect analysis based on grid resource sharing cooperative algorithm

Hand-drawn is one of the few visual descriptors that can directly represent visual content, and has significant research in the area of computer vision. Aiming at the problem of sparse features in the realm of hand-drawn image retrieval, hand-drawn images, and the easy deformation of hand-drawn images, this paper proposes a feature extraction method of grid resource sharing collaborative algorithm, which can be obtained utilizing precisely extracted semantic characteristics from hand-drawn images through computer multimedia-aided design Efficient and accurate retrieval results. First, the fundamental framework for obtaining semantic features is algorithm; then the attention model mechanism is the grid resource sharing collaborative introduced in the process of supervised training, and the attention structure block is introduced after the convolutional neural network’s bottom layer. To locate effective semantic features, In order to accomplish high-precision retrieval, the attention structure block combines channel attention structure and spatial attention structure to build the attention structure block. The last feature descriptor is then created by combining various semantic feature levels. The proposed strategy is practical and efficient, as demonstrated by the experimental findings on the comparison database Flickr15k. In addition, in the task of hand-drawn image classification, the proposed attention mechanism greatly improves the classification accuracy.

General structures of control area cooperation for variable renewable energy integration in electric power systems

To integrate large-scale variable renewable energy resources (RESs) in modern power grids, the coordinating control area (CA) operation is the most cost-effective method. This article reviews the technical aspects of CA cooperation. Firstly, a brief overview of the active balancing control within each CA is discussed. Secondly, three general control structures for CA cooperation are innovatively proposed, the corresponding implementation details are analyzed, and some representative technologies are also provided in the systematic analysis. Then, some future research directions such as large-scale power sharing by DC, active power control of RES bases, and the new structure for distributed energy resources in local power grids are prospected. Finally, the changes in power systems brought about by their evolution and importance for further promoting cooperation between CAs are summarized.

Investigating the role of smart technologies, financial, and environmental innovations in tackling the ecological sustainability: a global pathway toward low carbon energy transition.

Since the beginning of the twenty-first century, the rapid development of modern technologies has brought unprecedented social prosperity to mankind as technologies penetrate every sector of the economy. These technologies have given a new dimension to the energy sector. The key purpose of this study is to investigate the crucial impact of technological revolutions, namely, smart grids, smart devices, financial innovations, and environmental innovations, on greenhouse gas emissions (GHGs). To this end, the study utilized data from European, Asian, Middle Eastern, and African countries and employed first- and second-generation methods, such as DOLS, FMOLS, and CS-ARDL models. The research shows that smart grids are the only factor in reducing GHGs, regardless of geographic division. Hence, linking smart grid resources to climate change goals requires short-term deployment strategies with a clear long-term vision and the fundamental goal of transforming the power structure into a net zero-emission system. The study also demonstrates that the emergence of ICT in electricity consumption has not yet reached a level that can promote environmental excellence. The study documented the critical role of financial innovation and environmental innovation in addressing environmental degradation.

Prediction of the whole society electricity consumption in northeast China based on the BP neural network and Markov

Northeast China has been facing a severe power shortage situation. Since September 2021, “power rationing” events occurring in many places in the three provinces of northeast China have been causing inconvenience to people’s production and life. Therefore, it is particularly important to accurately predict the power load combined with the influencing factors of local power consumption. At the same time, the northeast region is about to enter the heating season, and the pressure on coal and electricity will further increase. In Heilongjiang Province, due to coal capacity control, limited production led to the high price of thermal coal; wind power photovoltaic output fluctuations, the epidemic, and other reasons also resulted in a large gap in the power supply side. Improving the power demand forecasting ability is of great significance to strengthen the reliability of people’s daily electricity consumption, rational distribution of power generation plans, and deployment of power grid resources. In order to improve the accuracy of electricity consumption prediction in Heilongjiang Province, Markov error correction is carried out on the basis of the backpropagation (BP) neural network prediction model so that the final prediction results have the advantages of the BP neural network prediction model and Markov model. In addition, it is more suitable for the prediction of random series data with high volatility, the prediction accuracy can be improved significantly, and the overall trend of electricity consumption can be predicted more accurately.