Power Load Demand Research Articles

Research on Energy Management Technology of Photovoltaic-FESS-EV Load Microgrid System

This study focuses on the development and implementation of coordinated control and energy management strategies for a photovoltaic–flywheel energy storage system (PV-FESS)-electric vehicle (EV) load microgrid with direct current (DC). A comprehensive PV-FESS microgrid system is constructed, comprising PV power generation, a flywheel energy storage array, and electric vehicle loads. The research delves into the control strategies for each subsystem within the microgrid, investigating both steady-state operations and transitions between different states. A novel energy management strategy, centered on event-driven mode switching, is proposed to ensure the coordinated control and stable operation of the entire system. Based on the simulation results, the PV system cannot cope with the load demand power when it is increased to a maximum of 2800 W, the effectiveness of the individual control strategies, the coordinated control of the subsystems, and the overall energy management approach are confirmed. The main contribution of this research is the development of a coordinated control mechanism that integrates PV generation with FESS and EV loads, ensuring synchronized operation and enhanced stability of the microgrid. This work provides significant insights into optimizing energy distribution and minimizing losses within microgrid systems, thereby advancing the field of energy management in DC microgrids.

Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.

A Multi-Layer Techno-Economic-Environmental Energy Management Optimization in Cooperative Multi-Microgrids with Demand Response Program and Uncertainties Consideration.

This paper presents a multi-layer, multi-objective (MLMO) optimization model for techno-economic-environmental energy management in cooperative multi-Microgrids (MMGs) that incorporates a Demand Response Program (DRP). The proposed MLMO approach simultaneously optimizes operating costs, MMG operator benefits, environmental emissions, and MMG dependency. This paper proposed a new hybrid ε-lexicography-weighted-sum that eliminates the need to normalize or scalarize objectives. The first layer of the model schedules MMG resources with DRP to minimize operating costs (local generation and power transactions with the utility grid) and maximize MMG profit. The second layer achieves the environmental operation of the MMG, while the third layer maximizes MMG reliability. This paper also proposed a new application of a recently developed enhanced equilibrium optimizer (EEO) for solving the three-layer EM problem. In addition, the uncertainties of solar power generation, wind power generation, load demand, and energy prices are considered based on the probabilistic 2m + 1 Point estimation method (PEM) approach. Three case studies are presented to verify the proposed MLMO approach on an MMG test system. In Case I, a deterministic EM is solved to simulate the MMG as a single layer to minimize costs and maximize benefits through DRP, while Case II solves the MLMO optimization problem. Simulation results show that the proposed MLMO technique reduces environmental emissions by 2.45% and 3.5% in its optimization layer and at the final layer, respectively. The independence index is also enhanced by 2.49% and 4.8% in its layer only and as a total increase, respectively. Case III is for the probabilistic EM simulation; due to the uncertain variables effect, the mean value in this case is increased by about 2.6% over Case I.

Study on the Economic Operation of a 1000 MWe Coal-Fired Power Plant with CO2 Capture

The flexible operation of carbon capture units is crucial for the economic performance of coal-fired power plants equipped with CO2 capture systems. This paper aims to investigate the impact of electricity, CO2, and fuel prices on the economic operation of such plants. A novel economic optimization model is proposed, integrating a static model of the carbon capture system with a particle swarm optimization algorithm. A new concept, the CO2 boundary price, is introduced as a key metric for determining the operating conditions of CO2 capture units. The CO2 boundary price rises when the power load decreases due to the decline in power generation efficiency, and it also increases with rising fuel prices, as the cost of steam for CO2 capture increases. Additionally, when the objective is to meet power load demand, CO2 prices have a great influence on the operation of CO2 capture units, assuming fixed coal and electricity prices. However, when the primary goal is to maximize plant profitability, the system’s operational conditions are strongly influenced by the relative prices of electricity and CO2. The proposed optimization model and the uncovered price-effect mechanisms provide valuable insights into the economic operation of carbon capture power plants.

Stochastic optimal allocation of grid-side independent energy storage considering energy storage participating in multi-market trading operation

The integration of large-scale intermittent renewable energy generation into the power grid imposes challenges to the secure and economic operation of the system, and energy storage (ES) can effectively mitigate this problem as a flexible resource. However, the conventional ES allocation is mostly planned to meet the regulation demands of individual entities, which is likely to result in low utilization of ES and difficult to recover the investment cost. Therefore, a two-stage stochastic optimal allocation model for grid-side independent ES (IES) considering ES participating in the operation of multi-market trading, such as peak-valley arbitrage, frequency regulation, and leasing, is proposed in this paper to improve the comprehensive benefits and utilization rate of ES. The first stage aims to allocate IES and develop a systematic scheduling plan based on the forecast of wind power output and load demand, while the second stage responds to the uncertainty of wind power output by re-dispatching generating units and invoking ES power leased by wind farms. Then, a two-layer loop iterative solution algorithm based on the Benders decomposition is formed to effectively solve the proposed model. Finally, the approach developed in this paper is applied to a modified IEEE RTS-79 test system, and the results verify that it is both feasible and effective.

Enhancing microgrid forecasting accuracy with SAQ-MTCLSTM: A self-adjusting quantized multi-task ConvLSTM for optimized solar power and load demand predictions

Accurate forecasting of solar power output and load demand is critical for the efficient operation and management of isolated microgrids, where reliability and sustainability are paramount. Traditional methods often struggle with data scarcity, limitations in capturing intricate temporal dynamics, and lack of scalability. This research introduces a novel multi-task learning (MTL) model, the Self-Aware Quantized Multi-Task ConvLSTM (SAQ-MTCLSTM), which addresses these challenges by jointly forecasting solar power and load demand while leveraging shared representations across these interdependent time series. The SAQ-MTCLSTM incorporates a sophisticated architecture that combines convolutional and LSTM layers with self-aware quantization to enhance computational efficiency and model adaptability. This allows for effective knowledge transfer, improved data utilization, and the capture of intricate temporal patterns. Evaluated on a real-world isolated microgrid dataset from the Micro Reseau Mafate research project, the model demonstrates significant improvements in forecasting accuracy, with MSE values of 0.0021 for solar power output and 0.0037 for load demand forecasting, compared to single-task and other advanced models. Extensive experiments highlight the impact of data scarcity, seasonality patterns, and microgrid topology on forecasting performance. Such forecasting is essential to optimize the integration and utilization of renewable resources, enhancing operational stability and reducing dependency on external energy supplies.

An universal energy-matching design and regulation method for combined cooling, heating, and power (CCHP) systems in different scenarios

In this study, the impact of the coupling matching mechanism between the systems and the users on the energy-saving characteristics of the systems was analyzed based on a universal representation and computation method to identify the energy saving potential of the systems. The physical model was detailed derived for the relationship between the load supply and user demands under different capacity design modes and operation strategies to construct a universal coupling matching diagram, and the impact of the coupling matching parameters (operational energy efficiency, load matching degree, etc) on system integration and operation in different energy use scenarios was quantitative analyzed to illustrate the inherent mechanism of energy saving characteristics. In addition, the coupling mechanism between the systems and the users from a global perspective was reconstructed, and a coupling method of capacity design factor and dual source regulation was proposed to realize the collaborative integration of centralized energy supply systems, combined cooling, heating, and power systems and load demands, as well as the active regulation of the internal cascade utilization of the systems, so as to improve the energy saving potential of the systems under all operating conditions. Finally, a case analysis and method verification were conducted using typical summer days at Kunming hotel as an example. The results show that the coupling matching relationship is the internal mechanism that affects the energy saving of the systems, and the average energy saving rate of the whole day can be increased by 7.45% through collaborative integration and active regulation.

A support vector regression-based interval power flow prediction method for distribution networks with DGs integration

In distribution networks with distributed generators (DGs), power generation and load demand exhibit increased randomness and volatility, and the line parameters also suffer more frequent fluctuations, which may result in significant state shifts. Existing model-driven methods face challenges in efficiently solving uncertain power flow, especially as the size of the system increases, making it difficult to meet the demand for rapid power flow analysis. To address these issues, this paper proposes an SVR-based interval power flow (IPF) prediction method for distribution networks with DGs integration. The method utilizes intervals to describe system uncertainty and employs Support Vector Regression (SVR) for model training. The input feature vector consists of the intervals of active power generation, load demand, and line parameters, while the output feature vector represents the intervals of voltage or line transmission power. Ultimately, the SVR-based IPF prediction model is established, capturing the linear mapping relationship between input data and output IPF variables. Simulation results demonstrate that the proposed method exhibits high prediction accuracy, strong adaptability, and optimal computation efficiency, meeting the requirements for rapid and real-time power flow analysis while considering the uncertainty in distribution networks with DGs integration.

Fault Diagnosis of Pumped Storage Units—A Novel Data-Model Hybrid-Driven Strategy

Pumped storage units serve as a crucial support for power systems to adapt to large-scale and high-proportion renewable energy sources by providing a stable and flexible energy supply. However, due to the coupling effects of electric power load demands and the complex multi-source factors within the water–mechanical–electrical system, the interrelationship between unit parameters becomes more intricate, posing significant threats to the operational reliability and health status of the units. The complexity of fault diagnosis is further aggravated by the intricate and varied nature of fault characteristics, as well as the challenges in signal extraction under conditions of strong electromagnetic interference and high noise levels. To address these issues, this paper proposes a novel data-model hybrid-driven strategy that analyzes vibration signals to achieve rapid and accurate fault diagnosis of the units. Firstly, the spectral kurtosis theory is employed to enhance the traditional empirical mode decomposition, achieving optimal decomposition and noise reduction effects for vibration signals. Secondly, the intrinsic mode functions (IMFs) obtained from the decomposition are reconstructed, and the entropy values of effective IMFs are calculated as fault feature vectors. Subsequently, the CNN-LSTM model is utilized for fault diagnosis. The effectiveness and feasibility of the proposed method are verified through actual operational data from pumped storage units in a specific region. Through analysis, the fault diagnosis accuracy of the method proposed in this paper can be maintained above 95%, demonstrating robustness in complex engineering environments and effectively ensuring the safe and stable operation of pumped storage units.

Improved Harmonic Mitigation and Power Injection Technique for Solar-fed DG System Using GA and PSO

This paper deals with Distributed Generation (DG) system requiring power quality features such as current harmonics, reactive power compensation, and power factor improvement in grid tied operation. The motivation is to combine DG system with the capabilities of shunt active filter to perform real power injection, current harmonics mitigation and power factor improvement. The control algorithm employed in this setup utilizes a reference current generator based on p-q theory together with a pulse width modulation controller for switching pulse generation. The control strategy utilizes PI controller with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for finding optimal PI controller parameters. The effectiveness of the proposed method is verified using MATLAB/Simulink and experimental model developed for 3kWp inverter. The PI-PSO controller injects 0.588kW active power into the grid results in sharing 54% of load power demand and the current harmonics is reduced from 23.70% to 1.63%. The obtained results demonstrate that the grid tied inverter maintains harmonic standards in accordance with the IEEE-519 standard while injecting maximum possible active power to the line.

A novel development of advanced control approach for battery-fed electric vehicle systems

In today’s context, there is a clear preference for DC microgrids over AC microgrids due to their better compatibility with generating sources, loads, and battery energy storage systems (BESS). However, the intermittent nature of renewable resources disrupts the balance between power generation and load demand. It raises concerns regarding power management and quality in the power system. Control strategies are essential to address these challenges. This article focuses on developing a novel control strategy to ensure stability in microgrid systems. The proposed control structure utilizes a second-order multi-agent system (MAS) to enhance the power-sharing and coordination in the microgrid network. For effective control of battery energy storage units, a Voltage–Power (V-P) reference-based droop control and leader–follower consensus method is employed. The control approach consists of primary and secondary control layers. The primary layer uses a V-P reference-based droop control strategy to allocate load components to storage units. The secondary control layer aims to restore DC bus voltage using a MAS-based consensus protocol. The MAS approach offers greater flexibility and requires less computational power than other strategies such as Model Predictive Control (MPC). The enhanced control structure incorporates a current ratio modification loop to adjust the current ratio between the converters, thereby modifying gain and improving the voltage profile. This novel control optimizes the reliability and stability of the proposed DC microgrid system. The effectiveness of the enhanced consensus-based secondary control strategy is demonstrated using the MATLAB/Simulink platform.

Integrated battery thermal and energy management for electric vehicles with hybrid energy storage system: A hierarchical approach

Battery cooling is crucial for electric vehicles’ thermal safety, energy consumption, and battery life in hot climatic conditions. For electric vehicles with battery/supercapacitor hybrid energy storage system, battery cooling is deeply coupled with load power split from the electrical-thermal-aging perspective, leading to challenging thermal and energy management issues. This paper proposes a hierarchical multi-horizon model predictive control (MH-MPC) method to optimize battery cooling and energy management simultaneously. First, the electrical-thermal-aging coupling relationship between battery cooling and energy management is systematically analyzed. Then, by decoupling a centralized MH-MPC, an upper-level MH-MPC is designed to optimize the battery capacity loss cost and battery cooling cost by generating optimal compressor power, then a lower-level MH-MPC tends to minimize the battery capacity loss cost by allocating the total load power demand. The prediction horizon and sampling time are determined. Numerical results show that, compared with the centralized method, the proposed hierarchical method provides a lower battery capacity loss for long-term driving with only about 20% computation burden. Compared with standalone energy management without battery cooling, the total cost can be reduced by 12%–16% under long-term driving. Compared with optimizing energy management with Bangbang cooling, the battery degradation and total costs can be reduced by 15%–52% under short-term driving without deteriorating long-term performance.

Improvement of Electrical Power Load Demand and Stability in Electrical Power System with Incorporation of Wind Power Generation

Improvement of Electrical Power Load Demand and Stability in Electrical Power System with Incorporation of Wind Power Generation

Design and Analysis of Load Frequency Control for a Two-Area Power System Using Conventional PID and FPID Controllers

Power system stability is required to maintain a continuous balance between power generation and load demand. Frequency control is also a major function of automatic generation control and one of the most important control problems in power system design and operation. So, a robust control system must be implemented for controlling the actual power and frequency. This paper mainly focuses on the design of a two-area power system with a rated frequency of 50 Hz, which is used in Myanmar, using the fuzzy-PID method to control the load frequency. It also aims to imply from one area to another when demand suddenly increases or decreases. It involves applying mathematical formulas and models to analyze how an automatic generation control works. The plan is first implemented in a single area before being modified to a two-area power system with and without controllers. The output results are simulated with MATLAB/ SIMULINK.

Harmonic modelling and control of dual active bridge converter for DC microgrid applications

Due to the intermittent power availability from renewable sources, the bidirectional DC-DC converter (BDC) must integrate with the energy storage system for bidirectional power flow. Among many BDCs, the Dual Active Bridge (DAB) converter is the most promising because of the system's power handling capacity and efficiency. DAB bidirectional DC-DC converter is a topology with the advantages of a decreased number of devices, soft-switching commutations, low cost, and high efficiency. This work describes the guidelines for designing a DAB converter for small DC microgrid applications. It is shown that the judicial selection of reactive elements leads to soft switching for all the switches in the converter in varying load conditions. A guideline for the controller design is given and the operations are validated through the simulation. A harmonic modelling technique has been implemented to model the DAB converter based on an efficient closed-loop regulator. A solar PV system has also been implemented here with a SEPIC converter that is used as an interfacing element between the PV system and the load bus. At last, a DC Microgrid is simulated in a PSIM environment to showcase its various modes of operation. It is shown that the battery bank can support variable loads independently through the DAB converter. In inadequate solar power generation, load power demand is shared between the battery and PV system. In case of deep discharge of the battery, the PV system can charge the battery through a DAB converter.

Security Event-Trigger-Based Distributed Energy Management Of Cyber-Physical Isolated Power System With Considering Nonsmooth Effects.

Due to cyber-physical fusion and nonsmooth characteristics of energy management, this article proposes a security event-trigger-based distributed approach to address these issues with developed smoothing technique. To tackle with nonconvex and nondifferentiable issue, a randomized gradient-free-based successive convex approximation is developed to smooth economic objective function. Due to resilience ability against security issue, a security event-triggered mechanism-based distributed energy management is proposed to optimize social welfare, which coordinately controls both power generators and load demand. The security event-triggered mechanism is designed to reduce power system security risks, and relieve communication burden caused by smoothing calculation, the convergence of proposed distributed algorithm is also properly proved. According to those obtained results on both IEEE 9-bus and IEEE 39-bus systems, it reveals that the proposed approach can achieve good convergence performance and have less security risks than other alternatives, which also proves that the proposed approach can be a viable and promising way for tackling with energy management issue of cyber-physical isolated power system.

Analysis of Model Predictive Control-Based Energy Management System Performance to Enhance Energy Transmission

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS.

ESG guidance and artificial intelligence support for power systems analytics in the energy industry.

In order to increase the precision and effectiveness of power system analysis and fault diagnosis, this study aims to assess the power systems in the energy sector while utilizing artificial intelligence (AI) and environmental social governance (ESG). First, the ESG framework is presented in this study to fully account for the effects of the power system on the environment, society, and governance. Second, to coordinate the operation of various components and guarantee the balance and security of the power system, the CNN-BiLSTM power load demand forecasting model is built by merging convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Lastly, the particle swarm optimization (PSO) algorithm is used to introduce and optimize the deep belief network (DBN), and a power grid fault diagnostic model is implemented using the PSO technique and DBN. The model's performance is assessed through experimentation. The outcomes demonstrate how the CNN-BiLSTM algorithm significantly increases forecasting accuracy while overcoming the drawback of just having one dimension of power load data. The values of 0.054, 0.076, and 0.102, respectively, are the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Effective processing of large-scale nonlinear data is achieved in the area of power grid fault diagnosis, resulting in prediction accuracy of 96.22% and prediction time of only 129.94s. This is clearly better than other algorithms and increases fault prediction efficiency and accuracy. Consequently, the model presented in this study not only produces impressive results in fault diagnosis and load demand forecasting, but also advances the field of power system analysis in the energy industry and offers a significant amount of support for the sustainable and intelligent growth of the energy industry.

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

As the adoption of renewable energy sources (RESs) continues to surge, and the concept of microgrids (MGs) gains widespread recognition, the need for efficient battery energy storage system (BESS) management within MGs has become increasingly prominent. This paper introduces a novel Energy Management System (EMS) designed for grid-connected MGs, with the primary objectives of enhancing the energy economics of the MG, minimizing BESS degradation, and ensuring that the point of common coupling (PCC) voltage remains within prescribed limits. The proposed EMS utilizes a finite-horizon Model Predictive Control (MPC) framework to seamlessly coordinate BESS operations, encompassing charging, discharging, and reactive power provision. This coordination is done in conjunction with solar power generation, load demand, electricity pricing, and feed-in tariffs. Furthermore, the EMS accounts for uncertainties related to load and solar demand using a Monte Carlo simulation approach. The optimization goal of the proposed EMS is to minimize the expected total operational cost of the MG over a 24-hour prediction horizon. This cost encompasses both economic costs and battery degradation costs. The optimization process adheres to a set of constraints that consider BESS limitations, such as state of charge (SoC) constraints and charging/discharging limits, as well as inverter capacity limits and PCC voltage requirements. Conducting rigorous case studies on the MATLAB Simscape Electrical platform and validating them in real-time using the OPAL-RT simulator demonstrate the effectiveness of the proposed EMS across diverse operational scenarios and pricing models. The findings of these case studies provide valuable insights into the effectiveness of the EMS in real-world MG applications.

Probabilistic photovoltaic generation and load demand uncertainties modelling for active distribution networks hosting capacity calculations

With the increasing integration of photovoltaic (PV) systems in active distribution networks (ADNs), accurate modelling of PV power generation and the network demand has become essential, especially for system operators (SO). However, existing studies have focused on deterministic representations of hourly profiles for PV generation and load consumption, which cannot thoroughly evaluate the existing uncertainties of PV power output and load demands. In this study, uncertain parameters load demand and PV power output profile will be modelled with forecasted values, and their profile will be obtained over probability density functions (PDFs). Firstly, a vast quantity of realistic load and PV generation profiles are produced over a day with 15-minute resolution, with a scenario generation method using the Monte Carlo methodology. Afterward, the generated scenarios are reduced to a set of scenarios to represent the span of all generated scenarios. A fully local reactive power regulation strategy is used in this study to evaluate the hosting capacity of the ADN. This proposed method is tested on modified 33-bus and 69-bus distribution test systems by using practical solar generation and load data. The proposed methodology results in the hosting capacity improvement by 20% besides the existing Q-Voltage and PF-Power local voltage control methods, where it has the flexibility to be implemented to any distribution feeder.