External Power Grid Research Articles

Review of Low Voltage Ride-Through Capabilities in Wind Energy Conversion System

The significance of low voltage ride-through (LVRT) capability in wind energy conversion systems (WECSs) is paramount for ensuring grid stability and reliability during voltage dips. This systematic review delves into the advancements, challenges, and methodologies associated with LVRT capabilities in WECSs. By synthesizing recent research findings, this review highlights technological innovations, control strategies, and regulatory requirements that influence LVRT performance. Key insights include the efficacy of various LVRT techniques, the role of grid codes in shaping LVRT standards, and the integration of advanced control algorithms to improve system resilience. The study offers a comprehensive understanding of the current landscape of LVRT in WECSs and pinpoints future research directions to optimize their performance in increasingly complex grid environments. During the LVRT process, the stator of a double-fed induction generator (DFIG) is directly linked to the power grid. When the external power grid experiences a failure, the stator flux produces a significant transient component, resulting in substantial overvoltage and overcurrent on the rotor side of the DFIG. Failure to implement preventative measures may result in damage to the converter, therefore compromising the safety and stability of how the power system functions.

A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties

The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.

Electricity Generation at Gas Distribution Stations from Gas Surplus Pressure Energy

At gas distribution stations (GDSs), the process of throttling (pressure reduction) of natural gas occurs on gas pressure regulators without generating useful energy. If the gas expansion process is created in a turbine, to the shaft where an electric generator is connected, then electricity can be obtained. At the same time, the recycling of secondary energy resources is provided, which is an important component in the efficient use of natural resources. The obtained electric power can be supplied to the external power grid and/or used for the GDS’s own needs. The process of generating electricity at the GDS from gas overpressure energy is an environmentally friendly, energy-saving technology that ensures an uninterrupted, autonomous operation of the GDS in the absence of an external energy supply. The power needs of a GDS with regard to electricity are relatively small (5 ÷ 20 kW). Expansion in throttling devices or turbine flow paths leads to gas cooling with a possible hydrate formation. It is prevented via gas preheating or vortex expansion equipment that keeps the further gas temperature at a necessary level. Turbogenerators can be created on the basis of vortex expansion turbomachines, which have many advantages compared to turbomachines of other types. This article studies how gas pressure (outlet: gas distribution station) and gas preheating (inlet: vortex expansion machine) influence turbogenerator parameters. Nine turbogenerator variants for the power needs of gas distribution stations have been assessed.

Optimization clearing strategy for multi-region electricity-heat market considering shared energy storage and integrated demand response

As a new type of energy storage, shared energy storage (SES) can help promote the consumption of renewable energy and reduce the energy cost of users. To this end, an optimization clearing strategy for a multi-region electricity-heat joint market is proposed with consideration of SES and integrated demand response (DR). Firstly, the concept of shared energy storage station (SESS) is proposed, its business operation model is analyzed and its advantages over traditional energy storage are compared. Secondly, to exploit the potential for user flexibility, an integrated DR model that includes shiftable, transferable, interruptible electricity and heat load is constructed. Then, a multi-region electricity-heat joint market clearing model is constructed considering SESS and integrated DR. Finally, a three-region electricity-heat joint market connecting the external power grid, gas grid, and the SESS is used as an example. A comparison is made between the configuration of independent energy storage in each region and the configuration of SESS, which concludes that the introduction of the SESS and integrated DR can reduce the energy cost of users and improve the utilization rate of the energy storage facilities.

Cooperative operation optimization for rural multi-microgrid and county-integrated energy operators considering typical energy scenarios

Despite being rich in renewable energy, China's rural areas are backwards in terms of energy use. Rural multi-microgrid cooperative operation optimization can effectively promote renewable energy use in rural areas. Many different microgrid energy scenarios have been developed for rural areas of China, and they have different resource endowments and unit compositions. Moreover, frequent power-related interactions occur between counties and villages. This study analyzes four typical microgrid energy scenarios in rural areas of China and optimizes their synergistic operation based on county-integrated energy operators. First, a mathematical model of rural microgrids for four energy scenarios and a trading mechanism between rural multi-microgrid and county-integrated energy operators were constructed. Subsequently, an upper-level optimization model that minimizes operating costs was developed for the county-integrated energy operator. A low-level optimization model was developed for rural multi-microgrid usage, and it minimized the operating costs. Finally, Stackelberg game theory was utilized to resolve the optimization issue. The results showed that the cooperative optimization of rural multi-microgrid and county-integrated energy operations can reduce the operating costs of both parties compared to that when each subject is operating alone. This optimization reduced the rural multi-microgrid cost from 12 773.64 yuan to 11 508.67 yuan and county-integrated energy operator cost from 3898.37 yuan to 1581.79 yuan. Moreover, it reduced both parties' dependence on external power grids; increased the self-balancing capacity of the rural multi-microgrid and county-integrated energy operator from 0.424 to 0.715 and 0.694 to 0.852, respectively; substantially increased the capacity of renewable energy consumption through power interaction; and reduced the risk of fluctuations in system operating costs.

Method for Predicting Linear Eddy Current Braking Performance of the High-Speed Maglev Train Based on Magnetic Circuit Structure

On high-speed magnetic levitation (maglev) trains, the linear eddy current brake (LECB) is a key piece of equipment to ensure the safety of train operation. In the event of a sudden failure of the traction motor or the external power grid, braking by the LECB, which comes with the maglev train, is currently the only emergency braking method that can ensure the train can be safely stopped while operating at high speed. The braking performance of eddy current brakes is usually predicted based on symmetrical magnetic equivalent circuits, which usually ignore the interactions between the magnetic circuits. For LECB, such interactions should not be neglected. In this paper, to investigate an accurate and efficient method for predicting the braking performance of LECB for high-speed maglev trains, based on the structural characteristics of LECB and Kirchhoff’s law, a full structural magnetic equivalent circuit model considering the interactions between magnetic circuits is developed, and then combined with the subdomain method, a braking force model, to predict the braking performance of LECB. The performance prediction of LECB with different numbers of magnetic poles is compared with the traditional method using the results of the finite element method and the experimental method as references. The results show that the proposed method is more accurate in predicting the braking performance of LECB and better reflects the physical characteristics of the actual magnetic field.

Operation characteristics analysis and optimal dispatch of solar thermal-photovoltaic hybrid microgrid for building

The optimal dispatch for hybrid microgrids is the crucial approach to decrease maintenance costs and enhance operational reliability. This paper aims to provide a feasible solution for the optimal dispatch of a solar thermal-photovoltaic hybrid microgrid. A distributed energy system of a building is established and the power load is analyzed. Operation parameters are optimized for hybrid microgrid in isolated operation mode and grid-connected operation mode. The operation performance with adequate and inadequate solar irradiation is evaluated by using fixed dispatch and optimal dispatch strategy. In addition, the performance of hybrid microgrid under power selling and purchasing conditions are analyzed. Results show that the operation mode of building hybrid microgrid can be adjusted to isolated/grid-connection modes based on actual power requirements. The optimal dispatch strategy shows great advantages on both solar adequate and inadequate conditions under isolated operation mode for hybrid microgrid, at most 14.3 % lower for maintenance than that of fixed strategy. In addition, the power selling to the external power grid can significantly improve the profit of the hybrid microgrid and generate earnings of $6102.1 per year under optimal dispatch. The research can offer an analysis approach for hybrid microgrid, and further increase clean energy utilization efficiency.

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

PV power generation is developing fast in both centralized and distributed forms under the background of constructing a new power system with high penetration of renewable sources. However, the control performance and stability of the PV system is seriously affected by the interaction between PV internal control loops and the external power grid. The impact of the PV system on the reliability, stability, and power quality of power systems has restricted them to further participate in power supplies with a large capacity. Traditional control methods have become ineffective at dealing with these problems as the PV system becomes increasingly complex and nonlinear. Intelligent control as a more advanced technology has been integrated into the PV system to improve system control performance and stability. However, intelligent control for the PV system is still in the early stages due to the extensive calculation and intricate implementation of intelligent algorithms. Further investigations should be carried out to effectively combine intelligent control with the PV system to constitute an intelligent PV power system with multiple functions, high stability, and high-performance. This paper provides a systematic classification and detailed introduction of various intelligent optimization methods in a PV inverter system based on the traditional structure and typical control. The future trends and research topics are given to provide a reference for the intelligent optimization control in the PV system.

Microgrids with day-ahead energy forecasting for efficient energy management in smart grids: hybrid CS-RERNN

ABSTRACT By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO).

Calculation Method for the Transient Hot-Spot Temperature Rise of Oil-Immersed Bushing in High-Speed Railway

Traction oil-immersed bushing is the only channel for connecting the high-speed railway system with the external power grid, and its transient hot spot is one of the most important factors reflecting its operation status. Since the traction load of a high-speed railway has obvious impulse characteristics, the existing calculation method for bushing given in IEEE Std C57.19-100™-2012 is incompatible because it is designed for steady electrical loads. Thus, in this paper, the equivalent hot spot temperature rise of the bushing under varying traction loads is calculated by the traction parameters (i.e., operating time <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_ot</i> , interval time <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_it</i> , and per unit transient traction current <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_tc</i> ). Then, the difference value of transient hot spot temperature rise is modified by analyzing the change of transient hot spot temperature rise under varying traction loads. Finally, a calculation method is proposed for the transient hot spot temperature rise of the oil-immersed bushing in high-speed railway under different traction loads. A case study of a 110 kV oil-immersed bushing shows that the proposed method is speedy and accurate.

Power load analysis and configuration optimization of solar thermal-PV hybrid microgrid based on building

Solar thermal power system and photovoltaic coupled system can supply electric energy based on renewable solar energy. To explore the optimal configuration of hybrid microgrid driven by solar energy and to achieve a stable and sufficient electric power supply for the distributed energy system, this paper configures a solar thermal-photovoltaic hybrid microgrid and compares the performance of several microgrids based on the power load analysis of the building. The working fluid and the microgrid configuration are determined based on the performance evaluation of single and hybrid microgrids. The effect of solar irradiation on economic and environmental performance is analyzed under the meteorological data of Xi'an and Lhasa in China. Furthermore, the configuration optimizations of a hybrid microgrid based on the actual load and basic load of the building are carried out to examine the performance of the system. Results indicate that economic and environmental performances are sensitive to solar irradiation. More than 50 % of the area of solar thermal collector can be saved for Lhasa compared with Xi'an. Solar thermal-photovoltaic hybrid microgrid with battery can decrease carbon emission and avoid dependence on the external power grid.

The potential of multiapartment rooftop PV systems as citizen’ energy communities in Latvia

Electricity generation from photovoltaic (PV) in Latvia is currently below the necessary capacity required to contribute to achieving climate neutrality by 2050. However, photovoltaic offer households and companies the opportunity to align their electricity consumption with environmental goals while supporting the European Green Deal objectives. In recent years, there was a growing interest in the installation of photovoltaic microgenerators in Latvia. By June 2023, the total number of households microgenerators connected to the grid has reached 15,000, boasting a collective production capacity exceeding 120 MW. This significant increase is in stark contrast to the situation 5 years ago when Latvia had a mere 3 MW of photovoltaic capacity. Several factors contributed to this growth, including a surge in electricity prices, at times surging more than tenfold, and the implementation of renewable energy (RE) use support measures to facilitate the acquisition of RE systems. Furthermore, 2023 witnessed numerous amendments to Latvia’s energy policy documents and the introduction of the concept of citizen energy communities (CEC), indicating a growing emphasis on harnessing solar energy potential in the country. However, it remains crucial to base these endeavors on accurate, economically viable information regarding solar technologies, their costs and their anticipated long-term outcomes. This manuscript aims to provide an overview of the grid-connected potential of rooftop photovoltaic systems within a Latvian urban setting. Through extensive research, a model has been developed, employing a thoroughly tested simulation program for evaluating the generation capacity of photovoltaic systems. This model considers real electricity consumption data, the existing infrastructure and economic factors. The findings affirm the technical and economic viability of urban rooftop photovoltaic systems within the Latvian context. It has been established that the implementation of such citizen energy communities energy systems holds significant potential. These systems have the potential to be a promising solution for future electricity generation, addressing some of the demands while relieving strain on external power grids. However, the full potential can only be realized with improved infrastructure, and the system’s profitability is heavily contingent on market dynamics and political conditions. This study may also be applicable to other photovoltaic systems facing similar climate conditions.

An efficient mixed-variable generation operator for integrated energy system configuration optimization

Energy consumption has skyrocketed as society has progressed, resulting in energy crises and environmental issues. New energy technologies are being developed to address these challenges, and technologies such as the cogeneration of electricity, heating, and cooling are being used to improve energy utilization. The configuration of an energy system has a critical impact on the economics of the system, its ability to supply energy, etc. This study establishes a multiobjective mixed-variable configuration optimization model for a comprehensive combined cooling, heating, and power energy system. The model considers equipment types (categorical variables), equipment quantities (integer variables), and critical parameters (real variables) as optimization variables, where economic performance, the proportion of renewable energy, supply shortage, and dependence on the external power grid are considered unoptimized objectives. This is a multiobjective and mixed-variable problem, and the combination of the two properties poses an excellent challenge for evolutionary algorithms in optimizing the model. Therefore, we propose an efficient generating operator based on a fully connected weighted neural network, called the FCWN operator. This operator overcomes the challenge of the discrete variable space and broken neighborhood relationships in mixed-variable problems. During the algorithm search process, search history information is used to update the fully connected weight network for distribution estimation of the variable space. Offspring are generated based on the fully connected network to improve the algorithm’s search efficiency. In the experimental section, we construct a model with 20 variables and conduct simulation-based configuration optimization for scenarios with 3, 5, and 8 available equipment types. The final obtained Pareto frontier solution set is evaluated using the hypervolume metric, a widely used multiobjective evaluation metric. The Wilcoxon rank sum test on the experimental results shows that the proposed algorithm has better results than other state-of-the-art algorithms at a 95% confidence level.

A self-driven solar air heater integrated with a thermal energy storage unit: Design and experiment study

The Self-driven solar air heater (SDSAH) can operate independently without reliance on an external power grid through the incorporation of photovoltaic (PV) panels, however it is still subject to intermittent operation and large temperature fluctuations. In this study, a novel SDSAH integrated with a thermal energy storage (TES) unit was proposed and the operational thermal and electrical performance of the SDSAH with TES unit were experimentally investigated. The results showed that the SDSAH could output heat continuously throughout the day (24 h) in a self-sustaining mode and automatically adjust the airflow rate according to solar irradiance. The overall thermal efficiency of this system averages at 52.73 % throughout the day. The introduction of the TES unit could lower the peak temperature of the outlet air by more than 9 K and improve the electrical efficiency of PV panels. However, due to heat loss at night, the whole-day thermal efficiency will be slightly lower than that of SDSAH without a TES unit. The energy payback time for this system was 42 months. This study offers new ideas and solutions for a distributed agricultural drying system.

Distribution Network Reconfiguration Considering the Impacts of Local Renewable Generation and External Power Grid

Distribution Network Reconfiguration Considering the Impacts of Local Renewable Generation and External Power Grid

Optimal allocation of hybrid energy storage capacity of DC microgrid based on model predictive control algorithm

To effectively enhance the safety, stability, and economic operation capability of DC microgrids, an optimized control strategy for DC microgrid hybrid energy storage system (HESS)(The abbreviation table is shown in Table 2) based on model predictive control theory is proposed. Based on the characteristics of supercapacitors and batteries, system safety requirements, and various constraints, a predictive model for a hybrid energy storage DC microgrid is established. By defining its optimization indicators, designing an energy optimization management strategy, and transforming it into a quadratic programming problem for solution, the reasonable scheduling of power in the DC microgrid has been achieved. In addition, a power control method was proposed for the system without constraints. The simulation experiment results show that at the initial sampling time, the system operates normally, and the MPC algorithm allocates two types of energy storage devices to discharge to meet the net load demand, without absorbing electricity from the external network. At the 30th sampling point, the net load increases, and the MPC controller obtains the optimal solution of the control problem based on the known net load prediction data at the previous sampling time. It outputs the operating reference values of each output unit at the next time. Starting from the 100th to 199th sampling points, SOC UC falls below the lower limit of the safety interval, and the system enters situation 4 mode. The external network output assists the battery in working. At the 131st sampling point, the net load decreases, the system enters Situation 3 mode, and the battery operates independently. Until the 179th point, SOC B was also below the lower limit of its safety interval, and the system entered situation 5 mode, completely maintaining system power balance by external network power. Starting from point 201, the net load becomes negative, and the system charges the HESS according to instructions and stops the external power grid energy transmission. Conclusion: The feasibility and effectiveness of the proposed optimization management strategy have been verified.

Optimization method of power supply mode and connected to external power grid of CZ railway traction station

Aiming at the problem of long slopes and weak power grids along the railway in high altitude mountainous areas, an optimization method of power supply mode and access to external power grid of CZ railway traction station is proposed. First, combined with the terrain and altitude along the railway and the characteristics of the regional power grid, a sample library of the power supply mode and access to external power grid of CZ railway traction station is designed. Secondly, combined with the train operation diagram and line ramp parameters, the full-day periodic load of each power supply arm is simulated. Then, according to the sample library, a “source-net-load” integrated simulation model is constructed in Simulink. Then, the three-phase voltage unbalance evaluation model of the power system is constructed in combination with relevant standards. Finally, the optimal wiring scheme is selected by using the evaluation model and the simulated voltage unbalance index.

Optimal Allocation Method for Energy Storage Capacity Considering Dynamic Time-of-Use Electricity Prices and On-Site Consumption of New Energy

Configuring energy storage devices can effectively improve the on-site consumption rate of new energy such as wind power and photovoltaic, and alleviate the planning and construction pressure of external power grids on grid-connected operation of new energy. Therefore, a dual layer optimization configuration method for energy storage capacity with source load collaborative participation is proposed. The external model introduces a demand-side response strategy, determines the peak, flat, and valley periods of the time-of-use electricity price-based on the distribution characteristics of load and new energy output, and further aims to maximize the revenue of the wind and solar storage system. With the peak, flat, and valley electricity price as the decision variable, an outer optimization model is established. Based on the optimized electricity price, the user’s electricity consumption in each period is adjusted, and the results are transmitted to the inner optimization model. The internal model takes the configuration power and energy storage capacity in the wind and solar storage system as decision variables, establishes a multi-objective function that comprehensively considers the on-site consumption rate of new energy and the cost of energy storage configuration, and feeds back the optimization results of the inner layer to the outer layer optimization model. Use ISSA-MOPSO algorithm to solve the optimized configuration model. Finally, the rationality of the proposed model and algorithm in terms of on-site consumption rate and economy of new energy is verified through numerical examples.

Enhanced transient angle stability control of isolated power grid based on phase sequence exchange

An isolated power grid (IPG) lacks the support of the external power grid, and a single generator or load accounts for a large proportion of the total capacity. Once transient angle instability, the generator and load shedding is not effectively. Phase sequence exchange (PSE) uses power electronic devices to switch the three-phase sequence, thereby reducing the power angle of the generator by 120° and preventing the system from losing stability. This paper proposed a PSE scheme for transient angle instability applied to IPG. The local measurements of IPG are used to identify the critical generator, and the system trajectory is obtained through large-step integration. The stability is assessed by the concavity of the trajectory. Once the trajectory swings from the concave area to the convex area, the IPG loses transient stability, at which point the generators in the critical cluster simultaneously PSE prevent transient instability. Based on Simulink and RTDS platform, the scheme is verified in a 39-bus 10-machine system and an existing IPG system. The results show that the proposed scheme can effectively restore the stability of the IPG, outperforming generators and load shedding.

Management and control optimization based on deep learning model

Microgrid technology is a key solution to improve distributed power consumption, complementary utilization of multiple energy sources, and power supply reliability. To guarantee the reliability of the microgrid system, a realistic strategy must be created. This work takes the microgrid as an object and uses simulation technology to construct a microgrid system. Then, using this simulation system and the double deep Q-learning, the goal is to minimize the 24-hour electricity consumption cost from the external power grid to meet the requirements of voltage deviation. Power balancing and energy storage loads for microgrid systems. Under the constraints of the electrical state and other constraints, the control variable is the energy storage's capacity for charging and discharging, and the optimization strategy of energy storage control is obtained through training. The results demonstrate that the DDQN algorithm will save 26.95% of the electricity purchase cost, which is significantly more than the MPPT algorithm's 12.43% savings. As a result, this work examines the efficacy of the charging and releasing approach for energy storage and confirms the potential of the suggested approach to reduce the cost of purchasing electricity.