Load Side Research Articles

Analysis and Design of an Unsymmetrical Multi-storey building with floating columns in a seismic zone using ETABS

Most high-rise structures today are designed with floating columns for aesthetic purposes and the need for efficient use of space. Lattice columns are vertical load-bearing elements that do not extend the entire height of the building, they are usually located on one or more intermediate floors and support beams. These design innovations have benefits such as increased architecture and space allocation. Loads include load and side load. Heavy loads, including dead loads and live loads, are evaluated to determine the overall load distribution. Lateral loads resulting from earthquakes and wind forces are calculated based on local building codes. In seismic design, multistorey buildings with floating columns are analysed at different levels on curved planes to determine safe floating column locations. Structures can be analysed with symmetric information and dynamic analysis methods such as response spectrum or potential time. Preliminary analysis and equivalent static analysis using ETABS 2022. A 3D structural model was created to accurately represent the geometry of the building and its behavior under different load combinations was used to compare the building's response to seismic forces. The result is the behavior of the structure: shear strength, bending moment, deflection, and displacement. The results are obtained in the form of tables and graphs. The structures were built with M30 concrete and various types of steel. Reinforcement spacing and detailing is done in CSI Detail.

Radiographic Estimation of Association between Condylar Morphology, Ramal Height, and Mandibular Plane Angle in Various Growth Patterns in District Solan Population in Himachal Pradesh

Introduction: In young adults, the mandibular condyle plays an essential role in the stability of long-term orthodontic and orthognathic treatments. Its morphology varies significantly between individuals and is associated with age, sex, facial type, occlusal force, functional load, malocclusion type, and right and left sides. Aim: The aim of the study was to estimate the association between morphology of condyle, ramus height, and mandibular plane (MP) angle in young adult patients with various vertical skeletal growth patterns. Materials and Methods: The present study was conducted on 90 pretreatment panoramic radiographs of subjects aged between 17 and 21 year of age; the patients were divided into three groups as hypodivergent, normodiveregent, and hyperdivergent with 30 subjects in each group on the basis of Sella-Nasion-MP angle. Various parameters were measured to record the condyle morphology, ramus height, and mandibular angle for all the groups. Results: The intergroup comparisons showed that hypodivergent group has statistically significantly greater values compared to those in the hyperdivergent group. A statistically significant difference was detected between the normodivergent and hypodivergent groups only in the condylar area measurements Conclusion: The condylar height was greater in patient with Class I malocclusion. The condylar height width and other perimeters were greater in males than females.

Optimal Scheduling of Micro-Energy Grid Based on Pareto Frontier Under Uncertainty and Pollutant Emissions

With the continuous improvement of micro-energy grid, in order to reduce carbon emissions and environmental pollution, a multi-objective optimization operation strategy considering the distributed energy generation uncertainty, environmental factors, and self-supply rate is proposed in this paper. First, to simulate the uncertainty of distributed energy, we used the three-parameter Weibull distribution to simulate the wind power. Then, for the load side, an innovative electric and thermal comprehensive demand response model is proposed. Different from the traditional micro energy network that only considers the heat energy balance, this paper also considers the thermal inertia of buildings to more accurately describe the characteristic of the heat load. Meanwhile, on the basis of economic factors, the abandon rate of distributed energy, environmental coefficient and self-sufficiency rate are also regarded as three objective functions. Finally, in order to obtaining a set of solutions at the Pareto front, the entropy-weighted TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is introduced to obtain the global optimal solution. Simulation study of multiple scenarios shows the effectiveness of the proposed method. Compared with the method that considers only economic costs, the pollutant emissions are reduced by 50kg and self-power supply rate is increased by 20%.

The damping problem in non-linear viscoelastic frames under varying temperature

A theoretical consideration of the damping problem in frame engineering structures made of functionally graded materials with non-linear viscoelastic behaviour is carried-out. The frames studied are under external mechanical load and varying temperature. In particular, damping in a two-legged frame with supports on different levels is analyzed. The frame is subjected to a cyclic side load with simultaneous variation of the temperature. The material of the frame is functionally graded in the thickness direction. Thus, the material properties change smoothly along the thickness. Equilibrium equations are used for determining the curvature and the neutral axis coordinates in the members of the frame structure. The damping energy density is integrated in the volume of the frame members in order to find-out the damping energy in the whole frame structure. A parametric investigation is performed by using the solution of the damping energy in order to clarify the influence of various factors (the frame geometry, loading, etc.) on the damping phenomenon at different temperatures.

Harmonic current suppression method of virtual DC motor based on fuzzy sliding mode

Abstract In the operation of power grid, the low frequency subharmonics in power lines are easy to cause harmonic current problems. A harmonic current suppression method is constructed based on fuzzy sliding mode and virtual DC motor (VDC). First, the adaptive linear network is introduced to compare and analyze the output value and the actual value in the power line, and the possible harmonic current is obtained, which is detected and discretized. After that, this study adopts Fuzzy sliding mode control (FSMC) strategy for VDC, uses FSMC to participate in on-off control of VDC, and then realizes the suppression of harmonic current in VDC ports. In addition, the model also uses the circuit superposition theorem and the amount of harmonic data to be compensated by FSMC. The whole method uses VDC control technology to ensure the voltage stability of DC bus and load side when the load changes. In this physical experiment, harmonic current generated by load disturbance is eliminated by reducing DC harmonic voltage, so as to achieve the suppression of DC port harmonic current. Experimental results show that the third and fifth harmonics in power lines can be eliminated by this method to a large extent. Therefore, the proposed method can accurately detect and eliminate the harmonic current caused by sudden load change, and then realize the suppression of VDC harmonic current.

Enhancing Power Grid System Analysis with Medium Voltage Cascaded H-Bridge Motor Driver Dynamic Model

Medium voltage cascaded H-Bridge motor drives (MV-CHBMD) are popular with renewable applications because of their scalability, reliability, and modularity. The MV-CBHMDs are used in different medium voltage applications and significantly affect grid flexibility, performance, and efficiency. Even with the famous use of MV-CHBMD, the provided simulation models need to be more detailed to cover grid connection dynamic investigations. This study provides a dynamic design of MV-CHBMD, connected with the power grid source and induction motor (IM) with load, which provides a compliance large-scale analysis for the dynamic act of the power grid system. The proposed model of dynamic MV-CHBMD is suitable for power system studies. The model provides an analytical analysis of the grid connection with the cascaded H-bridge motor driver (CHBMD) and IM on the load side of the driver. It accurately represents the dynamic of the total system under different disturbances. The main factors affected during any perturbation are variable frequency and voltage, which are deeply considered in the proposed model. A simulation analysis verifies the model’s accuracy, reliability, and effectivity. The mode sensitivity analysis depends on the impact of the variable parameters on the system acting and responding. The proposed model is easily insertable in the extensive grid-simulating system, which provides more accurate results in power grid dynamic studies.

Joint operation of mobile battery, power system, and transportation system for improving the renewable energy penetration rate

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines.

Low-carbon optimal scheduling for multi-source power systems based on source-load matching under active demand response

To reduce the source-load uncertainty and carbon emission levels of the power system, this study proposes a novel low-carbon economic stochastic optimization scheduling model. Firstly, to partially compensate for the instability of wind power (WP) and photovoltaic power (PV) generation, enhance energy efficiency, and reduce system carbon emissions, the introduction of concentrating solar power (CSP) as one of the power sources on the generation side. Secondly, to optimize load characteristics, a peak–valley time period division method based on fuzzy theory and an active demand response model for electric load using the Logistic fuzzy function are introduced on the load side. Thirdly, taking into account the role of the carbon trading market, a ladder-type carbon trading mechanism (LCTM) is introduced to control carbon emissions and reduce overall economic costs. Finally, considering WP-PV output fluctuations, the degree of matching between power output and load, WP-PV utilization, system economic efficiency, and carbon trading factors, with the objective of maximizing the tracking of the load curve and minimizing comprehensive system economic costs, a multi-objective stochastic optimized scheduling model for power systems is established. A multi-objective Runge–Kutta algorithm based on a population-based parallel search mechanism (PSMORUN) is proposed to solve the active demand response model and the low-carbon stochastic optimization scheduling model. Additionally, constraint repair techniques are employed to handle the complex constraints of the models. To verify the effectiveness of the proposed model, a 10-generator power system, including a WP farm, a PV plant, and a CSP plant, is used as a test case for simulation experiments and compared with other multi-objective scheduling models. The experimental results show that the proposed stochastic optimal scheduling model has higher safety, better economy, and lower carbon emissions.

Unit vector template control strategy‐based harmonic mitigation and charging with three phase–three level–three switch Vienna rectifier for Level 3 electric vehicle charging applications

SummaryFast battery charging units act as non‐linear loads, which induce a more harmonic effect in the utility grid. Hence, maintaining the stable voltage, current ripple, and total harmonic distortions within the permissible IEEE std level during the charging time of grid to electric vehicle (G2V) has become an important task for researchers in the future. In order to mitigate power fluctuations during every charging cycle, it will enhance the battery life cycle in electric vehicles (EVs). Three phase–three level–three switch Vienna rectifier with fuzzy logic controller tuned PID controller is used here for AC/DC conversion with minimum switching loss. A fuzzy intelligent control strategy has been employed with the DC link voltage regulator in order to sustain the DC link voltage as constant and progress the voltage/current profile by injecting active power at the point of common coupling. Unit vector template (UVT)‐based control strategy is employed here to identify the fundamental components from the load side and also estimate the reference phase current. This scheme enhances the performance and reduces the mathematical system complexity to diminish the harmonics in the distribution grid. The adaptability and feasibility of this proposed control method have been verified by the MATLAB simulation tool. To validate the response of the Vienna Rectifier with UVT controller are simulated with different unbalanced load conditions and also verified with 30 kW laboratory prototype experimental results using dsPIC30F4011 controller and the switch is IRFP260 with power diode RHRG30120.

A Whole-Segment Carbon Measurement Approach for Energy Systems Incorporating Knowledge Mapping Techniques

Abstract As the current carbon measurement method makes it difficult to reflect the time-space variability of carbon emission factors, it leads to the inability to clarify the carbon emission responsibility of grid-side losses. In this paper, starting from the internal power system, based on the power system carbon emission flow analysis theory and knowledge graph technology, a trend-tracking analytical algorithm based on the node conductivity matrix operation is proposed to solve the complex power allocation relationship between each generating unit and each node load, and between each generating unit and the line network loss. On the basis of this algorithm, the carbon flow tracking model of the whole power system is established according to the conversion relationship between the current and the carbon flow, and the analytical expressions of the carbon emission distribution among the power generation side, the load side and the line loss are obtained by combining with matrix operation, so as to measure the carbon emission measurement results of the whole power system and to provide data references for the responsibility sharing of carbon emission. Finally, simulation verification is carried out based on the actual system operation and load data for C city. The total amount of carbon emissions from electricity consumption is 98.8 million tCO2 when C city is used as the minimum spatial scale, while the total amount of carbon emissions from electricity consumption is 67.9 million tCO2, 14.0 million tCO2, 16.9 million tCO2, and the total amount of the three regions is 98.8 million tCO2, which is consistent with that calculated when C city is used as the minimum spatial scale. The calculation results are consistent in real-time. The carbon measurement method proposed in this paper can obtain higher spatial resolution carbon measurement results for electricity consumption.

Data association load uncertainty and risk aversion in electricity markets with data center participation in the demand response

Recently, demand response (DR) programs are being promoted in the power market to strengthen the role played by the load side in the power market and to increase the flexibility of loaders to participate in the power market. Data centers are uniquely positioned to participate as large-scale power users in the power market operation process. However, the issue of load uncertainty risk is not adequately considered in recent DR programs for data center participation in the power market. Regarding this issue, we propose a framework for facilitating bids for data center participation in DR programs in the power market. In the proposed framework, the data association Dirichlet process mixture model (DA-DPMM) is introduced to characterize the load uncertainty, and the association of load data is investigated to obtain higher prediction accuracy. To further reduce the uncertainty risk, we propose a two-stage market cleaning model based on the conditional value at risk (CVaR). We also analyze the CVaR, the social welfare derived from the Gaussian process model (GM), the Dirichlet process mixture model (DPMM), and the DA-DPMM. The results demonstrate the superiority of the DA-DPMM approach in a two-stage market cleaning model based on CVaR.

Analysis of Distribution Static Compensator Control Strategies for Mitigating Voltage Dip Impact on Distribution Network

Voltage dip, over-voltage, load unbalanced, and current and voltage harmonics distortions are the key characteristics of poor power quality (PQ) issues on the power distribution network with a significant negative impact. The performance of a custom power device, distribution static compensator (D-STATCOM), in reducing current total harmonic distortion (THD) during the mitigation process of voltage dip with fault is investigated in this study. To control the load side voltage, the D-STATCOM utilizes a three-phase voltage source converter and is coupled at the point of common coupling (PCC). To mitigate voltage dip effects, this study implements and compares the effectiveness of the conventional PI controller with an intelligent optimization-based PI controller using the dynamic gravitational search algorithm (DGSA). The performance of these controllers is validated by the MATLAB/Simulink simulation results obtained. Analysis of the results demonstrates that D-STATCOM operates flawlessly with an intelligently optimized PI control strategy reducing the current THD from 11.68% to 3.74%.

Mitigation of transient effects due to partial shading in a grid-connected photovoltaic farm through controlled vehicle to grid operation

Environmental issues and air pollution in urban areas are driving forces behind the shift to electric vehicles (EVs). The energy storage element of the EVs can be used in many effective ways, such as increasing spinning reserve, shaving peak load, load levelling, voltage regulation etc. So, establishing a smart grid with several renewable energy sources along with the grid integration of EVs is the recent trend in power systems. One of the commonly observed essential phenomena in the smart grid including a PV system is partial shading (PS), which implicates the reduction of solar irradiation over the PV module, mainly due to leaves or branches of trees, clouds, and dust. Though most previous researchers were concerned about the steady state power reduction during PS, in this research, we focused on the reduction of transient impact on a grid-connected PV system using the controlled Vehicle to Grid (V2G) operation of the EVs. A small-scale microgrid consisting of a PV farm and a diesel generator is considered, where the PV farm experiences different degrees of shading varying from 30 to 70%. The simulation results show that due to the increase of the PS, the percentage overshoot (% OS) and settling time of rotor speed, active power and load current are increased. To mitigate this issue, we proposed the integration of plugged-in electric vehicles (PEVs), through the controlled V2G operation, which is done here by a PI controller. The proposed system improves the transient effects at both the load and generator sides. It has been found that the PS creates an oscillation in rotor speed, which is settled down by the governor system of the generator at the 20s, while the controlled V2G operation reduced this settling time to 10s. On the load side, the % OS of active power has been reduced from 4.24% to 0.44% and the settling time has been decreased from 28s to 18s by the controlled integration of the PEVs.

Optimal Economic Scheduling Method for Power Systems Based on Whole-System-Cost Electricity Price

At present, the traditional scheduling mode of power grids generally dispatches according to the power generation cost within the safe range. Transmission costs are evenly distributed to customers according to their load ratios. There are no methods for the rational distribution of transmission costs according to the utilization degree of generation and load to transmission network resources. This traditional scheduling mode will render transmission cost distribution unfair, and it is difficult to guide reasonable load distribution in time and space. Therefore, an optimal economic scheduling method for power systems based on the whole-system-cost electricity price is proposed in this paper. For the power generation and the transmission sides, the whole-system-cost electricity price model was constructed according to the power flow tracking method. For the load side, a demand-side response model of users’ responses to electricity price changes was established. Finally, the IEEE 57 node standard model was used to simulate optimal economic scheduling. The results show that the proposed method can guide the rational distribution of power flow. The power flow is shifted moderately from far away to near the power generation center, allowing for the load demand to be guided to meet nearby customers’ demands and preventing the line from blocking, the latter of which is conducive to ensuring the safety of the power grid.

Probabilistic Power Flow Optimization of Source Network Load Storage in AC/DC Systems Based on SOC Characteristics

The randomness and fluctuation of wind turbines and photovoltaics affect the optimal operation of the power system. This paper aims to mitigate wind and solar fluctuations by integrating energy storage systems (ESS). One crucial metric for assessing the performance of energy storage systems is state of charge (SOC). Therefore, this paper proposes a power flow optimization method based on SOC characteristics. Firstly, we consider the uncertainties on both the generation and load sides of the power system and establish a source load model. Subsequently, an analysis of the SOC characteristics of the ESS is conducted, and associated constraints are provided. Then, a power flow model for the AC-DC system with voltage source converter (VSC) is developed based on the source load storage configuration. The optimization model is formulated as a multi-objective problem, utilizing penalty functions to minimize the overall economic cost, system losses, and line voltage deviations. Finally, the proposed method is validated through simulation analysis using an enhanced IEEE 30-node system as a case study. The simulation results confirm the correctness and effectiveness of the presented approach.

Photovoltaic-Based Residential Direct-Current Microgrid and Its Comprehensive Performance Evaluation

The “dual carbon” strategy has drawn attention to distributed PV systems for their flexibility and variability, but the rising need for direct-current (DC) loads on the load side has created additional difficulties for microgrid system upgrades. In this article, a PV-based microgrid design approach for residential buildings is suggested, working on the assumption that distributed PV systems are given top priority to handle domestic DC needs. The residential DC microgrid system’s overall design concept is first put out, and the circuit system is then concentrated to supply the main idea for the ensuing verification of the system’s viability. Secondly, the actual power generation in the selected area was clarified by testing, and then the electricity consumption of DC loads accounted for about 20.03% of the total power consumption according to the survey of 100 users. In addition, the circuit system is subjected to spectral model measurements and physical measurements to verify the operational performance of the circuit system; the feasibility of the PV microgrid system is further verified using dual testing of the PV system and the circuit system. The test results show that the proposed DC microgrid system can accurately provide the required voltage for small household DC appliances, such as 24 V, 14 V, 5 V, etc. Finally, the system economics were analyzed, and the equipment payback years were estimated. The supply and demand of PV power generation and DC appliances can be balanced via the construction of a microgrid. This study offers a fresh concept for the use of PV technology. The concept behind this research can serve as a model for the creation and application of other new energy sources.

A machine learning-driven support vector regression model for enhanced generation system reliability prediction

PurposeThe purpose of this paper is to achieve high accuracy in forecasting generation reliability by accurately evaluating the reliability of power systems. This study uses the RTS-79 reliability test system to measure the method’s effectiveness, using mean absolute percentage error as the performance metrics. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance, making this study relevant to power system planning and management.Design/methodology/approachThis paper proposes a novel approach that uses a radial basis kernel function-based support vector regression method to accurately evaluate the reliability of power systems. The approach selects relevant system features and computes loss of load expectation (LOLE) and expected energy not supplied (EENS) using the analytical unit additional algorithm. The proposed method is evaluated under two scenarios, with changes applied to the load demand side or both the generation system and load profile.FindingsThe proposed method predicts LOLE and EENS with high accuracy, especially in the first scenario. The results demonstrate the method’s effectiveness in forecasting generation reliability. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance. Therefore, the findings of this study have significant implications for power system planning and management.Originality/valueWhat sets this approach apart is the extraction of several features from both the generation and load sides of the power system, representing a unique contribution to the field.

Optimized Dispatch of Regional Integrated Energy System Considering Wind Power Consumption in Low-Temperature Environment

The wind abandonment phenomenon of cogeneration units in regional integrated energy systems (RIES) under the operation mode of “heat for electricity” and the improvement in the operation efficiency of the energy storage system under a low-temperature environment are problems that need to be solved urgently. To this end, a regional integrated energy system optimization scheduling method based on fine energy storage and wind power consumption is proposed in the paper. First, a fine energy storage model more adapted to a low-temperature environment is established on the power side to accurately simulate the actual working state of the energy storage components and quantify the uncertainty of the wind power output using the conditional value-at-risk (CVaR) theory. Then, a combined heat and power demand response mechanism is introduced on the load side to reduce the peak-to-valley difference in the heat and power loads, it is realized to promote the system’s consumption of wind power without increasing the transmission power of the contact line. Finally, the example is solved on the MATLAB platform with the objective of minimizing the total cost of the RIES optimal dispatch. The simulation results show that the proposed model is not only more adaptable to a low-temperature environment compared with the traditional model but also reduces the overall cost of the system by 2.58% while realizing the complete consumption of wind power. This innovative study provides a feasible and efficient solution to improve the performance of integrated energy systems, especially the operation capability in extreme environments.

Coordinated Planning of Power Systems under Uncertain Characteristics Based on the Multilinear Monte Carlo Method

The randomness of the power supply side and the load side of comprehensive energy systems is increasingly prominent. It is very difficult to meet demand through traditional planning methods. To solve this problem, this paper explores the coordinated planning of a power system under uncertain characteristics using the multilinear Monte Carlo method. The uncertain characteristic model and probability density function of the system’s power supply side and load side are established. Taking the optimal operating cost and the maximum wind power consumption as the system planning objectives, a system coordination planning scheme is established, and it is solved by multilinear Monte Carlo simulation. The superiority of this method is verified by taking the modified IEEE 39-bus test system as an example. This method can provide a reference for system planning.

A bi-layer wind-CCUS-battery expansion stochastic planning framework considering a source-load bilateral carbon incentive mechanism based on the carbon emission flow theory

The rapid development of low-carbon energy technologies and energy storage technologies has provided an important and feasible path to decarbonizing the power system. In this context, there is an increasing number of studies on renewable energy, carbon capture, utilization and storage (CCUS) and energy storage expansion planning. However, most of the existing studies attribute the carbon responsibilities to the source side and a small number to the load side. Expansion planning studies that consider the overall carbon emissions of the system to be shared between the source and the load side are still relatively few. Therefore, it is necessary for the source and the load side to share the responsibility for the total system carbon emissions. To fill this research gap, this paper proposes a source-load bilateral carbon incentive mechanism for wind-CCUS-battery power systems based on the carbon emission flow theory. Besides, a bi-layer wind-CCUS-battery expansion stochastic planning framework considering wind and load uncertainties is constructed. The first layer takes the minimum expectation of power generation costs, fixed investment costs of wind turbines and CCUS units and carbon incentive costs as the objective function from a source-side perspective. The second layer takes the minimum battery investment cost and the expectation of electricity purchasing costs and load-side carbon incentive costs as the objective function from a load-side perspective. Finally, the proposed model is tested on the IEEE 24 bus power system for validity and advantage. The results show that the current high investment cost is not favorable to CCUS construction. At this time, the bilateral carbon incentive mechanism is more conducive to promoting system carbon reduction than the unilateral carbon incentive mechanism. In the future, as the cost of CCUS decreases, the source-side carbon incentive mechanism is more conducive to system carbon reduction than the bilateral carbon incentive mechanism. Due to the consideration of the stochastic uncertainty of wind turbines and loads, the research in this paper is closer to the reality, which can provide a reference for the future carbon emission reduction path of the power system, especially for the quantitative analysis of carbon emission reduction of CCUS, which is an important guiding significance for the promotion of the engineering practice of CCUS.