Power Integration Research Articles

Evaluating the contribution of demand response to renewable energy exploitation in smart distribution grids considering multi-dimensional behavior-driven uncertainties

Demand Response (DR) is recognized as an efficient method for reducing operational uncertainties and promoting the efficient incorporation of renewable energy sources. However, since the effectiveness of DR is greatly influenced by consumer behavior, it is crucial to determine the degree to which DR programs can offer adaptable capability and facilitate the use of renewable energy resources. To address this challenge, the present paper proposes a methodological framework that characterizes the uncertainties in DR modeling. First, the demand-side activities within DR are segmented into distinct modules, encompassing load utilization, contract selection, and actual performance, to enable a multifaceted analysis of the impacts of physical and human variables across various time scales. On this basis, a variety of data-driven methods such as the regret matching mechanism is introduced to establish the analysis model to evaluate the impact of various factors on DR applicability. Finally, a multi-attribute evaluation framework is proposed, and the effects of implementing DR on the economic viability and environmental sustainability of distribution systems are examined. The proposed framework is demonstrated on an authentic regional distribution system. The simulation results show that compared to scenarios without considering uncertainty, the proposed method can fully consider the impact of DR uncertainty, thereby enabling a more realistic assessment of the benefits associated with DR in enhancing renewable energy accommodation for smart distribution grids. From the comparative analysis of new energy installation scenarios, with the integration of photovoltaic and wind power into the system, the presence of DR can increase the renewable energy consumption rate by 6.39% and 37.44%, respectively, and reduce the system operating cost by 1.37% and 3.32%. Through the comparative analysis of different load types, when DR is a shiftable load and a two-way interactive load, the renewable energy consumption rate increases by 20.57% and 26.35%, and the system operating cost decreases by 2.12% and 4.68%. In this regard, the proposed methodology, hopefully, could provide a reliable tool for utility companies or government regulatory agencies to improve power sector efficiency based on a refined evaluation of the potential for demand-side flexibility in future power grids incorporating renewable energies.

Optimization Study of Carbon Emissions in Wind Power Integrated Systems Based on Optimal Dispatch Algorithm

Abstract With the integration of wind power into the power system, dispatch becomes more complex and existing algorithms are no longer applicable. This paper focuses on optimizing carbon emissions in wind farm generation while considering issues related to wind power integration and carbon trading. An optimal dispatch algorithm was designed with the objective of minimizing total costs, which was then solved using the cuckoo search (CS) algorithm. Additionally, an adaptive improvement was made to the CS algorithm to obtain the improved cuckoo search (ICS) algorithm. An analysis was conducted on a case study with 10 units. The ICS algorithm obtained higher quality solutions, with a total cost of $ 632 719 and a calculation time of 0.51 minutes, which was superior to the solutions obtained by the particle swarm optimization and CS algorithms. Fluctuations in the confidence level of system rotation reserve capacity could lead to variations in the final system cost, which needs to be adjusted according to actual conditions. The dispatch scheme obtained by the ICS algorithm showed reduced carbon emissions, total costs, and better performance when compared with the optimal dispatch algorithm in different scenarios. The results show that the proposed methods are reliable and practical.

Integration Challenges and Solutions for Solar-Powered Electric Vehicle Charging Infrastructure: From Panel to Battery

The integration of solar power with electric vehicle (EV) charging infrastructure presents a promising avenue to foster sustainable transportation. This study delves into the multifaceted challenges encountered in the synthesis of solar-powered EV charging stations and proffers solutions that span the complete energy transfer chain from photovoltaic panels to EV batteries. Initial concerns address the intermittent nature of solar energy and its impact on the reliability of power delivery. Advanced energy management strategies are explored, incorporating predictive analytics and real-time adjustment mechanisms to enhance the consistency and efficiency of power flow. The second part of the investigation scrutinizes the power electronics interface, emphasizing the need for high-efficiency converters that can operate effectively over varied solar insolation levels. Novel circuit topologies are presented, alongside adaptive control algorithms designed to optimize the power conversion process. Collectively, the findings underscore the potential of solar-powered EV charging infrastructure to not only support the decarbonization of transportation but also to contribute to the stability and efficiency of the power grid. The intersection of power electronics, energy management, and grid integration forms the cornerstone of this endeavor, with each domain providing critical components to the holistic solution. It culminates in a set of recommendations for policy, design, and operation that can accelerate the adoption of this technology.

Hybrid Electrical Vehicle Design by Using Solar and Battery Sources

This paper of the modelling methods and software used to model EVs with BLDC motors are provided in this research. It examines the value of simulation in enhancing control, energy, and performance techniques for vehicles. This research also examines important variables, scenarios, and difficulties in simulating BLDC motor driven EVs, emphasising their significant contribution to the development of EV technology. The battery is charged using a power converter and a DC/DC converter. In order to charge the battery, it also used regenerative braking. This model enables a thorough examination of battery performance under various operating circumstances, such as temperature changes and charge and discharge cycles. The battery efficiency of this lithium-ion battery is outstanding. An electric vehicle with a solar power source and a totally battery-powered design. We create a comprehensive and adaptable simulation environment that faithfully replicates real-world EV behaviour by integrating the Simulink and Simscape libraries from MATLAB. It not only advances the technology of electric vehicles but also looks into the integration of solar power to increase the sustainability of electric mobility. This research highlights the potential for electric vehicles to be a key contributor to a cleaner and more sustainable transportation future by fusing the advantages of lithiumion batteries, BLDC motors, and solar power.

Event-triggered data-driven load frequency control for multi-area power system with wind penetration under deregulation environment

In order to solve the problem of power exchange between areas of multi-area power systems under the power market, this paper designs a power trading contract based on the generation participation matrix, so as to simulate the specific process of power change in each area. With the integration of wind power into the large-scale power system, it is difficult to model the multi-area power system. For this situation, this paper designs a data-driven model-free adaptive load frequency control algorithm based on collecting input and output data, which gets rid of the dependence of the power system on the model. Along with the frequent transmission of input and output data in each area, the communication load of the power system also increases. Aiming at saving communication resources, this paper designs an event-triggered mechanism to reduce the communication bandwidth. The stability of the control algorithm is demonstrated theoretically. Finally, a three-area power system with wind penetration is used as an example to simulate and verify the effectiveness of the proposed algorithm in this paper.

Power Electronics-Based Operation for Intelligent Energy Management in Microgrid

This article focuses on intelligent energy management in microgrid systems, providing a comprehensive control engineering perspective on power electronics-based operation. The proposed approach utilizes advanced control algorithms to optimize power flow, load balancing, and integration of renewable energy sources within the microgrid. This article proposes the integration of multiple control strategies to create a hybrid approach that overcomes their disadvantages and addresses the limitations of individual algorithms. We aim to achieve a more robust and adaptive microgrid control method by combining the strengths of different control techniques. This hybrid approach leverages the power electronics capabilities while mitigating their limitations.

Vertical Integration and Market Power in Supply Networks

Vertical Integration and Market Power in Supply Networks

Research on Low Temperature Drift Suppression of Micro-Machined Gyroscope Based on Self-Calibration Technology

Micro-machined gyroscope is an important inertial sensor, which has the advantages of high integration, small size and low power consumption. However, due to the temperature sensitivity of silicon and electronic devices, the bias and scale factor of micro-machined gyroscopes have temperature drift, which limits their engineering application the relatively large process error of micro-machining makes the frequency mismatch between driving mode and detection mode of micro-machined gyroscope, which not only deteriorates the mechanical sensitivity of the gyroscope, but also causes the frequency mismatch between driving mode and detection mode, it also leads to the quadrature error of driving coupling in gyro detection mode. Based on this, firstly, the relationship between the bias factor and the scale factor of MEMS gyroscope and its dynamic and electrical parameters is analyzed. Secondly, a self-calibration capacitance detection scheme based on triangular-electrode based (TEB) is proposed, and the temperature effect of the scheme is analyzed. Finally, the simulation results show that the temperature coefficient of the scale factor decreases from −8845 ppm/°C to 1660 ppm/°C when the temperature range is from −10 °C to 60 °C, the bias temperature coefficient decreased from −0.97°/s/°C to −0.42°/s/°C. The experimental results show that the scheme effectively reduces the temperature sensitivity of the bias and scale factor of the gyroscope.

Analytic Deep Learning and Stepwise Integrated Gradients-Based Power System Transient Stability Preventive Control

Trajectory sensitivity (TS) is a powerful alternative to linearize strong-nonlinear transient stability patterns. However, due to its incomplete view of an intact power system operational space, it can be readily to drive preventive control to stuck in local stationary solution. To mitigate this issue, an integrated gradient (IG)-based stepwise control method is proposed. The key idea is to develop IG to enable a control sensitivity taking both merits of local and global sensitivity, further help preventive control escape from local stationary points. Particularly, deep belief network (DBN) is firstly utilized to parameterize pattern between transient stability index (TSI) and steady-state dispatches. It is then leveraged to infer IG to linearize transient stability constraints in real-time. Interior point method is employed to solve the linearized model. A stepwise control strategy is finally proposed to adaptively tune the integrated interval of the applied IG, such that the preventive control path can be ensured to follow the trend of improving transient stability. Case studies on two benchmarks show that the proposed method outperforms traditional TS method in terms of efficiency and economy, and provides a promising way to enhance engineering sensitivity methods.

Integration of Power to X Economy in Existing Energy System – Case Finland

Hydrogen or Power-to-X economy is seen as the most promising way to carry out the energy transition from fossil to renewable carbon free energy system. Electrification of the society and green hydrogen-based energy system require huge capacity increase in renewable energy production and energy transmission. This paper introduces a simulation study about the integration of green hydrogen-based energy system to existing power system structure in Finland. Regional energy balances are studied with different options of locations of renewable energy and hydrogen production plants and needs for energy transportation either as electricity or hydrogen. The model includes sectoral couplings between electricity, hydrogen and heating grids and finds synergies of flexibility between different energy grids.

Control and Capacity Design of Station-Hybrid HVDC System with DRU and MMC in Parallel for Offshore Wind Power Integration

Control and Capacity Design of Station-Hybrid HVDC System with DRU and MMC in Parallel for Offshore Wind Power Integration

Enhancing Wind Power Plant Integration Using a T-NPC Converter with DR-HVDC Transmission Systems

This paper introduces an innovative control technique tailored for the seamless integration of wind power plants (WPP) into onshore grids. Our approach utilizes Diode-Rectifier-based High Voltage Direct Current (HVDC) system and incorporates a T-type Neutral Point Clamped (T-NPC) converter on the HVDC back, providing solutions for current challenges in wind power integration. While the use of Diode Rectifier (DR) as the Offshore converter for HVDC transmission has proven advantageous in terms of cost and loss reduction, it also introduces new challenges, such as maintaining AC grid voltage and frequency on the Offshore side and achieving the synchronization of the wind energy conversion system (WECS). This paper proposes a comprehensive control strategy to adresse these challenges by developing frequency and AC voltage control loops, ensuring the synchronization of WECS with the DR AC side. A notable distinctive feature of our proposed technique is to ensure the regulation of the DC voltage of the HVDC cable, accomplished through the onshore inverter. This crucial aspect ensures stable and efficient power transmission from wind power plants to the onshore grid.

A systematic approach for risk assessment and mitigation of wind integrated electricity markets

A risk-based double auction mechanism with dispatchable loads is one of the most cost-efficient trading mechanisms in the modern real-time electricity markets. Risks in the system due to various uncertainties, such as line failure, generator failure, load changes and other severe contingencies causes non-systematic execution of real-time electricity markets. This paper presents a simple and systematic procedure for reducing system risks in deregulated power markets considering wind power integrations. For maximizing the considerable system social welfare and minimizing the system’s economic risk, the optimal placement and operation of wind power producers (WPP) have been suggested. The critical buses have been identified by accessing the different assessment tools e.g., Value at Risk (VaR), Conditional Value at Risk (CVaR), and locational marginal pricing (LMP) based Marginal Pricing Index (MPI). The presented approach has been evaluated and validated on modified IEEE 30-bus system.

Optimized Signal and Power Integrity of Silicon Interposer for HBM2E in CoWoS Packaging

Optimized Signal and Power Integrity of Silicon Interposer for HBM2E in CoWoS Packaging

Efficient and Powerful Integration of Targeted Metabolomics and Transcriptomics for Analyzing the Metabolism Behind Desirable Traits in Plants.

Through current mass spectrometry methods and multiple RNA-Seq technologies, large metabolomics and transcriptomics datasets are readily obtainable, which provide a powerful and global perspective on metabolism. Indeed, one "omics" method is often not enough to draw strong conclusions about metabolism. Combining and interpreting multiple "omics" datasets remains a challenging task that requires careful statistical considerations and pre-planning. Here we describe a protocol for obtaining high-quality metabolomics and transcriptomics datasets in developing plant embryos followed by a robust approach to integration of the two. This protocol is readily adjustable and scalable to any other metabolically active organ or tissue.

A Thermal and Power Integrity Co-Optimization Framework for 2.5-D Integrated Microsystem

A Thermal and Power Integrity Co-Optimization Framework for 2.5-D Integrated Microsystem

Optimizing Grid Stability through the Integration of Wind Energy

It has become increasingly apparent that wind energy can contribute to reducing carbon emissions and reducing fossil fuel reliance. In spite of this, wind's intermittent nature makes it challenging for grids to maintain stability. The objective of this paper is to review the current state of wind energy integration, highlighting key research findings on the potential of wind power, turbine performance, and approaches to enhance grid stability. A particular part of the research focuses on the modelling of DFIG systems, which includes the rotor control mechanism as well as the grid control mechanism at the grid side of the generator. Voltage fluctuations, transients in the power system, and reactive power management are among the technical challenges associated with wind power integration. SFCLs (Superconducting Fault Current Limiters) are also explored in the paper as innovative solutions for improving grid stability. As a result of simulations, it is demonstrated that SFCLs can mitigate power deviations and improve overall system stability to an extremely high degree.

Research on the optimal configuration method of energy storage based on wind power randomness

Large-scale wind power integration into the power grid will bring some negative effects or even large faults to the power system, such as voltage instability, the change in the transient stability of the system, etc. This could lead to the low accuracy of the wind power prediction results and the unreasonable capacity configuration of the energy storage. In this paper, considering the uncertainty of wind power, the optimization model of minimizing the operating cost, voltage deviation, and pollutant emission is constructed. Additionally, the mixed probability optimization algorithm (PLF-NSGAII-MOPSO) is calculated and analyzed. The results show that the method proposed in this paper is beneficial to finding the optimal configuration of energy storage under the condition of wind power access.

Notice of Retraction: Signal and Power Integrity SPICE Cosimulation Versus 3-D EM Co-Extraction for DDR Memory Interface Modules

Notice of Retraction: Signal and Power Integrity SPICE Cosimulation Versus 3-D EM Co-Extraction for DDR Memory Interface Modules

Adaptive Neural Fixed-Time Tracking Control for High-Order Nonlinear Systems.

The problem of adaptive neural fixed-time tracking control for high-order systems is addressed in this article. In order to handle the difficulties from the uncertain nonlinearities within the original systems, the radial basis function neural networks (RBF NNs) are introduced to approximate the unknown nonlinear functions, and the adding a power integrator is applied to overcome the obstacle from high-order terms. It is proven that all signals in the closed-loop system are bounded and the output signal can eventually converge to a small neighborhood of the reference signal. Simulation results further verify the approaches developed.