Distributed Generation & Alternative Energy Journal

Superimposed Positive Sequence Impedance for Detecting Unintentional Islanding in Microgrid

Indradeo Pratap Bharti — 2024-07-16

Incorporation of environmentally friendly energy sources (RESs) into the electricity grid has many benefits, including economic, technological, and environmental. However, excessive renewable energy sources (RES) in the power grid provide technical problems, including equipment protection, DG operation, and islanding detection. One of the most serious challenges is the islanding phenomenon. Islanding can cause several problems, such as frequency instability and voltage fluctuations resulting in damage to electrical equipment or threatening utility workers who may be working/accessing the equipment. This research proposes an efficient islanding detection algorithm to lessen the impact of such threats. This novel passive islanding detection scheme is based on superimposed positive sequence impedance (SPSI). For calculating the superimposed positive sequence impedance (SPSI), the voltage and current signals are obtained from targeted DG points. The scheme’s performance is tested on multiple bus systems across islanding and non-islanding conditions using a MATLAB/Simulink environment. It is shown that even in the presence of noise, the algorithm can determine an islanding decision with high accuracy and a short detection time of 84 ms. In comparison to other algorithms, it operates at zero power mismatch (ZPM) and does not affect power quality.

A Two-Input Single-Output Boost Converter with Fault Tolerant Operation

K. Karishma — 2024-07-16

The need for multi-input DC-DC converters is highly demanded in the context of the integration of different energy sources. When integrating several energy sources, such as batteries, solar PV arrays, fuel cells, etc., multi-input DC-DC converters preferred over multiple single-input DC-DC converters. The vast majority of MISO converters in use today only have one mode of operation, which is conventional operation using all sources. Very few MISO topologies have been presented with fault-tolerant capability. In this work, a non-isolated two-input single-output (MISO) boost converter is investigated for low-voltage DC (LVDC) applications with fault tolerant capability. The presented converter works in three modes, such as DC sources of equal values, DC sources of unequal values, and one of the DC sources that is faulty or out of order. The converter is simulated in a MATLAB/Simulink environment and experimentally validated using a scaled prototype. The outcomes demonstrate that the converter can integrate systems with two DC energy sources, accommodates DC sources of equal values, DC sources of unequal values, and works even when one of the DC sources is faulty or out of order. This work points out that the presented MISO converter would be an apt solution for integrating varying input voltage sources with fault-tolerant capability.

Research on the Evolution Process of New Distribution Network Forms Considering Low Carbon and Sustainable Development

Zhengyuan Chen — 2024-07-16

To obtain the planning and development status of new distribution networks under the low-carbon and sustainable development goals, research the evolution process of new distribution network forms under the “carbon peak” and “carbon neutrality” goals (dual carbon goals). Firstly, combining the characteristics of high proportion renewable energy, construct a new distribution network morphology evolution system under the “dual carbon target”. Explore the energy pattern and structural form of the current and medium/long-term new distribution network, and obtain the corresponding evolution process. Then, considering the transmission characteristics of the new distribution network under the “dual carbon target”, an evaluation index system is established in three aspects: power supply, distribution, and transmission. And construct an evaluation model for the development form of distribution networks based on SWOT analysis, to evaluate the new development form of distribution networks. Finally, numerical examples were verified and analyzed using MATLAB simulation software. The method proposed in this article not only intuitively describes the evolution process of the new distribution network morphology, but also obtains the evolution of the new distribution network at different time stages under the “dual carbon target”, as well as the evolution results of the distribution network morphology in terms of carbon emissions and cost investment. The final research results of this article can provide theoretical basis for distribution network planning under the “dual carbon goal”.

Research on Distribution Substation Topology Identification Methods

Hu Weidong — 2024-07-16

With the advancement of digital transformation in distribution substations, a large number of smart devices are being integrated into substations. Addressing the challenges of automatic topology recognition and the issue of unstable recognition accuracy in distribution substations has become crucial. This paper proposes a substation topology recognition method based on an improved matrix approach and the Minimum Conditional Probability of Packet Loss Theorem. The improved matrix approach is utilized to calculate the topological signals, enabling automatic bottom-up topology recognition within the substation. The application of the Minimum Conditional Probability of Packet Loss Theorem in processing topological data significantly enhances the accuracy of substation topology recognition, reducing the impact of external factors on recognition accuracy. Experimental validation demonstrates that the proposed method is highly feasible and exhibits fault tolerance, indicating practical engineering applications.

KWH Cost Analysis of Energy Storage Power Station Based on Changing Trend of Battery Cost

Jie Huang — 2024-07-16

Energy storage plays a vital role in enhancing the resilience of the power grid. Utilizing typical capacity and power energy storage application scenarios, coupled with industry research data and technical analysis of energy storage, this study calculates the cost of energy storage per kilowatt-hour and the associated mileage cost. The findings indicate that the current cost per kilowatt-hour of electrochemical energy storage ranges from approximately 0.6 to 0.9 yuan/(kW⋅h), revealing a considerable gap between the target cost for widespread application and the range of 0.3 to 0.4 yuan/(kW⋅h). Therefore, the development of energy storage technologies (EST) should prioritize achieving “low cost, long life, high safety, and easy recycling,” taking into account a comprehensive assessment of system manufacturing, system lifespan, system safety, and recycling. This paper delves into the changing trend of battery costs and their impact on kilowatt-hours, presenting strategic suggestions to reduce the kilowatt-hour cost of ESP stations. The research underscores that a continuous reduction in battery costs will contribute to enhancing the economic benefits of ESP stations and provide robust support for the future development of the energy storage industry.

Research on Environmental Performance and Measurement of Smart City Power Supply Based on Non Radial Network DEA

Ying Sun — 2024-07-16

The continuous development of smart cities has put forward higher requirements for the supply of power systems. In response to the constraints in the environmental performance and measurement of smart city power supply, this paper proposes a research model for smart city power supply environmental performance and measurement based on non-radial network DEA based on the characteristics of DEA model and distance function. This model can combine different stages of power supply to conduct more reasonable statistics and analysis of efficiency in different regions. In addition, correlation coefficients were analyzed for the impact of efficiency factors on the phase ratio in the production and sales stages of the power supply system. The research results indicate that there is a positive correlation between the output value and power generation of electricity sales and the efficiency of the electricity sales stage, with correlation coefficients of 0.57 and 0.092, respectively; The length of newly added lines, capacity of new equipment, and line loss rate are all negatively correlated with their efficiency, with correlation coefficients of −0.42, −0.12, and −0.46, respectively. Based on the above analysis, this study provides more theoretical support for the study of environmental performance and measurement of smart city power supply.

Research on Electricity Balance and Measurement Optimization of New Energy Power System Considering Renewable Energy Consumption Mechanism

Zhihao Guo — 2024-07-16

With the rapid development of renewable energy, the new energy power system is facing the challenge of large-scale grid connection and consumption of renewable energy. In order to achieve efficient utilization and stable power supply of renewable energy, this study proposes a renewable energy consumption mechanism based on optimization methods. By establishing a power and electricity balance model, consider the relationship between different types of renewable energy generation and electricity demand. Various optimization strategies have been proposed for energy consumption issues in different scenarios, including power generation scheduling, energy storage optimization, and flexible load management. Validate the effectiveness of the proposed mechanism in terms of electricity balance and metering optimization through a model. The experimental results indicate that this mechanism can effectively enhance the renewable energy consumption capacity of the new energy power system, reduce energy waste, promote energy cleanliness and sustainable development, and has certain theoretical and practical significance for promoting the sustainable development of the new energy power system and responding to energy transformation.

Study on PV Power Prediction Based on VMD-IGWO-LSTM

Zhiwei Xu — 2024-07-16

This research proposes a combined approach for predicting photovoltaic power by integrating variational modal decomposition (VMD), an improved gray wolf optimization algorithm (IGWO), and long- and short-term memory neural network (LSTM) techniques. The model takes into account the impact of varying environmental factors on photovoltaic power and aims to enhance prediction accuracy. Firstly, the four environmental factors constraining the PV output power are decomposed into eigenfunctions (IMFs) through variational modal decomposition; then the improved gray wolf optimization algorithm is used to optimize the long and short-term memory neural network; finally, the dimensionality-reduced dataset is inputted into the LSTM neural network, and the dynamic temporal modeling and comparative analysis on the multivariate feature sequences are carried out. The results show that the VMD-LSTM model optimized by the improved Gray Wolf algorithm predicts better than the comparison models LSTM, VMD-LSTM and VMD-GWO-LSTM, and achieves the accurate prediction of time-volt power in the external environmental changes.

Analysis of Power Grid User Behavior Based on Data Mining Algorithms – System Design and Implementation

Yan Wang — 2024-07-16

A data mining based power grid user behavior analysis system has been designed to address the issues of insufficient stability and accuracy in existing power grid user behavior analysis systems. Design the overall structure of the power grid user behavior analysis system; In terms of system hardware design, select a core controller, build and install a server as the foundation for system information transmission and logical operation; Based on ZigBee wireless communication technology, a ZigBee wireless communication protocol stack and communication expansion board were designed; In terms of system software design, Python is used to crawl user behavior data in the system data collection layer, and Python language is used to maintain the crawling program; Use the K-means algorithm to perform secondary mining and clustering on power grid user behavior data, obtain the analysis results of power grid user behavior, and transmit them to the system visualization display layer. The weight and Rand coefficient of data analysis were used as indicators to test the application effect of the method in this paper. The experimental results showed that the system can stably and accurately analyze the behavior of power grid users, and has good application effect. This research achievement has important reference significance for the research in the field of power grid user behavior analysis in the world scientific community.

Stochastic Model Predictive Control Based on Polynomial Chaos Expansion With Application to Wind Energy Conversion Systems

Gang Liu — 2024-07-16

The wind energy conversion system (WECS) has a complex structure, and its state space model is highly nonlinear. Due to the random uncertainty of wind speed, it poses a huge challenge to achieve optimal control tasks and ensure the safe and stable operation of the system. Therefore, this article proposes a stochastic model predictive control strategy based on Polynomial Chaotic Expansion (PCE), which achieves the control tasks of MPPT and constant power regions in wind energy conversion systems. Firstly, a simple algorithm is proposed to obtain a set of basis functions that are suitable for the stochastic variable wind speed. Then, the obtained basis functions are used to propagate the uncertainty of the original uncertain differential equation of the wind energy conversion system through polynomial chaotic expansion. Combining the operating region and constraint conditions of the wind energy conversion system, the original stochastic uncertainty problem is transformed into a deterministic convex optimization problem. Using NREL 5MW wind turbine as the research object for simulation, the task of capturing maximum wind energy in MPPT area and tracking rated power points in constant power area was achieved. The experimental results show that the proposed control method can effectively improve the wind energy capture capability and achieve accurate tracking of output power to rated power.

Load Frequency Control Strategy of Interconnected Power System Based on Tube DMPC

Wang Xinshan — 2024-07-16

Solar thermal power generation shares technical characteristics with traditional thermal power generation. This enables rapid adjustment of turbine generator output to meet the demands of the power grid load for frequency modulation. However, fluctuations in light intensity lead to variations in interconnected power system parameters, posing challenges for load frequency control (LFC). In this study, we propose a Robust Distributed Model Predictive Control (RDMPC) method. This method achieves system trajectory tracking by solving the nominal system optimization problem. It also flexibly adjusts the weights of different Tube models to determine the optimal control law using the standard Tube online combination with various gain values. Additionally, we incorporate the states of adjacent areas into the feedback control law to achieve effective coordination between these areas. Using MATLAB/Simulink, we simulated the power system in two areas. Compared to standard Tube DMPC, our proposed algorithm effectively mitigates the impact of light intensity, enhances adjustment speed, reduces frequency fluctuation, and demonstrates superior control effectiveness.

Research on a Self-Coordinated Optimization Method for Distributed Energy Resources Targeting Risk Mitigation

Hongtao Li — 2024-07-16

This passage discusses a rapid restoration optimization strategy for short-term global coordination and the synergistic autonomous regulation of distributed energy sources based on a model predictive control framework. A short-term rapid recovery global coordination optimization model is established through the prediction of network states under abnormal conditions. This model includes the energy management of distributed energy sources and recovery plans for critical loads. In terms of the autonomous regulation of distributed energy sources, based on the results of global coordination optimization and aiming to minimize load shedding losses and grid losses, an ultra-short-term rolling control strategy is formulated using power output and load switching as control variables. Finally, simulation analysis on the IEEE 33-node distribution network system indicates that the proposed model and method significantly accelerate the recovery speed of the distribution network and effectively enhance its resilience level.