Distributed Power Generation Research Articles

Evaluation of radiality constraints for power distribution networks

This work carried out an empirical study with the objective of determining the most efficient strategy in guaranteeing the radiality of the distribution network during the reconfiguration. The reconfiguration problem in a distribution network aims to reduce technical losses by changing the paths that energy takes through the network until it reaches consumers. In this problem, a mandatory constraint is to ensure that the network operates radially. However, the literature does not present a consensus on the best way to guarantee this restriction. Computational experiments were carried out using the two main methodologies in the literature: limiting the number of input edges; and search with removal of cycles in each partial solution. A third and new strategy was proposed as an alternative to the literature using the concept of minimal cuts in graphs. Computational tests showed that the strategy of limiting the number of input edges was the most efficient in ensuring radiality. We also point out a drawback in the applicability of this strategy to networks with high penetrations of distributed generation (DG). For this reason, our experiments also analyze the other strategies that are suitable for DG power flows. For this case, we show that the methodology proposed in this paper provides a better way to tackle modern networks arising from applications of smart grids.

Energy Management System for Distributed Energy Resources using Blockchain Technology

: Power generation in today’s world is of utmost importance, due to which blockchain is used for the categorization and formation of decentralized structures. This paper has proposed decentralized energy generation using a nester, i.e., energy sharing without third-party intervention. Decentralized blockchain technology is applied to ensure power sharing between buyer and seller, and also to achieve efficient power transmission between prosumer and consumer. Energy management is associated with controlling and reducing energy consumption. Blockchain technology plays a major role in distributed power generation, for example, power-sharing (solar and wind energy), price fixation, energy transaction monitoring, and peer-to-peer power-sharing. These are operations performed by blockchain in renewable power generation. Solar power generation using blockchain technology can obtain an impact resting upon the power generation system. Distributed ledger is the key area of blockchain technology for recording and tracking each transaction in the distribution system to improve the efficiency of the overall transmission system. A smart contract is another important tool in the blockchain technology, which is issued to confirm an assent between buyer and seller before starting any energy transaction without external intervention and also to avoid time delay. Maximum power point tracking is conducted in PV cells using blockchain technology. Blockchain influences energy management systems to improve the utilization of energy, optimize energy usage, and also to reduce the cost.

Revenue loss reduction in the electrical distribution networks using distributed generators: A case of Tanzania electrical distribution network

Most power losses in power systems occur within distribution networks, resulting in poor service quality, higher electricity costs for consumers, and revenue loss for utility companies. This study suggests strategies for minimizing these revenue losses by integrating distributed generators (DGs) into the distribution networks. The process involves identifying optimal locations and determining the best power output for each DG based on fluctuating power system variables, making it a complex NP-hard optimization problem. Therefore, this study proposes the use of metaheuristic algorithms to solve the DG power dispatch problem and reduce revenue losses in the electrical distribution networks. The proposed technique has been tested in the Tanzanian electrical distribution network using an hourly load profile and tariff D1, T1 and T2 users. Four metaheuristic algorithms were used, namely Golden jackal optimization (GJO), Grey wolf optimizer (GWO), Walrus optimizer (WO) and Marine Predators Algorithm (MPA). The algorithms were evaluated based on convergence profiles and computational times, with GWO performing the best. The study analyzed the impact of the number of DGs on revenue loss reduction, finding that the revenue losses decrease with the increase in the number of DGs. The analysis of the impact of the tariffs category on revenue loss reduction shows that integrating DGs reduced revenue loss by over 56% in all cases. These results demonstrate that DG integration can effectively decrease revenue losses in distribution networks.

A twenty-year dataset of hourly energy generation and consumption from district campus building energy systems

Distributed energy resources (DERs) would play a crucial role in the transition towards decentralized and decarbonized energy systems. However, due to the limited availability of long-term, high-resolution datasets, there has been little research on the descriptive analysis of distributed energy systems throughout the lifespan of distributed power generators and beyond. To address this challenge, this study has collected and described a twenty-year dataset (from 2002 to 2021) consisting of hourly energy generation and consumption profiles from a campus distributed energy system, covering the entire lifespan of on-site generators. The dataset includes supply-side data such as gas consumption from combined heating and power (CHP) units (fuel cell, gas engine), absorption chiller, gas boiler, solar photovoltaic generation, CHPs output, and grid electricity import. Additionally, real-life electricity, hot water, space heating, and cooling energy load profiles from individual buildings within the campus were also collected. This long-term dataset can be utilized in various scenarios, providing researchers and policymakers with comprehensive insights into the energy efficiency of distributed energy systems.

Robust MG control considering uncertain constant power load

The destabilization of Microgrids (MGs) is attributed to the negative incremental resistance of constant power loads (CPLs). Furthermore, the inadequately damped modes of the droop control, typically employed for distributed generation (DG) power sharing, contribute to disturbances in MG dynamic performance. This paper introduces a novel control strategy aimed at mitigating instability issues associated with both CPL and droop controllers in MGs. The proposed approach utilizes an optimized H∞ control scheme, incorporating the Whale Optimization Algorithm (WOA), to enhance stability on both the CPL and distributed generation (DG) sides of the MG. The proposed controller is modeled and validated through comprehensive simulations in MATLAB, demonstrating its effectiveness under various uncertainty scenarios and disturbance conditions. These simulations demonstrate the controller's capability to effectively mitigate disturbances, optimize DG power sharing, and regulate the DC voltage of the CPL, thereby enhancing overall microgrid stability and performance. Additionally, a comparison between the proposed robust H∞ and conventional droop control schemes highlights the superiority of the proposed approach, with results confirming improved MG dynamic performance. The proposed controller exhibits a notable enhancement in Minimum Voltage Deviation (MVD), showing a substantial improvement of 96.19%. Additionally, both the Critical Response Time (CRT) and Settling Response Time (SRT) witness significant improvements of 95.33% and 97.69%, respectively, when subjected to system disturbances. Finally, the implemented MG is validated on a Real-Time Digital Simulator (RTDS), affirming the practicality of the proposed compensation technique.

Modelling of Biomass Gasification Through Quasi-Equilibrium Process Simulation and Artificial Neural Networks

In the past two decades, advancements in thermochemical technologies have improved biomass gasification for distributed power generation, enhancing efficiency, scalability, and emission control. This study aims to optimize syngas production from biomass gasification by comparing two computational models: a quasi-equilibrium thermodynamic model implemented in Aspen Plus and an artificial neural network (ANN) model. Operating at 850 °C with varying steam-to-biomass (S/B) ratios, both models were validated against experimental data. Results show that hydrogen concentration in syngas increased from 19.96% to 43.28% as the S/B ratio rose from 0.25 to 0.5, while carbon monoxide concentration decreased from 24.6% to 19.1%, consistent with the water–gas shift reaction. The ANN model provided rapid predictions, showing a mean absolute error of 3% for hydrogen and 2% for carbon monoxide compared to experimental data, though it lacks thermodynamic constraints. Conversely, the Aspen Plus model ensures mass and energy balance compliance, achieving a cold gas efficiency of 95% at an S/B ratio of 0.5. A Multivariate Statistical Analysis (MVA) further clarified correlations between input and output variables, validating model reliability. This combined modelling approach reduces experimental costs, enhances gasification process control and offers practical insights for improving syngas yield and composition.

Isolated microgrid frequency stabilization through nonlinear model predictive control of a gas engine generator set

Gas engine generator sets are applied in microgrids due to their capability to provide reliable, responsive and efficient distributed power generation, enhancing grid resilience and enabling the integration of renewable energy sources. This article proposes a methodology for stabilizing the frequency of an isolated microgrid subjected to a measurable disturbance by controlling the power of a premixed turbocharged natural gas engine. Control difficulties of this task include strongly nonlinear dynamics, time delay in the air-fuel path and constrained operating conditions. Conventional control methods like proportional-integral control have difficulties solving these problems, and require extensive tuning efforts and gain scheduling to deliver acceptable performance, prompting adoption of advanced control strategies. The presented approach utilizes a cascaded control architecture with a nonlinear model predictive controller (NMPC) for high level control and engine specific low-level controllers for controlling the intake manifold pressure and air-fuel ratio. The NMPC captures the inherent nonlinear dynamics, accounts for an input delay and handles state-dependent constraints. The cascaded control architecture enables the usage of a comparably simple model for the NMPC design, reducing the computational cost and the parametrization effort. This additionally enables application of the high level controller for different gas engine types and allows design of the controller in a simple simulation environment prior to the experiments on the real engine. Experimental results highlight the NMPC’s ability to control the engine at its physical limits over the entire load range without inducing frequency oscillations using just one parameter set. This emphasizes the robustness of the presented approach, making it a promising solution for real-world applications.

Multi-objective optimization and dynamic characteristic analysis of solid oxide fuel cell - Supercritical carbon dioxide brayton cycle hybrid system

Solid oxide fuel cells (SOFCs) are widely used in distributed power generation systems due to their high energy conversion efficiency, low emissions. Therefore, investigating the dynamic characteristics of fuel cell integration systems is significant for ensuring the efficient and stable operation of SOFC integrated systems. In this study, an SOFC integrated system with a supercritical CO2 Brayton cycle as the bottoming cycle is proposed. A dynamic model is established to investigate the dynamic characteristics of this system under optimal operating conditions. This integrated system is optimized from the perspectives of two objectives: energy conversion efficiency and operational cost. Using the Pareto frontier, the optimal solutions are a system electrical efficiency of 61.21 % and a total cost rate of 4583.8 $/hour. These optimized results are used as steady-state design points for the integrated system. At the steady-state design point, flow and voltage perturbation simulation experiments are conducted on the integrated system to analyze the dynamic characteristics of key system parameters. This study provides a theoretical foundation for the control structure design of SOFC integrated systems.

A Tri-Level Transaction Method for Microgrid Clusters Considering Uncertainties and Dynamic Hydrogen Prices

The advancement of hydrogen technology and rising environmental concerns have shifted research toward renewable energy for green hydrogen production. This study introduces a novel tri-level transaction methodology for microgrid clusters, addressing uncertainties and price fluctuations in hydrogen. We establish a comprehensive microgrid topology with distributed power generation and hydrogen production facilities. A polygonal uncertainty set method quantifies wind and solar energy uncertainties, while an enhanced interval optimization technique refines the model. We integrate a sophisticated demand response model for hydrogen loading, capturing users’ behavior in response to price changes, thereby improving renewable energy utilization and supporting economically viable management practices. Additionally, we propose a tri-level game-theoretic framework for analyzing stakeholder interactions in microgrid clusters, incorporating supply–demand dynamics and a master–slave structure for microgrids and users. A distributed algorithm, “KKT & supply-demand ratio”, solves large-scale optimization problems by integrating Karush–Kuhn–Tucker conditions with a heuristic approach. Our simulations validate the methodology, demonstrating that accounting for uncertainties and dynamic hydrogen prices enhances renewable energy use and economic efficiency, optimizing social welfare for operators and economic benefits for microgrids and users.

Research on carbon emission measurement techniques for accounting and grid auxiliary services

Abstract Comprehensive and accurate carbon measurement in industrial parks is an important prerequisite for grasping the current status of carbon emissions in industrial parks and exploring their carbon reduction potential. In industrial parks, a large amount of indirect or direct carbon emissions will be generated in common processes such as power use, fuel consumption, and industrial processes, and how to realize the real-time and accurate measurement of indirect and direct carbon emissions in industrial parks will become the key foundation for promoting carbon reduction and emission reduction in industrial parks. This paper focuses on the impact of auxiliary services provided by distributed power generation on carbon emissions and the integration of multi-energy carbon emission measurement technology to carry out carbon emission accurate measurement technology research, puts forward an accurate measurement method of indirect electricity carbon emission in industrial parks considering auxiliary services, and forms a carbon emission measurement technology solution in line with the new energy power generation and multiple energy use in industrial parks to provide data support for the low-carbon transformation of electric power in the parks. The study will provide data support for the low-carbon transformation of electric power and energy in the industrial park.

A multi-stage scheduling optimization method for distribution networks based on extreme learning algorithms

Abstract As the prevalence of distributed power sources grows, the distinctions between transmission and distribution networks will blur further. In addition to conventional and renewable energy sources, electric vehicles and energy storage are also regarded as dynamic resources for system operation adjustments. This article establishes response models based on the response strategies, constraints, and costs of controllable loads and energy storage resources, and proposes a multi-stage active distribution network optimization and scheduling optimization method for “source load storage” collaborative interaction. Secondly, a novel optimization scheduling method for distribution networks is formulated, aiming to enhance both reliability and cost-effectiveness while adhering to constraints such as network balance, distributed power generation, and energy storage charging and discharging. Finally, the feasibility and correctness of the optimized scheduling model and algorithm were verified using the IEEE-24 node system as an example.

Maximum power point tracking control and power distribution of marine current power system based on hybrid energy storage

Abstract With the development of industry in modern society leading to the energy crisis and the problem of the earth’s environmental pollution, it is especially important to generate electricity from renewable and clean energy sources. Marine current power generation, as a form of marine energy use, is also the object of widespread concern at home and abroad. A system consisting of supercapacitors and batteries can smooth out the fluctuations of the generating power. Power distribution is a necessary step to achieve smooth grid-connected power fluctuation by using the hybrid energy storage system (HESS). In the article, low-pass filter (LPF) and empirical mode decomposition (EMD) algorithms are used to distribute the marine current power generation, and the simulation experimental model of the present system is constructed in the Matlab/Simulink environment. Results of the simulation experiments show that the power curve of the generator side is obtained, and the generation power distribution is realized. Power signals of different frequencies are separated through the power distribution, so as to obtain the reference power of supercapacitors and the batteries.

Optimal planning method of distributed generation in AC/DC microgrid considering power balance

Abstract To optimize AC/DC hybrid microgrid power planning, improve operational efficiency, and balance power distribution, we constructed an optimization model for AC/DC microgrid power generation, covering power, economy, reliability, and environmental protection, and set constraints to solve optimal planning. The experiment shows that this method enhances voltage stability, improves power supply quality, enhances distributed power generation efficiency, and reduces carbon emissions.

Design and analysis of power decoupling based microinverter considering parasitic parameters

With fossil energy reducing year by year, more and more attention to the new energy sources greatly promotes the development of the distributed power generation system, especially the low-power photovoltaic system. However, the power injected into the single-phase grid is time-varying, an electrolytic capacitor with large bulk should be paralleled with PV to balance the input and output power ripple. But the electrolytic capacitor has a very short lifetime, which could shorten the whole inverter’s lifetime. In this paper, the operational principle of the power decoupling based microinverter considering parasitic parameters is proposed, in which a film capacitor with small capacitance value replaces the electrolytic capacitor to improve the lifetime of the inverter. Further, the operating mode of the transformer and the decoupling capability is analyzed, and obtains the reasons affecting the inverter steady-state performance. Finally, the simulation and experiment validation of the proposed microinverter have been accomplished. Base on the analysis method proposed in this paper, a more accurate device selection can be provided for industrial design.

A Modified Genetic Algorithm for Optimizing the Placement and Sizing of Distributed Generators in Radial Distribution Systems Including Security Analysis

In this paper, a Modified Genetic Algorithm (MGA) combined with ETAP (Electrical Transient Analyzer Program), is proposed to optimize the placement and sizing of Distributed Generation (DG) units while considering multiple objectives, such as improving voltage profiles, reducing line losses, and managing short-circuit levels. These factors are critical for maintaining power quality, system reliability, and overall stability. The MGA approach is validated through simulations using the IEEE 33-bus distribution system, modeled in both MATLAB and ETAP. The results show significant improvements in voltage stability, loss reduction, and short-circuit level management, enhancing energy management and operational efficiency. A comparative analysis with alternative algorithms, including the Bat Algorithm (BA), Bacterial Foraging Optimization Algorithm (BFOA), and Artificial Immune System (AIS), demonstrates that MGA achieves superior convergence speed and accuracy, making it highly effective for DG placement and power system performance optimization.

Optimization of shared energy storage configuration for village-level photovoltaic systems considering vehicle charging management

Distributed renewable energy is more abundant in rural areas, and a large amount of distributed photovoltaic grid-connected power brings challenges to the stable of the power grid. How to promote the self-generation and self-consumption of distributed renewable energy has become an urgent problem. In this paper, a village-level distributed photovoltaic power generation system including energy storage and electric vehicles is constructed. With the goal of minimizing the photovoltaic grid-connected power and maximizing the annual comprehensive revenue, the planning model of energy storage capacity allocation for village-level distributed power generation system is constructed. Considering the charging management for different numbers of electric vehicles, the optimal energy storage capacity allocation strategy is solved using the improved particle swarm algorithm.Five scenarios are set up as examples to be analyzed.The conclusions are:(1)After the configuration of a reasonable energy storage,the grid-connected generation of the system is significantly reduced,and the annual comprehensive revenue of the system is significantly increased.(2)After considering charging management for different numbers of electric vehicles, the capacity and power of the energy storage configuration can be significantly reduced, thus reducing the energy storage investment cost and operation and maintenance cost, and further significantly improving the annual comprehensive revenue of this system.

Efficient utilization of enriched oxygen gas in residential PEMFC-CHP system with hydrogen energy storage

The oxygen byproduct generated from water electrolysis can be utilized on-site in residential combined heat and power (CHP) systems to enhance economic efficiency. This study explores solutions for optimizing the use of enriched oxygen gas (EOG) and determining the ideal oxygen fraction to maximize economic viability. A novel EOG supply system is designed to regulate oxygen fraction, oxygen excess ratio (OER), and humidity. A dynamic model of a PEMFC integrated with the EOG supply system is developed using MATLAB/Simulink® software and partially validated. The dual effects of EOG on system efficiency and remaining useful lifetime (RUL) are analyzed using a comprehensive economic optimization algorithm, leading to the identification of optimal oxygen fractions for various hydrogen production modes. Additionally, a feedforward static decoupling controller is designed to independently control the OER and oxygen fraction. The results indicate that, under rated conditions (10 kW), system power increased by 15 %, along with a 10 % improvement in system efficiency, from 48 % to 58 %. In conclusion, the efficient use of enriched oxygen gas demonstrates significant economic feasibility and practical viability, making it highly suitable for distributed power generation applications.

Achieve the optimal capacities of renewable energy generating and storage equipment by loading factor indexes of commercial key-customers

Abstract According to the typical load characteristics of commercial key-customer, the loading factor index (LFindex) for 24 hours of the day in working day can be obtained. The solar power generation and the commercial key-customer load are estimated on the basis of the local sunshine which the optimal capacity of solar energy and power storage equipment is deduced under various load indicators in accordance with the supply-demand theory. The loading factor index is based on the average load to get the different loading factor indexes under different loads per hour. If the load index is greater than 1 that apply the distributed power generation is more applicable for solar power generation of renewable energy which can restrain the peak power consumption of commercial key-customers. Based on the selected key-customer when the loading factor index is equal to 1, the optimal solar energy setting capacity is 1.0268 PU, the optimal power storage device capacity is 0.3447 PU; the loading factor index is equal to 1.2 which get the optimal solar setting capacity is 0.618 PU and the optimal storage device capacity is 0.0261 PU; the loading factor index is equal to 1.5, both the optimal solar setting capacity is 0.0243 PU and storage device capacity is 0.0005 PU. The conclusion is that the loading factor index increases uniformly which the optimal solar installation capacity also decreases smoothly and the optimal energy storage equipment capacity decreases sharply.

Performance Evaluation of Distance Relay Operation in Distribution Systems with Integrated Distributed Energy Resources

This article presents the evaluation of the performance of the distance relay (ANSI function 21) when integrating Distributed Energy Resources (DERs) in a Local Distribution System (LDS). The aim is to understand the impacts of and the necessary modifications required in the operation of distance relays, considering different levels of DER aggregation, and identifying any threshold levels before issues arise. To achieve this, first, a comprehensive review was carried out to analyze the impacts generated in the protection systems. Second, by using the DigSilent Power Factory software, the implementation of the distance relay using a IEEE 13 Node Test Feeder was validated. The aggregation of the three fundamental types of DG, synchronous machines, solar panels, and wind turbines, was evaluated. The threshold at which distributed generation power injection begins to compromise distance protection performance was identified. This study compares the outcomes of using mho and quadrilateral protection schemes.

Adaptive control strategy for microgrid inverters based on Narendra model

In recent years, microgrid technology has been widely studied and applied. However, with times developing, the installed capacity of distributed power generation devices has been improved, and work is being carried out in increasingly complex situations, resulting in a decline in the control performance of microgrids. In view of this, to effectively improve inverter’s control performance, research is conducted on the fusion of Narendra model and adaptive control strategies for real-time voltage correction and compensation in complex situations. Compared to traditional inverters, inverters under research methods have faster voltage recovery speed when encountering load switching, and can recover in about one cycle, with good control performance. In the comparison between the improved inverter adaptive control system and the inverter adaptive system, the improved inverter voltage recovery speed is faster, can be restored within one cycle, and the control effect of the inverter is better. The harmonic rate of the port voltage has decreased from 10.43 to 1.92%. The applicability of the research method was verified. It indicats that the research method can improve inverter’s control effect and solve problems such as voltage deviation, three-phase asymmetry, harmonic pollution, etc. that are easily generated by the output terminal voltage. Simultaneously, research has provided theoretical basis and data support for the research of microgrids.