Performance Of Power System Research Articles

Enhancing PI controller performance in grid-connected hybrid power systems

The optimal operation of a microgrid was buildup of both uncontrollable (solar, wind) and controllable (batteries, diesel generators) electrical energy sources are enclosed in this paper. By replacing controllers, the variations in wavering power supply caused by load fluctuations are managed. The objective of the research paper is to optimize these controller gain settings for effective use of electrical energy. In this paper integral time square error principle is combined along with the Cuckoo search algorithm (CSA) and particle swarm algorithm (PSA) to obtain the accurate, precise and appropriate results. It enhances the microgrid's steady-state sensitive responsiveness in comparison to trial-and-error techniques, assuring a stable supply of electricity to the load.

Heuristic power management of hybrid source electric vehicle based on PV-battery-PEMFC

Renewable energy sources are now being focused on for usage in electric vehicles. Although majority of electric vehicles run solely on batteries, they have eliminated local emissions but not pollution. To tackle this issue, this research looks into the performance and power management aspects of an electric vehicle's hybrid power system, with a particular emphasis on the interactions between fuel cells, batteries, and photovoltaic (PV) cells. Stable operation is ensured via a bi-directional buck-boost converter, which maintains the voltage from all sources around reference 430 V. Initially, the fuel cell supplies electricity under low light conditions. The PV system and battery take over when the irradiance surpasses 100 W/m2, with the battery first draining and then charging in tandem with the increase in irradiance. 1750 RPM is the constant motor speed that is reached after making the required torque adjustments. A model full electric vehicle with multiple sources has been proposed with an efficient power management algorithm. When the battery is in stationary mode and the state of charge (SOC) is less than 100%, extra solar power is used to charge the battery. The model also takes into consideration using the car for entertainment or gadget charging when the ignition is off. The biogas output can be regulated using an inferential control technique using artificial neural network (ANN) based on the fuel demand or consumption. The system also includes anaerobic digestion of organic waste to provide hydrogen for the fuel cell sustainably from biogas. Power, current, SOC profiles, motor performance graphs, and other validations show that the extensive simulations exhibit efficient power management and a strong model for hybrid electric car power systems. The model has been simulated in the MATLAB Simulink environment and has been tested under realistic conditions.

Efficient reduction of power losses by allocating various DG types using the ZOA algorithm

The continuous challenge of ensuring energy supply is a significant concern. Traditionally, utilities have addressed increasing load demands by building new central power plants or expanding existing ones. However, these methods are time-consuming and costly, providing only short-term solutions. Additionally, power plants are often far from load centers, resulting in higher power losses during long-distance transmission. The integration of distributed generation (DG), characterized by smaller scale and lower capital costs, with nearby consumers can effectively address these challenges. Due to the significant influence of DG penetration and their specific locations on the distribution system's performance, this study offers a novel Zebra Optimization Algorithm (ZOA). The suggested ZOA is used to optimize the capacity and position of various DG units connected to a radial distribution system to reduce power losses and study the stability of the system after DG allocation using the Voltage Stability Index (VSI). The evaluation is conducted using the IEEE 33-bus test system, considering various scenarios including single and multiple DGs of different types. The results demonstrate that in the best scenario, the ZOA can reduce losses by up to 94.25 % and improve the VSI from 0.69643 to 0.9605. These findings highlight the superior performance of the ZOA over other techniques in optimizing DG placement and sizing for power system performance.

Design and Implementation of Self-Cleaning Coated Rooftop Photovoltaic System Supporting Energy Conservation in Small-Medium Enterprises

The implementation of rooftop solar power system for SMEs is one step to provide an examplified pilot project so that the clean energy transition in the economic sector can also be realized. However, difficult operations and maintenance for rooftop solar power plant require a self-cleaning coating design to passively clean the surface of the solar cell module and hence, the performance of rooftop solar power system is maintained. In this research, the design of a rooftop solar power system utilizes global climate data, i.e., air temperature data and duration of sunlight. Long-term trends in solar radiation levels were analyzed using the Mann-Kendall test and Sen's slope analysis to assess the long-term solar energy potential. The self-cleaning layer utilized a mixture of ZnO/TiO2 (1:1) dispersed in water/ethanol (7:3). The results of implementing the solar energy system for food-based SMEs in Indramayu showed a positive response. Solar energy supply from rooftop solar power system with the application of ZnO/TiO2 self-cleaning coating can save energy usage by up to 30% of the monthly electricity costs charged for MSME operations and domestic needs. The ZnO/TiO2 self-cleaning layer is found to be able to maintain rooftop solar power system performance of up to 85% during 9 months of observation.

Study on the Optimal Configuration of Battery Energy Storage System in Distribution Networks Considering Carbon Capture Units

Background: The implementation of Battery Energy Storage Systems (BESSs) and carbon capture units can effectively reduce the total carbon emissions of distribution networks. However, their widespread adoption has been hindered by the high investment costs associated with the BESSs and power generation costs of carbon capture units. Objective: The objective of this paper is to optimize the location and sizing of BESSs in distribution networks that comprise renewable power plants and coal-fired power units with carbon capture systems. The optimization process aims to minimize the grid’s impact from the configuration while maximizing economic cost savings and the benefits of reducing carbon emissions. Methods: A bi-layer optimization model is proposed to determine the configuration of BESSs. The upper layer of the model optimizes the size and operation strategy of the BESSs to minimize the configuration and power generation costs, using YALMIP and CPLEX optimization tools. Carbon emission reduction benefits are considered through deep peak-shaving and carbon tax. The lower layer of the model aims to optimizes the placement of the BESSs to minimize voltage fluctuation and network loss in the power grid. To achieve this, we improved the efficiency of the Nondominated Sorting Genetic Algorithm II (NSGA-II) to update the BESS’s placement. Results: The IEEE33-bus and IEEE118-bus systems were utilized for simulation and comparison in various scenarios. The findings demonstrate that the proposed configuration method can decrease the cost of investment and power generation. Furthermore, it reduces the degree of node voltage fluctuation and network loss in the distribution network. Conclusion: The study reveals that determining the optimal scale of BESSs can mitigate high energy consumption in carbon capture systems and improve the overall performance of power systems that integrate carbon capture technology and renewable power plants.

Power system instability prediction from the solution pattern of differential Riccati equations

AbstractPower system stability characteristics are typically evaluated in terms of small‐ and large‐signal (transient) stability. Access to the time‐varying A‐matrix of a state‐space‐based power systems model during transient conditions can be utilized to apply linear time‐varying system concepts for large‐signal stability analysis. In linear time‐varying system analysis, the differential Riccati equation (DRE) plays a vital role when the power system is subjected to a severe disturbance. The Möbius transformation is proposed in this paper to solve the DRE with singularity issues. It is shown that the solution of the DREs follows a specific mathematical pattern when the power system is stable but does not follow this pattern when the system progresses toward instability. The proposed method can be used in large‐signal stability analysis to predict instability and make the stability analysis more efficient. Additionally, the vector‐DRE is proposed to generalize the index in a large‐scale power system. Results show that analyzing the corresponding Riccati equation's behaviour can help researchers predict a power system's performance and improve the control and management of the system.

Investigation of startup process for underwater turbine power systems using computational fluid dynamics method

The start-up process is vital to the performance of underwater turbine power system. The current analysis is mostly relied on analytical models established from simplified models and empirical formulas and it cannot accurately predict the start-up process. This paper proposes numerical methods to simulate the startup process of the underwater turbine power system. The results from the analytical and numerical models are compared at different launching depths. It is shown that the analytical model is suitable at the shallow launching depth (30 m), and the maximum difference is 4.3 % compared against the numerical method. However, the difference between analytical and numerical methods is as high as 45.9 % at the large launching depth (300 m). In addition, the closed-loop control strategy of the fuel pump needs to be triggered within 1.2 s after the combustion chamber is fully occupied, the start-up process can then be successful at the large launching depth. The experimental validation is finally performed to test the start-up process of the underwater turbine power system at the launching depths of 300 m.

Multi-Objective Optimization Scheduling of Microgrids Considering Single Use Cost of Energy Storage and Unit Combination Startup and Shutdown Cost

Abstract Microgrids typically consist of various energy resources such as thermal power, solar energy, wind energy, energy storage, and so on. These energy resources have different characteristics and fluctuations. By utilizing the characteristics of different energy resources in a complementary and synergistic manner, their energy utilization efficiency can be promoted and the system stability can be improved. This paper considers the energy storage device in the completion of a single charge and discharge loss, that is, the single-use cost. Special attention to the impact of start-stop costs of unit combinations on microgrid operations. It proposes that in the daily dispatch and operation of microgrids, sinking costs can be reasonably reduced within a reasonable range, and power supply reliability can be appropriately lowered to improve the economic performance of power systems. Using the single-use cost of energy storage devices and the start-stop costs of unit combinations as variables, their impact on the economic operation of isolated microgrids is analyzed. For grid-connected microgrids with the objectives of minimizing operating costs and achieving optimal environmental protection, an optimal balance between economic and environmental performance is achieved through solving by Anti-Entropy weight method and Particle Swarm Optimization combination.

Impact of network degradation on load frequency control of large interconnected power system

Due to the extensive geographical coverage of the power system, the load frequency control (LFC) performance is impacted by communication delays and packet loss that occur during the exchange of control and measurement signals. In the literature on LFC of large interconnected power systems, communication delays are often assumed to be arbitrary or time-varying values based on probability distributions or estimation models. Most studies neglect realistic delays based on the actual communication technology present in the system despite its practical significance. Furthermore, the impact of packet loss in LFC studies has been largely overlooked in the existing literature. This research work presents a comprehensive investigation into the impact of real-time communication delay and packet loss on LFC performance in large interconnected power systems. The simulation results demonstrate the significant impact of communication network impairments on LFC performance. The classical simulation results are validated using Real-Time Hardware-in-the-Loop (RT-HIL) simulation.

P2P electricity trading model for urban multi-virtual power plants based on double-layer energy blockchain

The urban virtual power plant (VPP) enhances the overall performance of the urban power system by aggregating diverse distributed energy resources (DERs) to provide energy services. With the advancement of smart grid technology, peer-to-peer (P2P) electricity trading has emerged a new paradigm of electricity trading within urban VPPs. In this study, we propose a P2P electricity trading model for multi-VPPs in urban areas, based on a double-layer energy blockchain framework. Initially, users submit their electricity trading information for matching at the user layer; any remaining unfulfilled electricity demand is then further matched at the VPP layer. The user participation process in trading encompasses several stages: trading start, matching, validation, execution, and settlement. Additionally, this study introduces a double-layer blockchain structure consisting of one main chain and multiple side chains. The main chain records real-time trading information from the VPP layer while the side chains are dedicated to recording real-time trading information from the user layer. The feasibility of the proposed model is demonstrated through its implementation in Hyperledger Fabric. Experimental results indicate that the proposed model effectively reduces urban users' reliance on the main grid and decreases their electricity expenses.

Aggregation of building predictive energy flexibility in smart microgrid

Numerous demand-side resources possess significant potential to offer substantial flexible reserves and ancillary services, thereby enhancing the economic and technical performance of power systems within the demand response domain. A novel approach to aggregating the energy flexibility of buildings based on the virtual battery model is proposed in the smart microgrid, employing predictive control techniques while ensuring consumer privacy. The individual approximation process is implemented to extract the flexible capacity of local resources while protecting information privacy. Subsequently, the aggregator collects the flexibility information to develop an optimization strategy for maximum flexibility aggregation at the upper level, enabling the broader exploitation of global flexibility. The feasibility of power disaggregation is guaranteed according to the aggregation procedure. Furthermore, the comprehensive framework accommodates temporal preferences for flexibility requests from the aggregator. Numerical simulations are conducted to perform a comparative analysis with existing state-of-the-art methods. The results demonstrate that the proposed approach exhibits improved adaptability in characterizing the aggregate flexibility from buildings, effectively managing the heterogeneity of load characteristics and the variability in the collected flexibility information. Microgrids can realize cost savings and achieve dynamic flexibility utilization. This methodology can be extended and applied to diverse types of flexible resources.

Mechanism and simulation analysis of high electric field of NaNbO3 − based energy storage ceramics based on defect engineering design

Ceramic materials possessing high polarization and substantial breakdown electric fields represent a principal strategy for enhancing the performance of pulse power systems. To augment the energy storage capabilities of ceramic materials, numerous studies have suggested a variety of specific control methods. However, reports on the vacancy defects arising during the regulatory process and the relationship between these vacancy defects and energy storage performance are scarce. This paper introduces an optimal quantity of oxygen vacancy defects into the prepared NN–based ceramics for characterization and analysis, aiming to elucidate the impact of oxygen vacancy defects and their concentration on the structure and energy storage properties of ceramics. Through managing the content of oxygen vacancies, the 0.83NN–0.17SNS ceramic achieved a high energy storage density (Wrec) of 6.27 J/cm3 and an energy storage efficiency (η) of 82.79 % at 705 kV/cm. This work presents a novel approach to regulating the energy storage performance, offering a new method for optimizing the performance of lead-free energy storage materials.

Load frequency control of interconnected power system using cuckoo search algorithm

This paper presents a new time-domain multi-objective function approach for solving load frequency control issue in an interconnected power system. The performance of interconnected power system in each area is validated for overshoot and settling time values of frequency change and tie-line power exchange. An objective function is created with the goal of enhancing proportional integral derivative (PID) controller settings by reducing overshoot and achieving faster time-domain settling times. The efficiency of the proposed time-domain multi-objective function is evaluated in a two-area thermal power plant using a nature-inspired cuckoo search optimization (CSA) technique. By comparing the time-domain simulation results of the test system with the existing integral error-based objective functions IAE, ISE, ITAE, and ITSE, the proposed objective function is validated. Further, a sensitivity analysis were carried out to analyze the robustness of the proposed multi-objective function under various uncertain conditions.

Temperature Profile of a Biomass Gasification Power System

Abstract Biomass has been used as a renewable energy resource for years. The coconut tree, considered woody biomass, grows in the tropics and is abundant in the Philippines. To determine the performance of the biomass gasification power system (BGPS) using torrefied coconut shells (TCS), a 1.8-kVA BGPS was designed, fabricated, and tested. The TCS with a calorific value of 34.37 MJ/kg was used as feedstock to profile the temperature of BGPS. The temperature increased from the feeder at 79 °C to a maximum of 904 °C at the combustion zone and goes down to 779 °C at the reduction zone, which causes the breaking of the chemical structure of the torrefied coconut shells to release carbon monoxide (CO), hydrogen (H2) and other contaminants that form part the producer gas. The temperature at the inlet of the internal combustion engine (ICE) generating set had reduced to 37 °C. To improve the performance of the BGPS, it is recommended to review and enhance the cooler’s design to lessen the temperature of the producer gas to at least 25 °C.

Security concern and fuzzy output sliding mode load frequency control of power systems

This research investigates the concern of security in multi-area load frequency control (LFC) power systems using fuzzy logic output sliding mode control approach. A motion-trends-based deception attack model is proposed to represent malicious threats from communication networks. The motion-trends-based deception attack model enhances the destructive effect of deception attacks by monitoring changes in system state. In terms of the security concerns, a fuzzy-based output sliding mode control (OSMC) is applied to protect the power system from potential cyber-attacks. We provide sufficient conditions for the power systems stability under motion-trends-based deception attack. An initial controller gain that will bring the power system to stability is calculated by linear matrix inequalities (LMIs). The obtained controller gain provides a design method for fuzzy logic member functions. Fuzzy logic is then used to adjust the resulting controller gain to improve power system performance. The simulation results validate the usefulness of the proposed strategy, and indicate the viability of fuzzy logic in fine-tuning controllers.

From Time-Series to Hybrid Models: Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems.

Enhancing Hybrid Power System Performance with GWO-Tuned Fuzzy-PID Controllers: A Comparative Study

This study explores the implementation of a novel control strategy within hybrid power systems, leveraging a Grey Wolf Optimization (GWO)-tuned Fuzzy Proportional-Integral-Derivative (Fuzzy-P.I.D.) controller to enhance the integration of renewable energy sources. By addressing the critical challenge of grid frequency deviations, this approach significantly bolsters the stability and efficiency of power flow, ensuring a more reliable electricity supply. Employing MATLAB simulations, the research underscores the superior performance of the GWO-tuned Fuzzy-P.I.D. controller, which necessitates fewer control interventions and yields lower oscillation frequencies than its conventional P.I.D. and Fuzzy-P.I.D. counterparts. The robustness of this optimized controller is further validated through extensive tests, demonstrating its resilience across a spectrum of parameter adjustments and operational scenarios, including the hypothetical removal of system components. The findings reveal that this advanced control method markedly surpasses traditional solutions in maintaining stable electricity flow and enhancing the system's overall resilience and adaptability to the variable nature of renewable energy. Thus, the GWO-tuned Fuzzy-P.I.D. controller emerges as a significant innovation in hybrid power system management, heralding a new era of optimization and efficiency in renewable energy integration.

Assessing grid hardening strategies to improve power system performance during storms using a hybrid mechanistic-machine learning outage prediction model

Improvements of power system performance during severe weather events are targeted through grid hardening actions, such as strengthening aging infrastructure or performing vegetation management. However, due to the complexity of modeling power system interactions with the environment and the impacts of proposed reinforcements, the benefits of such hardening actions are often unclear. To support hardening assessments, comprehensive models capturing these intricate relationships are necessary. To this end, this study presents a novel application of a hybrid mechanistic-data-driven outage prediction model (OPM) designed to incorporate changes in infrastructure or environmental/vegetation parameters in the prediction of storm outages. The model incorporates physics-based structural fragility curves of the overhead pole-wire system within a machine learning OPM trained on meteorological, topographic, infrastructure, and historic outage data to quantify the benefits of adaptive change in reducing storm-related power outages. A prioritization scheme is formulated to inform improved strategies to strengthen grid resilience under budgetary constraints. As a case study of the distribution system in Connecticut, the model was trained on historical outage data from storms between 2005 and 2020, and a counterfactual analysis is conducted. The results indicate tree removal and pole strengthening are the most cost-effective strategies; for instance, under a $600 million budget, tree removal could have reduced up to 15,000 distribution grid outages over the events analyzed. The developed framework and models can be useful for utility companies, regulatory agencies, and governments to highlight areas of need, inform cost-risk-benefit analyses, and aid in the optimal allocation of funds and resources toward grid hardening.

A coordinated voltage control scheme based on linearized power flow functions for non-optimal and optimal problems

This paper proposes a coordinated voltage control scheme for transmission systems based on linearized power flow functions to determine the set points of local control voltage devices. The scheme considers models and interactions between different devices to attain the appropriate performance (non-optimal or optimal) of the whole power system. The sensitivity analysis is used to formulate a partly linearized model that relates the voltage variations of buses to the reactive power variation of synchronous machines, settings of static VAr compensators (SVCs), shunt capacitors susceptance, and tap position of load tap changers (LTCs) to determine the set of the settings of the voltage control devices. Two formulations are proposed by considering the susceptance of SVCs or the slope and reference voltage of characteristics of SVCs as control variables. The developed formulation based on the linearized power flow functions provides the possibility of the practical implementation of the coordinated voltage control scheme, especially the optimal one, by reducing the model complexity and solving time. The performance of the proposed formulations is investigated by some studies of voltage control and an optimally coordinated problem on the IEEE 30-bus and IEEE 118-bus test systems.

Parameters extraction of photovoltaic models using enhanced generalized normal distribution optimization with neighborhood search

The photovoltaic system has been widely integrated into electrical power grids to produce clean and sustainable energy sources. Precisely modeling of PV systems is crucial to simulate and asset the performance of such power system. Modeling of PV system is a challenge because the characteristic curve of current and voltage is nonlinear and has unknown parameters due to insufficient data points in manufacture’s data sheet. This work proposes generalized normal distribution optimization based on neighborhood search strategies (NSGNDO) to extract the parameter of single diode model (SDM), double diode model (DDM), and PV module model (PVM). The root means square error (RMSE) is used as a performance indicator. Two commercial PV models like RTC France solar cell and PWP201 are used to validate the ability of NSGNDO to precisely estimated the PV system’s parameters. The results show the superiority of NSGNDO over competitive optimization methods and can reduce the RMSE to 2.05296E-03 for PWP201 and to 9.8248E-04 for RTC France solar cell which prove that NSGNDO can be used as competitor method to identify the parameters of PV solar system. The statistical analysis shows the robustness of NSGNDO through statistical measurements and Wilcoxon rank test.’