Integrated Power System Research Articles

Hybrid PV and Fuel Cell Generating System for Shipboard Applications Based on Optimal Dispatch Evaluator Algorithm

The construction of an Energy Management System (EMS) for ships has been designed to improve the electrical performance of cruise lines. According to the target requirement, the specified electrical plant is an integrated power system characteristic of contemporary electric ships. Battery backup, low performance, and voltage instability are the drawbacks of the existing method. To overcome the limitations of the proposed method-based Photovoltaic (PV) renewable source using SEPIC DC–DC converters and a bidirectional fuel cell as an additional input power source. Power generation from the two different input sources is controlled through a DC bus and conversion through a centralized high-gain DC–DC converter. In this conversion, Pulse Width Modulation (PWM), which is the maximum power obtained using the optimal dispatch evaluation algorithm using an Artificial Neural Network (ANN), is obtained by adjusting the duty ratio of the Single-Ended Primary Inductance DC–DC converters (SEPIC). The electrochemical power source is a fuel cell. Hydrogen and oxygen are combined in fuel cells to create electricity, along with the consequences of heat and water being transformed into mechanical energy; in the battery management system, the battery is employed as an energy storage system to regulate any power imbalance or excess while taking the battery’s state of charge into account Electric Ships (AESs). The outcomes of the MATLAB Simulink output, fuel cell and battery storage performance analysis, efficiency, and THD analysis are summarized as follows.

A novel synchronized data-driven composite scheme to enhance photovoltaic (pv) integrated power system grid stability

The performance of power networks is enhanced by the penetration of solar energy, which helps to equate continuously the generation and demand power imbalance. However, the time margin that grids must adapt to unforeseen frequency fluctuations and restore generation-demand equivalency is reduced by these linkages. Consequently, it exerts the stability and performance of the power grid at risk. Thus, it becomes vital to assess real-time system data and to recognize and implement suitable remedies to maintain a healthy system performance. In order to improve grid stability in power networks that have solar energy penetration, this manuscript suggests a data driven integrated framework. The proposed approach is a two-step framework wherein the first stage assesses impending transient instability in the system through novel Instability Evaluation (IE). Step two involves creating and deploying a Decision Boundary based Control (DBC) to stabilize an unstable system following an emergency control strategy. An IE module employing short-synchronized movement data is presented for evaluating post-disturbance transient stability (TS). In the initial cycles following the fault initiation, the IE projects the impending transient instability. Next, an innovative DBC creates an emergency remedial system for unstable processes that determines the nature, magnitude and location of the remedial action. The DBC assesses pertinent action sets that it implements to sustain system stability using a proposed Decision Assisted Inference (DAI) technique. The simulation investigations validate the aptness of suggested analysis on the performance of power system with and without PV and topological variations.

Exploring the interdependence between control and protection philosophies in inverter integrated power system: A case study on directional relaying schemes

The potential impacts of the inverter-interfaced renewable energy resources (IIRERs) on the performance of sequence-based directional relaying schemes (SBDRS) have been investigated in this paper. The directional overcurrent relays such as 67POS, 67NEG, 67ZERO and 32Q have been considered for the investigation. CIGRE-14 bus system with IIRERs along with detailed modeling of inverter control scheme (with and without grid codes) and SBDRS have been done in the PSCAD environment. The mathematical analysis and circuit realization of the IIRERs have been attempted in the paper. The exclusive fault characteristics exhibited by IIRERs cause the conventional SBDRS to mal-operate. Significant change in levels of sequence currents and angular relationships between sequence voltages and currents during faults contributes to relay's unpredictable behavior. The comparative study based on different fault types, fault resistances and interconnecting transformer connections have also been done. The study reveals the impact of non-generalized angular differences between positive sequence voltage and current at the inverter terminal on performance of 67POS. It is found that 67NEG and 32Q mal-operated for all types of unbalanced faults while the 67ZERO can be utilized for grounded fault with delta-star grounded connection of interconnecting transformer.

Optimal Unit Commitment and Generation Scheduling of Integrated Power System with Plug-In Electric Vehicles and Renewable Energy Sources

The integration of wind energy sources and plug-in electric vehicles is essential for the efficient planning, reliability, and operation of modern electric power systems. Minimizing the overall operational cost of integrated power systems while dealing with wind energy sources and plug-in electric vehicles in integrated power systems using a chaotic zebra optimization algorithm (CZOA) is described. The proposed system deals with a probabilistic forecasting system for wind power generation and a realistic plug-in electric vehicle charging profile based on travel patterns and infrastructure characteristics. The objective is to identify the optimal scheduling and committed status of the generating unit for thermal and wind power generation while considering the system power demand, charging, and discharging of electric vehicles, as well as power available from wind energy sources. The proposed CZOA adeptly tackles the intricacies of the unit commitment problem by seamlessly integrating scheduling and the unit’s committed status, thereby enabling highly effective optimization. The proposed algorithm is tested for 10-, 20-, and 40-generating unit systems. The empirical findings pertaining to the 10-unit system indicate that the amalgamation of a thermal generating unit system with plug-in electric vehicles yields a 0.84% reduction in total generation cost. Furthermore, integrating the same system with a wind energy source results in a substantial 12.71% cost saving. Notably, the integration of the thermal generating system with both plug-in electric vehicles and a wind energy source leads to an even more pronounced overall cost reduction of 13.05%. The outcome of this study reveals competitive test results for 20- and 40-generating unit systems and contributes to the advancement of sustainable and reliable power systems, fostering the transition towards a greener energy future.

ANFIS Controlled MMC-UPQC to Mitigate Power Quality Problems in Solar PV Integrated Power System

This paper focuses on the implementation of a Modular Multilevel Converter (MMC) based Unified Power Quality Conditioner (UPQC) in a grid-integrated Photovoltaic (PV) system. The integration of photovoltaic (PV) systems into the grid brings numerous benefits in terms of renewable energy utilization like sustainable energy generation, energy security, grid resilience, and economic growth. However, the voltage source converters (VSCs) employed in PV systems can introduce power quality (PQ) issues, including voltage fluctuations, harmonics, and reactive power imbalances and grid instability. The objective of this work is to enhance power quality by mitigating voltage and current fluctuations and harmonics generated due to VSC, compensating reactive power requirements by the load and PV system, and regulating the DC link voltage. For DC voltage regulation, an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is employed to control for MMC-UPQC. The proposed control strategy is simulated using MATLAB/SIMULINK software and the results are compared with conventional Proportional Integral (PI) and fuzzy controllers. Various dynamic conditions such as voltage sag and swell at the point of common coupling (PCC), changes in irradiance for the PV system, and load variations are considered. The simulation results demonstrate that the ANFIS-controlled MMC-based UPQC outperforms the PI and fuzzy controllers in terms of power quality improvement. It effectively suppresses harmonics, maintains a stable DC link voltage, and compensates for reactive power fluctuations. The proposed control strategy provides superior performance and achieves better power quality compared to conventional controllers. The findings of this work highlight the potential of using an ANFIS controller in MMC-based UPQC systems for grid-integrated PV systems.

Enhancing load frequency control with plug-in electric vehicle integration in non-reheat thermal power systems

Enhancing load frequency control with plug-in electric vehicle integration in non-reheat thermal power systems

Integration of storage technology oversight: power system and computer engineering analogy

Energy storage, analogous to data storage in a computer system, is one of the enabling technologies that has emerged alongside the widespread use of renewable energy sources in the nation's power grid. This article shows that the underlying platforms for storing data and energy are quite similar. Batteries and hydrogen storage offer significant energy potential, much like a hard disk for storing vast amounts of data in a computer's central processing unit (CPU). A supercapacitor or flywheel storage device can be used to have emergency power on hand, with access times as fast as random access memory (RAM) in modern computers. In this study, we propose an energy-control scheme for caches that is akin to computer engineering and is used to coordinate the operation of multilevel storage systems that incorporate both capacity and access-oriented storage. By supporting the energy-management system, which in turn provides modern plug-and-play functionality, cache energy control helps optimise the system as a whole. Such an integrated system calls for renewable energy generation, local loads, fueling stations, and connections to gas and electric distribution grids. Distribution energy concepts with various storage systems can be easily grasped by drawing parallels between computer engineering and power system integration.

Driving Adoption of Advanced IC Packaging in Automotive Applications

For years automotive semiconductors, have been governed by small, low power Integrated Circuits (ICs) purpose built for specific functions. These Microcontroller Units (MCUs), deployed on the edge of the compute topology are typically low-power, low-performance parts. Built on legacy nodes (130nm+) on well-established packaging technology (often wirebond die on QFN-style packages), they demonstrated incremental improvements from a silicon and packaging perspective. With the adoption of software defined vehicles (SDV), the evolution of advanced driver assist systems (ADAS), and feature rich in-vehicle infotainment (IVI), the per vehicle compute power has grown exponentially per vehicle, colloquially, known as “data centers on wheels”. With this growth has come the need to adopt newer packaging techniques to improve system latency, bandwidth, compute power, and IP integration and reducing system footprint via 3D stacking – all fabricated on leading edge nodes. Advanced packaging has seen tremendous growth and adoption in the data center and client ecosystems – the next hurdle is addressing the safety and reliability focused requirements of automotives; extended useful lifespan of 10yrs+, and weathering harsher environmental conditions of thermal extremes, vibration, and EMC, and mixed IP integration. This talk will address the areas of focus for adopting heterogenous integration and advanced automotive packaging for automotives such as, qualification and formulation of materials to minimize traditional packaging yield concerns, introduction of new thermal interface materials aligned with new liquid cooled ECUs and testing to ensure zero defects across chiplets and memory; with broader test coverage and the adoption of Known Good Die (KGD). Additionally, the talk will cover the importance of driving industry standardization regarding die-to-die communication protocols, form factors, power delivery, system integration, and qualifying/enveloping new failure modes from the current baseline AEC-Q100 requirements brought on by various 2.5D and 3D packaging.

Design and Optimization of an Integrated Power System of Solid Oxide Fuel Cell and Marine Low-Speed Dual-Fuel Engine

A combined system including a solid oxide fuel cell (SOFC) and an internal combustion engine (ICE) is proposed in this paper. First, a 0-D model of SOFC and a 1-D model of ICE are built as agent models. Second, parameter analysis of the system is conducted based on SOFC and ICE models. Results show that the number of cells, current density, and fuel utilization can influence SOFC and ICE. Moreover, a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently, as achieved by the particle swarm optimization algorithm in this paper. The results demonstrate that the optimal system efficiency of 51.8% can be achieved from a 22.4%/77.6% SOFC-ICE power split at 6 000 kW power output. Furthermore, promising improvements in efficiency of 5.1% are achieved compared to the original engine. Finally, a simple economic analysis model, which shows that the payback period of the optimal system is 8.41 years, is proposed in this paper.

Innovative Approaches to Water and Power Systems Integration for Urban Sustainability

Innovative Approaches to Water and Power Systems Integration for Urban Sustainability

Optimizing Energy Production through Water-Smart Power Systems Integration

Optimizing Energy Production through Water-Smart Power Systems Integration

Optimal allocation of flexible AC transmission system (FACTS) for wind turbines integrated power system

AbstractThe importance of flexible alternating current transmission system (FACTS) devices is increasing day by day as they provide compensation for power systems. The installation of such devices solves many issues regarding the stability and power transferability of power systems. However, for optimizing the performance of FACTS devices, the location and sizes of FACTS devices must be selected very carefully. On the other side, the utilization of optimization techniques is increasing daily, as it is capable of solving nonlinear, random problems by its stochastic nature. As a result, it became a reliable tactic for solving stochastic problems almost in every aspect of our lives. Therefore, it is adopted for solving power system problems, like, FACTS devices allocation, distributed generators allocation, determining component's parameters, and more. In this paper, the optimal size and location of several FACTS devices are selected to achieve the following single objective functions separately: fuel cost minimization and power losses minimization, then combining the mentioned objective function into one multiobjective function for gross cost minimization. The power system optimized is the Institute of Electrical and Electronics Engineers 30‐bus standard system, and the FACTS devices installed are static VAR compensator, thyristor control series compensator, and thyristor‐controlled phase shifter, while the optimization is performed using different conventional and recent optimization algorithms, including a newly developed algorithm, typically Leader Walrus Optimization Algorithm “LWaOA,” making a comparison among such algorithms to investigate the algorithm that possesses the best performance. The results of the optimization processes show the superiority of LWaOA.

Coordination of preventive and emergency dispatch in renewable energy integrated power systems under extreme weather

AbstractIn recent years, frequent occurrence of extreme weather events has caused great losses to the power system. Meanwhile, the high penetration of renewable energy has brought new challenges to the safe operation of the system. Coordination of various measures against different stages of disaster can effectively reduce the overall loss and enhance system's resilience. This paper proposed a methodology to coordinate preventive and emergency dispatch stages based on the defender–attacker–defender model, in which various dispatching measures are modelled as defenders, whereas the extreme weather event and wind power's uncertainty are modelled as attackers. Specifically, a two‐stage three‐layer robust optimal dispatching model is established, in which conventional generator's output, wind farm's grid‐connected planning, and load shedding are taken as the main dispatching measures. To solve this model, the column and constraint generation algorithm is used to transform the three‐layer optimization model into a mixed integer linear problem . The effectiveness of the proposed model is verified in the modified IEEE RTS‐79 test system, and the results show that the coordination of preventive and emergency dispatch can effectively reduce the load outage cost under extreme weather, thus enhancing the ability of power system to cope with extreme scenarios.

Analysis of Power Quality and Technical Challenges in Grid-Tied Renewable Energy

The transition of power generation from fossil fuel to renewable energy is a cutting-edge phase in smart grid research. Renewable energy sources (RES), such as solar, photovoltaic, and wind are gradually overtaking other sources as the most attractive alternative within the power generation and distribution systems across many nations. Reduction in the carbon footprint is a major consideration in the choice of the RES. However, the technical challenges with RES pose a significant barrier to unified integration, even though the high penetration level appears plausible. The challenges are majorly caused by the variability and unpredictability of these sources. It is therefore a stimulating task to efficiently manage the electrical power distribution systems in the face of renewable energy integration. The purpose of this study is to examine the potential of renewable energy integration and the accompanying technical challenges that include power quality issues associated with grid-tied renewable energy (GtRE). The study also recommends techniques capable of mitigating prominent power quality challenges to guarantee seamless renewable energy integration in power systems.

Fuzzy semi-entropy based downside risk to low-carbon oriented multi-energy dispatch considering multiple dependent uncertainties

Increasing deployment of wind power into integrated energy system becomes a critical pathway for deep decarbonizing the power sector. It is imperative to investigate the impacts of uncertain wind power on integrated power system as the result of the heavy interdependencies among different types of energy in supplying the requirements of economy, low carbon and risk aversion. Here, the concept of fuzzy semi-entropy is presented to quantify the downside risk of uncertain wind power. After that, a mean-semi-entropy model for low-carbon oriented electrical-gas-heat energy system including carbon emission, carbon capture and carbon trading is proposed. In particular, the dependence of wind power outputs from different wind turbines is investigated by the multivariate Gaussian Copula. Results obtained from case studies demonstrate the feasibility of the proposed model in achieving a balanced trade-off among system economy, low carbon emission and operation risk properly. The proposed model enables the reduction in total cost and carbon emission by 2.29% and 5.18% in comparison to these of traditional cost minimization model. In addition, the accommodation level of wind power with multivariate Gaussian Copula improves by 2.17% compared to that without considering coupling.

Design and experimental validation of an artificial neural network-SVPWM controller for a novel micro grid-tied fuel cell-based 3-phase boost inverter

A grid-tied fuel cell (FC) system demands efficient power conversion, power quality preservation, grid stability, power flow management, renewable energy source (RES) integration, and enhanced grid resilience. Achieving these goals requires a precise inverter circuit switch approach. A boost power converter connected to the FC stack ensures voltage regulation, power conditioning, efficient power transfer, system integration, control, and protection. This enhances FC system adaptability and compatibility across various applications, minimizing input current ripples for prolonged FC lifespan.This study introduces a novel DC-DC boost converter with an artificial neural network (ANN) controller to reduce FC input current ripples and enhance FC stack-generated voltage for grid applications. It also presents a space vector sinusoidal pulse width modulation (SVPWM) technique for FC-based three-phase grid-tied inverters. This offers improved voltage utilization, precise voltage and current control, reduced harmonic distortion, rapid response, flexibility, scalability, reduced total harmonic distortion (THD), and over-modulation capability. The proposed SVPWM technique utilizes a digital signal processing (DSP)-based controller, combining high-speed processing, precision, and real-time capabilities to enhance system performance and efficiency.

Enhancing Islanded Power Systems: Microgrid Modeling and Evaluating System Benefits of Ocean Renewable Energy Integration

The energy transition hinges on the effective integration of renewable energy sources into the power grid. Islands can provide invaluable insights into the challenges and opportunities of integrating variable renewable energy into the grid due to their relatively small power systems, isolated grids, and diverse availability of renewable energy resources. This paper presents a study on the system benefits and challenges of marine energy integration in insular power systems, focusing on the Orkney Islands as a case study. A microgrid modeling approach that optimizes the mix of renewable sources and energy storage systems for future scenarios considering strategic time horizons (2030, 2040, and 2050) was employed. Results suggest that integrating ocean energies, namely, wave and tidal energy, yields notable benefits compared to traditional renewable energy sources exclusively. These benefits encompass reduced installed capacity, minimized energy storage requirements, lower excess generation, and overall cost-saving.

Cost‐effective secondary voltage control in dc shipboard integrated power system

AbstractFast and dramatic voltage disturbances caused by the electric propulsion and dynamic positioning process are severe issues in the DC shipboard integrated power system (DC‐SIPS). The secondary voltage control (SVC) equipped in the diesel genset (DG) and hybrid energy storage system (HESS) becomes an effective solution. However, the conventional SVC cannot distinguish the different voltage regulation characteristics between DG and HESS in a cost‐effective way when allocating the voltage regulation responsibility (VRR). The cost optimization achieved by the conventional tertiary control cannot be used for the DC‐SIPS due to frequent and unpredictable load fluctuations. In this paper, a cost‐effective secondary voltage control is proposed to realize voltage restoration and cost minimization simultaneously. First, the voltage regulation cost model is developed considering the different voltage regulation characteristics of DG and HESS. Then, the optimization problem of VRR distribution is addressed by minimizing the total voltage regulation cost function using a quadratic programming algorithm. Moreover, the voltage regulation ability of each energy storage system is fully used with the state of charge balance. Thus, the conflict among voltage restoration, cost minimization, and SoC balance is effectively addressed. Finally, promising hardware‐in‐loop test results illustrate the effectiveness of the proposed method.

A survey on voltage stability indices for power system transmission and distribution systems

Voltage stability is a critical aspect of power system operation, ensuring the reliable and efficient delivery of electrical energy to consumers. Recently, voltage stability in the power system has received much attention. The primary cause of voltage instability is the lack of real and reactive power generation to cope with the continuous demand increment. Maintaining voltage stability while planning, controlling, and assessing the system’s security is a difficult task for power system engineers. From knowledge of past incidents, a lack of reactive power is identified as the primary cause of voltage instability, which may further lead to the total collapse of the system. The importance of voltage stability assessment is essential to maintain the integrity of power systems. It helps prevent voltage instability, which can lead to cascading failures and widespread outages. This paper uses various parameters, including load demand, generator capacity, and system impedance, to develop voltage stability indices (VSIs). The paper mainly focuses on the idea of VSIs that have been developed in order to monitor and control the criticality of the power system. The voltage stability indices for transmission and distributed systems, as well as their subclasses, are thoroughly reviewed in this study. Furthermore, traditional voltage stability methods, as well as various software tools for monitoring the voltage stability problems, are also discussed. In addition, the development of VSIs and its related concepts are clearly described in this paper. This comprehensive survey provides a decent groundwork for future work in this area, and assists professionals in selecting the optimal VSI for various applications. Moreover, it provides a concise overview of methods and the importance of voltage stability assessment in both transmission and distribution systems.

Short-term wind power prediction based on anomalous data cleaning and optimized LSTM network

Wind power prediction values are often unstable. The purpose of this study is to provide theoretical support for large-scale grid integration of power systems by analyzing units from three different regions in China and using neural networks to improve power prediction accuracy. The variables that have the greatest impact on power are screened out using the Pearson correlation coefficient. Optimize LSTM with Lion Swarm Algorithm (LSO) and add GCT attention module for optimization. Short-term predictions of actual power are made for Gansu (Northwest China), Hebei (Central Plains), and Zhejiang (Coastal China). The results show that the mean absolute percentage error (MAPE) of the nine units ranges from 9.156% to 16.38% and the root mean square error (RMSE) ranges from 1.028 to 1.546 MW for power prediction for the next 12 h. The MAPE of the units ranges from 11.36% to 18.58% and the RMSE ranges from 2.065 to 2.538 MW for the next 24 h. Furthermore, the LSTM is optimized by adding the GCT attention module to optimize the LSTM. 2.538 MW. In addition, compared with the model before data cleaning, the 12 h prediction error MAPE and RMSE are improved by an average of 34.82% and 38.10%, respectively; and the 24 h prediction error values are improved by an average of 26.32% and 20.69%, which proves the necessity of data cleaning and the generalizability of the model. The subsequent research content was also identified.