Integrated Power System Research Articles

Event-Triggered T-S Fuzzy Load Frequency Control With Variable Probabilistic Release for Renewable Energy Integrated Power Systems

Event-Triggered T-S Fuzzy Load Frequency Control With Variable Probabilistic Release for Renewable Energy Integrated Power Systems

Chaos Control of RESs Integrated Power System Model using Adaptive Higher Order PID SMC and Comparison Among Different Adaptive SMC Techniques

Chaos Control of RESs Integrated Power System Model using Adaptive Higher Order PID SMC and Comparison Among Different Adaptive SMC Techniques

Design and economic analysis of integrated power systems with solar cell-diesel-battery-micro hydro off-grid (a case study of pinal village samosir district)

The need for electrical energy in remote areas in Indonesia is essential. The use of Renewable Energy in Indonesia until 2020 will only reach 2.5% or around 10.5 gigawatts of its total potential. The government is targeting % a national energy mix of 23% to be sourced from renewable energy in 2025. Pinal Village has an area of 295 Ha consisting of 48 heads of families with a population of approximately 182 people whose livelihoods are primarily farmers and servants. The Pinal village is one of the remote villages in the Sianjur Mula-mula sub-district, surrounded by mountains. There is no road access to the village, and it can only be accessed by boat, so the electricity network owned by the government (PLN) has not yet entered the Pinal village area. Therefore, efforts are needed to develop a power generation system to supply electricity in the village. Simulation with HOMER (Hybrid Optimization of Multiple Energy Resource), which shows a feasible generator configuration system to be built is a Solar PV-Battery-Diesel-Microhydro configuration system where the solar panel has a capacity of 54.5 kW, batteries with a total of 240 units, Diesel with a capacity of 25 kW and the output power of the micro hydro is 5.09 kW, the payback period is 15.8 years with a lifetime of the generating system for 20 years, the electricity production cost is IDR 1,744/kWh, fuel consumption is 6.2 liters/day, the emissions generated are 4,685 kg/year with land use of ±267.25 m2.

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

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With the integration of wind power into the power system, dispatch becomes more complex and existing algorithms are no longer applicable.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

Multiphase Solar Photovoltaic Prediction Model Based on Season, Hierarchical k‐Means Clustering, GRA‐PCC, SVM, and Neural Network

Solar photovoltaic (PV) has accounted for the highest percentage of power generation capacity among other renewables. However, solar PV power generation is highly variable because of different factors; therefore, accurate forecasting is critical for reliable integration into the power system. This paper proposes a multiphase solar PV prediction model that includes grouping, clustering, linking, classifying, and predicting using historical solar PV power and weather data. Seasonal variation is considered in the grouping phase, followed by hybrid hierarchical k‐means clustering to enhance data division in the clustering phase. A hybrid gray relational analysis‐Pearson correlation coefficient identifies significant weather factors impacting solar PV power in the linking phase. The classification phase employs a support vector machine to establish the relationship between the clusters and the relevant weather factors. Lastly, a neural network (NN) is trained to predict solar PV power. The solar PV power profiles are presented to show the variability in season and time. The simulation results of the proposed model showed relatively accurate forecasting results, including MAE of 0.408 MW, MSE of 460.51 MW, RMSE of 0.679 MW, nRMSE of 4.345%, and MRE of 2.266%. These results represent that the uncertainties of the proposed model are 6 and 12 times lower than those of the conventional methods (i.e., conventional NN and ARMAX). These results assure that the proposed model can provide more accurate solar PV power profiles for reliable power system integration.

Innovative Power Smoothing Technique for Enhancing Renewable Integration in Insular Power Systems Using Electric Vehicle Charging Stations

The reliance on imported fuels for electricity generation and internal transportation in insular electrical systems has historically posed a significant challenge due to their geographic isolation. The vulnerability of insular ecosystems to pollution has driven the need to transition toward renewable energy sources. Despite their inherent variability, wind and solar energy have gained acceptance. Integrating these renewable technologies into insular grids presents technical challenges that impact the quality of the power supply, particularly with the increasing presence of electric vehicles. Nevertheless, the batteries of these vehicles provide an opportunity to enhance network performance. This article introduces an innovative power smoothing technique that utilizes electric vehicle batteries to optimize self-consumption and reduce power fluctuations. The proposed method is an enhanced version of the ramp-rate energy smoothing method, incorporating adaptability through real-time control of the ramp-rate using fuzzy logic. It employs an aggregated model of lithium-ion batteries with a bidirectional power electronic converter. Experimental validation is carried out in the Micro-Grid Laboratory of the University of Cuenca, Ecuador. Experimental results demonstrate a significant 14% reduction in energy generation variability, resulting in a more stable electrical supply profile. Additionally, there is a marginal improvement in energy delivery, with an additional injection of 0.23 kWh compared to scenarios without the participation of electric vehicle batteries in power smoothing tasks. These findings support the effectiveness of the proposed approach in optimizing the integration of intermittent renewable generators and electric vehicle charging in insular energy systems.

Integration of Electrical power systems based on Renewable energy systems

Integration of Electrical power systems based on Renewable energy systems

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.