Optimal Power Research Articles

An efficient leakage power optimization framework based on reinforcement learning with graph neural network

Threshold voltage (Vth\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_{th}$$\\end{document}) assignment is convenient for leakage optimization due to the exponential relation between leakage power and Vth\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_{th}$$\\end{document} by swapping logic cells without routing effort. However, it poses great challenge in large scale circuit design as an NP-hard problem. Machine learning-based approaches have been proposed to solve this problem, aiming to achieve well tradeoff between leakage power reduction and runtime speed up without new induced timing violation. In this paper, a leakage power optimization framework based on reinforcement learning (RL) with graph neural network (GNN) is first-ever proposed to formulate Vth\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_{th}$$\\end{document} assignment as a RL process by learning timing and physical characteristics of each circuit instance with GNN. Multiple instances are selected in a non-overlapped manner for each RL action iteration to speed up convergence and decouple timing interdependence along circuit path, where the corresponding reward is carefully defined to tradeoff between leakage reduction and potential timing violation. The proposed framework was validated by the Opencores and IWLS 2005 benchmark circuits with TSMC 28 nm technology. Experimental results demonstrate that our work outperforms prior non-analytical and GNN-based methods with better leakage power optimization by additional 5% to 17% reduction, which is highly consistent with the commercial tool. When transferring the trained RL-based framework to unseen circuits, it achieves the roughly identical leakage optimization results as seen circuit and speed up the runtime by 5.7×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} to 8.5×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} compared with commercial tool.

PV Array Reconfiguration for Global Maximum Power Optimizing under Partial Shading Conditions based on PV Switched System

Background: Due to the partial shading effects on different photovoltaic (PV) modules in a PV array, PV module operating conditions are inconsistent, and PV array output power is significantly reduced. Although the maximum power point (MPP) of a non-uniform irradiance PV array can be observed through global maximum power point tracking (GMPPT), no evaluation of the array's energy potential has been done. Objective: One of the most effective solutions to overcome the negative effects of partial shading in PV systems is the PV array reconfiguration process. To optimize the electrical structure of the PV array as the PV modules are partially shaded in a non-uniform manner, this study proposes a promising technique for dynamic reconfiguring a PV array in order to improve the extracted maximum power from a PV array under partial shading conditions (PSC). Methods: PV modules are rearranged by iteratively sorting them to allow the PV array with nonuniform irradiance to produce as much power as possible. This is conducted by applying a switching matrix to implement a PV-switched system approach. The proposed system with different PV array dimensions (e.g., 3×4, 4×6, and 5×8) is assessed in order to validate the proposed algorithm. A MATLAB/Simulink PV array developed model is used to find the global maximum power point for the different PV array dimensions in both pre-reconfiguration and post-reconfiguration states. Results: A detailed numerical comparison of the extracted power from the proposed system has been provided. Conclusion: The results show that the proposed system has the potential to extract the exact global maximum power for a PV-switched system under PSC, irrespective of array dimensions, according to simulation results.

Transmit Power Optimization in Multihop Amplify-and-Forward Relay Systems with Simultaneous Wireless Information and Power Transfer

In this paper, we study a multi-hop amplify-and-forward (AF) simultaneous wireless information and power transmission (SWIPT) relay system. Each relay node harvests power using a power split (PS) method from a portion of the received signal, amplifies the remaining received signal, and passes it to the next relay. Based on this system model and signal flow, we derived and solved the convex power minimization problem with the optimal PS ratio. In this case, it was found that using the optimal PS ratio consumed a lower amount of power than when using a fixed PS ratio (0.5). We then investigated the impact of processing cost on the AF-SWIPT system using decoding and forwarding SWIPT as benchmarks, and found that AF-SWIPT was superior.

Energy Microgrids: Exploring Technology Trends and Prospects for Efficient Energy Management

Objective: The objective of this paper is to explore technology trends and prospects for efficient energy management in microgrids by identifying and analyzing distinct research lines in this field. Method: A Scopus search was conducted using keywords such as "microgrids" and "new technologies." The gathered information was processed using Bibliometrix software to cluster the data. This analysis identified five distinct research lines related to microgrids and energy management. Results and Discussion: The analysis revealed five distinct research lines: (1) microgrids related to energy generation and storage, (2) electric power system control, (3) electric power transmission networks, (4) optimization, and (5) power markets. The paper develops and explains these research lines comprehensively, detailing technology trends and prospects for efficient energy management. Additionally, a combined analysis of relevance and development classifies the research lines as either emerging or declining and determines whether they represent basic or motor themes. Research Implications: The findings provide insights into current and future trends in microgrid technology, offering valuable information for researchers, practitioners, and policymakers. Understanding these trends can help guide future research, development, and implementation strategies for efficient energy management in microgrids. Originality/Value: This paper offers a comprehensive analysis of technology trends and prospects in microgrids, utilizing advanced bibliometric tools to identify and classify research lines. The study provides a unique perspective on the development and relevance of different aspects of microgrid technology, contributing valuable insights to the field.

PEAK TO AVERAGE POWER COMPUTING AND OPTIMIZATION OF OPTICAL OTFS 5G WAVEFORM USING HYBRID FRACTAL-BASED SIGNAL PROCESSING ALGORITHM

Optical orthogonal time frequency space (OTFS) is better at handling Doppler shifts and multipath fading, making signals more reliable and valuable in places with much movement beyond the fifth generation (B5G). Using practical power amplifiers at the transmitter causes power inefficiency and signal distortion because of the OTFS system’s high peak-to-average power ratio (PAPR), severely reducing system efficiency. Combining partial transmit sequence (PTS) and selective mapping (SLM), a technique known as PTS+SLM, reduces peak power. While SLM generates numerous phase-modulated signal candidates and chooses the one with the lowest PAPR, PTS separates the signal into sub-blocks and optimizes their phases to decrease peak power. With few changes to the signal structure, this dual strategy effectively reduces PAPR while improving power spectral density (PSD) efficiency. As a result, we ensure the accuracy and dependability of the transferred data by maintaining the bit error rate (BER). Fractal optimization methods could be applied to these algorithms. For example, fractal-inspired optimization techniques might be used to explore the phase space more effectively or to discover new phase sequences that result in lower PAPR. According to the simulation findings, the suggested PTS+SLM method works better than the traditional PTS and SLM methods.

Analyses of the Effectiveness of Power Optimizers in Heterogeneous PV Plants

Analyses of the Effectiveness of Power Optimizers in Heterogeneous PV Plants

Simulation study of R1234ze and its mixed working medium in TEG‐ORC combined cycle

AbstractThermoelectric generation (TEG) and organic Rankine cycle (ORC) technologies both have their respective limitations in recovering waste heat from ships. However, the combination of TEG and ORC is an effective approach to achieve cascaded waste heat recovery and improve heat utilization efficiency. There has been some progress in research on waste heat recovery in maritime applications based on TEG‐ORC combined cycles. The selection of a working medium is a crucial element that directly influences the design and performance of the entire system, but there is limited research on the impact of different working fluids on combined cycle systems. In this study, a simulation model of a TEG‐ORC combined cycle system was established to examine its output potential using two different working fluids R1234ze and a mixture of R245fa/R1234ze. The results demonstrate that compared to employing the R1234ze working medium, the combined cycle system with the mixed working medium achieved a 13% increase in maximum output power, a 102% improvement in optimum thermal efficiency, and a 53% reduction in optimum power production cost. Additionally, the power output distribution in the system utilizing the mixed working fluid was more uniform compared to the system employing a single working medium. This confirms the great potential of using a mixed working fluid in a combined cycle system.

RSSI prediction and optimization of transmission power for improved LoRa communications performance

RSSI prediction and optimization of transmission power for improved LoRa communications performance

Optimization of a Biomass-Based Power and Fresh Water-Generation System by Machine Learning Using Thermoeconomic Assessment

Biomass is a viable and accessible source of energy that can help address the problem of energy shortages in rural and remote areas. Another important issue for societies today is the lack of clean water, especially in places with high populations and low rainfall. To address both of these concerns, a sustainable biomass-fueled power cycle integrated with a double-stage reverse osmosis water-desalination unit has been designed. The double-stage reverse osmosis system is provided by the 20% of generated power from the bottoming cycles and this allocation can be altered based on the needs for freshwater or power. This system is assessed from energy, exergy, thermoeconomic, and environmental perspectives, and two distinct multi-objective optimization scenarios are applied featuring various objective functions. The considered parameters for this assessment are gas turbine inlet temperature, compressor’s pressure ratio, and cold end temperature differences in heat exchangers 2 and 3. In the first optimization scenario, considering the pollution index, the total unit cost of exergy products, and exergy efficiency as objective functions, the optimal values are, respectively, identified as 0.7644 kg/kWh, 32.7 USD/GJ, and 44%. Conversely, in the second optimization scenario, featuring the emission index, total unit cost of exergy products, and output net power as objective functions, the optimal values are 0.7684 kg/kWh, 27.82 USD/GJ, and 2615.9 kW.

Proactive Eavesdropping in Relay Systems via Trajectory and Power Optimization

Proactive Eavesdropping in Relay Systems via Trajectory and Power Optimization

Multi-Timescale Reactive Power Optimization and Regulation Method for Distribution Networks Under a Multi-Source Interaction Environment

In the context of constructing new power systems, distribution networks are increasingly incorporating distributed resources such as distributed photovoltaic (PV) systems, decentralized wind turbines (WTs), and new types of energy storage system (ESS), which may lead to prominent issues such as voltage overruns and reverse heavy overloads in the distribution network. While distributed resources are valuable for voltage regulation, their regulation characteristics vary with their operation means, and the randomness and volatility of renewable power generation will also influence the optimization and regulation of voltage in the distribution network. This paper proposes a multi-timescale reactive power optimization and regulation method for distribution networks in a multi-source interactive environment. Firstly, the voltage regulation characteristics of distributed PV systems, decentralized ESSs, and distributed WTs are analyzed. Based on this analysis, a multi-timescale voltage optimization scheme for distribution networks using the MPC method is proposed, which optimizes the voltage regulation strategies for each distributed resource in a rolling manner. Furthermore, an event-triggered real-time voltage zoning control strategy based on voltage sensitivity is proposed to address the real-time sudden voltage overlimit problems. The modified IEEE 33-node system is used to verify the performance of the proposed method. Simulation results indicate that the issue of voltage overruns at distribution network nodes has been improved, and the intraday rolling optimization yields results are more realistic compared with the day-ahead optimization method.

Probabilistic Tide Calculation for Multi-Wind Farm Distribution Network Based on CATTSTS Model

Traditional probabilistic tide calculation methods often fall short in capturing the dynamic nature of these systems, leading to discrepancies between calculated and actual data. To address this, a dynamic probabilistic tide calculation model is proposed in this study, incorporating a wind farm probabilistic model enhanced by the CATTSTS model. This integration aims to optimize prediction accuracy by improving feature focusing and data fitting, thereby reducing prediction errors and providing more reliable data for power system adjustments and optimizations. Experimental results demonstrate that the model's predictions for wind speed series in multi-wind farm distribution networks closely match actual conditions, underscoring its practical applicability and reliability in real-world settings. Furthermore, the study highlights the impact of spatial and temporal correlations on model performance. This research contributes to advancing the management of renewable energy integration in power systems, offering insights for more informed decision-making and operational efficiency.

Design and implementation of a solar power optimizer for module level power electronics application

AbstractPartial shading on series‐connected photovoltaic (PV) panels in conventional PV systems results in lower harvested power. To resolve this, it is vital to utilize module level power electronics (MLPE) such as Solar Power Optimizers (SPOs). This paper introduced a non‐isolated common ground non‐inverting output voltage buck‐boost converter as an SPO. Proposed converter benefits from continuous input and output currents which has a significant role in designing SPOs. Having a quadratic gain, beside acceptable step‐down range are other features of the converter. Operating principle, design, steady‐state, small‐signal analysis, and dynamic performance of proposed converter are included. Proposed converter is compared with other buck‐boost converters in terms of voltage gain, voltage stresses, continuous input and output current, and output polarity. To validate the performance of introduced converter, experimental results for a prototype with input voltage 24 V, output voltage 72 V for step‐up and 15 V for step‐down modes are given and results are examined. The maximum efficiency of the prototype is 93% and 89% for step‐up and step‐down modes, respectively. To evaluate the effect of proposed SPO for extracting maximum available power from PVs, simulation results of a grid connected PV system with two series connected SPOs is discussed.

Multi-Level Sum of Product (SOP) Network Power Optimization Based on Switching Graph

The article presents the methodology of optimization of technology mapping of a multi-output function implemented in the form of sum of product (SOP) networks. The optimization is based on the concept of reducing the switching activity of combinational circuits. The aim of reducing network switching is to limit the consumption of dynamic power. Since dynamic power is one of the components of the total power consumed by digital systems, this leads to an optimization focused on the energy efficiency of digital systems. The basis of the proposed method is the technology mapping using a modified output graph describing the result of minimizing the multi-output function. The modified output graph, in terms of parameters associated with dynamic power, is defined as a switching graph. It was assumed that the key parameter associated with dynamic power is the switching activity of individual nodes of the logic network. The article presents elements of switching graph optimization which lead to the improvement of parameters associated with the dynamic power of the circuit. The essence of the proposed optimization methods is the appropriate movement of products and connections occurring in the logic network. Reducing the number of logic network vertices is also extremely important. The effectiveness of the proposed method was confirmed by the results of experiments performed on selected benchmarks. A significant reduction in the total value of switching activity was obtained for the optimized structures.

Advanced smart grid controller: a next-gen alternative to Fuzzy-logic for UPFC

A very valuable component of flexible AC transmission systems (FACTs) is the Unified Power Flow Controller (UPFC). By simultaneously regulating both active and reactive power on transmission lines, it maintains voltage stability on linked buses. This study devised a thorough control strategy for the Unitary Power Flow Controller (UPFC), combining traditional and artificial intelligence (AI)-based methods. To see how the system would react in different situations and how well artificial intelligence control worked compared to more traditional proportional-integral power control, we ran a lot of simulations. The study found that the control strategy based on artificial intelligence (AI) was better than the proportional-integral (PI) control at managing power flow, keeping the voltage stable, and responding to changes in the environment. The results of this study emphasize the capacity of artificial intelligence methods to improve the performance and stability of FACT components such as the UPFC. The research provides important insights into power system control and optimization, indicating that strategies based on artificial intelligence could greatly enhance power generation. In order to enhance the AI control strategy, future research should focus on its practical application in actual power systems.

AnFiS-MoH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms

The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces AnFiS-MoH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 scores, particularly excelling in handling high-dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://github.com/AmbitYuki/Metaheuristic-Adaptive-ANFIS.

Operational reliability and non-deterministic resilience estimation of active distribution network incorporating effect of real-time dynamic hosting capacity

Active distribution networks are increasingly recognized essential for achieving sustainable development goals. Traditionally, hosting capacity was considered as a static measure for planning distributed energy resources integration. This work introduces the concept of dynamic hosting capacity, which recurrently re-evaluates hosting capacity in response to erratic modern grid conditions. The introduction of dynamic hosting capacity facilitated testing variations of power injection from minimum to 100 %, sustaining power system governing parameter limits. This embarked the need of operational reliability assessment and enhancing situational awareness for optimum power injection and balance. To achieve operational reliability analysis based on dynamic hosting capacity, hybrid probability distribution function-based Monte Carlo simulation is proposed. This resulted in 85–90 %. improvisation of solar photovoltaic generation and load alignment, as this methodology provides comprehensive and accurate assessment of system performance under diverse uncertainties. The framework's validation includes projection of time-varying operational reliability indices, over time independent reliability indices i.e., dynamic loss of load probability, dynamic loss of load expectation, dynamic loss of load duration, dynamic loss of load frequency, dynamic grid margin, and dynamic grid dependency. This resulted in 30 % improvement in assessment of grid margin, facilitating reliable uncertainty handling competence. Additionally, expectation maximization algorithm is proposed to evaluate non-deterministic resilience due to ambiguities associated with solar photovoltaic distributed energy resources. The non-deterministic resilience assessment testified 80 % bounce-back rate, demonstrating better adaptability and robustness. The entire analysis is conducted in MATLAB, validated using Typhoon Hardware-in-Loop real-time platform, and compared with existing literatures to demonstrate its effectiveness.

Fast neural network inverse model to maximize throughput in ultra-wideband WDM systems

Ultra-wideband systems expand the optical bandwidth in wavelength-division multiplexed (WDM) systems to provide increased capacity using the existing fiber infrastructure. In ultra-wideband transmission, power is transferred from shorter-wavelength WDM channels to longer-wavelength WDM channels due to inelastic inter-channel stimulated Raman scattering. Thus, managing launch power is necessary to improve the overall data throughput. While the launch power optimization problem can be solved by the particle swarm optimization method it is sensitive to the objective value and requires intensive objective calculations. Hence, we first propose a fast and accurate data-driven deep neural network-based physical layer in this paper which can achieve 99%−100% throughput compared to the semi-analytical approach with more than 2 orders of magnitude improvement in computational time. To further reduce the computational time, we propose an iterative greedy algorithm enabled by the inverse model to well approximate a sub-optimal solution with less than 6% performance degradation but almost 3 orders of magnitude reduction in computational time.

The ART of Sharing Points-to Analysis: Reusing Points-to Analysis Results Safely and Efficiently

Data-flow analyses like points-to analysis can vastly improve the precision of other analyses, and enable powerful code optimizations. However, whole-program points-to analysis of large Java programs tends to be expensive – both in terms of time and memory. Consequently, many compilers (both static and JIT) and program-analysis tools tend to employ faster – but more conservative – points-to analyses to improve usability. As an alternative to such trading of precision for performance, various techniques have been proposed to perform precise yet expensive fixed-point points-to analyses ahead of time in a static analyzer, store the results, and then transmit them to independent compilation/program-analysis stages that may need them. However, an underlying concern of safety affects all such techniques – can a compiler (or program analysis tool) trust the points-to analysis results generated by another compiler/tool? In this work, we address this issue of trust in the context of Java, while accounting for the issue of performance. We propose ART: Analysis-results Representation Template – a novel scheme to efficiently and concisely encode results of flow-sensitive, context-insensitive points-to analysis computed by a static analyzer for use in any independent system that may benefit from such a precise points-to analysis. ART also allows for fast regeneration of the encoded sound analysis results in such systems. Our scheme has two components: (i) a producer that can statically perform expensive points-to analysis and encode the same concisely, (ii) a consumer that, on receiving such encoded results (called artwork), can regenerate the points-to analysis results encoded by the artwork if it is deemed “safe”. The regeneration scheme completely avoids fixed-point computations and thus can help consumers like static analyzers and JIT compilers to obtain precise points-to information without paying a prohibitively high cost. We demonstrate the usage of ART by implementing a producer (in Soot) and two consumers (in Soot and the Eclipse OpenJ9 JIT compiler). We have evaluated our implementation over various benchmarks from the DaCapo and SPECjvm2008 suites. Our results demonstrate that using ART, a consumer can obtain precise flow-sensitive, context-insensitive points-to analysis results in less than (average) 1% of the time taken by a static analyzer to perform the same analysis, with the storage overhead of ART representing a small fraction of the program size (average around 4%).

Impacts of Plug-in Electric Vehicles and Renewable Energy Source on Unit Commitment Problem using Cat Mouse Based Optimization A lgorithm

Objectives: The main objective of this paper is to solve the Cost Based Unit Commitment (CBIC) problem considering Renewable Energy Sources (RES) and Plug-in Electric vehicles (PEVs). The proposed problem minimizes the total operating of the interconnected system. Methods: A novel and recently developed successful optimization tool of Cat and Mouse Based Optimizer (CMBO) is proposed for optimal solution of Cost Based UC problem. The proposed CMBO approach is having two groups such as Cat group and Mice group for searching the global optimal solution with less computational time. It contains two phases like Cat’s movement towards Mice and Mice escape to a safe place is effectively updating the new position of Cat and Mice to easily reaching the best solutions. The control parameter of CMBO is number of agents is 50, number of variables is 10 and maximum number of iteration is 500. MATLAB 14.0 platform is utilized for the projected problem. Findings: The method tested on standard IEEE-39 bus system (10 generators and 24 hour) with an equivalent PEV and Wind farm. The CMBO approach minimize the total operating cost with various test cases. The outcomes such as UC schedule, Real power output of thermal, wind and PEV units, fuel cost, startup cost and total operating cost of the interconnected system are numerically and graphically reported. The total operating cost is 4.11 % minimized when compared with base case approach and 0.73 % reduced than the other existing method viz. Parallel UCs-RP method. Novelty: The CMBO is recently developed powerful optimizer and parameter free algorithm, so easily reaches the global optimal solutions. The obtained simulated results are compared with other mathematical and intelligent computational approaches for proving the efficiency and performance of the proposed CNBO technique. Keywords: Unit Commitment, Plug­in Electric Vehicles, Renewable Energy Sources, Cost minimization, Cat and Mouse Based Optimizer