Power Scheduling Scheme Research Articles

Optimal operation for P2H system based on a power–temperature–hydrogen coupling model for alkaline water electrolyzer plant

For the effective operation of a Power-to-Hydrogen (P2H) system that integrates renewable energy sources with an Alkaline Water Electrolyzer (AWE), precise modeling of the AWE plant is paramount. This research delves into the electrothermal dynamics of the AWE and constructs an advanced Power–Temperature–Hydrogen (P–T–H) coupling model of the AWE plant. The developed model intricately captures the pivotal role of temperature in modulating hydrogen production rates, power constraints, and system flexibility. Furthermore, to enable the application of the model to system-level optimization, we undertake a rational simplification process to transform it into a linear model. Building upon this model, we devise an optimized power scheduling strategy for the P2H system that enhances the system’s economic efficiency and adaptability. A comprehensive sensitivity analysis of crucial variables such as hydrogen demand, transmission line capacity, and AWE capacity underscores the strategic insights this model offers for system development and implementation. In a word, the developed P–T–H model is well feasible for the improvement of the system’s operation with AWE.

Cost-efficiency based residential power scheduling considering distributed generation and energy storage

The permeation of renewable energy into smart house is a key characteristic of the future power system that brings a significant challenge to the peak load management in the power sector. In this paper, we propose cost-efficiency based residential power scheduling scheme considering distributed generation and energy storage. In which, a cost-efficiency model designed by the ratio of consumption benefit to its cost is introduced to capture the residential users' electricity consumption behaviors and reflect their economic efficiency. A discomfort cost is constructed to represent the losses associated with power scheduling when facing the incentives given by the smart grid. Specially, not only residential load consumption utility, but also that of the renewable energy generation and energy storage devices are applied to compose the consumption benefit，and an energy storage model to be established to plan the charging and discharging of energy storage batteries. Day-Ahead Bidding Price and Real-time consumption management models are introduced to test the performance of the proposed scheduling scheme. Simulation results demonstrate that the proposed method increases the residential consumer's daily cost efficiency by 1.5834, and reduces its daily electricity bill by 38.43 % and that about 36.86 % of energy is saved in the smart grid while the proposed scheduling strategy for energy consumption can effectively smooth the load curve in smart grid.

Simultaneous capacity configuration and scheduling optimization of an integrated electrical vehicle charging station with photovoltaic and battery energy storage system

The implementation of an optimal power scheduling strategy is vital for the optimal design of the integrated electric vehicle (EV) charging station with photovoltaic (PV) and battery energy storage system (BESS). However, traditional design methods always neglect accurate PV power modeling and adopt overly simplistic EV charging strategies, which might result in suboptimal and infeasible designs. This study proposes a novel simultaneous capacity configuration and scheduling optimization model for PV/BESS integrated EV charging stations, which combines hybrid modeling for PV power prediction and optimal scheduling method for charging piles. The original model is then converted to a mixed integer linear programming problem by the Big-M method to greatly reduce the computational complexity. A typical scenario of commercial region is employed to demonstrate the effectiveness of the proposed approach. The results indicate that the approach could improve economic benefits by 15.67 % and reduce carbon emissions by 37.14 % compared with the entire grid-based mode. Besides, integrating the PV hybrid modeling method and optimal charging scheduling strategy could achieve superior capacity configurations and greatly improve the comprehensive performance of the system. It is thus illustrated that the approach could provide a promising solution for the optimal design and scheduling of PV/BESS integrated EV charging stations.

A robust auxiliary noise power scheduling strategy for online secondary path modeling in active noise control systems

Auxiliary noise injected in active noise control (ANC) systems for online secondary path modeling (SPM) contributes to the residual noise, and thus deteriorates the noise control performance of ANC systems. Moreover, sudden changes in secondary path may lead to easy divergence of the existing online SPM methods. This paper improves the existing methods from three aspects to further mitigate these problems: 1) the noise power is varied on the basis of the convergence status of SPM filter so that large-power noise is injected at the start or when secondary path changes and the power is reduced as the filter converges. 2) a novel ON/OFF controlling strategy is used in the method so that the noise injection is suspended at the optimum point when the modeling accuracy is sufficient. 3) continuous check for error outliers is applied in the method to detect changes in secondary path so that the injection of noise can be reactivated in the case of path perturbation. Comparative simulations show the effectiveness of the improved method both in convergence and noise reduction performance. In addition, the improved method is robust against sudden changes in secondary path.

SYNTONY: Potential-aware fuzzing with particle swarm optimization

Fuzzing has gained significant traction in academic research as well as industry thanks to its effectiveness for discovering software vulnerabilities. However, even the state-of-the-art fuzzers are not very efficient at identifying promising seeds. Coverage-guided fuzzers, while fast and scalable, usually employ single criterion to evaluate the quality of seeds, which may incur bias and pass up optimal seeds. In this paper, we devise a novel potential-aware fuzzing scheme, namely SYNTONY, which seeks to measure seed potential utilizing multiple objectives and prioritize promising seeds that are more likely to generate interesting seeds via mutation. More specifically, SYNTONY leverages efficient swarm intelligence techniques like Particle Swarm Optimization (PSO) to explore multi-criteria seed selection, which allows SYNTONY to choose effectively promising seeds. Furthermore, we introduce decent power scheduling strategy to discover significantly more paths or crashes by gravitating towards more potential seeds. We implement this scheme on top of several state-of-the-art fuzzers, i.e., AFL, AFL++, FairFuzz, and PTFuzz. Our evaluations on 11 popular real-world programs demonstrate that SYNTONY significantly increases the number of unique crashes triggered and edge coverage discovered by 132.06 % and 28.69 % over AFL++. Further comparison also shows that SYNTONY outperforms other state-of-the-art fuzzers, e.g., AFL, FairFuzz, and PTFuzz. Also, extensive evaluations illustrate that SYNTONY provides a great compatibility and expansibility, while introducing negligible overhead.

A multi-period design method for the steam and power systems coupling solar thermal energy and waste heat recovery in refineries

It is imperative to reduce carbon emissions by comprehensively utilizing renewable energies and waste heat in refineries. However, the intermittent supply of solar thermal energy, the periodic changes of waste heat, and the variable demands of steam and electricity in refineries hinder optimal design and stable operation of the steam and power systems. In this work, an optimal design method for coordination of solar thermal energy and waste heat to produce steam and electricity in refineries was proposed, which features a multi-period mathematical programming model to synchronously determine the economy, the optimal capacity configuration, and the power scheduling scheme for the steam and power system. The influence of the serial and parallel structures of solar thermal energy supply, the model processing method for continuity of energy storage among different subperiods, and parametric analysis on the results were analyzed and discussed in detail. The results show that the cost of the solar thermal collectors and the energy storage system accounts for the significant proportion of the investment cost of the system. The cost of solar thermal collectors accounts for the largest proportion of the investment cost of the system, which is about 60% in the case study. The parallel structure of solar thermal energy supply can significantly reduce the total cost of the system. The coordination of solar thermal energy and waste heat recovery can jointly meet the steam demand to ensure the stability of the energy supply of the system. The comprehensive interactions and coordination between the solar steam generation systems, waste heat systems, energy storage systems, and demands are analyzed. The battery is necessary for short-time energy storage, whereas thermal energy storage should be used for either long-term or short-term energy storage. These results provide fundamental support for the optimal design of the steam and power systems in refineries by the comprehensive utilization of solar thermal energy and waste heat.

Optimal Power Scheduling of Hydropower with Co-Located Floating Solar and Wind Generation Using Stochastic Weight Tradeoff Chaotic Particle Swarm Optimization

The increased use of solar and wind power generation worldwide has elevated the frequency and voltage regulation concerns due to their intermittent nature. Installing floating solar PV panels (FPV) and wind (WT) farms, at the same location as hydroelectric power plants (HPPs), should present a much cheaper option than using a battery storage. However, the operating strategy for the new co-located power plant needs to be revised from the conventional HPP operation. Hence, a novel optimal power scheduling strategy for co-located FPV-WT-HPP power plant using stochastic weight tradeoff chaotic particle swarm optimization (SWTC-PSO) is proposed in this paper. The objective is to either maximize the upper reservoir water or minimize the spill water. Test results obtained on the system with five major HPPs, two thermal, and one combined-cycle power plants ascertain that the proposed method can generate a significant amount of reservoir water saving up to 30%, which can later be used in the high demand period. The solar and wind energy penetration can be increased by up to 28% without disturbing frequency and voltage regulation using the co-located approach. Finally, the proposed modified HPP controller that uses the feedback signals from FPV and WT, can smooth out the combined co-located plant power output, leading to a dispatchable and firm power generation solution.

Multi-layer defense algorithm against deep reinforcement learning-based intruders in smart grids

The Internet of Energy envisions the next generation of smart grids as a highly interconnected network, including advanced metering infrastructures, distributed energy resources, and bidirectional communication systems. The open architecture of IoE-based smart grid results in manifold security concerns, especially the risk of False Data Injection Attacks. The attack may target the technical aspects of a system since fabricating the network's data misleads power scheduling and routing strategies and interrupts the healthy operation of the power system. Additionally, monetary motivation for the intruder sometimes is the main motivation. The conventional cyber defense strategies are unable to detect well-developed False Data Injection Attacks, particularly once the intruder takes advantage of a Deep Reinforcement Learning-based attack development framework that analyzes the dynamic nature of the smart grids. This paper primarily outlines various possible passive attacks using statistical methods. Then, a reinforcement learning-based intruder as an active attack generator is developed, initialized by modeled passive attacks. The attack generator algorithm can simulate the network environment and subsequently creates unclassified attacks. After creating a dynamic attacker, a multilayer defense framework is developed using Snapshot Ensemble Deep Neural Network and an adoptable Deep Auto Encoder network to detect known and unknown threats. Performance evaluations and a real-world simulation prove that the proposed framework can successfully detect both passive and active, where the accuracy and false positive detection rate of the developed framework are 98.82% and 97.42%, respectively.

Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks

Large-scale integration of battery energy storage systems (BESS) in distribution networks has the potential to enhance the utilization of photovoltaic (PV) power generation and mitigate the negative effects caused by electric vehicles (EV) fast charging behavior. This paper presents a novel deep reinforcement learning-based power scheduling strategy for BESS which is installed in an active distribution network. The network includes fast EV charging demand, PV power generation, and electricity arbitrage from main grid. The aim is to maximize the profit of BESS operator whilst maintaining voltage limits. The novel strategy adopts a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and requires forecasted PV power generation and EV smart charging demand. The proposed strategy is compared with Deep Deterministic Policy Gradient (DDPG), Particle Swarm Optimization and Simulated Annealing algorithms to verify its effectiveness. Case studies are conducted with smart EV charging dataset from Project Shift (UK Power Networks Innovation) and the UK photovoltaic dataset. The Internal Rate of Return results with TD3 and DDPG algorithms are 9.46% and 8.69%, respectively, which show that the proposed strategy can enhance power scheduling and outperforms the mainstream methods in terms of reduced levelized cost of storage and increased net present value.

Towards long-period operational reliability of independent microgrid: A risk-aware energy scheduling and stochastic optimization method

Independent microgrids (MGs) consisting of diesel generator (DG), photovoltaic (PV), and energy storage system (ESS) are becoming a cost effective solution for the power supply in remote areas. However, besides PV intermittence, limited reserve and time-consuming replenishment of diesel fuel in remote areas make it challenging to guarantee long-period reliable power supply. In this paper, a risk-aware energy scheduling and stochastic optimization method is proposed to enhance long-period operational reliability of independent MGs. The possible extreme scenarios in the future are considered in an energy scheduling optimization model (ESOM). Based on energy forecast results of PV and loads for the next 7 days, ESOM maximizes the reliable power supply probability by optimizing energy scheduling strategies and reserve requirement for future operational risk simultaneously. Subsequently, a day-ahead stochastic optimization model is established to determine optimal power scheduling strategies of DG, PV, ESS, and flexible loads. The conditional value at risk (CVaR) is used to address the operation risk caused by uncertainties of PV and loads. Compared with traditional day-ahead optimization methods by numerous simulations, the proposed method has less expected load losses and PV curtailment, as well as less total supply-demand deviation. The resistance for future operational risks of independent MGs is therefore significantly enhanced.

Energy-efficient power scheduling and allocation scheme for wireless sensor networks

The focus in this paper is on coverage energy-efficient of WSN (wireless sensor networks). Communication path lengths of a randomly distributed BSs and sensor nodes field are characterized by distance distributions. For sensor nodes, this paper will use theory of Poisson point process (PPP) and Boolean Model (BM) to purpose a power allocation scheme results in green communication of sensor nodes. The energy allocation scheme of wireless sensor networks can help to prolong the life of the whole network. In this paper, we prove that the scheduling and allocation scheme can achieve energy saving, so as to prolong the life cycle of the whole wireless sensor networks. In the uniform placement migration case, we simulation 1000 times. In Boolean Model, There is 71.3% for power scheduling and allocation schemes and 28.7% is not. From these results, we see that, in wireless sensor networks, 20%–30% of total energy, which was being consumed from the part of power scheduling and allocation. The scheduling scheme improves response time, throughput and overall energy consumption over base case. Compared with other allocation schemes, the advantage of the scheme proposed in this paper is that the less sensors selected can still provide more than 95% detection area coverage. It will enable robust communication against cross-tier interference and co-layer interference thereby obtaining a substantial improvement in link quality.

Power Scheduling Scheme for a Charging Facility Considering the Satisfaction of Electric Vehicle Users

This paper proposes a novel user-preference-aware power scheduling scheme for application at an electric vehicle (EV) charging facility. Here, the preference of the EV user is characterized as a utility function by considering two different factors: 1) a satisfaction factor according to the charged energy, and 2) payment for the received charging service. As a key component of the power scheduling method, this paper proposes a two-stage power charging method and analyzes the advantage of the proposed method from a monetary perspective. In addition, this paper analyzes the economic benefits of a charging facility and EVs using the single-leader, multi-follower Stackelkberg game. By showing the existence of a unique best response for each participant, this paper presents the improvement of financial profit of EV users and charging facility through comparison with the case where the charging facility does not apply a proper power management scheme. Based on the actual world datasets, this paper shows that the proposed power scheduling scheme is feasible for actual environment. With satisfying the constraints, it is possible to reduce overall electricity cost up to 8.59% compared to the case without considering the peak power in EV charging facility.

Resource Scheduling for Multi-Target Tracking in Multi-Radar Systems With Imperfect Detection

In this paper, an effective joint radar assignment and power scheduling (JRAPS) scheme is proposed for the multi-target tracking (MTT) task in multi-radar systems (MRSs) under imperfect detection, namely, target related measurements are collected with the probability of detection (<inline-formula><tex-math notation="LaTeX">$\bm {P}_{\bm {D}}$</tex-math></inline-formula>) less than 1. The centralized fusion architecture is adopted by the MRS. Specifically, during each sampling time interval, each radar of the MRS is assigned to track certain targets with controllable transmitting power. The measurements collected by all selected local radars, with <inline-formula><tex-math notation="LaTeX">$\bm {P}_{\bm {D}}\leq 1$</tex-math></inline-formula>, are sent to a central radar to obtain the global MTT results. To maximize the global MTT task performance, the proposed JRAPS scheme implements the online target-to-radar assignment and transmitting power allocation scheme based on the feedback of the MTT results. The posterior Cram&#x00E9;r-Rao lower bound (PCRLB) with <inline-formula><tex-math notation="LaTeX">$\bm {P}_{\bm {D}}\leq 1$</tex-math></inline-formula> is derived and utilized as the tracking performance metric since it provides a more accurate lower bound on the target state estimates under imperfect detection. Then, an overall cost function (OCF) is formulated based on the derived PCRLB to quantify the global MTT performance. Combined with the practical resource constraints of the MRS, the formulated JRAPS problem is shown to be non-convex. Therefore, we further propose a fast three-stage iterative method to solve this problem efficiently. Simulation results verify the superiority and effectiveness of the proposed JRAPS strategy in terms of both tracking accuracy and target detection performance.

Power Scheduling Scheme for DSM in Smart Homes with Photovoltaic and Energy Storage

This article presents a case study of a single-family house with several photovoltaic micro-installations oriented in different directions, in which the energy electricity storage systems have been operating for several months. In the house, the heat source is the air–water heat pump cooperating with heat buffers. The first photovoltaic installation was installed in 2016 and, in the subsequent five years, was expanded using microinverters. The final amount of energy from photovoltaics covers 50% of the energy demand of the building. The procedure for dealing with technical and economic aspects was presented, allowing us to determine whether it is profitable to install energy storage in the given conditions of energy prices, equipment efficiency, and prices, as well as government support. This paper presents the effects of the designed and built home energy management system that supervises energy storage in heat and batteries, mainly through its impact on the self-consumption of energy from the photovoltaic system and on final costs. Comparative calculations were performed with the demand-side management, which dictated the instantaneous energy costs. Attention was paid to the possibility of obtaining a high self-consumption, but the economic calculations showed that it was not always beneficial. An annual self-consumption increased by approximately one-sixth upon installation of the electrical energy storage system and by one-third from the start of use of the home energy management system. Concurrently, by utilising energy storage in heat and batteries, almost 95% of energy was consumed in the cheapest multi-zone tariff. The impact of inverters and battery charging systems on the power grid is also presented. Often, when the active energy was nearing zero, the capacitive reactive energy was significant.

Optimal residential load scheduling under dynamic pricing and demand-side management

Balancing electricity consumption and generation in the residential market is essential for power grids. The imbalance of power scheduling between energy supply and demand would definitely increase costs to both the energy provider and customer. This paper proposes a control function to normalize the peak cost and customer discomfort. In this work, we modify an optimization power scheduling scheme by using the inclined-block rate (IBR) and real-time price (RTP) technique to achieve a desired trade-off between electricity payment and consumer discomfort level. For discomfort, an average time delay between peak and off-peak is proposed to minimize waiting time. The simulation results present our model more practical and realistic with respect to the consumption constrained at peak hours.

Multi-objective design optimization of a multi-type battery energy storage in photovoltaic systems

In order to ensure economy and reliability of photovoltaic (PV) systems, battery energy storage systems (BESS) are usually utilized to accommodate various application scenarios. In this work, a multi-objective optimization method to design the BESS with multiple types of batteries was proposed, in which the total cost (TC) and the output power smoothing index (TP) of the PV systems were simultaneously minimized. The corresponding solution strategy to the proposed model was also developed, where the augmented ε-constraint method was adopted to solve the proposed multi-objective model, and the trade-off point determination method was used to attain the optimal design of the BESS in the PV system. A case study was adopted to illustrate the implementation and effectiveness of the proposed method. Results show that the Pareto front of the TC and TP of the PV system can be obtained by the proposed method. In the case study, the normalized TP 0.4 is selected as the trade-off point when the minimum total cost is taken as the primary target. The effects of the two objectives on the selection of battery types, battery capacities, and power scheduling schemes of the BESS in the PV system are comprehensively analyzed and discussed. This work provides meaningful guidance for the utilization of the BESS with multiple types of batteries under multi-objective application scenarios.

A Fuzzy Based Grid Maintenances and Load Balancing Technique for Feature Applications

In a Mobile Ad Hoc Network, this article suggests a Fuzzy dependent power scheduling and load balancing strategy for ZRP (MANET). The duty-cycles of border nodes are adaptively modified using this method, which takes into account the queue condition, expected residual capacity, and distance to border nodes. The nodes are active throughout each round and then go into sleep mode depending on the calculated duty-cycle time. When the load reaches a certain threshold, the zone leaders (ZL) are updated adaptively.

A multi-objective optimization model for fast electric vehicle charging stations with wind, PV power and energy storage

The construction of fast electric vehicle (EV) charging stations is critical for the development of EV industry. The integration of renewable energy into the EV charging stations comprises both threats and chances. A successful and reasonable capacity configuration and scheduling strategy is beneficial and significant. This paper studies the optimal design for fast EV charging stations with wind, PV power and energy storage system (FEVCS-WPE), which determines the capacity configuration of components and the power scheduling strategy. Firstly, an EV charging load simulation model considering demand response is built, which dynamically modified charging expectation under time-of-use electricity price. Secondly, based on the system design, a multi-objective optimization model is proposed with minimum objectives of cost of electricity and pollution emissions. Then, this model is solved by a hybrid optimization algorithm which combines multi-objective particle swarm optimization (MOPSO) algorithm and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Finally, the proposed optimization framework is applied to a case in Inner Mongolia, China. A scenario analysis is conducted and concludes that the renewable energy supplies, the connection with utility grid and demand response can help improve the performance on optimization objectives. A sensitivity analysis is also performed to verity the model’s effectiveness. In addition, the proposed method is compared with simulated annealing and genetic algorithm to show its faster computation speed and higher solution quality. • A fast EV charging station with renewable energies and ESS is proposed. • An EV charging load simulation model based on demand response is built. • An optimization design model for FEVCS-WPE is proposed with two objectives. • A hybrid optimization algorithm combining MOPSO and TOPSIS method is used.

Self-adapting variable step size strategies for active noise control systems with acoustic feedback

Self-adapting variable step size (SAVSS) normalized least mean square (NLMS) algorithms are derived and analysed for the active noise control systems with acoustic feedback path. The objective of the proposed SAVSS scheme is to resolve the conflicting requirements of rapid convergence and low mis-adjustment. A tuningless power scheduling scheme is proposed that varies the gain according to the output of the predictor filter, and switches to a low gain on the basis of a minimum criteria. In the steady state, a low gain auxiliary noise significantly improves noise reduction performance, and the update of the feedback compensation filter and predictor filter is discontinued to reduce computations. Simulations are performed with narrowband noises to validate the improved performance of the proposed method in comparison with established state-of-the-art methods.

Distributed power management with adaptive scheduling horizons for more electric aircraft

A more electric aircraft is expected to have low emissions, decreased fuel consumption, high efficiency, and low maintenance costs. To fully enjoy those benefits, major technical obstacles must be overcome first. In terms of power management, an increasing number of power electronic components used in aircraft power systems and their ever-increasing functional sophistication will lead to high modeling and computational complexities, making real-time power management a daunting challenge. To address this challenge, in this paper we will first propose a novel architecture for real-time power scheduling, which allows a user to easily incorporate fault diagnosis, stability analysis and power scheduling together, and then present a locally tactical power scheduling strategy based on a Lagrangian relaxation, which allows a user to adopt different computation techniques to undertake local computations within sub-systems in parallel to ensure good scalability of the distributed strategy. Moreover, the power scheduling is calculated based on an adaptive scheduling horizon to further reduce the computational complexity. Simulations are carried out to illustrate the effectiveness of our proposed distributed architecture, in particular, the optimality of the solution and the balanced computational complexity.