Energy Cost Function Research Articles

Optimal containment control for multi‐agent systems using fast adaptive dynamic programming

AbstractIn this paper, an innovative adaptive dynamic programming (ADP) algorithm with fast convergence speed is designed for the optimal containment control problem of discrete‐time linear multi‐agent systems. Precisely, a quadratic input energy cost function, including local containment error information and actuator information in the neighborhood, is designed for each follower. Solving the stationary condition of the cost function, the optimal containment controllers are obtained. Traditional ADP methods use actor–critic neural networks to approximate optimal costs and control strategies, it is time‐consuming to solve large‐scale multi‐agent problems due to the computational complexity of neural networks. In order to seek faster convergence speed of optimal containment control without knowing the model information, the fast ADP algorithm framework is designed, it is proved theoretically that the convergence speed is determined by some configurable parameters, and the whale optimization algorithm is employed to globally optimize the parameters of given spaces to derive the optimal configuration. Finally, numerical simulation results are given to verify the effectiveness of the designed algorithm.

Counterdiabatic optimized driving in quantum phase sensitive models

State preparation plays a pivotal role in numerous quantum algorithms, including quantum phase estimation. This paper extends and benchmarks counterdiabatic driving protocols across three one-dimensional spin systems characterized by phase transitions: the axial next-nearest neighbor Ising, XXZ, and Haldane–Shastry models. We perform a shallow quantum optimal control over the counterdiabatic protocols by optimizing an energy cost function. Moreover, we provide a code package for computing symbolically various adiabatic gauge potentials. This protocol consistently surpasses standard annealing schedules, often achieving performance improvements of several orders of magnitude. The axial next-nearest neighbor Ising model stands out as a notable example, where fidelities exceeding 0.5 are attainable in most cases. Furthermore, the optimized paths exhibit promising generalization capabilities to higher-dimensional systems, allowing for the extension of parameters from smaller models. Nevertheless, our investigations reveal limitations in the case of the XXZ and Haldane–Shastry models, particularly when transitioning away from the ferromagnetic phase. This suggests that finding optimal diabatic gauge potentials for specific systems remains an important research direction.

Elementary design and analysis of QCA-based T-flipflop for nanocomputing

Abstract This work presents a new T-flipflop design based on quantum-dot cellular automata technology, with the standard two inputs (T and clock) and two outputs (Q and Q̄). It adheres to the typical QCA layout design approach, which consists of two majority voters and one inverter (to produce the complementary output, Q̄). It is a single-layered design with no crossover. A memory loop is used to retain previous values and aid the toggling operation of the T-flipflop. This design achieves improved functionality and reduced area requirement compared to existing designs. In addition, the study investigated energy loss and cost functions. In particular, the total energy loss is reduced by 10% and 22% compared to the best design when analyzed with the QCAPro and QCADesigner-E (QDE) tools, respectively. The area-delay and energy-delay cost functions outperform the best current design by 1.3 and 1.07 times, respectively. Overall, this work advances QCA-based flipflop (QTFF) designs and emphasizes the potential of QCA technology for creating effective QCA circuits.

Cooperative trajectory shaping guidance law for multiple anti-ship missiles

Abstract To enhance the performance of anti-ship missiles cooperative attack, this paper proposes a finite-time trajectory shaping-based cooperative guidance law (TSCGL). Firstly, the cooperative guidance model is established on segmented linearisation of the missile’s heading angle. Then, a trajectory shaping guidance law for a single missile is derived by a weighted optimal energy cost function and Schwarz inequality. On this basis, a finite-time TSCGL is proposed combined with trajectory shaping technology and finite-time theory. The desirable finite-time convergence performance can ensure a simultaneous attack. Through an improved method of time-to-go estimation, it is independent of small-angle assumption and relaxes the launching conditions of the missiles. Additionally, the proposed finite-time TSCGL can achieve better damage performance through energy management. Finally, simulation results demonstrate the effectiveness and superiority of the proposed finite-time TSCGL.

A Safety Planning and Control Architecture Applied to a Quadrotor Autopilot

This letter presents a safety trajectory planning and tracking architecture for a quadrotor autopilot. Motor saturation constraints are explicitly considered in obstacle avoidance mission. Two challenging cases are covered: agile flight with a short task time and stable flight with actuator degradation. By explicitly considering the real physical constraints, the proposed method can make better use of the quadrotor maneuverability. Specifically, from control aspect, a sliding mode observer (SMO) based safety controller is implemented on the autopilot. Subsequently, the actuator saturation problem is analyzed and modeled as axis-coupled constraints. The B-spline curve is used to deal with the constraints due to its convex hull property. As a result, the planning problem can be transformed into a quadratically constrained quadratic programming (QCQP) problem with the proposed minimum energy cost function. Experiments show that the resulting trajectories can yield improvements in tracking accuracy in comparison of conventional planning method.

Bi-level fuzzy stochastic-robust model for flexibility valorizing of renewable networked microgrids

This paper presents a new bi-level multi-objective model to valorize the microgrid (MG) flexibility based on flexible power management system. It considers the presence of renewable and flexibility resources including demand response program (DRP), energy storage system and integrated unit of electric spring with electric vehicles (EVs) parking (IUEE). The proposed bi-level model in the upper-level maximizes expected flexibility resources profit subject to flexibility constraints. Also, in the lower level, minimizing MG energy cost and voltage deviation function based on the Pareto optimization technique is considered as the objective functions; it is bounded by the linearized AC optimal power flow constraints, renewable and flexibility resources limits, and the MG flexibility restrictions. In the following, the proposed bi-level model using Karush–Kuhn–Tucker (KKT) technique is converted to a single-level counterpart, and the fuzzy decision-making method is employed to achieve the best compromise solution. Further, hybrid stochastic-robust programming models uncertain parameters of the proposed model, so that stochastic programming models uncertainties associated with demand, energy price, and the maximum renewables active generation. Also, to capture the flexible potential capabilities of the IUEE, robust optimization models the EVs’ parameters uncertainty. Finally, numerical results confirm the proposed model could jointly improve operation, economic and flexibility conditions of the MG and turned it to a flexi-optimized-renewable MG. • Bi-level model to value the microgrid flexibility is presented. • Integrated unit of electric spring with electric vehicles is modeled. • The paper deals with maximizing flexibility value in renewable microgrids. • Fuzzy stochastic/robust optimization is proposed to handle uncertainty.

Suboptimal attitude control of unknown flexible space debris

This paper introduces a suboptimal controller with guaranteed global convergence for attitude control of space debris with unknown mass and flexibility properties. The proposed output-feedback control scheme can be implemented in real-time, does not need velocity measurements, and even under actuator faults and external disturbances converges to the vicinity of an energy-optimal solution. The controller essentially modifies the active disturbance rejection control scheme with variable state-dependent gains to minimize the energy cost function based on the solution of the Riccati equation. It is especially applicable to efficient detumbling of decommissioned or dysfunctional spacecraft with flexible solar panels, antennas, and booms, using a Deorbiter CubeSat. Simulations are carried out to demonstrate the effectiveness of the proposed scheme versus proportional-derivative controllers with an extended state observer, which need a system model for assigning their gains, as well as conventional active disturbance rejection controllers.

Adaptive clustering in energy efficient routing protocol for mobile nodes in WSNs

Introduction: Wireless Sensor Networks (WSN) is a collection of large number of small sensor nodes which communicate sensed data over a radio channel covering wide geographical region. Problem statement: A number of algorithms have been developed to enhance the network lifetime of WSN by efficiently utilizing the sources of energy. The most commonly used approach is clustering that is prone to uneven load balancing and instability issues. Furthermore, topological changes in WSN structure especially with mobile nodes significantly effect network lifetime. Methodology: In this study, we have proposed an Adaptive-Cluster-based Energy Efficient Routing Protocol (A-EECBRP), which employs a novel geometrical Voronoi-based configuration to solve load balancing and mobility issues while maintaining network stability and coverage. Furthermore, energy cost function and Energy Harvesters (EH) devices were implemented to reduce energy consumption and increase network life. Moreover, the concept of handshaking and random waypoint model for nodes movement between cluster groups was examined to define mobile nodes. Results: Simulation results obtained from network analysis performed on MATLAB® showed that A-EECBRP reduced energy consumption by almost 1500 rounds as compared to LEACH-M. This significantly improved the network lifetime of WSN as compared to the LEACH-M routing protocol. Therefore, our proposed scheme provides a huge potential for implementing energy-efficient routing protocols in mobile wireless sensor networks.

A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles

Recently, the ship energy consumption issue has been in the spotlight. Meanwhile, new conventions and standards of ship energy conservation as well as ship emission reduction have been continuously introduced by the International Maritime Organization and governments around the world. Energy conservation is a crucial factor in ship route planning. This paper proposes an improved ant colony algorithm that can handle a complicated ocean current environment and find an optimal energy-saving path. Firstly, this paper focuses on both the path length and energy consumption. To reduce energy consumption, a comprehensive energy cost function is introduced by a conventional ant colony algorithm. In addition, this paper analyzes the influence of the ocean current speed and setting on a marine vehicle and establishes an improved ant colony algorithm model based on the influence of the ocean current. Finally, simulation tests are carried out in Lianyungang and its surrounding areas to test the energy consumption performance of the improved ant colony algorithm. The simulation result shows that the improved ant colony algorithm can significantly reduce the energy consumption by 3.326% compared with the traditional algorithm.

Electrospraying Zwitterionic Copolymers as an Effective Biofouling Control for Accurate and Continuous Monitoring of Wastewater Dynamics in a Real-Time and Long-Term Manner.

Long-term continuous monitoring (LTCM) of water quality can provide high-fidelity datasets essential for executing swift control and enhancing system efficiency. One roadblock for LTCM using solid-state ion-selective electrode (S-ISE) sensors is biofouling on the sensor surface, which perturbs analyte mass transfer and deteriorates the sensor reading accuracy. This study advanced the anti-biofouling property of S-ISE sensors through precisely coating a self-assembled channel-type zwitterionic copolymer poly(trifluoroethyl methacrylate-random-sulfobetaine methacrylate) (PTFEMA-r-SBMA) on the sensor surface using electrospray. The PTFEMA-r-SBMA membrane exhibits exceptional permeability and selectivity to primary ions in water solutions. NH4+ S-ISE sensors with this anti-fouling zwitterionic layer were examined in real wastewater for 55 days consecutively, exhibiting sensitivity close to the theoretical value (59.18 mV/dec) and long-term stability (error <4 mg/L). Furthermore, a denoising data processing algorithm (DDPA) was developed to further improve the sensor accuracy, reducing the S-ISE sensor error to only 1.2 mg/L after 50 days of real wastewater analysis. Based on the dynamic energy cost function and carbon footprint models, LTCM is expected to save 44.9% NH4+ discharge, 12.8% energy consumption, and 26.7% greenhouse emission under normal operational conditions. This study unveils an innovative LTCM methodology by integrating advanced materials (anti-fouling layer coating) with sensor data processing (DDPA).

Energy consumption control in the two-machine Bernoulli serial production line with setup and idleness

In recent years, the topic of sustainable manufacturing system design with energy saving features has received increasing attention. For a typical production line setting, the machines are subject to random breakdown and restart besides blockage and starvation, and they are usually connected via buffer areas with finite capacity. In the present study, the energy consumption of the serial production line with two Bernoulli machines is considered, with an aim to minimise the total energy consumption under a desired production rate. The energy consumption of the two-machine system is composed of the energy needed to setup, to remain idle, and for actual manufacturing. In order to minimise the total energy expenditure, a nonlinear fractional polynomial optimisation model is constructed, which is first converted to a nonlinear polynomial optimisation problem. Then the property of the total energy cost is analysed via the sum of squares (SOS) method. To speed up the solving process, a new heuristic approach named energy consumption saving (ECS) algorithm is proposed considering the monotonicity and local optimality of the energy cost function. Finally, by presenting optimal configurations of the production line with different throughputs, buffer capacities, and energy parameters, a simulation-based study is performed to validate the SOS and ECS algorithms.

Using multi-objective optimisation with ADM1 and measured data to improve the performance of an existing anaerobic digestion system

This paper presents a method to model and optimise the substrate feeding rate of an anaerobic digestion (AD) system. The method is demonstrated for a case study plant in Bangalore, India, using onsite kitchen waste to provide biogas for cooking. The AD system is modelled using Anaerobic Digestion Model No. 1 (ADM1) and a genetic algorithm (GA) is applied to control the substrate feeding rate in order to simultaneously minimise the volume of flared biogas, unmet gas demand and energy cost. Our results show that ADM1 can predict biogas yield from a continuously operated digester well with mean percentage errors between daily predicted and measured data values of only 5.7% for March 2017 and 17.8% for July 2017. When biogas flaring and unmet gas demand were minimised, the amount of biogas flared reduced from 886.62 m3 to 88.87 m3 in March and from 73.79 m3 to 68.49 m3 in July. When the energy cost was also considered within the objective function, the biogas flared reduced from 886.62 m3 to 281.27 m3 for March, but increased from 73.79 m3 to 180.11 m3 for July. The amount of flaring increased in July as the energy cost function increased biogas yield without considering surplus gas production beyond demand and storage capacity. As AD systems are often operated to maximise biogas production, these results highlight the need for multi-objective optimisation, particularly for off-grid AD systems.

Optimized hybridized mathematical model for wastewater treatment and energy generation using microbial fuel cells

In the current era of urbanization and industrialization, water pollution and energy generation are the main concerns in the climate change context. Microbial Fuel Cells (MFCs) utilize biodegradable carbon compounds in organic waste to produce an electric current.MFC technology provides sustainable solutions for renewable power systems and energy-efficient wastewater treatment for distributed electricity systems. However, in wastewater treatment, generating usable energy from the MFC remains a significant challenge for the system scale and implementation. Hence, in this paper, the Optimized Hybridized Mathematical Model (OHMM) has been proposed for wastewater treatment and energy generation using microbial fuel batteries.Furthermore, statistical analysis has a role in synthesizing knowledge of fuel cells, defining and limiting factors in electricity production. The findings show that the proposed OHMM method, Sensitivity Analysis, Uncertainty Analysis, and Accuracy reduces energy consumption and cost function compared to other existing methods. With more miniature steam, it reduces the amount of harmful smoke that power plants emit, preserves the natural resources of a planet, and protects habitats against destruction. They will lead to a healthier and happier world by taking action to reduce the battery.

Optimal Target Control of Complex Networks With Selectable Inputs

Controlling a preselected subset of nodes (named “target control”) of complex networks with minimal control energy is a critically important physical issue. To address this issue, first, an energy cost function is established by designing an optimal controller, in which an input matrix $B$ is involved as a matrix variable to be determined so as to minimize the control cost. In particular, we integrate the design equations to obtain an equivalent expression of the cost function without solving the Riccati differential equation directly. Second, based on this expression, we derive its gradient with respect to $B$ by introducing some methods for matrix differentiation. Two different constraints, i.e., trace and positive element constraints, which are imposed on the matrix variable $B$ of the cost function, are considered. Last but not least, we propose two corresponding algorithms to solve these two different constraint optimization problems. Numerical examples in directed networks are provided to show the effectiveness of the proposed methods. This work suggests that the target control of complex networks can reduce both the minimum number of external control sources and the control cost.

Prospects of Hybrid Renewable Energy-Based Power System: A Case Study, Post Analysis of Chipendeke Micro-Hydro, Zimbabwe

Fossil fuel-based energy sources are the major contributors to greenhouse gas (GHG) emission and thus the use of renewable energy (RE) is becoming the best alternative to cater for the increasing energy demand in both developing and developed nations. Chipendeke is a rural community in Zimbabwe, in which electricity demand is partially served by the only micro-hydro plant and hence, load shedding is a regular practice to keep essential services running. This study explored a suitable opportunity to identify a feasible system with different energy sources that can fulfill the current and projected future load demand of the community. A techno-economic feasibility study for a hybrid RE based power system (REPS) is examined considering various energy sources and cost functions. Six different system configurations have been designed with different sizing combinations to identify the most optimum solution for the locality considering techno-economic and environmental viability. The performance metrics considered to evaluate the best suitable model are; Net Present Cost (NPC), Cost of Energy (COE), Renewable Fraction (RF), excess energy and seasonal load variations. In-depth, sensitivity analyses have been performed to investigate the variations of the studied models with a little variation of input variables. Of the studied configurations, an off-grid hybrid Hydro/PV/DG/Battery system was found to be the most economically feasible compared to other configurations. This system had the lowest NPC and COE of $ \$ $ 307,657 and $ \$ $ 0.165/kWh respectively and the highest RF of 87.5%. The proposed hybrid system could apply to any other remote areas in the region and anywhere worldwide.

Optimized flapping wing dynamics via DMOC approach

Outstanding aerial capabilities that insects present in nature inspire researchers to undertake a challenge to develop a flapping aerial vehicle with performances unmatched by any manmade object. However, the complex aerodynamic phenomena crucial for the insect flight are not easily understood, let alone modeled and utilized for flight. The researchers managed to develop a quasi-steady aerodynamic model capable of capturing the most important aspects of a fruit fly-like insect flight, while still being efficient enough to allow for the usage in the flapping mechanism optimization loop. This experimentally justified quasi-steady model is used in the paper as a building block for creating a novel optimization algorithm, based on the discrete mechanics and optimal control framework. When compared to the conventional approaches to design optimization, this framework includes the natural description of the energy cost function, while incorporating the physical laws in the form of a discrete Lagrange–d’ Alembert equations inherently in optimization constraints. This leads to the discrete description of the inherently continuous problem, allowing the algorithm to search for the optimal solutions in the whole domain. In other words, in contrast to the conventional approaches involving the assumption on the function family and subsequent optimization on the parameters of that function type, this approach is not constrained by the user input and is capable of yielding any solution that respects the physical laws. As presented by the numerical test cases, optimizing the flapping patterns of a fruit fly-like aerial vehicle in standstill hovering leads to both effective and robust optimization tool.

A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home

In recent years, energy demand has grown significantly relative to its production. The power companies have also offered a variety of schemes such as energy consumption management to meet this growing consumer demand. Energy consumption management is a set of strategies used to optimize energy consumption which includes a set of interconnected activities between the utility and customers to transfer the load from peak hours to off-peak hours. This reduces the electricity bill. This paper presents an optimal schedule for the consumption of residential appliances based on improved multi-objective antlion optimization algorithm to minimize the electrical cost and the user comfort. To prevent peaks, the peak-to-average ratio is considered as a constraint for the energy cost function. Also, two different tariff signals have been used to measure energy costs. The real-time pricing and critical peak pricing are considered as energy tariffs. The simulations results are compared with other meta-heuristic algorithms, including multi-objective particle swarm optimization, the second version of the non-dominated sorting genetic algorithm, and the basic antlion optimizer algorithm to show the superiority of the proposed algorithm. Final results show that using the proposed scheme reaches electricity bills less than 80%.

MANAGEMENT OF POWER IN ASPECTS OF ENERGY PRODUCTION PRICES FOR FUEL ENERGY GENERATORS

The article describes the impact of a fuel-consuming power generator on energy production costs. The research is based on a thermodynamic engine model for which the energy source is hydrocarbon fuel. The results of the studies on the thermodynamic engine are energy cost functions for which the variable is electrical power. The presented characteristics reflect the impact of fuel prices on the minima of the cost function. In addition, the characteristics reflect the location of the extremes of functions in the coordinate system relative to the maximum of the function of efficiency. The article concludes with a cost model for thermodynamic engines for which hydrocarbon fuel is purchased. Article proposes the cooperation of generators with renewable sources to increase and stabilize the power rating of power systems.

Multi-QoS constraint multipath routing in cluster-based wireless sensor network

Achieving the best quality as per user requirement is one of the important challenges in Wireless Sensor Network (WSN) containing numerous sensor nodes with limited battery power. The time-critical applications in WSN demand energy-efficient and reliable transmission of data with limited resource availability. To resolve these issues, our proposed optimal cluster head selection method choose node as CH subjected to minimal end-to-end delay and congestion index. The nodes with maximum remaining energy, not being subjected to delay and heavy traffic have been selected as a cluster head (CH) for the next round. The Inter-cluster routing path constructed based on energy, delay, and congestion index cost function to achieve aggregated bandwidth through the establishment of multipath. MQoScMR supports adaptive CH and path selection, tuning QoS parameter according to application requirement. The technique proposed outperforms when compared with the existing protocol in terms of minimized energy consumption, delay, packet drop ratio, and high throughput.

Evolutionary Algorithm-Based Complete Coverage Path Planning for Tetriamond Tiling Robots.

Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a self-reconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shape-shifting of the platform. Thus, during motion planning, the optimal navigation sequence of a self-reconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamond-based robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shape-shifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamond-based reconfigurable robots.