Power Cost Function Research Articles

Sharp PDE estimates for random two-dimensional bipartite matching with power cost function

Sharp PDE estimates for random two-dimensional bipartite matching with power cost function

Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method

Fuel cell lifetime is strongly affected by dynamic conditions. Most existing energy management works only focus on the fuel cell durability protection from the perspective of output power slope, without deeply considering the influence of the important parameters inside the stack. However, considering the variation of stack internal parameters (mechanism analysis) is more significant for fuel cell lifetime evaluation. In this paper, a health-aware model predictive control (HA-MPC) energy management strategy is proposed for fuel cell electric vehicle. A fuel cell health state model is established from the perspective of stack hydrogen excess ratio (HER), oxygen excess ratio (OER) and humidity through the hybrid modeling method. The fuel cell mechanism model and the low-dimensional data-driven model are established through the grey-box model estimation method and genetic algorithm-based radial basis function (GA-RBF) neural network. Then the objective function of energy management strategy is developed considering the total equivalent hydrogen consumption and stack improper parameter changes of HER, OER and humidity. Comparing with model predictive control strategy based on the typical power cost function, the HA-MPC can effectively reduce the steep drop of HER and OER in low power region by 3.58% and 4.41%, which can protect the fuel cell system lifetime.

Minimum Power Control Allocation for Incremental Control of Over-Actuated Transition Aircraft

Existing control allocation approaches usually minimize an or norm of the actuator commands that is loosely related to the thrust produced or the aerodynamic drag. This paper proposes a novel incremental control allocation method that directly minimizes the total power required for an over-actuated, propeller-driven transition aircraft. The minimum power cost function is derived in a convex form and leads to a closed-form algebraic solution for an additional command increment in the null space of the local control effectiveness matrix. The complete control allocation procedure consists of a two-step approach that is direction-preserving in the presence of actuator saturations and nonlinear due to the incremental formulation. The proposed control allocation is particularly advantageous in the case of a transition aircraft, where the tradeoff between using aerodynamic lift and vertical thrust can be solved naturally by including incremental attitude commands as virtual inputs in the minimum power control allocation. The resulting control allocation scheme allows to both maximize control authority and find minimum power trim solutions. Flight tests are performed on an over-actuated transition aircraft, showcasing the minimum power trim and control allocation without the use of flight mode switching or blending functions throughout all flight regimes.

Speed Scaling on Parallel Servers With MapReduce Type Precedence Constraints

A multiple server setting is considered, where each server has tunable speed, and increasing the speed incurs an energy cost. Jobs arrive to a single queue, and each job has two types of sub-tasks, map and reduce, and a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">precedence</b> constraint among them: any reduce task of a job can only be processed once all the map tasks of the job have been completed. In addition to the scheduling problem, i.e., which task to execute on which server, with tunable speed, an additional decision variable is the choice of speed for each server, so as to minimize a linear combination of the sum of the flow times of jobs/tasks and the total energy cost. The precedence constraints present new challenges for the speed scaling problem with multiple servers, namely that the number of tasks that can be executed at any time may be small but the total number of outstanding tasks might be quite large. We present simple speed scaling algorithms that are shown to have competitive ratios, that depend on the power cost function, and/or the ratio of the size of the largest task and the shortest reduce task, but not on the number of jobs, or the number of servers.

Piezoelectric patch vibration control unit connected to a self-tuning RL-shunt set to maximise electric power absorption

This paper presents a modular vibration control unit formed by a piezoelectric patch connected to a self-tuning RL-shunt, which can be bonded on thin structures to reduce the resonant response of a specific target low-order flexural mode due to a stationary stochastic excitation. The resistive and inductive components of the shunt are tuned online with an extremum seeking gradient search algorithm in such a way as to maximise the time-averaged electric power absorbed by the shunt from the resonant response of the target flexural mode of the structure. The tuning is thus implemented locally in the unit without the need of additional sensors to monitor the vibration of the structure. The paper presents both simulation and experimental results for a thin rectangular panel model structure, which is excited by a broadband stationary stochastic force and is equipped with five piezoelectric patches connected to the proposed self-tuning RL-shunts. To start with, the study shows that the resistance and inductance necessary to minimise the time-averaged resonant response of the target flexural mode closely match the values that would maximise the time-averaged electric power absorption by the shunt from the resonant response of the target flexural mode. Also, it shows that these two cost functions describe mirrored bell-surfaces with constant-inductance and constant-resistance principal axes. Hence, it proposes an independent inductance and resistance tuning sequence, which is based on an extremum seeking gradient search algorithm along the two principal directions of the electric power cost function. Finally, the online convergence to the optimal RL values of the proposed tuning algorithm is verified experimentally using a digital shunt.

Social welfare maximization based optimal energy and reactive power dispatch using ant lion optimization algorithm

In this paper an optimal energy and reactive power dispatch problem is solved by using the ant lion optimization (ALO) algorithm by considering the total cost minimization and social welfare maximization (SWM) objectives. Two different market models are proposed in this work, i.e., conventional/sequential market clearing and the proposed/simultaneous market clearing. In each market model, two objectives, i.e., total cost minimization and SWM are considered. The conventional social welfare (SW) consists the benefit function of consumers and the cost function of active power generation. In this paper, the conventional SW is modified by including the reactive power cost function. The reactive power cost calculation is exactly same as that in the conventional practice. The most important difference is that instead of doing cost calculation in post-facto manner as in conventional practice, simultaneous approach is proposed in this work. The scientificity and suitability of the proposed simultaneous active and reactive power methodology has been examined on standard IEEE 30 bus test system.

Energy optimal attitude control for a solar-powered spacecraft

In this article we aim to maximize the net energy a solar-powered spacecraft gains when performing a maneuver. The net energy can be defined as the integral of the power supplied by the solar panels minus the power used by the attitude control system, and is important since energy is a scarce resource in space. Previous research on optimal attitude control has focused on optimization with respect to other costs, such as time-optimal control and optimal attitude control with respect to the integral of the square of the input. The energy flow depends on both the power spent on actuation and the power received from the solar panels. Thus, the optimal attitude control problem should be formulated in such a way that the attitude of the spacecraft relative to the Sun during the maneuver is included in the calculations. This paper proposes a cost function based on net power to address this problem, introducing a new cost function that incorporates the incoming energy from the solar irradiance and the outgoing energy due to actuation. A simulation study comparing an optimal control solution of the proposed net power cost function using IPOPT in CasADi is presented for a 6U CubeSat equipped with solar cell arrays, where the net power based optimal control maneuver is shown to compare favorably to a sun-pointing PD controller.

Multi-Objective Optimal Placement of Distributed Generators by Modified BAT Algorithm

Day by day the need for electrical power is increasing due to the continuous growth and development in the social and economical status of the society. By and large in conventional electrical power system network, power flows from generating stations through transmission and distribution system to the end user. The demand for electrical power at the distribution systems is also increasing continuously. In order to meet the increase in the demand for the electrical power, it is required to increase the existing capacities of the generation and transmission &amp; distribution system that involves lot of money and is a time consuming process. The best solution for this problem of increase in load demand is perfectly met with electric power generating units of smaller capacity which are going to be installed locally in the distribution system and are termed as “Distributed Generators (DGs)”. DGs not only supplies electrical power that meets the increase in load demand but also reduces the losses, improves the voltage profile and reliability of the system. To get maximum of these benefits, DGs are to be installed at its best optimal locations and are to be operated at its optimal generating capacities. In this work an algorithm based on MBAT is proposed to find the optimal locations and sizes of the DG units by considering the minimization of multiple objective functions like minimization of real power loss, cost function and total voltage deviation. The proposed algorithm is tested on IEEE-33 radial distribution systems and results are presented.

Double‐sided bidding strategy for power suppliers and large buyers with amalgamation of wind and solar based generation in a modern energy market

The deployment of renewable energy sources has been rapidly increasing due to environmental constraints but renewable electricity suppliers face an inevitable problem of uncertainty which is caused by the intermittent nature of renewable sources. In real-time operation, compromises have been made in cost and power to balance the power which reduces supplier benefits. Therefore, the functions of Weibull and Beta distribution of probability are used to handle the adverse impact of wind speed uncertainty and solar irradiation, respectively, and scenarios are reduced using the forward-reduction algorithm. Moreover, to measure the deviation of renewable power, underestimation and overestimation cost functions are utilised. This study proposes a suitable double-sided strategic bidding problem as a multi-objective optimisation problem to maximise the profits of suppliers and buyers to minimise uncertainty of rivals and renewable power. The formulated problem is solved by Technique for Order of Preference by Similarity to Ideal Solution along with Gravitational Search Algorithm, and simulation results are obtained in absence and presence of both solar and wind power on IEEE standard 30-bus and 57-bus test systems. The obtained results prove the suitability of the proposed bidding strategy in the presence of uncertainty of solar and wind power.

Multi-objective optimal power flow considering wind power cost functions using enhanced PSO with chaotic mutation and stochastic weights

A multi-objective optimal power flow (OPF) solution using an enhanced NSPSO, incorporating chaotic mutation and stochastic weight trade-off features, is proposed here. The objective functions considered embody the different aspects of the power system, viz. financial, reliability and operational efficiency. The proposed OPF algorithm balances the exploration of global and the utilization of local bests with the stochastic weight and dynamic coefficient trade-off techniques, thus enhancing the searching capability. Also, for countering the premature convergence issue and to improve diversity, the feature of chaotic mutation is incorporated. The pareto-optimal front is provided by the combination of crowding distance approach and non-dominated sorting principle. The best solution can be obtained from a dual-stage process, by selecting from the collective of local best compromises using a fuzzy function. To assess the performance, the proposed method is tested on a standard IEEE 30-bus test system and is compared with popular multi-objective algorithms. IEEE 118-bus system is also used for testing applicability to large systems. Based on the obtained results, the proposed method gives a more reliable operating solutions, better optimal front and hence an improved solution providing a better trade-off.

Modified harmony search algorithm for combined economic emission dispatch of microgrid incorporating renewable sources

Nowadays, there is a growing interest in the microgrid systems with a high penetration of renewable sources. In this paper, the modified harmony search (MHS) algorithm is proposed to solve the combined economic emission dispatch (CEED) problem of the microgrid taking into account the solar and wind power cost functions. The proposed algorithm can be derived by not only adjusting the parameters but also improving the structure and operation of the original harmony search (HS) algorithm. The solution of the CEED problem of the microgrid taking into account the solar and wind power cost functions is obtained for different scenarios using the MHS algorithm and some recently published algorithms. The results of all scenarios show the effectiveness of the MHS algorithm over other published algorithms employing same data.

Model Predictive Direct Power Control for Nonredundant Fault Tolerant Grid-Connected Bidirectional Voltage Source Converter

This paper proposes a model predictive direct power control scheme for nonredundant fault tolerant grid-connected bidirectional voltage source converter (BVSC) with balanced dc-link split capacitor voltage and high reliability. Based on the operation analysis of fault-tolerant BVSC with phase leg faults, a power predictive model of three-phase four-switch fault-tolerant topology in αβ coordinates is established, and the space voltage vectors with unbalanced dc-link split capacitor voltage are analyzed. According to the power predictive model and cost function, the optimal space voltage vector is selected to achieve a flexible, smooth transition between inverter and rectifier mode with direct power control. Pulse width modulation and phase locked loop are not required in the proposed method. The constraint of dc-link voltage constraint is designed for the cost function to achieve a central point of dc-link voltage offset suppression, which can reduce the risk of electrolytic capacitor failure for over-voltage operation. With the proposed control method, the converter can work continuously in both inverter mode and rectifier mode, even if phase leg faults occur. The simulation and experimental results show good steady-state and dynamic performance of the proposed control scheme to enhance the reliability of bidirectional power conversion.

Analyzing the Effect of Multi-fuel and Practical Constraints on Realistic Economic Load Dispatch using Novel Two-stage PSO

In power system restructuring, pricing the electrical power plays a vital role in cost allocation between suppliers and consumers. In optimal power dispatch problem, not only the cost of active power generation but also the costs of reactive power generated by the generators should be considered to increase the effectiveness of the problem. As the characteristics of reactive power cost curve are similar to that of active power cost curve, a nonconvex reactive power cost function is formulated. In this paper, a more realistic multi-fuel total cost objective is formulated by considering active and reactive power costs of generators. The formulated cost function is optimized by satisfying equality, in-equality and practical constraints using the proposed uniform distributed two-stage particle swarm optimization. The proposed algorithm is a combination of uniform distribution of control variables (to start the iterative process with good initial value) and two-stage initialization processes (to obtain best final value in less number of iterations) can enhance the effectiveness of convergence characteristics. Obtained results for the considered standard test functions and electrical systems indicate the effectiveness of the proposed algorithm and can obtain efficient solution when compared to existing methods. Hence, the proposed method is a promising method and can be easily applied to optimize the power system objectives.

Efficient Usage of Renewable Energy in Communication Systems Using Dynamic Spectrum Allocation and Collaborative Hybrid Powering

In this paper, we introduce a new green resource allocation problem using hybrid powering of communication systems from renewable and nonrenewable sources. The objective is to efficiently allocate the power delivered from the different micro-grids to satisfy the network requirements. Minimizing a defined power cost function instead of the net power consumption aims to encourage the use of the available renewable power through collaboration between the base stations within and outside the different micro-grids. The different degrees of freedom in the system, ranging from assignment of users to base stations, possibility of switching the unnecessary base stations to the sleep mode, dynamic power allocation, and dynamic allocation of the available bandwidth, allow us to achieve important power cost savings. Since the formulated optimization problem is a mixed integer-real problem with a nonlinear objective function, we propose to solve the problem using the branch and bound (B&B) approach, which allows to obtain the optimal or a suboptimal solution with a known distance to the optimal. The relaxed problem is shown to be a convex optimization which allows to obtain the lower bound. For practical applications with large number of users, we propose a heuristic solution based on decomposing the problem into two subproblems. The users-to-base stations assignment is solved using an algorithm inspired from the bin-packing approach while the bandwidth allocation is performed through the bulb-search approach. Simulation results confirm the important savings in the nonrenewable power consumption when using the proposed approach and the efficiency of the proposed disjointed algorithms.

Minimizing peak current in combinational circuit test

In the VLSI era where millions of transistors are packed in a single chip, the demand for proper power management is of a paramount importance. The problem of low power test is taking center stage for both combinational and sequential circuits in recent years due to its impact on overall yield. This paper proposes a technique that targets the reduction of peak current during combinational circuit test to realize peak power reduction. Unlike previous methods, this approach utilizes peak current defined by the direction of switching activity to find minimum peak power. The proposed framework consists of two main phases where the test set is first reordered using a combined peak current/peak power cost function followed by an x-refilling technique based on Fiduccia-Mattheyses concept to refill unspecified bit values. Experimental results show that the proposed approach reduces peak current, peak power, and total power by 33%, 32%, and 43% respectively compared to Hamming distance-based ordering with random filling.

Economic Concerns in Optimal Power Dispatch in the Presence of a Generalized Unified Power Flow Controller under Practical Constraints

In power system restructuring, pricing the electrical power plays a vital role in cost allocation between suppliers and consumers. In optimal power dispatch problem, not only the cost of active power generation but also the costs of reactive power generated by the generators, shunt capacitors, transmission losses and device investment costs should be considered. In this paper, a more realistic multi-fuel total cost objective is formulated by considering all the above mentioned costs. As the characteristics of reactive power cost curve are similar to that of active power cost curve, a non-convex reactive power cost function is formulated. The formulated cost function is optimized by satisfying equality, in-equality and practical constraints and also device limits using the proposed uniform distributed two-stage particle swarm optimization. In this paper, power injection model of generalized unified power flow controller (GUPFC) including converter switching losses is presented. The proposed objective functions are optimized in the presence of UPFC and GUPFC and finally suitable device is identified for the standard IEEE-30 bus test system.

Optimization of Microgrid Using Quantum Inspired Evolutionary Algorithm

This paper presents a generalized formulation for determining the optimal operating strategy and cost optimization scheme as well as reducing the emissions of a MicroGrid (MG). In this article a microgrid including a wind turbine, pv array and a CHP system consisting of fuel cells and a microturbine is studied and then the modeling of various DERs is conducted and the objective functions and constraints are developed. The model takes into consideration the operation and maintenance costs as well as the reduction in emissions of NOx, SO2, and CO2 In the end the Quantum-Inspired Evolutionary Algorithm is employed to solved the optimal model and an operation scheme is achieved while meeting various constraints on the basis of tariff details, equipment performance, weather conditions and forecasts, load details and forecasts and other necessary information and then the economic costs and environmental impacts are analyzed and a conclusion that the QEA can achieve high environmental benefits and spend as low operation cost as possible. according to power Output functions and cost function of the various units , can be achieve to minimize cost.

선형 근사 평활 발전 비용함수를 이용한 경제급전 문제의 최적화

Abstract This paper proposes a simple linear function approximation method to solve an economic load dispatch problem with complex non-smooth generating cost function. This algorithm approximates a non-smooth power cost function to a linear approximate function and subsequently shuts down a generator with the highest operating cost and reduces the power of generator with more generating cost in order to balance the generating power and demands. When applied to the most prevalent benchmark economic load dispatch cases, the proposed algorithm is found to dramatically reduce the power cost than does heuristic algorithm. Moreover, it has successfully obtained results similar to those obtained through a quadratic approximate function method. Key Words : Economic load dispatch, Optimization, Shutdown, Smooth and non-smooth function, Function approximation * 정회원, 강릉원주대학교 과학기술대학 멀티미디어공학과접수일자 : 2013년 2월 11일, 수정완료 : 2014년 5월 3일게재확정일자 : 2014년 6월 13일Received: 11 February, 2014 / Revised: 3 May, 2014 /Accepted: 13 June, 2013

A New Approach to the Solution of Economic Dispatch Using Particle Swarm Optimization with Simulated Annealing

A new approach to the solution of Economic Dispatch using Particle Swarm Optimization is presented. It is the progression of allocating production amongst the dedicated units such that the restriction forced are fulfilled and the power needs are reduced. More just, the soft computing method has received supplementary concentration and was used in a quantity of successful and sensible applications. Here, an attempt has been made to find out the minimum cost by using Particle Swarm Optimization Algorithm using the data of three generating units. In this work, data has been taken such as the loss coefficients with the max-min power limit and cost function. PSO and Simulated Annealing are functional to put out the least amount for dissimilar energy requirements. When the outputs are compared with the conventional method, PSO seems to give an improved result with enhanced convergence feature. All the methods are executed in MATLAB environment. The effectiveness and feasibility of the proposed method were demonstrated by three generating units case study. Output gives hopeful results, signifying that the projected method of calculation is competent of economically formative advanced eminence solutions addressing economic dispatch problems.

Multi-objective voltage island floorplanning using sequence pair representation

In the nanometer era of VLSI design, high power consumption is considered to be a “show-stopper” for many applications. Voltage island design has emerged as a popular method for addressing this issue. This technique requires multiple supply voltages on the same chip with blocks assigned to different supply voltages. Implementation challenges force blocks with similar supply voltages to be placed contiguous to one another, thereby creating “islands”. Classical floorplanners assume a single supply voltage in the entire chip and thus require additional design steps to realize voltage islands. In this paper we present a new multi-objective floorplanning algorithm based on the sequence pair representation that can floorplan blocks in the form of islands. Given the possible supply voltage choices for each block, the floorplanner simultaneously attempts to reduce power and area of the chip. Our floorplanner integrates the tasks of assigning blocks to different supply voltages and the placing of the blocks in the chip. Compared to previous work, the proposed floorplanner on average reduces the area overhead of the chip by 13.5% with 34% runtime improvement. As power and area are jointly optimized, we also explore the trade-off between these cost functions. Experimental results show that equal weight to area and power cost function do not provide the best floorplan solution.