Voltage-based reactive power pricing in deregulated environment using hybrid multi-objective particle swarm optimisation

Reactive power market management along with voltage stability is needed to maintain system reliability and to meet the required security levels in deregulated power system. This paper explores the technical and economic issues of reactive power pricing mechanism along with improvement in voltage stability in deregulated system. The algorithm proposes a multi-objective optimisation problem for minimising reactive power pricing and cost of energy loss along with voltage stability improvement using Hybrid Multi-Objective Particle Swarm Optimization . The voltage magnitude of generators, reactive power settings of shunt capacitors and the transformer tap settings are taken as the control variables. The proposed problem uses a well-defined reactive power-bid structure based on long-term capacity bids provided by the generators and capacitors to the system operator to ensure voltage security. The proposed cost-based pricing strategy along with stability enhancement has been implemented in IEEE 30-bus test system under normal and stressed system conditions.     

Improving the energy efficiency of an islanded distribution network using classical and innovative computation methods

The paper presents the analysis of some potentially suitable actions for reducing the energy losses of an islanded Medium Voltage distribution network, with the aim of improving electricity distribution efficiency. For this purpose, four actions are considered: 1) increasing the network's rated voltage; 2) reactive power compensation through static capacitor banks; 3) reactive power compensation through switchable capacitor banks; 4) installation of distributed photovoltaic (PV) generation. The first two measures are typically taken into account by the distribution system operators and can be examined by means of classical design methods, whereas the latter two more innovative actions are tested here using specialized software based on the NSGA-II multi-objective optimization algorithm. The fourth action, expanding distributed PV generation, can be motivated through public or private incentives. It is investigated here using an innovative approach to the optimization problem formulation considering the perspectives of both the utility and the consumer. A case study on a small Mediterranean island real existing network is also presented.     

Multi-objective modelling of simultaneous reconfiguration of NMG-based distribution network and operation of different DERs

ABSTRACT

A Micro-Grid (MG) is envisaged with ever increasing distributed energy resources (DERs) e.g. distributed generation, demand response and electrical vehicles. This paper suggests a method based on multi-objective modelling for day-ahead scheduling of Networked-MGs based distribution network in the presence of different DERs and also this method is able to find the daily reconfiguration instants. The proposed scheme is solved using NSGA-II from distribution network operator viewpoints, who is responsible for providing power demand in higher reliability level and lower costs. Besides, in the suggested scheme voltage deviation and voltage stability as efficient power quality criteria in distribution networks and emission pollutant reduction are incorporated as independent objective functions. Moreover, to quantify the influence of different load models, a 33-node distribution network is adopted with a load class mix of residential, industrial and commercial loads. Eventually, the obtained results are reported which verify the efficiency of the proposed method.     

Simultaneous placement of renewable DGs and protective devices for improving the loss,reliability and economic indices of distribution system with nonlinear load model

ABSTRACT

In this paper, the simultaneous placement of renewable distributed generation (DG) and protective devices are discussed in the distribution system with nonlinear load model. The load model is considered as a combination of various customers’ daily load patterns and sensitive to voltage frequency. The considered DGs are wind turbine and photovoltaic. The breaker, relay and fuse are considered as protective devices. The objective functions of the problem consist of the active and reactive power loss, the reliability index and the profit of company of distribution system. The whale optimisation algorithm is used for multi-objective optimisation. Moreover, a method based on fuzzy set theory is employed to extract one of the Pareto-optimal solutions as the best compromise one. The proposed algorithm is implemented on the 69-bus distribution system and actual 101-bus distribution system in Khoy–Iran. The results indicate the high performance of the proposed method in improving the technical and economic indices of the network.     

Multi-objective modelling of day ahead scheduling of MMG-based distribution networks accompanied by DEA considering economics,emissions and power quality

ABSTRACT

This paper presents the state of the art relating to multi-objective modelling for day ahead scheduling of multi micro grid-based distribution networks, using optimal power flow (OPF) accompanied by data envelopment analysis (DEA). In this paper eco-reliability cost function, power quality enhancement and emission reduction are treated as the objective functions and the uncertainties of renewable distributed generations (DGs), load demand and market price are incorporated into the problem. This method is able to find the optimum operation of DGs in grid-connected or isolated MGs, power transaction between each MG and upstream networks/other MGs and hourly reconfiguration instants. For this purpose, firstly OPF is applied to the problem, then the obtained optimal solutions are prioritised by DEA and ranking is done, based on the efficiencies of the optimal solutions. Finally, the provided results validate the practicability of the proposed method and accuracy of the outcomes.     

高效非逆变光伏技术理论基础

关于光伏应用形式的主要争论之一,是光伏电力的逆变应用与非逆变应用之争。其实"逆变"只是电力技术中一种直流变交流的方法,逆变与非逆变的本质区别,并不在于采用该方法与否。在逆变应用中也有直流线路,非逆变应用中也有逆变装置。关键问题是,被统称为"逆变并网"的逆变应用,是推崇用光伏电力取代市电的一种思潮,并长期以来作为主流观点在光伏应用领域占统治地位。而非逆变应用就不赞成这种应用形式,并且有针对性地提出了许多不同意见,归结起来有3条:第一,不必要,因为直流电、交流电都可以应用,将直流低压的光伏电力变成高压交流电去适应普通电气应用是多余而又降低效率的环节;第二,问题复杂化,因为光伏电力的输出功率不稳定,又不采用储能装置,依附电网上的负载卸载,势必给网电造成影响,从而发生一系列技术问题和与电力部门的协调问题,人为增加了光伏电力应用的困难;第三,经济上不合算,无论如何,光伏电力成本的价格还是远高于市电。而逆变应用反驳得不太有说服力,除了第一条所说的应用方便之外,其余2条对于实际问题的解决,至今没有实质性的进展。     

Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization

In this article, multi-objective optimization of braced frames is investigated using a novel hybrid algorithm. Initially, the applied evolutionary algorithms, ant colony optimization (ACO) and genetic algorithm (GA) are reviewed, followed by developing the hybrid method. A dynamic hybridization of GA and ACO is proposed as a novel hybrid method which does not appear in the literature for optimal design of steel braced frames. Not only the cross section of the beams, columns and braces are considered to be the design variables, but also the topologies of the braces are taken into account as additional design variables. The hybrid algorithm explores the whole design space for optimum solutions. Weight and maximum displacement of the structure are employed as the objective functions for multi-objective optimal design. Subsequently, using the weighted sum method (WSM), the two objective problem are converted to a single objective optimization problem and the proposed hybrid genetic ant colony algorithm (HGAC) is developed for optimal design. Assuming different combination for weight coefficients, a trade-off between the two objectives are obtained in the numerical example section. To make the final decision easier for designers, related constraint is applied to obtain practical topologies. The achieved results show the capability of HGAC to find optimal topologies and sections for the elements.     

低压阻抗型无功补偿与谐波滤波装置及应用

从GB/T 26870-2011《滤波器和并联电容器在受谐波影响的工业交流电网中的应用》出发,利用仿真实例分析阻抗型无功补偿与谐波滤波装置的原理和功能异同,及其在低压系统应用中应该注意的问题。在谐波电流发生量较小或谐波电流注入系统引起的谐波电压不超标的低压系统,建议采用失谐滤波器;反之,则建议采用调谐滤波器治理低压系统的谐波和无功问题。     

Optimal size and cost analysis of stand-alone hybrid wind/photovoltaic power-generation systems

Design of sustainable energy systems for the supply of electricity need correct selection and sizing to reduce investment costs. In this article, a new sizing methodology is developed for stand-alone hybrid wind/photovoltaic (PV) power systems, using multi-objective optimisation algorithms. Multi-objective particle swarm optimisation algorithm and non-dominated sorting genetic algorithm-II are selected related to their match with the nature of renewable energy sizing problem. A match evaluation method is developed based on renewable energy supply/demand match evaluation criteria, to size the proposed system in the lowest cost. As an example of application of this technique, six different wind turbines (WTs) and also six different PV modules have been considered. The sizing methodology determines a multi-objective design, obtaining the best solutions that the applied algorithm has found simultaneously considering three objectives: inequality coefficient, correlation coefficient, and annualised cost of system. The optimal number of WTs, PV modules, and batteries ensuring that the system total cost is minimised while guaranteeing a highly reliable source of load power is obtained. A management strategy has been designed to achieve higher electricity match rate. Based on the proposed technique, the algorithm developed for different cases, using the climatic condition data of the city Zabol, located in south-east of Iran. Additionally, a study of operating hours of diesel generator in optimal configuration is carried out.     

基于遗传算法的飞艇结构多目标优化

首先,根据软式飞艇结构的力学特点建立了飞艇结构的多目标优化模型,以外荷载作用下飞艇“应力最小”、“变形最小”及“自重最小”为优化目标,确定膜材厚度、膜材弹性模量、飞艇内气压及飞艇细长比为优化变量.其次通过加权系数法将多目标优化转化为单目标问题,并采用遗传算法进行求解.最后,采用此方法对某一飞艇结构进行多目标优化.研究结果表明：飞艇的优化模型是有效的;结合加权系数法和遗传算法,可以获得各个目标均较理想的优化结果;通过进行多目标优化可以为飞艇结构的设计提供合理建议.     

绝缘子污秽闪络的研究现状及思索

绝缘子污秽闪络严重威胁着高压电网的可靠性。论述了人工污秽试验、污闪机理、污闪特征检测以及污闪模型建立等方面的研究现状,认为人工试验主要存在指标和污闪间的关系、电源配置及污秽模拟和运行现场的差异等问题。从工程的角度概括对比了可以有效用于污闪检测和风险预测的几种方法:距离判别法、模糊分类法、极值风险预测法及聚类分析法。指出解决污闪问题的关键是建立科学有效的污闪预测机制,基于模糊理论的信息融合法在污闪检测和风险预测上值得深入研究,而污闪模型研究就工程来讲,意义不大,并提出了未来防污闪工作的建议。     

Inverse analysis of deformation moduli for high arch dams using the displacement reconstruction technique and multi-objective optimization

The inverse analysis of the deformation moduli of high arch dams based on displacement monitoring data is essential for structural safety assessment. In traditional inverse analysis methods, the deformation moduli are identified based on the single-objective optimization and the hydrostatic component derived from the statistical model. This type of method has two main shortcomings: First, it treats the essential multi-objective optimization problem as a single-objective problem; second, the extracted hydrostatic component may be biased due to the multicollinearity of variables in the statistical model. This paper presents a methodology for the inverse analysis of the deformation moduli of high arch dams under a multi-objective optimization strategy. The methodology employs empirical mode decomposition to extract the aging component from displacement monitoring data. Then, thermomechanical analysis is used to reconstruct the remaining hydrostatic and temperature components, thereby avoiding the biases encountered in solving the statistical model. The adaptive polynomial chaos expansion method is embedded in the NSGA-III algorithm to establish and solve multi-objective functions in the inverse analysis. Additionally, a composite decision index considering errors and test information is proposed to determine acceptable deformation moduli from the Pareto solution set. A high arch dam is selected to illustrate this methodology with static and dynamic monitoring data. The results show that the identified deformation moduli have errors of 3.8% and 7.2% in displacement and acceleration, respectively. The proposed methodology can yield deformation modulus values that are more consistent with the physical implications than those of the single-objective optimization method.     

消防应急照明配电设计细节探讨

新国标《消防应急照明和疏散指示系统技术标准》实施后,配电距离较长的消防应急照明线路,存在末端电压降过大或计算值偏保守以及末端短路电流过小等问题。针对末端电压降过大或计算值偏保守问题,基于电压降计算公式,提出了一种新的实用计算方法,可以精确计算线路的电压降;针对线路末端短路电流过小问题,基于熔断器的熔断时间—电流特性曲线,提出了可以在规定时间内熔断的最小短路电流概念,计算出了安全电压下常用导线的极限供电距离。解决了电压降简化计算误差过大问题,提出了一种应急照明线路短路电流的便捷计算方法,可有效减少消防应急照明配电设计在此方面存在的安全隐患。     

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently.Many multi-objective optimization algorithms have been developed;however few of them are tested in solving building design problems.This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building(n ZEB) where more than 1.610 solutions would be possible.The compared algorithms include a controlled non-dominated sorting genetic algorithm witha passive archive(p NSGA-II),a multi-objective particle swarm optimization(MOPSO),a two-phase optimization using the genetic algorithm(PR_GA),an elitist non-dominated sorting evolution strategy(ENSES),a multi-objective evolutionary algorithm based on the concept of epsilon dominance(ev MOGA),a multi-objective differential evolution algorithm(sp MODE-II),and a multi-objective dragonfly algorithm(MODA).Several criteria was used to compare performance of these algorithms.In most cases,the quality of the obtained solutions was improved when the number of generations was increased.The optimization results of running each algorithm20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity,followed by the p NSGA-II,ev MOGA and sp MODE-II.Uncompetitive results were achieved by the ENSES,MOPSO and MODA in most running cases.The study also found that 1400-1800 were minimum required number of evaluations to stabilize optimization results of the building energy model.     

Post-Pareto optimality approach to enhance budget allocation process for bridge rehabilitation management

A variety of conflicting criteria in the form of objective functions exist in budget allocation optimisation problem for bridge rehabilitation projects. Budget allocation decision-making for such transportation assets is generally a combinatorial problem. The nature of the problem is a good reason for decision-makers to apply multi-objective optimisation techniques. However, manually choosing an acceptable solution from a set of optimal solutions is a time-consuming task, which would be avoided if the optimisation technique could be followed by a ranking method to obtain unique acceptable solution. To enhance the budget allocation process, this paper develops a posteriori approach to prioritise Pareto-Optimal (PO) solutions generated by genetic algorithm in order to identify a unique package of bridge rehabilitation activities. By identifying the most conventional objective functions for bridge rehabilitation based on technical and managerial criteria, a multi-objective knapsack problem is constructed. PO solutions will then be prioritised applying ‘Technique for Order Preference by Similarity to an Ideal Solution’. The feasibility of the study will be finally demonstrated through an illustrative example. The proposed ranking approach may facilitate the budget allocation optimisation process for bridge rehabilitation where one or a few acceptable solutions are demanded.     

空间网格结构优化设计的模糊判决方法研究

在空间网格结构的优化设计过程中,必然会遇到大量的不确定性信息和因素,而且往往要考虑多个目标函数,而各个目标之间存在矛盾及各目标的解具有冲突性,多目标优化问题要求各个目标函数都达到最优一般很难．本在经典模糊判决法的基础上,提出了四种改进的模糊判决法,并利用隶属函数建立模糊优化数学模型转化为非模糊的单目标优化模型,从而可以使用普通优化程序得到模糊优化解．最后,通过二十五杆塔架的数值例题来说明本方法的求解过程并验证了其模糊判决方法的有效性和可行性．     

Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony(ABC)

The aim of this paper is to present a powerful simulation-based multi-objective optimization of building energy efficiency and indoor thermal comfort to obtain the optimal solutions of the comfort-energy efficient configurations of building envelope. The optimization method is developed by integrating a multi-objective artificial bee colony(MOABC) optimization algorithm implemented in MATLAB with EnergyP lus building energy simulation tool. The proposed optimization approach is applied to a single office room; and the building parameters, including the room rotation, window size, cooling and heating setpoint temperatures, glazing and wall material properties are considered as decision variables. In the present study, single-objective and multi-objective optimization analyses of the total annual building electricity consumption and the Predicted Percentage of Dissatisfied(PPD)are investigated to bring down the total energy cost as well as the thermal discomfort in four major climate regions of Iran, i.e. temperate, warm-dry, warm-humid and cold ones. In the results part,the achieved optimal solutions are presented in the form of Pareto fronts to reveal the mutual impacts of variables on the building energy consumption and the thermal discomfort. Finally, the ultimate optimum solution on the Pareto fronts are selected by TOPSIS decision-making method and the results of double-objective minimization problem are compared with the single-objective ones as well as the base design. The results of double-objective optimization problem indicate that in different climates, even though the total building electricity consumption increases a bit about2.9-11.3%, the PPD significantly decreases about 49.1-56.8%compared to the baseline model. In addition, the comparisons of single-objective and double-objective optimization approaches clearly show that multi-objective optimization methods yield more appropriate results respect to the single ones, mainly because of the lower deviation index value from the ideal solution.     

电梯群控问题的数学模型及智能群控技术

电梯群控问题本质上是一类调度优化问题。描述了电梯群控问题的数学模型，分析了电梯群控问题的特点及其与其它调度问题的不同；对人工智能技术在电梯群控问题研究中的应用情况进行了分析、总结，简要提出并分析了当前电梯群控研究领域可进一步深入研究的几个方向。     

Optimal placing and sizing of parking lots including different levels of charging stations in electric distribution networks

Background: Low emission, high efficiency, and convenience of using plug-in electric vehicles (PEVs) are considered as significant benefits of PEVs. However, installation of parking lots (PLs) in order to supply PEVs causes some financial and technical challenges for electric distribution networks. The changes in system reliability, power loss, voltage drop, and costs associated with the installation of PLs are considered as some of the aforementioned challenges. Therefore, optimal placing and sizing of PEV PLs including different levels of charging stations ((CS); slow, medium, and fast) are presented in this paper. Since PLs have the potential to exchange electricity by electric network, they can be taken into account as distributed generations (DGs) and their installation can be considered from the perspective of installation of DGs. An objective function including system reliability, power loss, voltage drop, and PL cost/revenue is proposed for the optimal planning. Results: Genetic algorithm is employed to solve the optimisation problem. Simulation is carried out on a 33-bus radial distribution network. For the planning purposes of PLs, three different levels of CSs (slow, medium, and fast) in PLs are considered. The effect of increasing the penetration of PEVs in PLs is also examined on planning the PLs. The effect of different dispatch times on the selection of different CS levels (slow, medium, and fast) is investigated as well. Furthermore, the effect of applying tariffs and incentives for the customers is analysed for the selection of CS levels. Finally, the effect and importance of combinations of the CSs with different levels are investigated.     

Dispatching ready mixed concrete trucks under demand postponement and weight limit regulation

This study formulates the dispatching operations of ready mixed concrete (RMC) trucks as a job shop problem with recirculation, which includes time windows and demand postponement, as well as the external cost of transport, in a multi-objective programming model. This investigation divides factors that affect truck dispatching of RMC plants into intrinsic and imposed constraints, where intrinsic constraints refer to limits that must be satisfied during the concrete distribution process; imposed constraints are those that should be fulfilled to the greatest extent possible during dispatching. The results will help concrete plant dispatchers of small to medium-sized enterprises (SMEs) determine a cost effective and reliable day-to-day distribution scheme and achieve better customer service. This work contributes to both modeling and dispatching operations of RMC trucks as a job shop problem by applying optimization tools that enable RMC SMEs to detect and subsequently take advantage of opportunities that may exist for improving daily operation efficiency.