GA and PSO culled hybrid technique for economic dispatch problem with prohibited operating zones

This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.

Addressing Economic Dispatch Problem with Multiple Fuels Using Oscillatory Particle Swarm Optimization

Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in revenue.Various non-linearity are added to make the fossil fuel-based power systems more practical.In order to achieve an accurate economical schedule,valve point loading effect,ramp rate constraints,and prohibited operating zones are being considered for realistic scenarios.In this paper,an improved,and modified version of conventional particle swarm optimization(PSO),called Oscillatory PSO(OPSO),is devised to provide a cheaper schedule with optimum cost.The conventional PSO is improved by deriving a mechanism enabling the particle towards the trajectories of oscillatory motion to acquire the entire search space.A set of differential equations is implemented to expose the condition for trajectory motion in oscillation.Using adaptive inertia weights,this OPSO method provides an optimized cost of generation as compared to the conventional particle swarm optimization and other new meta-heuristic approaches.

Predictive Mathematical and Statistical Modeling of the Dynamic Poverty Problem in Burundi: Case of an Innovative Economic Optimization System

The mathematical and statistical modeling of the problem of poverty is a major challenge given Burundi’s economic development. Innovative economic optimization systems are widely needed to face the problem of the dynamic of the poverty in Burundi. The Burundian economy shows an inflation rate of -1.5% in 2018 for the Gross Domestic Product growth real rate of 2.8% in 2016. In this research, the aim is to find a model that contributes to solving the problem of poverty in Burundi. The results of this research fill the knowledge gap in the modeling and optimization of the Burundian economic system. The aim of this model is to solve an optimization problem combining the variables of production, consumption, budget, human resources and available raw materials. Scientific modeling and optimal solving of the poverty problem show the tools for measuring poverty rate and determining various countries’ poverty levels when considering advanced knowledge. In addition, investigating the aspects of poverty will properly orient development aid to developing countries and thus, achieve their objectives of growth and the fight against poverty. This paper provides a new and innovative framework for global scientific research regarding the multiple facets of this problem. An estimate of the poverty rate allows good progress with the theory and optimization methods in measuring the poverty rate and achieving sustainable development goals. By comparing the annual food production and the required annual consumption, there is an imbalance between different types of food. Proteins, minerals and vitamins produced in Burundi are sufficient when considering their consumption as required by the entire Burundian population. This positive contribution for the latter comes from the fact that some cows, goats, fishes, ···, slaughtered in Burundi come from neighboring countries. Real production remains in deficit. The lipids, acids, calcium, fibers and carbohydrates produced in Burundi are insufficient for consumption. This negative contribution proves a Burundian food deficit. It is a decision-making indicator for the design and updating of agricultural policy and implementation programs as well as projects. Investment and economic growth are only possible when food security is mastered. The capital allocated to food investment must be revised upwards. Demographic control is also a relevant indicator to push forward Burundi among the emerging countries in 2040. Meanwhile, better understanding of the determinants of poverty by taking cultural and organizational aspects into account guides managers for poverty reduction projects and programs.

A Low-carbon Operation Optimization Method of ETG-RIES Based on Adaptive Optimization Spiking Neural P Systems

To enhance multi-energy complementarity and foster a low carbon economy of energy resources,this paper proposes an innovative low-carbon operation opti-mization method for electric-thermal-gas regional inte-grated energy systems.To bolster the low-carbon operation capabilities of such systems,a coordinated operation framework is presented that integrates carbon capture devices,power to gas equipment,combined heat and power units,and a multi-energy storage system.To address the challenge of high-dimensional constraint imbalance in the optimization process,a novel low-carbon operation opti-mization method is then proposed.The new method is based on an adaptive single-objective continuous optimiza-tion spiking neural P system,specifically designed for this purpose.Furthermore,simulation models of four typical schemes are established and employed to test and analyze the economy and carbon environmental pollution degree of the proposed system model,as well as the performance of the operation optimization method.Finally,simulation results show that the proposed method not only considers the economic viability of the target integrated energy sys-tem,but also significantly improves the wind power utili-zation and carbon reduction capabilities.Index Terms—Spiking neural P system,power-to-gas,membrane computing,regional integrated energy system,low-carbon operation optimization.

Comparison of Economic Dispatch, OPF and Security Constrained-OPF in Power System Studies RN

Electricity network is a very complex entity that comprises several components like generators, transmission lines, loads among others. As technologies continue to evolve, the complexity of the electricity network has also increased as more devices are being connected to the network. To understand the physical laws governing the operation of the network, techniques such as optimal power flow (OPF), Economic dispatch (ED) and Security constrained optimal power flow (SCOPF) were developed. These techniques have been used extensively in network operation, planning and so on. However, an in-depth presentation showcasing the merits and demerits of these techniques is still lacking in the literature. Hence, this paper intends to fill this gap. In this paper, Economic dispatch, optimal power flow and security-constrained optimal power flow are applied to a 3-bus test system using a linear programming approach. The results of the ED, OPF and SC-OPF are compared and presented.

基于最优经济运行区域的主动配电网日前日内协同调度方法

传统的日前-日内协同调度通常以与日前时序计划曲线偏差最小作为日内目标函数,当日内新能源出力预测值与日前相差较大时,储能装置(energy storage systems,ESS)等由于其时间耦合约束日内调整范围有限,导致经济性和灵活性较差。对此,提出了基于最优经济运行区域(optimal economic operation region,OEOR)的主动配电网(active distribution networks,ADN)日前-日内协同调度方法。在日前阶段,构建线性化ADN调度模型,基于拉丁超立方采样法生成的大量随机场景下调控设备优化曲线,以全时间段内设备出力上下界内所包含的随机场景优化结果数量最大为目标,考虑储能装置荷电状态的相邻时段约束和微型燃气轮机的爬/滑坡率,构建OEOR生成模型。最后,在日内阶段,调控设备在OEOR内滚动优化调整,当该时段优化值贴近OEOR边界时,考虑相邻时段出力约束,将OEOR扩展为最优经济极限运行区域(E-OEOR)。算例结果表明,所提方法相比于传统方法能够更有效地提升配电网经济性。

考虑净负荷不确定性的配电网协同调度

如何实现可再生能源的最大化消纳同时保证配电网的安全经济运行成为亟须解决的问题。首先,将节点上的可再生能源出力和负荷聚合为净负荷并以区间描述其不确定性,同时将系统中调节能力在节点上的映射以端点可优化的区间描述,以调节能力区间和净负荷扰动区间的公共部分反映该节点净负荷扰动的可接纳域。然后,基于优先目标规划,构建了以最大化净负荷扰动可接纳域、最小化配电网运行总成本、最小化电压偏差为目标的考虑净负荷不确定性的3层优化模型,进而得到协同考虑新能源最大化消纳、运行成本合理、节点电压波动最小化的优化方案,保证了配电网运行的安全性、可靠性与经济性。最后,基于改进的IEEE 33节点配网算例,验证了该模型和方法的有效性。

大型水利工程梯级泵站短期优化调度方案

针对泵站高能耗难题,采用粒子群优化算法对南水北调东线徐洪河段的短期经济性调度方案进行研究.基于水量平衡方法,耦合流量、水位、蓄量及水力损失函数关系式,动态模拟渠道水情变化过程.将泵站扬程动态调整过程中产生的电费纳入考虑范围,以调度期内梯级输水系统产生的总电费和调控后水位与目标偏差最小为优化目标,其中目标水位为适应短期内调节的梯级扬程优化分配结果.基于实际运行数据对模型结果进行分析和验证,结果表明:渠道水情模拟方法的模拟精度较高,水位模拟值与实测值平均误差在10 cm左右;梯级短期优化调度模型引入目标水位增强了优化效果,进一步减少4.7元/万m3用电费用,优化方案的单位运行成本相比实际调度可有效降低6.7%.

基于龙格库塔算法的梯级泵站优化运行

梯级泵站在调水、灌溉与生态修复等建设中发挥了巨大作用。为提高梯级泵站运行效率、节约运行费用,结合某梯级泵站明渠引水工程,以总效率最高为目标建立了梯级泵站优化调度模型,提出了一种基于龙格库塔算法(RUN)的优化调度方法。计算结果表明,与该泵站现状运行方案相比,采用RUN算法计算所得方案可提高运行效率0.73%,节省运行费用271195元/年,优于利用PSO、GA算法所得优化结果,证实了所提方法可良好地服务于梯级泵站优化运行。

计及需求侧管理与碳排放的综合能源系统光-储设备优化配置方法

合理调整综合能源系统(IES)的配置结构能够有效提升其低碳经济运行水平,基于此,提出了一种计及需求侧管理与碳排放的IES光-储设备优化配置方法。基于IES典型拓扑及供能设备的工作机理,建立其冷-热-电-气能流模型;根据不同柔性负荷特点,从不同维度构建用能体验的评价指标,并提出一种考虑用户综合满意度的需求侧能量管理策略;在此基础上,为进一步提升系统的低碳性能,设计了一种计及全寿命周期碳排放的奖惩阶梯型碳交易机制;在同时考虑所提需求侧管理与碳交易机制的前提下,以设备全寿命周期内创造的总收益最大为优化目标,构建了从运行到规划层面的源-网-荷-储多环节协同的光-储设备优化配置模型及运行方案;基于某真实IES开展算例研究,结果验证了所提方法在避免设备冗余配置、减碳与提高经济运行水平方面的有效性。

考虑能量-调频辅助服务下的用户侧储能经济运行策略

目前用户侧储能的投资回报期一般长达6~7年。为进一步提升用户侧储能经济运行收益,提出一种电能量和调频辅助服务市场规则下的经济运行策略。首先综合考虑峰谷价差套利和参与调频建立用户侧储能经济收益模型;其次,分析电力用户生产运行需求并建立用户侧储能运行约束;基于此,提出用户侧储能的两阶段经济运行策略,在日前给出用户侧储能最优调频容量申报量,在日内基于改进直接前瞻方法滚动优化用户侧储能充放电功率,提高整体运行经济性。最后,仿真结果表明所提策略能够有效提高用户侧储能运行经济性。

考虑需求响应的微电网最优经济运行及改进人工蜂群算法

为降低微电网并网对大电网的影响并降低微电网的发电成本,提出一种基于负荷转移的激励型需求响应微电网最优经济运行模型.在此基础上,针对人工蜂群算法寻优精度不高、易陷入局部最优等不足,提出一种多策略改进人工蜂群算法.首先,提出双精英个体引导的新搜索方程降低搜索的随机性和盲目性;其次,提出免疫-提前自适应转换机制,平衡全局搜索性能和局部开发能力;最后,引入基于Levy飞行的变邻域搜索策略,强化算法跳出局部最优的能力,通过仿真实例验证了所提模型和算法的可行性和有效性.试验结果表明,所提模型实现削峰填谷的同时可以有效降低发电成本;通过与其他算法在微电网算例上收敛速度和寻优精度的比较,验证了多策略改进人工蜂群算法的优越性.

融合阶梯式碳交易与需求响应的源荷协同优化

为实现多能互补的区域综合能源系统的低碳经济运行,提出一种融合阶梯式碳交易需求响应的源荷协同优化运行方法。首先,通过引入阶梯式碳交易机制和电转气设备来提高源侧可再生能源的消纳水平并降低碳排放量;其次,在负荷侧引入考虑用户满意度的电气热多能耦合的激励型需求响应,并结合调峰收益实现多元负荷的削峰填谷;基于此,建立考虑碳交易与需求响应的区域电气热综合能源系统优化运行模型;最后,算例结果表明所提调度模型能够优化电气热负荷曲线并降低系统碳排放,实现区域综合能源系统的源荷协同降碳和经济性运行。

考虑源-荷不确定性的微能源网日前鲁棒优化调度

微能源网集成园区/社区的分布式能量系统、多元荷载及控制装置,能够实现多能源联产联供功能,有利于提升各类能源网络运行灵活性和经济性。为有效应对微能源网内部负荷和新能源出力的不确定性,首先建立多面体不确定集刻画负荷和光伏出力波动情况,构建描述微能源网网络拓扑和能流耦合关系的能源集线器(energy hub,EH)耦合矩阵。然后,以最小化系统运行成本为目标建立微能源网在并网和孤岛运行模式下的两阶段鲁棒优化经济调度模型,并运用线性决策规则(linear decision rule,LDR)和对偶理论制定模型求解策略。最后,基于IEEE 33节点配电网改进设计的微能源网进行算例验证分析。结果表明,LDR能够在一定精度范围内通过线性仿射函数有效近似刻画决策变量和不确定变量间的关系,降低两阶段鲁棒优化经济调度模型的求解难度。

基于含多微网接入的主动配电系统自主优化经济调度

针对含多微网接入的主动配电系统经济调度方法在中断负荷的影响下,调度结果与微网出力情况不一致导致的经济性差的问题,提出基于含多微网接入的主动配电系统自主优化经济调度方法。以多微网的电源特性为依据,计算主动配电系统的运行成本和环境成本,以运行成本和环境成本经济效益最大化为目标,建立自主优化经济调度目标函数,并从环境成本和运行成本两方面设计目标函数的约束条件,进行支路潮流约束,限制微网的输出功率等,使配电系统输出功率变化与电池出力情况变化趋势保持一致,在约束条件下求解目标函数,获得目标函数解集,求解集合中各个解与理想权重的接近程度,筛选出最优解,实现主动配电系统的自主优化经济调度。实验结果表明:设计的调度方法在中断负荷的情况下,运行成本低,优化水平高,微网输出功率稳定,并且与电池出力情况一致,该方法的经济性得到了提高。

原油管道节能降耗优化技术研究

基于降低原油管道水力消耗和热力消耗的目的,通过核算管输经济成本分析了不同输量对油单耗、电单耗及单位周转量运行能耗等参数的影响,研究了能耗最优和经济成本最优对应的最佳输量,分析了以气代油和用电方式改变对管输经济成本的影响。得到如下结论,单位周转量运行能耗随着输量的增大先增大后减小,在输量为1 050 m^(3)/h时的运行能耗最优;不同年份的燃料价格和电价对经济成本的影响较大,最低经济成本对应的输量也有所不同;将燃油加热炉改为燃气加热炉后,预计全年可节约热力成本1 840万元;将用电方式从一般工商业调整为大工业用电,且计价遵循最大容量时,预计全年可节约水力成本1 610万元。研究结果可为同类原油管道的运行优化提供实际参考。

建设新型电力系统目标下的湖南省中长期电源优化配置研究

在加快构建新型电力系统、助力新型能源体系规划建设的时代背景下,湖南省电源结构正逐步由“水火并济”转变为新能源作为电力电量主体的新格局。考虑到新能源发电的随机性和波动性,湖南电网能源结构的优化重构和调节电源的进一步发展对电力系统安全运行和清洁化发展至关重要。利用新型电力系统运行模拟模型,综合考虑扩展电源建设运行成本、新能源消纳等因素,建立了电源扩展优化经济模型,并以湖南电网为模型应用实例,深入分析了电力系统中长期电源资源的配置需求,经技术经济比选明确了系统中长期电源优化配置的推荐方案。

基于遗传优化图神经网络的企业贸易经济预测

在智能化算法研究的基础上,设计基于遗传优化的图神经网络(GOGNN)模型,用于实现企业贸易经济预测功能。根据经济贸易特征因子,通过引入种群适应度的平均值和适应度值的离散程度来动态调整交叉概率和变异概率。根据种群的进化情况动态调整这些概率,避免模型陷入局部最优解。选择影响贸易经济的评价指标构建经济贸易预测图数据,使用图神经网络对指标因素进行聚合,以预测准确率作为适应度函数的输出值。结合来自国家统计局、国家外汇管理局、行业调查研究等网站的历史外贸经济数据,将提出的GOGNN模型与其他模型做对比。实验结果表明:GOGNN模型对训练数据的均方误差大约为0.74335,小于其他模型;GOGNN模型对测试数据集的预测误差值在0.03以下,且相对于其他模型有着较好的收敛精度。

W火焰锅炉扩烧烟煤的运行优化研究

针对因贵州省煤炭供应变化导致的原设计煤种供应减少及发电量下降问题,国能安顺第二发电有限公司针对W型火焰汽包锅炉实施了掺烧烟煤的可行性研究,旨在寻求解决方案。研究过程中,针对掺烧烟煤可能导致的自燃、防爆、结焦及拱上燃烧器烧损等关键问题,提出了系列技术措施,有效控制了这些风险。掺烧烟煤不仅降低了氨耗和用油量,还因其高热值而减少了供电煤耗,进而降低了燃煤成本。烟煤的低含硫量也有助于防止受热面的高温腐蚀,延长了设备的使用寿命。掺烧后的试验结果表明,烟煤能够满足电网的高负荷需求,且环保指标未超标,证实了其对环境的影响是可控的。

Adjustable robust low-carbon dispatch for interconnected power systems in Northeast Asian countries

Interconnected power systems that link several countries and fully utilize their individual resources in a complementary manner are becoming increasingly important.As these systems enhanee accommodation of renewable energy,they also represent a move toward low-carbon and low-emissi on power systems.In this paper,a low-carb on dispatch model is proposed to coo rd i nate the gen erati on output betwee n several coun tries where the carb on emissi on constraint is a priority.An adjustable robust optimization approach is used to find the optimal solution under the worst-case scenario to address the uncertainties associated with renewable energy resources.A specific constraint is that the area control error for each country should be self-balanced.Furthermore,a reformation using participation factors is presented to simplify the proposed robust dispatch model.Simulation results for practical interconnected power systems in northeast Asian countries verify the effectiveness of the proposed model.