Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

Optimal Utilization of Renewable Energy in Aquaponic Systems

Aquaponic systems require energy in different forms, heat, solar radiation, electricity etc. Typical actuator components of an aquaponic system include pumps, aerators, heaters, coolers, feeders, propagators, lights, etc., which need electrical energy to operate. Hybrid Energy Systems (HES) can help in improving the economic and environmental sustainability of aquaponic systems with respect to energy aspects. Energy management is one of the key issues in operating the HES, which needs to be optimized with respect to the current and future change in generation, demand, and market price, etc. In this paper, a Decision Support System (DSS) for optimal energy management of an aquaponic system that integrates different energy sources and storage mechanisms according to priorities will be presented. The integrated model consists of photovoltaic and solar thermal modules, wind turbine, hydropower, biomass plant, CHP, gas boiler, energy and heat storage systems and access to the power grid and district heating. The results show that the proposed method can significantly increase the utilization of HES and reduce the exchange with the power grid and district heating and consequently reduce running costs.

Wind Power Flow Optimization and Control System Based on Rapid Energy Storage

风电功率的间歇与波动致使电场容量可信度低、可调度性差；同时易引起局部电网的电压不稳、频率波动，影响了系统的电能质量及稳定性。针对此现象，将超级电容器与蓄电池组成快速储能装置，用于风电的潮流优化控制。采用三重双向直流变换电路控制储能元件间的功率流动；采用四象限交直流变换电路控制储能与电网间的能量交换。提出基于超级电容器电压低频波动抑制的功率分配方法，可显著减少蓄电池的充放次数；提出基于储能元件荷电状态的储能能量调整规则，可避免储能元件的过充和频繁深度放电，以优化其功率调节能力。实验结果表明，系统可实现2种储能元件的优势互补，能有效平滑调节风电注入电网的有功功率，并实时补偿控制风电接入点的无功功率。

Optimal convergence rates for three-dimensional turbulent flow equations

In this paper, the convergence turbulent flow equations are considered. By rates of solutions to the three-dimensional combining the LP-Lq estimate for the linearized equations and an elaborate energy method, the convergence rates are obtained in various norms for the solution to the equilibrium state in the whole space when the initial perturbation of the equilibrium state is small in the H3-framework. More precisely, the optimal convergence rates of the solutions and their first-order derivatives in the L2-norm are obtained when the LP-norm of the perturbation is bounded for some p ε [1, 6）.

Coordinated restoration optimization of power-gas integrated energy system with mobile emergency sources

In an integrated energy system(IES) composed of multiple subsystems, energy coupling causes an energy supply blockage or shutdown in one subsystem, thereby affecting the energy flow distribution optimization of other subsystems.The energy supply should be globally optimized during the IES energy supply restoration process to produce the highest restoration net income. Mobile emergency sources can be quickly and flexibly connected to supply energy after an energy outage to ensure a reliable supply to the system, which adds complexity to the decision. This study focuses on a powergas IES with mobile emergency sources and analyzes the coupling relationship between the gas distribution system and the power distribution system in terms of sources, networks, and loads, and the influence of mobile emergency source transportation. The influence of the transient process caused by the restoration operation of the gas distribution system on the power distribution system is also discussed. An optimization model for power-gas IES restoration was established with the objective of maximizing the net income. The coordinated restoration optimization decision-making process was also built to realize the decoupling iteration of the power-gas IES, including system status recognition, mobile emergency source dispatching optimization, gas-to-power gas flow optimization, and parallel intra-partition restoration scheme optimization for both the power and gas distribution systems. A simulation test power-gas IES consisting of an 81-node medium-voltage power distribution network, an 89-node medium-pressure gas distribution network, and four mobile emergency sources was constructed. The simulation analysis verified the efficiency of the proposed coordinated restoration optimization method.

Microgrid Optimal Scheduling

This paper presents the optimal scheduling of renewable resources using interior point optimization for grid-connected and islanded microgrids (MG) that operate with no energy storage systems. The German Jordanian University (GJU) microgrid system is used for illustration. We present analyses for islanded and grid-connected MG with no storage. The results show a feasible islanded MG with a substantial operational cost reduction. We obtain an average of $1 k daily cost savings when operating an islanded compared to a grid-connected MG with capped grid energy prices. This cost saving is 10 times higher when considering varying grid energy prices during the day. Although the PV power is intermittent during the day, the MG continues to operate with a voltage variation that does not 10%. The results imply that MGs of GJU similar topology can optimally and safely operate with no energy storage requirements but considerable renewable generation capacity.

Design of Poiseuille Flow Controllers Using the Method of Inequalities

This paper investigates the use of the method of inequalities （MoI） to design output-feedback compensators for the problem of the control of instabilities in a laminar plane Poiseuille flow. In common with many flows, the dynamics of streamwise vortices in plane Poiseuille flow are very non-normal. Consequently, small perturbations grow rapidly with a large transient that may trigger nonlinearities and lead to turbulence even though such perturbations would, in a linear flow model, eventually decay. Such a system can be described as a conditionally linear system. The sensitivity is measured using the maximum transient energy growth, which is widely used in the fluid dynamics community. The paper considers two approaches. In the first approach, the MoI is used to design low-order proportional and proportional-integral （PI） controllers. In the second one, the MoI is combined with McFarlane and Glover＇s H∞ loop-shaping design procedure in a mixed-optimization approach.

ODFF: Optimized Dual Fuzzy Flow Controller Based Voltage Sag Compensation for SMES-Based DVR in Power Quality Applications

The booming electronics itself carries an impact on power quality. Superconducting Magnetic Energy Storage (SMES) is proposed to enhance power quality in three-phase systems under various loads. This paper aimed to compensate the voltage sags under various faults in the grid systems. The SMES is selected as an energy storage unit to improve the capability of voltage sag compensation. Optimized Dual Fuzzy Flow (ODFF) logic controller is designed to prevent the voltage sag time during excessive phase voltage variation. Hence the proposed controller strategy reduces the total harmonic distortion during various fault conditions. To regulate the contribution of active power, the least possible value is improved using ODFF. The depth of voltage sags compensation is achieved by the over modulation and an iterative loop is designed in the control block. While protecting sensitive loads from voltage disturbances, and sags initiated by the power system, the proposed configuration is advantageous for an industrial implementation. It is found that the proposed method can result in more than 50% additional sag support time when compared with the previous methods such as PI and PSO. Utilizing MATLAB Simulink, compensation of sag and minimization of THD is established, and the simulation tests are performed to evaluate the performance of the proposed control method.

Energy Efficient Modelling of a Network

Most of the networks are generally less energy efficient and most of the time resources are underutilized. Even resources of busy networks are also underutilized and thus networks show energy inefficient management system. This paper focuses on how to obtain minimum resources for the current situation of the network to maintain connectivity, power saving and quality of service. Four different models are proposed in this perspective with different purposes and functions. These models determine the minimum resources under certain constrains. Two types of services namely, "minimum bandwidth" and "trivial file transfer" are considered. For "minimum bandwidth" service, minimum edge, minimum delay and minimum change models are proposed. Here data rate switch and enable/disable of edges are placed in these models for power saving strategy. Another model, multi flow is proposed for "trivial file transfer" service. It is proposed for transferring files through multiple flows in multiple paths from source to destination. All models except multi flow model are mixed integer programming optimization problem.

考虑能耗和运输的有限缓冲区混合流水车间调度

为解决生产调度不及时、加工过程中能耗过大等问题,构建了有限缓冲区混合流水车间调度优化模型。模型以最小化最大完工时间和车间总能耗为目标,将运输时间、广义能耗和缓冲区容量等资源限制作为约束,并应用开关机节能策略减少待机能耗,验证了优化模型的可行性;设计一种狮群算法,采用随机产生与贪婪选择相结合的种群初始化方法,提高初始解质量和求解效率,验证了狮群算法的优越性。实验结果表明:该算法能有效解决考虑能耗和运输时间的有限缓冲区混合流水车间调度问题,优化模型能依照实际需要进行柔性调节,达到制造型企业合理排产、节能减排的目的。

海上风氢系统与沿海电网能量协同优化调度

为实现海上风电的高效消纳和绿色氢能的制备,提出一种海上风氢系统与沿海电网能量协同优化调度方法。首先,通过评估海上风电场间的风能耦合效应,建立考虑风能异域强化利用的风电场群发电转移模型。其次,量化分析风电场的疲劳载荷与疲劳成本之间的关系,构建考虑检修便利性的风电场群均衡载荷模型。基于此,提出风电场群综合效能调控模型,以协调发电与疲劳寿命之间的关系。然后,考虑海上风流因素对船舶航行时间的影响,建立电氢并举的能量外送模型。最后,以系统运行成本最小为目标,提出考虑主动孤岛、制氢效率特性以及系统旋转备用的海上风氢系统与沿海电网能量协同优化调度模型。算例验证了所提方法的有效性和优越性,为海上风电制氢能量调度提供了一种新的思路。

基于混合离散状态转移算法的多目标节能无等待流水车间调度

针对以完工时间和总能耗为目标的节能无等待流水车间调度问题(energy-efficient no-wait flow shop scheduling problem,EENWFSP),设计一种混合离散状态转移算法(hybrid discrete state transition algorithm,HDSTA)进行求解。根据问题特性设计工序和速度矩阵的编码方式,采用启发式算法获得优质的初始解。根据EENWFSP性质,对4个离散操作算子进行任务分配,将嵌入二次状态转移的交换、移动、对称算子用于工序优化,替换算子用于机器速度优化,并在替换算子中嵌入基于关键路径的速度替换策略。设计了一种改进的破坏重构操作,用于进一步提高Pareto解的质量。通过与4种算法在测试实例上的实验结果分析,表明HDSTA在解决EENWFSP时具有较强优势。

具有紧时、高能耗特征的混合流水车间多目标调度优化问题

针对具有紧时、高能耗工序特征的混合流水车间调度问题,以优化产品暴露时间、最大完工时间和能源消耗为目标,建立混合流水车间调度模型,并提出一种改进的多目标粒子群算法进行有效求解。首先构建了基于ISDE指标的档案维护策略及局部邻域搜索策略,辅助算法跃出局部极值及减少生产阻塞。之后,提出一种基于模糊理论的决策分析方法选取最优调度方案。最后,通过仿真实验验证提出的多目标调度模型与算法的可行性和优越性。

考虑风电不确定性和储能灵活性的有源配电网有功无功协同鲁棒优化

提出考虑风电不确定性和储能灵活性的有源配电网有功无功协同鲁棒优化方法。首先建立计及储能四象限功率可调特性的有源配电网有功无功协同优化模型;其次给出储能约束条件和交流潮流方程的线性化表征方式;然后利用盒式集描述风电出力的不确定性,建立有源配电网有功无功协同鲁棒优化模型,并通过对偶变换得到混合整数线性规划形式的两阶段鲁棒主、子问题,采用列约束生成算法交互迭代求解;最后以IEEE33节点系统为例进行仿真验证。仿真结果表明,所提方法能够克服模型高度非凸非线性对其收敛精度和求解效率的影响,充分利用储能的无功调节能力,实现有源配电网有功无功协同鲁棒优化运行。

考虑多元协同互动的微能源网多目标优化运行

微能源网是促进新能源消纳和碳减排的重要载体,可提供电、热、气、氢等多类型的能源,有利于实现多种能源的统一协同调度。首先提出了含高比例风电的低碳微能源网架构,电、热系统通过包含电-热-氢多能转化和存储的能量枢纽互联;其次,综合考虑微能源网对上级网络能源价格及节点边际碳排放强度的响应、微能源网中配电网和热网自身的优化运行、电-热-氢能量枢纽内部灵活调节、终端负荷柔性能量需求,建立了考虑多元协同互动的微能源网多目标优化运行模型。通过分段线性化、二阶锥松弛、多目标加权模糊化处理技术将优化模型转化为易于求解的混合整数二阶锥单目标规划问题。最后,通过电-热-氢能量枢纽将改进的IEEE33节点电力系统和改进的巴厘岛32节点热力系统互联组成微能源网进行仿真验证,结果表明考虑多元协同互动的能源供需在系统运行经济性和低碳性方面具有优势。

面向海量分布式新能源业务流的通存资源无交互协同预留算法

考虑到传统配电通信网对新型业务流支撑能力不足,结合运营商公网的跨域融合组网模式被广泛应用。其中,资源预留是提升数据传输确定性的重要技术,但公网通道性能存在不可预知的波动性,对基于有交互模式的资源预留效用影响机理难以量化表征。为此,提出面向海量分布式新能源业务流的通存资源无交互协同预留算法。首先,采用通感算一体化模式,基于业务流特征构建异质资源无交互协同预留系统模型;其次,设计对应无交互协同预留效用函数,并以效用最大化为目标提出对应优化问题;最后,基于块坐标下降法将原问题解耦,结合凸优化理论快速给出最优无交互资源预留方案。仿真结果表明,所提业务流特征评估方法能够合理量化表征通道性能波动性,进而在无交互模式下有效优化异质资源协同预留过程,显著提升海量分布式光伏数据感知确定性。

考虑煤气调度的钢铁园区能量流-物质流协同优化方法

为促进钢铁行业绿色低碳转型,提出一种钢铁园区能量流-物质流协同优化调度方法,充分挖掘生产系统与能源系统协同优化潜力。通过分析园区高炉、转炉、电炉工序的时序特性与物料关系,建立面向生产系统的物质流模型。考虑各生产工序的能耗特性和煤气产耗情况,建立计及煤气调度的能量流-物质流耦合模型。基于上述模型,以系统运行成本最小化和最低碳排为目标,对钢铁园区能量流-物质流进行协同优化调度,得到高炉、转炉、电炉生产设备的最优生产策略以及电力、煤气、焦炭能源的最佳调度结果。以山西某钢铁园区为算例对所提方法进行验证,结果表明,在满足钢铁园区生产计划要求的前提下,所提方法通过物质流与能量流的协同调控可使钢铁园区的日碳排放量降低9.56%,系统总成本降低6.12%。

高风电渗透率下液流电池储能系统调峰优化控制策略

【目的】在“双碳”目标背景下,解决高风电渗透率系统建设带来的调峰安全性和经济性问题。【方法】采用电池储能系统削峰填谷的解决方案,提出了一种兼顾技术及经济性的锌溴液流电池(zinc-bromine flow battery,ZBB)储能的调峰优化控制方法。根据实际电池装置,对ZBB储能进行结构解析及数学模型构建。考虑调峰技术性效果,以调峰后的负荷曲线标准差最小为目标函数,提出一种考虑调峰效果的储能双向寻优控制策略。在此基础上,依据电网分时(time of use,TOU)电价政策,以技术性及经济性最优为目标函数,提出一种基于TOU电价机制的储能调峰经济模型,得出储能优化功率时序结果。最后,以东北某地区负荷及风电数据为例,对比验证所提策略的有效性。【结果】所提策略相较于原负荷,在日均负荷峰谷差、峰谷差率指标上分别降低了35.973%和34.205%,在调峰经济性优化方面提高了5.582%,且合并缓解了电网弃风消纳问题。【结论】所提策略在达到一定调峰效果的同时,在其全寿命周期内仍保持较好的调峰经济性。

云边端协同驱动的陶瓷制造过程能效调度方法

陶瓷制造企业作为典型的多品种小批量高能耗制造企业,面临着能耗高、生产周期长、品种繁多、生产工艺复杂等问题,因此如何提高陶瓷制造的能源和资源利用效率,并进一步实现节能降耗,已经成为陶瓷企业亟需解决的重要问题。针对这一目标,提出了一种云边端协同驱动的陶瓷制造过程能效调度架构,该架构依赖于云边端协同技术来支持陶瓷制造过程的调度优化。以最小化最大完工时间和最小化总能耗为优化目标,建立陶瓷制造过程柔性流水车间调度模型。利用灰狼优化算法具有较强的全局搜索能力的特性,使用灰狼优化算法求解调度模型。结合陶瓷工厂实际情况设计算例,并以其作为实验数据,验证了灰狼优化算法在陶瓷制造过程中柔性流水车间调度优化的有效性。

油田天然气全流程节能降碳技术研究与应用

为精准管控油田天然气用能情况,在对天然气全流程生产工艺及重点设备研究分析基础上,集成整合现有信息化应用系统数据资源,利用GoJS技术绘制天然气业务可视化流程图,实现天然气全流程的动态可视化管控。通过分析梳理注汽锅炉近年相关运行数据,建立注汽锅炉“两线三区”运行评价图版和设备能效优化模型,实现注汽锅炉用能的在线评价优化,促进用能效率逐步提升。天然气全流程节能降碳技术实施以来,年节气率达5.62%,减排二氧化碳4.78×10^(4)t,为油田节能降碳工作提供良好借鉴。