A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.

Economic Dispatch of Electrical Power System Based on the Multi-objective Co-evolutionary Algorithm

It is important to distribute the load efficiently to minimize the cost of the economic dispatch of electrical power system. The uncertainty and volatility of wind energy make the economic dispatch much more complex when the general power systems are combined with wind farms. The short term wind power prediction method was discussed in this paper. The method was based on the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD). Furthermore,the effect of wind farms on the traditional economic dispatch of electrical power system was analyzed. The mathematical model of the economic dispatch was established considering the environmental factors and extra spinning reserve cost. The multi-objective co-evolutionary algorithm was used to figure out the model. And the results were compared with the NSGA-Ⅱ(non-dominated sorting genetic algorithm-Ⅱ) to verify its feasibility.

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.

Solution of Combined Heat and Power Economic Dispatch Problem Using Direct Optimization Algorithm

This paper presents the solution to the combined heat and power economic dispatch problem using a direct solution algorithm for constrained optimization problems. With the potential of Combined Heat and Power (CHP) production to increase the efficiency of power and heat generation simultaneously having been researched and established, the increasing penetration of CHP systems, and determination of economic dispatch of power and heat assumes higher relevance. The Combined Heat and Power Economic Dispatch (CHPED) problem is a demanding optimization problem as both constraints and objective functions can be non-linear and non-convex. This paper presents an explicit formula developed for computing the system-wide incremental costs corresponding with optimal dispatch. The circumvention of the use of iterative search schemes for this crucial step is the innovation inherent in the proposed dispatch procedure. The feasible operating region of the CHP unit three is taken into account in the proposed CHPED problem model, whereas the optimal dispatch of power/heat outputs of CHP unit is determined using the direct Lagrange multiplier solution algorithm. The proposed algorithm is applied to a test system with four units and results are provided.

Grey Wolf Optimizer to Real Power Dispatch with Non-Linear Constraints

A new and efficient Grey Wolf Optimization(GWO)algorithm is implemented to solve real power economic dispatch(RPED)problems in this paper.The nonlinear RPED problem is one the most important and fundamental optimization problem which reduces the total cost in generating real power without violating the constraints.Conventional methods can solve the ELD problem with good solution quality with assumptions assigned to fuel cost curves without which these methods lead to suboptimal or infeasible solutions.The behavior of grey wolves which is mimicked in the GWO algorithm are leadership hierarchy and hunting mechanism.The leadership hierarchy is simulated using four types of grey wolves.In addition,searching,encircling and attacking of prey are the social behaviors implemented in the hunting mechanism.The GWO algorithm has been applied to solve convex RPED problems considering the all possible constraints.The results obtained from GWO algorithm are compared with other state-ofthe-art algorithms available in the recent literatures.It is found that the GWO algorithm is able to provide better solution quality in terms of cost,convergence and robustness for the considered ELD problems.

Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm

Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm （POA） in combination with the genetic algorithm （GA）. After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.

Reliable Scheduling Method for Sensitive Power Business Based on Deep Reinforcement Learning

The main function of the power communication business is to monitor,control and manage the power communication network to ensure normal and stable operation of the power communication network.Commu-nication services related to dispatching data networks and the transmission of fault information or feeder automation have high requirements for delay.If processing time is prolonged,a power business cascade reaction may be triggered.In order to solve the above problems,this paper establishes an edge object-linked agent business deployment model for power communication network to unify the management of data collection,resource allocation and task scheduling within the system,realizes the virtualization of object-linked agent computing resources through Docker container technology,designs the target model of network latency and energy consumption,and introduces A3C algorithm in deep reinforcement learning,improves it according to scene characteristics,and sets corresponding optimization strategies.Mini-mize network delay and energy consumption;At the same time,to ensure that sensitive power business is handled in time,this paper designs the business dispatch model and task migration model,and solves the problem of server failure.Finally,the corresponding simulation program is designed to verify the feasibility and validity of this method,and to compare it with other existing mechanisms.

计及工况预测误差的主动配电网日前无功优化调度策略

为解决工况预测误差较大时,日前无功优化调度方案优化效果不佳的问题,提出了计及工况预测误差的主动配电网日前无功优化调度策略。首先,使用轻量级梯度提升机算法建立日前工况功率预测模型;其次,考虑大规模高比例分布式电源接入主动配电网,以调度时段内所有时间断面的多目标加权累加和为目标函数建立日前无功优化调度模型;最后,设计了一种变寻优粒子空间的改进引力搜索算法对日前无功优化调度模型进行求解,该算法根据历史工况预测误差评价指标调整寻优粒子空间各维度的上下限矩阵,从而抑制了当无功区域内工况预测误差较大时可控设备调度异常的缺陷。最后采用拓展的IEEE 33节点系统算例进行有效性验证。

Q学习差分进化算法求解热电动态经济排放调度

热电联产动态经济排放调度同时考虑了燃料成本花费和污染气体排放两个目标值,且下一时间段的热电产量受当前时间段热电产量的影响,这是近年来电力系统运行中的一个重要问题。文中提出一种基于Q学习强化多目标差分进化(Q Learning Multi-Objective Differential Evolution,QLMODE)算法,以此求解热电联产动态经济排放调度(Combined Heat and Power Dynamic Economic Emission Dispatch,CHPDEED)问题。在QLMODE中,采用Q学习技术调整算法的比例因子参数,即在迭代过程中利用子代解和父代解之间的支配关系确定动作奖励和惩罚,并通过Q学习调整参数值,以获得最适合环境模型的算法参数。文中将所提QLMODE用于求解11机组和33机组的热电联产动态经济排放调度问题。仿真结果表明,与4种成熟的多目标优化算法相比,QLMODE算法燃料成本最小,污染气体排放最少,收敛性和多样性指标优于其他4种算法,且QLMODE在两组问题上都获得了更好的Pareto最优前沿。

考虑源荷不确定性和N-1安全约束的电力系统优化调度

大规模风电并网增加了系统运行的不确定因素,文章利用快速可调资源平抑风电和负荷波动引发的功率不平衡,并考虑备用调用过程减少对系统潮流的影响。首先分析了风电和负荷预测误差的概率分布模型;然后,建立含N-1网络安全机会约束和旋转备用机会约束的电力系统优化调度模型,以系统调度总成本最小为目标优化机组出力;最后,基于起作用整数变量的识别方法的Benders分解法处理N-1网络安全约束,提高求解效率。利用修正的IEEE30节点系统,验证了所提模型能够保障系统备用可用性、经济性和安全性,充分挖掘抽水蓄能的快速可调能力。

考虑新能源出力不确定性的风-光-柴-储系统调度策略

文章以风-光-柴-储系统为研究对象,为了研究新能源出力不确定性对该系统的影响,提出了一种新能源出力复合预测模型。为提高风-光-柴-储系统运行的经济性、环保性和安全性,提出了考虑新能源出力不确定性的风-光-柴-储系统调度模型,并采用了带有Monte Carlo模拟的遗传算法对模型进行求解。文章采用了负荷缺失率(load loss rate,LLR)和置信概率对系统的安全性进行评价,并分析了其对系统调度结果的影响。仿真结果表明,文中所提出的考虑新能源出力不确定性的风-光-柴-储系统调度模型,可以降低新能源出力不确定性对系统的影响,且该方法可以有效地平衡系统的经济性和安全性。

考虑新能源不确定性的调度计划安全稳定校核方法

基于发生概率最高的新能源功率预测数据进行确定性调度计划安全稳定校核,其结论与电网实际情况相差较大。为提高安全稳定校核结果的适应性,提出一种考虑新能源不确定性的调度计划安全稳定校核方法。基于传统确定性调度计划安全稳定量化评估结果,构建计及新能源场站预测功率置信区间约束的安全稳定裕度最小化模型,实现高风险计划方式的有效识别。基于有功灵敏度对新能源场站进行等值聚合和不确定性变量降维,提高安全稳定裕度最小化模型的求解效率。基于并行处理平台进行调度计划多方式分组并行安全校核,得到考虑新能源功率预测不确定性的安全稳定校核结果。所提方法可有效提升高比例新能源电网调度计划安全稳定校核的准确性和时效性。实际电网算例验证了所提方法的有效性。

一种标识互信认证的设备终端安全接入新方法

为解决电力监控调度网络中存在的安全接入认证控制、交互难的问题,提出了一种基于标识互信认证的网络设备终端安全接入新方法。对电力监控与调度终端进行安全初始化,配置安全接入IP地址,并将带有自身硬件序列号信息的加密数据上传至局端设备;局端设备对接收到的电力监控与调度终端的硬件序列号进行解密,在增加路由信息后将数据传输至安全接入平台。安全接入平台采用标识互信认证技术,对电力监控与调度终端的硬件序列号进行识别,验证电力监控与调度终端的合法性。在某省电力监控与调度系统中进行实例运行,其结果验证了所提方法的有效性。

电力调度系统的优化策略分析

阐述优化电力调度安全管理的策略,包括更新和维护设备、加大现代信息技术运用、加强人员培养培训、完善各项制度规定,为电力调度安全管理提供硬件和软件、人力资源和制度的支持。

电力调度中的智能算法分析

阐述电力调度的特点和面临的挑战,介绍人工智能算法的原理和分类,探讨在相关领域的应用。分析电力调度与人工智能算法的集成方法,通过实验设计和结果分析,评估其在电力调度中的效果。

考虑平行系统特征的双活调度系统的设计与应用

为提高电网运行的稳定性,需要构建稳定可靠的电网调度系统,现提出一种考虑平行系统特征的双活调度系统。通过构建主备两个数据中心,根据应用业务量在主备调间进行调整,实现对资源的充分利用。发生故障时,另一个正常运行的中心将承担所有业务。并将平行系统视为电网虚拟运行系统,将真实与虚拟系统结合后得到电网双活调度系统,并具有管理和控制模块、实验和评估模块、学习和培训模块等功能。该系统通过对数据进行初始化处理,计算出电力分配结果,建立双层任务调度模型,利用遗传算法求解模型,从而实现双活调度系统的优化调度。对系统进行试验测试,试验结果表明:设计系统所提方法达到最优分配值所需时间短、效率高、电网信息调度总量大,调度能力强,调度信息的安全性高。说明所设计的双活调度系统具有可行性,利于区域电网的优化调度。

考虑个体自私的热电联产系统完全分布式经济调度

针对传统集中式经济调度策略需要获取全局信息以及无法适应系统灵活拓扑的缺陷,同时考虑能量以电能和热量形式并存出现的情况,基于一致性算法提出了一种完全分布式热电联产经济调度策略.将热电联产系统中各机组电价和热价的增量成本作为一致性变量进行迭代计算,直至增量成本达到收敛,实现系统经济最优.此外,还分析了个别机组为了提升自身利益,向相邻机组传输带有偏差增量成本值的情况,设计了一个增量成本补偿项来消除该自私行为的影响.在3个仿真算例中得到的结果表明,所提策略在解决热电联产系统经济调度问题中具有有效性和可行性.

面向恶意网络攻击的电力监控系统防御策略设计

为保障电力系统正常运行,针对电力监控系统中能源优化调度过程面临的安全问题,提出一种有向网络拓扑下面向恶意网络攻击的防御策略。首先,对于非隐蔽型网络攻击,将有向连通支配集作为系统的安全区域,进而设计了一种基于弹性一致性的优化调度算法,确保系统在非隐蔽型攻击下可靠运行。然后,对于隐蔽型网络攻击,引入水印信号的标签机制,以防止隐蔽型攻击对优化调度算法收敛性的影响。最后,收敛性分析和数值仿真结果表明,在所设计的防御策略下,所有被攻击的节点都能被检测到,剩余未被攻击的节点可在新的发电机组负荷分配下达到最优解。

基于安全限定义的新能源电力系统区间经济调度模型及算法

大规模风电等新能源机组接入电网,其不确定性和波动性对电网的安全运行造成冲击。目前采用的鲁棒调度和区间调度无法同时考虑不确定性条件下系统功率平衡和全场景下线路潮流安全双重约束。为此,将风电出力视为区间数,考虑极端场景下的功率平衡约束和可转移、可削减柔性负荷的参与建立了计及风电不确定性和电力平衡的区间经济调度模型。为求解该模型,提出了基于安全限定义的区间经济调度算法,该算法采用优化场景法求解区间直流潮流得到安全限,进而通过安全限定义法将区间优化模型转化为确定性的二次规划模型求解。采用IEEE 118节点系统测试,结果表明了所提区间经济调度算法既能满足极端场景下的电力平衡需求,又能保证所有场景下线路传输功率的安全性,同时降低了调度成本,发挥了柔性负荷削峰填谷的作用,且风电出力较小或较大时通过削减负荷或弃风实现经济性,为新能源电力系统安全、经济运行提供技术支撑。

计及备用风险性的新型电力系统源网协同安全经济调度

为合理利用源侧备用资源,提出基于随机潮流和半不变量法的备用风险评估指标,以准确量化因备用不足而造成的潮流越限风险;为了实现源侧备用资源和网侧可控资源的协同优化,将网侧有载调压变压器和电容器组无功补偿装置纳入决策变量,以系统运行成本最低和系统运行越限风险指标值最小为优化目标,构建计及备用风险性的新型电力系统源网协同安全经济调度模型;将布朗运动和莱维飞行随机游走机制融入多目标微分进化算法,设计新型的布朗-莱维多目标微分进化算法,对模型进行高效求解。算例分析结果验证了所提方法的有效性和优越性。