Two-stage stochastic approach for spinning reserve allocation in dynamic economic dispatch

A novel approach was proposed to allocate spinning reserve for dynamic economic dispatch.The proposed approach set up a two-stage stochastic programming model to allocate reserve.The model was solved using a decomposed algorithm based on Benders' decomposition.The model and the algorithm were applied to a simple 3-node system and an actual 445-node system for verification,respectively.Test results show that the model can save 84.5 US $ cost for the testing three-node system,and the algorithm can solve the model for 445-node system within 5 min.The test results also illustrate that the proposed approach is efficient and suitable for large system calculation.

Dynamic Economic Dispatch for Wind Power System Considering System Security and Spinning Reserve

In this paper, dynamic economic dispatch model is proposed for power systems with bulk wind power integration. The wind turbine generators are assumed to partially undertake the spinning reserve for the thermal generator. A double-layer optimization model is proposed. The outer layer use the differential evolution to search for the power output of thermal generators, and the inner layer use the primal-dual interior point method to solve the OPF of the established output state. Finally, the impact of spinning reserve with wind power on power system operating is validated.

Dynamic Economic Dispatch for Microgrids Including Battery Energy Storage

Microgrids integrate distributed renewable energy resources, controllable loads and energy storage in a more economic and reliable fashion. Battery energy storage units are essential for microgrid operation, which make microgird become a strong coupling system in the time domain. Hence, the traditional methods of static dispatch are no longer suitable for microgrids. This paper proposes a dynamic economic dispatch method for microgrids. Considering microgrid as a discrete time system, the dynamic economic dispatch is to find the optimal control strategy for the system in finite time period. Based on this idea, the dynamic economic dispatch model for microgrids is established, and then the corresponding dynamic programming algorithm is designed. Finally, an example of microgrid is given, and the dynamic economic dispatch results are compared with that of the static dispatch. The comparison confirms the effectiveness of the proposed dynamic dispatch method.

Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch

The hydro unit economic load dispatch （ELD） is of great importance in energy conservation and emission reduction. Dynamic programming （DP） and genetic algorithm （GA） are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA＇s performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases.

Security and Stability Aspects of Multi Objective Dynamic Economic Dispatch with Renewable Energy and HVDC Transmission Lines

Renewable sources of energy are being integrated into the power grids due to their economic and environmental merits as compared with the traditional fossil-fuel-fired power generation. However, their significant penetration demands a thorough research in terms of system reliability, that is, security and stability. In this paper, Security Constrained Multi Objective Dynamic Economic Dispatch (SCMODED) problem considering cubic thermal cubic cost function, wind, solar penetration, cubic transmission power losses and cubic emissions cost function as objectives is first formulated. Both HVDC and HVAC lines are included in their formulation. Various approaches like probabilistic load flow (PLF), scenario based method, participation factors and Harmony Search algorithm etc. are employed in the solution process. Security and stability effects of renewable energy (RE) penetration are investigated and analyzed. The simulated results reveal that RE penetration leads to reduced cost and emissions and increased security concerns. Further, there is increased power system instability and hence increased load shedding so as to help the power system attain steady state stability. Inclusion of HVDC lines facilitates rapid and fast control to increase the transient stability limit by the action of the converter ignition angle (CIA) and converter extinction angle (CEA).

An Efficient Multi-objective Approach Based on Golden Jackal Search for Dynamic Economic Emission Dispatch

Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific times.Although many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching solutions.To address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED problem.The proposed algorithm first stores non-dominated optimal solutions found so far into an archive.Then,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index method.This mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant emissions.Moreover,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto solutions.To this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching solutions.The proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance indicators.Also,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED methods.Finally,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs and pollution emissions simultaneously,compared to the latest published DEED solutions.

考虑超碳需求响应的综合能源系统低碳优化调度

西北地区新能源发展迅速,充分利用当地独有的光热资源优势,同时结合火电低碳化改造与新能源协同运行,有利于推动当地能源系统绿色低碳转型。为尽最大限度地提升系统的减碳能力,提出一种基于源荷协同降碳的超碳需求响应模型,将动态碳排放因子作为分时电价的惩罚因子,从而将源侧碳信号传递至荷侧,驱使用户侧进行低碳性状态转移。首先,在日前调度阶段,构建预调度-再调度两阶段调度运行机制,再调度根据预调度的系统状态信息进行超碳需求响应,来深度降低系统碳排量。其次,将光热电站引入综合能源系统,与风电场、碳捕集电厂协同运行,从而构建高比例新能源场景,来验证超碳需求响应在此场景下的减碳效益。最后,建立了基于超碳需求响应的预调度-再调度两阶段低碳调度模型。经算例仿真分析表明,所提源荷协同降碳的新思路能有效提高系统的减碳能力,深入挖掘系统的降碳空间,提升系统的经济效益。

基于动态掺氢策略的综合能源系统低碳经济调度

针对现有综合能源系统中传统碳捕集电厂灵活性较差,电负荷系数、管道长度和管道运维成本等因素对计及气网掺氢的综合能源系统调度的影响尚未明确等问题,提出了一种基于动态掺氢策略的综合能源系统低碳经济调度模型。首先,建立综合灵活碳捕集电厂模型,对二氧化碳的吸收与再生环节进行解耦;其次,建立基于动态掺氢的管道运维成本模型,实现掺氢比的动态调整,提高系统调度的灵活性;然后,引入阶梯式碳交易机制,构建考虑气网掺氢管道运维成本的综合能源系统低碳经济调度模型,利用McCormick松弛方法将非线性问题转化为线性问题,并进行求解。最后,算例仿真结果表明:气网各节点处管道运维成本系数与气体体积、输送距离等因素相关,应综合考虑上述因素对气体定价;在综合能源系统调度中综合考虑电负荷系数、气网管道长度等因素合理选择气网掺氢比上限、制定动态掺氢策略,系统的经济效益与环境效益分别提高了44.1%、47.8%。

基于改进堆优化算法求解电动汽车并网动态经济调度

针对堆优化算法HBO处理含电动汽车的动态经济调度问题时存在收敛慢和精度低等问题,提出一种改进的堆优化算法RDHBO。首先,在RDHBO中引入了最优成员区域搜索和双种群交互策略。前者引导最优成员移动到更有希望的区域,提升了算法的收敛精度和收敛速度;后者充分利用被淘汰的劣势个体,丰富了种群的多样性,避免了算法陷入局部最优。然后,将RDHBO应用于四种充电场景的10机组电动汽车动态经济调度问题。仿真结果表明,与已有的代表性方法相比,RDHBO在产生低燃料成本和稳定性方面具有很强的竞争力。最后,对RDHBO的两种改进策略进行了消融实验,验证了两种改进策略的有效性。

考虑碳-绿证耦合机制的电-气-热综合能源系统分布式鲁棒低碳经济调度

为了保护综合能源系统中不同运营主体的信息隐私,权衡系统运行低碳性和经济性,解决天然气网络鲁棒模型难以求解的问题,提出了考虑碳-绿证耦合机制的电-气-热综合能源系统分布式鲁棒低碳经济调度方法。考虑天然气网络和热力网络的动态特性、碳-绿证耦合机制,构建综合能源系统动态低碳经济调度模型;为了保护各网络运营商的数据隐私,根据能量耦合关系对综合能源系统解耦,建立电-气-热网络的分布式协同优化模型;在此基础上,考虑风电出力和多能负荷的不确定性,提出基于一致性交替方向乘子法的分布式鲁棒优化框架,并利用二阶锥对偶理论与交替优化方法实现含二阶锥约束和二元变量的鲁棒子问题求解。以修改的IEEE 39节点电力网络、比利时20节点天然气网络和15节点热力网络为算例进行仿真分析,验证所提方法能够在源荷不确定性条件下实现综合能源系统各网络分散自治运行,同时兼顾系统运行的低碳性和经济性。

Reserve Constrained Dynamic Economic Dispatch with Valve-point Effect:A Two-stage Mixed Integer Linear Programming Approach

This paper proposes a deterministic two-stage mixed integer linear programming(TSMILP)approach to solve the reserve constrained dynamic economic dispatch(DED)problem considering valve-point effect(VPE).In stage one,the nonsmooth cost function and the transmission loss are piecewise linearized and consequently the DED problem is formulated as a mixed integer linear programming(MILP)problem,which can be solved by commercial solvers.In stage two,based on the solution obtained in stage one,a range compression technique is proposed to make a further exploitation in the subspace of the whole solution domain.Due to the linear approximation of the transmission loss,the solution obtained in stage two dose not strictly satisfies the power balance constraint.Hence,a forward procedure is employed to eliminate the error.The simulation results on four test systems show that TSMILP makes satisfactory performances,in comparison with the existing methods.

基于碳势-能源价格双响应的综合能源系统低碳经济调度

利用电-气-热多元负荷节点碳势引导需求响应是实现综合能源系统(IES)低碳运行的有效手段。IES中多能流耦合关系复杂、动态特性交织,碳排放特性难以准确表达。文中针对IES中低碳运行潜力挖掘不足的问题进行了研究,提出了一种基于节点碳势-能源价格双响应的经济调度方法,用于IES源-荷协同,实现低碳运行。一方面,通过剖析气、热动态特性对碳排放流的影响机理,引入动态特性等价替换思想,得出计及动态特性的碳排放流模型,进而得到负荷节点的碳势,实现碳排放责任的分摊;另一方面,从碳视角出发,考虑多元负荷的碳排放责任,设计了基于多元负荷节点碳势-能源价格双响应的IES源-荷低碳互动机制,充分挖掘IES中多元负荷的低碳潜力。通过对IES中碳排放产生、传输、消费全过程的追踪与计量,制定兼顾经济性与低碳性的调控策略。以电网14节点-热网6节点-天然气网6节点(E14-H6-G6)和E57-H12-G12测试系统为例,验证了所提模型和方法的有效性。

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。

计及调节资源AGC动态特性的离散-连续时间随机前瞻经济调度

随着具有波动性的可再生能源渗透比例不断提高,系统对调频备用的需求显著增加。为了制定更合理的调度方案和调频备用,有必要研究经济调度和自动发电控制(automatic generation control,AGC)之间的协调优化。该文提出建立考虑调节资源AGC动态特性的离散-连续时间随机前瞻经济调度模型,以制定兼顾能量平衡和调频需求的调度决策,并实现快慢资源的最优协同,增强系统的动态调节能力。为降低模型复杂度,在当前时段将调节资源的AGC动态特性建模为精细化离散模型,在前瞻时段采用简化的连续时间模型刻画调节资源的AGC动态特性,显著降低问题规模。通过在目标函数中引入合适的储能充放电成本惩罚项,将储能充放电互补约束松弛,从而消除01变量。最后,采用Benders分解算法实现并行求解,进一步降低了计算时间。该文在PJM5节点系统和IEEE118节点系统进行算例验证。结果表明,与传统模型相比,所提模型可以有效降低系统的频率偏差;与在前瞻时段考虑精细化AGC动态约束的模型相比,所提模型可显著降低计算时间。

考虑碳排放强度约束和负荷转移的风火打捆发电系统经济调度模型

在当前“碳中和、碳达峰”的背景下,风火打捆发电系统将不断提高输送功率,加快构建清洁能源为主体的新型电力系统。为此,本文提出一种考虑碳排放强度约束和负荷转移的风火打捆发电系统经济调度模型。首先,对风火打捆发电系统中灵活性改进后的火电机组,考虑其爬坡能力有了较大提升,采用阶梯式爬坡建模方式来表征其快速爬坡响应能力;其次,考虑改进后的火电机组仍然存在碳排放特性,故将碳排放强度约束加入发电系统的经济调度模型。但是这样可能会限制火电机组的阶梯式爬坡响应能力。为解决这一矛盾,本文将风火打捆发电系统与城市高压配电网进行耦合建模,让城市高压配电网中灵活负荷资源参与风火打捆发电系统经济调度,最终形成计及负荷转移的经济调度模型。最后,本文对某实际地区的多个风火打捆发电系统与当地高压配电网络进行联合仿真分析,验证了所提模型的有效性。

基于模型预测控制的源荷储低碳经济调度

为提高电力系统风电消纳的经济性以及实现低碳排放的目标,基于模型预测控制方法,构建了多时间尺度下含碳捕集电厂的源荷储低碳经济调度模型。源侧分析碳捕集电厂的运行机理及能量时移特性;荷侧根据需求响应时间尺度的差异性,调用不同响应速度的价格型、动态激励型需求响应资源分别参与日前-日内调度;储能快速调整充放电功率提高运行经济性,日前调度以运行成本和碳排放量最小为目标,通过协调调度源荷储资源优化系统各单元出力计划。日内调度在滚动优化平滑功率波动的基础上,以跟踪和修正日前调度计划为目标,实时应对风光和负荷的功率波动,通过算例验证所提方法可降低运行成本和碳排放量,实现对经济与低碳协同优化。

Data-driven Optimal Dynamic Dispatch for Hydro-PV-PHS Integrated Power Systems Using Deep Reinforcement Learning Approach

To utilize electricity in a clean and integrated manner,a zero-carbon hydro-photovoltaic(PV)-pumped hydro storage(PHS)integrated power system is studied,considering the uncertainties of PV and load demand.It is a challenge for operators to develop a dynamic dispatch mechanism for such a system,and traditional dispatch methods are difficult to adapt to random changes in the actual environment.Therefore,this study proposes a real-time dynamic dispatch strategy considering economic operation and complementary regulatory ability.First,the dynamic dispatch of a hydro-PV-PHS integrated power system is presented as a multi-objective optimization problem and the weight factor between different goals is effectively calculated using information entropy.Afterwards,the dispatch model is converted into the Markov decision process,where the dynamic dispatch decision is formulated as a reinforcement learning framework.Then,a deep deterministic policy gradient(DDPG)is deployed towards the online decision for dispatch in continuous action spaces.Finally,a case study is applied to evaluate the performance of the proposed method based on a real hydroPV-PHS integrated power system in China.Simulations show that the system agent reduces the power volatility of supply by 26.7%after hydropower regulating and further relieves power fluctuation at the point of common coupling(PCC)to the upperlevel grid by 3.28%after PHS participation.The comparison results verify the effectiveness of the proposed method.

基于多区域全分布算法的主动配电网动态经济调度方法

由于传统方法在进行主动配电网动态经济调度过程中,没有对目标函数内状态变量存在的越界情况进行处理,主动配电网电压超越上限,从而导致调度效果不佳,因此,提出基于多区域全分布算法的主动配电网动态经济调度方法。利用多区域全分布算法实施机会约束规划,根据规划结果,建立对应的多目标模型,通过罚函数形式对目标函数内状态变量存在的越界情况进行处理,避免主动配电网电压超越上限,使电压在上限之内。根据柴油机发电爬坡方面、线路传输方面、主网与区域配电网最大交互传输功率方面和储能单元方面,通过蒙特卡罗模拟方法对经济调度时的约束条件进行选取,改进传统粒子群算法,并利用改进结果对模型进行求解,从而实现动态性经济调度的目的。仿真实验结果表明,该方法的调度结果是有效、动态的,并且调度精度较高,调度时间较短,适用于主动配电网的光伏与风电等的调度,继续精进后有良好的应用前景。

水电站机组负荷分配中动态规划与遗传算法性能比较

以三峡水库26台机组为研究背景,以计算精度、计算时间和算法稳定性为评价指标,采用数值试验方法,开展了动态规划和遗传算法在机组负荷分配中的性能比较研究。设计了计算精度相同比较计算时间和计算时间相同比较计算精度的具有可比性的数值实验方案,得到了动态规划和遗传算法的性能随机组数变化的关系。结果表明,在机组数小于10台时,遗传算法的性能优于动态规划;当机组数大于10台时,遗传算法性能低于动态规划法。

基于改良和声搜索的电力系统动态经济调度

针对电力系统动态经济调度(Dynamic economic dispatch,DED)问题提出了一种改良和声搜索算法(Reformed harmony search,RHS)求解。RHS在原始和声搜索算法的基础上进行了四方面改进:第一,以100%概率执行和声记忆步骤,排除了随机化步骤。第二,使用基于混沌序列的随机数对基音调整率进行扰动,使其在整个迭代过程中始终处于动态变化的状态。第三,根据当前和声记忆库获得每个变量的动态上下限,以此确定基音调整步长。第四,引入了一种全局搜索步骤,该步骤根据全局最优和声向量对当前和声向量进行更新。上述四个步骤有利于提高RHS算法的收敛性、种群多样性以及对问题的自适应性。实验表明,RHS在求解三种DED问题时能获得比其他两种算法更低的发电成本,因此,它具有更好的优化性能,是求解DED问题的一种有效方法。