Power Purchase Model of Provincial Grids Based on Diverse Energy Coordination Hedging Optimization

Under the background of energy conservation, the grid companies should give priority to consumptive hydropower, wind power and other clean electricity to fulfill their social responsibility and promote the carbon emission reduction in power industry. But under the current power purchase mode, grid companies must first perform the contract. This is extremely uneconomical and not environmentally friendly. Based on hedging theory, this paper proposes a power purchase optimization model using the strategy of “compression and compensation”. If outer price is lower than the contract price, the grid can compress contract power appropriately, leaving more space for purchasing electricity;if outer price is not attractive enough, the grid should timely improve contract proportion, compensating the deviations of contract caused by "compression". Based on the strategy of "compression and compensation", it can effectively reduce the abandoned wind and water, enhance the economic and social benefits of provincial power grid.

Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.

Wind Farm Coordinated Control for Power Optimization

以降低风电场尾流损失、优化风场出力为目标,设计基于Laguerre函数非线性预测控制（nonlinear modelpredictive control,NLMPC）方案的风场集群控制器。该控制器应用风场动态尾流模型,通过NLMPC统一调整风场内各机组转速以提升风场功率。在控制器设计中,使用有效风速预测误差校正对预测模型失配及超短期风速预测误差进行补偿,引入Laguerre函数降低滚动时域优化计算负担并分析了控制器对风速预测误差的鲁棒性能。仿真研究表明,集群控制器能够在不同风速条件下提升风场功率、降低优化计算负担,且对风速预测模型失配与风场自然风速预测误差具有鲁棒性。

Optimal coordination of directional over current relays using evolutionary programming

Co-ordination of directional over current relays(DOCR) requires the selection and setting of relays so as to sequentially isolate only that portion of the power system where an abnormality has occurred.The problem of coordinating protective relays in electrical power systems consists of selecting suitable settings such that their fundamental protective function is met,given operational requirements of sensitivity,selectivity,reliability and speed.Directional over current relays are best suited for protection of an interconnected sub-station transmission system.One of the major problems associated with this type of protection is the difficulty in coordinating relays.To insure proper coordination,all the main/back up relay pairs must be determined.This paper presents an effective algorithm to determine the minimum number of break points and main/back up relay pairs using relative sequence matrix(RSM).A novel optimization technique based on evolutionary programming was developed using these main/back up relay pairs for directional over current relay coordination in multi-loop networks.Since the problem has multi-optimum points,conventional mathematics based optimization techniques may sometimes fail.Hence evolutionary programming(EP) was used,as it is a stochastic multi-point search optimization algorithm capable of escaping from the local optimum problem,giving a better chance of reaching a global optimum.The method developed was tested on an existing 6 bus,7 line system and better results were obtained than with conventional methods.

Multi-Stage Voltage Control Optimization Strategy for Distribution Networks Considering Active-Reactive Co-Regulation of Electric Vehicles

The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.

Transient Stability Analysis of Power System by Coordinated PSS-AVR Design Based on PSO Technique

In this paper, Power System Stabilizer (PSS) and Automatic Voltage Regulator (AVR) are coordinated to improve the transient stability of generator in power system. Coordinated design problem of AVR and PSS is formulated as an optimization problem. Particle Swarm Optimization (PSO) technique is an advanced robust search method by the swarming or cooperative behavior of biological populations mechanism. The performance of PSO has been certified in solution of highly non-linear objectives. Thus, PSO technique has been employed to optimize the parameters of PSS and AVR in order to reduce the power system oscillations during the load changing conditions in single-machine, infinite-bus power system. The results of nonlinear simulation suggest that, by coordinated design of AVR and PSS based on PSO technique power system oscillations are exceptionally damped. Correspondingly, it’s shown that power system stability is superiorly enhanced than the uncoordinated designed of the PSS and the AVR controllers.

Electricity-Carbon Interactive Optimal Dispatch of Multi-Virtual Power Plant Considering Integrated Demand Response

As new power systems and dual carbon policies develop,virtual power plant cluster(VPPC)provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems.To solve the coordinated optimal operation and low-carbon economic operation problem in multi-virtual power plant,a multi-virtual power plant(VPP)electricity-carbon interaction optimal scheduling model considering integrated demand response(IDR)is proposed.Firstly,a multi-VPP electricity-carbon interaction framework is established.The interaction of electric energy and carbon quotas can realize energy complementarity,reduce energy waste and promote low-carbon operation.Secondly,in order to coordinate the multiple types of energy and load in VPPC to further achieve low-carbon operation,the IDR mechanism based on the user comprehensive satisfaction(UCS)of electricity,heat as well as hydrogen is designed,which can effectively maintain the UCS in the cluster within a relatively high range.Finally,the unit output scheme is formulated to minimize the total cost of VPPC and the model is solved using theCPLEX solver.The simulation results showthat the proposed method effectively promotes the coordinated operation among multi-VPP,increases the consumption rate of renewable energy sources and the economics of VPPC and reduces carbon emissions.

Coordination of PSS and TCSC controller using modified particle swarm optimization algorithm to improve power system dynamic performance

This paper develops a modified optimization procedure for coordination of a power system stabilizer (PSS) and a thyristor controlled series compensator (TCSC) controller to enhance the power system small signal stability.The new approach employs eigenvalue-based and time-domain simulation based objective functions simultaneously to improve the optimization convergence rate.A modified particle swarm optimization (MPSO) algorithm is used as the optimization algorithm.The results of simulations and eigenvalue analysis for a single machine infinite bus (SMIB) system equipped with the proposed PSS and TCSC controllers confirm that the new approach is effective in enhancing the system stability.

Coordinative Optimization of Hydro-photovoltaic-wind-battery Complementary Power Stations

The depletion of fossil energy and the deterioration of the ecological environment have severely restricted the development of the power industry.Therefore,it is extremely urgent to transform energy production methods and vigorously develop renewable energy sources.It is therefore important to ensure the stability and operation of a large multi-energy complementary system,and provide theoretical support for the world’s largest single complementary demonstration project with hydro-wind-PV power-battery storage in Qinghai Province.Considering all the multiple power supply constraints,an optimization scheduling model is established with the objective of minimizing the volatility of output power.As particle swarm optimization(PSO)has a problem of premature convergence and slow convergence in the latter half,combined with niche technology in evolution,a niche particle swarm optimization(NPSO)is proposed to determine the optimal solution of the model.Finally,the multiple stations’coordinated operation is analyzed taking the example of 10 million kilowatt complementary power stations with hydropower,wind power,PV power,and battery storage in the Yellow River Company Hainan prefecture.The case verifies the rationality and feasibility of the model.It shows that complementary operations can improve the utilization rate of renewable energy and reduce the impact of wind and PV power’s volatility on the power grid.

考虑大规模风光分层接入的配电网多层协调无功优化方法

大量风光新能源分层、多点接入给配电网运行带来了电压越限等负面影响,增加了不同电压等级无功协同优化的难度。兼顾不同电压等级调节需求,从模型降维等值的角度,提出了基于等值模型的配电网多层协调无功优化方法。该方法分别针对含有无功调节设备及没有无功调节设备的两种台区系统,利用神经网络对大量台区相关运行数据进行训练得到台区拟合模型,形成不可控和可控两种类型的台区等值模型,并用这两类拟合模型分别代替两类台区系统的物理模型,使台区以数据驱动的方式参与配电网无功优化,形成馈线物理模型和台区拟合模型组成的单一电压等级物数混合优化调度模型。将原多层无功优化问题转换为单层系统优化问题,再对该单层系统无功优化调度问题进行求解。该方法可降低配电网系统优化计算规模,从而减小计算量,以无功优化数学模型与数据驱动方法相结合的方式,实现配电网馈线-台区多层协调无功优化。通过算例验证了所提方法对于解决分布式风光分层接入所导致电压越限问题的可行性、有效性和优越性,能够保障配电网的经济安全稳定运行。

区域新能源并网暂态功角稳定分析与紧急控制策略优化

随着新型电力系统发展和新能源机组不断投产,南方区域将形成多区域新能源与邻近常规电源联合集中接入系统的场景。新能源集中接入常与常规电源共用输电通道,将影响区域系统暂态稳定特性及输送通道的传输能力,使南方区域系统的安全稳定运行和风险防控面临了前所未有的挑战。为深入研究区域新能源送出的稳定特性建立了区域新能源交流集中送出的等值系统模型,分析了新能源并入对区域系统对暂态稳定特性影响的主要机理。基于等面积法则对区域送出系统暂态过程的加、减速面积进行了定量分析,提出了紧急控制切机策略的协调优化方法,实现区域送出传统机组与新能源机组紧急控制切机量及控制代价的最小化的切机策略配置。最终,基于广东区域海风火电打捆送出系统为例,对分析结果的正确性进行了验证。

计及光伏波动性的主动配电网双层有功无功协调优化

针对含有高比例分布式电源的配电网在源荷不确定性和协调多种无功补偿设备等方面存在的问题,提出一种主动配电网双层有功无功协调优化策略。该策略首先针对离散型出力的有载调压变压器和电容器组,以网损和电压偏差为目标,建立上层优化模型;其次,下层优化模型通过储能出力平抑光伏的有功波动,同时通过调整静止无功补偿装置、光伏逆变器弥补光伏并网点的无功缺额,以配电网的有功波动和光伏并网点的电压偏差为目标,并采用改进的多目标粒子群优化算法求解该模型;最后,通过IEEE 33节点算例验证所提策略的有效性。该优化策略不仅提高了有功与无功的协调性,且实时性强,不依赖精准的设备控制,使配电网的经济性和可靠性得到提升。

考虑建筑物热惯性的综合能源系统主从博弈协调优化策略

从用户角度出发,提出一种考虑建筑物热惯性的综合能源系统主从博弈协调优化策略。首先,建立热网特性模型和建筑物热惯性模型,并构建以微网运营商为领导者,用户为跟随者的主从博弈数学模型。在光伏出力大时,用户通过电制热设备将电能转化为热能,并利用建筑物热惯性将热能加以存储,无需将光伏余电尽数售给微网运营商,可增强用户权益,同时可有效避免大量光伏余电接入电网,进而缓和余电上网的压力。其次,考虑用户用能满意度及温度舒适度,建立微网运营商与用户的定价、定量模型,并证明Stackelberg博弈均衡解的存在性与唯一性。最后通过算例验证所提策略的有效性,极大地增强了用户权益,同时避免用户光伏余电大量接入电网。

风电-光伏发电-抽水蓄能联合优化调度方法

为解决电力市场环境下的风电、光伏发电、抽水蓄能协同互补问题,提出了一种同时参与日前市场和实时平衡市场的风电-光伏发电-抽水蓄能联合优化调度方法。该方法考虑风电和光伏发电出力的不确定性,基于生成对抗网络和峰值密度聚类算法生成风电、光伏发电联合出力典型场景;基于抽水蓄能的灵活调节特性,并考虑风电和光伏发电实时出力引起的平衡成本,构建了风电-光伏发电-抽水蓄能联合参与日前市场和实时平衡市场的两阶段随机联合优化调度模型,最大化风电-光伏发电-抽水蓄能联合发电系统的收益。算例仿真结果表明,该方法具有有效性和合理性。

计及风电PSS与PLL耦合对功角振荡影响的DFIG控制参数协调优化

双馈感应发电机(doubly fed induction generator,DFIG)装设电力系统稳定器(power system stabilizer,PSS),有助于抑制同步发电机间功角振荡,但抑制效果受DFIG锁相环(phase-locked loop,PLL)跟踪误差影响。考虑PSS与PLL耦合特性对功角振荡的影响,提出改善振荡抑制效果的DFIG控制参数协调优化算法。首先基于DFIG有功控制的分解等效结构绘制DFIG-PSS与锁相误差的耦合路径,提出耦合特性解析表达。然后建立耦合解析表达对控制参数的轨迹灵敏度向量,以向量2-范数之比定义耦合强度,量化耦合特性对功角振荡的影响程度。最后基于耦合强度指标,提出带有PLL参数动态不等式约束的多步优化模型,以协调DFIG控制参数取值,提高并网系统对功角振荡的抑制效果。仿真结果证实了耦合特性对功角振荡的影响,验证了所提协调优化算法的有效性。

电液馈能互联悬架侧倾与俯仰协调优化

针对复合变工况下,侧倾与俯仰模式的电液馈能互联悬架(Electro-hydraulic Energy Regeneration Interconnected Suspension,EERIS)协调优化问题,设计一种包含环境选择策略的高维多目标粒子群优化算法。建立EERIS减振器阻尼力模型,通过试制样机进行模型试验验证并分析其参数变化在侧倾与俯仰模式下的影响规律;融合全局排序规则与全局密度估计方法,设计高维多目标粒子群优化算法;通过仿真对比EERIS优化前、单独优化侧倾模式后、单独优化俯仰模式后、协调优化侧倾与俯仰模式后的性能参数响应及均方根值。结果表明:复合变工况下,协调优化后的性能参数响应峰值降低;簧载质量加速度均方根值降低10.77%,侧倾角加速度均方根值降低24.77%,俯仰角加速度均方根值降低25.05%,提高车辆的平顺性与抗侧倾、抗俯仰能力;悬架动挠度均方根值降低7.9%,轮胎动载荷均方根值降低3.79%,车辆的操纵稳定性得到改善。

二代高温超导带材失超特性研究

高温超导带材失超会降低其通流能力,从而影响高温超导装置的运行稳定性。目前,高温超导带材失超仿真模型多为二维计算模型,无法准确模拟其失超过程中的电磁热行为。该文采用有限元方法对二代高温超导带材失超特性进行多物理场耦合分析。在三维直角坐标系下,以磁场强度矢量为求解变量,建立了二代高温超导带材失超演化电磁热仿真模型,研究了二代高温超导带材长度方向与宽度方向在不同热扰动工况下的电磁热特性及其演化过程,分析了高温超导带材过电流失超过程中的各层分流状况与超导层电阻率变化行为,区分了不同类型的过电流失超行为,并提出了高温超导带材过电流冲击失超的电阻率判据。

基于双向安德森加速分解协调算法的输配协同无功优化

新型电力系统中新能源渗透率不断增加,输电网和配电网协同运行环境复杂多变,对输配协同优化算法的收敛性提出了更高的挑战。鉴于此,应用广义主从分裂法将输配协同无功优化问题分解为输电网和多个配电网的并行优化问题;为改善广义主从分裂法的计算效率,提出了基于功率、电压等多元信息交互及历史信息修正的双向安德森加速策略;在此基础上,提出了融合原对偶内点法、启发式算法和辅助函数的输配协同无功优化方法,实现了输电网和配电网多种类灵活性资源的协调配合和优化控制。算例仿真结果表明,所提方法能有效改善主从分裂法边界的收敛性能,在大规模分布式能源接入的输配协同无功优化场景中具有更好的应用价值。

基于安全强化学习的主动配电网有功-无功协调优化调度

提出一种基于离线策略的安全强化学习方法,通过离线训练大量配电网历史运行数据,摆脱了传统优化方法对完备且准确模型的依赖。首先,结合配电网络参数信息,建立了基于约束马尔可夫决策过程的有功无功优化模型;其次,基于原始对偶优化法设计了新型安全强化学习方法,该方法在最大化未来折扣奖励的同时最小化成本函数;最后,在配电系统上进行仿真。仿真结果表明:所提方法能够根据配电网实时观测信息,在线生成满足复杂约束条件且具有经济效益的调度策略。

面向两个细则考核的风-储场站日前-日内两阶段功率优化上报策略

针对两个细则考核风功率上报准确率的问题,提出一种可有效降低风-储场站风险考核电量的日前-日内两阶段功率优化上报策略。首先,提出面向两个细则的风-储场站日前-日内功率协同上报框架;然后,以期望考核电量最小化为目标,建立计及储能调节的日前-日内两阶段风-储功率优化上报模型,模型中,日前、日内风功率场景集通过基于预测误差分箱的Cornish-Fisher级数与Cholesky分解法生成;最后,针对模型的多重非线性特征与不同时间尺度的求解效率要求,制定基于小步长线性化迭代的求解算法。算例仿真结果表明,相较其余上报策略与求解算法,该文提出的日前-日内协同上报策略及相应算法有效地提升了上报准确率及求解性能。