The Optimal Steam Pressure of Thermal Power Plant in a Given Load

As the large change of the grid load, many large capacity units of our country had to change the load in order to meet the gird need. When a thermal power plant receives a given load instruction from the grid, it is necessary to set an optimal steam pressure to maintain the high efficiency of the plant. In the past optimization methods, during the process of calculation, the output of the turbine often changed, it was hard to maintain the output constant. Therefore, in combination with the theory of variable condition of turbine, calculation of governing stage and the matrix equation of thermal power system, an optimization method were put forward and an optimal solution was got in a given load.

Distributionally Robust Optimal Dispatch of Virtual Power Plant Based on Moment of Renewable Energy Resource

Virtual power plants can effectively integrate different types of distributed energy resources,which have become a new operation mode with substantial advantages such as high flexibility,adaptability,and economy.This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal day-ahead dispatch under uncertainties of renewable energy sources.The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments.In this regard,the faults of stochastic optimization and traditional robust optimization can be overcome.Firstly,a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed,and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in day-ahead electricity markets.Then,an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem,which improves the computational efficiency of the model.Finally,the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy.They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output.

Solving Load Flow Problems of Power System by Explicit Pseudo-Transient Continuation (E-ψtc) Method

This paper is a further study of two papers [1] and [2], which were related to Ill-Conditioned Load Flow Problems and were published by IEEE Trans. PAS. The authors of this paper have some different opinions, for example, the 11-bus system is not an ill-conditioned system. In addition, a new approach to solve Load Flow Problems, E-ψtc, is introduced. It is an explicit method;solving linear equations is not needed. It can handle very tough and very large systems. The advantage of this method has been fully proved by two examples. The authors give this new method a detailed description of how to use it to solve Load Flow Problems and successfully apply it to the 43-bus and the 11-bus systems. The authors also propose a strategy to test the reliability, and by solving gradient equations, this new method can answer if the solution exists or not.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Collaborative robust dispatch of electricity and carbon under carbon allowance trading market

The launch of the carbon-allowance trading market has changed the cost structure of the power industry.There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch.In this study,a data-driven model of the uncertainty in the annual carbon price was created.Subsequently,a collaborative,robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation.The model is solved using the column-and-constraint generation algorithm.An operation and cost model of a carbon-capture power plant(CCPP)that couples the carbon market and the economic operation of the power system is also established.The critical,profitable conditions for the economic operation of the CCPP were derived.Case studies demonstrated that the proposed low-carbon,robust dispatch model reduced carbon emissions by 2.67%compared with the traditional,economic,dispatch method.The total fuel cost of generation decreases with decreasing,conservative,carbon-price-uncertainty levels,while total carbon emissions continue to increase.When the carbon-quota coefficient decreases,the system dispatch tends to increase low-carbon unit output.This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

Multi-Objective Optimal Dispatch Considering Wind Power and Interactive Load for Power System

With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.

Accelerated Particle Swarm Optimization for Controlling Virtual Power Plant Consisting of Renewable Energy Sources

RES （renewable energy sources）, such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of VPP （virtual power plant） has been developed. The VPP is composed of several RES, from which at least one of them is fully controllable. Because the production of noncontrollable RES can not be forecasted perfectly, therefore an optimal dispatch schedule within VPP is needed. To address this problem, an APSO （accelerated particle swarm optimization） is used to solve the constrained optimal dispatch problem within VPP. The experimental results show that the proposed optimization method provides high quality solutions while meeting constraints.

A Multi-Agent Particle Swarm Optimization for Power System Economic Load Dispatch

A new versatile optimization, the particle swarm optimization based on multi-agent system (MAPSO) is presented. The economic load dispatch (ELD) problem of power system can be solved by the algorithm. By competing and cooperating with the randomly selected neighbors, and adjusting its global searching ability and local exploring ability, this algorithm achieves the goal of high convergence precision and speed. To verify the effectiveness of the proposed algorithm, this algorithm is tested by three different ELD cases, including 3, 13 and 40 units IEEE cases, and the experiment results are compared with those tested by other intelligent algorithms in the same cases. The compared results show that feasible solutions can be reached effectively, local optima can be avoided and faster solution can be applied with the proposed algorithm, the algorithm for ELD problem is versatile and efficient.

Grid Power Optimization Based on Adapting Load Forecasting and Weather Forecasting for System Which Involves Wind Power Systems

This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather forecasting are used for collecting predicting data which are required for optimizing the performance of the grid. The stability of each power systems on the grid highly affected by load varying, and with the presence of the wind power systems on the grid, the grid will be more exposed to lowering its performance and increase the instability to other power systems on the gird. This is because of the intermittence behavior of the generated power from wind turbines as they depend on the wind speed which is varying all the time. However, with a good prediction of the wind speed, a close to the actual power of the wind can be determined. Furthermore, with knowing the load characteristics in advance, the new load curve can be determined after being subtracted from the wind power. Thus, with having the knowledge of the new load curve, and data that collected from SACADA system of the status of all power plants, the power optimization, load distribution and redistribution of the power flows between power plants can be successfully achieved. That is, the improvement of performance, more reliable, and more stable power grid.

风光制氢合成氨系统的多时段可调度域分析

为直观描述风光发电制氢合成氨(power-to-ammonia,P2A)系统的柔性负荷调节潜力,计及制氢、合成氨化工过程的负载调控特性,基于动态规划思想和计算几何理论,提出其多时段可调度域(multi-stagedispatchableregion,MSDR)的刻画方法。所提方法将多时段间相互耦合的鲁棒约束变换为“氢缓冲罐贮量–合成氨产率”状态空间中的确定性多面体约束,使得对于可调度域内的任意系统状态,均存在满足完整调度过程安全约束的“制氢流量–氨产率爬坡速率”控制序列,从而实现P2A系统可调度能力的可视化。所提MSDR模型可进一步用于构造多时段鲁棒优化调度,在保证可行性的前提下优化P2A系统的经济收益。最后,基于内蒙古自治区某在建示范工程构造算例,验证所提方法的有效性。

基于改进深度Q网络的虚拟电厂实时优化调度

深度强化学习算法以数据为驱动,且不依赖具体模型,能有效应对虚拟电厂运营中的复杂性问题。然而,现有算法难以严格执行操作约束,在实际系统中的应用受到限制。为了克服这一问题,提出了一种基于深度强化学习的改进深度Q网络(improved deep Q-network,MDQN)算法。该算法将深度神经网络表达为混合整数规划公式,以确保在动作空间内严格执行所有操作约束,从而保证了所制定的调度在实际运行中的可行性。此外,还进行了敏感性分析,以灵活地调整超参数,为算法的优化提供了更大的灵活性。最后,通过对比实验验证了MDQN算法的优越性能。该算法为应对虚拟电厂运营中的复杂性问题提供了一种有效的解决方案。

含整体煤气化燃料电池-碳捕集电厂的风火储系统分布鲁棒调度方法

整体煤气化燃料电池(integrated gasification fuel cell,IGFC)是与碳捕集技术高度适配的清洁、高效、稳定的绿色煤电技术,有望克服传统碳捕集技术在低浓度CO_(2)环境下产生的高成本、高能耗问题。该文构建含IGFC碳捕集电厂的风火储发电系统。研究IGFC碳捕集电厂的运行机理,对IGFC内部各环节建立数学模型,提出工况匹配时燃烧利用率和阴极空气利用率需要满足的运行条件,并针对该型碳捕集电厂的电碳特性进行分析。为计及风电出力的不确定性,构建风火储系统的两阶段分布鲁棒经济调度模型,引入k阶适应性理论,提出基于正交支撑子集策略的求解方法来获得鲁棒对等式。最后,在改进IEEE-30节点系统中进行算例分析,结果表明,该型碳捕集电厂可利用阳极碳富集的优势,实现系统低碳经济调度的目标,并验证所提优化方法能为可行解提供可解释性。

基于耦合约束解耦的虚拟电厂动态可行域求解方法

构建以新能源为主体的新型电力系统需深入挖掘需求侧灵活性资源的调度潜力,使其参与电网调度调节任务以提升电网平衡调节能力。灵活性资源的海量异构特性使其聚合可行域的求解计算复杂度提升、约束满足难度增大。该文基于顶点搜索法,建立计及分布式电源出力不确定性的虚拟电厂动态可行域求解模型,进而通过李雅普诺夫优化将求解模型内部耦合约束松弛解耦为虚拟队列稳定性问题,提出以顶点搜索与队列稳定为目标的数学模型,最终通过建立虚拟时间队列形成求解算法。所提算法通过耦合约束解耦以提高求解效率;通过满足调度能力约束以提升资源调度能力。算例结果表明,在日前与日内场景下,所提算法具备提升计算效率与约束满足能力等方面的优势。

考虑梅雨汛期防洪安全的水电富集型虚拟电厂优化调度

梅雨季节是中国长江中下游地区的特殊汛期,持续性降雨和突发性暴雨特性导致水电站群的调度策略尤其要兼顾流域防洪安全。为此,提出了考虑梅雨汛期防洪风险的水电富集型虚拟电厂(HEVPP)调度策略。首先,基于典型梅雨时空分布以及水电站防洪安全裕度特性,设计了考虑防洪风险偏好及降雨量不确定性的梅雨汛期库容调控方法;其次,针对HEVPP进行防洪风险的精细化建模,定义了基于剩余防洪库容理论的HEVPP防洪性能评估指标,并进一步提出了基于安全裕度的条件风险价值(CVaR)防洪风险成本计算方法;然后,以虚拟电厂经济效益最大化为目标,构建了考虑梅雨季节降雨特性及防洪风险成本的HEVPP优化调度模型;最后,通过中国浙江省丽水市某HEVPP的算例分析表明,所提调度策略能够反映流域水电站的拓扑特性,充分挖掘梅雨季节水电与风光资源间的协同调度能力,优化水电站群在不同防洪风险偏好下的蓄水策略,在保证流域防洪安全的基础上提升HEVPP的运行效益。

热电联供电厂厂级供热集中智能管控系统开发及应用研究

为了解决多台机组供热负荷调节分散、供热系统压力波动大、供热经济性差的问题,首先提出基于MODBUS通信方式下多台机组供热单元互联控制的厂级供热集中智能管控系统;然后,结合机组实际性能试验数据建立机组性能仿真模型,获得不同电负荷下冷热再抽汽量与煤耗量的关系;最后,考虑机组安全运行边界,建立适用于多台机组热负荷经济分配的数学模型,以厂级总耗煤量最低寻优为目标,获得各机组热负荷分配方案,并成功在某装机6台、总容量3320 MW燃煤电厂实现了工程示范应用。结果表明:厂级供热集中智能管控系统可采用流量指令方式在机组安全运行边界内对各机组供热流量进行自动调节;全厂对外供热流量在50~400 t/h之间变化时,厂级供热集中智能管控系统可将供热母管压力控制在设定值的±0.03 MPa以内;通过对总供热负荷智能寻优分配,全厂总煤耗量可降低0.13~3.53 t/h。

考虑光热电站和柔性负荷的电氢热综合能源系统联合优化运行

为解决现有综合能源系统运行灵活性差及风、光消纳难的问题,同时充分利用氢能实现碳减排目标,本文提出一种考虑光热电站和柔性负荷的电氢热综合能源系统联合优化运行方法。首先,基于光热电站和含电转氢余热利用的氢能系统模型构建电氢热综合能源系统。其次,考虑需求侧电氢热柔性负荷的特征和调节价值,构建含可平移、可转移、可削减负荷的电氢热柔性负荷模型。然后,进一步构建考虑电氢热柔性负荷调节特性的电氢热综合能源系统联合优化运行模型。最后,通过算例仿真验证所提方法能够兼顾经济运行与低碳效果,不仅可以提高光热电站与氢能系统的联合运行能力,而且能够有效提升系统运行的经济性和低碳性,电、氢、热负荷峰谷差分别降低6.68%、11.95%、8.35%,系统运行总成本降低24.4%。

大规模变频空调的可信聚合优化与自适应分散协调方法

变频空调(IAC)可观的调节潜力使其成为电力系统的重要灵活性资源,但其海量、异质和隐私敏感的特点使得集中式调控实施性不佳。现有的弱中心化方法较为关注计算效率的提高,对结果的可行性和解聚合的自适应性考虑不足,极易导致实际负荷分配越界且收敛困难。因此,首先基于IAC物理模型,通过构建保守能量场景得到个体可信边界,以保证集群调度曲线始终处于可调范围内。然后,为了敏捷地分配负荷,提出基于分层分散式架构的自适应解聚合策略,使集群在各时段精确跟踪调度目标并最小化效用损失。其控制过程轻量、无参数化,且仅需极少迭代即可快速得到可行次优解;数据经分层汇总后实现降维,避免了个体隐私泄露,更具实施性。最后,为进一步挖掘实时调节潜力,利用IAC集群进行三相负载均衡,开创了应用场景,并通过“迭代即调整”方式平抑负荷与光伏波动,实现能量优化、分配、平衡的完整统一。算例结果验证了所提方法的高效性,为海量IAC的灵活性挖掘提供了新思路。

计及碳交易和需求响应的虚拟电厂低碳经济运行分析

为应对碳排放增加和清洁能源消纳问题,根据柔性负荷的可转移、可平移、可削减特性,建立以系统运行成本和碳交易成本最低为优化目标的虚拟电厂低碳经济运行模型,选用Matlab+Yalmip工具箱调用Cplex求解器对算例进行求解。通过设置不同场景进行对比分析,验证在碳交易体系下,柔性负荷的合理调度对虚拟电厂进一步减少碳排放、降低系统成本可发挥的作用。仿真结果表明,在碳交易体系下,柔性负荷的合理调度能够有效提高系统的经济环境综合效益和电力系统的灵活性及抗风险能力,为我国电力市场的健康发展提供有力支持。

考虑风电消纳及不确定性的负荷聚合商运行策略

针对“碳达峰、碳中和”背景下新能源大规模并网而引发的风电消纳困难问题,提出了一种考虑风电消纳及不确定性负荷聚合商日前-日内两阶段的经济调度模型。首先分析了考虑风电消纳的负荷聚合商源荷协同运行模式,提出了考虑用能特性与不确定性的柔性负荷响应模型;并基于风电场的消纳需求,以柔性负荷调度成本最小为目标,建立日前-日内两阶段的负荷聚合商经济调度模型;最后采用三角模糊数表征柔性负荷功率的不确定性,采用清晰等价转化求解混合整数线性规划问题。仿真算例验证了该模型能够有效提高风电消纳水平,减少由柔性负荷不确定性带来的影响。

考虑碳排放流与需求响应的电力系统两阶段优化调度

风电接入从发电侧降低了电力系统碳排放,而引入需求响应消纳弃风为负荷侧降碳提供了新思路。文中综合考虑风电和需求响应,基于电力系统碳排放流理论提出日前、日内两阶段电网低碳优化调度方法。首先,分析电力系统碳排放流理论,建立负荷侧的节点碳势模型;其次,将柔性负荷分为可转移负荷和可削减负荷,基于负荷节点碳势模型设计调用这两类负荷降碳的响应机制;然后,建立考虑低碳性和经济性的源荷协调日前优化调度模型,基于模型预测控制求解日前优化调度模型,并通过反馈校正调整日内调度结果。最后,分别通过改进PJM-5节点和IEEE 300系统进行仿真验证,证明了文中所提优化调度方法能有效促进柔性负荷消纳风电,减少弃风,同时实现负荷侧降碳。