Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities.However,the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data.This article proposes a physics-informed artificial intelligence(AI)surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance.The incomplete dataset,built with numerical weather prediction data,historical wind power generation,and weather factors data,is augmented based on generative adversarial networks.After augmentation,the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind power.Therefore,the forecasting models’accuracy and generalization ability are improved by mining the implicit physics information from the incomplete dataset.An incomplete dataset gathered from a wind farm in North China,containing only 15 days of weather and wind power generation data withmissing points caused by occasional shutdowns,is utilized to verify the proposed method’s performance.Compared with other probabilistic forecastingmethods,the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset,which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities.

Day-ahead scheduling based on reinforcement learning with hybrid action space

Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal scheduling,the total cost of the ADN can be reduced.However,the optimal dayahead scheduling problem is challenging since the future electricity price is unknown.Moreover,in ADN,some schedulable variables are continuous while some schedulable variables are discrete,which increases the difficulty of determining the optimal scheduling scheme.In this paper,the day-ahead scheduling problem of the ADN is formulated as a Markov decision process(MDP)with continuous-discrete hybrid action space.Then,an algorithm based on multi-agent hybrid reinforcement learning(HRL)is proposed to obtain the optimal scheduling scheme.The proposed algorithm adopts the structure of centralized training and decentralized execution,and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables.The simulation experiment results demonstrate the effectiveness of the algorithm.

基于综合水龄指数评价的供水管网优化调度

研究了通过计算节点平均水龄分析该地区供水管网的水质状况的方法,在此基础上,提出供水管网综合水龄指数评价指标,并基于该指数构建管网水力优化调度模型,采用遗传算法实现优化问题的求解,并以江苏某镇城乡统筹供水管网为实例进行了应用.结果表明,水力优化调度能够改善管网的综合水龄情况,尤其是对于流量较大的节点有明显的效果,但是对于管网末端水量相对较小节点,则需要结合水力调度以及管段冲洗等方式来改善管网水龄情况.

风水火系统长期优化调度方法

含梯级水电站的风水火联合优化调度对于减少煤炭消耗量、提高清洁能源利用效率具有重要意义。长期优化调度不仅需要考虑不同类型电源运行的差异性和互补性,同时也面临多种的不确定因素,建模和求解的难度均较大。为此,综合考虑检修计划影响、梯级水电站间水力约束以及风力、热力与电力相互耦合的复杂约束,建立了风水火长期优化调度模型。为了降低求解难度,将这一大规模、多约束、非线性的优化问题转换为线性混合整数模型予以求解。同时针对模型中各时段水文径流与风速的随机性,利用点估计法构造随机变量的估计点,结合每一个估计点向量进行确定性的长期优化调度模型的求解,进而得到优化调度决策变量的期望值。算例分析结果表明,所提出的调度方法计算精度高,求解速度快,能够方便地处理不确定因素,提高系统的经济性和消纳清洁能源的能力。

考虑可控光伏系统概率模型的主动配电网日前优化调度

针对含可控光伏主动配电网的日前调度问题,采用合适的功率控制策略使DC-DC变换器和DC-AC并网逆变器实现有功无功解耦控制。通过仿真探索计及有功可控能力的光伏发电系统的概率特性,得到有功参考值与其累积密度函数、均值之间的关系。进而考虑机会约束,设计能计算储能系统充放电次数的约束,建立以配网企业运行成本最小化为目标的有功无功协调的主动配电网日前优化调度模型。将非凸约束松弛,利用二阶锥规划方法求解确定性优化步骤,加入割集重复优化保证松弛为紧。结合基于随机响应面法的概率潮流方法检验机会约束,最终得到考虑光伏可控性的满足全部机会约束的效益最大化调度方案。算例结果验证了上述模型和算法是正确性的,且能够满足安全经济运行要求。

智能电网环境下多电动汽车协同充电优化调度算法

大量电动汽车通过居民侧家庭能源管理系统接入电网充电时,若各用户独自对充电过程进行优化,会使充电负荷集中在低电价时段,形成巨大的负荷峰值,造成变压器和线路严重超载,威胁电力系统的稳定运行。引入了一种智能电网环境下电动汽车协同充电模式,建立了最小化用户用电费用的最优充电模型,提出了一种多电动汽车协同充电优化调度算法。该算法能有效避免用户独自优化调度时出现的弊端,同时显著降低用户的充电费用。通过仿真实验验证了算法的有效性。

人力资源调度的蚁群算法模型

针对企业人力资源管理中的生产安排、工作分配和设备布置的优化调度问题,在对蚁群算法进行总结分析的基础上,提出了求解该问题的蚁群算法模型.并对蚁群算法模型进行了改进,提高了算法的全局搜索能力.提出了基于蚁群算法的人力资源调度策略,并用数学模型对求解问题进行描述,给出了算法步骤.实例证明:改进蚁群算法能有效节省人力资源成本,为人力资源调度提供参考.

基于改进蚁群算法的云计算任务调度研究

为了找到最优的云计算任务调度方案,减少任务的完成时间,提出了基于改进蚁群算法的云计算任务调度算法。首先建立云计算任务调度的目标函数,然后采用蚁群算法模拟蚂蚁搜索食物过程对目标函数进行求解,并引入局部、全局信息深度更新方式进行改进,加快搜索速度,最后在CloudSim仿真平台进行性能测试实验.结果表明,改进蚁群算法不仅大幅度减少了云计算任务执行时间,而且解决了资源负载不均衡难题,很好地实现了云计算任务的最优调度.

基于模拟退火的自适应布谷鸟算法求解公交调度问题

根据实际应用中布谷鸟算法体现出的局部搜索能力差的问题,本文采用算法结合的方式把模拟退火算法结合其中,同时动态更改发现概率以及搜索步长,使之变成自适应混合布谷鸟算法。利用标准测试函数进行检验,发现此结合算法能够很好地提高算法运算质量,收敛速度较快。通过实际应用,将该算法引入到公交调度系统当中,效果较好,这为公交系统优化研究提供了一个新颖的方法。

基于SCADA系统应用DDE技术开发并行应用程序

本文介绍了利用SCADA系统提供的对应用程序间的动态数据交换(DDE)机制的支持,开发具有自主知识产权的并行应用程序的方法,并将此方法应用于水泵站的优化调度系统。

储能应急车优化调度的模糊机会约束方法

移动储能技术作为一种新型的灵活性资源,其在电力系统的防灾与应急支援等多种场景中发挥了非常重要作用。在此背景下,提出了基于模糊机会约束规划的储能应急车优化调度模型,以最大程度地恢复重要电力用户的供电并减少停电损失。首先,介绍了储能应急车的工作原理,提出了考虑负荷分级和行驶时间不确定性的储能应急车优化调度的模糊机会约束规划模型,该模型以最小化总停电损失为优化目标,实现对一级负荷的持续供电。然后,针对行驶时间的不确定性,采用模糊理论中的模糊参数表示行驶时间,并采用梯形隶属度参数的清晰等价类将含模糊参数的机会约束清晰化,便于对模糊机会约束模型进行求解,最后采用了遗传算法对所提的模型进行求解。算例分析结果表明,所提出的储能应急车优化调度模型较好地处理了电力应急过程中行驶时间的不确定性,实现了储能应急车的优化分配与调度,确保了重要电力用户的供电恢复,有效地降低了停电损失。

基于模型预测控制和机会约束的主动配电网实时调度优化策略

合理利用分布式电源和可调负荷的灵活调节特性是实现主动配电网主动控制的关键,但其中分布式电源的随机性和波动性影响了实时调度的可靠性。为实现主动配电网的实时优化调度,提出基于模型预测控制和机会约束的主动配电网实时调度优化策略,将模型预测控制与机会约束相结合,降低了分布式能源随机性和波动性对小时间尺度调度的影响。首先,对主动配电网的小时间尺度调度体系进行了分析;在此基础上,以最优经济调度为优化目标,通过模型预测控制将系统未来状态感知与实时状态反馈相结合,对实时调度进行滚动优化,尽可能减小配电网不确定性影响;在滚动优化中采用机会约束进一步降低分布式能源和可调负荷随机波动的影响;实现了主动配电网实时调度的高可靠性和高经济性。最后通过全面的运行实例验证了所提策略的适用性和优越性。

计及不确定因素的梯级水电站短期优化调度

以一定时期内期望发电效益最大化为目标,采用马尔可夫链对梯级水电站机组未来调度时段的预想故障及上网电价进行概率预测,构建了一种新的梯级水电站短期概率优化调度的模型,并且采用服从正态分布的负荷波动来分析时变负荷对优化调度的影响。该模型全面考虑了梯级水电站蓄水量、弃水量、水位、发电引用流量等约束条件,实现了机组运行状态概率预测与优化调度决策的密切结合。利用微分进化算法鲁棒性强、搜索效率高的特点,与蒙特卡洛方法对模型进行求解。以一梯级水电站系统为例进行计算分析,表明所提出的模型合理和有效。

含风电及电动汽车虚拟电厂参与电力市场的优化调度策略

可再生能源发电及电动汽车充电的不确定性给电力系统运营带来新的挑战。针对含有风力发电和电动汽车充放电的虚拟电厂参与到电力市场中包含的不确定性问题,提出了一种混合储能虚拟电厂参与电力市场的优化调度策略。基于轮盘赌机制建立风力发电不确定性模型,将电动汽车的不确定性参数引入该模型,通过分析电力市场需求,制定基于不确定模型的随机优化调度方案。通过算例验证该方案的有效性和适用性,为混合储能虚拟电厂参与电力市场调度提供指导。

Day-ahead Optimization Schedule for Gas-electric Integrated Energy System Based on Second-order Cone Programming

This paper proposes an optimal day-ahead opti-mization schedule for gas-electric integrated energy system(IES)considering the bi-directional energy flow.The hourly topology of electric power system(EPS),natural gas system(NGS),energy hubs(EH)integrated power to gas(P2G)unit,are modeled to minimize the day-ahead operation cost of IES.Then,a second-order cone programming(SOCP)method is utilized to solve the optimization problem,which is actually a mixed integer nonconvex and nonlinear programming issue.Besides,cutting planes are added to ensure the exactness of the global optimal solution.Finally,simulation results demonstrate that the proposed optimization schedule can provide a safe,effective and economical day-ahead scheduling scheme for gas-electric IES.

Robust coordination of interdependent electricity and natural gas systems in day-ahead scheduling for facilitating volatile renewable generations via power-to-gas technology

The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics.This paper proposes a robust day-ahead scheduling model for the optimal coordinated operation of integrated energy systems while considering key uncertainties of the power system and natural gas system operation cost. Energy hub,with collocated gas-fired units, power-to-gas(Pt G) facilities, and natural gas storages, is considered to store or convert one type of energy(i.e., electricity or natural gas)into the other form, which could analogously function as large-scale electrical energy storages. The column-andconstraint generation(C&CG) is adopted to solve the proposed integrated robust model, in which nonlinear natural gas network constraints are reformulated via a set of linear constraints. Numerical experiments signify the effectiveness of the proposed model for handling volatile electrical loads and renewable generations via the coordinated scheduling of electricity and natural gas systems.

Day-ahead Scheduling of Multi-carrier Energy Systems with Multi-type Energy Storages and Wind Power

The integration of large-scale wind power brings challenges to the operation of integrated energy systems(IES).In this paper,a day-ahead scheduling model for IES with wind power and multi-type energy storage is proposed in a scenario-based stochastic programming framework.The structure of the IES consists of electricity,natural gas,and heating networks which are all included in the model.Operational constraints for IES incorporating multi-type energy storage devices are also considered.The constraints of the electricity network,natural gas network and heating network are formulated,and non-linear constraints are linearized.The calculation method for the correlation of wind speed between wind farms based on historical data is proposed.Uncertainties of correlated wind power were represented by creating multiple representative scenarios with different probabilities,and this was done using the Latin hyper-cube sampling(LHS)method.The stochastic scheduling model is formulated as a mixed integer linear programming(MILP)problem with the objective function of minimizing the total expected operation cost.Numerical results on a modified PJM 5-bus electricity system with a seven-node natural gas system and a six-node heating system validate the proposed model.The results demonstrate that multi-type energy storage devices can help reduce wind power curtailments and improve the operational flexibility of IES.

Day-ahead scheduling of large numbers of thermostatically controlled loads based on equivalent energy storage model

Due to their heat/cool storage characteristics, thermostatically controlled loads(TCLs) play an important role in demand response programmers. However, the modeling of the heat/cool storage characteristic of large numbers of TCLs is not simple. In this paper, the heat exchange power is adopted to calculate the power instead of the average power, and the relationship between the heat exchange power and energy storage is considered to develop an equivalent storage model, based on which the time-varying power constraints and the energy storage constraints are developed to establish the overall day-ahead schedulingmodel. Finally, the proposed scheduling method is verified using the simulation results of a six-bus system.

An economic and low-carbon day-ahead Pareto-optimal scheduling for wind farm integrated power systems with demand response

Demand response(DR)and wind power are beneficial to low-carbon electricity to deal with energy and environmental problems.However,the uncertain wind power generation(WG)which has anti-peaking characteristic would be hard to exert its ability in carbon reduction.This paper introduces DR into traditional unit commitment(UC)strategy and proposes a multi-objective day-ahead optimal scheduling model for wind farm integrated power systems,since incentive-based DR can accommodate excess wind power and can be used as a source of system spinning reserve to alleviate generation side reserve pressure during both peak and valley load periods.Firstly,net load curve is obtained by forecasting load and wind power output.Then,considering the behavior of DR,a day-ahead optimal dispatching scheme is proposed with objectives of minimum generating cost and carbon emission.Non-dominated sorting genetic algorithm-II(NSGA-II)and satisfaction-maximizing method are adopted to solve the multi-objective model with Pareto fronts and eclectic decision obtained.Finally,a case study is carried out to demonstrate that the approach can achieve economic and environmental aims and DR can help to accommodate the wind power.

Approximating Nash Equilibrium in Day-ahead Electricity Market Bidding with Multi-agent Deep Reinforcement Learning

In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies.Considering unobservable information in the problem,a model-free and data-driven approach,known as multi-agent deep deterministic policy gradient(MADDPG),is applied for approximating the Nash equilibrium(NE)in the above Markov game.The MAD-DPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks.The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested case and a congested case.Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient(DDPG)demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains.In addition,the comparison with a conventional-model-based method shows that the MADDPG algorithm has higher computational efficiency,which is feasible for real-world applications.