The IES dynamic time-scale scheduling strategy based on multiple load forecasting errors

Considering the complex coupling of multiple energies and the varying load forecasting errors for an integrated energy system (IES), this study proposes a dynamic time-scale scheduling strategy based on long short-term memory (LSTM) and multiple load forecasting errors. This strategy dynamically selects a hybrid timescale which is suitable for a variety of energies for each month. This is obtained by combining the mean absolute percentage error (MAPE) curve of the load forecasting with the error restriction requirements of the dispatcher. Based on the day-ahead scheduling plan, the output of the partial equipment is selectively adjusted at each time-scale to realize multi-energy collaborative optimization and gives full play to the comprehensive advantages of the IES. This is achieved by considering the differences in the response speed for each piece of equipment within the intra-day scheduling. This study uses the IES as an example, and it dynamically determines the time scale of the energy monthly. In addition, this investigation presents a detailed analysis of the output plan of the key equipment to demonstrate the necessity and the advantages of the strategy.     

短期水电系统发电调度的优化方法综述

对国际上短期水电调度中采用的优化技术进行了系统地综述,简述了部分方法的基本思想,分析了各种优化方法在短期水电优化调度中的特点,并探讨了深入研究的难点和可能的方法.     

水电主导的省级多能互补调度系统关键技术及应用

随着西南地区水电和新能源电站规模的不断增大,开发实用的高比例可再生能源电网发电调度决策系统具有很强的迫切性和现实性。为此,以云南电网为依托,设计开发了多能源电网互补优化调度系统,构建了分层协同的总体技术架构,开发了覆盖新能源预测、径流预测、中长期运行方式优化、短期发电计划编制等全过程发电调度业务功能,集成了多尺度风光水互补优化调度模型与方法。实际应用结果表明,该系统可有效提升水电主导的多能源电网发电调度精细化、自动化、实用化水平。     

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Proton exchange membrane fuel cell (PEMFC) long-term prognostic facilitates reducing the time/cost of the durability tests and is a critical starting point for control/maintenance suggestions. Long short-term memory (LSTM) recurrent neural networks have excellent time series processing capabilities and are proved to be useful for the short-term prognostic of PEMFC. However, LSTM prognostic models usually suffer from accumulated errors and model recognition uncertainties, which make it difficult to break the historical degradation data limitations, resulting in unsatisfactory long-term prediction performance. To tackle the problem, this paper proposes a novel model named navigation sequence driven LSTM (NSD-LSTM) for long-term prognostic. In the strategy, a navigation sequence is firstly generated by using an autoregressive integrated moving average model with exogenous variables. The sequence is then fed iteratively into LSTM in the implementation stage to achieve long-term perdition. The proposed strategy is evaluated using the aging experimental data of two types of PEMFC under different operating conditions. The long-term prognostic performance of the proposed model and other two state-of-the-art prognostic models, namely, nonlinear autoregressive exogenous and echo state network, are evaluated through comparison experiments. The simulation and experimental results show that the proposed prognostic strategy has better long-term degradation trend prediction consistency and remaining useful life estimation robustness.     

Integrating price-based resources in short-term scheduling ofelectric power systems

This paper discusses the application of a Lagrangian relaxation algorithm for solving short-term power system resource scheduling problems in the presence of “endogenously priced” resources. The authors demonstrate how such resources can be incorporated into the scheduling model as opposed to the prevailing practice of activating them through post-dispatch price signals derived from the short term scheduling of resources directly controlled by the electric utility. Through a simulation study, the authors compare operations and costs of integrated scheduling to those resulting from the post-dispatch approach. Their analysis demonstrates that integrated scheduling can produce significant improvements     

考虑时间窗约束的海上风电机组运维方案优化

降低运维成本是保障海上风电经济效益的关键,运维方案优化对降低海上风电机组运维成本和提高发电量起着双重作用。根据风电机组零部件的可靠度模型,计算出每台风电机组最佳维修时机对应的时间窗,考虑提前维修和故障后维修的经济损失,建立包含时间窗约束的海上风电机组运维方案优化模型,然后设计基于参数优化的改进遗传算法计算出最优运维方案。最后采用某海上风电场内风电机组运维案例验证模型和算法,结果表明考虑时间窗约束的运维方案可大幅度提高海上风电的经济效益,改进遗传算法比传统遗传算法具有更强的寻优能力。     

考虑电网络影响的水火电力系统短期优化调度

为提高发电计划实际执行的可行性,提出考虑电网络影响的水火电力系统短期优化调度方案,即在传统水火电优化调度中引入输电网络潮流约束,构建水火电力系统短期优化调度数学模型。该模型以火电站的总煤耗量最小为优化目标,考虑水火电的发电特性、梯级水电复杂关系、系统运行约束、输电网络约束及传输功率限制。同时为模型求解引入迁徙操作和惯性权重非线性递减策略的改进粒子群算法,并设计了五种约束处理规则以应对复杂的约束条件。最后以典型水火电系统和IEEE-9节点的电网络拓扑为例对所构建的模型和算法进行验证。结果表明,所构建的优化模型和设计的求解方法能满足复杂运行约束的短期调度方案。     

A resource-task network approach for optimal short-term/periodic scheduling and heat integration in multipurpose batch plants

This article aims to integrate the task scheduling and heat recovery problems into an unified framework for multi-purpose batch processes. The batch scheduling formulation is extended from the continuous Resource-Task Network (RTN) formulation which was originally proposed by Castro et al. [P.M. Castro, A.P. Barbosa-Póvoa, H.A. Matos, Optimal periodic scheduling of batch plants using RTN-based discrete and continuous-time formulations: a case study approach, Ind. Eng. Chem. Res. 42 (2003) 3346–3360; P.M. Castro, A.P. Barbosa-Póvoa, H.A. Matos, A.Q. Novais, Simple continuous-time formulation for short-term scheduling of batch and continuous processes, Ind. Eng. Chem. Res. 43 (2004) 105–118]. Besides the integrated heat recovery and short-term batch scheduling, a full set of heat-integrated periodic scheduling for batch processes, which is more useful in industrial environments, is also presented. The heat-integrated batch scheduling problem can be formulated as a mixed-integer linear program (MILP), where the new formulation can also be solved in standalone or heat-integrated modes by user’s preferences. In the heat-integrated mode, the processes can be precisely defined while keeping the operation flexibility with slightly expanded model size. Two literature examples are demonstrated to show the efficiency and flexibility of the model.     

Accelerated solution of the transmission maintenance schedule problem: A Bayesian optimization approach

To maximize the maintenance willingness of the owner of transmission lines, this study presents a transmission maintenance scheduling model that considers the energy constraints of the power system and the security constraints of on- site maintenance operations. Considering the computational complexity of the mixed integer programming (MIP) problem, a machine learning (ML) approach is presented to solve the transmission maintenance scheduling model efficiently. The value of the branching score factor value is optimized by Bayesian optimization (BO) in the proposed algorithm, which plays an important role in the size of the branch-and-bound search tree in the solution process. The test case in a modified version of the IEEE 30-bus system shows that the proposed algorithm can not only reach the optimal solution but also improve the computational efficiency.     

Long-term forecasts for energy commodities price: What the experts think

The ability to forecast energy prices in the long-term is important for a wide range of reasons, from the formulation of countries' energy and transportation policies to the defensive strategies of nations to investment decisions within the private sector. Despite the importance of these predictions, however, forecasters and market pundits face a difficult challenge when trying to forecast over the long-term. While statistical models can credibly rely on assumptions about the relationship between variables in the short-term, they are frequently less reliable in the long-term as political and technological transformations profoundly change how the economy works over time. Towards improving long-term predictions for energy commodities, this paper uses the elicitation and aggregation of experts' beliefs to put forward forecasts for crude oil and natural gas prices by incentivizing experts to tell the truth and minimising their own biases through the application of the Bayesian Truth Serum. With this approach, we generated both short-term and long-term forecasts, and used the short-term forecast to validate the quality of the experts' predictions.     

In situ check of collector array performance

Within the scope of the EU-ALTENER project “Guaranteed Yield of Solar Water Heaters”, a procedure has been proposed for in situ check of collector array performance. The goal of the procedure is prediction of collector array long-term performance, yearly energy yield in particular. The prediction is carried out using the array parameters identified by short-term monitoring of array performance under non-stationary measurement conditions i.e. transient weather and the system operating conditions. The variability of the influencing variables determines accuracy of the identified parameters for the particular array. The necessary variability may be determined by the detailed performance simulation of a particular array, using the components design parameters of the solar system and the long-term, site-related weather data. The procedure is checked out on a solar water heater with 60 m² collector array. Satisfactory accuracy and repeatability of the significant identified array parameters are achieved. The prediction of the array yearly energy yield is accurate within ±5%.     

一种改进的合成流量法及其在水文预报中的应用

合成流量法对下游站流量作出预报的关键是确定各上游站流量到达下游站的时间。在实际中常采用平均传播时间,然而不同水情下真实传播时间与平均传播时间存在一定的差距,影响预报精度。对此,提出时间窗口概念,以平均传播时间为中心,向前、后各开一个时间窗口,用上游各站时间窗口内流量的线性组合表示其到达下游站的流量,以提高模型对不同水情的适应能力和预报精度。以三峡水库2009~2015年流量数据率定参数,以2016~2018年流量数据作为预报对象,试验结果表明时间窗口法能够显著改善预报精度。     

基于乘子法的梯级水电站短期优化运行研究

本文从分析电力系统中梯级水电子系统短期优化调度的最优准则出发,建立了梯级水电站短期优化调度的非线性规划模型。该模型考虑了水电站水头、效率的变化等因素影响,并通过一系列转换,使模型成为以电站下泄流量为独立变量的形式,利用乘子法求解。实例优化结果表明乘子法用于求解梯级水电站短期优化调度的有效性。     

Optimal hydro scheduling and offering strategies considering price uncertainty and risk management

Hydro energy represents a priority in the energy policy of Portugal, with the aim of decreasing the dependence on fossil fuels. In this context, optimal hydro scheduling acquires added significance in moving towards a sustainable environment. A mixed-integer nonlinear programming approach is considered to enable optimal hydro scheduling for the short-term time horizon, including the effect of head on power production, start-up costs related to the units, multiple regions of operation, and constraints on discharge variation. As new contributions to the field, market uncertainty is introduced in the model via price scenarios and risk management is included using Conditional Value-at-Risk to limit profit volatility. Moreover, plant scheduling and pool offering by the hydro power producer are simultaneously considered to solve a realistic cascaded hydro system.     

汽车发动机喷油器清洗设备的研制分析

随着市场汽车保有量的增加,汽车保养在汽车的使用中,决定着汽车使用性能、寿命,保养设备质量、便捷程度影响着汽车保养的质量和保养时间。针对目前市场汽车电控汽油发动机喷油器的保养还未能完全满足客户需求,故设计实用可靠的汽车喷油器清洗设备是这保养方向的课题。     

An Interactive Package for Risk Evaluation and Maintenance Scheduling

The calculation of the Loss of Load Probability (LOLP) is an important element in generation planning studies. LOLP is calculated either independently to evaluate the risk of a certain generation system or in conjunction with several planning application programs such as, generator maintenance scheduling, probabilistic production costing, hydrothermal scheduling, generation expansion, etc. Exact calculation of the LOLP requires the calculation of the Capacity Outage Probability Table (COPT). This table takes into consideration the capacity rating the the forced outage rates of the generating units of the system and it provides the probability values associated with various amounts of system capacity on outage. The capacity outage table is calculated by utilizing the well known recursive convolution formula. Usually the table is simplified by rounding the capacity outage table to selected discrete capacity levels. The size of the round off increment depends on the desired accuracy. The classic approach is time consuming and requires a considerable amount of computer memory. In order to reduce the computational time and computer storage several methods have been proposed, in which the capacity outage probabilities are approximated with analytic continuous probability distributions. Most of these methods utilize the Central Limit Theorem, Fourier techniques and the Gram Charlier's expansion. Closely related to the LOLP is the maintenance scheduling problem. Preventive maintenance is required for all generating units in order to reduce the probability of capacity shortage and improve the overall reliability of the power system.     

我国电力需求影响因素实证分析

应用协整分析方法,建立了我国电力需求的长期均衡方程和短期误差修正模型,并进行G ranger因果检验,依据此模型与检验能较好地解释我国电力需求与国内生产总值、电价水平、经济结构、人口之间存在的长期均衡关系及短期变动的影响因素。     

Energy consumption and economic activity in industrialized economies—a note

In industrialized countries the income elasticity of primary energy demand is not far off unity. Furthermore, it can be argued that there is little use in distinguishing between short-and long-term income elasticities. A long-term income elasticity does not have a direct impact on energy demand and hence it is of doubtful use in energy demand projections. Via an aggregate OECD model it was estimated that the short-term impact of prices on primary energy demand is about — 0.15 while the long-term impact is — 0.43, but increasing over time.     

基于多状态空间划分的风电机组非完美维修决策

对风电机组的合理维护维修是减少风电场运维成本的重要方式。同一风电场的多台风力发电机构成了一个典型的多部件系统,各风力发电机的运行性能共同决定了系统整体的运行效率和维修需求。同时,对各风力发电机的维修效果也将影响到系统后续的可利用率和维修决策。该文以同一风电场中多台风力发电机的主轴组成的同型多部件系统为对象,在考虑非完美维修的条件下制定基于周期检测的视情机会维修策略;构建考虑非完美维修的多状态退化空间划分模型,以定义系统状态与维修需求的表示及关系,并归纳推导系统维修需求概率的计算模型和非完美维修干预下的系统退化及维修恢复过程中的状态转移概率;在此基础上,建立系统平均费用率解析模型,以确定最优的检测周期和维修阈值。通过某风电场的主轴实际运行数据进行数值实验,验证策略和模型的正确性和有效性,并对参数进行灵敏度分析以说明模型的适用性。结果表明该策略能有效减少风电场的运维成本。     

Testing the oil price efficiency using various measures of long-range dependence

In this paper, we empirically examine the application of a host of techniques employed to measure price efficiency through long-range dependence using prices of monthly oil contracts. Using a series of methods, we analyse the volatility (daily absolute returns) of WTI and Brent oil prices for nine different contracts with maturity, ranging from 1 to 9 months, during the sample period of 1990-2017. We use bootstrapping to compute the confidence interval of the parameter of long-range dependence. Our results indicate that on an average, there is no long-range dependence in the volatility of oil price contracts at least at the 10-percent level of significance. Moreover, our results of rolling estimates suggest that the normality assumption does not affect the results considerably, and the results are robust to different rolling window sizes. While the results of the efficiency index suggest that the efficiency of oil returns vary with time, the futures contracts for Brent oil are found to be less efficient compared to WTI oil. The long-term futures contracts are more efficient than short-term contracts.