Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes

This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production scheduling in mining complexes.The datasets for the throughput prediction model include penetration rates from blast hole drilling(measurement while drilling),geological domains,material types,rock density,and throughput rates of the operating mill,offering an accessible and cost-effective method compared to other geometallurgical programs.First,the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates.A regression model was constructed to predict throughput rates as a function of blended rock properties,which are informed by a material tracking approach in the mining complex.Finally,the throughput prediction model was integrated into a stochastic optimization model for short-term production scheduling.This way,common shortfalls of existing geometallurgical throughput prediction models,that typically ignore the non-additive nature of hardness and are not designed to interact with mine production scheduling,are overcome.A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8.By integrating the prediction model and new stochastic components into optimization,the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill.Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7%per period can be mitigated this way.

Application of Approximation Full-Load Distribution Method to Pass Scheduling on Plate Mill With Hydro-Bending System

The influence of positive bending system on plate crown control was researched. The approximation fullload distribution method for pass scheduling was put forward. This method can not only make full use of the mill capacity with decreased pass number, but also give quality product of excellent flatness.

Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction accuracy.Taking the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction model.The experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.

Mid-long Term Optimal Dispatching Method of Hydro-thermal Power System Considering Scheduled Maintenance

在中长期水火发电调度中考虑检修计划的影响是目前中长期水火发电调度面临的难题。利用现代整数代数建模技术，建立发电计划和检修计划协调优化的多场景调度模型。在该模型中，鉴于设备检修计划的连续性，在预测场景树的基础上，将场景节点划分成不同的场景，通过节点和场景关联矩阵，实现多场景下设备检修模型的构建。同时，鉴于中长期调度计划中发电计划和检修计划对时段间隔要求的不同，分别设置电量相关节点和电力相关节点，实现中长期发电计划和检修计划的协调。上述模型是一个大规模混合整数线性规划（mixed integer linear programming，MILP）问题，采用商用MILP求解器进行求解。大规模实际水火电系统的实例分析结果表明，所提模型和方法是可行、有效的。

On the Short-Term Optimisation of a Hydro Basin with Social Constraints

In this paper, an optimisation problem for calculating the best energy bids of a set of hydro power plants in a basin is proposed. The model is applied to a real Spanish basin for the short-term (24-hour) planning of the operation. The algorithm considers the ecological flows and social consumptions required for the actual operation. One of the hydro plants is fluent, without direct-control abilities. The results show that the fluent plant can be adequately controlled by using the storage capacities of the other plants. In the simulations, the costs related to the social consumptions are more significant than those due to the ecological requirements. An estimate of the cost of providing water for social uses is performed in the study.

基于源荷匹配的梯级水风光蓄系统优化运行研究

在“双碳”目标下,我国亟需进行能源结构转型,提高可再生能源在电力系统中的比例。调节性水电站和抽蓄电站具有良好的调蓄能力,可以作为调节电源与风光电源联合运行,从前端减缓风光不稳定出力对电力系统的冲击,促进新能源集中上网消纳。因此,充分利用梯级水电站及抽水蓄能电站的调节性能,制定合理的一体化调度策略,有利于在发电侧实现水风光蓄打捆送出和协同消纳,助力“双碳”目标实现。基于对梯级水风光蓄互补发电系统在短期调度中电网侧负荷需求和梯级水电水资源利用率的考虑,构建以源荷差异最小、梯级水电蓄能增量最大为目标,以常规水电站、风光电站、抽水蓄能电站安全运行条件为约束的短期互补调度模型,并采用粒子群-动态规划内外层嵌套算法对模型进行求解。选取西南某流域常规梯级水电站、抽水蓄能电站及周边风光电站组成的水风光蓄互补发电系统为研究对象进行模拟计算,结果表明:(1)抽水蓄能电站倾向于在梯级电站水头较低时选择泄水发电,而在梯级电站水头较高时选择抽水或维持当前水位不变;(2)所构建模型有效提高了系统总出力曲线与电网负荷曲线的匹配度,实现了梯级水电站之间负荷与水量的合理分配,一定程度上减少了梯级水电站效益损耗,提高了水能利用率,增加了梯级水电蓄能增量,有利于源网协同运行和梯级电站之间的稳定运行。

基于客观权重赋权-优劣解距离法的水光互补能源基地补偿效益分摊

针对水光互补系统各电站补偿关系复杂且补偿效益分摊难以量化的问题,提出一种适用于量化水光互补系统效益补偿关系的求解方法,通过对梯级优化调度及水光互补优化调度后发电量的对比,得出水光互补动态过程指标和静态特性指标,建立水光互补补偿效益分摊模型,并采用改进的客观权重赋权-优劣解距离法(criteria importance through intercriteria correlation-technique for order preference by similarity to an ideal solution,CRITIC-TOPSIS)求解补偿效益分摊比例,明晰各电站之间的损益关系。以澜沧江上游西藏段水光互补清洁能源基地为例进行求解,结果表明该方法考虑梯级水电站与光伏电站间的利益关系,同时兼顾梯级水电站内部的效益得失,分摊结果公平合理,可以平衡各主体之间的利害关系,有利于调动施益主体参与互补运行的积极性,实现清洁能源基地整体效益最大化。

基于改进Shapley值法的风-光-水-储多主体互补发电系统合作增益分配策略

为了充分发挥系统中可调节资源的自身优势,利用变速抽水蓄能机组配合小水电机组进行常规调节,并考虑变速抽水蓄能机组的快速响应特性,提出了兼顾系统小时级以及秒级安全性的风-光-水-储多主体互补发电系统的联合优化调度模型。为了降低合作博弈高效性算法线性增长的计算复杂度,基于改进Shapely值法提出了一种大规模利益主体的合作增量效益(增益)分配策略。通过资源聚合,对高维度问题进行降维处理,利用Shapley值法进行初始分配;构建合作增益贡献指标,采用非对称纳什谈判理论对同类型的不同主体进行细化分配。以某流域风-光-水-储10主体互补发电系统为仿真算例,结果表明:抽水蓄能机组与小水电机组互补运行可以提升系统的灵活性和安全性;基于改进Shapley值法的合作增益分配策略具有计算高效性以及应用可行性。

水风光多能互补运行关键技术研究现状及展望

在全球能源结构转型背景下,水风光多能互补系统作为现代能源体系关键组成部分,正日益受到重视。探讨了我国水风光资源分布情况,并分析了互补基地建设规划。剖析了水风光多能互补系统在规划期资源评估与系统集成、运行期预报调度与高效运行、消纳期电力交易与市场营销等关键阶段技术挑战。总结了当前在资源评估方法创新、系统集成优化、智能调度系统开发以及储能技术应用等方面最新研究进展,并对未来研究趋势进行了前瞻性展望,研究成果可为相关领域研究和实践提供参考和借鉴。

基于MIQP-GWO的水火风光近似区间需求响应调度

在新型电力系统背景下,水能、风能、太阳能等清洁能源占比显著提升,由于风电和光电的随机波动性及不稳定性,增加了并网的难度。为了更好的应对风电和光电的不确定性,充分发挥梯级水电、火电的调节能力,开展多种能源的联合优化调度,已经成为当前电网调度亟需解决的问题。利用切比雪夫扩张函数对风光发电模型进行近似区间求解,在传统的需求响应模型的基础上,提出水火风光区间需求响应调度模型。利用启发式方法对约束条件进行优化处理,并提出一种嵌套混合整数二次规划法(MIQP)的灰狼算法(MIQP-GWO)。实例分析表明与其他智能算法对比,MIQP-GWO算法在求解区间需求响应调度模型问题的稳定性和性能上有显著的提升。

考虑源荷协同的水风光一体化多时间尺度优化调度研究

以大规模、高比例新能源为主体的新型电力系统背景下,为解决中长期、短期水风光互补系统的电力质量、源荷协同矛盾的问题,本文提出了一种考虑不同时间尺度下水风光互补目标的水库优化调度决策,可实现不同时间尺度下的水风光一体化优化调度,指导水风光一体化系统的全周期运行过程。首先,从水风光一体化互补基本原理层面,阐述水风光一体化互补的机理、原则以及具体方法;其次,提出基于多时间尺度的以送电量为主、适应受端负荷的水风光互补调度多目标函数,构建水风光中长期—短期优化调度的多层嵌套调度模型,引入分层联合解耦、区间二分搜索算法求解;最后,通过不同时间尺度的水风光一体化场景与受端负荷特性确定边界,结合多目标函数构建水电中长期、短期优化调度方案。研究结果表明:利用水电灵活调节性能可有效提升中长期、短期水风光互补发电质量效益,同时增加对受端电力系统负荷的适应性。本次研究成果对水风光一体化系统的调度运行指导提供一定的经验借鉴和参考。

基于改进麻雀算法的风光水储优化配置方法

再生能源发电是构建我国清洁能源体系的核心,风光水可再生能源发电系统是新型电力系统的重要特征。针对风光水分布式可再生能源发电系统的随机性、不确定性等特点,提出了综合考虑风光水不确定性的风光水储优化配置及调度策略,并利用改进麻雀算法进行求解。针对麻雀算法的早熟和收敛性慢等问题,从改善种群多样性、增强全局探索能力和边界处理机制等方面对麻雀搜索算法进行改进;并利用改进的麻雀算法结合实际电网风光水互补特性进行优化配置计算。结果表明,所构建的风光水联合优化调度模型能够较好地提升风光消纳水平,提出的算法具有较高的求解效率。

Simulation and optimization approach for uncertainty-based short-term planning in open pit mines

Accuracy in predictions leads to better planning with a minimum of opportunity lost. In open pit mining,the complexity of operations, coupled with a highly uncertain and dynamic production environment,limit the accuracy of predictions and force a reactive planning approach to mitigate deviations from original plans. A simulation optimization framework/tool is presented in this paper to account for uncertainties in mining operations for robust short-term production planning and proactive decision making. This framework/tool uses a discrete event simulation model of mine operations, which interacts with a goalprogramming based mine operational optimization tool to develop an uncertainty based short-term schedule. Using scenario analysis, this framework allows the planner to make proactive decisions to achieve the mine's operational and long-term objectives. This paper details the development of simulation and optimization models and presents the implementation of the framework on an iron ore mine case study for verification through scenario analysis.

Non-dominated sorting culture differential evolution algorithm for multi-objective optimal operation of Wind-Solar-Hydro complementary power generation system

Due to the intermittency and instability of Wind-Solar energy and easy compensation of hydropower, this study proposes a Wind-Solar-Hydro power optimal scheduling model. This model is aimed at maximizing the total system power generation and the minimum ten-day joint output. To effectively optimize the multi-objective model, a new algorithm named non-dominated sorting culture differential evolution algorithm(NSCDE) is proposed. The feasibility of NSCDE was verified through several well-known benchmark problems. It was then applied to the Jinping Wind-Solar-Hydro complementary power generation system. The results demonstrate that NSCDE can provide decision makers a series of optimized scheduling schemes.

考虑CCER机制的碳-电耦合市场水火电协同竞价模型

当前,全国统一电力市场体系和全国碳交易市场正在同步改革,发电企业面临着在电力市场和碳市场竞争的双重压力。如何适应新的市场环境,在碳-电耦合市场中统筹考虑市场细则及自身电源特征制定竞价计划,是当前发电企业亟须解决的实际问题。针对这一问题,提出了考虑国家核证自愿减排量(CCER)机制的碳-电耦合市场水火电协同竞价模型。该模型详细考虑了碳市场中的碳清缴、CCER抵消、违约惩罚等机制及电力市场特征,兼顾梯级水电站水力-电力的时空耦合联系及非凸非线性的发电运行特征,描述了水火电在耦合市场中的协同竞价要素。针对模型中的非凸及逻辑表达式,引入多种线性化方法将其转为混合整数二次规划问题予以求解。以中国澜沧江流域梯级水电站与火电厂为工程背景验证了模型的有效性,并分析了碳-电耦合市场机制的作用效果。

考虑短期波动与弃电风险的水光互补系统中长期双层嵌套优化调度

由于中长期调度对短期水电调节能力与光电随机波动性考虑不足,不利于高比例新能源渗透下水光互补系统长期消纳与短期并网的有效承接。该文提出统筹中长期消纳电量效益与短期波动、弃电风险的调度方案,构建外层月度库容决策搜索与内层短期水光互补策略相耦合的双层嵌套调度模型与求解方法。首先,从出力形状、电量角度分析光电波动幅度与水电调节能力的互补关系;然后,制定基于电量区域标识和波动平抑方法的短期水光互补策略;接着,引入水光消纳矢量关系加速模型求解;最后,通过模拟水光场景与选取波动参数确定边界,结合目标函数建立风险与效益方案集。实例研究表明:波动参数与发电并网质量具有良好的适配性,对互补系统调度决策影响显著;波动与弃电风险控制能够有效兼顾能源消纳电量与电力系统平稳运行;方案集提供了不确定性场景、波动参数向量集下的决策具象化平台,所呈现的电量效益与波动风险偏好信息能够为互补系统与电力系统的协调运行提供应用指导。

考虑水光互补系统参与电力辅助服务的短期优化调度

梯级水电站与光伏进行互补对平抑光伏波动、提高光伏消纳具有重要意义。为此,首先从发电功率波动最小和发电经济效益最大2个目标函数出发,考虑水光互补系统参与电网有功平衡辅助服务,通过整定水光互补系统出力设计值,使其日内出力趋势能在一定范围内跟随电网负荷的波动,建立梯级水光互补系统短期多目标优化调度模型。其次计及梯级水电站水量约束和光伏电站的晴、阴、雨3种典型日发电特性,采用基于分解的多目标进化算法进行求解。最后对3种典型场景下的梯级水光互补系统的日内优化调度进行验证和分析。实验结果表明梯级水光互补系统可有效平抑光伏发电的间歇性、波动性,并且互补系统出力能及时在电网负荷高峰时段做出相应调整;梯级水光互补系统优化调度方案兼顾了功率波动性和发电经济效益。

含流域梯级水电的水火风联合低碳调度模型

针对大规模风电并网导致的电力系统运行风险和水电、风电出力的互补特性,提出一种计及风电出力不确定性的含流域梯级水电的水火风联合低碳调度模型。采用高斯混合模型-吉布斯采样法生成融入不同时间尺度关联特性的风电出力动态场景集。综合考虑水头、流量多重因素及多种复杂运行约束的影响,建立流域梯级水电站群耦合模型。在此基础上,构建以基准风电场景下机组发电、排放成本和风电动态场景下机组调整成本为整体优化目标的两阶段随机规划调度模型。以改进的IEEE-24节点系统为例进行仿真分析,验证了所提模型的有效性,从而为大规模水火风互补发电系统的联合优化调度提供了参考。

Multi-stage Robust Scheduling for Community Microgrid with Energy Storage

Energy storage devices can effectively balance the uncertain load and significantly reduce electricity costs in the community microgrids(C-MGs) integrated with renewable energy sources. Scheduling of energy storage is a multi-stage decision problem in which the decisions must be guaranteed to be nonanticipative and multi-stage robust(all-scenario-feasible). To satisfy these two requirements, this paper proposes a method based on a necessary and sufficient feasibility condition of scheduling decisions under the polyhedral uncertainty set. Unlike the most popular affine decision rule(ADR) based multistage robust optimization(MSRO) method, the method proposed in this paper does not require the affine decision assumption, and the feasible regions(the set of all feasible solutions) are not reduced, nor is the solution quality affected. A simple illustrative example and real-scale scheduling cases demonstrate that the proposed method can find feasible solutions when the ADR-based MSRO fails, and that it finds better solutions when both methods succeed. Comprehensive case studies for a real system are performed and the results validate the effectiveness and efficiency of the proposed method.

An efficient stochastic algorithm for mid-term scheduling of cascaded hydro systems

Due to the stochastic and correlated attributes of natural inflows, the mid-term generation scheduling problem for cascaded hydro systems is a very challenging issue.This paper proposes a novel stochastic optimization algorithm using Latin hypercube sampling and Cholesky decomposition combined with scenario bundling and sensitivity analysis(LC-SB-SA) to address this problem.To deal with the uncertainty of natural inflows, Latin hypercube sampling is implemented to provide an adequate number of sampling scenarios efficiently, and Cholesky decomposition is introduced to describe the correlated natural inflows among cascaded stations.In addition, to overcome the difficulties in solving the objectives of all the scenarios, scenario bundling and sensitivity analysis algorithms are developed to improve the computational efficiency.Simulation results from both two-station and tenstation systems indicate that the proposed method has the merits in accuracy as well as calculation speed for the midterm cascaded hydro generation scheduling.The consideration of natural inflow correlation makes the formulated problem more realistic.