Source-Load Coordinated Optimal Scheduling Considering the High Energy Load of Electrofused Magnesium and Wind Power Uncertainty

In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional units obviously can not solve the new energy as the main body of the scheduling problem.To enhance the systemscheduling ability,based on the participation of thermal power units,incorporate the high energy-carrying load of electro-melting magnesiuminto the regulation object,and consider the effects on the wind unpredictability of the power.Firstly,the operating characteristics of high energy load and wind power are analyzed,and the principle of the participation of electrofusedmagnesiumhigh energy-carrying loads in the elimination of obstructedwind power is studied.Second,a two-layer optimization model is suggested,with the objective function being the largest amount of wind power consumed and the lowest possible cost of system operation.In the upper model,the high energy-carrying load regulates the blocked wind power,and in the lower model,the second-order cone approximation algorithm is used to solve the optimizationmodelwithwind power uncertainty,so that a two-layer optimizationmodel that takes into account the regulation of the high energy-carrying load of the electrofused magnesium and the uncertainty of the wind power is established.Finally,the model is solved using Gurobi,and the results of the simulation demonstrate that the suggested model may successfully lower wind abandonment,lower system operation costs,increase the accuracy of day-ahead scheduling,and lower the final product error of the thermal electricity unit.

Optimal Scheduling Strategy of Source-Load-Storage Based onWind Power Absorption Benefit

In recent years,the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing,but the peak regulation capacity of the power grid in the three north regions of China is limited,resulting in insufficient local wind power consumption capacity.Therefore,this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid’s wind power consumption capacity.The objective of the uppermodel is tominimize the peak-valley difference of the systemload,which ismainly to optimize the system load by using the demand response resources,and to reduce the peak-valley difference of the system load to improve the peak load regulation capacity of the grid.The lower scheduling model is aimed at maximizing the system operation benefit,and the scheduling model is selected based on the rolling schedulingmethod.The load-side schedulingmodel needs to reallocate the absorbed wind power according to the response speed,absorption benefit,and curtailment penalty cost of the two DR dispatching resources.Finally,the measured data of a power grid are simulated by MATLAB,and the results show that:the proposed strategy can improve the power grid’s wind power consumption capacity and get a large wind power consumption benefit.

An Optimal Dynamic Generation Scheduling for a Wind-Thermal Power System

In this paper, a dynamic generation scheduling model is formulated, aiming at minimizing the costs of power generation and taking into account the constraints of thermal power units and spinning reserve in wind power integrated systems. A dynamic solving method blended with particle swarm optimization algorithm is proposed. In this method, the solution space of the states of unit commitment is created and will be updated when the status of unit commitment changes in a period to meet the spinning reserve demand. The thermal unit operation constrains are inspected in adjacent time intervals to ensure all the states in the solution space effective. The particle swarm algorithm is applied in the procedure to optimize the load distribution of each unit commitment state. A case study in a simulation system is finally given to verify the feasibility and effectiveness of this dynamic optimization algorithm.

Optimized scheduling of integrated energy systems for low carbon economy considering carbon transaction costs

With the introduction of the“dual carbon”goal and the continuous promotion of low-carbon development,the integrated energy system(IES)has gradually become an effective way to save energy and reduce emissions.This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs.With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads,an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established.The role of flexible loads in improving the economy of an energy system was investigated using examples,and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios.The results showed that the total cost of the system in different scenarios was reduced by 18.04%,9.1%,3.35%,and 7.03%,respectively,whereas the total carbon emissions of the system were reduced by 65.28%,20.63%,3.85%,and 18.03%,respectively,when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously.Flexible electrical and thermal loads did not have the same impact on the system performance.In the analyzed case,the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account.Photovoltaics have an excess of carbon trading credits and can profit from selling them,whereas other devices have an excess of carbon trading and need to buy carbon credits.

A Robust Reserve Scheduling Method Considering Asymmetrical Wind Power Distribution

Nowadays, limited predictability and controllability of wind power are regarded as some bottlenecks to wind generation integration with the power system. This paper introduces a robust reserve scheduling method, where the spinning reserve allocation among conventional units is considered as well.The method applies to asymmetrical wind power distribution,and offers control on the degree of solution's conservatism by changing the robustness budget. Meanwhile, distributional information of wind power is represented by mean value and asymmetrical bounds. Furthermore, the model is converted into a deterministic programming problem with dual theory. Case studies for asymmetrically distributed wind power illustrate its effectiveness.

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。

Optimization of Minimum Power Output for Combined Heat and Power Units Considering Peak Load Regulation Ability

Northern China has rich wind power and photovoltaic renewable resources. Combined Heat and Power (CHP) Units to meet the load demand and limit its peaking capacity in winter, to a certain extent, it results in structural problems of wind-solar power and thermoelectric. To solve these problems, this paper proposes a plurality of units together to ensure supply of heat load on the premise, by building a thermoelectric power peaking considering thermal load unit group dynamic scheduling model, to achieve the potential of different thermoelectric properties peaking units of the excavation. Simulation examples show, if the unit group exists obvious relationship thermoelectric individual differences, the thermal load dynamic scheduling can be more significantly improved overall performance peaking unit group, effectively increase clean energy consumptive.

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.

Three-Level Optimal Scheduling and Power Allocation Strategy for Power System ContainingWind-Storage Combined Unit

To mitigate the impact of wind power volatility on power system scheduling,this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy.And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit.The strategy takes smoothing power output as themain objectives.The first level is the wind-storage joint scheduling,and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster(WPC),respectively,according to the scheduling power of WPC and ESS obtained from the first level.This can ensure the stability,economy and environmental friendliness of the whole power system.Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system(ESS),this paper decides the charging and discharging intervals of ESS,so that the energy storage and wind power output can be further coordinated.Considering the prediction error and the output uncertainty of wind power,the planned scheduling output of wind farms(WFs)is first optimized on a long timescale,and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale.Finally,the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis.

Distributed Robust Scheduling Optimization ofWind-Thermal-Storage System Based on Hybrid Carbon Trading andWasserstein Fuzzy Set

A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems.A hybrid carbon trading mechanism that combines shortterm and long-term carbon trading is constructed,and a fuzzy set based onWasserstein measurement is proposed to address the uncertainty of wind power access.Moreover,a robust scheduling optimization method for wind–fire storage systems is formed.Results of the multi scenario comparative analysis of practical cases show that the proposed method can deal with the uncertainty of large-scale wind power access and can effectively reduce operating costs and carbon emissions.

SMO Algorithm to Unravel CEED Problem using Wind and Solar

This research proposes a more advanced way to address Combined Economic Emission Dispatch(CEED)concerns.Economic Load Dispatch(ELD)and Economic Emission Dispatch(EED)have been implemented to reduce generating unit fuel costs and emissions.When both economics and emission tar-gets are taken into account,the dispatch of an aggregate cost-effective emission challenge emerges.This research affords a mathematical modeling-based analyti-cal technique for solving economic,emission,and collaborative economic and emission dispatch problems with only one goal.This study takes into account both the fuel cost target and the environmental impact of emissions.This bi-inten-tion CEED problem is converted to a solitary goal function using a price penalty factor technique.In this case,a metaheuristic and an environment-inspired,intel-ligent Spider Monkey Optimization technique(SMO)are used to address the CEED dilemma.By following the generator’s scheduling process,the SMO meth-od is used to regulate the output from the power generation system in terms of pollution and fuel cost.The Fission-Fusion social(FFS)structure of spider mon-keys promotes them to utilize a global optimization method known as SMO dur-ing foraging behaviour.The emphasis is mostly on lowering the cost of generation and pollution in order to improve the efficiency of the power system and han-dle dispatch problems with constraints.The economic dispatch has been reme-died,and the improved result demonstrates that the system’s performance is stable andflexible in real time.Finally,the system’s output demonstrates that the system has improved in resolving CEED difficulties.When compared to ear-lier investigations,the proposed model’sfindings have improved.As the gener-ating units,wind and solar are used to explore the CEED crisis in the IEEE 30 bus system.

On optimal charging scheduling for electric vehicles with wind power generation

We consider the scheduling of battery charging of electric vehicles(EVs)integrated with renewable power generation.The increasing adoption of EVs and the development of renewable energies contribute importance to this research.The optimization of charging scheduling is challenging because of the large action space,the multi-stage decision making,and the high uncertainty.To solve this problem is time-consuming when the scale of the system is large.It is urgent to develop a practical and efficient method to properly schedule the charging of EVvs.The contribution of this work is threefold.First,we provide a sufficient condition on which the charging of EVs can be completely self-sustained by distributed generation.An algorithm is proposed to obtain the optimal charging policy when the sufficient condition holds.Second,the scenario when the supply of the renewable power generation is deficient is investigated.We prove that when the renewable generation is deterministic there exists an optimal policy which follows the modified least laxity and longer remaining processing time first(mLLLP)rule.Third,we provide an adaptive rule-based algorithm which obtains a near-optimal charging policy efficiently in general situations.We test the proposed algorithm by numerical experiments.The results show that it performs better than the other existing rule-based methods.

How to Use Wind Power Efficiently for Seawater Reverse Osmosis Desalination

Due to water scarcity and the global trends in climate change, winning drinking water through desalination is increasingly becoming an option, especially using reverse osmosis (RO) membrane technology. Operating a reverse osmosis desalination plant is associated with several expenses and energy consumption that take a very large share. Several studies have shown that wind power incurs lower energy costs compared to other renewable energy sources, therefore, should be the first choice to be coupled to an RO desalination system to clean water using sustainable energy. Therefore, in this paper, we investigate the feasibility of driving an RO desalination system using wind power with and without pressure vessel energy storage and small scale energy recovery using Clark pump based on simulation models. The performance of both variants was compared with several scenarios of wind patterns. As expected buffering and energy recovery delivered higher water production and better water quality demonstrating the importance of an energy storage/recovery system for a wind-power-supplied desalination plant.

Five Major Concerns about China’s Wind Power Development

Economic issue is the very focus of China's wind power development.Although all the security problems can be dealt with through technical measures,the compensation for supportive thermal power units and the subsidy from economically developed regions to resource-outputting regions would require a higher level of strategic considerations.The core purpose of developing wind energy is to reduce pollutant emissions,so planners should take into account the overall cost of all sides,without touching the bottom of social affordability.

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.

A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration

The volatile wind power generation brings a full spectrum of problems to power system operation and management, ranging from transient system frequency fluctuation to steady state supply and demand balancing issue. In this paper, a novel wind integrated power system day-ahead economic dispatch model, with the consideration of generation and reserve cost is modelled and investigated. The proposed problem is first formulated as a chance constrained stochastic nonlinear programming U+0028 CCSNLP U+0029, and then transformed into a deterministic nonlinear programming U+0028 NLP U+0029. To tackle this NLP problem, a three-stage framework consists of particle swarm optimization U+0028 PSO U+0029, sequential quadratic programming U+0028 SQP U+0029 and Monte Carlo simulation U+0028 MCS U+0029 is proposed. The PSO is employed to heuristically search the line power flow limits, which are used by the SQP as constraints to solve the NLP problem. Then the solution from SQP is verified on benchmark system by using MCS. Finally, the verified results are feedback to the PSO as fitness value to update the particles. Simulation study on IEEE 30-bus system with wind power penetration is carried out, and the results demonstrate that the proposed dispatch model could be effectively solved by the proposed three-stage approach. © 2017 Chinese Association of Automation.

风光热储互补发电系统容量配置技术研究

针对无常规电源支撑的风光热储互补发电系统,协调规划装机容量对提高发电系统运行经济性和利用率具有重要意义。提出了一种双层优化配置方法,上层以最小度电成本及弃电率为目标,确定系统装机容量;下层以新能源发电消纳最大为目标,解决功率分配问题。通过反复迭代寻优,得到系统容量配置;然后;通过纳什谈判对优化结果进行选择;最后,对甘肃河西地区数据进行仿真分析。结果表明:风光热储互补发电系统最优容量配置下的度电成本为0.3064元/(k W·h);风电场加光伏电站装机容量与光热电站装机容量的最优比为6:1,对比相同装机容量的风光互补发电系统,风光热储互补发电系统具有更高的稳定性。

计及UPFC最优配置的电力系统鲁棒调度协同优化策略

统一潮流控制器(UPFC)应用于潮流调控时,计及UPFC调控参数的交流潮流计算是非凸、非线性问题,且多台装置间的非线性交叉耦合特性也会直接影响优化配置方案。为此,基于UPFC的潮流调控特性,构建了计及UPFC的松弛型交流潮流二阶锥规划模型;计及风电的不确定性,协同考虑UPFC的规划和电力系统的调度问题,建立了计及UPFC最优配置的电力系统鲁棒协同优化模型,并采用列和约束生成算法进行求解。以IEEE RTS-24节点系统为算例进行仿真分析,结果表明所提协同优化策略有效提升了UPFC配置方案的适应性,提高了系统运行经济性和风电消纳能力,增强了系统运行调控的灵活性。

基于TOPSIS-Shapley值法的梯级水电站补偿效益分摊研究

针对流域内联合电站数目众多但利益分配存在纠纷的情况,旨在提出一种更为合理的梯级电站利益分配方法。基于Shapley值法和TOPSIS法,综合考虑电站贡献及电站对不同客观分摊方法的相对满意程度,建立TOPSIS-Shapley值法梯级电站补偿效益分摊模型,同时为解决Shapley值法在求解多利益主体梯级电站联合调度增益分配问题中的局限性,引入聚合降维思想。模型思路如下:将上游电站联合调度前后梯级发电效益的增量作为补偿效益,首先采取四种不同的客观分摊方法对电站特性参数指标进行计算,构成TOPSIS的基础决策矩阵,利用TOPSIS原理得出电站对各个分摊方法的相对满意度,同时采用联盟博弈理论,计算电站所有可能联盟方式下的发电效益,得到基于Shapley值的补偿效益分摊结果,最后将TOPSIS原理得出的分摊系数与由Shapley值法所得的分摊系数结合形成综合分摊权重,应用于黑河上游的八级电站补偿效益分摊中。工程应用实例表明:该方法不仅考虑了联盟电站对整体的边际贡献和各电站的分摊满意程度,且兼顾了电站的特性指标差异,合理量化和分配了各电站间的发电补偿效益,分摊结果公平合理、易被各方接受,可促进流域各级电站参与联合调度从而达到整体效益最大的目标,对于数量较多的梯级水电站补偿效益分配问题具有重要参考意义。

基于主动尾流控制的风电机群协同优化调度

针对机组间尾流效应严重影响风电机组发电效率的问题,提出了风电机组安全偏航约束计算方法、尾流特性混合半机理建模方法以及风电机群多目标协同优化调度方法。基于FAST.FARM平台完善了多自由度可控机组与尾流的动态交互集成仿真环境,对比分析了2台机组串列式排布以及华东地区某海上风电场7台机组实际排布下的协同运行优化性能。结果表明:所建立的集成仿真模型能够合理表征风电机群与空气流场的多领域动态交互特性,所提方法能够有效提升风电机群发电效能,促进经济效益、资源利用和成本控制的均衡优化。