As cascade hydropower stations increasingly participate in energy and ancillary service markets, operators must determine joint bidding and scheduling strategies while accounting for strong hydraulic-electric coupling and the physical impacts of reserve deployment. Joint participation across multiple markets directly links market decisions with cascade dispatch feasibility, and actual reserve activation can substantially reshape short-term scheduling coordination, posing new challenges under market price uncertainty. To address this challenge, this paper proposes a short-term multi-market joint scheduling framework for cascade hydropower stations that explicitly incorporates reserve deployment scenarios and coupled hydraulic-electric constraints. The framework jointly considers medium- and long-term contract decomposition, day-ahead energy bidding, and spinning and regulating reserve markets. A non-probabilistic information-gap decision theory (IGDT) approach is employed to characterize price uncertainty in both energy and reserve markets. Accordingly, risk-averse and opportunity-seeking bilevel decision models are formulated to describe robustness-profit trade-offs without assuming price probability distributions, and the bilevel models are further reformulated into equivalent single-level mixed-integer linear programs. Finally, the proposed framework is applied to the cascade hydropower stations in the Lancang River Basin, China. Numerical results demonstrate that, compared with deterministic and stochastic benchmark models, the IGDT-based framework maintains feasible joint scheduling under reserve deployment uncertainty while capturing explicit multi-market risk-profit trade-offs. Moreover, the results indicate that reserve deployment probabilities significantly reshape cascade dispatch coordination and bidding behavior, highlighting the importance of explicitly modeling deployment effects.

On the path to energy transition, substantial increases in wind and solar power are expected to heighten the complexity of ensuring the continuous load-generation balance for grid stability. Hydropower could be the low-carbon source of flexibility to integrate wind and solar power, but seasonal fluctuations and multiple coupling uncertainties would shape traditional hydropower operations. Here we propose a new simulation-optimization-learning approach that addresses uncertainties and nonlinear dynamic hydropower operation characteristics to extract long-term operational rules for cascade hydropower plants under energy transition. The approach consists of three key steps: Simulation, using Kirsch-Nowak Streamflow Generator and ARIMA to track hydrological and meteorological uncertainties; Optimization, developing the objective-driven optimal model that consider nonlinear dynamic hydropower operation characteristics to obtain optimal schemes before and after energy transition; and Learning, constructing physics-constrained LSTM networks (PCLSTM) that incorporate physical reservoir operation constraints to learn operational rules from the optimal schemes. Case studies are conducted for a hydro-wind-solar hybrid system in Southwest China's Wujiang River Basin. Results show that: (1) The effective operational rules adapted to energy transition can be extracted; (2) Compared to long-short term memory networks, the operational rules extracted by PCLSTM can enhance simulation accuracy and reduce the degree of reservoir level penalty, effectively guiding hydropower operations both before and after energy transition. The average degree of reservoir level penalty in all hydropower plants could decrease from 22 % to 5 % before the energy transition and from 10 % to 4 % after energy transition; (3) Compared to stochastic dual dynamic programming (SDDP), the real simulation results from the proposed approach are close to those of the state-of-the-art existing approaches and can address problems that the SDDP cannot solve. (4) Hydropower could perform intraannual hydraulic-electricity spatial-temporal exchange to accommodate wind and solar power, at the expense of sacrificing a portion of hydropower generation.

Hydropower can play a transformative role in supporting the substantial increase in variable renewable energy (VRE) via flexible regulation ability, but the capability of cascade hydropower to buffer VRE is indistinct. This study proposes a systematic framework to assess hydropower capability for integrating VRE considering the peak shaving demands of multiple power grids. A chance-constrained multi-time aggregate model for the integrated hydro-wind-solar operation is established to determine the optimal wind and solar capacity and coordinated operational strategies. An independent hydropower peak shaving operation model is proposed for comparison. The proposed models are recast as mixed integer linear programming formulations based on the proposed solution method. Comparative situation experiments divided by different influencing factors are implemented to analyze hydropower capability for integrating VRE. The results revealed the following: (1) Integrating VRE would reduce the peak shaving effectiveness of hydropower - the lowest load rate decreases to 0.899. (2) Hydropower capability for integrating wind and solar power is affected by hydrological conditions. Hydropower capability is limited under fixed hydrological conditions, and it prioritizes expanding wind power and restricting solar power. (3) Expanding the transmission capacity will increase hydropower capability from 13 300 to 17 000 MW and enhance peak shaving performance through the increased hydropower contribution.

如何破解梯级上下游电站报价不匹配导致的竞争性弃水,是高比例水电现货市场出清中的基础性理论和实践难题.为此,基于节点电价出清机制,从弃水原因出发,提出一种耦合水电弃能消纳的多阶段日前现货市场出清方法.该方法第一阶段引入弃能电站报价修正策略,通过等比例调减弃能电站报价,实现市场优先出清;第二阶段引入上游电站电量控制策略,利用下游电站弃能反向推算上游电站应削减电量,构建了多时段耦合电量控制约束,通过减少上游放水削减下游弃能;第三阶段引入上游电站出力控制策略,考虑梯级滞时精细化推算上游电站各时段出力上限,构建了出力控制约束,进一步调减弃能;最后基于"谁获利、谁负责"的公平性原则,构建了多阶段结算补偿策略.实例分析表明,提出的方法能够有效协调上下游电站出清电量,避免报价不匹配导致的竞争性弃水,为高比例水电市场现货交易提供了新的思路.

How to handle the complex characteristics of cascade hydropower and solve the competitive spillage and power shortage problem is one of the key issues in the clearing of the high-proportion hydropower day-ahead spot electricity market.Therefore,combined with the project in Yunnan,this paper proposes a day-ahead clearing method for the high-proportion hydropower electricity market.In this method,the hydraulic constraints in the clearing model are replaced by the post-hydraulic check.Combined with the essential causes of competitive spillage and power shortage problems,the paper constructs a conditional constraint set including cascaded hydropower linkage constraints and spillage control constraints,and corresponding trigger conditions respectively.Moreover,the constraint boundaries are dynamically updated to gradually reduce competitive spillage and power shortage in the iterative solution procedure.A settlement compensation strategy is finally designed to provide corresponding benefits for plants involved in solving the spillage.The proposed method is validated by a real-world hydropower-dominated provincial system ranking second in China.The results show that competitive spillage and power shortage can be avoided or reduced substantially.It is demonstrated that this method meets the requirements of practicability and timeliness for day-ahead spot clearing of hydropower-dominated provincial power systems.

Promoting a hydro-dominated electricity market (HEM) is greatly challenging. This study analyzes impacts and challenges of electricity market on hydro-dominated power system operations. The analysis indicates that the HEM exhibits unique difficulties including spatiotemporal hydraulic coupling of cascaded hydropower plants, runoff uncertainties, nonlinear operation constraints, coordination of multiple time scales, as well as transprovincial and transregional power transmission. Two kinds of suggestions are presented to overcome these difficulties for different market objects. The hydropower enterprises are suggested to improve generation prediction level, reconstruct market-based operation rules, implement multiscale nested operations and bidding, strengthen collaborative bidding of different stakeholders, and analyze market demands and competitors. The goal is to improve their competitiveness. The market operators should make efforts to develop accuracy and efficiency of clearing models and predetermine feasible hydropower bidding intervals to ensure power balance and prevent unreasonable spillage. Moreover, a practical and convenient electricity trading platforms is required. It is implied that there is strong necessity to explore characteristic market theories and application technologies combined with hydropower characteristics and actual dispatching needs.

Over the past few decades, China has built a batch of high proportion hydropower systems (HPHSs) in southwest China. In these HPHSs, it is very common that the upstream and downstream stations belong to different stakeholders. This situation often leads to unreasonable clearing results with a mismatch between power gen-eration and water release when the upstream and downstream power stations independently participate in the electricity spot market. In addition, HPHSs have highly non-convex nonlinear constraints. The integrated consideration of the above factors and other operational constraints make the day-ahead market clearing (DAMC) for HPHSs very challenging. Therefore, this paper proposes a novel practical DAMC method to deter-mine the optimal quarter-hourly clearing plan of hydropower stations for HPHSs. First, the non-convex nonlinear hydraulic constraints of cascade hydropower stations are replaced by a set of linear dynamic constraints (LDCs), reducing the computational complexity. Second, a mixed-integer linear programming model coupled with LDCs is constructed to minimize the total purchase cost. Third, a control boundary update strategy is proposed to update the LDCs through accurate hydraulic checks. The above steps constitute an iterative solution framework of "model optimization-constraint update", which ensures the efficiency of the model solution and the feasibility of clearing results. The developed method is applied to China Yunnan Power Grid, a high proportion hydropower grid, to optimize the DAMC results for hydropower stations. Different hydrological and price scenarios such as high bid price, low bid price, flood season, and dry season are selected to verify the effectiveness of the presented method. The experiments demonstrate that the proposed method can effectively avoid the mismatch problem between power generation and water release and obtain reasonable hydropower clearing results in an acceptable computational time. The study provides a valuable technical approach for the DAMC of HPHSs in China and other places worldwide.

Renewable energies such as hydro, wind, and solar power, are susceptible to the impacts of climate change. Energy Impact Assessment models under climate change are useful tools for understanding these impacts, but still face some challenges, such as the limited spatial resolution, the lack of utilization of the latest climate models, the inadequate analysis of uncertainties and extreme behaviors, and having not enough capability in simulating the coordinated operation of different power sources. To improve the situation, this study utilizes Random Forest Regression and its derivative Quantile Regression Forest to downscale the CMIP6 General Circulation Models for projecting the means and quantiles of the renewable energy resources for each individual power station under seven combined pathways of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). Based on the projected results, a coordinated operation strategy is established to quantify the impacts of climate change on the regional hydro-wind-solar energy supply system. The assessment reveals: (1) changes of hydro-meteorological variables will present spatially and seasonal inhomogeneous; (2) at the end of the century, the projected increased level of electricity supply will be the highest under the SSP5RCP8.5 and the increment will be 8.680 TWh; (3) the extreme lack electricity supply will occur under the SSP3-RCP7.0 for the period 2041-2060 and the twenty-year average value will be reduced by 16.83 TWh relative to the historical period reference period.

It is popular to combine the hydropower plant with the wind and solar power plants to supply electricity, and the joint is termed as the hydro-wind-solar renewable energy supply system (RESS). The long-term optimal operation of the RESS is a challenging task, due to the nature of different spatial and temporal variabilities associated with renewable energy resources, and the significant operation uncertainties due to the changing natural environment. To address the task, the stochastic model predictive control (MPC), based on probabilistic forecasting and rolling stochastic optimization, is designed and implemented for the long-term operations of the RESSs in Yunnan province, China. The paper tests out the system efficiencies for different penetration levels of wind and solar power, and finds out that (1) the Long Short-Term Memory performs best among candidate point prediction algorithms; (2) the stochastic MPC considering the correlations among renewable resources helps the RESS operate with a higher efficiency; (3) the large-scale hydropower plant has great potential to offset the effects of seasonal uncertainties and demand-generation mismatch, but cannot completely avoid the electricity shortage enabled by unexpected scenarios of renewable resources.

中长期交易电量与短期发电计划衔接不当是引发结构性弃水或缺电的关键问题之一,如何分解交易电量96点曲线对于高比例水电电网发电计划编制至关重要.本文依托云南电网实际工程,提出计及电量曲线分解的短期发电调度方法,引入电站分类策略以贯序确定标准负荷曲线,兼顾公平性提出耦合标准负荷曲线的日电量分解方法,并以交易电量完成度相对偏差最大值最小为目标,集成水电站限制区和电网输电断面校核处理策略,迭代优化初始分解曲线以快速获得可行的电站群发电出力过程.通过电网短期调度实例验证,结果表明该方法能够减少交易电量执行偏差,且满足高比例水电电网发电计划编制的实用性和时效性要求.

高比例水电电力市场中水电是重要的市场主体,对出清结果有重要和决定性影响,梯级电站间紧密的水力时空耦合关系和水电清洁消纳政策,使得其现货出清方法较火电为主的市场复杂和困难得多.针对高比例水电市场特点,结合云南电网电力市场的真实需求,以购电费用最小为目标将梯级水电站日电量动态控制纳入系统约束,基于迭代的成交电量,建立上下游梯级的联动控制边界,将上述目标和约束自动加载到商业求解器优化框架,通过混合整数线性规划模型迭代优化求解.以云南电网122座水电站和11座火电站真实的日发电计划数据作为参考,实现了日前现货竞价出清仿真,结果表明水电现货出清计划都能满足梯级水力约束条件,且计算时间均控制在3min以内,满足了高比例水电系统日前市场出清时效性和实用性要求.

电力中长期市场与现货市场衔接的核心问题之一是中长期物理执行合同到短时间尺度的合理分解.为缓解现货市场电价的剧烈波动和促进市场的平稳运行,建立了考虑余留现货竞价空间均衡化并兼顾各流域梯级水电站间合同分解公平性的中长期合同电量分解非线性规划模型.该模型考虑了梯级水电站间紧密的水力和电力联系以及合同电量的执行要求,并采用多项式拟合技术处理模型中多变量耦合非线性关系.文中以中国西南地区水电富集的云南电力市场为背景进行案例分析,结果表明该模型能够合理地分解各流域梯级水电站中长期交易合同,并为现货市场提供均衡化的竞价空间.

Nowadays, China is carrying on its power sector reform, with establishing long- and medium-term markets as its feature. All the contracts there are physical and month-ahead, which need to be decomposed into power curves before daily delivery. However, no widely acceptable solution had been proposed to solve the contracts decomposition problem (CDP), especially in the hydropower dominated market, in which nonlinear hydraulic connections and head effects have to be considered. A fair and load-following decomposition scheme is firstly proposed in this paper, considering optimal operation of cascaded hydropower in different basins. The scheme is developed as a multi-objective optimization model with the balance between plants generation fairness and fluctuation of daily residual load. The original model is transformed into a single objective non-linear one and solved with LINGO. Different cases are investigated based on Yunnan Electricity Market (YNEM), showing that the scheme proposed can give reasonable solutions to the CDP in various scheduling periods.