Cascade Hydropower System Research Articles

Short term hydropower scheduling considering cumulative forecasting deviation of wind and photovoltaic power

The cumulative forecasting errors of wind and photovoltaic (PV) power pose serious challenges to the short-term scheduling of hydropower stations that connected to the same regional power grid. The variable and intermittent nature of wind and PV power necessitate a flexible power source to achieve power balance. Consequently, hydropower, with its flexible adjustment capability, plays a crucial role in supporting the consumption of wind and PV power stations in short-term scheduling. Meanwhile, to address the grid's peak shaving requirement, the scheduling of cascaded hydropower system becomes increasingly complex. Given the limitations of current wind and PV power output prediction technologies, achieving precise forecast remains challenging. In practice, the actual outputs of wind and PV power stations often diverge considerably from predicted values over multiple consecutive periods. This variability may result in the underutilization of wind and PV power if the reserve capacity of hydropower stations is insufficient. Therefore, it is essential to account for the cumulative deviation in electricity from wind and PV sources. By computing probability distribution and establishing boundary condition of the cumulative deviation, the operation of the cascaded hydropower system can be controlled more effectively, thus enhancing the stability of a multi-energy complementary system. This study employs Gaussian Mixture Model (GMM) to characterize the probability distributions of cumulative electricity deviation across multiple periods of wind and PV power and takes it as restriction to regulate the operation of hydropower stations. In order to balance the deviation electricity, the downward electricity reserve, upward electricity reserve and the storage energy of the cascaded reservoirs are introduced as restrictive conditions. The proposed Hydro-wind-PV peak shaving model is then applied to the short-term optimal scheduling of the Lancang River cascaded hydropower system to validate the feasibility and efficacy.

Short-term load distribution model for cascade giant hydropower stations with complex hydraulic and electrical connections

The cascade giant hydropower station system (CGHSS) has been widely applied in countries with abundant water resources. However, the dense hydropower stations and power grids bring complex hydraulic and power connections, including the backwater effect, multiple hydroplants sharing one reservoir and one-reservoir multiple dispatch power grid, which leads to modelling difficulties and inefficient model solving. To address these challenges, a short-term load distribution (SLD) model considering complicated hydraulic and electricity constraints is developed. First, the discharge of each hydroplant and the downstream forebay water level are used as inputs to construct a multivariate nonlinear function, and the average tailwater levels are obtained. Second, a head constraint considering the shared water level is added to the model to calculate the head of each hydroplant, making the output more accurate. Finally, the nonlinear constraints are linearly reconstructed using the Big-M method, triangulation technique and special ordered sets to reduce the model solution time. The SLD model is applied to the Xiluodu-Xiangjiaba cascade hydropower system in the Jinsha River. The results show that the developed model reduces water consumption by 4.02 %, 4.70 %, 5.79 % and 5.28 % compared to the constant proportional distribution model and the real operation, generating a more executable SLD scheme.

Revealing electricity conversion mechanism of a cascade energy storage system

With the increasing penetration of renewable energy in the power system, it is necessary to develop large-scale and long-duration energy storage technologies. Deploying pump stations between adjacent cascade hydropower plants to form a cascade energy storage system (CESS) is a promising way to accommodate large-scale renewable energy sources, yet the mechanism how renewable curtailment is converted to hydroelectricity is still unclear. In this paper, we aim to clarify this mechanism by evaluating the CESS's long-term operational efficiency and changes compared to the cascade hydropower system. First, operational features and principle of the CESS was outlined. Then, long-term operations of the CESS and cascade hydropower system were, respectively, optimized using a simulation-based optimization framework. Finally, the operational efficiency of the CESS, defined as a ratio of reduced renewable energy curtailment to increased hydropower production, was calculated based on the above two optimized operation results. China's Longyangxia-Laxiwa CESS was selected as a case study. Results show that: (1) long-term operational efficiency of the CESS reached 81.6 %, i.e., 81.6 % of the curtailed renewable could be converted to hydroelectricity; (2) the increase in hydropower production was mainly attributed to the upper hydropower plant, accounting for about 96.5 % of the total increment; and (3) the increase in generation flow was the main factor to impact the increased hydropower production of the upper plant, although it could decrease the hydropower plant's hydraulic head.

Short-term optimization scheduling method of cascade hydropower and photovoltaic complementary system based on pumping station operation strategy

With the rapid development of photovoltaic power generation, how to improve the photovoltaic grid connection rate is an urgent problem to be solved. This article proposes an optimized scheduling method for the water and photovoltaic complementary system, taking into account the operation strategy of pump stations to improve the photovoltaic grid connection rate. Firstly, a multi-objective optimization scheduling model is constructed to consider both power generation and output fluctuation, and the uncertainty of photovoltaic power generation is analyzed from multiple perspectives. Then, taking the cascade hydropower stations and surrounding photovoltaic power stations in a river basin in Sichuan as an example, the operation strategy of pump stations is introduced into the water and photovoltaic complementary system, considering different weather scenarios, to reduce the photovoltaic curtailment rate. The study verifies that the introduction of pump stations can effectively increase the photovoltaic grid connection rate, and quantitatively analyzes the pump station capacity configuration under different photovoltaic penetration rates.

Fast Coordinated Predictive Control for Renewable Energy Integrated Cascade Hydropower System Based on Quantum Neural Network

The increasing penetration of renewable energy poses intractable uncertainties in cascade hydropower systems, such that excessively conservative operations and unnecessary curtailment of clean energies can be incurred. To address these challenges, a quantum neural network (QNN)-based coordinated predictive control approach is proposed. It manipulates coordinated dispatch of multiple clean energy sources, including hydro, wind, and solar power, leverages QNN to conquer intricate multi-uncertainty and learn intraday predictive control patterns, by taking renewable power, load, demand response (DR), and optimal unit commitment as observations. This enables us to exploit the stability and exponential memory capacity of QNN to extrapolate diversified dispatch policies in a reliable manner, which can be hard to reach for traditional learning algorithms. A closed-loop warm start framework is finally presented to enhance the dispatch quality, where the decisions by QNN are fed to initialize the optimizer, and the optimizer returns optimal solutions to quickly evolve the QNN. A real-world case in the ZD sub-grid of the Sichuan power grid in China demonstrates that the proposed method hits a favorable balance between operational cost, accuracy, and efficiency. It realizes second-level elapsed time for intraday predictive control.

Bidding optimization for cascade hydropower plants in multi-regional electricity markets

Efficient cross-regional allocation of hydropower is critical for the low-carbon energy transition. However, co-optimizing bidding curves for different regional markets presents significant challenges for hydropower producers, due to the complex interplay of bidding strategies, multiple factors that contribute to uncertainty, and the diversity of regional markets. This paper proposes a novel bi-level model to optimize the bidding curves of cascade hydropower plants for multi-regional electricity markets with detailed modeling of their spatio-temporal correlations. The proposed model can adapt to regional markets with varying information transparency by employing a flexible approach to market modeling. A modified interval programming method is proposed to address the multiple uncertainties in the bidding, which mitigates the excessive conservatism inherent in traditional interval programming while preserving its robustness to the distribution estimation error. To efficiently solve the mathematical model, the complex nonlinear dependencies among interval variables are first eliminated through Taylor expansion and a modified multi-dimensional interpolation method. Subsequently, the order relation of interval and Karush-Kuhn-Tucker conditions are utilized to reformulate the proposed model into a tractable mixed-integer linear programming model. Numerical tests conducted on a real cascade hydropower system located in China validate the effectiveness and advantages of the proposed method.

Changes in flow and sediment transport caused by cascade hydropower in the upper reaches of Yangtze River and their influence on spawning of Coreius heterodon

The upper reaches of the Yangtze River (YR) constitute the largest group of hydropower cascade projects in the world. The construction of cascade hydropower systems has a significant impact on the flow regimes and sediment transport in rivers, ultimately affecting fish spawning. Previous studies have examined the effects of fluctuating flow and water temperature on spawning; however, insufficient attention has been paid to their effects on sediment transport. This study analysed changes in runoff and sediment load (SL) at the Xiangjiaba hydrologic station (XJB) and Zhutuo hydrologic station (ZT) in the upper reaches of the YR over the past 60 years. Based on the indicators of hydrological regimes and sediment transport, we used the generalized additive models (GAM) to investigate the effects of cascade hydropower operations from 2009 to 2018 on the spawning abundance of Coreius heterodon (C. H) in the upper YR Fish Reserve. Following the operation of cascade hydropower, the SL and sediment concentration decreased by 56.8% and 56.6%, respectively, while the median size of the bed-material load demonstrated an initial thinning trend followed by subsequent coarsening. In addition, the frequency of the high-flow pulse increased by 32.1%, whereas the peak flow and duration of the high-flow pulse decreased by 41.2% and 62.6%, respectively. However, no significant changes were noted in the indicators of the flooding process. The decrease in sediment transport and changes in the flow regime led to a reduction in the spawning abundance of C. H. Reductions in sediment transport and changes in high-flow pulses caused by cascade hydropower can negatively affect C. H spawning, and SL is the most important indicator of impacts on spawning abundance (contribution of 77.7%). These findings are critical for evaluating the long-term impacts of cascade hydropower on fish habitats.

Economically optimal hydropower development with uncertain climate change

Climate change affects the planning, construction, operation and management of hydropower plants in various ways. Evaluating hydropower generation with nonstationary climate changes is important for planning electric power infrastructure development. For a new hydropower plant with climate change, specifying too large a reservoir may result in excessive investment if the future becomes drier, while too small a reservoir may cause insufficient utilization of hydropower resources. Whether to build a new hydropower station at once or build it dynamically in stages over time is challenged by uncertainty in future conditions. This study proposes a planning method for developing a flexible hydropower construction schedule to adapt to long-term uncertain hydrology with climate changes. Projected uncertain nonstationary hydrology scenarios were developed first, and Bayes’ theorem is used to update the probability of each scenario based on observed hydrologic conditions over time. A profit maximization model integrates stochastic dynamic programming and linear programming for a cascade hydropower system to evaluate hydropower plant expansion and how to build it dynamically with uncertain nonstationary climate conditions. In this, stochastic dynamic programming evaluates and prescribes inter-stage reservoir upgrades and linear programming prescribes inner-stage hydroelectric power generation. The case study of Jinsha cascade hydropower system in China’s Yunnan province illustrates the method. The intensity and trend of climate change effects on streamflow directly affect the optimized results. If streamflow decreases in the future, the best strategy is to build a small reservoir initially, without future changes. If streamflow increases in the future, it is better to build a small reservoir first, and gradually increase capacity according to observed changes in streamflow and economies of scale in capacity construction. If there is no clear trend in streamflow change, it is also better to build a small reservoir at first, then decide whether to expand the reservoir according to future observed changes in streamflow. This method with uncertain nonstationary climate change and upgrading adaptively with observed hydrology usually produced better results than optimization for a deterministic nonstationary hydrology.

Issues and Strategies for the Dispatching and Trading of the Three Gorges Large Hydropower System

China’s electricity market reform has posed a real challenge to the large-scale hydropower system. Taking the world’s largest watershed hydropower system, the Three Gorges large hydropower system (TGLHS), as the engineering background, this study analyzes the issues and strategies of dispatching and trading in the electricity market. The analysis indicates that the TGLHS exhibits unique difficulties because of transprovincial and transregional power transmission. Major issues including the multi-dimensional and multi-time-scale nested allocation of hydropower energy, the bidding and performance of cascaded hydropower plants in multiple electricity markets, as well as multiple uncertainties in the runoff; electricity prices in multiple markets are also elaborated upon. The corresponding suggested strategies are proposed to cope with the aforementioned issues: (1) for multi-dimensional and multi-scale nested allocation problems, it is necessary to comprehensively consider monthly market transactions and priority generation plans, and establish a profit maximization model; (2) propose a bidding decision-making linkage and segmented bidding optimization model for cascades upstream and downstream hydropower stations; (3) construct a model for decomposing the annual and monthly planned electricity consumption curves and developing operational plans for giant cascade power stations that are suitable for cross-provincial and cross-regional power transmission and transformation; (4) a runoff, electricity price, and market distribution model has been proposed, laying the foundation for further research on multi-scale optimization models for hydropower. Finally, prospects for research on the participation of large-scale hydropower systems in the electricity market are summarized, expecting to promote the marketization of large cascaded hydropower systems. The dispatching and trading of the TGLHS implies that it is important and necessary to explore market theories and methods considering hydropower characteristics and operation needs.

Techno-economic feasibility of hybrid hydro-FPV systems in Sub-Saharan Africa under different market conditions

Floating photovoltaic (FPV) systems are an emerging and increasingly competitive application of solar PV, especially in land area-constrained countries. This study focuses on the optimal dimensioning and scheduling of a grid-connected hybrid hydro-FPV system. The case study is based on a cascade hydropower system located in Sub-Saharan Africa. The techno-economic feasibility of the hybrid system is analysed under different types of revenue streams and load commitments. Moreover, the resource complementarity between solar irradiation and reservoir water inflow in different weather years is analysed. A linear programming model for optimal dimensioning/scheduling of a hybrid hydro-FPV system is proposed. The results indicate that hybridisation with FPV can under the proposed PPA and spot market structure increase the annual producer profits by 18–21% and 0–4%, respectively compared to a hydro-only system. Furthermore, it is estimated that the CAPEX of FPV should be around 42–57% lower than that of ground-mounted PV (GPV) with single-axis tracking for the hydro-FPV system to reach the same annual producer profit as the hydro-GPV system. Considering improved efficiency by cooling the FPV modules, the revenues increase by 0–3% depending on the selected weather year and market scheme.

Stochastic Day-ahead operation of cascaded hydropower systems with Bayesian neural network-based scenario generation: A Portland general electric system study

Coordinating water usage of cascaded dams along a river to serve electric power generation and other non-power needs is challenging, due to the volatile and time-variate streamflow, the tight hydrologic couplings between the dams, and the strict regulation of individual dams. With this, cascaded hydropower system (CHS) operators, such as Portland General Electric (PGE), usually adopt empirical strategies to sidestep these difficulties, but at the expense of water usage inefficiency and economic losses. Motived by this, a stochastic optimization-based day-ahead operation model of CHSs is developed to determine scenario-independent hourly forebay levels of individual dams, which could assist operators in effectively utilizing available water resources against inflow uncertainties and maximizing the profit of hydropower. A common penstock effect model is also integrated to accurately capture the physical operating characteristics of CHSs. Moreover, a data-driven Bayesian neural network (BNN)-based scenario generation model is seamlessly connected to the stochastic day-ahead operation model for preparing water inflow scenarios of the impending operating day. Leveraging actual operation data of the PGE’s Round Butte-Pelton CHS, numerical simulations demonstrate that the day-ahead operation schedules out of the proposed approach could deliver more efficient water usage and higher economic benefits than the PGE’s current practice.

Short-term operation of cascade hydropower system sharing flexibility via high voltage direct current lines for multiple grids peak shaving

The escalating pressure for peak shaving presents a significant challenge for the power grid's operation, particularly with the rapid growth of renewable energies. Huge cascade hydropower stations (CHS) send power to the local power grid and multiple power grids via long-distance high voltage direct current (HVDC) transmission lines, how to share hydropower flexibility for multiple power grids in peak shaving while considering constraints of hydropower and HVDC lines challenges power dispatching management. This study proposed a multiple power grids peak shaving model of cascade hydropower stations with HVDC sharing flexibility for receiving power grids. Firstly, the objective of minimizing the peak-valley difference of multiple power grids is adopted. The model then takes into account the operation constraints of CHSs and HVDC tie-lines to share hydropower flexibility. Finally, the model is transformed into a Mixed-Integer Linear Programming problem through piecewise linearization. The case studies of practical examples in southwest China show that the proposed model reduces peak-valley differences by 23.3%, 46.9%, 99.1% and 19.6%, 34.3%, 99.5% for three receiving power grids in two typical days by sharing hydropower flexibility. Moreover, adjusting the HVDC daily power regulation times and electricity deviation tolerance helps coordinate power grids to improve their peak shaving performance.

Solution framework for short-term cascade hydropower system optimization operations based on the load decomposition strategy

The optimal operation of short-term cascade hydropower systems, with dozens of hydropower stations, multistage calculation periods and complex hydraulic constraints, faces a serious “curse of dimensionality” problem, and it is difficult to solve this problem directly or obtain a precise optimal hydropower dispatching plan in an acceptable time. This study presents an efficient solution framework based on the load decomposition strategy to alleviate the dimensionality problem. First, the load decomposition strategy is exploited by reducing the number of optimization stages to reduce the dimensionality of the original complicated optimization problem. By continuously adjusting the number of time periods and load of each stage, the load decomposition strategy can divide the original optimization problem into two subproblems with different optimization periods, namely, the segmented load process and burr load process. Second, a hydropower station classification method is proposed, in which all stations are divided into balanced power stations for the burr load and main dispatching stations for the base load, significantly reducing the participation of most hydropower stations in the frequent dispatch process. Finally, according to the transformation of the water balance relationship, a mathematical expression that accurately describes the complex hydraulic connection problem between the two subproblems is constructed to obtain more refined solution results. Practical project cases involving a large-scale hydropower system with 7 stations on the Wu River of China are used to test the sensitivity and efficiency of the proposed method. The sensitivity analysis indicates that the different optimization stages in this method can quickly obtain a globally optimal result and improved computational efficiency. Moreover, compared with the 24-point and 96-point traditional optimization scheduling methods, the water discharge of the presented method is reduced by 6.6% and 7.8% in the dry season and 4.0% and 4.9% in the flood season in a limited time, respectively, which suggests that the solving difficulties caused by the “curse of dimensionality” can be effectively alleviated and the solution efficiency can be greatly improved. The simulation calculations for different seasons and scales also indicate that the proposed method is an effective tool for the optimal operation of large-scale hydropower systems.

Cascade Hydropower System Operation Considering Ecological Flow Based on Different Multi-Objective Genetic Algorithms

Cascade Hydropower System Operation Considering Ecological Flow Based on Different Multi-Objective Genetic Algorithms

Spatial patterns of diffusive greenhouse gas emissions from cascade hydropower reservoirs

Greenhouse gas (GHG) emissions from reservoirs have received increasing attention in recent years. Despite extensive studies in single reservoirs, GHG emission patterns in cascades of multiple reservoirs, which are becoming increasingly common worldwide, remain unknown. This study investigated the spatial patterns of diffusive carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions, as well as their total CO2-equivalent (CO2-eq), for a cascade hydropower system in the heavily dammed upper Mekong River, China. Results demonstrated that GHG emissions in cascade reservoirs were higher than that in the upstream channel since the accumulated sediments fueled microbes for GHG production. In cascade reservoirs, CO2 made the largest contribution (58.6%–84.8%) to total CO2-eq, while the contribution of N2O was marginal. Deep reservoirs emitted less CO2, which was attributed to higher CO2 consumption by phytoplankton. Reservoirs formerly occupying the most upstream position for the longest period of time in the cascade emitted the most CH4, perhaps due to accumulations of river borne sediments. The total CO2-eq generally increased with distance downstream except within deep reservoirs. These findings indicate that, with respect to mitigating GHG emissions, the deepest, most upstream reservoir should be constructed first in the configuration of cascade hydropower reservoirs, and less sediment will enter downstream reservoirs, which have higher CO2-eq emissions.

Application of Delft3D computational model to estimate the influence of El Bala run-of-the-river dam on the morphological activity of Beni River, Bolivia

Beni river is an important tributary in the Amazon basin. There, the Bolivian government decided the construction of a two-dam cascade hydropower system, including Chepete dam and El Bala run-of-the-river dam. The main concern is the high sediment transport in Beni river. Then, the aim of the research is the application of Delft3D model to estimate the influence of El Bala run-of-the-river dam on the sediment transport and morphological activity of Beni river. The model was calibrated for natural conditions based on ADCP data and sediment transport measurements collected from the river. The implemented model was able to reproduce hydrodynamics and sediment transport in natural conditions. Hence, several scenarios were tested. A hydrodynamic and morphological model was set to test the influence of the dam on the river for hydrodynamics, sedimentation and erosion processes in Beni river. Results show that Delft3D performed very well in modelling sedimentation and erosion processes in Beni river, the model was able to predict the expected erosion process due to the presence of Chepete dam, upstream of El Bala, which retains the sediment load of Beni river affecting the morpho-dynamics of the river. Additionally, the model was able to predict a sedimentation in the reservoir.

Water-energy-food nexus in the Yarlung Tsangpo-Brahmaputra River Basin: Impact of mainstream hydropower development

Study regionYarlung Tsangpo-Brahmaputra River Basin Study focusA large-scale hydropower system with reservoir storage on a transboundary river brings both opportunities and challenges to water, food, and energy securities of the riparian countries. This is the case for the possible cascade hydropower system in the Great Canyon of Yarlung Tsangpo. From the perspective of Water-Energy-Food (WEF) nexus, this study analyzed the possible trade-offs and synergies among hydropower production, waterway navigation and agricultural irrigation. With streamflow simulated by the THREW distributed Hydrological model, multiple-objective optimization has been performed by the WEF nexus model which is aiming at revealing the potential for transboundary cooperation on enhancing basin-wide benefits. New hydrological insights for the regionApart from bringing 33.7∼75.0 % more hydropower productivity for each month, the reservoir storage of YB River mainstream hydropower system can also increase the minimum flow during dry season, extending the period with navigability by one to four months per year from the Majuli Island to downstream. It provides higher assurance rate of irrigation water requirement in Bangladesh, effectively mitigating flood damage by reducing downstream flood-affected area in India and Bangladesh by up to 32.56 % and 14.8 % respectively. The possible mainstream hydropower development can contribute significantly to basin-wide benefits through transboundary cooperation in the YB River basin.

Joint operation rules considering the uncertainty of energy available for multiple cascaded hydropower system

AbstractMulti‐reservoir operation rules have been widely used in practice operation. The operation rules are often derived from historical or simulated run‐off information through implicit stochastic optimization or parameterization‐simulation‐optimization. The output decisions of operation rules are usually obtained without considering inflow forecasting or using perfect runoff forecast information, which is hardly implemented in practical applications. This paper proposes robust joint operation rules for multiple cascaded reservoirs considering the uncertainty of energy available. The rule parameters are optimized using multi‐step genetic algorithm for minimum‐power maximization. A case study for a three‐cascaded‐reservoirs system shows that, compared with deterministic joint operation rules, the accuracy of energy available estimation of joint operation rules is increased by 2.3%, considering inflow uncertainty. The simulated minimum‐power decision of joint operation rules considering the uncertainty of energy available is 40.4% higher than that of determinate joint operation rules. Results indicate that there is a possibility of obtaining greater and more effective power decisions through the joint operation rules considering the uncertainty of available energy.

Risk Control in Optimization of Cascade Hydropower: Considering Water Abandonment Risk Probability

Water abandonment risk caused by inflow uncertainties is a major problem in cascade hydropower generation operation and water resources management. In this study, we propose a theoretical estimation method (TEM) to calculate water abandonment risk probability (WARP) utilizing sample inflow data and historical forecast errors. The method analyzes the differences in risk quantification between the head hydropower station and downstream stations, and the corresponding probability formula is provided. Then, a short-term optimal operation model of cascade hydropower stations considering the additional WARP water level constraints is constructed to dynamically control water level risk. The Dahuashui (DHS)-Geliqiao (GLQ) cascade hydropower stations in the Wujiang River Basin of China is investigated as a case study. The results show that compared with the historical scheme, the total amount of water abandonment of DHS, GLQ and DHS-GLQ in the WARP optimal scheme decreases by 11.69%, 47.69% and 28.27%, respectively, and the flood peak of GLQ is reduced by 21.6%. In conclusion, compared with the traditional control approach and actual operation processes, the proposed risk control in optimization of cascade hydropower considering WARP can improve the comprehensive utilization efficiency of cascade hydropower systems by reducing the occurrence of water abandonment and thereby increase generation profits.

Impact of Climate and Land Use Change on Economic Development in the Baoxing River Watershed in Giant Panda National Park

ABSTRACTChina established the Giant Panda National Park for ecological and species protection. New management requirements will impact the socioeconomic sustainability of the Baoxing River watershed, the core area of the national park. In this study, the Soil and Water Assessment Tool model was used to explore future temporal distribution and magnitude of discharge in the Baoxing River watershed under climate and land use change to evaluate potential impacts on the local economy. Six scenarios were explored according to land use changes and climate emission scenarios. Peak discharge is predicted to shift to May through July in contrast to July through September during the base evaluation period. The gross industrial output and tax revenue of the cascade hydropower system in the watershed were predicted to increase by 68 and 95 million USD/year and 2.9 and 3.8 million USD/year, respectively, compared to the base year 2016 under the Representative Concentration Pathway 4.5 (RCP4.5) and RCP8.5 emission scenarios, respectively. However, due to the overall loss of the mining industry, the gross industrial output and tax revenue of hydropower and mining in the watershed were predicted to decrease by 125 and 233 million USD/year and 4.1 and 9 million USD/year, respectively, compared to the base year 2016. Findings are important for policy development to maintain ecological security and achieve sustainable economic development within the watershed.