Cascade Hydropower Reservoirs Research Articles

Multi-objective solution and decision-making framework for coordinating the short-term hydropeaking-navigation-production conflict of cascade hydropower reservoirs

The transformation of energy structure motivates hydropower to participate in peak shaving operations for grid stability, which conflicts with the multiple uses of cascade hydropower reservoirs. To coordinate the principal contradictory tasks of peak shaving, ship navigation, and power generation in day-ahead scheduling, a mixed integer linear programming model for the short-term multi-objective optimal scheduling is constructed. Specifically, a novel power release-based indicator is adopted to describe the navigation objective, while the minimization of the peak-valley difference of the residual load and the total water released are taken as the other two objective functions. Constraint aggregation and rectangular meshing methods are employed to handle the challenging nonlinear and nonconvex constraints. Subsequently, the Normalized Normal Constraint method, integrated with the mathematical optimization solver Gurobi, generates a set of well-distributed Pareto solutions, each representing a scheduling scheme. Finally, the Combined Weighting - Technique for Order Preference by Similarity to Ideal Solution is utilized as a decision-maker to trade off and determine a compromise solution for final implementation. Case studies on the cascade hydropower reservoirs of the Wujiang River in China demonstrate that the proposed model can adequately characterize the demands of various stakeholders, and the suggested framework can obtain an even and extensive Pareto front, mediate conflicts, and make reasonable decisions. Compared to the original peak-shaving scheme, the finalized compromise scheme reduces the peak-shaving effect by just 27.82% but offers simultaneous boosts of 96.54% in navigation benefits and 10.88% in power generation benefits. Consequently, the presented framework can be a promising alternative for conflict resolution of cascade hydropower reservoirs.

Optimal Scheduling of Cascade Reservoirs Based on an Integrated Multistrategy Particle Swarm Algorithm

The optimal scheduling of cascade reservoirs is an important water resource management and regulation method. In the actual operation process, its nonlinear, high-dimensional, and coupled characteristics become increasingly apparent under the influence of multiple constraints. In this study, an integrated multistrategy particle swarm optimization (IMPSO) algorithm is proposed to realize the optimal operation of mid- and long-term power generation in cascade reservoirs according to the solution problem in the scheduling process of cascade reservoirs. In IMPSO, a variety of effective improvement strategies are used, which are combined with the standard PSO algorithm in different steps, among which beta distribution initialization improves population diversity, parameter adaptive adjustment accelerates convergence speed, and the Lévy flight mechanism and adaptive variable spiral search strategy balance the global and local search capabilities of the algorithm. To handle complex constraints effectively, an explicit–implicit coupled constraint handling technique based on constraint normalization is designed to guide the update process into the feasible domain of the search space. The feasibility of the proposed method is verified in the mid- and long-term power generation optimization scheduling of the lower reaches of the Jinsha River–Three Gorges cascade hydropower reservoirs. The results show that the proposed method outperforms the other methods in terms of search accuracy and has the potential to improve hydropower resource utilization and power generation efficiency significantly.

EDNA metabarcoding reveals differences in fish diversity and community structure in heterogeneous habitat areas shaped by cascade hydropower.

Freshwater ecosystems are under great threat from humans, among which habitat heterogeneity is the most obvious, being one of the important reasons for the decline of fish diversity. This phenomenon is particularly prominent in the Wujiang River, where the continuous rapids of the mainstream have been divided into 12 mutually isolated sections by 11 cascade hydropower reservoirs. Based on the fact that conventional survey methods are more harmful to the ecological environment, the efficient and noninvasive environmental DNA metabarcoding (eDNA) approach was used in this study to conduct an aquatic ecological survey of the 12 river sections of the mainstream of the Wujiang River. A total of 2299 operational taxonomic units (OTUs) were obtained, corresponding to 97 species, including four nationally protected fish species and 12 alien species. The results indicate that the fish community structure of the Wujiang River mainstream, which was originally dominated by rheophilic fish species, has been changed. And there are differences in fish species diversity and species composition among the reservoir areas of the mainstream of the Wujiang River. The fish species in the area have gradually declined under the influence of anthropogenic factors such as terraced hydropower and overfishing. The fish populations consequently have demonstrated a tendency to be species miniaturized, and the indigenous fish are severely threatened. In addition, the fish composition monitored by the eDNA approach was found to be close to the fish composition of historical information on the Wujiang River, indicating that eDNA approach may be used as a complementary tool to conventional methods in this basin.

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.

Third-Monthly Hydropower Scheduling of Cascaded Reservoirs Using Successive Quadratic Programming in Trust Corridor

The third-monthly (about 10 days in a time-step) hydropower scheduling, typically a challenging nonlinear optimization, is one of the essential tasks in a power system with operational storage hydropower reservoirs. This work formulates the problem into quadratic programming (QP), which is solved successively, with the linearization updated on the nonlinear constraint of the firm hydropower yield from all the cascaded hydropower reservoirs. Notably, the generating discharge is linearly concaved with two planes, and the hydropower output is defined as a quadratic function of reservoir storage, release, and generating discharge. The application of the model and methods to four cascaded hydropower reservoirs on the Jinsha River reveals several things: the successive quadratic programming (SQP) presented in this work can derive results consistent with those by the dynamic programming (DP), typically with the difference in water level within 0.01m; it has fast convergence and computational time increasing linearly as the number of reservoirs increases, with the most significant improvement in the objective at the second iteration by about 20%; and it is capable of coordinating the cascaded reservoir very well to sequentially maximize the firm hydropower yield and the total hydropower production.

Investigation on Water Levels for Cascaded Hydropower Reservoirs to Drawdown at the End of Dry Seasons

Operators often have a dilemma in deciding what water levels the over-year hydropower reservoirs should drawdown at the end of dry seasons, either too high to achieve a large firm hydropower output during the dry seasons in the current year and minor spillage in coming flood seasons, or too low to refill to the full storage capacity at the end of the flood seasons and a greater firm hydropower output in the coming year. This work formulates a third-monthly (in an interval of about ten days) hydropower scheduling model, which is linearized by linearly concaving the nonlinear functions and presents a rolling strategy to simulate many years of reservoir operations to investigate how the water level at the end of dry seasons will impact the performances, including the energy production, firm hydropower output, full-refilling rate, etc. Applied to 11 cascaded hydropower reservoirs in a river in southwest China, the simulation reveals that targeting a drawdown water level between 1185–1214 m for one of its major over-year reservoirs and 774–791 m for another is the most favorable option for generating more hydropower and yielding larger firm hydropower output.

A Monthly Hydropower Scheduling Model of Cascaded Reservoirs with the Zoutendijk Method

A monthly hydropower scheduling determines the monthly flows, storage, and power generation of each reservoir/hydropower plant over a planning horizon to maximize the total revenue or minimize the total operational cost. The problem is typically a complex and nonlinear optimization that involves equality and inequality constraints including the water balance, hydraulic coupling between cascaded hydropower plants, bounds on the reservoir storage, etc. This work applied the Zoutendijk algorithm for the first time to a medium/long-term hydropower scheduling of cascaded reservoirs, where the generating discharge capacity is handled with an iterative procedure, while the other head-related nonlinear constraints are represented with exponential functions fitting to discrete points. The procedure starts at an initial feasible solution, from which it finds a feasible improving direction, along which a better feasible solution is sought with a one-dimensional search. The results demonstrate that the Zoutendijk algorithm, when applied to six cascaded hydropower reservoirs on the Lancang River, worked very well in maximizing the hydropower production while ensuring the highest firm power output to be secured.

Integrating wind and photovoltaic power with dual hydro-reservoir systems

Hydropower facilities can be dispatched to offset wind and photovoltaic energy variability in power systems. But the abrupt water discharges needed to cope with wind and photovoltaic intermittency causes harmful riverine disruptions with significant environmental costs. In systems with hydropower cascade consisting of two or more reservoirs, the river flow disruptions created by the upstream hydro-reservoir can be mitigated by dispatching the downstream hydro-reservoir in a restorative way. The hypothesis of this paper is that it is possible to operate cascaded hydropower reservoirs to integrate wind and photovoltaic power in a way that reduces total power output variability and downstream water flow disruptions. The upstream hydro-reservoir is operated in such a way that it complements the variable power (from photovoltaic and wind), while the downstream hydro-reservoir runs mitigating runoff disruption (i.e., re-regulating). An optimization model finds the hydro-wind-photovoltaic power’s operational strategy that minimizes the variability in total power output and the river flow’s alteration. The results of a case study of the world largest hydro-junction, Three Gorges Dam - Gezhouba Dam, illustrate that 1) the proposed strategy is feasible; 2) the water head and reservoir storage capacity are the two key features for determining the hydropower facility’s re-regulation capacity; 3) the upstream hydro-reservoir alone can integrate up to 1.7 GW of wind and 1.7 GW of photovoltaic -i.e., about 15% of the upstream hydro-reservoir's 22.5 GW of capacity- without altering the river's flow, and the downstream hydro-reservoir can integrate an additional 2.8 GW from wind and 2.8 GW of photovoltaic, hence the dual hydro-reservoir can integrate 9 GW of wind plus photovoltaic -i.e., 35% of the upstream + downstream hydro-reservoir capacity; 4) increasing the upstream hydro-reservoir's water-head and/or downstream hydro-reservoir's storage capacity would increase the size of wind and photovoltaic they can integrate. The strategy proposed is helpful to power system managers planning and operating systems with high penetration of variable renewable energy.

Comparison of efficient procedures for hydropower unit commitment

This paper explores three new competitive heuristic procedures, which are compared with the mixed integer linear programming (MILP), all used to commit units within a hydroplant to give the maximal electrical production during a study horizon, given the cascade operation has already determined the quarter-hourly releases from individual reservoirs. The problem incorporates constraints on the unit up/down hours and its shutdown times. The procedures are applied to seven hydroplants, with 2, 3, 4, 5, 6, 9 and 18 units installed respectively. The application suggests that the MILP performs the best in minimizing spillages and improving generating efficiency but volatile in computation time when the number of units is more than 4 in a hydroplant, while the heuristic procedures can obtain fairly good results and have a significant advantage in computation time when committing more than 6 units in a hydroplant. The heuristic procedures are valuable when incorporating unit commitment into a large-scale operational problem of cascaded hydropower reservoirs.

Weekly hydropower scheduling of cascaded reservoirs with hourly power and capacity balances

A medium/long-term hydropower scheduling routinely assumes the energy produced in each week will be fully utilized in peak-shaving the power demands during every day in that week, ignoring the fluctuation of the hourly power loads. This work improves the traditional weekly hydropower scheduling by integrating the hourly power and capacity balances (HPCB), which has rarely been investigated, mainly due to great challenges imposed in solving the model. The HPCB are formulated into mixed integer linear constraints, involving the spare, maintenance, disabled, reserve and working capacities, as well as the order and levels of hydroplants in peak-shaving the hourly power load curve, all to be optimized. The formulation is then improved with successive strategies in updating water heads and peak-shaving hours, aiming to boost the solution efficiency by excluding more binary variables. The case studies involving 6 cascaded hydropower reservoirs in Lancang River reveal that the traditional weekly hydropower scheduling overestimates the benefits significantly, strongly suggesting the necessity to include the HPBC into a long/mid-term hydropower scheduling. The experiments also recommend an improved model formulation that achieves very consistent results on the top three prioritized objectives, while taking less than 0.1 s to solve the problem involving all six cascaded reservoirs, demonstrating a great prospect in solving problems in large scale.

Joint Optimization of Forward Contract and Operating Rules for Cascade Hydropower Reservoirs

AbstractForward contract is a useful tool for hydropower producers to hedge revenue uncertainty sourced from varied spot prices. However, few studies have been explored to jointly derive the optima...

Derivation of operating rule curves for cascade hydropower reservoirs considering the spot market: A case study of the China's Qing River cascade-reservoir system

The spot price varies in deregulated markets, and is important for the revenue of cascade hydropower reservoirs. However, the conventional operating rules maximize hydropower generation without considering the spot price. To address this issue, this study focuses on deriving operating rule curves to maximize revenue by integrating reservoir operations and a spot market. Spot prices are simulated by aggregating supply bids and estimating demand. A parameter-simulation-optimization approach is then used to maximize the hydropower revenue and reliability, and minimize spill water simultaneously. With China's Hubei province as the market boundary, the Qing River cascade-reservoir system was selected as the case study. The spot price is significantly affected when the Qing River cascade-reservoir system participates, compared with the market without the cascade system. The spill water reduction (1.33%) and average water head increase (0.56%) contributes to the 1.74% hydropower generation increment of the derived rule curves compared with the conventional operating rules, resulting in hydropower revenue increase by 2.25% with the mean spot price raised by 0.36%. The firm-output operational zone is expanded during the refill period, compared with that derived in an independent pricing scenario. The derived operating rule curves are effective to maximize hydropower revenue for long-term operation.

Bacterial communities in cascade reservoirs along a large river

AbstractDam construction is widespread, changing the hydrological and biogeochemical conditions and thereby the bacterial communities in the rivers of the earth. To date, knowledge is lacking about bacterial communities in cascade reservoirs. Here, we investigated the bacterial communities and potential functions of nine cascade hydropower reservoirs in 1290 km of the upper Mekong River (Lancang River in China). Along the reservoir cascade, the water temperature, rather than the presence of dams, was the main cause for the geographical patterns of bacterial community composition. Within a reservoir, significant spatial differences in sediment bacterial communities were observed between the tail, middle, and head of a reservoir. The differences in sediment properties resulted by flow velocity‐sieved sedimentation from the tail to head of a reservoir caused the spatial variation in sediment bacteria communities, forming potential hotspots for biogeochemical cycling in the middle of the reservoir. In contrast, unlike in deep lakes and deep single reservoirs, the bacterioplankton community composition had no distinctly layered features in the deep cascade reservoirs, because density‐induced underwater currents and convection resulting from hydropower production reduced the vertical hydro‐environmental gradients. This study provides a novel perspective on the processes affecting the distribution and function of bacterial communities in river‐reservoir cascades, and is a first step toward forecasting the consequences of microbially mediated biogeochemical cycling in existing and future reservoirs worldwide.

Cooperation Search Algorithm for Power Generation Production Operation Optimization of Cascade Hydropower Reservoirs

Over the past decades, the reservoirs operation plays an increasingly important role in coping with the serious water, food and energy crisis. However, the curse of dimensionality poses huge challenges for operators because the computation cost often grows exponentially with the expansion of hydropower system. With strong search performance and high execution efficiency, metaheuristic search algorithms become the research hotspot in the reservoir operation field. Here, cooperation search algorithm (CSA) inspired by team cooperation behaviors in modern enterprise is introduced for power generation operation of cascade hydropower reservoirs. In CSA, the team communication operator is designed to determine promising search areas, and then the reflective learning operator is adopted to enhance the local search, while the internal competition operator is used to retain the elite individuals. The feasibility of the CSA method in numerical optimization problems is fully proved by the simulation results of several test functions. Then, the CSA method is used to solve the power generation operation of cascade hydropower reservoirs. The results show that the CSA method outperforms several traditional methods in both convergence rate and search precision. Thus, an effective tool with strong reliability and robustness is provided for the complex reservoir operation problem.

Activity and structure of methanogenic microbial communities in sediments of cascade hydropower reservoirs, Southwest China

Freshwater reservoirs are an important source of the greenhouse gas methane (CH4). However, little is known about the activity and structure of microbial communities involved in methanogenic decomposition of sediment organic matter (SOM) in cascade hydropower reservoirs. In this study, we targeted on sediments of three cascade reservoirs in Wujiang River, Southwest China. Our results showed that the content of sediment organic carbon (SOC) was between 3% and 11%, and it's positively correlated with both C/N ratio and recalcitrant organic carbon content of SOM. Meanwhile, SOC content was positively correlated with CH4 production rates but had no significant correlation with total CO2 production rates of the sediments, when rates were normalized to sediment volume. Resultantly, the sediment anaerobic decomposition rates hardly significantly increase along with the SOC content. These results suggested that the terrestrial organic matter accumulated after damming stimulated CH4 production from the reservoir sediments even though its decomposition rate was limited. Meantime, high throughput sequencing of 16S rRNA genes indicated that not only the hydrogenotrophic and acetoclastic, but also the methylotrophic methanogens (Methanomassiliicoccus) are abundant in the reservoir sediments. Moreover, metagenomic sequencing also suggested that methylotrophic methanogenesis are potentially important in the sediment of cascade reservoirs. Finally, the hydraulic residence time of the reservoir could be the key controlling factor of the structures of bacterial and archaeal communities as well as the CH4 production rates of the reservoir sediments.

Multi-time-scale nested optimal scheduling model for cascaded hydropower reservoirs

As more hydropower plants are in operation, it is difficult to balance the medium-and long-term benefits with the short-term benefits when developing an optimal scheme for power generation from cascaded hydropower reservoirs. And traditional models have difficulty making full use of the latest runoff forecast information. To overcome these problems, a multi-time-scale nested optimal scheduling model for cascaded hydropower reservoirs is developed. This model analyzes the level-by-level control strategy and rolling update mechanism between upper and lower level submodels. An efficient algorithm for solving the model is also given. In this study, the model is applied to four cascaded hydropower reservoirs located at the Yangtze River. The case study shows that the developed model can better coordinate the medium- and long-term benefits with the short-term benefits, make full use of the latest forecast information, and enhance the power generation efficiency of the cascaded hydropower reservoirs. Compared with the results of the existing scheduling model, the total power generation from the proposed model is improved under the same boundary conditions.

Enhanced harmony search algorithm for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem

With the merits of superior performance and easy implementation, the harmony search (HS), a famous population-based evolutionary method, has been widely adopted to resolve global optimization problems in practice. However, the standard HS method still suffers from the defects of premature convergence and local stagnation in the complex multireservoir operation problem. Thus, this study develops an enhanced harmony search (EHS) method to improve the HS’s search ability and convergence rate, where adaptive parameter adjustment strategy is used to enhance the global search performance of the swarm, while the elite-learning evolutionary mode is used to improve the converge trajectory of the population. To verify its practicability, EHS is applied to solve numerical optimization and multireservoir operation problems. The results show that EHS can produce better results than several existing methods in different cases. For instance, the mean objective of EHS is improved by about 23.9%, 28.7% and 26.8% compared with particle swarm optimization, differential evolution and gravitational search algorithm in 1998–1999 typical runoff case. Hence, an effective optimizer is developed for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem.

CO2 emissions from karst cascade hydropower reservoirs: mechanisms and reservoir effect

Carbon dioxide (CO2) emissions from aquatic surface to the atmosphere has been recognized as a significant factor contributing to the global carbon budget and environmental change. The influence of river damming on the CO2 emissions from reservoirs remains poorly constrained. This is hypothetically due to the change of hydraulic retention time (HRT) and thermal stratification intensity of reservoirs (related to the normal water level, NWL). To test this hypothesis, we quantified CO2 fluxes and related parameters in eight karst reservoirs on the Wujiang River, Southwest China. Our results showed that there was a significant difference in the values of pCO2 (mean = 3205.7 μatm, SD = 2183.4 μatm) and δ 13CCO2 (mean = −18.9‰, SD = 1.6‰) in the cascade reservoirs, suggesting that multiple processes regulate CO2 production. Moreover, the calculated CO2 fluxes showed obvious spatiotemporal variations, ranging from −9.0 to 2269.3 mmol m−2 d−1, with an average of 260.1 mmol m−2 d−1. Interestingly, the CO2 flux and δ 13CCO2 from reservoirs of this study and other reservoirs around the world had an exponential function with the reservoir effect index (Ri , HRT/NWL), suggesting the viability of our hypothesis on reservoir CO2 emission. This empirical function will help to estimate CO2 emissions from global reservoirs and provide theoretical support for reservoir regulation to mitigate carbon emission.

A New Peak-Shaving Model Based on Mixed Integer Linear Programming with Variable Peak-Shaving Order

Peak-shaving is a very efficient and practical strategy for a day-ahead hydropower scheduling in power systems, usually aiming to appropriately schedule hourly (or in less time interval) power generations of individual plants so as to smooth the load curve while enforcing the energy production target of each plant. Nowadays, the power marketization and booming development of renewable energy resources are complicating the constraints and diversifying the objectives, bringing challenges for the peak-shaving method to be more flexible and efficient. Without a pre-set or fixed peak-shaving order of plants, this paper formulates a new peak-shaving model based on the mixed integer linear programming (MILP) to solve the scheduling problem in an optimization way. Compared with the traditional peak-shaving methods that need to determine the order of plants to peak-shave the load curve one by one, the present model has better flexibility as it can handle the plant-based operating zones and prioritize the constraints and objectives more easily. With application to six cascaded hydropower reservoirs on the Lancang River in China, the model is tested efficient and practical in engineering perspective.

Developing MSA Algorithm by New Fitness-Distance-Balance Selection Method to Optimize Cascade Hydropower Reservoirs Operation

Optimal operation of cascade hydropower reservoirs is a complex high-dimensional engineering problem. Developing an appropriate model to solve such problems requires an efficient search method proportional to the dimensions of the problem. Accordingly, this research employed the new fitness-distance-balance (FDB) selection method in the moth swarm algorithm (MSA) to achieve promoted FDB-MSA with a high performance in solving complex large-scale problems. To ensure the efficiency of the developed algorithm, five benchmark functions of Shekel, Six-Hump Camel, McCormick, Goldstein-Price and Rosenbrock were used. Then, the FDB-MSA was used for optimization of hydropower generation of a real five-reservoir system along Karun River at Iran. This is the largest cascade reservoir system in Iran, which supplies more than 90% of the country’s hydropower demand. The results of the developed algorithm were compared with those of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. It was found that the FDB-MSA could successfully increase the hydropower generation by 59.5% (6724 GW) compared to the actual generation of energy over a 180-months operational period. The corresponding values for PSO and GA algorithms were 54.3% and 9.2% respectively. In addition, the results revealed the superiority of FDB-MSA to GA and PSO, so that, it demonstrated the smallest difference (3.41%) between nominal and optimal power generation compared to the PSO (6.58%) and GA (33.89%).