Considering water propagation impact in short-term optimal operation of cascade reservoirs using Nested Progressive Optimality Algorithm

In Short-term Cascade Reservoirs Optimal Operation (SCROO), the flow from upstream reservoir to downstream reservoir which propagates in the natural channel can be generalized as not only transposition but also attenuation. In order to get the relative accurate inflow series of downstream reservoir, the paper tries to adopt the Muskingum model to simulate the inflow of the downstream reservoir instead of the easier processing in most papers of SCROO: ignoring the flow attenuation in natural channel. Considering the flow attenuation between the cascade reservoirs, the SCROO problem do not satisfy the “Principe of Optimality” anymore, so the Dynamic Programming(DP) is no longer applicable. The paper proposes a new improved DP named Multi-Stage Dynamic Programming (MSDP) based on DP. In MSDP, multi-stage’s outflow of upstream reservoir is taken into account at the same time and then inflow of downstream reservoir can be calculated by the Muskingum model, which can include much more flow information than the easier processing with DP, and the inflow of downstream reservoir will be closer to the actual one. The paper takes the cascade hydro-power stations consisting of Jindong and Guandi in Yalong river basin as an example to solve the SCROO problem with MSDP. By comparing the operation result of MSDP and DP with the easier processing, the operation strategy of MSDP can gain further benefits than DP’s in actual operation.

The reservoirs (hydro plants) along the upper Yellow River are typical cascade reservoirs, with multiple objectives regarding flood control, ice control, water supply, power generation, and ecological security. The competition among these multiple objectives reflects the competition among various agencies with different interests. There has been a certain degree of conflict between ‘power scheduling’, which aims at obtaining greater power generation from the cascade reservoirs, and ‘water regulation’, which is currently being implemented. Questions of how to reasonably use the comprehensive regulation capacity of the cascade reservoirs, in order to relieve the conflicts among multiple objectives, and understand the nature of the competition between ‘power scheduling’ and ‘water regulation’, require urgent research and solutions. Based on an analysis of the current situation regarding water supply, electricity demand, flood control, ice control, and ecology, a multi-objective optimal operation model for the cascade reservoirs in the upper and middle reaches of the Yellow River has been constructed to reveal the relationships between power generation and other objectives. This study provides theoretical evidence for the informed operation of the cascade reservoirs and will be of great significance for coordinating the relationship between power generation and water regulation.

A multi-objective cooperation search algorithm for cascade reservoirs operation optimization considering power generation and ecological flows

Alpine hydropower reservoirs play a crucial role in the energy system as a source of renewable energy and energy storage, as well as in water management mitigating the impacts of extreme events and augmenting freshwater availability. The effective operation of hydropower reservoirs requires knowledge of the expected inflows, and the inflows prediction methods usually require the historical series of observed inflows. The reservoir inflow is often estimated because it is hardly measurable due to its spatial distribution along the reservoir sides. However, traditional methods such as the Simple Water Balance for estimating inflows can yield fluctuating and potentially negative results due to errors in water level measurement and stage-storage relationships. This study focuses on the estimation of inflow to ten reservoirs belonging to three different hydropower cascade systems situated in the Italian Alps. Two new methodologies to estimate reservoir inflow are proposed. The first (Optimized Inflow Estimation from Water Balance, OIEWB) consists of an optimization-based method and extends a known literature optimization technique to cascade reservoirs. In particular, the OIEWB method estimates the inflows to cascade reservoirs solving a bi-objective optimization problem aiming to minimize both the differences between consecutive inflow and the differences between observed and estimated water levels. It also includes an automatic calibration of the weight of the objectives according to the physical characteristics of each reservoir, avoiding any a priori calibration. The second (Filtered Inflow Estimation from Water Balance, FIEWB) consists of a low-pass filter shaped as a piecewise linear function whose slope is defined, again, by the physical characteristics of each reservoir. The low-pass filter is applied to the SWB cascade reservoir inflow to remove the high-frequency fluctuations that can be generated by measurement and estimation errors. The proposed procedures have been compared with the traditionally used ones in terms of Inflow Variability (the difference between inflow at two consecutive time steps) and Storage Error (the difference between the estimated reservoir storage and the observed one). Results show that both the OIEWB and FIEWB methods generate smoother inflows compared to the SWB, reducing the average Inflow Variability standard deviation, of 86.6% and 79.3%, respectively. However, the FIEWB does not guarantee the positivity of the inflows and can lead to large Storage Errors. The OIEWB method has been found to be more flexible and automatically adaptable to reservoirs with a wide range of physical characteristics. Nevertheless, a relationship between the OIEWB and the FIEWB has emerged. This relationship can be used to design new low-pass filters that can emulate the behavior of the OIEWB, combining the flexibility of the latter with the simplicity of the FIEWB. By contributing to provide more accurate and reliable inflow predictions, the proposed methodologies reveal their utility in optimizing cascade reservoir operation, thereby facilitating better decision-making.

Synergistic gains from the multi-objective optimal operation of cascade reservoirs in the Upper Yellow River basin

The current issue of river damming causing the release of greenhouse gas nitrous oxide (N2O) from reservoirs has led to a debate over whether hydropower can still provide clean energy. In January (dry season) and August 2019 (wet season), we detected the N2O concentrations from cascade reservoirs in Lancang‐Mekong River Yunnan section by using static headspace gas chromatography and estimated the emission flux by the boundary layer approach to explore the influencing factors, especially the impact of cascade reservoirs development and operation. In the Lancang River (Yunnan section), the longitudinal distribution of N2O concentration along the cascade reservoirs showed seasonal differences. The average concentrations of N2O during the dry season and the wet season were 13.50 ± 2.00 nmol/L and 16.40 ± 4.10 nmol/L, respectively. The reservoirs with thermal stratification in both seasons were the Xiaowan (XW), Nuozhadu (NZD), and Jinghong (JH) reservoirs. The vertical distribution of N2O concentration was depth dependent (i.e., the deeper the location, the higher the concentration) in reservoirs with thermal stratification and uniform in reservoirs without thermal stratification. Dissolved oxygen (DO), water temperature, and other biogenic elements were the main factors affecting N2O distribution. The average N2O emission flux in the cascade reservoirs was 12.80 ± 2.60 μmol m2·day−1 during the dry season and 16.10 ± 4.75 μmol m2·day−1 during the wet season. The annual average N2O emission fluxes of all reservoirs were in an order of JH > XW > Dachaoshan (DCS) > NZD > Miaowei (MW) > Gongguoqiao (GGQ) > Manwan (Man W). In 2019, a total of 3.37 × 106 mol year−1 (94.39 ton N2O‐N year−1) was released from the water surface of the cascade reservoirs, which accounted for approximately 0.31% of the total N2O emissions from reservoirs worldwide. This study shows the cascade reservoirs development and operations have made the Lancang River (Yunnan section) a major area for N2O emissions.

This paper presents a multi-objective optimization model for the coordinated regulation of flow and sediment in cascade reservoirs. The model was developed to address two contradicting issues: sediment trapping and flow regulation. The benefits of flood control, hydropower generation and navigation, and sedimentation in cascade reservoirs were considered as the target functions; then the corresponding submodels for reservoir operation and sediment computation were established. The model was implemented by reducing it to a single objective nonlinear model using the constraint method. Non-inferior solutions were obtained by solving the model with catfish effect particle swarm optimization algorithm. The model was applied to the cascade system of Xiluodu and Xiangjiaba reservoirs in the lower Jinsha River in the flood recession period. Under the safety of flood control and navigation, a non-inferior set for impounding time, power generation, and siltation-loss rate of capacity was obtained and optimal solutions with different weights were derived. The results demonstrate that the model is a useful tool in coordinated operations of cascade reservoirs.

Determination of suitable ecological flow regimes for spawning of four major Chinese carps: A case study of the Hongshui River, China

Impact assessment of cascade freshwater reservoir using the ecological security assessment (ESA) model across a four-year timescale

In this paper, linear regression method is used to deal with the flow transmission. Applying large -scale system decomposition-coordination method, the paper analyzes the optimal operation problem of cascade hydropower stations. In order to research the uncertainty of the optimal operation of cascade reservoirs, this paper presents a method which combines it with the random simulation, which uses the method of Latin hypercube sampling to improve Monte Carlo method. The study case of Three Gorges cascade reservoirs shows that the method is practical

Optimal operation of reservoirs is very essential for water resource planning and management, but it is very challenging and complicated when dealing with climate change impacts. The objective of this paper was to assess existing and future hydropower operation at the Tekeze reservoir in the face of climate change. In this study, a calibrated and validated Soil and Water Assessment Tool (SWAT) was used to model runoff inflow into the Tekeze hydropower reservoir under present and future climate scenarios. Inflow to the reservoir was simulated using hydro-climatic data from an ensemble of downscaled climate data based on the Coordinated Regional climate Downscaling Experiment over African domain (CORDEX-Africa) with Coupled Intercomparison Project Phase 5 (CMIP5) simulations under Representative Concentration Pathway (RCP)4.5 and RCP8.5 climate scenarios. Observed and projected inflows to Tekeze hydropower reservoir were used as input to the US Army Corps of Engineer’s Reservoir Evaluation System Perspective Reservoir Model (HEC-ResPRM), a reservoir operation model, to optimize hydropower reservoir release, storage and pool level. Results indicated that climate change has a clear impact on reservoir inflow and showed increase in annual and monthly inflow into the reservoir except in dry months from May to June under RCP4.5 and RCP8.5 climate scenarios. HEC-ResPRM optimal operation results showed an increase in Tekeze reservoir power storage potential up to 25% and 30% under RCP4.5 and RCP8.5 climate scenarios, respectively. This implies that Tekeze hydropower production will be affected by climate change. This analysis can be used by water resources planners and mangers to develop reservoir operation techniques considering climate change impact to increase power production.

Methane (CH4) emissions from freshwaters to the atmosphere have a profound impact on global atmospheric greenhouse gas (GHG) concentrations. Anthropogenic footprints such as dam construction and reservoir operation significantly changed the fate and transport of CH4 in freshwaters. The type of particulate organic carbon (POC) in reservoirs is a critical factor controlling CH4 production and emissions. However, little is known of how reservoir operation mediates the distribution of POC and regulates CH4 accumulation in cascade hydroelectric reservoirs. Here, spatial and temporal variations in POC and CH4 were explored in the Xiluodu (XLD) and Xiangjiaba (XJB) reservoirs which are deep valley dammed cascade reservoirs located in the main channel of the upper Yangtze River. Based on the δ13C-POC and N / C mole ratios of particulate organic matter, the results of multi-endmember stable isotope mixing models by a Bayesian model show that terrestrial POC and autochthonous POC accounted for approximately 56 ± 19 % and 42 ± 19 % (SD, n = 181) of POC, respectively. CH4 concentrations and δ13C-CH4 in the cascade reservoirs were potentially influenced by CH4 oxidation. Together with other physicochemical parameters and structural equation model, these results suggested that the input of terrestrial POC was dominantly influenced by water level variations and flow regulation due to reservoir operation. The cumulative effect of POC caused by cascade reservoirs was not apparent at a bimonthly scale. Terrestrial POC was more likely to dominate CH4 accumulation in cascade reservoirs under reservoir operation.

Methane (CH4) emissions from freshwaters to the atmosphere have a profound impact on global atmospheric greenhouse gas (GHG) concentrations. Anthropogenic footprints such as dam construction and reservoir operation significantly changed the fate and transport of CH4 in freshwaters. The type of particulate organic carbon (POC) in reservoirs is a critical factor controlling CH4 production and emissions. However, little is known of how reservoir operation mediates the distribution of POC and regulates CH4 accumulation in cascade hydroelectric reservoirs. Here, spatial and temporal variations in POC and CH4 were explored in the Xiluodu (XLD) and Xiangjiaba (XJB) reservoirs which are deep valley dammed cascade reservoirs located in the main channel of the upper Yangtze River. Based on the δ13C-POC and N / C mole ratios of particulate organic matter, the results of multi-endmember stable isotope mixing models by a Bayesian model show that terrestrial POC and autochthonous POC accounted for approximately 56 ± 19 % and 42 ± 19 % (SD, n = 181) of POC, respectively. CH4 concentrations and δ13C-CH4 in the cascade reservoirs were potentially influenced by CH4 oxidation. Together with other physicochemical parameters and structural equation model, these results suggested that the input of terrestrial POC was dominantly influenced by water level variations and flow regulation due to reservoir operation. The cumulative effect of POC caused by cascade reservoirs was not apparent at a bimonthly scale. Terrestrial POC was more likely to dominate CH4 accumulation in cascade reservoirs under reservoir operation.

Modeling competitive-cooperative relationship between flood control and hydropower generation within hierarchical gaming structure of reservoir operation

Optimal reservoir operation is a complex problem that involves multiple objectives, multiple constraints as well as considerable risk and uncertainty. Eco-friendly reservoir operation makes it more complicated by taking into account a conflicting objective or highly nonlinear constraint related to ecosystem requirement. The study developed a model to optimize reservoir operation in an Eco-friendly manner by using Genetic Algorithm(GA) and applied it to two cascade reservoirs of Yalongjiang River in the Southwest of China. In order to improve its performance, GA was adapted in transferring objective function and operating mutation dynamically. In addition, a time-nested model was proposed to optimize monthly-based data to daily one, thereby avoiding too much state variables being involved when reservoir require daily operation policy. It is shown that the adapted GA can certainly fulfill the goal of eco-friendly reservoir operation and it was enhanced in search accuracy and global searching ability with objective function transfer and dynamic mutation operator. Moreover, the time-nested model was greatly help to build a daily-based optimization model which can cut computing times dramatically and improve the GA efficiency.