Rule Curves Research Articles

Synchronizing short-, mid-, and long-term operations of hydro-wind-photovoltaic complementary systems

Hydro-wind-photovoltaic (PV) complementary power systems (HWPCSs) offer a promising solution for integrating intermittent wind and PV power, leveraging the long-term energy storage capacity of reservoirs and the short-term flexible adjustability of hydro units. However, existing studies often neglect the reciprocal influences among short-, mid-, and long-term operations of HWPCSs, resulting in suboptimal operating strategies. To tackle this issue, the study proposes a generic framework for synchronizing operations of HWPCSs across multiple time scales. The framework simultaneously optimizes long-term operating rule curves, mid-term water consumption allocation, day-ahead power generation plan scheduling, and intraday economic operation of HWPCSs. A HWPCS located in the Yalong River Basin is selected as the case study. The results indicate that: (1) synchronized operation effectively reduces the output shortage risk (from 4.86 % to 0 %) and average absolute water consumption deviation (from 2.07 % to 0.09 %) compared to the conventional method; (2) synchronized operation provides more reasonable boundaries for short-term operations, reducing the average online time percentage (from 65.08 % to 63.57 %) and start-up/shut-down times (from 3185 to 2981) of hydro units, leading to an increase in the average hydropower efficiency (from 9.05 to 9.12). This study highlights the impracticality of the conventional method, underscoring the necessity of synchronized operation for HWPCSs. The proposed method provides a practical framework for the efficient operation of HWPCSs.

Adapting reservoir operations for optimal water management under varying climate and demand scenarios using metaheuristic algorithms

The operational rule curve is significant for proper reservoir operations and water resources management of the dam. The optimisation of the release policy is rather a complicated and stochastic problem. Various types of algorithms have been used to develop the release policy for the Klang Gate Dam (KGD), but alas, climate parameters associated with climate change, be it regional or globally, e.g., temperature factors, had not been considered then. In this study, the consideration of maximum and minimum temperature factors is given emphasis, while water demand is evaluated in the optimisation problem using simulation-optimisation approaches. The support vector regression simulation model (SVRS), and the multilayer perceptron simulation model (MLPS) were used to simulate the demand for the scenario cases. SVRS utilises the hyperplane theory to study the relationship between the input and output data, whereas MLPS employs the human neural system to weight and process the inputs via multiple layers of neurons into a targeted output. Four distinct types of simulation-optimisation combination model, including the SVRS-firefly algorithm (SVRS-FA), the SVRS-particle swarm optimisation (SVRS-PSO), the MLPS-firefly algorithm (MLPS-FA), and the MLPS-particle swarm optimisation (MLPS-PSO), were applied and investigated. The ultimate goal of this study was to minimise the water deficit. The PSO optimisation models overall have outperformed the FA optimisation models in all aspects. The periodic reliability of the MLPS-PSO was found to be the highest when the minimum temperature scenario was considered, and is 0.6491, with the least shortage period of 80 months. The MLPS-PSO is also the most resilient model in the same scenario, with a resilience value of 0.6750. Whereas the SVRS-PSO was the least vulnerable model in the minimum temperature scenario with the lowest shortage index of 0.0047 and a volumetric deficiency of about 2.44 MCM in total. The SVRS-PSO model showed excellent performance for high and low inflow conditions, while the MLPS-PSO model was better during medium inflows. In short, each of the models investigated has its particular field of advancement, but since the maximum temperatures sounded critical, additional research for the maximum temperature case could be pursued further in depth.

Optimal operation of the dam reservoir in real time based on generalized structure of group method of data handling and optimization technique

The historical data on water intake into the reservoir is collected and used within the framework of a deterministic optimization method to determine the best operating parameters for the dam. The principles that have been used to extract the best values of the flow release from the dam may no longer be accurate in the coming years when the inflow to dams will be changing, and the results will differ greatly from what was predicted. This represents this method’s main drawback. The objective of this study is to provide a framework that can be used to guarantee that the dam is running as efficiently as possible in real time. Because of the way this structure is created, if the dam’s inflows change in the future, the optimization process does not need to be repeated. In this case, deep learning techniques may be used to restore the ideal values of the dam’s outflow in the shortest amount of time. This is achieved by accounting for the environment’s changing conditions. The water evaluation and planning system simulator model and the MOPSO multi-objective algorithm are combined in this study to derive the reservoir’s optimal flow release parameters. The most effective flow discharge will be made feasible as a result. The generalized structure of the group method of data handling (GSGMDH), which is predicated on the results of the MOPSO algorithm, is then used to build a new model. This model determines the downstream needs and ideal release values from the reservoir in real time by accounting for specific reservoir water budget factors, such as inflows and storage changes in the reservoir. Next, a comparison is drawn between this model’s performance and other machine learning techniques, such as ORELM and SAELM, among others. The results indicate that, when compared to the ORELM and SAELM models, the GSGMDH model performs best in the test stage when the RMSE, NRMSE, NASH, and R evaluation indices are taken into account. These indices have values of 1.08, 0.088, 0.969, and 0.972, in that order. It is therefore offered as the best model for figuring out the largest dam rule curve pattern in real time. The structure developed in this study can quickly provide the best operating rules in accordance with the new inflows to the dam by using the GSGMDH model. This is done in a way that makes it possible to manage the system optimally in real time.

Operational analysis of lake Tana under climate change, Upper Blue Nile Basin, Ethiopia

The excessive abstraction of lake water affects its sustainability and is common in Lake Tana. Hence this study aims to develop operation rule curves for optimum operation of Lake Tana reservoir by considering the effects of small and large-scale irrigation water abstraction, Hydropower demand, and climate change. The data used in this study were produced by two Global Circulation Models (GCMs) (MIROC5 and MPI) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) in the 2040s (2020–2049) and 2070s (2050–2079). For RCP4.5 and RCP 8.5 scenarios of the 2040s- and 2070s-time domains, lake temperature exhibits an upward trend. The lake rainfall also will show an increase for the 2040s and 2070s under both RCP scenarios. The outcome showed that in all future scenarios, lake evaporation under RCP4.5 of 2040s and 2070s rise between 3.6 % to 7 % and 4.6 % to 19, respectively, while for RCP8.5 of 2040s and 2070s increases between 4.6 % to 9.9 % and 9.2 % to 24.7 %. The surface water inflow increased for RCP4.5 (2070s) and RCP8.5 (2040s), whereas it declined for RCP4.5 (the 2040s) and RCP8.5 (2070s. Under both scenarios, the average value of the time-based and volumetric reliability in the 2040s and 2070s is less than 80 %. The reservoir will take a very long time to recover from the shortfall as the resilience levels are below 50 %. Additionally, the dimensionless vulnerability was less than 50 %, indicating that there won't be a shortage of inflow to meet demand at the reservoir. Based on evaluation, the reservoir will not probably perform well in any way because of the rise in upstream abstractions. To guide the operation of the reservoir, operation rule curves were developed using the Hec-ResSim model. Generally, the government, decisions, and policymakers should set a water resources management plan not trigger normal functioning of the Lake system.

Insights from a Comprehensive Capacity Expansion Planning Modeling on the Operation and Value of Hydropower Plants under High Renewable Penetrations

This paper presents a quantitative assessment of the value of hydroelectric power plants (HPPs) in power systems with a significant penetration of variable renewable energy sources (VRESs). Through a capacity expansion planning (CEP) model that incorporates a detailed representation of HPP operating principles, the study investigates the construction and application of HPP rule curves essential for seasonal operation. A comparative analysis is also conducted between the proposed rule curve formulation and alternative modeling techniques from the literature. The CEP model optimizes installed capacities per technology to achieve predefined VRES penetration targets, considering hourly granularity and separate rule curves for each HPP. A case study involving twelve reservoir hydropower stations and two open-loop pumped hydro stations is examined, accounting for standalone plants and cascaded hydro systems across six river basins. The study evaluates the additional generation and storage required to replace the hydropower fleet under high VRES penetration levels, assessing the resulting increases in total system cost emanating from introducing such new investments. Furthermore, the study approximates the storage capabilities of HPPs and investigates the impact of simplified HPP modeling on system operation and investment decisions. Overall, the findings underscore the importance of reevaluating hydro rule curves for future high VRES penetration conditions and highlight the significance of HPPs in the energy transition towards carbon neutrality.

Mitigating adverse impacts of reservoir impoundment on lake ecology: A case study of the Three Gorges Reservoir and Dongting Lake

Upstream reservoir impoundment has brought adverse impacts on the downstream river-connected lakes, including lower water levels, frequent droughts, and reduced biodiversity. Existing research on ecological reservoir operation focused more on the effectiveness of operation redesign for ecology improvement, but less discussion on why they are eco-friendly. In this paper, the reservoir operation mechanisms targeting lake ecology are explored from three perspectives: ecological impoundment strategy, dominant characteristics of ecology and conservation tradeoffs, and critical parameters of the rule curves. Therefore, a reservoir operation framework, which couples the lake hydrological response model and multi-objective optimization model, is developed in this study. This framework is to maximize the water conservation and lake ecology benefits, assessing the latter by ecological reliability and ecological water shortage rate. Taking the Three Gorges Reservoir and Dongting Lake as the case study, operation rule curves are optimized for different impoundment strategies. The results indicate that impoundment with early storage time and slow storage rate can improve the ecological reliability and ecological water shortage by 9.90 % and 16.39 % than conventional operation. The ecological objectives are weakly competitive or synergistic when impounding fast, but strong competitive when impounding slow. Meanwhile, as the storage rate declines, the ecological improvement causes a slight decrease in power generation and a significant decrease in water supply. Finally, the crucial parameters of the rule curves are explored and appropriate values are recommended for different ecological preferences.

An Alternative Approach Using the Firefly Algorithm and a Hybrid Method Based on the Artificial Bee Colony and Cultural Algorithm for Reservoir Operation

In reservoir operation rule curves, it is necessary to apply rule curves to guide long-term reservoir management. This study proposes an approach to optimizing reservoir operation rule curves (RORCs) using intelligent optimization techniques from the firefly algorithm (FA) and a unique combination method utilizing the artificial bee colony and cultural algorithm (ABC-CA). The aim is to establish a connection with the simulation model to determine the optimal RORCs for flood control. The proposed model was used to determine the optimal flood control RORC for the Nam-Oon Reservoir (NOR) in northeastern Thailand. A minimum frequency and minimum average of excess water were provided as an objective function for assessing the efficiency of the search process. The evaluation of the effectiveness of flood control RORCs involved expressing water scarcity and excess water situations in terms of frequency, magnitude, and duration using historical inflow data synthesized from 1000 events. The results demonstrated that when using the obtained RORC to simulate the NOR system for reducing flooding in long-term operations, excess water scenarios were smaller than those using the current RORC. The results showed that the excess water scenario using the RORC obtained from the proposed model can reduce the excess water better than the current RORC usage scenario. In decreasing flood situations, the newly acquired RORC from the suggested FA and ABC-CA models performed better than the current RORC.

Optimization of Dualistic Reservoir System Two-Dimensional Rule Curve with Three Allocation Rules

A two-dimensional operation chart is commonly used to manage the operation of a dual-reservoir system, where the water storage in each reservoir is accurately considered in the water-supply decision. The dual reservoir chart should be combined with one allocation rule to better represent water supply distribution between reservoirs. In this study, the 2D rule curve was coupled with three allocation rules: variable allocation ratios, fixed allocation ratios, and compensation regulation, to identify the efficiency of using these rules with the 2D rule curve in operating the dual reservoirs. Mosul-Dukan dual reservoirs in Iraq were implemented as a study area using monthly data extended from 2001 to 2020. The Shuffled Complex Evolution Algorithm was used to optimize the water allocation ratios. The results revealed that the variable allocation ratios were superior to the other two rules in terms of water deficit, in which the total water shortage of the variable allocation ratios rule was 56590 Mm3. The total shortage was less than that obtained by the fixed allocation ratio and compensation regulation rules by 0.9% and 56%, respectively. Finally, the variable allocation ratio was more suitable for application with a 2D reservoir rule curve than the two remaining rules (fixed allocation ratio and compensation regulation rules). The variable allocation ratios sustainably manage reservoirs in the regions that suffer from water scarcity and represent the most vulnerable to the impact of climate change. Doi: 10.28991/CEJ-2024-010-02-04 Full Text: PDF

Study on the Basic Form of Reservoir Operation Rule Curves for Water Supply and Power Generation

Reservoir operation rule curves are crucial for managing water supply and power generation in reservoirs. As the number of objectives and management requirements increase, there is a growing demand for optimized operation rule curves. The objective of this study is to explore the most effective forms of reservoir operation rule curves, focusing on the case of the Nierji Reservoir and considering the dual objectives of water supply and power generation. The parameter–simulation–optimization framework, specifically employing the NSGA-II algorithm, was used to analyze and compare two basic forms of operation rule curves: the shared type and independent type. The impact of these curves on water supply potential and multi-objective optimization results with various water demand scenarios was assessed. The analysis revealed that the choice of operation rule curve form can influence the maximum water supply potential of the reservoir to some extent. The independent type operation rule curve was significantly more effective in enhancing the water supply potential for industrial and domestic users, resulting in a notable increase of 3.5 × 108 m3. Additionally, it also proved beneficial for environmental users, with an increase of 1 × 108 m3. Conversely, the shared type operation rule curve demonstrated similar functionality to the independent type curve with fewer decision variables, particularly when the water demand was relatively low. In scenarios with high water demand, the independent type curve outperformed the shared type curve by generating 6549 superior, non-dominated solutions for multi-objective optimization, specifically focused on maximizing reservoir operation benefits. In conclusion, selecting the appropriate form of reservoir operation rule curve is crucial to balance different reservoir functional objectives and achieve optimal results. Further research could focus on quantifying the specific benefits and trade-offs associated with each type of curve in order to provide more robust evidence for the advantages of a complex reservoir system.

Dynamic reservoir rule curves – Their creation and utilization

This paper presents a methodology for the creation of dynamic reservoir rule curves on the basis of the results of implicit stochastic optimization coupled with optimized demand hedging embedded as constraints to optimization. The novelty of the method is a dynamic rule curve that always starts from the current storage level and projects a range of anticipated target levels in the immediate future based on the statistical analyses of the results of implicit stochastic optimization. The method is particularly useful in dry years when storage is not completely filled at the end of wet seasons. Such situations cannot be addressed with standard traditional rule curves, thus causing reservoir operators to base their decisions on mere judgment. The proposed method can be helpful in such situations. The method has been demonstrated on the Tawa reservoir in the Narmada River Basin in India.

Integrated hydrological modelling of two contrasting watersheds with a terminal reservoir in the Upper Tapi River basin, India

Abstract The present study developed an integrated hydrologic model for sustainable utilisation and water management in two complex watersheds with varying physioclimatic features and reservoirs. The soil and water assessment tool (SWAT) is used for predicting integrated inflows into the Hatnur reservoir from the Burhanpur and Purna watersheds of the Upper Tapi River basin, while outflows are simulated using a rule curve. The influence of watershed complexities on hydrological model parameters and the watershed processes are investigated using extensive multisite and multivariable calibration (1998–2007) and validation (2008–2013) approaches, including sensitivity and uncertainty analyses. The sensitive parameters are related to curve number (CN), groundwater, slope, and main channel characteristics. The annual streamflow (m3/km2/mm of rainfall) in the Burhanpur watershed is 568.7, which is 4.2 times higher than the Purna watershed's streamflow of 136.2. The hypsometric analysis, areal rainfall, and flow duration curves revealed a substantially different streamflow pattern and a larger coefficient of variation in the spatial distribution of water balance components over sub-watersheds in the Burhanpur watershed compared to the Purna watershed due to diverse topographic features. The developed model would be useful for planning controlled releases from the terminal reservoir to mitigate hazards in the downstream reaches of the Tapi River basin.

Climate-associated risks for reliability, resilience, vulnerability and sustainability of irrigation reservoirs in small ungauged watersheds

Climate change poses an existential threat to the functional integrity of the network of agricultural reservoirs responsible for sustaining rice production in South Korea. Anthropogenic interventions have triggered a cycle of droughts and floods by magnifying the already existing supply–demand gap, which is seriously hampering reservoir performance. The watersheds of most agricultural reservoirs in South Korea are ungauged, and inflow and outflow data are not measured. In this study, recorded volumetric water storage data from five reservoirs were used to derive observed inflow data based on the hydrological reasoning and reservoir operation rule curves. These data were then used to evaluate the performance of a lumped rainfall-runoff model in simulating the runoff generated in upstream ungauged watersheds. A water balance analysis was conducted for 400 major agricultural reservoirs spread throughout the country during 1973–2017. Performance indices were estimated based on inflow availability in the reservoirs and downstream irrigation demands. Empirical relationships were derived to predict performance indices by using the reservoirs’ physical characteristics as input variables. Generally, reservoirs received substantially higher inflows compared to irrigation demands, suggesting sufficient water availability during the rice growing season (Apr−Sep). An out-of-phase cycle of peak inflow generation and water demand periods induced frequent failure events during the rice nursery transplanting period (May−Jun), and reservoir sustainability was minimal in these months. Southern and southwestern major agricultural regions with a higher density of small- to medium-sized reservoirs would be at higher risk of climate-induced water scarcity. Upsizing storage capacity could alleviate the water deficit in reservoirs associated with watersheds featuring higher means and lower standard deviations of runoff. A watershed-to-irrigated area ratio > 5 indicated acceptable reservoir sustainability.

Optimal Rule Curves for Operation the Euphrates River Concerning Tharthar Reservoir as a Case Study

Optimal Rule Curves for Operation the Euphrates River Concerning Tharthar Reservoir as a Case Study

Optimizing Solution in Decision Supporting System for River Basin Management Consisting of a Reservoir System

Decision support systems tackle problems and require systematic planning. They consider physical data, hydrological data, and sediment levels to achieve efficiency and adaptability in various situations. Therefore, this research aims to identify alternative engineering choices for the management of a river basin with a single reservoir system. Optimization techniques, including marine predator algorithm (MPA), genetic algorithm (GA), genetic programming (GP), tabu search (TS), and flower pollination algorithm (FPA), were applied to find the optimal reservoir rule curves using a reservoir simulation model. The study focused on the Ubolratana Reservoir in Thailand’s Khon Kaen Province, considering historic inflow data, water demand, hydrologic and physical data, and sedimentation volume. Four scenarios were considered: normal water scarcity, high water scarcity, normal excess water, and high excess water. The optimal rule curves derived from the reservoir simulation model, incorporating sedimentation and hedging rule (HR) criteria, were found to be the best engineering choices. In the normal and high water scarcity scenarios, they minimized the average water shortage to 95.558 MCM/year, with the lowest maximum water shortage 693.000 MCM/year. Similarly, in the normal and high excess water scenarios, the optimal rule curves minimized the average excess water, resulting in a minimum overflow of 1087.810 MCM/year and the lowest maximum overflow 4105.660 MCM/year. These findings highlight the effectiveness of integrating optimization techniques and a reservoir simulation model to obtain the optimal rule curves. By considering sedimentation and incorporating HR criteria, the selected engineering alternatives demonstrated their ability to minimize water shortage and excess water. This contributes to improved water resource management and decision-making in situations of scarcity and excess.

Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

This paper presents a comprehensive review of optimization algorithms utilized in reservoir simulation-optimization models, specifically focusing on determining optimal rule curves. The study explores critical conditions essential for the optimization process, including inflow data, objective and smoothing functions, downstream water demand, initial reservoir characteristics, evaluation scenarios, and stop criteria. By examining these factors, the paper provides valuable insights into the effective application of optimization algorithms in reservoir operations. Furthermore, the paper discusses the application of popular optimization algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and tabu search (TS), highlighting how researchers can utilize them in their studies. The findings of this review indicate that identifying optimal conditions and considering future scenarios contribute to the derivation of optimal rule curves for anticipated situations. The implementation of these curves can significantly enhance reservoir management practices and facilitate the resolution of water resource challenges, such as floods and droughts.

Derivation of Optimal Two Dimensional Rule Curve for Dualistic Reservoir Water-Supply System

In arid and semi-arid regions particularly vulnerable to climate change, optimizing the long-term operation of multi-purpose reservoirs is paramount. This study derived an optimum two-dimensional rule curve to jointly operate the parallel reservoirs of Mosul and Dukan, Northern Iraq. A hybridized optimization technique combining conventional dynamic programming with the shuffled complex evolution algorithm (SCE-UA) was developed to solve this problem. The results showed that the proportion of normal water supply areas increased from the beginning of the flood season (October) to its highest levels in April (58.77% of the total water supply area). The proportion decreased to its lowest in September (25.04% of the total water supply area). The newly derived 2D rule cure was compared with the current operation policy and was found to optimize the amount of water shortage by 21.1% during the operational period. It also reduced the shortage period and avoided catastrophic water shortages during droughts. In addition, the developed model optimized the amounts of water more than the joint water requirements, suffering from a significant deficit in meeting the demand during some months of the operational years. As a result, the storage in each reservoir was improved and thence can be adapted to face water shortages during future climate changes. This study proved the new hybridized model's applicability and can serve as a tool for sustainable water management. Doi: 10.28991/CEJ-2023-09-07-016 Full Text: PDF

Application of Harmony Search Algorithm for Improvement of Reservoir Rule Curves Under Dynamic Data of Water Requirements

Application of Harmony Search Algorithm for Improvement of Reservoir Rule Curves Under Dynamic Data of Water Requirements

Probability-Based Rule Curves for Multi-Purpose Reservoir System in the Seine River Basin, France

Multiple reservoir operation is of paramount importance due to tradeoffs in water supply and their cost functions. Understanding this complexity is important for optimizing water supply and increasing synergies gained from the joint operation. Therefore, this study aimed to develop a conceptual framework for addressing the effects of climate change on water security under the operating rules of the multiple reservoir system in northern France. A dynamic programming approach (DP) was employed to find the cost–benefit analysis that best fit with the objectives of reservoir operation, while the space rule was applied to balance the available space in each reservoir of a parallel system. A finite-horizon optimal regulation was then adopted for determining daily reservoir storage based on probability-based rule curves. The results indicated that the predicted inflow during the drawdown–refill cycle period to the Marne and Pannecière reservoirs would be the largest and lowest, respectively. The proposed upper rule curves during high-flow conditions suggested that the release from Aube reservoir should be postponed from July to August until September. At 50- and 100-year return periods, quite a high release rate from Seine and Marne reservoirs was observed during the dry season. A decrease in future water supply from Pannecière reservoir was found during summer, while the withdrawal in November could cause excessive water in the Seine tributary and Paris City. Under low-flow conditions in all return periods, the proposed lower rule curves recommended that the reservoir storage should go below the current operating rule, with a clear difference in July (the largest in Marne and the smallest in Pannecière) and almost no difference in November. Moreover, the web-based support system IRMaRA was developed for revising operating rules of four main reservoirs located in the Seine River Basin. The novelty of this modeling framework would contribute to the practice of deriving optimal operating rules for a multi-reservoir system by the probability-based rule curve method. Based on the evaluation of the effects of applying the estimated reservoir storage capacity under different return periods, both less overflow and water shortage represented by different levels of quantity and severity can be expected compared to the existing target storage at specified control points. Finally, the obtained finding revealed that the application of dynamic programming for reservoir optimization would help in developing a robust operating policy for tackling the effects of climate change.

Application of Optimization Techniques for Searching Optimal Reservoir Rule Curves: A Review

This paper reviews applications of optimization techniques connected with reservoir simulation models to search for optimal rule curves. The literature reporting the search for suitable reservoir rule curves is discussed and examined. The development of optimization techniques for searching processes are investigated by focusing on fitness function and constraints. There are five groups of optimization algorithms that have been applied to find the optimal reservoir rule curves: the trial and error technique with the reservoir simulation model, dynamic programing, heuristic algorithm, swarm algorithm, and evolutionary algorithm. The application of an optimization algorithm with the considered reservoirs is presented by focusing on its efficiency to alleviate downstream flood reduction and drought mitigation, which can be explored by researchers in wider studies. Finally, the appropriate future rule curves that are useful for future conditions are presented by focusing on climate and land use changes as well as the participation of stakeholders. In conclusion, this paper presents the suitable conditions for applying optimization techniques to search for optimal reservoir rule curves to be effectively applied in future reservoir operations.

Design of optimal operating rule curves for hydropower multi-reservoir systems by an influential optimization method

Hydropower plants play an integral role in energy supply and are one of the main renewable energy sources. The efficiency of hydropower reservoir systems is controlled by operating rules that need to be optimized to maximize energy production. Here, a novel optimization algorithm, influential flower pollination algorithm (IFPA), is developed in this paper. IFPA has three enhancements: an efficient exploration mechanism based on an adaptive coefficient, a new exploitation mechanism around the best solution, and a rank-based strategy to smoothly transition from exploration to exploitation. IFPA is tested in two benchmark multi-reservoir systems and a real-world five-reservoir hydropower system in Iran. The results show IFPA outperforms other advanced methods in terms of convergence speed and preciseness to achieve the global solution. The tested algorithms include adaptive guided differential evolution (AGDE), composite DE (CODE), hybridizing sum-local search optimizer (HSLSO), improved teaching learning-based optimization (ITLBO), self-adapting control parameters in DE (jDE), self-adaptive TLBO (SATLBO), and a multi-strategy hybrid of DE and particle swarm optimization (MS-DEPSO). IFPA also achieved better total power production in the real-world case study compared to other algorithms. Overall, IFPA can provide better solutions with less power deficit and shows as a promising method for optimizing hydropower reservoir problems.