Multi-reservoir Hydropower System Research Articles

Influence of hydrogen import prices on hydropower systems in climate-neutral Europe

AbstractWhile climate and energy policy targets require fundamental changes and expansions in the energy infrastructure, hydropower systems across Europe remain essential for low-carbon energy systems. With renewable fuel import prices being subject to large uncertainties, this work aims to substantiate the relationship between these fuel import prices and multireservoir hydropower systems in a climate-neutral energy system. To that end, three green hydrogen import price scenarios are combined with two aggregated modelling approaches for pan-European hydropower assets. Using the integrated energy system model SCOPE SD, the analysis shows that import prices for green hydrogen have a significant impact on European electricity generation (+ 595 GW$$_\text {el}$$ el and + 650 TWh$$_\text {el}$$ el /yr), domestic hydrogen production (+ 396 TWh$$_\text {th}$$ th /yr), and water values of European hydropower assets (+ 33 % of average value in Norway). The results further indicate that the different aggregation methods only have a minor impact, suggesting that the computationally more efficient approach with up to 90% reductions in solution time provides suitable approximations of hydropower generation and flexibility in future analyses.

Extract nonlinear operating rules of multi-reservoir systems using an efficient optimization method

Hydropower plants are known as major renewable energy sources, usually used to meet energy demand during peak periods. The performance of hydropower reservoir systems is mainly affected by their operating rules, thus, optimizing these rules results in higher and/or more reliable energy production. Due to the complex nonlinear, nonconvex, and multivariable characteristics of the hydropower system equations, deriving the operating rules of these systems remains a challenging issue in multi-reservoir systems optimization. This study develops a self-adaptive teaching learning-based algorithm with differential evolution (SATLDE) to derive reliable and precise operating rules for multi-reservoir hydropower systems. The main novelty of SATLDE is its enhanced teaching and learning mechanism with three significant improvements: (i) a ranking probability mechanism is introduced to select the learner or teacher stage adaptively; (ii) at the teacher stage, the teaching mechanism is redefined based on learners’ performance/level; and (iii) at the learner stage, an effective mutation operator with adaptive control parameters is proposed to boost exploration ability. The proposed SATLDE algorithm is applied to the ten-reservoir benchmark systems and a real-world hydropower system in Iran. The results illustrate that the SATLDE achieves superior precision and reliability to other methods. Moreover, results show that SATLDE can increase the total power generation by up to 23.70% compared to other advanced optimization methods. Therefore, this study develops an efficient tool to extract optimal operating rules for the mentioned systems.

Managing chance-constrained hydropower with reinforcement learning and backoffs

The control of multi-reservoir hydropower systems is a crucial cogwheel in the current transition of power systems towards renewable energy. In particular in northern regions where inflows vary enormously through the year, the satisfaction of storage constraints cannot be ensured at all times. We thus add chance constraints to the underlying Markov Decision Process problem, and solve it with a Reinforcement Learning, policy gradient approach. “Backoffs”, common in other areas of optimal control, are introduced to better manage the numerous chance constraints as a single joint constraint. Stochastic Dynamic Programming (SDP) is used as a benchmark approach. We show numerically that our approach can deliver high quality policies, and just as importantly, that a three-reservoir system is solved in proportionally little more time than a one-reservoir system.

Incorporating reliability into the optimal design of multi-hydropower systems: A cellular automata-based approach

In recent years, Cellular Automata (CA) has emerged as a powerful tool for solving optimization problems in water resources engineering and, in particular, reservoir operation problems. This study utilizes the capabilities of the CA-based method to maximize the firm energy yield of multi-reservoir hydropower systems. Installed capacities (ICs) are selected as decision variables in the design phase and be determined in an iterative procedure. The reservoir storages at the beginning and the end of the periods are used as the decision variables in the operation phase and be calculated by the CA. The process starts with specifying an arbitrary initial installed IC for each reservoir determined based on long-term average annual inflow. Then, the system is optimally operated to maximize the energy yield under user-specified reliability of the system using a hybrid approach, Cellular Automata-Simulating Annealing (CA-SA). The system’s ICs are then increased/decreased depending on whether the system’s energy yield reliability is greater/less than the target reliability. This iterative process lasts till the system’s energy yield reliability is equal to the target reliability. The proposed method is used to optimally design the three-reservoir Khersan hydropower system and also the largest hydropower reservoir system in Iran composed of 16 reservoirs. The results are presented and compared with those of the existing methods in the literature. The results show that the proposed method can be efficiently and effectively used for improving the firm energy yield of real-world multi-reservoir hydropower systems.

An accelerated gradient-based optimization development for multi-reservoir hydropower systems optimization

Hydropower is one of the significant renewable energy resources. It is regularly requested at peak time steps to meet the load requirements of power systems resources allocation. Therefore, modeling the optimal operation of hydropower systems to maximize the entire energy production of reservoir systems can be a vital task for energy investment. Deriving optimal unknown decision parameters of these reservoir systems is a nonlinear, nonconvex, and complex optimization problem. Herein, a novel optimization algorithm, called an accelerated version of gradient-based optimization (AGBO), is developed to solve a complex multi-reservoir hydropower system. This advised technique uses an efficient adaptive control parameters mechanism to stabilize the global and local search; utilizing an enhanced local escaping operator (ELEO) to extend the chances of getting away from local optima; expanding the exploitation search by applying the sequential quadratic programming (SQP) technique. At first, the developed AGBO algorithm is employed to solve the optimal operation of a complex 10-reservoir hydropower system. Secondly, the possibility of the AGBO algorithm within the global optimization problems is illustrated by numerical tests of 23 mathematical benchmark functions. Optimal results show that the proposed AGBO can approach to 0.9999% of the optimal global solution. As a result, the advised method is the most superior one compared to the other advanced optimization algorithms for maximizing the load demands in hydropower system. In conclusion, this offers a productive tool to solve the complex hydropower multi-reservoir optimization systems.

Optimisation model for multi-reservoir systems based on profust reliability

A large number of multi-reservoir hydropower systems are in operation all over the world in order to meet increasing power demand. To date, different optimisation methods have been applied to multi-reservoir systems. However, overcoming the computational effort required to find an optimal operating policy for multi-reservoir systems still remains a difficult task. The aim of this study was to optimise the operation of complex multi-reservoir hydropower systems using profust reliability – a theory used in the reliability analysis of manufactured systems. The profust-reliability-based optimisation model was applied to a system with reservoirs connected in series and parallel to determine monthly operating policies for synthetically generated streamflow. The results show that the model is applicable to multi-reservoir systems.

A simulation – Optimization models for multi-reservoir hydropower systems design at watershed scale

Hydropower energy is one of the most economical and cleanest energies in the world. Due to the huge investment in hydropower projects, economic feasibility and determining the main design parameters related to plant sizing of hydro projects are extremely important. In this research, a simulation–optimization model for optimal design of hydropower systems with a systematic view of the basin (which is a complex nonlinear problem) has been developed. For water resources planning and management in the water basin, WEAP model has been used. Despite various capabilities, there are also limitations for hydropower systems modeling in WEAP. In this regard, a hydropower computation module has been employed to resolve these disadvantages. Then, PSO algorithm, linked to the simulation model and the developed optimization-simulation model has been used to solve the problem of optimal design of Garsha, Kuran Buzan, Sazbon and Tange mashoore power plant projects in Karkhe river basin. Objective function has been formulated based on two conventional methods in the economic analysis of hydro projects, i.e. electricity market price method and alternative thermal plant method. The objective function is maximization of net benefits of hydropower projects considering all resources, uses, and demands in the basin. Normal water level (NWL), Minimum Operation Level (MOL), Installed Capacity (IC) of dams and power-plants were chosen as decision variables of the problem. Finally, the results of optimal design of hydropower system have been discussed for both economic objective functions, followed by comparison and analysis of the results. the total generated energy and firm energy of the hydropower system is calculated bigger in alternative thermal plant method compared to market price method. However, the peak generated energy is computed larger in the market price.

Optimizing Multiple Linear Rules for Multi-Reservoir Hydropower Systems Using an Optimization Method with an Adaptation Strategy

Water resources crisis has a significant impact on hydropower energy production, which highlights the importance of water resources management. Reservoirs are effective and powerful systems to manage water resources. Due to the growing water demands and the limited water resources, optimizing these systems to maximize the production of hydropower energy is an essential task. In this study, an effective differential evolution (DE) algorithm with mutation strategy adaptation (MSA-DE) is developed to promote the local and global search capabilities in a feasible domain. In addition, an elitist strategy is applied to escape local optimum trap. In the present study, the MSA-DE algorithm was applied to optimize the multiple linear rules for two multi-reservoir operation systems (3- and 4-reservoir systems) in Iran. In the 3-reservoir system, the best objective function value in 10 runs was 123.57 for the MSA-DE, while the corresponding values for the DE, artificial bee colony (ABC), and genetic algorithm (GA) were 126.42, 147.38, and 126.68, respectively. For the 4-reservoir system, the best objective function values for the MSA-DE, DE, ABC, and GA were 130.50, 159.75, 174.41, and 140.63, respectively. The results demonstrated that the MSA-DE algorithm can be used to derive optimal operating rules for multi-reservoir systems by enhancing appropriate solutions, while it still preserves the accuracy and efficiency of the solutions.

Developing optimal policies for reservoir systems using a multi-strategy optimization algorithm

It is still a challenge to effectively optimize operation policies for reservoir systems, due to their large-scale and stochastic natures. The development and improvement of the optimization methods for optimizing reservoir operation systems are, therefore, a worthy undertaking. Hence, the objective of this study is to develop an effective hybrid of differential evolution (DE) and particle swarm optimization (PSO) with multi-strategy (MS-DEPSO) to optimize the operating policies for reservoir systems. The proposed MS-DEPSO promotes the local and global search capabilities of the basic DE algorithm to obtain an effective optimal operating policy. Fourteen mathematical functions were applied to verify the performance of the proposed optimization method. Furthermore, a multi-reservoir hydropower system with three various monthly operation periods over 10, 15, and 20 years was used as a real case study to evaluate the efficiency of MS-DEPSO in hydropower energy generation. Finally, the optimal operating rules were obtained based on the reservoir rule curves for a single reservoir with the purpose of agricultural water supply. The results highlighted the competency of the proposed optimization model to reduce the impact of severe drought periods. It is demonstrated in this study that the proposed algorithm has a superior ability to extract the optimal operating rules for reservoir systems.

Optimal operation of multi-reservoir hydropower systems using enhanced comprehensive learning particle swarm optimization

Metaheuristics are promising optimization algorithms for tackling reservoir-system operation. Comprehensive learning particle swarm optimization (CLPSO) is a state-of-the-art metaheuristic that is strong in exploration. Recently we have proposed enhanced CLPSO (ECLPSO) to improve the exploitation performance of CLPSO. In this paper, we apply ECLPSO to the optimal operation of multi-reservoir hydropower systems. Two novel strategies are proposed to handle the various physical and operational constraints. First, the outflow and storage volume constraints are appropriately enforced to achieve a tradeoff between preserving diversity and facilitating convergence. Second, with the penalty function technique adopted to penalize the constraint violations and convert the original constrained problem into an unconstrained one, the penalty factor is dynamically adjusted in order to encourage exploration of the search space in the beginning and gradually guide the search to concentrate in the feasible region. The short-term scheduling of a 4-reservoir hydrothermal power system and the long-term planning of China's Xiluodu–Xiangjiaba–Threegorges 3-reservoir hydropower system are studied. Experimental results demonstrate that ECLPSO helps to robustly derive feasible high quality solutions for the two cases studied. The contribution to performance improvement by ECLPSO as well as the constraint enforcement and penalty factor adjustment strategies are analyzed.

Parallel discrete differential dynamic programming for multireservoir operation

The curse of dimensionality and computational time cost are a great challenge to operation of large-scale hydropower systems (LSHSs) in China because computer memory and computational time increase exponentially with increasing number of reservoirs. Discrete differential dynamic programming (DDDP) is one of the most classical algorithms for alleviating the dimensionality problem for operation of LSHSs. However, the computational time performed on DDDP still increases exponentially with increasing number of reservoirs. Therefore, a fine-grained parallel DDDP (PDDDP) algorithm, which is based on Fork/Join parallel framework in multi-core environment, is proposed to improve the computing efficiency for long-term operation of multireservoir hydropower systems. The proposed algorithm is tested using a huge cascaded hydropower system located on the Lancang River in China. The results demonstrate that the PDDDP algorithm enhances the computing efficiency significantly and takes full advantage of multi-core resources, showing its potential practicability and validity for operation of LSHSs in future.

Evaluation of a multi-reservoir hydropower system using a simulation model

In the present study, the operation of a multi-reservoir hydropower system is evaluated using a monthly simulation model for power production and irrigation releases. The hydropower production is assessed for two operating scenarios, namely, unconstrained and constrained scenarios based on hard bound constraint on powerhouse releases and also for various duration of hydropower plants operation. Further, the behaviour of the system is analysed using the statistical performance indices such as reliability, resilience and vulnerability. The results show that the power production is high for the unconstrained operating scenario due to unrestricted releases to the powerhouses. On the other hand, the constrained scenario has resulted in lesser but reliable steady power production irrespective of duration of operation. It is found that the power production can be increased by 14% without compromising the irrigation releases. The statistical performance analyses show that lesser the duration of operation of powerhouses, higher is the reliability and resilience with less vulnerability.

PSO-MODSIMP: Optimization-Simulation Model for Optimal Design and Operation of Multireservoir Hydropower Systems

Application of simulation-optimization models to the problem of optimal design and operation of multi-reservoir hydropower systems with control on the reliability of meeting the system’s energy yield could be a suitable approach to solve the resulting mixed integer nonlinear, nonconvex optimization model. An optimization-simulation model has been developed in this study by integrating MODSIM river basin simulation model and Particle Swarm Optimization (PSO) algorithm. First, the possibility of defining an energy demand-node and applying a hydropower standard operating policy (HSOP) in MODSIM was provided. Then the extended simulation model (MODSIMP) was linked to the PSO algorithm with the objective function of maximization of the net benefit from the system’s design and operation over its projected life period. In the developed model, each objective function evaluation of PSO is performed by running MODSIMP. Analysis of the PSO-MODSIMP model results was done through its application to the problem of optimal design and operation of the Khersan multireservoir hydropower system as a real case study. Computational efficiency of the MODSIMP in simulating the operation of multireservoir hydropower systems and the flexibility of the PSO algorithm in considering the model nonlinearity has provided a promising modeling tool in tackling the problem of optimal design and operation of multi-reservoir hydropower systems.

Short-term scheduling of cascade reservoirs using an immune algorithm-based particle swarm optimization

This paper presents a new approach for short-term hydropower scheduling of reservoirs using an immune algorithm-based particle swarm optimization (IA-PSO). IA-PSO is employed by coupling the immune information processing mechanism with the particle swarm optimization algorithm in order to achieve a better global solution with less computational effort. With the IA-PSO technique, the hydro-electrical optimization model of reservoirs is formulated as a high-dimensional, dynamic, nonlinear and stochastic global optimization problem of a multi-reservoir hydropower system. The purpose of the proposed methodology is to maximize total hydropower production. Here it is applied to a reservoir system on the Qingjiang River, in the Yangtze watershed, that consists of two reservoirs. The results are compared with the results obtained through conventional operation method, the dynamic programming and the standard PSO algorithm. From the comparative results, it is found that the IA-PSO approach provides the most globally optimum solution at a faster convergence speed.

Performance of the Equivalent Reservoir Modelling Technique for Multi-Reservoir Hydropower Systems

This paper investigates the validity of a simplified equivalent reservoir representation of a multi-reservoir hydroelectric system for modelling its optimal operation for power maximization. This simplification, proposed by Arvanitidis and Rosing (IEEE Trans Power Appar Syst 89(2):319–325, 1970), imputes a potential energy equivalent reservoir with energy inflows and outflows. The hydroelectric system is also modelled for power maximization considering individual reservoir characteristics without simplifications. Both optimization models employed MINOS package for solution of the non-linear programming problems. A comparison between total optimized power generation over the planning horizon by the two methods shows that the equivalent reservoir is capable of producing satisfactory power estimates with less than 6% underestimation. The generation and total reservoir storage trajectories along the planning horizon obtained by equivalent reservoir method, however, presented significant discrepancies as compared to those found in the detailed modelling. This study is motivated by the fact that Brazilian generation system operations are based on the equivalent reservoir method as part of the power dispatch procedures. The potential energy equivalent reservoir is an alternative which eliminates problems with the dimensionality of state variables in a dynamic programming model.

A NEURAL NETWORK OPTIMIZER FOR SCHEDULING HYDROPOWER GENERATIONS

An approach based on neural network optimizer is proposed for determining the optimal short-term scheduling of multi-reservoir hydropower system. The proposed method is based on the Lagrange multiplier theory and search for solutions satisfying the necessary conditions of optimality in the state space. The equilibrium point of the network optimizer corresponds to the Lagrange solution of the problem and satisfies the Kuhn-Tucker condition for the problem. Here the main objective is to determine the optimal amounts of water to be released from each reservoir during each interval so as to minimize the overall energy shortages over the complete planning horizon. The method takes into account the water transportation delays between upstream and downstream reservoirs and a nonlinear hydropower generation function. An algorithm based on the proposed neural network optimizer has been developed and implemented on a multi-chain cascade of reservoir type hydropower system. Results so achieved have been compared with those obtained using conventional augmented Lagrange multiplier method. From the results, it is concluded that the proposed method is very effective in providing a good optimal solution along with constraint satisfaction.

Hydropower planning coordinated with wind power in areas with congestion problems for trading on the spot and the regulating market

In this paper a day-ahead planning algorithm for a multi-reservoir hydropower system coordinated with wind power is developed. Coordination applies to real situations, where wind power and hydropower are owned by different utilities, sharing the same transmission lines, though hydropower has priority for transmission capacity. Coordination is thus necessary to minimize wind energy curtailments during congestion situations. The planning algorithm accounts for the uncertainty of wind power forecasts and power market price uncertainty. Planning for the spot market and the regulating market is considered in the algorithm. The planning algorithm is applied to a case study and the results are summarized in the paper.

Reliability-Based Simulation-Optimization Model for Multireservoir Hydropower Systems Operations: Khersan Experience

A common method for assessing the energy potential of hydropower systems in Iran is the sequential streamflow routing with control on the energy-yield reliability. The method results in developing a reliability-based simulation (RBS) model that is used to analyze single-reservoir hydropower systems. In most of the cascade hydropower systems in the country, a single-reservoir RBS model is usually employed in the design and operation of each of the systems reservoirs. This paper presents a multireservoir RBS model considering the integrated operation of the systems. The model employs the general algorithm of the single-reservoir RBS model; however, with a single-period optimization submodel in determining releases from reservoirs during the system simulation. The objective function of the submodels minimizes the sum of reservoir releases and/or maximizes the sum of reservoir storages in each of the time periods, subject to a reliability constraint on the system’s energy yield. The single and multireservoir ...

A Bayesian Stochastic Optimization Model for a Multi-Reservoir Hydropower System

This paper presents the development of an operating policy model for a multi-reservoir system for hydropower generation by addressing forecast uncertainty along with inflow uncertainty. The stochastic optimization tool adopted is the Bayesian Stochastic Dynamic Programming (BSDP), which incorporates a Bayesian approach within the classical Stochastic Dynamic Programming (SDP) formulation. The BSDP model developed in this study considers, the storages of individual reservoirs at the beginning of period t, aggregate inflow to the system during period t and forecast for aggregate inflow to the system for the next time period t + 1, as state variables. The randomness of the inflow is addressed through a posterior flow transition probability, and the uncertainty in flow forecasts is addressed through both the posterior flow transition probability and the predictive probability of forecasts. The system performance measure used in the BSDP model is the square of the deviation of the total power generated from the total firm power committed and the objective function is to minimize the expected value of the system performance measure. The model application is demonstrated through a case study of the Kalinadi Hydroelectric Project (KHEP) Stage I, in Karnataka state, India.

Optimality with hydropower system

This paper studies an electricity producer's long-term optimality in the case of a multireservoir hydropower system. The model solves the optimal production process and trading strategy of electricity and weather derivatives by maximizing the utility from production and terminal water reservoir level. The optimal trading strategy hedges the rainfall and electricity price uncertainties.