In this paper, a hybrid improved particle swarm optimization （IPSO） algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimizati...In this paper, a hybrid improved particle swarm optimization （IPSO） algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization （PSO） algorithm is improved in two ways： （1） The linearly decreasing inertia weight coefficient （LDIWC） is replaced by a self-adaptive exponential inertia weight coefficient （SEIWC）, which could make the PSO algorithm more balanceable and more effective in both global and local searches. （2） The crossover and mutation idea inspired by the genetic algorithm （GA） is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation （DPSA） algorithm.展开更多
Increasing demand for water from all sectors presents a challenge for policy makers to improve water allocation policies for storage reservoirs. In addition, there are many other organisms and species present in river...Increasing demand for water from all sectors presents a challenge for policy makers to improve water allocation policies for storage reservoirs. In addition, there are many other organisms and species present in river waters that also require water for their survival. Due to the lack of awareness many times the minimum required quantity and quality of water for river ecosystem is not made available at downstream of storage reservoirs. So, a sustainable approach is required in reservoir operations to maintain the river ecosystem with environmental flow while meeting the other demands. Multi-objective, multi-reservoir operation model developed with Python programming using Fuzzy Linear Programing method incorporating environmental flow requirement of river is presented in this paper. Objective of maximization of irrigation release is considered for first run. In second run maximization of releases for hydropower generation is considered as objective. Further both objectives are fuzzified by incorporating linear membership function and solved to maximize fuzzified objective function simultaneously by maximizing satisfaction level indicator (λ). The optimal reservoir operation policy is presented considering constraints including Irrigation release, Turbine release, Reservoir storage, Environmental flow release and hydrologic continuity. Model applied for multi-reservoir system consists of four reservoirs, i.e., Jayakwadi Stage-I Reservoir (R1), Jayakwadi Stage-II Reservoir (R2), Yeldari Reservoir (R3), Siddheshwar Reservoir (R4) in Godavari River sub-basin from Marathwada region of Maharashtra State, India.展开更多
Water is the soul of the world. It is the most important element for the survival of humans, animals, birds, plants and all other living things on earth. Water is essential for the beginning of life as well as regular...Water is the soul of the world. It is the most important element for the survival of humans, animals, birds, plants and all other living things on earth. Water is essential for the beginning of life as well as regular availability of water ensuring the survival, growth and overall nourishment. Thus, proper planning and use of reservoir water are essential for all. To tackle this issue different optimization techniques underline their need and importance in the reservoir operations. In the present study, multi-reservoir optimization model is developed using Python programing language considering the objective of maximization of total annual release for hydropower generation. Model is applied to 3 reservoirs from Godavari River basin from Maharashtra state India. Water essential for conservation of environment has also been made available in river as environmental flow as per the recommendations of Central Water Commission (CWC) India. Developed optimization model provides optimal monthly operation policies.展开更多
以黄河上游多年调节水库龙羊峡及下游刘家峡水库为研究对象,考虑来水不确定性影响,建立梯级水库群多目标随机优化模型,并基于理想点法(technique for order preference by similarity to an ideal solution,TOPSIS)决策方法,求解当年发...以黄河上游多年调节水库龙羊峡及下游刘家峡水库为研究对象,考虑来水不确定性影响,建立梯级水库群多目标随机优化模型,并基于理想点法(technique for order preference by similarity to an ideal solution,TOPSIS)决策方法,求解当年发电量与年末水位Pareto方案集中最佳方案,探讨来水频率及起调水位对年末水位和发电量的影响,验证年末期望水位保证多年调节水库发电效益的可靠性。结果表明:在不确定来水条件下,龙羊峡水库年末期望水位与龙羊峡水库、刘家峡水库年期望发电量之间存在竞争关系;龙羊峡水库来水频率越低、起调水位越高时,龙羊峡水库年末水位越高,龙羊峡水库、刘家峡水库发电量越大;基于提出的年末水位优化方法可大幅缩减龙羊峡水库现行调度方式下年末水位范围,且保障发电效益的可靠性在98%以上。展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 50679011)
文摘In this paper, a hybrid improved particle swarm optimization （IPSO） algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization （PSO） algorithm is improved in two ways： （1） The linearly decreasing inertia weight coefficient （LDIWC） is replaced by a self-adaptive exponential inertia weight coefficient （SEIWC）, which could make the PSO algorithm more balanceable and more effective in both global and local searches. （2） The crossover and mutation idea inspired by the genetic algorithm （GA） is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation （DPSA） algorithm.
文摘Increasing demand for water from all sectors presents a challenge for policy makers to improve water allocation policies for storage reservoirs. In addition, there are many other organisms and species present in river waters that also require water for their survival. Due to the lack of awareness many times the minimum required quantity and quality of water for river ecosystem is not made available at downstream of storage reservoirs. So, a sustainable approach is required in reservoir operations to maintain the river ecosystem with environmental flow while meeting the other demands. Multi-objective, multi-reservoir operation model developed with Python programming using Fuzzy Linear Programing method incorporating environmental flow requirement of river is presented in this paper. Objective of maximization of irrigation release is considered for first run. In second run maximization of releases for hydropower generation is considered as objective. Further both objectives are fuzzified by incorporating linear membership function and solved to maximize fuzzified objective function simultaneously by maximizing satisfaction level indicator (λ). The optimal reservoir operation policy is presented considering constraints including Irrigation release, Turbine release, Reservoir storage, Environmental flow release and hydrologic continuity. Model applied for multi-reservoir system consists of four reservoirs, i.e., Jayakwadi Stage-I Reservoir (R1), Jayakwadi Stage-II Reservoir (R2), Yeldari Reservoir (R3), Siddheshwar Reservoir (R4) in Godavari River sub-basin from Marathwada region of Maharashtra State, India.
文摘Water is the soul of the world. It is the most important element for the survival of humans, animals, birds, plants and all other living things on earth. Water is essential for the beginning of life as well as regular availability of water ensuring the survival, growth and overall nourishment. Thus, proper planning and use of reservoir water are essential for all. To tackle this issue different optimization techniques underline their need and importance in the reservoir operations. In the present study, multi-reservoir optimization model is developed using Python programing language considering the objective of maximization of total annual release for hydropower generation. Model is applied to 3 reservoirs from Godavari River basin from Maharashtra state India. Water essential for conservation of environment has also been made available in river as environmental flow as per the recommendations of Central Water Commission (CWC) India. Developed optimization model provides optimal monthly operation policies.
基金This study was jointly supported by the National Key Research and Development Program of China （2016YFC0402208, 2016YFC0401903, and 2016YFC0400903）, the National Natural Science Foundation of China （51709276）, and the State Key Laboratory of Simulation and Regulation of the Water Cycle in River Basins （2016CG05）.
文摘以黄河上游多年调节水库龙羊峡及下游刘家峡水库为研究对象,考虑来水不确定性影响,建立梯级水库群多目标随机优化模型,并基于理想点法(technique for order preference by similarity to an ideal solution,TOPSIS)决策方法,求解当年发电量与年末水位Pareto方案集中最佳方案,探讨来水频率及起调水位对年末水位和发电量的影响,验证年末期望水位保证多年调节水库发电效益的可靠性。结果表明:在不确定来水条件下,龙羊峡水库年末期望水位与龙羊峡水库、刘家峡水库年期望发电量之间存在竞争关系;龙羊峡水库来水频率越低、起调水位越高时,龙羊峡水库年末水位越高,龙羊峡水库、刘家峡水库发电量越大;基于提出的年末水位优化方法可大幅缩减龙羊峡水库现行调度方式下年末水位范围,且保障发电效益的可靠性在98%以上。
