Shuffled complex evolution coupled with stochastic ranking for reservoir scheduling problems

This paper introduces an optimization method(SCE-SR)that combines shuffled complex evolution(SCE)and stochastic ranking(SR)to solve constrained reservoir scheduling problems,ranking individuals with both objectives and constrains considered.A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals.This method is suitable for handling multiple conflicting constraints,and is easy to implement,requiring little parameter tuning.The search properties of the method are ensured through the combination of deterministic and probabilistic approaches.The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system,and its performance is compared with that of two classical methods(the dynamic programming and genetic algorithm).The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly,with sufficient global convergence properties and robustness.The operation schedules obtained satisfy the basic scheduling requirements of reservoirs.

Finding middle ground:Flow regimes designed for salmon and energy value

In regulated rivers,shaping seasonal flows to recover species at risk depends on understanding when to expect conflicts with competing water users and when their interests are aligned.Multi-objective optimization can be used to reveal such conflicts and commonalities.When species are involved,multi-objective optimization is challenged by the need to simulate complex species responses to flow regimes.Previously,we addressed that challenge by developing a simplified salmon model(Quantus)that defines cohorts of salmon by the river section and time in which they were spawned.Salmon in these space-time cohorts are tracked from the time redds(nests)are constructed until the cohort exits the tributary en route to the ocean.In this study,we modeled seasonal patterns in energy value and developed a Pareto-optimal frontier of seasonal flow patterns to maximize in-river salmon survival and hydropower value.Candidate flow regimes were characterized by two pulse flows varying in magnitude,timing,and duration and constrained by a total annual flow near the historical median.Our analysis revealed times when economic and salmon objectives were aligned and times when they differed.Pulse flows that favored higher energy value were timed to meet demand during extreme temperatures.Both salmon and hydropower objectives produced optimal flow regimes with pulse flows in early summer,but only solutions favoring hydropower value included high flows in mid-winter.Solutions favoring higher age-0 salmon survival provided an extended pulse flow in late winter/early spring,which suggests that access to productive floodplain habitat allowed faster growth and earlier out-migration and reduced the need for higher temperature-moderating flows later in spring.Minimum flows were also higher among solutions favoring salmon over energy.The tools used to produce these results can help to design simplified seasonal flow regimes by revealing compromise solutions that satisfy both fish and energy producers and highlighting when potential conflicts are likely.