Multi-Objective Reservoir Optimization Balancing Energy Generation and Firm Power

Li, Fang‐Fang; Qiu, Jun

doi:10.3390/en8076962

Cited by 25 publications

(13 citation statements)

References 26 publications

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“…Since both the revenue from energy production and cost from energy purchase would affect the net revenue, determining the optimal levels of the two decision variables necessitates the analysis of their conflicting mechanisms from the background supports of reservoir operation. Previous studies [23,24] have noted that the maximization in total energy production is conflicted with the maximization of firm power output, due to difference between the mechanism of energy maximization and the mechanism of firm power output maximization. Specifically, the maximization in total energy production could result in heterogeneous power output when the peak power output is often produced at the highest water head, while the maximization in firm power output may yield homogenized power output sequences for maximizing the minimum power output over the planning horizon.…”

Section: Conflicting Decisions On Energy Production and Energy Purchasementioning

confidence: 99%

Analysis of a Stochastic Programming Model for Optimal Hydropower System Operation under a Deregulated Electricity Market by Considering Forecasting Uncertainty

Zhong

Du³

et al. 2018

Water

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In a deregulated electricity market, optimal hydropower operation should be achieved through informed decisions to facilitate the delivery of energy production in forward markets and energy purchase level from other power producers within real-time markets. This study develops a stochastic programming model that considers the influence of uncertain streamflow on hydropower energy production and the effect of variable spot energy prices on the cost of energy purchase (energy shortfall). The proposed model is able to handle uncertainties expressed by both a probability distribution and discretized scenarios. Conflicting decisions are resolved by maximizing the expected value of net revenue, which jointly considers benefit and cost terms under uncertainty. Methodologies are verified using a case study of the Three Gorges cascade hydropower system. The results demonstrate that optimal operation policies are derived based upon systematic evaluations on the benefit and cost terms that are affected by multiple uncertainties. Moreover, near-optimal operation policy under the case of inaccurate spot price forecasts is also analyzed. The results also show that a proper policy for guiding hydropower operation seeks the best compromise between energy production and energy purchase levels, which explores their nonlinear tradeoffs over different time periods.

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Section: Conflicting Decisions On Energy Production and Energy Purchasementioning

confidence: 99%

Analysis of a Stochastic Programming Model for Optimal Hydropower System Operation under a Deregulated Electricity Market by Considering Forecasting Uncertainty

Zhong

Du³

et al. 2018

Water

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“…In the modeling process, the following equality and inequality constraints should be strictly satisfied to guarantee the safe operation of power system in the entire scheduling horizon [55][56][57][58].…”

Section: Operation Constraintsmentioning

confidence: 99%

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

et al. 2019

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In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid sine cosine algorithm (HSCA) is proposed to deal with the complex peak operation problem of cascade hydropower reservoirs. In HSCA, the elite-guide evolution strategy is embedded into the standard sine cosine algorithm to improve the convergence rate of the swarm. The Gaussian local search strategy is used to increase the diversity of the population. The random mutation operator is adopted to enhance the search capability of the individuals in the evolutionary process. The proposed method is applied to solve the complex peak operation problem of two hydropower systems. The simulations indicate that in different cases, HSCA can generate the scheduling results with higher quality than several benchmark methods. Hence, this paper provides a feasible method for the complex peak operation problem of cascade hydropower reservoirs.

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“…After randomly generating the initial population in the search space, three basic evolutionary operators-the selection operator, crossover operator and mutation operator-are used to produce the new population composed of elite individuals. Moreover, using the fast sorting method and crowding distance strategy, all the Pareto solutions obtained at each cycle will be dynamically updated [38,39]. The iterative process will be not stopped until the terminal condition is met, then the final individuals represent the approximate optimal Pareto solutions.…”

Section: Overview Of Multi-objective Genetic Algorithmmentioning

confidence: 99%

Parallel Multi-Objective Genetic Algorithm for Short-Term Economic Environmental Hydrothermal Scheduling

et al. 2017

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Abstract:With the increasingly serious energy crisis and environmental pollution, the short-term economic environmental hydrothermal scheduling (SEEHTS) problem is becoming more and more important in modern electrical power systems. In order to handle the SEEHTS problem efficiently, the parallel multi-objective genetic algorithm (PMOGA) is proposed in the paper. Based on the Fork/Join parallel framework, PMOGA divides the whole population of individuals into several subpopulations which will evolve in different cores simultaneously. In this way, PMOGA can avoid the wastage of computational resources and increase the population diversity. Moreover, the constraint handling technique is used to handle the complex constraints in SEEHTS, and a selection strategy based on constraint violation is also employed to ensure the convergence speed and solution feasibility. The results from a hydrothermal system in different cases indicate that PMOGA can make the utmost of system resources to significantly improve the computing efficiency and solution quality. Moreover, PMOGA has competitive performance in SEEHTS when compared with several other methods reported in the previous literature, providing a new approach for the operation of hydrothermal systems.

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Multi-Objective Reservoir Optimization Balancing Energy Generation and Firm Power

Cited by 25 publications

References 26 publications

Analysis of a Stochastic Programming Model for Optimal Hydropower System Operation under a Deregulated Electricity Market by Considering Forecasting Uncertainty

Analysis of a Stochastic Programming Model for Optimal Hydropower System Operation under a Deregulated Electricity Market by Considering Forecasting Uncertainty

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

Parallel Multi-Objective Genetic Algorithm for Short-Term Economic Environmental Hydrothermal Scheduling

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