Long -term multi-objective power generation operation for cascade reservoirs and risk decision making under stochastic uncertainties

The long-term multi-objective power generation operation (LTMOPGO) inherently exists multiple uncertainties coming from streamflow forecasting and decision-making process. With help of multi-criteria decision making (MCDM), reservoir scheduling solutions are evaluated and ranked. However, conventional MCDM provide decision makers (DMs) with deterministic rank sequence ignoring uncertainty effects which may lead to unignorable risk of decision error. Furthermore, algorithm improvements are greatly emphasized on multi-objective reservoir operation, while uncertainty analysis and decision risk are ignored to some degree. To this end, we establish framework for solving LTMOPGO and MCDM under multiple uncertainties, including criteria values (CVs) and criteria weights (CWs). First, reservoir operation solutions with uncertain information are acquired by improved multi-objective particle swarm optimization (IMOPSO) and LHS-Monte Carlo simulation. Then, stochastic multi-criteria acceptability analysis (SMAA) model coupling with grey correlation analysis (GCA) and TOPSIS is developed to assist stochastic decision making. Finally, we conduct simulation experiments for cascade reservoirs in Qingjiang river and disclose effect of uncertain factors on LTMOPGO and MCDM with probabilistic rank sequence and risk information. Comparison analysis indicates feasibility and efficiency of novel SMAAGCA&TOPSIS model compared with SMAA-2. Overall, novel framework proposed are effective ways for DMs to make highly reliable and risk-informed decisions under stochastic environment. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The article introduces a framework for solving long-term multi-objective power generation operation and multi-criteria decision making under multiple uncertainties, including improved multi-objective particle swarm optimization and LHS-Monte Carlo simulation. A stochastic multi-criteria acceptability analysis model coupled with grey correlation analysis and TOPSIS is developed to assist stochastic decision making. Simulation experiments reveal the effect of uncertain factors on LTMOPGO and MCDM with probabilistic rank sequence and risk information.