Abstract
In this study, in order to improve regional energy system adjustment, a multistage stochastic inexact robust programming (MSIRP) is proposed for electric-power generation planning and structure adjustment management under uncertainty. Scenario-based inexact multistage stochastic programming and stochastic robust optimization were integrated into general programming to reflect uncertainties that were expressed as interval values and probability distributions in the objective function and constraints. An MSIRP-based energy system optimization model is proposed for electric-power structure management of Zibo City in Shandong Province, China. Three power demand scenarios associated with electric-power structure adjustment, imported electricity, and emission reduction were designed to obtain multiple decision schemes for supporting regional sustainable energy system development. The power generation schemes, imported electricity, and emissions of CO2 and air pollutants were analyzed. The results indicated that the model can effectively not only provide a more stable energy supply strategies and electric-power structure adjustment schemes, but also improve the balanced development between conventional and new clear power generation technologies under uncertainty.
Highlights
Rapid power consumption increment, increasing deterioration of environmental quality, and imperfect energy system management have led to unsustainable energy resources exploitation and utilization, unreasonable electric-power structure, and serious environmental issues [1,2,3]
A multistage stochastic inexact robust programming was proposed for supporting regional electric-power system structure optimization and management
The model covered the district heating supply, power generation, and air pollutant mitigation coupled with relevant technique constraints and governmental policies
Summary
Rapid power consumption increment, increasing deterioration of environmental quality, and imperfect energy system management have led to unsustainable energy resources exploitation and utilization, unreasonable electric-power structure, and serious environmental issues [1,2,3]. Multiple forms of uncertain information are involved in energy system management and the related social-economic factors and/or technical-economic parameters, causing a variety of complexities in decision support and policy analysis for regional energy planning [4]. Such complexities would pose great challenges in formulating more scientific and reasonable development strategies for Energies 2018, 11, 2108; doi:10.3390/en11082108 www.mdpi.com/journal/energies. It is desirable to develop effective uncertain optimization models/techniques for energy system management and the related decision analysis.
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