Optimization Online


Assessing the Cost of the Hazard-Decision Simplification in Multistage Stochastic Hydrothermal Scheduling

Alexandre Street (street***at***ele.puc-rio.br)
Andre Lawson (andrelawsonps***at***gmail.com)
Davi Valladão (davimv***at***puc-rio.br)
Alexandre Velloso (alevelloso***at***gmail.com)

Abstract: Hydropower is one of the world’s primary renewable energy sources whose usage has profound economic, environmental, and social impacts. We focus on the dispatch of generating units and the storage policy of hydro resources. In this context, an accurate assessment of the water opportunity-cost is cru- cial for driving the sustainable use of this scarce resource. Nevertheless, tra- ditional computational tools use stochastic dual dynamic programming under the Hazard-Decision (HD) modeling simplification, where dispatch decisions are determined assuming perfect information about the current inflows. In practice, however, some dispatch decisions are made before water inflows are observed, i.e., under a Decision-Hazard (DH) scheme. This inconsistency generates an optimistic assessment of the opportunity cost of the water, in- ducing a sub-optimal use of energy resources. Thus, our objectives are: (i) to raise awareness of the HD issue bridging research and implementation with a clear recommendation to system operators and regulators; (ii) to incorporate the DH scheme into the long-term hydrothermal scheduling problem; (iii) to assess the economic impacts and other market distortions due to the actual energy usage policy based on the HD simplification. For a representative case-study with realistic data from the Brazilian power system, results show that the HD simplification introduces a regret cost of 12%. Furthermore, we show that incorporating the DH scheme into the hydrothermal planning model, the energy supply cost can be reduced up to 5.3%. We also show that spot-price distortions and expensive thermal generation could be mitigated in the DH scheme.

Keywords: Multistage hydrothermal scheduling, Stochastic dual dynamic programming, hazard-decision vs decision-hazard, time consistency.

Category 1: Stochastic Programming

Category 2: Other Topics (Dynamic Programming )

Category 3: Applications -- Science and Engineering (Control Applications )

Citation: Accepted for publication in Applied Energy, September 2020.

Download: [PDF]

Entry Submitted: 10/10/2018
Entry Accepted: 10/10/2018
Entry Last Modified: 09/24/2020

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society