Data-Driven Chance-Constrained Planning for Distributed Generation: A Partial Sampling Approach

Shiyi Jiang, Jianqiang Cheng, Kai Pan, Feng Qiu, Boshi Yang

Research output: Contribution to journalArticlepeer-review


The planning of distributed energy resources has been challenged by the significant uncertainties and complexities of distribution systems. To ensure system reliability, one often employs chance-constrained programs to seek a highly likely feasible solution while minimizing certain costs. The traditional sample average approximation (SAA) is commonly used to represent uncertainties and reformulate a chance-constrained program into a deterministic optimization problem. However, the SAA introduces additional binary variables to indicate whether a scenario sample is satisfied and thus brings great computational complexity to the already challenging distributed energy resource planning problems. In this paper, we introduce a new paradigm, i.e., the partial sample average approximation (PSAA) using real data, to improve computational tractability. The innovation is that we sample only a part of the random parameters and introduce only continuous variables corresponding to the samples in the reformulation, which is a mixed-integer convex quadratic program. Our extensive experiments on the IEEE 33-Bus and 123-Bus systems show that the PSAA approach performs better than the SAA because the former provides better solutions in a shorter time in in-sample tests and provides better guaranteed probability for system reliability in out-of-sample tests. All the data used in the experiments are real data acquired from Pecan Street Inc. and ERCOT. More importantly, our proposed chance-constrained model and PSAA approach are general enough and can be applied to solve other valuable problems in power system planning and operations.

Original languageEnglish (US)
Pages (from-to)5228-5244
Number of pages17
JournalIEEE Transactions on Power Systems
Issue number6
StatePublished - Nov 1 2023


  • Planning
  • chance-constrained programming
  • data-driven
  • distributed energy resources
  • energy storage
  • renewable distributed generation
  • stochastic programming

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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