Joint Energy Procurement and Demand Response Towards Optimal Deployment of Renewables

Xuanyu Cao, Junshan Zhang, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


In this paper, joint energy procurement and demand response is studied from the perspective of the operator of a power system. The operator procures energy from both renewable energy sources (RESs) and the spot market. We observe the fact that the RESs may incur considerable infrastructure cost. This cost is taken into account and the optimal planning of renewables is examined by controlling the investment in RES infrastructures. Due to the uncertainty of renewables, the operator can also purchase energy directly from the spot market to compensate for the possible deficit incurred by the realization of the random renewable energy. By setting appropriate prices, the operator sells the collected energy to heterogeneous end users with different demand response characteristics. We model the decision making process of the operator as a two-stage optimization problem. The optimal decisions on the renewable deployment, energy purchase from the spot market, and pricing schemes are derived. Several solution structures are observed and a computationally efficient algorithm, requiring only closed-form calculation and simple bisection search, is proposed to compute the optimal decisions. Finally, numerical experiments are conducted to verify the optimality of the proposed algorithm and the solution structures observed theoretically. In particular, the impact of renewable penetration and the importance of its optimal design are highlighted.

Original languageEnglish (US)
Article number8378242
Pages (from-to)657-672
Number of pages16
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number4
StatePublished - Aug 2018

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


  • Renewable energy sources
  • demand response
  • optimization
  • pricing
  • resource allocation
  • smart grid


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