Data-Driven Adjustable Robust Unit Commitment of Integrated Electric-Heat Systems

Wang, Cheng; Gong, Zhihao; He, Chuan; Gao, Hongjun; Bi, Tianshu

doi:10.1109/tpwrs.2020.3019412

Cited by 17 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the HS constraints (8)-( 12), constraint (8) depicts the supply and return pipelines for the nodal heat power, respectively. Constraint (9) states that the inevitable heat loss leads to the temperature drop along the pipeline, constraint (10) defined the mixture temperature at confluence nodes, constraint (11) enforces the same temperature for the mixed fluid at the confluence node, and constraint (12) provides the temperature bound. The CHP constraints ( 13)-( 14) represent the CHP capacity limits.…”

Section: Epsmentioning

confidence: 99%

“…The state-of-the-art solutions for uncertain RESs are focused on stochastic programming (SP), robust optimization (RO), and distributionally robust optimization (DRO). However, SP is hard to obtain the exact probability distributions, which requires a large number of scenarios, while RO that focused on worst-case scenarios may produce overconservative solutions [7]- [12]. DRO hedges against the shortcoming between SP and RO, which can seek the solution associated with an appropriate conversion by incorporating available distribution information into an ambiguity set [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multistage Model Predictive Control Co-Risk Dispatch for Coupled Electricity and Heat System

et al. 2021

View full text Add to dashboard Cite

The energy transition can facilitate the rapid development of the coupled electricity and heat system (CEHS), accommodating the higher share of renewable energy. However, uncertain renewable energy sources (RESs) and overlimit risk can bring significant challenges for the safe operation of the CEHS. Considering uncertain RESs integration and overlimit risk within the upper and lower bounds jointly with high probability, the multistage model predictive control (MPC) co-risk dispatch model for the CEHS can be proposed in this paper. The model can hedge against the operational risk for the multistage rolling dispatch. In the context of the multistage real-time risk dispatch, the MPC framework is presented, which contains three processes, autoregressive moving average (ARMA) model prediction process, risk-based optimization process, and penalty-based feedback correction process. In the prediction process, the ARMA method can be employed to accurately predict the RESs power, which is characterized as the merit of the short-term forecast. In the optimization process, the violation risk of overlimit chance constraints caused by the output power fluctuation can be mitigated using the distributionally robust chance-constrained (DRCC) under the Wasserstein ambiguity set. The tractable approximation reformulation can be obtained by adopting the Worst-Case Conditional Value-at-Risk (WC-CVaR) approximation method. In the feedback correction process, penalty-based load shedding and RESs spillage can lower the risk level by minimizing the risk penalty cost. In the numerical case, the risk performance analysis based on the modified Barry Island system is implemented. The analysis demonstrates that the daily average violation probability can be mainly affected by the Wasserstein radius while the total cost can be affected by the violation probability . The dispatch energy for the CEHS is obtained using the proposed approach, which outperforms the existing robust optimization (RO) approach.

show abstract

Section: Epsmentioning

confidence: 99%