Abstract
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 $V_{p}$ can be mainly affected by the Wasserstein radius $\rho $ while the total cost $C_{obj}$ can be affected by the violation probability $\varepsilon $ . The dispatch energy for the CEHS is obtained using the proposed approach, which outperforms the existing robust optimization (RO) approach.
Highlights
Driven by the goals of clean energy and zero carbon emissions, the energy transition is an urgent need to decarbonize electricity and heat sectors [1]–[3]
model predictive control (MPC)-BASED MULTISTAGE CO-RISK DISPATCH FRAMEWORK The growing uncertainty associated with the increasing time scale has produced operation risk for the coupled electricity and heat system (CEHS)
The multistage MPC co-risk dispatch model for the CEHS can be developed to explicitly hedge against the ambiguity of uncertainty distributions, which is composed of three processes, autoregressive moving average (ARMA)-based Prediction process, risk-based optimization process, and penalty-based feedback correction process
Summary
Driven by the goals of clean energy and zero carbon emissions, the energy transition is an urgent need to decarbonize electricity and heat sectors [1]–[3]. MPC-BASED MULTISTAGE CO-RISK DISPATCH FRAMEWORK The growing uncertainty associated with the increasing time scale has produced operation risk for the CEHS. The multistage MPC co-risk dispatch model for the CEHS can be developed to explicitly hedge against the ambiguity of uncertainty distributions, which is composed of three processes, ARMA-based Prediction process, risk-based optimization process, and penalty-based feedback correction process. Since real-time dispatch time scale usually varies from minutes to hours, the short-term forecast methodology can provide a more accurate power output of RESs in the prediction process. Since the multistage real-time rolling dispatch range from a few minutes to an hour, short-term forecasts for the RESs power output can hedge against the risk that produces extensive forecasts error used by other forecast methods. The forecast result in each step provides data used as input for the optimization process
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