With the application and development of combined heat and power (CHP) techniques, the coupling of electrical and heat energy is gradually increasing. State estimation (SE) is essential to achieve the comprehensive and accurate operating status in an integrated electricity-heat system (IEHS). This paper proposes a robust SE method based on second-order conic programming (SOCP) for IEHS. Firstly, auxiliary state variables and measurements are introduced to obtain the exact linear measurement equations for IEHS. Then, a robust SE model based on weighted least absolute values (WLAV) for IEHS is proposed. To further improve the estimation performance, the constraint conditions satisfied by auxiliary state variables are relaxed into second-order cone constraints and added to the proposed WLAV model, leading a SOCP-based robust SE method. Simulations demonstrate that the proposed method ensures a global optimal solution and has good robustness and high estimation accuracy.

With the increasing proportion of wind power connected to grid, power system dispatching is facing more and more challenges from uncertainty. To cope with this uncertainty, robust optimization has been applied in unit commitment (UC) problem. In this paper, a multi‐band uncertainty set considering the temporal correlation (MBUSCTC) of wind/load prediction error is proposed firstly, which has two characteristics: (1) The MBUSCTC rigorously and realistically reflect the distribution characteristics of uncertainties in uncertainty intervals, thereby effectively reducing the conservatism of the traditional singe‐band uncertainty set; (2) the temporal correlation constraints of wind power/load prediction errors in MBUSCTC could limit the realization of uncertainties fluctuating frequently in uncertain intervals, thereby eliminating scenarios with lower probability in uncertainty sets. Then the proposed MBUSCTC is applied to UC problem, leading a robust UC model based on MBUSCTC, which is solved by Benders decomposition method and C&CG method. Finally, case studies based on the modified IEEE‐118 bus system and an actual power system of China demonstrate that the proposed method can effectively reduce the conservativeness of the robust UC model and ensure the robustness of the unit commitment solution.

The real-time monitoring of the grid-connected renewable energy generations is of great significance to the operation and control of active distribution systems (ADS). Considering the detailed photovoltaic (PV) system model and ambient influencing factors, this paper develops a state estimation framework for the ADS with integrated PV power plants. Firstly, suitable models of PV arrays and power converters for steady-state state estimation are developed. Based on these models, the state variables of a PV generation system are selected, and the measurement equations are derived. Finally, combined with the state estimation model of distribution networks, a framework for state estimation and bad data processing of an ADS with PV power plants is presented. A parameter estimation approach for PV power plants based on multiple measurement scans is also developed in order to address the practical situation of unknown model parameters. Simulation results show that the proposed framework can effectively capture the operating state of the distribution network and PV power plants. In addition, it enhances the estimation accuracy and bad data processing capability compared to conventional state estimation frameworks.

Robust optimisation has been applied to the dispatching problem considering fault outage of power systems. The traditional fault outage uncertainty set are too conservative. To solve this problem, a robust contingency constrained unit commitment (CCUC) model considering the outage probability of units and transmission lines is proposed. The proposed model has the following characteristics: (i) the outage probability of units is determined by normal operation time and fault repair time, whereas the outage probability of transmission lines is determined by length and load rate; (ii) based on the probability criterion of unit outage contingencies, the outage probabilities of units and transmission lines are both incorporated into the modelling of uncertainty sets; (iii) the robust CCUC model obtains UC decision of base case and the UC decision under uncertainty scenarios can adjust the output of units by spinning reserve capacity to cope with stochastic fault outage scenarios. The proposed robust CCUC model is solved with Benders‐column and constraint generation (C&CG) method combined with Benders decomposition method and C&CG method. Simulations on the modified IEEE‐RTS‐79 system and IEEE‐RTS‐96 system demonstrate the effectiveness and reliability of the proposed robust CCUC model.