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This paper proposes a resilience enhancement strategy for power transmission system against ice storms by the optimal coordination of power system schedule with the pre-positioning and routing of mobile dc de-icing devices (MDIDs). A two-stage robust optimization model is established to accommodate the variable ice thickness on transmission lines. The first stage coordinates the pre-positioned MDIDs and unit commitment in day-ahead. These decisions, which are based on a robust approach, can accommodate the variable ice thickness in which the coordinated real-time schedule would always be feasible with respect to day-ahead decisions. At the second stage, the real-time operation, which integrates the power system dispatch, de-icing schedule, and MDID routing, is scheduled according to the real-time ice thickness. Auxiliary variables are adopted to convert the proposed nonconvex nonlinear model to a mixed-integer second-order cone programming (MISOCP) problem. The nested column-and-constraint generation algorithm is utilized to solve the two-stage robust MISOCP problem. Several computational enhancement strategies including Lagrangian relaxation are proposed to improve the performance of the proposed resilience enhancement strategy. Numerical results for an integrated 6-bus 6-node electricity-road network and a real-world example employed in China show the effectiveness of the proposed model and solution technique for enhancing the transmission grid resilience. [ABSTRACT FROM AUTHOR]

Highlights • A security-constrained co-planning of power lines and energy storage is proposed. • The corrective control with multi-energy storages and generator units is studied. • The large-scale formulation is solved using Benders decomposition algorithm. • Case studies on the real-world power system in Gansu, China are conducted. Abstract This paper presents a security-constrained co-planning of transmission line expansion and energy storage with high penetration of wind power. The energy storage can not only improve the accommodation of renewable generation but also help to mitigate the emergency overflow under the post-contingency state. Also, the adjustment capabilities of the wind farms are considered, and the risk of excessive wind curtailment under the post-contingency state is assessed quantitatively. The coordinated multistage corrective control strategy for the battery storage, the pumped storage, thermal generators, and wind farms is studied. The mixed-integer linear programming problem based on the proposed corrective control strategy has been formulated. The Benders decomposition algorithm is introduced to divide the co-planning problem into one master problem and two sub-problems. In the master problem, the system is operating under normal conditions, so the security constraints are not considered. It minimizes the sum of the investment cost of newly built facilities and the operational cost. In the sub-problems, the corrective control during different post-contingency stages is formulated, minimizing the risk costs under fault conditions. The two-step technique is developed to generate the Benders cuts from the mixed-integer linear programming sub-problems, and return to the master problem. The real-world power system in Gansu, China is studied to validate the applicability and scalability of the proposed model and solution technique. The results denote that the co-planning model with security constraints is profitable in the real power system, with savings of 25% for the newly built transmission lines and a 5.5% reduction in total cost. [ABSTRACT FROM AUTHOR]

• The evolution process of subsequent commutation failures (SCFs) is analyzed. • The influence of electrochemical energy storage (EES) power on SCFs is analyzed. • A coordinated power control strategy of EES to mitigate SCFs is proposed. • The influence of active power amplitude of EES on SCFs is discussed. Commutation failure is one of the most common faults in high voltage direct current (HVDC) transmission system. Subsequent commutation failures (SCFs) caused by commutation failure will cause large voltage and current swing. Several SCFs may cause DC transmission power interruption, which seriously threatens the safe and stable operation of AC/DC hybrid power grid. With the construction and commissioning of grid-side electrochemical energy storage (EES), it is possible to mitigate SCFs of adjacent HVDC transmission lines using EES with fast power response characteristics. In this paper, the influence mechanism of active and reactive power output of EES on commutation conditions is studied by combining the evolution of cascading outages and SCFs. It is found that reactive power mainly affects AC voltage and active power mainly affects DC current. Based on the mechanism analysis, a coordinated power control strategy for EES is presented. This strategy, combined with EES capacity constraints, can control EES active and reactive power output instructions adaptively according to the degree of AC failure at different stages of SCFs to fully release the transient support capacity of EES. Case studies are undertaken on the Henan multi-infeed UHVDC system and CIGRE-HVDC standard test system, respectively. The simulation results verify the correctness of the proposed EES power control strategy and the effectiveness of the strategy for SCFs mitigation in different systems, different fault types, and different fault degrees. [ABSTRACT FROM AUTHOR]

• Proposed an improved two-stage optimization method for network and load recovery. • Decouples integer and continuous decision variables and solves them separately. • Proposed method significantly can improve the computational efficiency. • Verified the advantage in computational efficiency and plan optimality. • Demonstrated potential application through simulation in a practical system. Network and load recovery (NLR) during power system restoration is a multi-step mixed-integer nonlinear programming (MINP) problem. The NLR is difficult to be solved as it is NP-hard. Thus, NLR is commonly solved step by step, which is short-sighted and will result in longer restoration time. To obtain a far-sighted NLR plan, this paper proposes an improved two-stage optimization method for NLR. The first stage adopts a mixed-integer linear programming (MILP) model to obtain optimal solutions of integer variables in NLR, namely the load pick-up schedules and transmission line charging schedules. Then in the second stage, a continuous non-linear optimization method based on AC power flow with frequency constraints and load model is established to minimize the restoration duration of the plan generated in the first stage step by step. Case studies are undertaken on a 10-machine 39-bus system and Southeast Hubei Provincial power system of China. Simulation results indicate that the restoration plan obtained from the improved two-stage optimization method is highly effective, while the computational efficient meets the intensive need for restoration scheduling after blackouts. [ABSTRACT FROM AUTHOR]