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The increasing penetration of variable energy resources in modern electric power systems requires additional flexibility in reserve provision to maintain reliable and efficient grid operations. However, full recognition and appropriate compensation of this flexibility is difficult to ensure within current power market designs due to rigidity in reserve definitions and requirements. This paper proposes a new mixed-integer linear programming formulation for the optimal clearing of a day-ahead market based on swing contracts with dynamically updated regulation reserve zones. Five-bus and 30-bus test cases are used to illustrate the effectiveness of the proposed new market design. [ABSTRACT FROM AUTHOR]

Climatic hazards, such as hurricanes and ice storms, pose a top threat to power distribution systems. This paper proposes a resilience-enhancing strategy to make distribution systems more resilient to climatic hazards. The proposed strategy consists of three resilience-oriented design (ROD) measures, namely line hardening, installing backup distributed generators (DGs), and adding automatic switches. The main challenges of this problem are modeling the spatio-temporal correlation among ROD decisions and uncertainties, capturing the entire failure-recovery-cost process, and solving the resultant large-scale mixed-integer stochastic problem efficiently. To deal with these challenges, we propose a hybrid stochastic process with deterministic casual structure to model the spatio-temporal correlations of uncertainties. A new two-stage stochastic mixed-integer linear program is formulated to capture the impacts of ROD decisions and uncertainties on system's responses to climatic hazards. The objective is to minimize the ROD investment cost in the first stage and the expected costs of loss of load, DG operation, and damage repairs in the second stage. A dual decomposition algorithm with branch-and-bound is developed to solve the proposed model with binary variables in both stages. Case studies on the IEEE 123-bus test feeder show the proposed approach can improve the system resilience at minimum costs. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to co-optimize the distribution system operation and repair crew routing for outage restoration after extreme weather events. A two-stage stochastic mixed integer linear program is developed. The first stage is to dispatch the repair crews to the damaged components. The second stage is distribution system restoration using distributed generators, and reconfiguration. We consider demand uncertainty in terms of a truncated normal forecast error distribution, and model the uncertainty of the repair time using a lognormal distribution. A new decomposition approach, combined with the progressive hedging algorithm, is developed for solving large-scale outage management problems in an effective and timely manner. The proposed method is validated on modified IEEE 34- and 8500-bus distribution test systems. [ABSTRACT FROM AUTHOR]

This paper proposes a resilience-oriented design (ROD) technique to protect distribution grids against high-impact but low-probability extreme weather events. The problem is formulated as a two-stage stochastic mixed integer problem. The first stage is to make ROD decisions, i.e., hardening existing distribution lines and deploying ROD resources such as back-up distributed generators and automatic switches. The second stage evaluates the system operation cost during a realized extreme weather event and repair cost after the event. A novel modeling strategy is proposed to deal with the decision-dependent uncertainty of distribution line damage status, which is affected by the first-stage hardening decisions. As both stages have binary variables, a modified and computationally efficient progressive hedging algorithm with scenario bundling is introduced. The algorithm performance is evaluated by calculating lower bounds of solutions. The proposed model and algorithms are demonstrated on 34-bus and 123-bus test feeders. [ABSTRACT FROM AUTHOR]