This paper proposes a resilience-constrained unit commitment (RCUC) framework in which the system operation constraints, heterogeneity of power flow distribution and lines forced outages are simultaneously addressed. The proportional hazard model (PHM) is adopted to shape line forced outage rates with the cumulative effect of weather conditions and line loading rates. A sequential and Monte Carlo-based framework is established for RCUC using the recursive sampling process. The RCUC solution in each period affects the previously accumulated outage probabilities, thus it would have an impact on the outage process. In turn, the sampled line outages affect the RCUC solution in the next period and the loading of remaining lines. This is a sequential process in which the RCUC and the outage sampling of power system components are computed alternatively until the scheduling horizon is culminated. During each period, two penalty terms are introduced into the RCUC model to seek a tradeoff between operation cost and the homogeneity of flow distribution in power network and the loading rates of local lines affected by extreme weather. Since the penalty terms are modeled by absolute value functions, a general linearization method for absolute value functions in the mix-integer programming is presented. The overall convergence of the Monte Carlo technique is quantified by the coefficient of variation of total costs over multiple simulations. If the convergence criterion is not satisfied, the Monte Carlo solution approach will be continued. The case studies illustrate the effectiveness of the proposed RCUC model.

Voltage instability is a serious phenomenon that can occur in a power system because of critical or stressed conditions. To prevent voltage collapse caused by such instability, accurate voltage collapse prediction is necessary for power system planning and operation. This paper proposes a novel collapse prediction index (NCPI) to assess the voltage stability conditions of the power system and the critical conditions of lines. The effectiveness and applicability of the proposed index are investigated on the IEEE 30-bus and IEEE 118-bus systems and compared with the well-known existing indices (Lmn, FVSI, LQP, NLSI, and VSLI) under several power system operations to validate its practicability and versatility. The study also presents the sensitivity assumptions of existing indices and analyzes their impact on voltage collapse prediction. The application results under intensive case studies prove that the proposed index NCPI adapts to several operating power conditions. The results show the superiority of the proposed index in accurately estimating the maximum load-ability and predicting the critical lines, weak buses, and weak areas in medium and large networks during various power load operations and contingencies. A line interruption or generation unit outage in a power system can also lead to voltage collapse, and this is a contingency in the power system. Line and generation unit outage contingencies are examined to identify the lines and generators that significantly impact system stability in the event of an outage. The contingencies are also ranked to identify the most severe outages that significantly cause voltage collapse because of the outage of line or generator.

Global changes in climate conditions and increasing number of weather-related disasters in the world have elevated the quest for better load restorations in power distribution systems. This paper proposes a distribution system restoration (DSR) model where non-exponential time-dependent cold load pickup (CLPU) characteristics and soft open point operations are simultaneously addressed. We develop a time-dependent CLPU model for attaining a more realistic and secure DSR in which we demonstrate that the traditional delayed exponential model (DEM) for a long outage duration is a special case of the adopted CLPU model. The current soft open point (SOP) models would typically relax the operation constraints without devising any tightening procedures, which might lead to inaccurate or even infeasible SOP states. In this paper, we analyze the reasons traditional SOP constraints would lead to unsecure operations and present a set of novel SOP operation constraints for recovering bindings. Also, we adopt the sequential second order cone programming (SSOCP) algorithm to establish the sequential framework of DSR and solve the proposed model effectively. Our presented case studies discuss the rationality of the proposed DSR model and algorithm.

In Day-ahead Power Market (DAM), Load Serving Entities (LSEs) are required to submit their future load schedule to market operator. Due to the cost computation, we have found the inconformity between load accuracy and cost of power purchase. It means that more accurate load forecasting model may not lead to a lower cost for LSEs. Accuracy pursuing load forecast model may not target a solution with optimal benefit. Facing this issue, this paper initiates a Beneficial Correlated Regularization (BCR) for Neural Network (NN) load prediction. The training target of neural network contains both accuracy section and power cost section. Also, this paper establishes a virtual neuron and a Modified-Levenberg-Marquardt algorithm for network training. A numerical study with practical data is presented and the result shows that neural network with BCR can reduce power cost with acceptable accuracy level

The high penetration of customer-side variable PVs could change power distribution networks (PDNs) operations and lead to frequent nodal voltage violations. The grid-side static var compensators (SVCs), controlled by distribution system operators (DSO), and customer-side PV inverters are usually used as fast devices to mitigate voltage problems caused by PVs. To effectively coordinate these two types of VAR resources with different ownerships, this paper proposes the multi-agent deep reinforcement learning (MADRL) approach, which uses the asymmetric Markov game (ASMG) method to implement a cooperative bi-level Volt/VAR optimization (VVO) framework. In this framework, DSO is the leader, which makes decisions at the upper level to minimize the PDNs energy losses by regulating SVCs, and customers are the followers, which make decisions at the lower level by regulating PV inverters to mitigate nodal voltage deviations. Furthermore, a model-free Bi-level Actor-Critic (Bi-AC) algorithm is proposed to solve the ASMG problem, which defines the agents' decision priorities so that the follower agents can always execute the best response policy. The effectiveness of the proposed Bi-AC method is verified by utilizing improved IEEE 33-bus and IEEE 118-bus case with practical grid operation data.

Unified power flow controller (UPFC) gains growing application in power systems due to its power flow regulation ability. However, there is still a dilemma in the incorporation of UPFC into security-constrained economic dispatch (SCED). The modeling of UPFC involves nonlinear and nonconvex operational constraints, which is difficult to solve efficiently and accurately. Conventional methods adopted Taylor expansion or sensitivity analysis to realize the convexification of UPFC models, which leads to a loop structure and is subject to initial point choice. In this paper, a novel three-terminal injected UPFC model is proposed. In the proposed model, the voltage source variables are eliminated completely. Instead, only power injection variables are introduced to equivalently describe physical characteristics and constraints of UPFC. The nonlinear and nonconvex operational constraints of UPFC are transformed into mixed integer second-order cone form. Moreover, compared with conventional UPFC models, the control characteristic of UPFC is considered in a more accurate and practical manner in the proposed SCED, which helps to prevent potential risks of power flow over-limit under N -1 contingencies. Simulation results show the effectiveness and efficiency of the established solution.