The locational marginal pricing (LMP) methodology has become the dominant approach in power markets. Moreover, the dc optimal power flow (DCOPF) model has been applied in the power industry to calculate locational marginal prices (LMPs), especially in market simulation and planning owing to its robustness and speed. In this paper, first, an iterative DCOPF-based algorithm is presented with the fictitious nodal demand (FND) model to calculate LMP. The algorithm has three features: the iterative approach is employed to address the nonlinear marginal loss; FND is proposed to eliminate the large mismatch at the reference bus if FND is not applied; and an offset of system loss in the energy balance equation is proved to be necessary because the net injection multiplied by marginal delivery factors creates doubled system loss. Second, the algorithm is compared with ACOPF algorithm for accuracy of LMP results at various load levels using the PJM 5-bus system. It is clearly shown that the FND algorithm is a good estimate of the LMP calculated from the ACOPF algorithm and outperforms the lossless DCOPF algorithm. Third, the DCOPF-based algorithm is employed to analyze the sensitivity of LMP with respect to the system load. The infinite sensitivity or step change in LMP is also discussed.

In power market studies, the forecast of locational marginal price (LMP) relies on the load forecasting results from the viewpoint of planning. It is well known that short-term load forecasting results always carry certain degree of errors mainly due to the random nature of the load. At the same time, LMP step changes occur at critical load levels (CLLs). Therefore, it is interesting to investigate the impact of load forecasting uncertainty on LMP. With the assumption of a certain probability distribution of the actual load, this paper proposes the concept of probabilistic LMP and formulates the probability mass function of this random variable. The expected value of probabilistic LMP is then derived, as well as the lower and upper bound of its sensitivity. In addition, two useful curves, alignment probability of deterministic LMP versus forecasted load and expected value of probabilistic LMP versus forecasted load, are presented. The first curve is designed to identify the probability that the forecasted price in a deterministic LMP matches the actual price at the forecasted load level. The second curve is demonstrated to be smooth and therefore eliminates the step changes in deterministic LMP forecasting. This helps planners avoid the possible sharp changes during decision-making process. The proposed concept and method are illustrated with a modified PJM five-bus system and the IEEE 118-bus system.

This paper presents a novel big-M based mixed integer quadratic programming (MIQP) method to solve economic load dispatch problem with disjoint prohibited zones. By adding artificial 0-1 binary variables for each prohibited operating zone of generators and employing a binary coding scheme, disjoint feasible regions are represented by complementary linear constraints. Compared to existing MIQP method, the proposed method can achieve global optimal solution with much reduced problem complexity.

As the penetration of intermittent renewable energy increases in bulk power systems, flexible generation resources, such as quick-start gas units, become important tools for system operators to address the power imbalance problem. To better capture their flexibility, we proposed a two-stage distributionally robust unit commitment framework with both regular and flexible generation resources, in which the unit commitment decisions for flexible generation resources can be adjusted in the second stage to accommodate the renewable energy intermittency. In order to tackle this challenging two-stage distributionally robust mixed-binary model, to which traditional separation algorithms won’t apply, we designed a revised integer L-shaped algorithm with lift-and-project cutting plane techniques. In comparison to the traditional distributionally robust unit commitment, the proposed approach can reduce the system cost through an improved flexible resource quantification in the modeling.

Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A high-level research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of three fusing approaches, i.e., model identification and parameter tuning, supplementary signal generation, and controller substitution, with the existing control framework. Finally, the fundamental challenges associated with adopting MFRL in microgrid control and corresponding insights for addressing these concerns are fully discussed.