The short-term forecasting of real-time locational marginal price (LMP) and network congestion is considered from a system operator perspective. A new probabilistic forecasting technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability distribution of the real-time LMP congestion is obtained. The proposed method incorporates load generation forecast, time varying operation constraints, and contingency models. By shifting the computation cost associated with multiparametric programs offline, the online computation cost is significantly reduced. An online simulation technique by generating critical regions dynamically is also proposed, which results in several orders of magnitude improvement in the computational cost over standard Monte Carlo methods.
There are several prior studies on LMP congestion forecasting from the system operator perspective. The proposed technique in @cite_16 employs an online Monte Carlo sampling technique that, for each Monte Carlo sample path, solves an optimal power flow (OPF) problem, which is computationally expensive. Monte Carlo technique was also used in @cite_3 where a reduction of the random variable dimension is made using a nonhomogeneous Markov chain model based on a partition of the system state space.
@cite_16 @cite_3
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This paper introduces a probabilistic method for short-term transmission congestion forecasting, which is recently developed by EPRI. The proposed method applies the sequential Monte Carlo simulation (MCS) in a probabilistic load flow as the conceptual framework, adds all the significant uncertainties and their probability distributions to be modeled, develops the models, and most importantly specifies how to accurately model the key input assumptions in order to derive valid confidence levels of the forecasted congestion variables. The developed probabilistic method is successfully applied to the four-area WECC equivalent system. Focus is on the confidence levels of making such forecasts, so that a window of forecast-ability is defined, beyond which any forecast would be considered to contain little actionable information. Within the window of forecast-ability, the probabilistic forecasts of congestion would provide confidence limits and information for ranking the potential benefits of alleviating congestion at the various transmission bottlenecks. Short-term congestion forecasting is highly important for market participants in wholesale power markets that use locational marginal prices (LMPs) to manage congestion. Accurate congestion forecasting facilitates market traders in bidding and trading activities and assists market operators in system planning. This study proposes a new short-term forecasting algorithm for congestion, LMPs, and other power system variables based on the concept of system patterns - combinations of status flags for generating units and transmission lines. The advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce forecast error. The advantage relative to previously proposed structural forecasting methods is that data requirements are substantially reduced. Forecasting results based on a NYISO case study demonstrate the feasibility and accuracy of the proposed algorithm. The focus of this paper is the online load flow optimization of power systems in closed loop. In contrast to the conventional approach where an AC OPF solution is computed before being applied to the system, our objective is to design an adaptive feedback controller that steers the system in real time to the optimal operating point without explicitly solving an AC OPF problem. Our approach can be used for example to simultaneously regulate voltages, mitigate line congestion, and optimize operating costs under time-varying conditions. In contrast to related work which is mostly focused on distribution grids, we introduce a modeling approach in terms of manifold optimization that is applicable in general scenarios. For this, we treat the power flow equations as implicit constraints that are naturally enforced and hence give rise to the power flow manifold (PFM). Based on our theoretical results for this type of optimization problems, we propose a discrete-time projected gradient descent scheme on the PFM. In this work, we confirm through a detailed simulation study that the algorithm performs well in a more realistic power system setup and reliably tracks the time-varying optimum of the underlying AC OPF problem. The problem of forecasting the real-time locational marginal price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future locational marginal prices with forecast horizons of 6–8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques.