ISO New England

About: ISO New England is a based out in . It is known for research contribution in the topics: Electric power system & Electricity market. The organization has 122 authors who have published 243 publications receiving 7253 citations. The organization is also known as: ISO-NE & Independent System Operator New England.

Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem

Abstract: Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.

Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks

Abstract: In deregulated electricity markets, short-term load forecasting is important for reliable power system operation, and also significantly affects markets and their participants. Effective forecasting, however, is difficult in view of the complicated effects on load by a variety of factors. This paper presents a similar day-based wavelet neural network method to forecast tomorrow's load. The idea is to select similar day load as the input load based on correlation analysis, and use wavelet decomposition and separate neural networks to capture the features of load at low and high frequencies. Despite of its "noisy" nature, high frequency load is well predicted by including precipitation and high frequency component of similar day load as inputs. Numerical testing shows that this method provides accurate predictions.

Marginal loss modeling in LMP calculation

Abstract: This paper discusses the pricing of marginal transmission network losses in the locational marginal pricing approach recently deployed in the ISO New England (ISO-NE) standard market design (SMD) project implemented by ALSTOM's T&D Energy Automation and Information (EAI) Business. The traditional loss model is studied and a new model is proposed. The new model achieves more defendable and predictable market-clearing results by introducing loss distribution factors to explicitly balance the consumed losses in the lossless dc power system model. The distributed market slack reference is also introduced and discussed. The LMP components produced by the two models are studied and compared under changes in slack reference. Numerical examples are presented to further compare the two models.

A Linearized OPF Model With Reactive Power and Voltage Magnitude: A Pathway to Improve the MW-Only DC OPF

Abstract: In this study, a linearized and convergence-guaranteed optimal power flow (OPF) model with reactive power ( Q ) and voltage magnitude ( v ) is proposed. Based on a linearized network model, a fully linearly-constrained OPF model is formulated with constraints on Q and v and limits on the apparent branch flow. Compared with the commonly used DC OPF method, the proposed method narrows the deviation from the AC OPF solution without requiring any additional information of the power grid. The locational marginal price (LMP) of the proposed method is closer to the AC OPF solution than the DC OPF method. The marginal price of the reactive power (Q-LMP) is provided, which offers the opportunity to price the reactive power. Case studies on several IEEE and Polish benchmark systems show that the proposed OPF method substantially enhances the performance of the prevalent DC OPF method. In addition, it is shown that if the accuracy of the linearized network model needs to be further improved, such as that during the iterative quasi-optimization process that reconstitutes the AC feasibility, a solution that is notably close to the optimum of the AC OPF model can be obtained by taking only one more iteration.