Dynamic Optimal Power Flow Model Research Articles

Coordinating Distributed Energy Resources and Utility-Scale Battery Energy Storage System for Power Flexibility Provision Under Uncertainty

Relying on the power flexibility of distributed energy resources (DERs) located in an active distribution network (ADN), this ADN will be able to provide power flexibility to the upper-layer grid at their point of common coupling (PCC). The power flexibility is defined as additional bi-directional active/reactive powers a resource can provide to the grid by adjusting its operating point. In this context, this paper presents a two-stage ADN management method to deliver, at the PCC, the power flexibility that the upper-layer grid operator would request minutes-ahead real-time operation. The first stage updates the power set-points of DERs considering their offer curves as well as the uncertainties stem from the short-term forecast errors of demand and renewable generation profiles. The inter-temporal constraints and losses of the grid are accounted for by exploiting a linearized dynamic optimal power flow model, whereby the first stage is implemented as a linear scenario-based optimization problem. Then, in real-time operation, relying on a linear optimization problem, the second stage adjusts the power flexibility injection of a utility-scale battery energy storage system (ESS) to mitigate the imbalance at the PCC inherent in the above-mentioned uncertainties. The performance of the proposed method is tested in the case of a real ADN located in the city of Aigle in southwest of Switzerland.

Robust Stochastic Dynamic Optimal Power Flow Model of Electricity-Gas Integrated Energy System considering Wind Power Uncertainty

The application of gas turbines and power to gas equipment deepens the coupling relationship between power systems and natural gas systems and provides a new way to absorb the uncertain wind power as well. The traditional stochastic optimization and robust optimization algorithms have some limitations and deficiencies in dealing with the uncertainty of wind power output. Therefore, we propose a robust stochastic optimization (RSO) model to solve the dynamic optimal power flow model for electricity-gas integrated energy systems (IES) considering wind power uncertainty, where the ambiguity set of wind power output is constructed based on Wasserstein distance. Then, the Wasserstein ambiguity set is affined to the eventwise ambiguity set, and the proposed RSO model is transformed into a mixed-integer programming model, which can be solved rapidly and accurately using commercial solvers. Numerical results for EG-4 and EG-118 systems verify the rationality and effectiveness of the proposed model.

Generalized Benders Decomposition Based Dynamic Optimal Power Flow Considering Discrete and Continuous Decision Variables

The dynamic optimal power flow (DOPF) is a mixed-integer nonlinear programming problem. This article builds a DOPF model with discrete and continuous variables, and then proposes the iterative method based on the master and sub-problems obtained from the generalized Benders decomposition (GBD). Firstly, the power output of conventional generators and the reactive power of the wind farm are modeled as the continuous decision variables, and the transformer taps ratio is built as a discrete decision variable. Secondly, the objective function is to minimize the total power generation cost and network loss. Thirdly, the DOPF problem is decomposed into the master problem and sub-problems by fixing a complex variable, which reduces the complexity of DOPF. Then, the proposed algorithm is used to solve the master and sub-problems. Finally, simulation results show that the proposed method has advantages in terms of reducing computational time and enhancing accuracy.

A Mixed-Integer Second-Order Cone Programming Algorithm for the Optimal Power Distribution of AC-DC Parallel Transmission Channels

For the controllability of the transmission power of DC transmission channels, the optimal power distribution (OPD) of AC-DC parallel transmission channels is an effective measure for improving the economic operation of an AC-DC interconnected power grid. A dynamic optimal power flow model for day-ahead OPD of AC-DC parallel transmission channels is established in this paper. The power flow equation constraints of an AC-DC interconnected power grid and the constraints of the discrete regulation requirement of the transmission power of DC channels are considered, which make the OPD model of the AC-DC parallel transmission channels a mixed-integer nonlinear non-convex programming (MINNP) model. Through a cone relaxation transformation and a big M method equivalent transformation, the non-convex terms in the objective function and constraints are executed with the convex relaxation, and the MINNP model is transformed into a mixed-integer second-order cone programming model that can be solved reliably and efficiently using the mature optimization solver GUROBI. Taking an actual large-scale AC-DC interconnected power grid as an example, the results show that the OPD scheme of the AC-DC parallel transmission channels obtained by the proposed algorithm can effectively improve the economical operation of an AC-DC interconnected power grid.

A Small Compressed-Air-Assisted Wind Turbine System Serves the Safe and Reliable Operation of Power System

Based on the dynamic characteristics of small wind power generation system with the compressed air energy storage, this paper proposes a dynamic optimal power flow model of distribution network with small Compressed-Air- Assisted Wind Turbines. The model aimed to minimize network loss. Through dynamic adjustment of the wind power output of a small Compressed-Air-Assisted Wind Turbine, it can seek optimal operation strategy of distribution network. Meanwhile, it guarantees the active power loss is reduced, the economic operation of the distribution network and the utilization rate of wind energy are improved. This article mainly provides services from two aspects. One is to serve grid operators to reduce the impact of wind fluctuations on the grid. On the premise of ensuring the safe and reliable operation of the power grid, the wind energy absorption level of the power grid should be improved. The second is to serve new energy equipment suppliers and provide reference for their construction of new energy equipment. Numerical tests on an IEEE 33bus and IEEE 123 bus distribution system show that the proposed model is feasible and the method is efficient and optimization accuracy

Dynamic optimal power flow model incorporating interval uncertainty applied to distribution network

Dynamic optimal power flow (DOPF) in active distribution networks generally relies on a perfect forecasting of uncertainties such as intermittent distributed generations and time-varying loads, which is generally difficult to achieve in practise. To make DOPF possess the ability to deal with uncertainties, especially for the satisfaction of operating constraints in an uncertain environment, an interval DOPF (I-DOPF) model is derived in this study, by using affine arithmetic and interval Taylor expansion. To solve the I-DOPF problem efficiently, the solving method based on successive linear approximation (SLA) and distributed optimisation strategy is further discussed. The proposed I-DOPF model and its solving method are subsequently applied to a modified IEEE 33-bus network and a real 113-bus distribution network. The simulation results demonstrate that the I-DOPF model has a good performance on boundary constraint satisfaction under uncertainties; the SLA-based solving method can be well integrated with distributed optimisation to meet the practical requirements of data exchange in large-scale active distribution networks.