Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

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Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Simulation and the monte carlo method

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.

https://ieeexplore.ieee.org/ielaam/5165411/8075182/7990560-aam.pdf

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

Electrifying transportation is a promising approach to alleviate the climate change issue. The adoption of electric vehicle into market has introduced significant impacts on various fields, especially the power grid. Various policies have been implemented to foster the electric vehicle deployment and the increasing trend of electric vehicle adoption in the recent years has been satisfying. The continual development of electric vehicle power train, battery and charger technologies have further improved the electric vehicle technologies for wider uptake. Despite the environmental and economical benefits, electric vehicles charging introduce negative impacts on the existing network operation. Appropriate charging management strategies can be implemented to cater for this issue. Furthermore, electric vehicle integration in the smart grid can bring many potential opportunities, especially from the perspective of vehicle-to-grid technology and as the solution for the renewable energy intermittency issue. This paper reviews the latest development in electric vehicle technologies, impacts of electric vehicle roll out and opportunities brought by electric vehicle deployment.

A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects

The main objective of this two-part state-of-the-art paper called “Recent Advances in the Design, Modeling, and Control of Multiphase Machines” is to present latest contributions in the multiphase machines' field. The first part of this paper focuses on the recent progress in the design, modeling, and control, whereas the drive is in healthy operation. This second part presents relevant contributions in two not analyzed fields. The first is in relation with the use of the additional degrees of freedom of multiphase machines and the exploitation of their fault-tolerant capabilities without adding extra hardware. The second one analyzes multiphase generation, particularly in grid-connected wind energy conversion systems and stand-alone applications. Recent progresses are shown and open challenges and future research directions are discussed.

Recent Advances in the Design, Modeling, and Control of Multiphase Machines—Part II

This paper provides a framework to carry out a multi-area optimal power flow in a coordinated decentralized fashion. A DC nonlinear optimal power flow model is used. Losses are incorporated through additional loads based on cosine approximations. The model makes it possible the independent optimal dispatch of each area while the global economical optimum of the whole electric energy system is achieved. This is possible by means of the Lagrangian relaxation decomposition procedure. Optimal energy pricing rates for the energy traded through the interconnections are derived. The developed algorithm can be run in parallel either to carry out numerical simulations or in an actual multi-area electric energy system.

Multi-area coordinated decentralized DC optimal power flow

This paper introduces the cumulant method for the probabilistic optimal power flow (P-OPF) problem. By noting that the inverse of the Hessian used in the logarithmic barrier interior point can be used as a linear mapping, cumulants can be computed for unknown system variables. Results using the proposed cumulant method are compared against results from Monte Carlo simulations (MCSs) based on a small test system. The Numerical Results section is broken into two sections: The first uses Gaussian distributions to model system loading levels, and cumulant method results are compared against four MCSs. Three of the MCSs use 1500 samples, while the fourth uses 20 000 samples. The second section models the loads with a Gamma distribution. Results from the proposed technique are compared against a 1000-point MCS. The cumulant method agrees very closely with the MCS results when the mean value for variables is considered. In addition, the proposed method has significantly reduced computational expense while maintaining accuracy.

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Cumulant-based probabilistic optimal power flow (P-OPF) with Gaussian and gamma distributions

Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support

The problem of optimally sizing hybrid energy storage systems (HESS) installed in electric railway systems, considering the effect of regenerative braking is studied in this paper HESSs combine traditional batteries and newly developed ultracapacitors, taking advantage of the high energy capacity of batteries and of the flexibility and ability to capture high power density of ultracapacitors A novel mixed integer linear programming formulation that includes the counting of battery cycles is presented Some particularities of battery operation are included in the model, like the dependence of its performance on the number of cycles and the depth of discharge (DOD) Results are reported first for a illustrative case study, allowing us to perform sensitivity studies for several parameters; results are also reported and for a larger case, closely resembling the problem faced by railway energy systems planners and operators

Optimal Sizing of Energy Storage for Regenerative Braking in Electric Railway Systems

This paper proposes a methodology for optimal operation of railway electric energy systems considering renewable energy sources (PV panels and wind turbines), regenerative braking capabilities and hybrid electric energy storage systems (ultracapacitors and batteries). The uncertainties associated to renewable energies are taken into account through a scenario tree approach. All these elements are integrated into a multi-period AC optimal power flow problem. This problem is then cast as a large-scale nonlinear optimization problem. The aim is to study the potential for energy and economic savings of the railway operation as well as energy storage systems behavior. A real case study is analyzed for a Spanish high-speed railway line. Results are reported for several cases comparing four different operation modes.

José A. Aguado

Papers

Multi-area coordinated decentralized DC optimal power flow

Cumulant-based probabilistic optimal power flow (P-OPF) with Gaussian and gamma distributions

Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support

Optimal Sizing of Energy Storage for Regenerative Braking in Electric Railway Systems

Optimal Operation of Electric Railways With Renewable Energy and Electric Storage Systems