Mohamed E. El-Hawary

A Survey of Particle Swarm Optimization Applications in Electric Power Systems

Particle swarm optimization (PSO) has received increased attention in many research fields recently. This paper presents a comprehensive coverage of different PSO applications in solving optimization problems in the area of electric power systems. It highlights the PSO key features and advantages over other various optimization algorithms. Furthermore, recent trends with regard to PSO development in this area are explored. This paper also discusses PSO possible future applications in the area of electric power systems and its potential theoretical studies.

The Smart Grid—State-of-the-art and Future Trends

This presentation introduces Smart Grid and associated technical, environmental and socio-economic, and other non-tangible benefits to society, and articulates the need for the concept and the fact that it is a dynamic interactive, real-time infrastructure that responds to the challenges of designing and building the power system of the future, rather than being simply a marketing term. To illustrate the diversity of terminology, we compare an Electric Power Research Institute (EPRI) definition with that suggested by a study group of the International Electrotechnical Commission (IEC). Next, a paper sponsored by the Canadian Electricity Association (CEA) that cites three example definitions to highlight the diversity of views of Smart Grid is briefly reviewed. Early misconceptions and characterizations of Smart Grid are discussed as a prelude to addressing challenging issues that motivate developing and implementing related innovative technologies, products and services. We then discuss the potential promise of the Smart Grid, which is embedded in its often-cited attributes of efficiency, accommodating, quality focus, enabling and self-healing to name some. The presentation then addresses some of the often-cited impediments to accepting Smart Grid which are based on concerns and issues confronting its forward progress, adoption and acceptance. Distribution Automation (DA) and embedded intelligence are discussed emphasizing self-healing, optimizing operation and facilitating recreation and recovery from abnormal events. Functional and integration requirements of Distributed Energy Resources (DER,) are detailed. Smart Consumption Infrastructure elements of Distribution Management Systems (DMS,) Automated Metering Infrastructure (AMI,) Smart Homes (SH), and Smart Appliances (SA,) are discussed. We discuss smart grid activities in China, India, and the development of a Smart Grid roadmap for the US State of Kentucky. The approaches of each of these cases reflect the diversity of policy initiatives in these jurisdictions. State of the art reviews of distribution network active management and future development trends in technologies and methods, where centralized and decentralized management frameworks and applying agent-based coordination are discussed. A review of smart home technologies and the goals of an energy management system (SHEMS) are also discussed.

A survey of particle swarm optimization applications in power system operations

Particle swarm optimization (PSO) has been getting added attention in many research fields. This article presents a comprehensive coverage of PSO applications in solving optimization problems in the area of electric power systems.

Reformulating Three-Phase Power Components Definitions Contained in the IEEE Standard 1459–2000 Using Discrete Wavelet Transform

Power components definitions contained in the IEEE Standard 1459-2000 for unbalanced three-phase systems with nonsinusoidal situations are represented in the frequency domain based on Fourier transform (FT). However, FT suffers from the high computational effort especially when the number of phases increases and it is unable to provide information concerning time content because it provides only an amplitude-frequency spectrum. On the other hand, the discrete wavelet transform (DWT) preserves both time and frequency information while reducing the computational effort through dividing the frequency spectrum into bands and thus overcomes the limitations of FT. In this paper the three-phase power components definitions contained in the IEEE Standard 1459-2000 are reformulated using the DWT and thus redefined in the time-frequency domain. Also in order to study system unbalance, the concept of symmetrical components is defined in the wavelet domain. The results obtained from applying the IEEE Standard definitions and the DWT-based definitions to balanced and unbalanced three-phase systems under nonsinusoidal operating conditions, indicate that the DWT-based definitions are very accurate and the problem of spectral leakages can be reduced by suitable choice of the mother wavelet and the wavelet family. The DWT-based definitions are useful in studying nonstationary waveforms.

Defining Power Components in Nonsinusoidal Unbalanced Polyphase Systems: The Issues

Summary form only given. Defining power components for unbalanced three phase systems operating under nonsinusoidal conditions is an area of active research which spawned many theories. The approaches may be classified into two groups: the first is a bottom-up approach that extends well accepted definition of power components for single phase systems operating under ideal sinusoidal situations to the case of unbalanced three-phase systems in nonsinusoidal situations. The second group involves a top-down approach that tries to offer a definition for nonlinear polyphase systems in nonsinusoidal situations and apply it to linear single phase systems in sinusoidal situations by considering the former as the general case and the latter as a special case. This paper summarizes previously published approaches and concludes that none of the approaches in the literature can lead to power components definitions that satisfy all required power properties. Different analysis tools other than those based on frequency or time domain are recommended in this paper for future work.

Emission-Economic Dispatch using a Novel Constraint Handling Particle Swarm Optimization Strategy

This paper presents a particle swarm optimization (PSO) algorithm to solve an economic-emission dispatch problem (EED) which gained recent attention due to the deregulation of the power industry and strict environmental regulations. The problem treats four objective functions, namely economic cost, COx, SOx, and NOx emission functions. It is formulated as a highly nonlinear constrained multiobjective optimization problem with conflicting objectives. The inequality constraints considered are the generating unit capacity limits while the equality constraint is generation-demand balance with network losses included. A novel equality constraint handling mechanism is proposed in this paper. PSO algorithm is tested on a 30-bus standard test system. Results obtained show that PSO algorithm has a great potential in handling multiobjective optimization problems. PSO was able to find the Pareto optimal solution set for each pair of objective functions. The overall problem is solved based on the decision maker utility function that assigns proper weights for each objective based on its importance. Comparison results signify the effectiveness and robustness of PSO as a promising optimization tool

A new and fast power flow solution algorithm for radial distribution feeders including distributed generations

This paper presents a new procedure for solving the power flow for radial distribution feeders taking into account embedded distribution generation sources and shunt capacitors. Also, new approximation formulas are proposed to reduce the number of required solution iterations. The method used is different from the conventional well-known power flow methods. The proposed algorithm procedures are tested on sample feeder systems. Results are compared with those obtained using other distribution power flow methods.

Metropolis biogeography-based optimization

Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm (EA). Although the exploitation level of BBO is good researchers found some weaknesses in its exploration. This study proposes a new hybridization between BBO and simulated annealing (SA) to enhance its performance. In this proposed algorithm, the inferior migrated islands will not be selected unless they pass the Metropolis criterion of SA and so the new algorithm is called MpBBO. The performance of MpBBO is evaluated using 36 benchmark functions with five different cooling strategies of SA and then compared with the original and essentially modified BBO models. The results show the exponential, inverse, and inverse linear cooling strategies provide best solutions, but they are the slowest. Among these three strategies, the exponential cooling rate can compromise between the solution quality and CPU time compared with the others. Also, the inverse cooling rate is competitive and wins when the mutation stage is completely disabled. The F-test and T-test show that MpBBO has significant differences. Further, it has been observed, through sensitivity analysis, that MpBBO behaves like BBO and SA. The parameters of SA and BBO can affect the performance of MpBBO, which has more immunity against trapping into local optimums. Moreover, the superiority of MpBBO appears when it is compared with non-simplified migration-based BBO models as well as other hybrid/non-hybrid EAs.

Pattern search optimization applied to convex and non-convex economic dispatch

Economic dispatch is a classic power system problem. It is formulated with either a non-linear quadratic objective function only or by adding another sinusoidal term to the quadratic function in order to incorporate the valve-point effect. Both formulations are subjected to equality and non-equality constraints. In solving such optimization problem, deterministic and heuristic methods were utilized. Direct search-pattern search is a novel heuristic technique used in solving the economic dispatch problems in this paper. This derivative-free method shows optimistic results in dealing with simple and large power systems. The proposed approach has been been tested on three, six and twenty generation units to validate usage of this heuristic technique in the economic dispatch area. Results are compared with deterministic and other heuristic methods.

Fuzzy Model Based Bilateral Control Design of Nonlinear Tele-Operation System Using Method of State Convergence

This paper presents the design of a state convergence (SC)-based bilateral controller for a nonlinear teleoperation system, which has been approximated by a Takagi-Sugeno (TS) fuzzy model. The selection of SC is made due to the advantages offered by this scheme both in the modeling and control design stages. The modeling stage considers master/slave systems, which can be represented by the n th order differential equations, while the control design stage offers an easy way to determine the control gains required for assigning desired closed loop dynamics to teleoperation system. After the master/slave systems are represented by TS fuzzy models, a stabilizing fuzzy law is adopted which allows deploying the SC scheme with all its benefits to design the fuzzy bilateral controller. In this way, not only the simplicity of the design scheme is ensured but also the existing SC scheme is able to control a nonlinear teleoperation system based on its TS fuzzy model description. As an additional advantage, the SC-based existing linear bilateral controller can be easily derived from the SC-based proposed fuzzy bilateral controller. Various cases of master/slave systems originally reported in terms of their linear model representation and communication in the absence/presence of time delay are all discussed in the corresponding fuzzy framework. The MATLAB simulations, considering a one-degree-of-freedom teleoperation system, are performed to validate the proposed methodology for controlling a nonlinear teleoperation system.