The integration of distributed generation (DG) units in power distribution networks has become increasingly important in recent years. The aim of the optimal DG placement (ODGP) is to provide the best locations and sizes of DGs to optimize electrical distribution network operation and planning taking into account DG capacity constraints. Several models and methods have been suggested for the solution of the ODGP problem. This paper presents an overview of the state of the art models and methods applied to the ODGP problem, analyzing and classifying current and future research trends in this field.

/pdf/optimal-distributed-generation-placement-in-power-4b69hs7usu.pdf

Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods, and Future Research

This paper, prepared by a special task force of the IEEE PES Renewable Technologies Subcommittee, is a review of hybrid renewable/alternative energy (RE/AE) power generation systems focusing on energy sustainability. It highlights some important issues and challenges in the design and energy management of hybrid RE/AE systems. System configurations, generation unit sizing, storage needs, and energy management and control are addressed. Statistics on the current status and future trend of renewable power generation, as well as some critical challenges facing the widespread deployment of RE/AE power generation technologies and vision for future research in this area are also presented. The comprehensive list of references given at the end of the paper should be helpful to researchers working in this area.

A Review of Hybrid Renewable/Alternative Energy Systems for Electric Power Generation: Configurations, Control, and Applications

Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this field.

/pdf/a-survey-on-metaheuristics-for-stochastic-combinatorial-58c1pv8m6a.pdf

A survey on metaheuristics for stochastic combinatorial optimization

Public awareness of the need to reduce global warming and the significant increase in the prices of conventional energy sources have encouraged many countries to provide new energy policies that promote the renewable energy applications. Such renewable energy sources like wind, solar, hydro based energies, etc. are environment friendly and have potential to be more widely used. Combining these renewable energy sources with back-up units to form a hybrid system can provide a more economic, environment friendly and reliable supply of electricity in all load demand conditions compared to single-use of such systems. One of the most important issues in this type of hybrid system is to optimally size the hybrid system components as sufficient enough to meet all load requirements with possible minimum investment and operating costs. There are many studies about the optimization and sizing of hybrid renewable energy systems since the recent popular utilization of renewable energy sources. In this concept, this paper provides a detailed analysis of such optimum sizing approaches in the literature that can make significant contributions to wider renewable energy penetration by enhancing the system applicability in terms of economy.

Optimum design of hybrid renewable energy systems: Overview of different approaches

The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems.

Multiobjective evolutionary algorithms for electric power dispatch problem

Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm

Multisource hybrid power generation systems are a type of representative application of the renewables' technology. In this investigation, wind turbine generators, photovoltaic panels, and storage batteries are used to build hybrid generation systems that are optimal in terms of multiple criteria including cost, reliability, and emissions. Multicriteria design facilitates the decision maker to make more rational evaluations. In this study, an improved particle swarm optimization algorithm is developed to derive these nondominated solutions. Hybrid generation systems under different design scenarios are designed based on the proposed approach. First, a grid-linked hybrid system is designed without incoroprating system uncertainties. Then, adequacy evaluation is conducted based on probabilistic methods by accounting for equipment failures, time-dependent sources of energy, and stochastic generation/load variations. In particular, due to the unpredictability of wind speed and solar insolation as well as the random load variation, time-series models are adopted to reflect their stochastic characteristics. An adequacy evaluation procedure including time-dependent sources, is adopted. Sensitivity studies are also carried out to examine the impacts of different system parameters on the overall design performance.

/pdf/multicriteria-design-of-hybrid-power-generation-systems-3vvlmcsn43.pdf

Multicriteria Design of Hybrid Power Generation Systems Based on a Modified Particle Swarm Optimization Algorithm

Optimal placement of protection devices and distributed generators (DGs) in radial feeders is important to ensure power system reliability. Distributed generation is being adopted in distribution networks with one of the objectives being enhancement of system reliability. In this paper, an ant colony system algorithm is used to derive the optimal recloser and DG placement scheme for radial distribution networks. A composite reliability index is used as the objective function in the optimization procedure. Simulations are carried out based on two practical distribution systems to validate the effectiveness of the proposed method. Furthermore, comparative studies in relation to genetic algorithm are also conducted.

Reliability-Constrained Optimum Placement of Reclosers and Distributed Generators in Distribution Networks Using an Ant Colony System Algorithm

The advancement of renewable energy technologies has seen the emergence of customer owned grid tied wind and solar microgrids. These microgrids offer an opportunity to energy users to lower their energy costs as well as enabling the power suppliers to regulate the utility grid. However, the integration of the renewable energy based sources into the smart grid increases the complexity of the main grid. The success of this scheme will be heavily reliant on accurate real-time information exchange between the microgrid, the main grid, and the consumers. The communication between these agents will be critical in implementation of intelligent decisions by the smart grid. The microgrids will be required to relay energy forecasts information to the utility grid. Similarly, customers will be expected to submit energy demand schedules, to actively monitor energy price signals, to participate in energy bids, and to respond to energy management signals in real time. This kind of grid-user interaction will be overwhelming and could result in consumer apathy. There is therefore a need to develop smart systems that will autonomously execute all these tasks without the prompting of the customers. This paper presents one such approach. In this study, we proposed a demand side energy management for a grid connected household with a locally generated photovoltaic energy. To ensure efficient household energy management, smart scheduling of electrical appliances has also been presented.

Autonomous Appliance Scheduling for Household Energy Management

As information and communication networks are highly interconnected with the power grid, cyber security of the supervisory control and data acquisition (SCADA) system has become a critical issue in the electric power sector. By exploiting the vulnerabilities in cyber components and intruding into the local area networks of the control center, corporation, substations, or by injecting false information into communication links, the attackers are able to eavesdrop critical data, reconfigure devices, and send trip commands to the intelligent electronic devices that control the system breakers. Reliability of the power system can thus be impacted by various cyber attacks. In this paper, four attack scenarios for cyber components in networks of the SCADA system are considered, which may trip breakers of physical components. Two Bayesian attack graph models are built to illustrate the attack procedures and to evaluate the probabilities of successful cyber attacks. A mean time-to-compromise model is modified and adopted considering the known and zero-day vulnerabilities on the cyber components, and the frequencies of intrusions through various paths are estimated. With increased breaker trips resulting from the cyber attacks, the loss of load probabilities in the IEEE reliability test system 79 are estimated. The simulation results demonstrate that the power system becomes less reliable as the frequency of successful attacks on the cyber components increases and the skill levels of attackers increase.

Lingfeng Wang

Papers

Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm

Multicriteria Design of Hybrid Power Generation Systems Based on a Modified Particle Swarm Optimization Algorithm

Reliability-Constrained Optimum Placement of Reclosers and Distributed Generators in Distribution Networks Using an Ant Colony System Algorithm

Autonomous Appliance Scheduling for Household Energy Management

Power System Reliability Evaluation With SCADA Cybersecurity Considerations