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8 pages, 1970 KiB

Open AccessEditorial

Optimization Methods Applied to Power Systems

by Francisco G. Montoya, Raúl Baños, Alfredo Alcayde and Francisco Manzano-Agugliaro

Energies 2019, 12(12), 2302; https://doi.org/10.3390/en12122302 - 17 Jun 2019

Cited by 8 | Viewed by 3392

Abstract

Continuous advances in computer hardware and software are enabling researchers to address optimization solutions using computational resources, as can be seen in the large number of optimization approaches that have been applied to the energy field [...] Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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Research

Jump to: Editorial, Review

20 pages, 5097 KiB

Open AccessArticle

Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks

by Javier Leiva, Rubén Carmona Pardo and José A. Aguado

Energies 2019, 12(7), 1295; https://doi.org/10.3390/en12071295 - 4 Apr 2019

Cited by 7 | Viewed by 2933

Abstract

A growing presence of distributed energy resources (DER) and the increasingly diverse nature of end users at low-voltage (LV) networks make the operation of these grids more and more challenging. Particularly, congestion and voltage management strategies for LV grids have usually been limited [...] Read more.

A growing presence of distributed energy resources (DER) and the increasingly diverse nature of end users at low-voltage (LV) networks make the operation of these grids more and more challenging. Particularly, congestion and voltage management strategies for LV grids have usually been limited to some elemental criteria based on human experience, asset oversizing, or grid reinforcement. However, with the current massive deployment of sensors in modern LV grids, new approaches are feasible for distribution network assets operation. This article proposes a multi-objective particle swarm optimization (MOPSO) approach, combined with data analytics through affinity propagation clustering, for congestion threshold determination in LV grids. A real case study from the smart grid of Smartcity Malaga Living Lab is used to illustrate the proposed approach. Within this approach, distribution system operators (DSOs) can take decisions in order to prevent situations of risk or potential failure at LV grids. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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13 pages, 1307 KiB

Open AccessArticle

Optimization of the Contracted Electric Power by Means of Genetic Algorithms

by Alfredo Alcayde, Raul Baños, Francisco M. Arrabal-Campos and Francisco G. Montoya

Energies 2019, 12(7), 1270; https://doi.org/10.3390/en12071270 - 2 Apr 2019

Cited by 4 | Viewed by 4682

Abstract

An adequate selection of an energy provider and tariff requires us to analyze the different alternatives to choose one that satisfies your needs. In particular, choosing the right electricity tariff is essential for reducing company costs and improving competitiveness. This paper analyzes the [...] Read more.

An adequate selection of an energy provider and tariff requires us to analyze the different alternatives to choose one that satisfies your needs. In particular, choosing the right electricity tariff is essential for reducing company costs and improving competitiveness. This paper analyzes the energy consumption of large consumers that make intensive use of electricity and proposes the use of genetic algorithms for optimizing the tariff selection. The aim is to minimize electricity costs including two factors: the cost of power contracted and the heavy penalties for excess of power demand over the power contracted in certain time periods. In order to validate the proposed methodology, a case study based on the real data of energy consumption of a large Spanish university is presented. The results obtained show that the genetic algorithm and other bio-inspired approaches are able to reduce the costs associated to the electricity bill. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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17 pages, 379 KiB

Open AccessArticle

Quadrature Current Compensation in Non-Sinusoidal Circuits Using Geometric Algebra and Evolutionary Algorithms

by Francisco G. Montoya, Alfredo Alcayde, Francisco M. Arrabal-Campos and Raul Baños

Energies 2019, 12(4), 692; https://doi.org/10.3390/en12040692 - 21 Feb 2019

Cited by 12 | Viewed by 4315

Abstract

Non-linear loads in circuits cause the appearance of harmonic disturbances both in voltage and current. In order to minimize the effects of these disturbances and, therefore, to control the flow of electricity between the source and the load, passive or active filters are [...] Read more.

Non-linear loads in circuits cause the appearance of harmonic disturbances both in voltage and current. In order to minimize the effects of these disturbances and, therefore, to control the flow of electricity between the source and the load, passive or active filters are often used. Nevertheless, determining the type of filter and the characteristics of their elements is not a trivial task. In fact, the development of algorithms for calculating the parameters of filters is still an open question. This paper analyzes the use of genetic algorithms to maximize the power factor compensation in non-sinusoidal circuits using passive filters, while concepts of geometric algebra theory are used to represent the flow of power in the circuits. According to the results obtained in different case studies, it can be concluded that the genetic algorithm obtains high quality solutions that could be generalized to similar problems of any dimension. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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15 pages, 2705 KiB

Open AccessArticle

A Two-Stage Feature Selection Method for Power System Transient Stability Status Prediction

by Zhen Chen, Xiaoyan Han, Chengwei Fan, Tianwen Zheng and Shengwei Mei

Energies 2019, 12(4), 689; https://doi.org/10.3390/en12040689 - 20 Feb 2019

Cited by 15 | Viewed by 2870

Abstract

Transient stability status prediction (TSSP) plays an important role in situational awareness of power system stability. One of the main challenges of TSSP is the high-dimensional input feature analysis. In this paper, a novel two-stage feature selection method is proposed to handle this [...] Read more.

Transient stability status prediction (TSSP) plays an important role in situational awareness of power system stability. One of the main challenges of TSSP is the high-dimensional input feature analysis. In this paper, a novel two-stage feature selection method is proposed to handle this problem. In the first stage, the relevance between features and classes is measured by normalized mutual information (NMI), and the features are ranked based on the NMI values. Then, a predefined number of top-ranked features are selected to form the strongly relevant feature subset, and the remaining features are described as the weakly relevant feature subset, which can be utilized as the prior knowledge for the next stage. In the second stage, the binary particle swarm optimization is adopted as the search algorithm for feature selection, and a new particle encoding method that considers both population diversity and prior knowledge is presented. In addition, taking the imbalanced characteristics of TSSP into consideration, an improved fitness function for TSSP feature selection is proposed. The effectiveness of the proposed method is corroborated on the Northeast Power Coordinating Council (NPCC) 140-bus system. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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18 pages, 2673 KiB

Open AccessArticle

A Cross-Entropy-Based Hybrid Membrane Computing Method for Power System Unit Commitment Problems

by Min Xie, Yuxin Du, Peijun Cheng, Wei Wei and Mingbo Liu

Energies 2019, 12(3), 486; https://doi.org/10.3390/en12030486 - 2 Feb 2019

Cited by 4 | Viewed by 2857

Abstract

The cross-entropy based hybrid membrane computing method is proposed in this paper to solve the power system unit commitment problem. The traditional unit commitment problem can be usually decomposed into a bi-level optimization problem including unit start-stop scheduling problem and dynamic economic dispatch [...] Read more.

The cross-entropy based hybrid membrane computing method is proposed in this paper to solve the power system unit commitment problem. The traditional unit commitment problem can be usually decomposed into a bi-level optimization problem including unit start-stop scheduling problem and dynamic economic dispatch problem. In this paper, the genetic algorithm-based P system is proposed to schedule the unit start-stop plan, and the biomimetic membrane computing method combined with the cross-entropy is proposed to solve the dynamic economic dispatch problem with a unit start-stop plan given. The simulation results of 10–100 unit systems for 24 h day-ahead dispatching show that the unit commitment problem can be solved effectively by the proposed cross-entropy based hybrid membrane computing method and obtain a good and stable solution. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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20 pages, 947 KiB

Open AccessArticle

Optimal Decision-Making to Charge Electric Vehicles in Heterogeneous Networks: Stackelberg Game Approach

by Shijun Chen, Huwei Chen and Shanhe Jiang

Energies 2019, 12(2), 325; https://doi.org/10.3390/en12020325 - 21 Jan 2019

Cited by 11 | Viewed by 3877

Abstract

Electric vehicles (EVs) are designed to improve the efficiency of energy and prevent the environment from being polluted, when they are widely and reasonably used in the transport system. However, due to the feature of EV’s batteries, the charging problem plays an important [...] Read more.

Electric vehicles (EVs) are designed to improve the efficiency of energy and prevent the environment from being polluted, when they are widely and reasonably used in the transport system. However, due to the feature of EV’s batteries, the charging problem plays an important role in the application of EVs. Fortunately, with the help of advanced technologies, charging stations powered by smart grid operators (SGOs) can easily and conveniently solve the problems and supply charging service to EV users. In this paper, we consider that EVs will be charged by charging station operators (CSOs) in heterogeneous networks (Hetnet), through which they can exchange the information with each other. Considering the trading relationship among EV users, CSOs, and SGOs, we design their own utility functions in Hetnet, where the demand uncertainty is taken into account. In order to maximize the profits, we formulate this charging problem as a four-stage Stackelberg game, through which the optimal strategy is studied and analyzed. In the Stackelberg game model, we theoretically prove and discuss the existence and uniqueness of the Stackelberg equilibrium (SE). Using the proposed iterative algorithm, the optimal solution can be obtained in the optimization problem. The performance of the strategy is shown in the simulation results. It is shown that the simulation results confirm the efficiency of the model in Hetnet. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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17 pages, 3470 KiB

Open AccessArticle

Intelligent Energy Management Algorithms for EV-charging Scheduling with Consideration of Multiple EV Charging Modes

by Tian Mao, Xin Zhang and Baorong Zhou

Energies 2019, 12(2), 265; https://doi.org/10.3390/en12020265 - 16 Jan 2019

Cited by 45 | Viewed by 5512

Abstract

Electric vehicles (EVs) are now attracting increasing interest from both industries and countries as an environmentally friendly and energy efficient mode of travel. Therefore, the EV charging and/or discharging issue has become an important challenge and research topic in power systems in recent [...] Read more.

Electric vehicles (EVs) are now attracting increasing interest from both industries and countries as an environmentally friendly and energy efficient mode of travel. Therefore, the EV charging and/or discharging issue has become an important challenge and research topic in power systems in recent years. An advanced and economic EV charging process, however, should employ smart scheduling, which depends on effective and robust algorithms. To that end, a comprehensive intelligent scatter search (ISS) algorithm within the frame of a basic scatter search has been designed with both unidirectional and bidirectional charging considered. The ISS structure also supports both a flexible and constant charging power rate by respectively employing filter-SQP (sequential quadratic programming) and mixed-integer SQP as local solvers with module control. The detailed design of ISS is presented and the objectives of smoothing the daily load profile and minimizing the charging cost have been tested. Compared with methods based on GS (global search), GA (genetic algorithm), and PSO (particle swarm optimization), the outcome-verified ISS can produce attractive results with a significantly short computational time. Moreover, to handle a large scale EV charging scenario, a hybrid method comprised of a GA and ISS approach has been further developed. Simulation results also verified its prominent performance, plus superbly low computational time. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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16 pages, 2451 KiB

Open AccessArticle

Applications of a Strong Track Filter and LDA for On-Line Identification of a Switched Reluctance Machine Stator Inter-Turn Shorted-Circuit Fault

by Li Xiao, Hexu Sun, Liyi Zhang, Feng Niu, Lu Yu and Xuhe Ren

Energies 2019, 12(1), 134; https://doi.org/10.3390/en12010134 - 1 Jan 2019

Cited by 5 | Viewed by 2836

Abstract

Reliability is pivotal significance for switched reluctance machine drives (SRD) applied to safety essential transportation and industrial fields. An inter-turn shorted-circuit fault (ISCF) could incite the machine to operate in unbalanced status, resulting in the noise increases. In the event such a fault [...] Read more.

Reliability is pivotal significance for switched reluctance machine drives (SRD) applied to safety essential transportation and industrial fields. An inter-turn shorted-circuit fault (ISCF) could incite the machine to operate in unbalanced status, resulting in the noise increases. In the event such a fault remains untreated, the fault will further destroy the rest of the normal phases, even leading to a tragic incident for the entire drive application. To improve the reliability of SRD, an efficient on-line fault diagnosis method for ISCF should be proposed. This paper is focused on employing the strong track filter (STF) to achieve real-time phase resistance differences between before and after ISCF, which are used as features to diagnose the fault occurrence and the fault phase. Furthermore, a classification namely as linear discriminant analysis (LDA) is selected to estimate fault severity. Finally, simulation and experiments correspond to various running statuses are executed and their results can verify that the diagnosis method has accuracy and robustness. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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16 pages, 2082 KiB

Open AccessArticle

Two States for Optimal Position and Capacity of Distributed Generators Considering Network Reconfiguration for Power Loss Minimization Based on Runner Root Algorithm

by Anh Viet Truong, Trieu Ngoc Ton, Thuan Thanh Nguyen and Thanh Long Duong

Energies 2019, 12(1), 106; https://doi.org/10.3390/en12010106 - 29 Dec 2018

Cited by 25 | Viewed by 3599

Abstract

Although the distributed generator (DG) placement and distribution network (DN) reconfiguration techniques contribute to reduce power loss, obviously the former is a design problem which is used for a long-term purpose while the latter is an operational problem which is used for a [...] Read more.

Although the distributed generator (DG) placement and distribution network (DN) reconfiguration techniques contribute to reduce power loss, obviously the former is a design problem which is used for a long-term purpose while the latter is an operational problem which is used for a short-term purpose. In this situation, the optimal value of the position and capacity of DGs is a value which must be not affected by changing the operational configuration due to easy changes in the status of switches compared with changes in the installed location of DG. This paper demonstrates a methodology for choosing the position and size of DGs on the DN that takes into account re-switching the status of switches on distribution of the DN to reduce power losses. The proposed method is based on the runner root algorithm (RRA) which separates the problem into two states. In State-I, RRA is used to optimize the position and size of DGs on closed-loop distribution networks which is a mesh shape topology and power is delivered through more than one line. In State-II, RRA is used to reconfigure the DN after placing the DGs to find the open-loop distribution network which is a tree shape topology and power is only delivered through one line. The calculation results in DN systems with 33 nodes and 69 nodes, showing that the proposed method is capable of solving the problem of the optimal position and size of DGs considering distribution network reconfiguration. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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19 pages, 6022 KiB

Open AccessArticle

Grounding System Cost Analysis Using Optimization Algorithms

by Jau-Woei Perng, Yi-Chang Kuo and Shih-Pin Lu

Energies 2018, 11(12), 3484; https://doi.org/10.3390/en11123484 - 13 Dec 2018

Cited by 12 | Viewed by 6053

Abstract

In this study, the concept of grounding systems is related to the voltage tolerance of the human body (human body voltage tolerance safety value). The maximum touch voltage target and grounding resistance values are calculated in order to compute the grounding resistance on [...] Read more.

In this study, the concept of grounding systems is related to the voltage tolerance of the human body (human body voltage tolerance safety value). The maximum touch voltage target and grounding resistance values are calculated in order to compute the grounding resistance on the basis of system data. Typically, the grounding resistance value is inversely proportional to the laying depth of the grounding grid and the number of grounded copper rods. In other words, to improve the performance of the grounding system, either the layering depth of the grounding grid or the number of grounded copper rods should be increased, or both of them should be simultaneously increased. Better grounding resistance values result in increased engineering costs. There are numerous solutions for the grounding target value. Grounding systems are designed to find the combination of the layering depth of the grounding grid and the number of grounded copper rods by considering both cost and performance. In this study, we used a fuzzy algorithm on the genetic algorithm (GA), multi-objective particle swarm optimization (MOPSO) algorithm, Bees, IEEE Std. 80-2000, and Schwarz’s equation based on a power company’s substation grounding system data to optimize the grounding resistance performance and reduce system costs. The MOPSO algorithm returned optimal results. The radial basis function (RBF) neural network curve is obtained by the MOPSO algorithm with three variables (i.e., number of grounded copper rods, grounding resistance value, and grounding grid laying depth), and the simulation results of the electrical transient analysis program (ETAP) system are verified. This could be a future reference for substation designers and architects. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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18 pages, 2390 KiB

Open AccessArticle

Decentralized and Collaborative Scheduling Approach for Active Distribution Network with Multiple Virtual Power Plants

by Xiangyu Li, Dongmei Zhao and Baicang Guo

Energies 2018, 11(11), 3208; https://doi.org/10.3390/en11113208 - 19 Nov 2018

Cited by 10 | Viewed by 3220

Abstract

In order to build an active distribution system with multi virtual power plants (VPP), a decentralized two-stage stochastic dispatching model based on synchronous alternating direction multiplier method (SADMM) was proposed in this paper. Through the integration of distributed energy and large-scale electric vehicles [...] Read more.

In order to build an active distribution system with multi virtual power plants (VPP), a decentralized two-stage stochastic dispatching model based on synchronous alternating direction multiplier method (SADMM) was proposed in this paper. Through the integration of distributed energy and large-scale electric vehicles (EV) in the distribution network by VPP group, coordinative complementarity, and global optimization were realized. On the premise of energy autonomy management of active distribution network (AND) and VPP, after ensuring the privacy of stakeholders, the power of tie-line was taken as decoupling variable based on SADMM. Furthermore, without the participation of central coordinators, the optimization models of VPPs and distribution networks were decoupled to achieve fully decentralized optimization. Aiming at minimizing their own operating costs, the VPPs aggregate distributed energy and large-scale EVs within their jurisdiction to interact with the upper distribution network. On the premise of keeping operation safe, the upper distribution network formulated the energy interaction plan with each VPP, and then, the global energy optimization management of the entire distribution system and the decentralized autonomy of each VPP were achieved. In order to improve the stochastic uncertainty of distributed renewable energy output, a two-stage stochastic optimization method including pre-scheduling stage and rescheduling stage was adopted. The pre-scheduling stage was used to arrange charging and discharging plans of EV agents and output plans of micro gas turbines. The rescheduling stage was used to adjust the spare resources of micro gas turbines to deal with the uncertainty of distributed wind and light. An example of active distribution system with multi-VPPs was constructed by using the improved IEEE 33-bus system, then the validity of the model was verified. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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23 pages, 4494 KiB

Open AccessArticle

Interval Power Flow Analysis Considering Interval Output of Wind Farms through Affine Arithmetic and Optimizing-Scenarios Method

by Weijie Cheng, Renli Cheng, Jun Shi, Cong Zhang, Gaoxing Sun and Dong Hua

Energies 2018, 11(11), 3176; https://doi.org/10.3390/en11113176 - 15 Nov 2018

Cited by 11 | Viewed by 3568

Abstract

Wind power belongs to sustainable and clean energy sources which play a vital role of reducing environment pollution and addressing energy crisis. However, wind power outputs are quite difficult to predict because they are derived from wind speeds, which vary irregularly and greatly [...] Read more.

Wind power belongs to sustainable and clean energy sources which play a vital role of reducing environment pollution and addressing energy crisis. However, wind power outputs are quite difficult to predict because they are derived from wind speeds, which vary irregularly and greatly all the time. The uncertainty of wind power causes variation of the variables of power grids, which threatens the power grids’ operating security. Therefore, it is significant to provide the accurate ranges of power grids’ variables, which can be used by the operators to guarantee the power grid’s operating security. To achieve this goal, the present paper puts forward the interval power flow with wind farms model, where the generation power outputs of wind farms are expressed by intervals and three types of control modes are considered for imitating the operation features of wind farms. To solve the proposed model, the affine arithmetic-based method and optimizing-scenarios method are modified and employed, where three types of constraints of wind control modes are considered in their solution process. The former expresses the interval variables as affine arithmetic forms, and constructs optimization models to contract the affine arithmetic forms to obtain the accurate intervals of power flow variables. The latter regards active power outputs of the wind farms as variables, which vary in their corresponding intervals, and accordingly builds the minimum and maximum programming models for estimating the intervals of the power flow variables. The proposed methods are applied to two case studies, where the acquired results are compared with those acquired by the Monte Carlo simulation, which is a traditional method for handling interval uncertainty. The simulation results validate the advantages, effectiveness and the applicability of the two methods. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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17 pages, 830 KiB

Open AccessArticle

SOCP Relaxations of Optimal Power Flow Problem Considering Current Margins in Radial Networks

by Yuwei Chen, Ji Xiang and Yanjun Li

Energies 2018, 11(11), 3164; https://doi.org/10.3390/en11113164 - 15 Nov 2018

Cited by 18 | Viewed by 3654

Abstract

Optimal power flow (OPF) is a non-linear and non-convex problem that seeks the optimization of a power system operation point to minimize the total generation costs or transmission losses. This study proposes an OPF model considering current margins in radial networks. The objective [...] Read more.

Optimal power flow (OPF) is a non-linear and non-convex problem that seeks the optimization of a power system operation point to minimize the total generation costs or transmission losses. This study proposes an OPF model considering current margins in radial networks. The objective function of this OPF model has an additional term of current margins of the line besides the traditional transmission losses and generations costs, which contributes to thermal stability margins of power systems. The model is a reformulated bus injection model with clear physical meanings. Second order cone program (SOCP) relaxations for the proposed OPF are made, followed by the over-satisfaction condition guaranteeing the exactness of the SOCP relaxations. A simple 6-node case and several IEEE benchmark systems are studied to illustrate the efficiency of the developed results. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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Graphical abstract

14 pages, 2207 KiB

Open AccessArticle

Intelligent Regulation Method for a Controllable Load Used for Improving Wind Power Integration

by Jia-Jue Li, Bao-Zhu Shao, Jun-Hui Li, Wei-Chun Ge, Jia-Hui Zhang and Heng-Yu Zhou

Energies 2018, 11(11), 3085; https://doi.org/10.3390/en11113085 - 8 Nov 2018

Cited by 2 | Viewed by 2651

Abstract

Improving the safety and stability of power systems by adjusting the controllable load to improve the wind power integration has become a hot research topic. However, the methodology of accurately controlling the load and fundamentally improving the wind power integration capacity has yet [...] Read more.

Improving the safety and stability of power systems by adjusting the controllable load to improve the wind power integration has become a hot research topic. However, the methodology of accurately controlling the load and fundamentally improving the wind power integration capacity has yet to be studied. Therefore, this paper proposes an intelligent regulation method for a controllable load. This method takes the new energy consumption assessment as feedback, and it combines the wind power acceptance assessment and scheduling plan to form the internal and external loop control structure, and it derives the controllable load intelligent regulation architecture. The load curve is decomposed by an interactive load observer, and the load curve is adjusted by the interactive load controller according to a given standard, thereby improving the new energy acceptance capability. Finally, based on the actual grid operation data of a provincial power grid in Northeastern China, the source grid load balancing process and the interactive load regulation model of the wind power system are simulated. The above method verifies the validity and rationality of the proposed method. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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8 pages, 600 KiB

Open AccessArticle

A Novel Integrated Method to Diagnose Faults in Power Transformers

by Jing Wu, Kun Li, Jing Sun and Li Xie

Energies 2018, 11(11), 3041; https://doi.org/10.3390/en11113041 - 5 Nov 2018

Cited by 12 | Viewed by 2730

Abstract

In a smart grid, many transformers are equipped for both power transmission and conversion. Because a stable operation of transformers is essential to maintain grid security, studying the fault diagnosis method of transformers can improve both fault detection and fault prevention. In this [...] Read more.

In a smart grid, many transformers are equipped for both power transmission and conversion. Because a stable operation of transformers is essential to maintain grid security, studying the fault diagnosis method of transformers can improve both fault detection and fault prevention. In this paper, a data-driven method, which uses a combination of Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), and Support Vector Machines (SVM) to enable a better fault diagnosis of transformers, is proposed and investigated. PCA is used to reduce the dimension of transformer fault state data, and an improved PSO algorithm is used to obtain the optimal parameters for the SVM model. SVM, which is optimized using PSO, is used for the transformer-fault diagnosis. The diagnostic-results of the actual transformers confirm that the new method is effective. We also verified the importance of data richness with respect to the accuracy of the transformer-fault diagnosis. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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23 pages, 1336 KiB

Open AccessFeature PaperArticle

Hybrid Imperialist Competitive and Grey Wolf Algorithm to Solve Multiobjective Optimal Power Flow with Wind and Solar Units

by Jalel Ben Hmida, Mohammad Javad Morshed, Jim Lee and Terrence Chambers

Energies 2018, 11(11), 2891; https://doi.org/10.3390/en11112891 - 24 Oct 2018

Cited by 19 | Viewed by 3019

Abstract

The optimal power flow (OPF) module optimizes the generation, transmission, and distribution of electric power without disrupting network power flow, operating limits, or constraints. Similarly to any power flow analysis technique, OPF also allows the determination of system’s state of operation, that is, [...] Read more.

The optimal power flow (OPF) module optimizes the generation, transmission, and distribution of electric power without disrupting network power flow, operating limits, or constraints. Similarly to any power flow analysis technique, OPF also allows the determination of system’s state of operation, that is, the injected power, current, and voltage throughout the electric power system. In this context, there is a large range of OPF problems and different approaches to solve them. Moreover, the nature of OPF is evolving due to renewable energy integration and recent flexibility in power grids. This paper presents an original hybrid imperialist competitive and grey wolf algorithm (HIC-GWA) to solve twelve different study cases of simple and multiobjective OPF problems for modern power systems, including wind and photovoltaic power generators. The performance capabilities and potential of the proposed metaheuristic are presented, illustrating the applicability of the approach, and analyzed on two test systems: the IEEE 30 bus and IEEE 118 bus power systems. Sensitivity analysis has been performed on this approach to prove the robustness of the method. Obtained results are analyzed and compared with recently published OPF solutions. The proposed metaheuristic is more efficient and provides much better optimal solutions. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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16 pages, 2573 KiB

Open AccessArticle

Hierarchical Scheduling Scheme for AC/DC Hybrid Active Distribution Network Based on Multi-Stakeholders

by Chang Ye, Shihong Miao, Yaowang Li, Chao Li and Lixing Li

Energies 2018, 11(10), 2830; https://doi.org/10.3390/en11102830 - 19 Oct 2018

Cited by 5 | Viewed by 2980

Abstract

This paper presents a hierarchical multi-stage scheduling scheme for the AC/DC hybrid active distribution network (ADN). The load regulation center (LRC) is considered in the developed scheduling strategy, as well as the AC and DC sub-network operators. They are taken to be different [...] Read more.

This paper presents a hierarchical multi-stage scheduling scheme for the AC/DC hybrid active distribution network (ADN). The load regulation center (LRC) is considered in the developed scheduling strategy, as well as the AC and DC sub-network operators. They are taken to be different stakeholders. To coordinate the interests of all stakeholders, a two-level optimization model is established. The flexible loads are dispatched by LRC in the upper-level optimization model, the objective of which is minimizing the loss of the entire distribution network. The lower-level optimization is divided into two sub-optimal models, and they are carried out to minimize the operating costs of the AC/DC sub-network operators respectively. This two-level model avoids the difficulty of solving multi-objective optimization and can clarify the role of various stakeholders in the system scheduling. To solve the model effectively, a discrete wind-driven optimization (DWDO) algorithm is proposed. Then, considering the combination of the proposed DWDO algorithm and the YALMIP toolbox, a hierarchical optimization algorithm (HOA) is developed. The HOA can obtain the overall optimization result of the system through the iterative optimization of the upper and lower levels. Finally, the simulation results verify the effectiveness of the proposed scheduling scheme. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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24 pages, 2820 KiB

Open AccessArticle

PLDAD—An Algorihm to Reduce Data Center Energy Consumption

by Joao Ferreira, Gustavo Callou, Dietmar Tutsch and Paulo Maciel

Energies 2018, 11(10), 2821; https://doi.org/10.3390/en11102821 - 19 Oct 2018

Cited by 4 | Viewed by 4320

Abstract

Due to the demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the produced data. While these new technologies require high levels of availability, a reduction in the cost and [...] Read more.

Due to the demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the produced data. While these new technologies require high levels of availability, a reduction in the cost and environmental impact is also expected. The present paper proposes a power balancing algorithm (power load distribution algorithm-depth (PLDA-D)) to optimize the energy distribution of data center electrical infrastructures. The PLDA-D is based on the Bellman and Ford–Fulkerson flow algorithms that analyze energy-flow models (EFM). EFM computes the power efficiency, sustainability and cost metrics of data center infrastructures. To demonstrate the applicability of the proposed strategy, we present a case study that analyzes four power infrastructures. The results obtained show about a 3.8% reduction in sustainability impact and operational costs. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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15 pages, 5067 KiB

Open AccessArticle

Building Energy Management Strategy Using an HVAC System and Energy Storage System

by Nam-Kyu Kim, Myung-Hyun Shim and Dongjun Won

Energies 2018, 11(10), 2690; https://doi.org/10.3390/en11102690 - 10 Oct 2018

Cited by 22 | Viewed by 5058

Abstract

Recently, a worldwide movement to reduce greenhouse gas emissions has emerged, and includes efforts such as the Paris Agreement in 2015. To reduce greenhouse gas emissions, it is important to reduce unnecessary energy consumption or use environmentally-friendly energy sources and consumer products. Many [...] Read more.

Recently, a worldwide movement to reduce greenhouse gas emissions has emerged, and includes efforts such as the Paris Agreement in 2015. To reduce greenhouse gas emissions, it is important to reduce unnecessary energy consumption or use environmentally-friendly energy sources and consumer products. Many studies have been performed on building energy management systems and energy storage systems (ESSs), which are aimed at efficient energy management. Herein, a heating, ventilation, and air-conditioning (HVAC) system peak load reduction algorithm and an ESS peak load reduction algorithm are proposed. First, an HVAC system accounts for the largest portion of building energy consumption. An HVAC system operates by considering the time-of-use price. However, because the indoor temperature is constantly changing with time, load shifting can be expected only immediately prior to use. Therefore, the primary objective is to reduce the operating time by changing the indoor temperature constraint at the forecasted peak time. Next, numerous research initiatives on ESSs are ongoing. In this study, we aim to systematically design the peak load reduction algorithm of ESS. The structure is designed such that the algorithm can be applied by distinguishing between the peak and non-peak days. Finally, the optimization scheduling simulation is performed. The result shows that the electricity price is minimized by peak load reduction and electricity usage reduction. The proposed algorithm is verified through MATLAB simulations. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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30 pages, 7055 KiB

Open AccessArticle

Analyzing of a Photovoltaic/Wind/Biogas/Pumped-Hydro Off-Grid Hybrid System for Rural Electrification in Sub-Saharan Africa—Case Study of Djoundé in Northern Cameroon

by Nasser Yimen, Oumarou Hamandjoda, Lucien Meva’a, Benoit Ndzana and Jean Nganhou

Energies 2018, 11(10), 2644; https://doi.org/10.3390/en11102644 - 3 Oct 2018

Cited by 101 | Viewed by 11432

Abstract

Traditional electrification methods, including grid extension and stand-alone diesel generators, have shown limitations to sustainability in the face of rural electrification challenges in sub-Saharan Africa (SSA), where electrification rates remain the lowest in the world. This study aims at performing a techno-economic analysis [...] Read more.

Traditional electrification methods, including grid extension and stand-alone diesel generators, have shown limitations to sustainability in the face of rural electrification challenges in sub-Saharan Africa (SSA), where electrification rates remain the lowest in the world. This study aims at performing a techno-economic analysis and optimization of a pumped-hydro energy storage based 100%-renewable off-grid hybrid energy system for the electrification of Djoundé, which is a small village in northern Cameroon. Hybrid Optimization of Multiple Energy Resources (HOMER) software was used as an analysis tool, and the resulting optimal system architecture included an 81.8 kW PV array and a 15 kW biogas generator, with a cost of energy (COE) and total net present cost (NPC) of €0.256/kWh and €370,426, respectively. The system showed promise given the upcoming decrease in installation cost of photovoltaic systems. It will be viable in parts of SSA region but, significant investment subsidies will be needed elsewhere. The originality of this study can be emphasized in three points: (1) the modelling with the recently introduced pumped-hydro component of HOMER; (2) broadening sensitivity analysis applications to address practical issues related to hybrid renewable energy systems (HRES); and, (3) consideration of the agricultural sector and seasonal variation in the assessment of the electricity demand in an area of SSA. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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22 pages, 1940 KiB

Open AccessArticle

Demand Bidding Optimization for an Aggregator with a Genetic Algorithm

by Leehter Yao, Wei Hong Lim, Sew Sun Tiang, Teng Hwang Tan, Chin Hong Wong and Jia Yew Pang

Energies 2018, 11(10), 2498; https://doi.org/10.3390/en11102498 - 20 Sep 2018

Cited by 24 | Viewed by 3557

Abstract

Demand response (DR) is an effective solution used to maintain the reliability of power systems. Although numerous demand bidding models were designed to balance the demand and supply of electricity, these works focused on optimizing the DR supply curve of aggregator and the [...] Read more.

Demand response (DR) is an effective solution used to maintain the reliability of power systems. Although numerous demand bidding models were designed to balance the demand and supply of electricity, these works focused on optimizing the DR supply curve of aggregator and the associated clearing prices. Limited researches were done to investigate the interaction between each aggregator and its customers to ensure the delivery of promised load curtailments. In this paper, a closed demand bidding model is envisioned to bridge the aforementioned gap by facilitating the internal DR trading between the aggregator and its large contract customers. The customers can submit their own bid as a pairs of bidding price and quantity of load curtailment in hourly basis when demand bidding is needed. A purchase optimization scheme is then designed to minimize the total bidding purchase cost. Given the presence of various load curtailment constraints, the demand bidding model considered is highly nonlinear. A modified genetic algorithm incorporated with efficient encoding scheme and adaptive bid declination strategy is therefore proposed to solve this problem effectively. Extensive simulation shows that the proposed purchase optimization scheme can minimize the total cost of demand bidding and it is computationally feasible for real applications. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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13 pages, 2295 KiB

Open AccessArticle

Using Piecewise Linearization Method to PCS Input/Output-Efficiency Curve for a Stand-Alone Microgrid Unit Commitment

by Ha-Lim Lee and Yeong-Han Chun

Energies 2018, 11(9), 2468; https://doi.org/10.3390/en11092468 - 17 Sep 2018

Cited by 5 | Viewed by 3456

Abstract

When operating a stand-alone micro grid, the battery energy storage system (BESS) and a diesel generator are key components needed in order to maintain demand-supply balance. Using Unit Commitment (UC) to calculate the optimal operation schedule of a BESS and diesel generator helps [...] Read more.

When operating a stand-alone micro grid, the battery energy storage system (BESS) and a diesel generator are key components needed in order to maintain demand-supply balance. Using Unit Commitment (UC) to calculate the optimal operation schedule of a BESS and diesel generator helps minimize the operation cost of the micro grid. While calculating the optimal operation schedule for the microgrid, it is important that it reflects the actual characteristics of the implanted devices, in order to increase the schedule result accuracy. In this paper, a piecewise linearization, on the actual power conditioning system (PCS) input/output-efficiency characteristic curve, has been considered while calculating the optimal operation schedule using UC. The optimal schedule result calculated by the proposed method has been examined by comparing the schedule calculated by a fixed input/output-efficiency case, which is conventionally used while solving UC for a stand-alone microgrid. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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16 pages, 3735 KiB

Open AccessArticle

Dynamic Optimization of Combined Cooling, Heating, and Power Systems with Energy Storage Units

by Jiyuan Kuang, Chenghui Zhang, Fan Li and Bo Sun

Energies 2018, 11(9), 2288; https://doi.org/10.3390/en11092288 - 30 Aug 2018

Cited by 16 | Viewed by 3865

Abstract

In this paper, a combined cooling, heating, and power (CCHP) system with thermal storage tanks is introduced. Considering the plants’ off-design performance, an efficient methodology is introduced to determine the most economical operation schedule. The complex CCHP system’s state transition equation is extracted [...] Read more.

In this paper, a combined cooling, heating, and power (CCHP) system with thermal storage tanks is introduced. Considering the plants’ off-design performance, an efficient methodology is introduced to determine the most economical operation schedule. The complex CCHP system’s state transition equation is extracted by selecting the stored cooling and heating energy as the discretized state variables. Referring to the concept of variable cost and constant cost, repeated computations are saved in phase operating cost calculations. Therefore, the most economical operation schedule is obtained by employing a dynamic solving framework in an extremely short time. The simulation results indicated that the optimized operating cost is reduced by 40.8% compared to the traditional energy supply system. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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21 pages, 6217 KiB

Open AccessArticle

A Hybrid DA-PSO Optimization Algorithm for Multiobjective Optimal Power Flow Problems

by Sirote Khunkitti, Apirat Siritaratiwat, Suttichai Premrudeepreechacharn, Rongrit Chatthaworn and Neville R. Watson

Energies 2018, 11(9), 2270; https://doi.org/10.3390/en11092270 - 29 Aug 2018

Cited by 81 | Viewed by 6266

Abstract

In this paper, a hybrid optimization algorithm is proposed to solve multiobjective optimal power flow problems (MO-OPF) in a power system. The hybrid algorithm, named DA-PSO, combines the frameworks of the dragonfly algorithm (DA) and particle swarm optimization (PSO) to find the optimized [...] Read more.

In this paper, a hybrid optimization algorithm is proposed to solve multiobjective optimal power flow problems (MO-OPF) in a power system. The hybrid algorithm, named DA-PSO, combines the frameworks of the dragonfly algorithm (DA) and particle swarm optimization (PSO) to find the optimized solutions for the power system. The hybrid algorithm adopts the exploration and exploitation phases of the DA and PSO algorithms, respectively, and was implemented to solve the MO-OPF problem. The objective functions of the OPF were minimization of fuel cost, emissions, and transmission losses. The standard IEEE 30-bus and 57-bus systems were employed to investigate the performance of the proposed algorithm. The simulation results were compared with those in the literature to show the superiority of the proposed algorithm over several other algorithms; however, the time computation of DA-PSO is slower than DA and PSO due to the sequential computation of DA and PSO. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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17 pages, 2582 KiB

Open AccessArticle

Adaptive Consensus Algorithm for Distributed Heat-Electricity Energy Management of an Islanded Microgrid

by Xiaofeng Dong, Xiaoshun Zhang and Tong Jiang

Energies 2018, 11(9), 2236; https://doi.org/10.3390/en11092236 - 26 Aug 2018

Cited by 6 | Viewed by 2970

Abstract

This paper proposes a novel adaptive consensus algorithm (ACA) for distributed heat-electricity energy management (HEEM) of an islanded microgrid. In order to simultaneously satisfy the heat-electricity energy balance constraints, ACA is implemented with a switch between unified consensus and independent consensus according to [...] Read more.

This paper proposes a novel adaptive consensus algorithm (ACA) for distributed heat-electricity energy management (HEEM) of an islanded microgrid. In order to simultaneously satisfy the heat-electricity energy balance constraints, ACA is implemented with a switch between unified consensus and independent consensus according to the dynamic energy mismatches. The feasible operation region of a combined heat and power (CHP) unit is decomposed into eight searching sub-regions, thus its electricity and heat energy outputs can simultaneously match the incremental cost consensus requirement and the heat-electricity energy balance constraints. Case studies are thoroughly carried out to verify the performance of ACA for distributed HEEM of an islanded microgrid. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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17 pages, 1552 KiB

Open AccessArticle

Using Generalized Generation Distribution Factors in a MILP Model to Solve the Transmission-Constrained Unit Commitment Problem

by Guillermo Gutierrez-Alcaraz and Victor H. Hinojosa

Energies 2018, 11(9), 2232; https://doi.org/10.3390/en11092232 - 26 Aug 2018

Cited by 7 | Viewed by 4002

Abstract

This study proposes a mixed-integer linear programming (MILP) model to figure out the transmission-constrained direct current (DC)-based unit commitment (UC) problem using the generalized generation distribution factors (GGDF) for modeling the transmission network constraints. The UC problem has been reformulated using these linear [...] Read more.

This study proposes a mixed-integer linear programming (MILP) model to figure out the transmission-constrained direct current (DC)-based unit commitment (UC) problem using the generalized generation distribution factors (GGDF) for modeling the transmission network constraints. The UC problem has been reformulated using these linear distribution factors without sacrificing optimality. Several test power systems (PJM 5-bus, IEEE-24, and 118-bus) have been used to validate the introduced formulation. Results demonstrate that the proposed approach is more compact and less computationally burdensome than the classical DC-based formulation, which is commonly employed in the technical literature to carry out the transmission network constraints. Therefore, there is a potential applicability of the accomplished methodology to carry out the UC problem applied to medium and large-scale electrical power systems. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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13 pages, 3070 KiB

Open AccessArticle

Maintenance Factor Identification in Outdoor Lighting Installations Using Simulation and Optimization Techniques

by Ana Ogando-Martínez, Javier López-Gómez and Lara Febrero-Garrido

Energies 2018, 11(8), 2169; https://doi.org/10.3390/en11082169 - 20 Aug 2018

Cited by 10 | Viewed by 4043

Abstract

This document addresses the development of a novel methodology to identify the actual maintenance factor of the luminaires of an outdoor lighting installation in order to assess their lighting performance. The method is based on the combined use of Radiance, a free and [...] Read more.

This document addresses the development of a novel methodology to identify the actual maintenance factor of the luminaires of an outdoor lighting installation in order to assess their lighting performance. The method is based on the combined use of Radiance, a free and open-source tool, for the modeling and simulation of lighting scenes, and GenOpt, a generic optimization program, for the calibration of the model. The application of this methodology allows the quantification of the deterioration of the road lighting system and the identification of luminaires that show irregularities in their operation. Values lower than 9% for the error confirm that this research can contribute to the management of street lighting by assessing real road conditions. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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21 pages, 5558 KiB

Open AccessArticle

Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm ^†

by Zahir Sahli, Abdellatif Hamouda, Abdelghani Bekrar and Damien Trentesaux

Energies 2018, 11(8), 2134; https://doi.org/10.3390/en11082134 - 16 Aug 2018

Cited by 56 | Viewed by 5653

Abstract

This paper presents an efficient approach for solving the optimal reactive power dispatch problem. It is a non-linear constrained optimization problem where two distinct objective functions are considered. The proposed approach is based on the hybridization of the particle swarm optimization method and [...] Read more.

This paper presents an efficient approach for solving the optimal reactive power dispatch problem. It is a non-linear constrained optimization problem where two distinct objective functions are considered. The proposed approach is based on the hybridization of the particle swarm optimization method and the tabu-search technique. This hybrid approach is used to find control variable settings (i.e., generation bus voltages, transformer taps and shunt capacitor sizes) which minimize transmission active power losses and load bus voltage deviations. To validate the proposed hybrid method, the IEEE 30-bus system is considered for 12 and 19 control variables. The obtained results are compared with those obtained by particle swarm optimization and a tabu-search without hybridization and with other evolutionary algorithms reported in the literature. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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15 pages, 2175 KiB

Open AccessFeature PaperArticle

Parameter Estimation of Electromechanical Oscillation Based on a Constrained EKF with C&I-PSO

by Yonghui Sun, Yi Wang, Linquan Bai, Yinlong Hu, Denis Sidorov and Daniil Panasetsky

Energies 2018, 11(8), 2059; https://doi.org/10.3390/en11082059 - 8 Aug 2018

Cited by 3 | Viewed by 2974

Abstract

By combining together the extended Kalman filter with a newly developed C&I particle swarm optimization algorithm (C&I-PSO), a novel estimation method is proposed for parameter estimation of electromechanical oscillation, in which critical physical constraints on the parameters are taken into account. Based on [...] Read more.

By combining together the extended Kalman filter with a newly developed C&I particle swarm optimization algorithm (C&I-PSO), a novel estimation method is proposed for parameter estimation of electromechanical oscillation, in which critical physical constraints on the parameters are taken into account. Based on the extended Kalman filtering algorithm, the constrained parameter estimation problem is formulated via the projection method. Then, by utilizing the penalty function method, the obtained constrained optimization problem could be converted into an equivalent unconstrained optimization problem; finally, the C&I-PSO algorithm is developed to address the unconstrained optimization problem. Therefore, the parameters of electromechanical oscillation with physical constraints can be successfully estimated and better performed. Finally, the effectiveness of the obtained results has been illustrated by several test systems. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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25 pages, 2697 KiB

Open AccessFeature PaperArticle

A Novel Multi-Population Based Chaotic JAYA Algorithm with Application in Solving Economic Load Dispatch Problems

by Jiangtao Yu, Chang-Hwan Kim, Abdul Wadood, Tahir Khurshiad and Sang-Bong Rhee

Energies 2018, 11(8), 1946; https://doi.org/10.3390/en11081946 - 26 Jul 2018

Cited by 48 | Viewed by 4519

Abstract

The economic load dispatch (ELD) problem is an optimization problem of minimizing the total fuel cost of generators while satisfying power balance constraints, operating capacity limits, ramp-rate limits and prohibited operating zones. In this paper, a novel multi-population based chaotic JAYA algorithm (MP-CJAYA) [...] Read more.

The economic load dispatch (ELD) problem is an optimization problem of minimizing the total fuel cost of generators while satisfying power balance constraints, operating capacity limits, ramp-rate limits and prohibited operating zones. In this paper, a novel multi-population based chaotic JAYA algorithm (MP-CJAYA) is proposed to solve the ELD problem by applying the multi-population method (MP) and chaotic optimization algorithm (COA) on the original JAYA algorithm to guarantee the best solution of the problem. MP-CJAYA is a modified version where the total population is divided into a certain number of sub-populations to control the exploration and exploitation rates, at the same time a chaos perturbation is implemented on each sub-population during every iteration to keep on searching for the global optima. The proposed MP-CJAYA has been adopted to ELD cases and the results obtained have been compared with other well-known algorithms reported in the literature. The comparisons have indicated that MP-CJAYA outperforms all the other algorithms, achieving the best performance in all the cases, which indicates that MP-CJAYA is a promising alternative approach for solving ELD problems. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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19 pages, 2541 KiB

Open AccessArticle

Optimal Micro-PMU Placement Using Mutual Information Theory in Distribution Networks

by Zhi Wu, Xiao Du, Wei Gu, Ping Ling, Jinsong Liu and Chen Fang

Energies 2018, 11(7), 1917; https://doi.org/10.3390/en11071917 - 23 Jul 2018

Cited by 24 | Viewed by 5426

Abstract

Micro-phasor measurement unit (μPMU) is under fast development and becoming more and more important for application in future distribution networks. It is unrealistic and unaffordable to place all buses with μPMUs because of the high costs, leading to the necessity of determining optimal [...] Read more.

Micro-phasor measurement unit (μPMU) is under fast development and becoming more and more important for application in future distribution networks. It is unrealistic and unaffordable to place all buses with μPMUs because of the high costs, leading to the necessity of determining optimal placement with minimal numbers of μPMUs in the distribution system. An optimal μPMU placement (OPP) based on the information entropy evaluation and node selection strategy (IENS) using greedy algorithm is presented in this paper. The uncertainties of distributed generations (DGs) and pseudo measurements are taken into consideration, and the two-point estimation method (2PEM) is utilized for solving stochastic state estimation problems. The set of buses selected by improved IENS, which can minimize the uncertainties of network and obtain system observability is considered as the optimal deployment of μPMUs. The proposed method utilizes the measurements of smart meters and pseudo measurements of load powers in the distribution systems to reduce the number of μPMUs and enhance the observability of the network. The results of the simulations prove the effectiveness of the proposed algorithm with the comparison of traditional topological methods for the OPP problem. The improved IENS method can obtain the optimal complete and incomplete μPMU placement in the distribution systems. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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30 pages, 3375 KiB

Open AccessArticle

Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours

by Lin Lin, Lin Xue, Zhiqiang Hu and Nantian Huang

Energies 2018, 11(7), 1899; https://doi.org/10.3390/en11071899 - 20 Jul 2018

Cited by 8 | Viewed by 3584

Abstract

To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to [...] Read more.

To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to the previous 168 h considering the calendar feature. Second, a feature selection combined with a predictor process was carried out to select the optimal feature for building a reliable predictor with respect to each hour. The final modular model consisted of 24 predictors with a respective optimal feature subset for day-ahead load forecasting. New England and Singapore load data were used to evaluate the effectiveness of the proposed method. The results indicated that the accuracy of the proposed modular model was higher than that of the traditional method. Furthermore, conducting a feature selection step when building a predictor improved the accuracy of load forecasting. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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18 pages, 719 KiB

Open AccessArticle

Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach

by Nian Liu, Bin Guo, Zifa Liu and Yongli Wang

Energies 2018, 11(7), 1891; https://doi.org/10.3390/en11071891 - 20 Jul 2018

Cited by 16 | Viewed by 3866

Abstract

Community Energy Internet (CEI) integrates electric network and thermal network based on combined heat and power (CHP) to improve the economy of energy system in Smart Community. In the CEI, an energy sharing framework for prosumers equipped with photovoltaic-thermal (PVT) system and heat [...] Read more.

Community Energy Internet (CEI) integrates electric network and thermal network based on combined heat and power (CHP) to improve the economy of energy system in Smart Community. In the CEI, an energy sharing framework for prosumers equipped with photovoltaic-thermal (PVT) system and heat pump (HP) is introduced. Supporting by the PVT and HP, the prosumer has four role attributes with either heat or electricity producer/consumer. A social welfare maximization model is built for the CEI, including PVT-HP prosumers, CHP system, and utility grid. Considering there are multiply participants in the local market of CEI, the social welfare maximization problem is decoupled by using Lagrange multiplier method. Moreover, a consensus-based fully distributed algorithm is designed to solve the problem. Finally, six residential buildings are selected as the case study to validate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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16 pages, 1124 KiB

Open AccessArticle

Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer

by Mostafa Abdo, Salah Kamel, Mohamed Ebeed, Juan Yu and Francisco Jurado

Energies 2018, 11(7), 1692; https://doi.org/10.3390/en11071692 - 28 Jun 2018

Cited by 57 | Viewed by 4095

Abstract

The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey Wolf [...] Read more.

The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey Wolf Optimizer (DGWO) to solve the optimal power flow (OPF) problem by an efficient way. Although the GWO is an efficient technique, it may be prone to stagnate at local optima for some cases due to the insufficient diversity of wolves, hence the DGWO algorithm is proposed for improving the search capabilities of this optimizer. The DGWO is based on enhancing the exploration process by applying a random mutation to increase the diversity of population, while an exploitation process is enhanced by updating the position of populations in spiral path around the best solution. An adaptive operator is employed in DGWO to find a balance between the exploration and exploitation phases during the iterative process. The considered objective functions are quadratic fuel cost minimization, piecewise quadratic cost minimization, and quadratic fuel cost minimization considering the valve point effect. The DGWO is validated using the standard IEEE 30-bus test system. The obtained results showed the effectiveness and superiority of DGWO for solving the OPF problem compared with the other well-known meta-heuristic techniques. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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13 pages, 7106 KiB

Open AccessArticle

A Thermal Probability Density–Based Method to Detect the Internal Defects of Power Cable Joints

by Li Zhang, Xiyue LuoYang, Yanjie Le, Fan Yang, Chun Gan and Yinxian Zhang

Energies 2018, 11(7), 1674; https://doi.org/10.3390/en11071674 - 27 Jun 2018

Cited by 11 | Viewed by 3560

Abstract

Internal defects inside power cable joints due to unqualified construction is the main issue of power cable failures, hence in this paper a method based on thermal probability density function to detect the internal defects of power cable joints is presented. First, the [...] Read more.

Internal defects inside power cable joints due to unqualified construction is the main issue of power cable failures, hence in this paper a method based on thermal probability density function to detect the internal defects of power cable joints is presented. First, the model to calculate the thermal distribution of power cable joints is set up and the thermal distribution is calculated. Then a thermal probability density (TPD)-based method that gives the statistics of isothermal points is presented. The TPD characteristics of normal power cable joints and those with internal defects, including insulation eccentricity and unqualified connection of conductors, are analyzed. The results indicate that TPD differs with the internal state of cable joints. Finally, experiments were conducted in which surface thermal distribution was measured by FLIR SC7000, and the corresponding TPDs are discussed. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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20 pages, 7544 KiB

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A Survey on Optimization Techniques Applied to Magnetic Field Mitigation in Power Systems

by Juan Carlos Bravo-Rodríguez, Juan Carlos del-Pino-López and Pedro Cruz-Romero

Energies 2019, 12(7), 1332; https://doi.org/10.3390/en12071332 - 8 Apr 2019

Cited by 48 | Viewed by 5130

Abstract

With the continuous increase in the number and relevance of electric transmission lines and distribution networks, there is a higher exposure to the magnetic fields generated by them, leading to more cases of human electrosensitivity, which greatly necessitates the design and development of [...] Read more.

With the continuous increase in the number and relevance of electric transmission lines and distribution networks, there is a higher exposure to the magnetic fields generated by them, leading to more cases of human electrosensitivity, which greatly necessitates the design and development of magnetic field mitigation procedures and, at the same time, the need to minimize both performance degradation and deterioration in the efficiency as well. During the last four decades, fruitful results have been reported about extremely low frequency magnetic field mitigation, giving a wide variety of solutions. This survey paper aims to give a comprehensive overview of cost-effective optimization techniques destined to magnetic field mitigation in power systems, with particular attention to the results reported in the last decade. Full article

(This article belongs to the Special Issue Optimization Methods Applied to Power Systems)

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