Power System Control
V.K. Peddiny; B. Datta; A. Banerjee
Abstract
Changes in the electric supply can significantly affect electronic devices since they are very sensitive. Due to a nonlinear system with multiple interconnected and unpredictable demands in the smart grid, the electricity system is facing several issues, including power quality, reactive power management, ...
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Changes in the electric supply can significantly affect electronic devices since they are very sensitive. Due to a nonlinear system with multiple interconnected and unpredictable demands in the smart grid, the electricity system is facing several issues, including power quality, reactive power management, and voltage drop. To address these problems, a static synchronous compensator (STATCOM) is frequently used to compensate and correct the voltage level at the power bus voltage. In this study, an Artificial Neural Network (ANN) and GWO based controlled STATCOM has been developed to replace the traditional PI based controller and enhance the overall STATCOM performance. The ANN controller is preferred due to its simplicity, adaptability, resilience, and ability to consider the non-linearities of the power grid. To train the classifier offline, data from the PI controller was utilized. The MATLAB/Simulink software was employed to assess the effectiveness of STATCOM on a 25 Km transmission line during increased load and three faults. The combined results of the PI and ANN controllers indicate that the ANN controller significantly improves STATCOM efficiency under different operating conditions. Moreover, the ANN controller outperforms the traditional PI controller in terms of results.
A. Namvar; J. Salehi
Abstract
One of the crucial challenges within the optimal operation of smart cities is coordinated management of multiple energy carriers in the residential buildings owing to disparate and often conflicting objectives. In response to this challenge, this paper proposes a novel conceptual cost-emission-based ...
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One of the crucial challenges within the optimal operation of smart cities is coordinated management of multiple energy carriers in the residential buildings owing to disparate and often conflicting objectives. In response to this challenge, this paper proposes a novel conceptual cost-emission-based scheme for optimal energy-gas use in a smart home in the context of residential energy hubs considering a meaningful trade-off between cost saving and environmental protection. Various energy conversion resources containing energy and heat storage systems, rooftop photovoltaic modules, and also combined heat and power units along with responsible electrical and thermal loads are taken into account in the proposed model. Furthermore, an efficient stochastic scenario-based method is executed to tackle the intense uncertainty associated with photovoltaic production. The proposed model reduces domestic energy consumption and utility costs by incorporating a weighted summation mixed objective function under various system constraints and user preferences, while at t the same time optimal task scheduling and comfort for the resident that it can guarantee a good lifestyle. The presented scheme is carried out on a realistic case study equipped with energy hubs and as expected, introduces its applicability and effectiveness in the optimal energy management of the proposed residential energy hub problem. The simulation results confirm that energy procurement costs can be saved by up to 46.16% and emission costs by 34.07% while maintaining the desired level of comfort for the head of the household.
F. Shamsini Ghiasvand; K. Afshar; N. Bigdeli
Abstract
In the restructured electricity industry, the electricity retailer, as a profit-oriented company, buys electricity from wholesale electricity markets and sells it to end customers. On the other hand, with the move of the electricity networks towards smart grids, small customers who, in addition to receiving ...
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In the restructured electricity industry, the electricity retailer, as a profit-oriented company, buys electricity from wholesale electricity markets and sells it to end customers. On the other hand, with the move of the electricity networks towards smart grids, small customers who, in addition to receiving energy from the distribution network, can generate power on a small scale, have emerged as prosumers in the electricity market environment. Therefore, the prosumers' aggregator is defined to maximize the profit of a set of prosumers in this environment. In this paper, the energy exchange between the retailer and the aggregator has been modeled as a bi-level game. At a higher level, the retailer, as a leader to maximize its profit or minimize its expenses, offers a price to buy or sell energy to the prosumers' aggregator. The aggregator also decides on the amount of exchange energy to buy or sell, to minimize the energy supply costs required of its consumers according to the retailer's bid price. In this paper, a combined method based on~MILP (Mixed Integer Linear Programming)~and MO (Mayfly Optimization) has been used to find the optimal point of this modeled game. To evaluate the efficiency of the proposed method, the three pricing methods FP (Fixed Pricing),~TOU (Time Of Using), and RTP (Real Time Pricing) as price-based demand response programs have been compared using the proposed algorithm. The simulation results show that among the three pricing methods for customers, the RTP pricing method has the highest profit for the retailer and the lowest cost for the aggregator.
Energy Management
A.Y. Dewi; M.Y. Arabi; Z.F. Al-lami; M.M. Abdulhasan; A.S. Ibrahim; R. Sattar; D.A. Lafta; B.A. Usmanovich; D. Abdullah; Y. Yerkin
Abstract
Sustainable and efficient energy solutions are needed in the fast-growing energy sector. Meeting these objectives requires smart distribution networks that maximize energy utilization, eliminate losses, and improve system reliability. However, these networks' usefulness and durability depend on their ...
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Sustainable and efficient energy solutions are needed in the fast-growing energy sector. Meeting these objectives requires smart distribution networks that maximize energy utilization, eliminate losses, and improve system reliability. However, these networks' usefulness and durability depend on their ability to quickly recover from faults. Intelligent distribution networks can self-heal, which speeds up restoration and ensures energy delivery. This paper proposes a comprehensive strategy for intelligent distribution network self-healing after flaws. Restoration involves identifying and isolating the damaged area using offline and online methods. Online approaches, notably islanding, have helped restore services in the affected region. This paper presents a novel linear mathematical approach to optimize online islanding. The model estimates the boundaries of islanded microgrids and the appropriate number of microgrids for faults, enabling quick restoration. This analysis also seeks to determine the fault-affected area's system layout. A mathematical model defines the ideal arrangement in the first layer of the two-layered approach. The next layer analyzes unit participation in the intelligent distribution system, focusing on rescheduling, allocation, and organization. Additionally, the study identifies the best energy storage solutions to aid restoration. The recommended strategy uses adaptive load reduction and demand response to maximize system recovery. The mathematical model benefits from various strategies, including faster execution and better outcomes. This research advances intelligent distribution networks by combining advanced mathematical modeling, self-healing, and smart load control. These upgrades boost distribution networks' effectiveness.
Distribution Systems
Soheil Derafshi Beigvand; Hamdi Abdi,
Volume 3, Issue 2 , December 2015, , Pages 102-115
Abstract
This paper proposes an Optimal Power Flow (OPF) algorithm by Direct Load Control (DLC) programs to optimize the operational cost of smart grids considering various scenarios based on different constraints. The cost function includes active power production cost of available power sources and a novel ...
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This paper proposes an Optimal Power Flow (OPF) algorithm by Direct Load Control (DLC) programs to optimize the operational cost of smart grids considering various scenarios based on different constraints. The cost function includes active power production cost of available power sources and a novel flexible load curtailment cost associated with DLC programs. The load curtailment cost is based on a virtual generator for each load (which participates in DLC program). To implement the load curtailment in the objective function, we consider incentive payments for participants and a load shedding priority list in some events. The proposed OPF methodology is applied to IEEE 14, 30-bus, and 13-node industrial power systems as three examples of the smart grids, respectively. The numerical results of the proposed algorithm are compared with the results obtained by applying MATPOWER to the nominal case by using the DLC programs. It is shown that the suggested approach converges to a better quality solution in an acceptable computation time.
Smart Grid
Hamidreza Arasteh; Mohammadsadegh Sepasian; Vahid Vahidinasab
Volume 3, Issue 2 , December 2015, , Pages 116-130
Abstract
This paper presents a novel concept of "smart distribution system expansion planning (SDEP)" which expands the concept of demand response programs to be dealt with the long term horizon time. The proposed framework, integrates demand response resources (DRRs) as virtual distributed generation (VDG) resources ...
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This paper presents a novel concept of "smart distribution system expansion planning (SDEP)" which expands the concept of demand response programs to be dealt with the long term horizon time. The proposed framework, integrates demand response resources (DRRs) as virtual distributed generation (VDG) resources into the distribution expansion planning. The main aim of this paper is to develop and initial test of the proposed model of SDEP to include DRRs which are one of the most important components to construct smart grid. SDEP is modeled mathematically as an optimization problem and solved using particle swarm optimization algorithm. The objective function of the optimization problem is to minimize the total cost of lines’ installation, maintenance, demand response persuasion, energy losses as well as reliability. Furthermore, the problem is subject to the constraints including radiality and connectivity of the distribution system, permissible voltage levels, the capacity of lines, and the maximum penetration level of demand response. Based on two sample test systems, the simulation results confirmed that the consideration of DRRs simultaneously with distribution system expansion can have economical profit for distribution planners.
Application of Automatic Control in Power System
N. Zendehdel
Volume 3, Issue 1 , June 2015, , Pages 1-22
Abstract
This paper presents a comprehensive robust distributed intelligent control for optimum self-healing activities in smart distribution systems considering the uncertainty in loads. The presented agent based framework obviates the requirements for a central control method and improves the reliability of ...
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This paper presents a comprehensive robust distributed intelligent control for optimum self-healing activities in smart distribution systems considering the uncertainty in loads. The presented agent based framework obviates the requirements for a central control method and improves the reliability of the self-healing mechanism. Agents possess three characteristics including local views, decentralizations and autonomy. The message, exchanged among neighboring agents, is used to develop a global information discovery algorithm and updates the topology information of out-of-service areas, available supply capacity and routing information. Fuzzy description is employed to take into account the uncertainties of measurements in which are exchanged between agents. Moreover, to find the optimal restoration plan, incorporating the discovered data, a routing problem is developed as a fuzzy binary linear optimization problem. This problem is approached by a novel method using a specific ranking function. Finally, robustness and applicability of the proposed self-healing method is tested on two standard case studies. The obtained results emphasize that ignoring the uncertainties may lead to non-realistic solutions.
M. Allahnoori; Sh. Kazemi; H. Abdi; R. Keyhani
Volume 2, Issue 2 , December 2014, , Pages 113-120
Abstract
The microgrid concept provides attractive solutions for reliability enhancement of power distribution systems. Normally, microgrids contain renewable-energy-based Distributed Generation (DG) units, which their output power varies with different environmental conditions. In addition, load demand usually ...
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The microgrid concept provides attractive solutions for reliability enhancement of power distribution systems. Normally, microgrids contain renewable-energy-based Distributed Generation (DG) units, which their output power varies with different environmental conditions. In addition, load demand usually changes with factors such as hourly and seasonal customer activities. Hence, these issues have to be considered in evaluating the reliability of such a power distribution system. This paper evaluates the reliability performance of distribution systems with considering uncertainties in both generation and load demands. The results of applying the proposed approach on a case study system verify its advantages compared to the previous studies.
