scholarly journals Control Strategy for MGT Generation System Optimized by Improved WOA to Enhance Demand Response Capability

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3101
Author(s):  
Jiecheng Zhu ◽  
Xitian Wang ◽  
Da Xie ◽  
Chenghong Gu
Keyword(s):  
Power Systems ◽  
Demand Response ◽  
Control Strategy ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimization Method ◽  
Generation System ◽  
Whale Optimization ◽  
Valve Opening ◽  
Response Capability

The grid-connected micro gas turbine (MGT) generation system is playing an important role in power systems because of its demand response capability and application in combined heat and power (CHP) systems. When applied to promote demand response, the generation system is expected to respond to follow instructions quickly, but a rapid response harms the safety and is not conducive to the benefits of customers, which leads to a contradiction. In this paper, a closed-loop power control is introduced for the MGT to improve demand response capability. The rate of fuel valve opening is limited so as to protect the equipment from thermal fatigue threats. An optimization method is developed for identifying the control parameters, balancing the response time and unrealized energy in the regulation process. An improved whale optimization algorithm (IWOA) is proposed to implement the optimization. Results of the algorithm performance verify that WOA is competitive with other heuristic algorithms, and IWOA is more suitable for parameter optimization problems than WOA because of better efficiency and exploitation capability. Results of power response further indicate that the proposed control strategy can achieve expected aims and is suitable for the MGT generation system.

scholarly journals Robust operation of microgrid energy system under uncertainties and demand response program

2020 ◽  
Vol 17 (2) ◽  
pp. 1005
Author(s):  
Sahar Seyyedeh Barhagh ◽  
Amin Mohammadpour Shotorbani ◽  
Behnam Mohammadi-Ivatloo ◽  
Kazem Zare ◽  
Ali Farzamnia
Keyword(s):  
Power Systems ◽  
Demand Response ◽  
Optimization Problems ◽  
Energy Systems ◽  
Stochastic Scheduling ◽  
Energy System ◽  
Optimization Method ◽  
Operating Conditions ◽  
Mixed Integer ◽  
Demand Response Program

<span>Microgrid energy systems are one of suitable solutions to the available problems in power systems such as energy losses, and resiliency issues. Local generation by these energy systems can reduce the role of the upstream network, which is a challenge in risky conditions. Also, uncertain behavior of electricity consumers and generating units can make the optimization problems sophisticated. So, uncertainty modeling seems to be necessary. In this paper, in order to model the uncertainty of generation of photovoltaic systems, a scenario-based model is used, while the robust optimization method is used to study the uncertainty of load. Moreover, the stochastic scheduling is performed to model the uncertain nature of renewable generation units. Time-of–use rates of demand response program (DRP) is also utilized to improve the system economic performance in different operating conditions. Studied problem is modeled using a mixed-integer linear programming (MILP). The general algebraic modeling system (GAMS) package is used to solve the proposed problem. A sample microgrid is studied and the results with DRP and without DRP are compared. It is shown that same robustness is achieved with a lower increase in the operation cost using DRP.</span>

scholarly journals A hybrid cuckoo–harmony search algorithm for optimal design of water distribution systems

2015 ◽  
Vol 18 (3) ◽  
pp. 544-563 ◽  
Author(s):  
Razi Sheikholeslami ◽  
Aaron C. Zecchin ◽  
Feifei Zheng ◽  
Siamak Talatahari
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Search Space ◽  
Optimization Method

Meta-heuristic algorithms have been broadly used to deal with a range of water resources optimization problems over the past decades. One issue that exists in the use of these algorithms is the requirement of large computational resources, especially when handling real-world problems. To overcome this challenge, this paper develops a hybrid optimization method, the so-called CSHS, in which a cuckoo search (CS) algorithm is combined with a harmony search (HS) scheme. Within this hybrid framework, the CS is employed to find the promising regions of the search space within the initial explorative stages of the search, followed by a thorough exploitation phase using the combined CS and HS algorithms. The utility of the proposed CSHS is demonstrated using four water distribution system design problems with increased scales and complexity. The obtained results reveal that the CSHS method outperforms the standard CS, as well as the majority of other meta-heuristics that have previously been applied to the case studies investigated, in terms of efficiently seeking optimal solutions. Furthermore, the CSHS has two control parameters that need to be fine-tuned compared to many other algorithms, which is appealing for its practical application as an extensive parameter-calibration process is typically computationally very demanding.

scholarly journals Power system voltage instability risk mitigation via emergency demand response-based whale optimization algorithm

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Mohammed Amroune ◽  
Tarek Bouktir ◽  
Ismail Musirin
Keyword(s):  
Power Systems ◽  
Power System ◽  
Demand Response ◽  
Optimization Algorithm ◽  
Voltage Stability ◽  
Risk Mitigation ◽  
Stability Margin ◽  
Whale Optimization Algorithm ◽  
Voltage Instability ◽  
Whale Optimization

AbstractIn recent years, due to the economic and environmental issues, modern power systems often operate proximately to the technical restraints enlarging the probable level of instability risks. Hence, efficient methods for voltage instability prevention are of great importance to power system companies to avoid the risk of large blackouts. In this paper, an event-driven emergency demand response (EEDR) strategy based on whale optimization algorithm (WOA) is proposed to effectively improve system voltage stability. The main objective of the proposed EEDR approach is to maintain voltage stability margin (VSM) in an acceptable range during emergency situations by driving the operating condition of the power system away from the insecure points. The optimal locations and amounts of load reductions have been determined using WOA algorithm. To test the feasibility and the efficiency of the proposed method, simulation studies are carried out on the IEEE 14-bus and real Algerian 114-bus power systems.

scholarly journals Multi-Objective Artificial Bee Colony Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3187
Author(s):  
Nien-Che Yang ◽  
Danish Mehmood ◽  
Kai-You Lai
Keyword(s):  
Power Systems ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimization Method ◽  
Manhattan Distance ◽  
Third Order ◽  
Multi Objective ◽  
Power Filters ◽  
Bee Colony ◽  
Passive Power

Passive power filters (PPFs) are most effective in mitigating harmonic pollution from power systems; however, the design of PPFs involves several objectives, which makes them a complex multiple-objective optimization problem. This study proposes a method to achieve an optimal design of PPFs. We have developed a new multi-objective optimization method based on an artificial bee colony (ABC) algorithm with a minimum Manhattan distance. Four different types of PPFs, namely, single-tuned, second-order damped, third-order damped, and C-type damped order filters, and their characteristics were considered in this study. A series of case studies have been presented to prove the efficiency and better performance of the proposed method over previous well-known algorithms.

scholarly journals Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable Energies

2021 ◽  
Vol 5 (4) ◽  
pp. 140
Author(s):  
Jianqiang Hu ◽  
Jinde Cao
Keyword(s):  
Power Systems ◽  
Demand Response ◽  
Control Strategy ◽  
Programming Model ◽  
Response Strategy ◽  
Chance Constraint ◽  
Optimal Dispatch ◽  
Renewable Power ◽  
Power Injection ◽  
And Control

Demand response (DR) flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a demand response optimization dispatch control strategy for flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs) with stochastic renewable power injection. Firstly, a chance constraint look-ahead programming model is proposed to maximize the social welfare of both units and load agents, through which the optimal power scheduling for TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed, respectively, based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. It has been shown that the proposed dispatch and control strategy can coordinate the flexible load agents and the renewable power injection. Finally, the simulation results on a modified IEEE 39 bus system demonstrate the effectiveness of the proposed demand response strategy.

scholarly journals A Swarm Intelligence Approach to the Power Dispatch Problem

2010 ◽  
Vol 5 (3) ◽  
pp. 375 ◽  
Author(s):  
Dinu Călin Secui ◽  
Ioan Felea ◽  
Simona Dzitac ◽  
Laurențiu Popper
Keyword(s):  
Power Systems ◽  
Optimization Problems ◽  
Optimization Method ◽  
Economic Power ◽  
Swarm Optimization ◽  
Power Dispatch ◽  
Complex Optimization ◽  
Economic Power Dispatch ◽  
The Mathematical Model ◽  
Intelligence Approach

This paper examines how two techniques of the Particle Swarm Optimization method (PSO) can be used to solve the Economic Power Dispatch (EPD) problem. The mathematical model of the EPD is a nonlinear one, PSO algorithms being considered efficient in solving this kind of models. Also, PSO has been successfully applied in many complex optimization problems in power systems. The PSO techniques presented here are applied to three case studies, which analyze power systems having four, six, respectively twenty generating units.

scholarly journals Optimal Operation of Stand-Alone Microgrid Considering Emission Issues and Demand Response Program Using Whale Optimization Algorithm

2021 ◽  
Vol 13 (14) ◽  
pp. 7710
Author(s):  
Mehrdad Tahmasebi ◽  
Jagadeesh Pasupuleti ◽  
Fatemeh Mohamadian ◽  
Mohammad Shakeri ◽  
Josep M. Guerrero ◽  
...  
Keyword(s):  
Power Systems ◽  
Demand Response ◽  
Optimization Algorithm ◽  
New Technologies ◽  
Renewable Energy Sources ◽  
Optimization Methods ◽  
Optimal Operation ◽  
Whale Optimization Algorithm ◽  
Whale Optimization ◽  
Demand Response Program

Microgrids are new technologies for integrating renewable energies into power systems. Optimal operation of renewable energy sources in standalone micro-grids is an intensive task due to the continuous variation of their output powers and intermittant nature. This work addresses the optimum operation of an independent microgrid considering the demand response program (DRP). An energy management model with two different scenarios has been proposed to minimize the costs of operation and emissions. Interruptible/curtailable loads are considered in DRPs. Besides, due to the growing concern of the developing efficient optimization methods and algorithms in line with the increasing needs of microgrids, the focus of this study is on using the whale meta-heuristic algorithm for operation management of microgrids. The findings indicate that the whale optimization algorithm outperforms the other known algorithms such as imperialist competitive and genetic algorithms, as well as particle swarm optimization. Furthermore, the results show that the use of DRPS has a significant impact on the costs of operation and emissions.

scholarly journals Opposition-Based Multi-Objective Whale Optimization Algorithm with Multi-Leader Guiding

2021 ◽  
Author(s):  
Yang Li ◽  
Wei-gang Li ◽  
Yun-tao Zhao ◽  
Ao Liu
Keyword(s):  
Optimization Algorithm ◽  
Pareto Front ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Benchmark Problems ◽  
Initial Population ◽  
Multi Objective ◽  
Opposition Based Learning ◽  
Whale Optimization

Abstract Over the years, heuristic algorithms have been widely studied, especially in multi-objective optimization problems (MOPs). The multi-objective whale optimization algorithm based on multi-leader guiding (MOWOAMLG) is proposed in this paper, which is the multi-objective version of whale optimization algorithm (WOA). The proposed algorithm adopts several improvements to enhance optimization performance. First, multiple leadership solutions guide the population to search the sparse space to achieve more homogeneous exploration in per iteration, and the leadership solutions are selected on the Pareto front by grid mechanism and the principle of maximum crowding distance. Second, the differential evolution (DE) is employed to generate the offspring for the leadership solutions, while WOA is employed for the ordinary solutions. In addition, a novel opposition-based learning (OBL) strategy is developed to improve the distribution of the initial population. To show the significance of the proposed algorithm, it is tested on the 20 bi-objective and tri-objective unconstrained benchmark problems of varying nature and complexities. The result of numerical experiments shows that the proposed algorithm has competitive advantages in convergence and distribution while compared with other 10 classic or state-of-the-arts algorithms. The convergence curve of IGD indicates that MOWOAMLG is able to obtain good Pareto front in cost of fewer optimization iterations. Moreover, it is tested on load distribution of hot rolling, and the result proves its good performance in real-world applications. Thus, all of the aforementioned results have indicated that MOWOAMLG is comparatively effective and efficient to solve MOPs.

scholarly journals Research on ordered charge and discharge of cluster electric vehicle based on index selection

2019 ◽  
Vol 272 ◽  
pp. 01023
Author(s):  
Chunxuan Hu ◽  
Tianran Li ◽  
Chao Yuan
Keyword(s):  
Power Systems ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Demand Response ◽  
Control Strategy ◽  
Assessment Model ◽  
Cost Constraints ◽  
Basic Characteristics ◽  
The Cost ◽  
The Impact

The basic characteristics of electric vehicles are important basis for studying the behavior of electric vehicles. According to the basic characteristics of electric vehicles, this paper establishes an electric vehicle convergence model and its control strategy with demand-side response. Taking into account the demand for electric vehicles, electric vehicle aggregators and power companies, reducing the cost of control, while reducing the impact on electric vehicles. Based on the real-time state of charge, the conditions of electric vehicle in the network and other factors to build the assessment model of the scheduling potential, and then put forward the demand response indicators of electric vehicles, and give the corresponding aggregation strategy. considering the multiple constraints , such as the cost constraints of electric vehicles participating in grid regulation, the charging requirements of electric vehicle owners, and the battery consumption of electric vehicles, a control strategy model is proposed for electric vehicles participating in demand response of power systems. The simulation test shows that the aggregation strategy can not only meet the travel needs of electric vehicle owners, but also reduce the impact on the electric vehicle caused by frequent switching of charge and discharge status. In addition, it can also reduce the cost of grid regulation.

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