Harmony Search Method: Theory and Applications

The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm, which has been employed to cope with numerous challenging tasks during the past decade. In this paper, the essential theory and applications of the HS algorithm are first described and reviewed. Several typical variants of the original HS are next briefly explained. As an example of case study, a modified HS method inspired by the idea of Pareto-dominance-based ranking is also presented. It is further applied to handle a practical wind generator optimal design problem

Improving the delivered power quality from WECS to the grid based on PMSG control model

Renewable energy has become one of the most energy resources nowadays, especially, wind energy. It is important to implement more analysis and develop new control algorithms due to the rapid changes in the wind generators size and the power electronics development in wind energy applications. This paper proposes a grid-connected wind energy conversion system (WECS) control scheme using permanent magnet synchronous generator (PMSG). The model works to improve the delivered power quality and maximize its value. The system contained one controller on the grid side converter (GSC) and two simulation packages used to simulate this model, which were PSIM software package for simulating power circuit and power electronics converters, and MATLAB software package for simulating the controller on Simulink. It employed a meta-heuristic technique to fulfil this target effectively. Mine-blast algorithm (MBA) and harmony search optimization technique (HSO) were applied to the proposed method to get the best controller coefficient to ensure maximum power to the grid and minimize the overshoot and the steady state error for the different control signals. The comparison between the results of the MBA and the HSO showed that the MBA gave better results with the proposed system

Self-Adaptive Global-Best Harmony Search Algorithm-Based Airflow Control of a Wells-Turbine-Based Oscillating-Water Column

The Harmony Search algorithm has attracted a lot of interest in the past years because of its simplicity and efficiency. This led many scientists to develop various variants for many applications. In this paper, four variants of the Harmony search algorithm were implemented and tested to optimize the control design of the Proportional-Integral-derivative (PID) controller in a proposed airflow control scheme. The airflow control strategy has been proposed to deal with the undesired stalling phenomenon of the Wells turbine in an Oscillating Water Column (OWC). To showcase the effectiveness of the Self-Adaptive Global Harmony Search (SGHS) algorithm over traditional tuning methods, a comparative study has been carried out between the optimized PID, the traditionally tuned PID and the uncontrolled OWC system. The results of optimization showed that the Self-Adaptive Global Harmony Search (SGHS) algorithm adapted the best to the problem of the airflow control within the wave energy converter. Moreover, the OWC performance is superior when using the SGHS-tuned PID.This work was supported in part by the Basque Government, through project IT1207-19 and by the MCIU/MINECO through RTI2018-094902-B-C21 / RTI2018-094902-B-C22 (MCIU/AEI/FEDER, UE)

Analysis of the Effect of Distributed Generation on Loss Reduction in Electrical Distribution Network

Distribution network is said to be the most visual part of the electric production and the most observed by the utilities for investment, maintenance and operation. The system have been operated under stressed conditions due to limited structure and increasing day to day requirement of power consumption, which have a significant economic and social impact on the system. Due to the system high resistance to impendence ratio, large amount of power loss occur in the network. This loss is the most severity factors affecting the power quality delivered to the end users and depend on power network expansion and load complexity. Among the support methods available for power loss minimization in distribution network, strategic allocation of Distributed Generation (DG) in distribution system is widely considered a viable option. DGs are electrical sources connected to the power network located to consumer’s side but very small when compared with the centralized power plant. They can be in form of wind, mini-hydro, photovoltaic and fuel-based system such as fuel cells and micro-turbines. Therefore, in this study, different approaches for power loss minimization in electrical distribution system with the incorporation of DG by various researchers were reviewed. These approaches have become powerful tools to overcome the problem of power loss minimization in distribution system. Keywords: Distribution System, Power Loss. Distributed Generation, Power Consumption, Photovoltaic System, Centralized Power Plant. DOI: 10.7176/JETP/11-6-02 Publication date: November 30th 202

A modified whale optimization algorithm-based adaptive fuzzy logic PID controller for load frequency control of autonomous power generation systems

An autonomous power generation system (APGS) contains units such as diesel energy generator, solar photovoltaic units, wind turbine generator and fuel cells along with energy-storing units such as the flywheel energy storage system and battery energy storage system. The components either run at lower/higher power output or may turn on/off at different instants of their operation. Due to this, the conventional controllers will not provide desired performance under varied load conditions. This paper proposes an adaptive fuzzy logic PID (AFPID) controller for load frequency control. In order to achieve an improved performance, a modified whale optimization algorithm (mWOA) was also proposed in this paper for tuning of the AFPID parameters. The proposed algorithm was first evaluated using standard test functions and compared with other recent algorithms to authenticate the competence of algorithm. The proposed mWOA algorithm outperforms PSO, GSA, DE and FEP algorithms in five out of seven unimodal test functions and four out of six multimodal test functions. The effectiveness of the AFPID compared with the conventional PID and the proposed AFPID provides better performance. Reduction of 39.13% in error criteria (objective function) compared with WOA-PID controller. The proposed approach was also compared with some recently proposed frequency control approaches in a widely used two-area test system

Tilt Integral Derivative Controller Optimized by Battle Royale Optimization for Wind Generator Connected to Grid

Globally the countries are focusing on reducing the carbon footprint leading to a greater effort for electrical energy generation by renewable energy sources, particularly wind. The wind turbines are invariably using doubly fed asynchronous generator. In this paper a controller has been designed for a doubly fed induction motor. The proposed Tilt Integral Derivate controller for was compared with commonly used PI, PID controllers. Several optimization algorithms were used for tuning of controllers and the best one was selected for each type of controller. The controller has been optimized using battlefield optimization. It had been compared with proportional integral controller, fractional order proportional integral derivative controller. Other controllers were optimized using meta heuristic algorithms. The controller enhanced the system response in terms of settling time, rise time and other parameters. The Tilt controller gave the overall superior performance in terms of parameters like rise time, settling time, settling minimum, peak, and peak time. The results were obtained using MATLAB. This paper discusses operation of doubly fed induction motor operation and optimization methods

Understanding the Electricity-Water-Climate Change Nexus Using a Stochastic Optimization Approach

Climate change has been shown to cause droughts (among other catastrophic weather events) and it is shown to be exacerbated by the increasing levels of greenhouse gas emissions on our planet. In May 2013, CO2 daily average concentration over the Pacific Ocean at Mauna Loa Observatory reached a dangerous milestone of 400 ppm, which has not been experienced in thousands of years in the earth\u27s climate. These levels were attributed to the ever-increasing human activity over the last 5-6 decades. Electric power generators are documented by the U.S. Department of Energy to be the largest users of ground and surface water and also to be the largest emitters of carbon dioxide and other greenhouse gases. Water shortages and droughts in some parts of the U.S. and around the world are becoming a serious concern to independent system operators in wholesale electricity markets. Water shortages can cause significant challenges in electricity production having a direct socioeconomic impact on surrounding regions. Several researchers and institutes around the world have highlighted the fact that there exists an inextricable nexus between electricity, water, and climate change. However, there are no existing quantitative models that study this nexus. This dissertation aims to ll this vacuum. This research presents a new comprehensive quantitative model that studies the electricity-water-climate change nexus. The first two parts of the dissertation focuses on investigating the impact of a joint CO2 emissions and H2O usage tax on a sample electric power network. The latter part of the dissertation presents a model that can be used to study the impact of a joint CO2 and H2O cap-and-trade program on a power grid. We adopt a competitive Markov decision process (CMDP) approach to model the dynamic daily competition in wholesale electricity markets, and solve the resulting model using a reinforcement learning approach. In the first part, we study the impacts of dierent tax mechanisms using exogenous tax rate values found in the literature. We consider the complexities of a electricity power network by using a standard direct-current optimal power flow formulation. In the second part, we use a response surface optimization approach to calculate optimal tax rates for CO2 emissions and H2O usage, and then we examine the impacts of implementing this optimal tax on a power grid. In this part, we use a multi-objective variant of the optimal power flow formulation and solve it using a strength Pareto evolutionary algorithm. We use a 30-bus IEEE power network to perform our detailed simulations and analyses. We study the impacts of implementing the tax policies under several realistic scenarios such as the integration of wind energy, stochastic nature of wind energy, integration of PV energy, water supply disruptions, adoption of water saving technologies, tax credits to generators investing in water saving technologies, and integration of Hydro power generation. The third part, presents a variation of our stochastic optimization framework to model a joint CO2 and H2O cap-and-trade program in wholesale electricity markets for future research. Results from the research show that for the 30-bus power grid, transition from coal generation to wind power could reduce CO2 emissions by 60% and water usage about 40% over a 10-year horizon. Electricity prices increase with the adoption of water and carbon taxes; likewise, capacity disruptions also cause electricity prices to increase