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Exploiting Meta-heuristic Technique for Optimal Operation of Microgrid
Abstract
A power system with different types of micro-sources are very popular in recent years. The aim of the paper is to make the environment green by reducing green house gases and meet the load demand in an efficient way. However, we propose a grid-connected microgrid system which meets the load demand in an efficient manner to achieve our objectives. The objective of this work is to find the optimal set points of controllable micro-sources in terms of cost minimization. The grid-connected microgrid also helps to exchange power with utility during different intervals of a day to meet the load demand. The significance and performance of the proposed strategy is proved through performing simulations in MATLAB. However, the overall cost of MG is less, while in schedulable microsources the cost of FC is less as compared to MT and DE.
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Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.
Micro-grids have spread in many distribution systems worldwide. They offer safe and reliable operation for their consumers by managing the micro-grids' power generation and trading with the main grid. Furthermore, micro-grids can help in integrating and promoting for Renewable Energy Sources (RES)
Based on the characteristics of electric vehicles (EVs), this paper establishes the load models of EVs under the autonomous charging mode and the coordinated charging and discharging mode. Integrating the EVs into a microgrid system which includes wind turbines (WTs), photovoltaic arrays (PVs), diesel engines (DEs), fuel cells (FCs) and a storage battery (BS), this paper establishes multi-objective economic dispatch models of a microgrid, including the lowest operating cost, the least carbon dioxide emissions, and the lowest pollutant treatment cost. After converting the multi-objective functions to a single objective function by using the judgment matrix method, we analyze the dynamic economic dispatch of the microgrid system including vehicle-to-grid (V2G) with an improved particle swarm optimization algorithm under different operation control strategies. With the example system, the proposed models and strategies are verified and analyzed. Simulation results show that the microgrid system with EVs under the coordinated charging and discharging mode has better operation economics than the autonomous charging mode. Meanwhile, the greater the load fluctuation is, the higher the operating cost of the microgrid system is.
Energy management in microgrids is typically formulated as a nonlinear optimization problem. Solving it in a centralized manner does not only require high computational capabilities at the microgrid central controller (MGCC), but may also infringe customer privacy. Existing distributed approaches, on the other hand, assume that all generations and loads are connected to one bus, and ignore the underlying power distribution network and the associated power flow and system operational constraints. Consequently, the schedules produced by those algorithms may violate those constraints and thus are not feasible in practice. Therefore, the focus of this paper is on the design of a distributed energy management strategy (EMS) for the optimal operation of microgrids with consideration of the distribution network and the associated constraints. Specifically, we formulate microgrid energy management as an optimal power flow problem, and propose a distributed EMS where the MGCC and the local controllers jointly compute an optimal schedule. We also provide an implementation of the proposed distributed EMS based on IEC 61850. As one demonstration, we apply the proposed distributed EMS to a real microgrid in Guangdong Province, China, consisting of photovoltaics, wind turbines, diesel generators, and a battery energy storage system. The simulation results demonstrate the effectiveness and fast convergence of the proposed distributed EMS.
Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.
The use of a new control method for grid-connected inverters for reducing the output current harmonic distortion in a wide range of grid-connected distributed generation (DG) applications, including wind turbine (WT) and fuel cell (FC) inverters is proposed in this paper. The control method designed to eliminate main harmonics in a microgrid (MG) and between MG and point of common coupling (PCC) and responsible for the correction of the system unbalance. Another advantage of the proposed control method is that it can be easily adopted into the DG control system without the installation of extra hardware. The proposed control method is comprised of the synchronous reference frame method (SRF). Results from the proposed control method are provided to show the feasibility of the proposed approach. © 2017 Institute of Advanced Engineering and Science. All rights reserved.
In this paper, we comparatively evaluate the performance of home energy management controller which is designed on the basis of heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO). In this regard, we introduce a generic architecture for demand side management (DSM) which integrates residential area domain with smart area domain via wide area network. In addition, problem formulation is carried via multiple knapsack problem. For energy pricing, combined model of time of use tariff and inclined block rates is used. Simulation results show that all designed models for energy management act significantly to achieve our objections and proven as a cost-effective solution to increase sustainability of smart grid. GA based energy management controller performs more efficiently than BPSO based energy management controller and ACO based energy management controller in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.
Hybrid photovoltaic (PV)–wind turbine (WT) systems with battery storage have been introduced as a green and reliable power system for remote areas. There is a steady increase in usage of hybrid energy system (HES) and consequently optimum sizing is the main issue for having a cost-effective system. This paper evaluates the performance of different evolutionary algorithms for optimum sizing of a PV/WT/battery hybrid system to continuously satisfy the load demand with the minimal total annual cost (TAC). For this aim, all the components are modeled and an objective function is defined based on the TAC. In the optimization problem, the maximum allowable loss of power supply probability is also considered to have a reliable system, and three well-known heuristic algorithms, namely, particle swarm optimization (PSO), tabu search (TS) and simulated annealing (SA), and four recently invented metaheuristic algorithms, namely, improved particle swarm optimization (IPSO), improved harmony search (IHS), improved harmony search-based simulated annealing (IHSBSA), and artificial bee swarm optimization (ABSO), are applied to the system and the results are compared in terms of the TAC. The proposed methods are applied to a real case study and the results are discussed. It can be seen that not only average results produced by ABSO are more promising than those of the other algorithms but also ABSO has the most robustness. Also considering set to 5%, the PV/battery is the most cost-effective hybrid system, and in other values, the PV/WT/battery is the most cost-effective systems.
Recently, the use of wind generation has rapidly increased in micro-grids. Due to the fluctuation of wind power, it is difficult to schedule wind turbines (WTs) with other distributed energy resources (DERs). In this paper, we propose an expert energy management system (EEMS) for optimal operation of WTs and other DERs in an interconnected micro-grid. The main purpose of the proposed EEMS is to find the optimal set points of DERs and storage devices, in such a way that the total operation cost and the net emission are simultaneously minimized. The EEMS consists of wind power forecasting module, smart energy storage system (ESS) module and optimization module. For optimal scheduling of WTs, the power forecasting module determines the possible available capacity of wind generation in the micro-grid. To do this, first, an artificial neural network (ANN) is used to forecast wind speed. Then, the obtaining results are used considering forecasting uncertainty by the probabilistic concept of confidence interval. To reduce the fluctuations of wind power generation and improve the micro-grid performances, a smart energy storage system (ESS) module is used. For optimal management of the ESS, the comprehensive mathematical model with practical constraints is extracted. Finally, an efficient modified Bacterial Foraging Optimization (MBFO) module is proposed to solve the multi-objective problem. An interactive fuzzy satisfying method is also used to simulate the trade-off between the conflicting objectives (cost and emission). To evaluate the proposed algorithm, the EEMS is applied to a typical micro-grid which consists of various DERs, smart ESS and electrical loads. The results show that the EEMS can effectively coordinate the power generation of DERs and ESS with respect to economic and environmental considerations.
Recently, due to technology improvements, governmental incentives for the use of green energies and rising concerns about high cost of energy from fossil fuels, renewable energy sources (RESs) appears to be a promising approach for producing local, clean, and inexhaustible energy. This motivates the implementation of microgrids (MGs) introduced as a cluster of electrical and/or thermal loads and different RESs. Due to different uncertainties linked to electricity supply in renewable microgrids, probabilistic energy management techniques are going to be necessary to analyze the system. This paper proposes a probabilistic approach for the energy and operation management (EOM) of renewable MGs under uncertain environment. The proposed framework consists of 2m point estimate method for covering the existing uncertainties in the MGs and a self-adaptive optimization algorithm based on the gravitational search algorithm (GSA) to determine the optimal energy management of MGs. This paper considers uncertainties in load demand, market prices and the available electrical power of wind farms and photovoltaic systems. In this study, a self-adaptive mutation technique is offered to enhance the convergence characteristics of the original GSA and avoid being entrapped into local optima. The Weibull and normal distributions are employed to model the input random variables. Moreover, the Gram–Charlier expansion is used to find an accurate distribution of the total energy and operational cost of MGs for the next day-ahead. The effectiveness of the proposed method is validated on a typical grid-connected MG including energy storage and different power generating units.
We compare two models to determine the size of grid units and dispatch in a wind-diesel power system with hydrogen storage. Both take as data 1-year time series of hourly wind speed and electricity demand, and their objective is to minimise cost. Our first model, based on linear programming, generates as output a combination of capacities and a year time series for the dispatch variables. Our second model runs a fixed dispatch rule over several capacity combinations and selects the cheapest option. The dispatch rule can then be improved through comparison with the linear programming solution. At present costs, the hydrogen storage-conversion system is excluded from the solutions, so the interesting operation rules associated with the option of harvesting do not arise. However, the costs of hydrogen storage technologies are decreasing with investment. By running our model with prospective costs for year 2010, we see storage emerge in the optimum, and thus a sample of the operation patterns that will occur in a renewable dominated grid.