Refine Search

New Search

Results: 2

(searched for: Modelling of an Optimized Microgrid Model by Integrating DG Distributed Generation Sources to IEEE 13 Bus System)
Save to Scifeed
Page of 1
Articles per Page
by
Show export options
  Select all
Bisma Imtiaz, Imran Zafar, Cui Yuanhui
European Journal of Electrical Engineering and Computer Science, Volume 5, pp 18-25; doi:10.24018/ejece.2021.5.2.309

Abstract:
Due to the rapid increase in energy demand with depleting conventional sources, the world’s interest is moving towards renewable energy sources. Microgrid provides easy and reliable integration of distributed generation (DG) units based on renewable energy sources to the grid. The DG’s are usually integrated to microgrid through inverters. For a reliable operation of microgrid, it must have to operate in grid connected as well as isolated mode. Due to sudden mode change, performance of the DG inverter system will be compromised. Design and simulation of an optimized microgrid model in MATLAB/Simulink is presented in this work. The goal of the designed model is to integrate the inverter-interfaced DG’s to the microgrid in an efficient manner. The IEEE 13 bus test feeder has been converted to a microgrid by integration of DG’s including diesel engine generator, photovoltaic (PV) block and battery. The main feature of the designed MG model is its optimization in both operated modes to ensure the high reliability. For reliable interconnection of designed MG model to the power grid, a control scheme for DG inverter system based on PI controllers and DQ-PLL (phase-locked loop) has been designed. This designed scheme provides constant voltage in isolated mode and constant currents in grid connected mode. For power quality improvement, the regulation of harmonic current insertion has been performed using LCL filter. The performance of the designed MG model has been evaluated from the simulation results in MATLAB/ Simulink.
H. Lan, S. Wen, Q. Fu, D. C. Yu, L. Zhang
Mathematical Problems in Engineering, Volume 2015, pp 1-8; doi:10.1155/2015/493560

Abstract:
The consumption of conventional energy sources and environmental concerns have resulted in rapid growth in the amount of renewable energy introduced to power systems. With the help of distributed generations (DG), the improvement of power loss and voltage profile can be the salient benefits. However, studies show that improper placement and size of energy storage system (ESS) lead to undesired power loss and the risk of voltage stability, especially in the case of high renewable energy penetration. To solve the problem, this paper sets up a microgrid based on IEEE 34-bus distribution system which consists of wind power generation system, photovoltaic generation system, diesel generation system, and energy storage system associated with various types of load. Furthermore, the particle swarm optimization (PSO) algorithm is proposed in the paper to minimize the power loss and improve the system voltage profiles by optimally managing the different sorts of distributed generations under consideration of the worst condition of renewable energy production. The established IEEE 34-bus system is adopted to perform case studies. The detailed simulation results for each case clearly demonstrate the necessity of optimal management of the system operation and the effectiveness of the proposed method.1. IntroductionRecently an increasing amount of renewable energy is introduced into the local and regional utility grids in order to reduce the serious environmental pollution, improve energy efficiency, and reinforce the power system stability. However, a high penetration of renewable energy raises a problem of system instability, caused by the nature of wind and solar uncertainty. The integration of ESS has become one of the most viable solutions to ensure the stability and power quality of power system with facilitating increased penetration of distributed generation resources.An optimal management of ESS with distributed generators in power system can achieve significant benefits as follows [1]: enhance power system reliability and power quality; minimize the power loss and improve the voltage profiles; reduce the power system cost and control high cost energy imbalance charges; serve the demand for peak load and decrease the environmental negative influence.Optimization is an important consideration among the distribution network; in recent years, a few researches have been performed to determine the optimal location and capacity of ESS. The study in [2] presents a smart energy management system to optimize the operation of the microgrid. The system power loss and cost are minimized by the smart management of ESS and operation optimization of distributed generation. The authors in [3] make use of linear programming to develop a new methodology for determining the optimal allocation of embedded generation with respect to the system constraints. In [4], a multiobjective optimization combined with nondominated sorting genetic algorithm is presented to minimize the power generation cost and to maximize the useful life of lead-acid batteries. The research in [5] proposes a new approach based on the cost benefit analysis for optimal sizing of an ESS in microgrid. Two mathematical models have been built for both the islanded and grid-connected modes of microgrid and different operation situations are analyzed. Reference [6] presented an integrated electricity production cost analysis for autonomous electrical networks based on renewable energy sources and energy storage configurations. The initial cost of the energy storage, the input electricity and fuel cost, and the fixed and variable M&O cost of the entire installation were taken into consideration. A vulnerability assessment for power system planning and operation has been discussed in [7]. The authors in [8] focused on the optimal ESS for maximizing the support to the network voltage control in a distributed system.Particle swarm optimization has become an efficient tool for solving the nonlinear, nondifferentiable, multiobjective, and discrete variables optimization problems due to its flexible applications and better robustness in controlling parameters. The authors in [9] made use of PSO to achieve the best performance by effectively utilizing controllable loads and ESS. The study in [10] proposed the PSO algorithm to solve economic dispatch problem in a microgrid. With the help of the approach, the system cost, power loss and emission are reduced sharply. The research in [11] employs the PSO technique to search the optimal settings of the optimized parameters for enhancing the microgrid stability. Linear and nonlinear models of a microgrid operating in grid-connected and autonomous modes have been presented. In [12], Lee utilizes PSO approach for optimal wind-battery coordination in a power system. The optimal operating schedule for a battery energy storage system and thermal unit was reached while minimizing the total operation cost. Reference [13] establishes an economic operation model of microgrid including PV, wind energy, heat recovery boiler, and battery. PSO is employed to reduce the emission of pollutant for greenhouse gases and minimize the operation cost. In [14], a novel PSO based on multiagent systems is presented as a solution to the reactive power dispatch problem. A modified PSO based on multiobjective optimization algorithm to solve the energy storage design problem which includes not only energy storage capacity and power rate but also the operation strategy is proposed in [15].In this paper, a microgrid based on IEEE 34-bus system is set up, which consists of wind power generation system, photovoltaic (PV) generation system, diesel generation system, ESS, and various types of loads. Due to the concern of the power quality and surety in microgrid, particle swarm optimization to allocate the ESS for minimizing the total power loss is proposed. With the help of the optimal allocation of ESSs, the system power loss is dramatically reduced and the voltage profile is improved much. Furthermore, the system reliability is enhanced with a smaller voltage fluctuation. The performance of the microgrid in islanded mode with different operation situation is analyzed in detail and the uncertainty of wind and solar power is taken into consideration.The rest of this paper is organized as follows. In Section 2, a microgrid configuration based on IEEE 34-bus distribution system is presented and different types of distributed generations are modeled. Section 3 states the problem in formulas. Section 4 proposes the algorithm of PSO. In Section 5, the established 34-bus microgrid system is adopted to implement and verify the proposed algorithm. Several case studies are conducted and compared.2. Microgrid Configuration2.1. System StructureDue to its role in driving modern power systems to achieve clean energy generation, the concept of microgrid has been considered more frequently in recent years [16]. A primary goal of a microgrid is to operate a cluster of DGs which are placed in an area power system to provide power and energy with higher reliability, surety, and quality to the local loads. In this paper, a stand-alone 34-bus microgrid is set up for studying the behavior of the renewable energy systems and diesel generator and their effects on the voltage and frequency in a microgrid. The system with the scale of 12 KV and 6 MVA consisting of two 750 KW wind turbines, one 1 MW solar PV, one 1.5 MW diesel generator, and one 500 KW energy storage system is shown in Figure 1.Figure 1: The construction of 34-bus microgrid.2.2. Microgrid Components2.2.1. Wind TurbineThe large-scale integration of wind power into modern power systems has challenged the wind turbine modeling and analysis innovatively. Accurate modeling of different wind turbine technologies is becoming a necessity as wind turbines replace conventional units in the production side [17]. In the paper, a 750 KW induction wind generator is modeled, which is shown in Figure 2.Figure 2: The model for a 750 KW wind generator.2.2.2. Solar PVPV intermittency, or discontinuity of power output, is one of the most important traits used in evaluation and it is often the largest barrier to introducing DGs on a utility system [18], so a detailed solar PV model with maximum power point tracing control method is set up in the paper. The structure of PV model is described in Figure 3.Figure 3: The model for 1 MW PV generation system.2.2.3. Diesel GeneratorIn order to obtain an acceptable efficiency and moderate operation condition, diesel generators are widely used to maintain the voltage in microgrid, especially when more renewable energy generations are introduced into the system. This paper proposes a 1.5 MW synchronous diesel generator model and voltage and frequency drool control is developed, which is shown in Figure 4.Figure 4: Synchronous diesel generation system.2.2.4. Energy Storage SystemDue to the randomness of the natural condition, the output power of renewable energy generations varies drastically and these fluctuations make undesirable effects on the voltage, frequency, and transient stability of the utility grid [19]. In this paper, a 500 KW energy storage system is employed to support the short-term shortcomings of renewable energy and the construction of the ESS model is depicted in Figure 5.Figure 5: 500 KW energy storage system.2.2.5. LoadsThe system includes a total of 53 loads, consisting of fixed and variable PQ loads and fixed impedance loads. The total microgrid load profile is shown in Figure 6. The maximum load occurs at 7 PM with the capacity of 1100 KW, which is selected as a peak load condition to conduct PSO algorithm in case studies.Figure 6: Total load in microgrid.3. Problem FormulationA higher power loss always occurs in microgrids so it is significant to optimally allocate the ESS. The optimal storage placement and sizing problem is formulated as a constrained nonlinear integer optimization
Page of 1
Articles per Page
by
Show export options
  Select all
Back to Top Top