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Impact assessment of a hybrid energy-generation system on a residential distribution system in Taiwan
Accepted Manuscript Title: Impact assessment of a hybrid energy generation system on a residential distribution system in Taiwan Author: Nien-Che Yang Wei-Chih Tseng PII: DOI: Reference:
S0378-7788(15)00030-4 http://dx.doi.org/doi:10.1016/j.enbuild.2015.01.024 ENB 5629
To appear in:
ENB
Received date: Revised date: Accepted date:
16-5-2014 29-10-2014 5-1-2015
Please cite this article as: N.-C. Yang, W.-C. Tseng, Impact assessment of a hybrid energy generation system on a residential distribution system in Taiwan, Energy and Buildings (2015), http://dx.doi.org/10.1016/j.enbuild.2015.01.024 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Impact assessment of a hybrid energy generation system on a residential distribution system in Taiwan Nien-Che Yang*, Wei-Chih Tseng
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Department of Electrical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 32003, Taoyuan, Taiwan, ROC E-mail: [email protected] Tel: +886-3-4638800 Ext.7114; Fax:+886-3-4639355
Abstract: In this paper, a Monte Carlo based three-phase power flow method is proposed to evaluate the impact of a variety of
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a hybrid energy generation system on a residential distribution system. First, the proposed deterministic power flow method is exploited based on graph theory, circuit theory and nonlinear numerical solution methods. The proposed Monte Carlo based power
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flow method is developed based on the proposed deterministic power flow method and a commercial software package OPTIMUS. Various distributed generations (DGs) are also combined in the proposed method. In this paper, the hybrid energy system in the
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University of Yuan Ze is used as a sample case. The hybrid energy system consists of a 5kW photovoltaic (PV) system, a 3kW wind power system and a 2kW hydrogen-fuel cell (HFC) system. Then, a comprehensive study of the hybrid energy microgrid system, in
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terms of steady-state voltage deviations, power flows, power losses, reverse power flows, short-circuit fault currents, maximum allowable DGs capacity and voltage unbalance factor, is performed to evaluate the impacts of the hybrid energy system on the utility
distribution systems.
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power grid. The research results are of value to the pre-determination of impacts of the hybrid energy system on the residential
1 Introduction
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Keywords: Distributed energy resources, distributed generations, deterministic power flow analysis, graph theory, injection current, microgrids, Monte Carlo power flow analysis.
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With the purpose of reducing greenhouse gas emissions and local pollutions, many new developments in power systems have occurred in recent years. Hybrid energy microgrid systems may offer a revolutionary application in power systems [1-3]. In general, there are several distributed generations (DGs) involved in a hybrid energy microgrid system. The DGs may employ the traditional energy sources, such as diesel, gas and coal or the renewable energy resources, such as wind, solar and hydrogen. Due to the advantages of DGs applications, the use of DGs, such as photovoltaic (PV) [4-7], wind turbines [8, 9] and fuel cell systems, is rapidly growing throughout the world. To improve the reliability, flexibility and efficiency of a utility system, some experts and scholars have concentrated on the economic and environmental evaluation of renewable energy use in residential distribution systems. In Ref. [10], the sizing and techno-economical optimization of a stand-alone hybrid PV/wind system with battery storage was presented. In Ref. [11], an electrical energy analysis of a building has been performed to evaluate the power quality of the hybrid energy system. In this hybrid energy system, electrical energy is generated by a 5kW PV system and is stored by a 2.4kW
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hydrogen-fuel cell (HFC) system. In Ref. [12], an analysis of the technical and financial viability of grid-only, renewable energy supply (RES) only and grid/RES hybrid power supply configurations for a large-scale grid-connected hotel has been presented. In Ref. [13], two typical micro combined heat and power (CHP) technologies, namely, gas engine and fuel cell for residential buildings, were analyzed. By using an evaluation model for residential micro CHP systems, two different operating modes including minimum-cost operation and minimum-emission operation are taken into account. In other words, the economic and environmental
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potentials of the micro CHP systems are taken into account. In Ref. [14], a study of a grid-connected PV system installed in an institutional building has been proposed. The findings are useful for planning grid-connected PV installations in terms of energy,
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environment and financial aspects. In Ref. [15], the impact of roof integrated PV orientation on the residential electricity peak demand has been proposed. The energy efficiency methods in the building with an oriented PV system can reduce the peak energy
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demand by 62% compared to a code standard building of the same size. In Ref. [16], a feasibility study of small-scale hydro/PV/Wind based hybrid electric supply system was proposed. Each possible combination of the resources is taken into
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account. The feasible combinations that can meet the required system load demands and constraints are determined. In Ref. [17], the Hybrid Optimization Model for Electric Renewables (HOMER) software was used to perform a comprehensive economic and
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environmental analysis of hybrid solar and wind energy system based on both solar data and wind data. In Ref. [18], a techno-economic feasibility study of an autonomous hybrid wind/PV/battery power system for a household has been proposed
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based on renewable energy resources and load data research. Besides the advantages of DGs applications, several system issues which may be encountered as DGs penetrate into distribution systems were discussed in [19]. The power quality issues may cover
and LTC operations.
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the impact of DGs on the system voltage, interaction of DGs and capacitor operations, and interaction of DGs and voltage regulator
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This paper differs greatly from the above studies that focused on the economic and environmental evaluation of renewable energy utilization in power systems. The main objective of this paper is to diagnose the power quality and safety of hybrid energy microgrid systems. The proposed Monte Carlo based method can be used to deal with the uncertainty problems of hybrid energy microgrid system operating states. That is, the solution sets obtained by the proposed method can cover all the possible combinations of the critical operating cases of the hybrid energy system. The hybrid energy system of the University of Yuan Ze is used as a case study. The hybrid energy system consists of a 5kW PV system, a 3kW wind power system and a 2kW HFC system. First, a Monte Carlo based three-phase power flow method for microgrid decision-making is proposed. In an easy-to-use manner, the proposed Monte Carlo based power flow method is developed on the MATLAB/Simulink based deterministic power flow method and a commercial software package OPTIMUS. Then, a comprehensive analysis of the hybrid energy system, in terms of steady-state voltage deviations, power flows, power losses, reverse power flows, short-circuit fault currents, maximum allowable DGs capacity and voltage unbalance factor, is performed to evaluate the impact of the hybrid energy system on the residential
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distribution system. That is, the proposed method is designed for predetermining the impact of the hybrid energy system on the residential distribution system. The healthy, marginal and at risk system operation conditions can be distinguished. The critical damage situations for systems and equipment can therefore be avoided. This makes the person processing the DG interconnection has confidence in making judgments on the planning and design of DG interconnection. This paper is organized as follows. Section II introduces the proposed Monte Carlo based power flow method for microgrid
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comprehensive analysis of the hybrid energy system. In Section V, a brief conclusion is drawn.
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decision-making. Section III introduces the hybrid energy system of the University of Yuan Ze in Taiwan. Section IV presents a
2 Proposed Algorithm
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The proposed Monte Carlo based three-phase power flow method for microgrid decision-making is developed based on a deterministic three-phase power flow analysis and a commercial software package OPTIMUS. The proposed method is described
2.1 Deterministic three-phase power flow analysis
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as follows.
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The proposed deterministic power flow analysis technique is designed and implemented with the MATLAB/Simulink platform. For a precise power flow simulation, the system components must be represented by exact mathematical models that can describe their physical behaviors. These components [20] include (1) overhead lines and underground cables, (2) capacitors and reactors, (3)
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three phase distribution transformers and single phase distribution transformers with mid-tap, (4) automatic voltage regulators, (5) constant power loads, constant current loads and constant impedance loads, and (6) distributed resources.
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The IEEE 13 Bus benchmark system was used to demonstrate the validity of the proposed method. The IEEE 13 Bus benchmark
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system implemented in MATLAB/Simulink is shown in Fig. 1. The final converged results obtained by the proposed algorithm for the IEEE 13 Bus benchmark system are shown in Table 1. Table 2 and Table 3 show the mismatches of the magnitudes and angles of bus voltages for the final converged voltage solutions obtained by the proposed algorithm and the IEEE results, respectively. The maximum mismatches of the magnitudes and angles of bus voltages between the final converged solutions and the IEEE results are less than 0.0006 p.u. (0.0610%) and 0.02 (0.8032%), respectively. In other words, the final converged voltage results obtained by the proposed algorithm would approximate to the IEEE results. Fig. 1 to be placed here Table 1 to be placed here Table 2 to be placed here
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Table 3 to be placed here
2.2 Monte Carlo based three-phase power flow analysis The proposed Monte Carlo based three-phase power flow analysis is implemented by MATLAB/Simulink and OPTIMUS. The most commonly used continuous distributions include the continuous uniform, normal, exponential, gamma and beta distributions.
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The actual operating conditions of the hybrid energy microgrid system may depend on the changes of load demands, power productions of DGs, system configurations and operating statuses of voltage control equipment. To take the uncertainty problems of microgrid operating states, the action probability distribution is represented by normal distribution function with mean and standard
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deviation. The simulation workflow for the commerical software package OPTIMUS is shown in Fig. 2.
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Fig. 2 to be placed here
3 Hybrid Energy Microgrid System
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Yuan Ze University is the first university in the world passing the Energy Management System ISO 50001 Standards. Therefore, Yuan Ze University was selected by the Taiwan Ministry of Education as a model for implementing the Green University Project
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with communities and nearby universities to share the experiences of improving energy efficiency, carbon emission reduction and the utilization of green energy and materials. In this paper, the hybrid energy system of the University of Yuan Ze in Taiwan, shown in Fig. 3, is used as a sample system to demonstrate the performance of the proposed Monte Carlo based power flow method. The
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scheme of the hybrid energy microgrid system is shown in Fig. 4. The technical data of the AC meter is shown in Table 4. The one-line diagram of the hybrid energy microgrid system is shown in Fig. 5. The parameters of this sample system are listed as
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follows.
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Table 4 to be placed here Fig. 3 to be placed here Fig. 4 to be placed here Fig. 5 to be placed here
1) The system short-circuit capacity at the primary side of the substation transformer is 500MVA. The voltage level of the primary distribuiton network is 22.8kV.
2) The rated capacity and rated voltage of the distribution transformer T1 are 1000kVA and 22.8kV−0.38kV/0.22kV, respectively. The percent impedance of the distribution transformer T1 is 4.8%. The rated capacity and rated voltage of the distribution transformer T2 are 5kVA and 0.22kV−0.11kV, respectively. The percent impedance of the distribution transformer T2 is 3%. The rated capacity and rated voltage of the distribution transformer T3 are 7.5kVA and 0.38kV/0.22kV−0.22kV, respectively. The percent impedance of the distribution transformer T3 is 3%.
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3) The main feeder conductors in the sample system are underground cables. The parameters of main feeder conductors are shown in Table 5. 4) The type and rated capacity of loads for the sample system are shown in Table 6. 5) The rated capacity of the PV system is 5kW. The technical data of the PV module is shown in Table 7. 6) The rated capacity of the wind power system is 3kW. The wind power curve of WT 2000 DF wind turbine is illustrated in Fig.
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6. The technical data of the wind turbine is shown in Table 8.
7) The rated capacity of the HFC system is 2kW. The technical data of the HFC is shown in Table 9.
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Table 5 to be placed here
Table 7 to be placed here Fig. 6 to be placed here
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Table 8 to be placed here
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Table 6 to be placed here
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Table 9 to be placed here
4 Results and Discussion
To clarify the applications of the proposed method, a comprehensive analysis of the hybrid energy system in Yuan Ze University
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is performed. The steady-state voltage deviations, power flows, power losses and reverse power flows, as well as short-circuit fault currents, maximum allowable DGs capacity, and voltage unbalance factor are examined in this impact analysis study. In the figures
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shown below, the output quantities of phase R are drawn by red points (.); the output quantities of phase S are drawn by magenta
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crosses (x); and the output quantities of phase T are drawn by blue diamonds (◊).
4.1 Steady-state voltage deviation
To understand the impact of the hybrid energy system on the power grid, the ranges of system parameters, probabilistic characteristics of loads and stochastic changes of generations are all taken into account. In this steady-state voltage deviation study, the normal distribution function is adopted. Both the load demands and power generations are generated randomly. And then, the proposed Monte Carlo based power flow solution is performed. The statistics of bus voltage magnitude at each bus for the hybrid energy system are shown in Fig. 7. In the hybrid energy system, the hydrogen (H2) generator is installed on phase S of Bus A9. When the H2 generator is operated to generate hydrogen gas, the bus voltages on phase S will drop considerably. The minimum bus voltage on phase S is about 0.97 pu (213.4V). Under peak load conditions, the H2 generator must avoid operating for the production of hydrogen gas. The fuel cell energy system is installed on phase R of Bus A18 and the wind power system is installed on phase T of Bus A19. The PV energy system can provide a three-phase power supply and is installed at Bus A21. The maximum bus voltage
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is about 1.048 pu (230.56V). Therefore, the bus voltages of the whole hybrid energy system are between 0.97 pu and 1.048 pu. The voltage deviations at the pivot bus (Bus A7) are between 0.38% and 1.01%. The limitation of the steady-state voltage deviation of 2.5% can be met in all cases [21-25]. During the period of September 2013-August 2014, the voltage duration curve of the whole distribution system in Yuan Ze University is shown in Fig. 8. The minimum bus voltage and the maximum bus voltage are 0.97 pu and 1.03 pu, respectively. The critical operation situations of the hybrid energy system were evaded. Therefore, the bus voltage
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magnitudes of the whole distribution system in Yuan Ze University were constrained within the predicted limits. That is, the hybrid energy system was operated at the healthy system operation conditions
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Fig. 7 to be placed here
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Fig. 8 to be placed here
4.2 Power flow
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The statistics of bus power injection at each bus and power flow at each line segment for the hybrid energy system are shown in Fig. 9 and Fig. 10, respectively. When the hybrid energy system is operated with the minimum load demand and the rated power output of DGs, the reverse power flow will occur around 5kW. According to the Chinese National Standard (CNS), the maximum
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continuous operation current of 600V/PVC/14mm2 cable is 50A. That is, for a 380V distribution system, the maximum continuous operation power of each cable is about 11kVA.
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When the hybrid energy system is operated with the maximum load demand and no power production of DGs, the branch currents on phase S of line segment L6 is close to the maximum continuous operation power 11kVA, where the H2 generator may
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consume up to 8kVA. To ensure the distribution system be operated safely, the operation schedules for the H2 generator shall be considered carefully. In other words, the operation period of the H2 generator should be set for the off-peak loading periods.
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Therefore, the operation currents of feeders and transformers in the hybrid energy system will not exceed the safety limit. Fig. 9 to be placed here Fig. 10 to be placed here
4.3 Power loss
The power losses of the hybrid energy system can be estimated by a three-phase power flow solution [26, 27] or an annual energy loss evaluation [28, 29]. To evaluate the daily energy loss, weekly energy loss, monthly energy loss and annual energy loss in detail, the daily load curves of each load and hourly power generation curves of each DG should be taken into consideration. The statistics of power loss at each line segment for the hybrid energy system can be determined by using the proposed Monte Carlo based power flow algorithm. The statistics of power losses for the hybrid energy system are shown in Fig. 11. After the DGs are connected to the power grid, the power losses of the hybrid energy system can be reduced up to 103.5W. That is, the annual energy loss of the hybrid
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energy system can be decreased up to 894kWh. Fig. 11 to be placed here
4.4 Reverse power flow When the hybrid energy system is operated with the minimum load demand and the rated power output of DGs, the maximum
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reverse power flow will occur on the line segment L6 of feeders. Table 10 shows the distributions of the reverse power flows for the hybrid energy system. According to CNS, the maximum continuous operation current of 600V/PVC/14mm2 cable is 50A. For a 380V distribution system, the maximum continuous operation power of each cable is up to 11kVA. The maximum reverse power
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flow is 4553.34W. The operation currents of feeders and transformers in the hybrid energy system will not exceed the security limit.
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Table 10 to be placed here
4.5 Short-circuit fault current
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In this short-circuit fault current analysis, the complex short-circuit MVA method [30] is adopted. The short circuit MVA value at Bus A7 is 1.2229MVA. When a three-phase bolted fault occurred at Bus A7, the short circuit fault current is about 1.858kA. All
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DGs involved in the hybrid energy system are the inverter-based sources. Therefore, the short circuit fault currents contributed by DGs are almost two times the rated currents of DGs. When a three-phase bolted fault occurred at Bus A7, the DGs will contribute a fault current of 0.0608kA. The total short-circuit fault current at Bus A7 is up to 1.9188kA. The fault currents provided by DGs
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are much smaller than the fault currents contributed by the power grid. In this case, the effect of the fault currents provided by DGs
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on the setting of the protective relays is not obvious.
4.6 Maximum allowable DGs capacity
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In practice, (1) is widely adopted to evaluate the maximum allowable installed capacity of DGs corresponding to the steady-state voltage deviations limits [31, 32].
PDG
d% SS.C. cos cos +
(1)
where d% is the steady-state voltage deviation as a percentage of the nominal voltage; SS.C. is the network short circuit capacity at the point of DG interconnection; and and are the phase angle of the system driving-point impedance and the phase angle between the output voltage and current of DG, respectively. The network short-circuit capacity and grid driving-point impedance angle at Bus A7 are 1.23MVA and 23.66 , respectively. The DGs are assumed to be operated at unity factor and the steady-state voltage deviations due to DGs are limited to ±2.5%, as a percentage of the nominal voltage. The maximum possible installed capacity of DGs is 33.57kVA. According to the steady-state voltage deviation and the maximum continuous operation current limitations ruled by local interconnection codes, the remaining installation capacity of DGs for the hybrid energy system is about
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20kVA.
4.7 Voltage unbalance factor Voltage unbalance exists in transmission and distribution systems. In three-phase systems, these problems exist due to un-transposed transmission lines, single-phase loads, open delta and open wye connections of transformers, blown fuses on
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three-phase capacitor banks and so on. “Voltage Unbalance” is usually expressed in terms of a voltage unbalance factor (VUF), which can be used to quantify the situation of system voltage unbalance and evaluate some performance of a power system.
application constraints of the different voltage unbalance definition are not the same.
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Nowadays, the definitions of voltage unbalance applied by different communities are not consistent. The complexity and
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In the VUF, the symmetrical components of the phase voltages are used to replace the phase voltages to describe the voltage unbalance in power systems. The negative-sequence voltage unbalance factor d2 and the zero-sequence voltage unbalance factor d0
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are defined as the ratio of the negative-sequence voltage component to the positive-sequence voltage component and the ratio of the zero-sequence voltage component to the positive-sequence voltage component, respectively. Although the calculation of the VUF is more complicated, both the magnitudes and phase displacements of three-phase voltages are taken into consideration. In other
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words, the characteristics of the voltage unbalance of the power system can be held accurately. In addition, the VUF is known as the “true definition” of the voltage unbalance. It is usually used for more rigorous requirements. The statistics of voltage unbalance
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factor at Bus A7 for the hybrid energy system are shown in Fig. 12. According to IEC 1000 2-1/2 and IEC 1000-2-2, the voltage unbalance factor d2 in low voltage distribution systems shall be less than 2%. The maximum value of the voltage unbalance factor
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d2 at Bus A7 is 1%. In all cases, the requirements of IEC standards are satisfied.
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Fig. 12 to be placed here
5 Conclusions
A comprehensive analysis has been performed on the impacts of the hybrid energy system on the utility power grid. In this paper, the hybrid energy system of Yuan Ze University was used as a case study. In the hybrid energy system, there are three types of DGs including PV, wind and fuel cell energy systems. In the demonstration field, lamps, computers, electric heater, window type air-conditioners and split air-conditioners are used for testing. In order to ensure the power quality and operation security of the hybrid energy system, the comprehensive analysis was used to diagnose the steady-state voltage deviations, power flows, power losses, reverse power flows, short-circuit fault currents, maximum allowable DGs capacity and voltage unbalance factor for the hybrid energy system. The analysis results are summarized as follows:
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1) The steady-state voltage deviations at Bus A7 are between 0.38% and 1.01%. In all cases, the limitation of the steady-state voltage deviation of 2.5% is satisfied. 2) When the hybrid energy system is operated with maximum load demand and no power productions of DGs, the branch currents on phase S of line segment L6 is close to the maximum continuous operation power 11kVA. To ensure the hybrid energy system is operated safely, the operated period of the H2 generator is set for off-peak loading periods.
is, the annual energy loss of the hybrid energy system can be decreased up to 894kWh.
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3) After the DGs are connected to the power grid, the power losses of the hybrid energy system can be reduced up to 103.5W. That
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4) When the hybrid energy system is operated with the minimum load demand and the rated power output of DGs, the maximum reverse power flow occurs on the line segment L6 of feeders. According to CNS, the maximum continuous operation current of
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600V/PVC/14mm2 cable is 50A. For a 380V distribution system, the maximum continuous operation power of each cable is close to 11kVA. The maximum reverse power flow is up to 4553.34W. Therefore, the operation currents of feeders and
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transformers in the hybrid energy system will not exceed the security limit.
5) All DGs involved in the hybrid energy system are the inverter-based sources. The maximum short circuit fault currents of DGs
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are two times the rated currents of DGs. Therefore, the fault currents provided by DGs will be much smaller than the fault currents contributed by the power grid. The effect of the fault currents provided by DGs on the setting of the protective relays is
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not obvious in this case.
6) According to the steady-state voltage deviation and the maximum continuous operation current limitations ruled by local
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interconnection codes, the remaining installation capacity of DGs for the hybrid energy system is about 20kVA. 7) The voltage unbalance factors at Bus A7 are up to 1%. In all cases, the requirements of IEC standards are satisfied.
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In terms of the electrical impact assessment, the proposed method based on Monte Carlo simulations can be used for the pre-determination of the critical impact of a variety of a hybrid energy system on a residential distribution system. In the proposed method, the all uncertainties in the actual network configurations and features, as well as load and hybrid energy generation system states can be taken into consideration. That is, all possible combinations of system operation conditions within the feasible regions of the system generations and system loads states are all involved. The healthy, marginal and at risk system operation conditions can be distinguished. This makes the person processing the DG interconnection has confidence in making judgments on the planning and design of DG interconnection.
6 Acknowledgements This work was supported in part by the National Science Council of Taiwan, R.O.C. through grant numbers NSC 102-3113-P-155-001 and NSC 102-2221-E-155-040.
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Research Highlights Monte Carlos based three-phase power flow method. Implemented by MATLAB/Simulink and OPTIMUS.
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Hybrid energy system consists of a 5kW PV system, a 3kW wind power system and a 2kW HFC system.
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Table 1 Accuracy comparison for IEEE 13 Bus distribution system. Vbn
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Calculated
IEEE results
Calculated
IEEE results
1.00000.00 1.0625-0.00 1.0211-2.51 1.0181-2.57 0.9940-3.25
1.00000.00 1.06250.00 1.0210-2.49 1.0180-2.56 0.9941-3.23
0.9903-5.34 0.9903-5.34 0.9884-5.36
0.9900-5.30 0.9900-5.30 0.9881-5.32
1.0000-120.00 1.0500-120.00 1.0419-121.68 1.0400-121.73 1.0216-122.19 1.0327-121.87 1.0310-121.95 1.0528-122.26 1.0528-122.26
1.0000-120.00 1.0500-120.00 1.0420-121.72 1.0401-121.72 1.0218-122.22 1.0329-121.90 1.0311-121.98 1.0529-122.34 1.0529-122.34
1.0000120.00 1.0687120.00 1.0176117.76 1.0150117.75 0.9961117.27 1.0157117.78 1.0137117.83 0.9781115.87 0.9781115.87 0.9761115.77 0.9740115.61
1.0000120.00 1.0687120.00 1.0174117.83 1.0148117.82 0.9960117.34 1.0155117.86 1.0134117.90 0.9778116.02 0.9778116.02 0.9758115.92 0.9738115.78
0.9821-5.29 0.9903-5.34 0.9841-5.59
0.9818-5.25 0.9900-5.31 0.9835-5.56
1.0528-122.26 1.0550-122.42
1.0529-122.34 1.0553-122.52
0.9777116.02 0.9758116.03
ip t
IEEE results
cr
Van Calculated
0.9781115.87 0.9759115.88
us
Node ID 650 RG60 632 633 634 645 646 671 680 684 611 652 692 675
Table 2 Mismatches of magnitudes of bus voltages between the calculated solutions and IEEE results.
0.0003 0.0003 0.0003
0.0303 0.0303 0.0304
0.0003 0.0003 0.0006
0.0306 0.0303 0.0610
(%)
p.u.
(%)
0.0000 0.0000 -0.0001 -0.0001 -0.0002 -0.0002 -0.0001 -0.0001 -0.0001
0.0000 0.0000 -0.0096 -0.0096 -0.0196 -0.0194 -0.0097 -0.0095 -0.0095
0.0000 0.0000 0.0002 0.0002 0.0001 0.0002 0.0003 0.0003 0.0003 0.0003 0.0002
0.0000 0.0000 0.0197 0.0197 0.0100 0.0197 0.0296 0.0307 0.0307 0.0307 0.0205
-0.0001 -0.0003
-0.0095 -0.0284
0.0004 0.0001
0.0409 0.0102
M
0.0000 0.0000 0.0000 0.0000 0.0001 0.0098 0.0001 0.0098 -0.0001 -0.0101
Vcn
p.u.
an
Vbn (%)
pt
650 RG60 632 633 634 645 646 671 680 684 611 652 692 675
Van p.u.
ed
Node ID
Node ID 650 RG60 632 633 634 645 646 671 680 684 611 652 692 675
Ac ce
Table 3 Mismatches of angles of bus voltages between the calculated solutions and IEEE results. Van
Vbn
Vcn
Degree
(%)
Degree
(%)
Degree
(%)
0.0000 0.0000 -0.0200 -0.0100 -0.0200
0.0000 0.0000 0.8032 0.3906 0.6192
-0.0400 -0.0400 -0.0400
0.7547 0.7547 0.7519
0.0000 0.0000 0.0400 -0.0100 0.0300 0.0300 0.0300 0.0800 0.0800
0.0000 0.0000 -0.0329 0.0082 -0.0245 -0.0246 -0.0246 -0.0654 -0.0654
0.0000 0.0000 -0.0700 -0.0700 -0.0700 -0.0800 -0.0700 -0.1500 -0.1500 -0.1500 -0.1700
0.0000 0.0000 -0.0594 -0.0594 -0.0597 -0.0679 -0.0594 -0.1293 -0.1293 -0.1294 -0.1468
-0.0400 -0.0300 -0.0300
0.7619 0.5650 0.5396
0.0800 0.1000
-0.0654 -0.1500 -0.0816 -0.1500
-0.1293 -0.1293
Page 13 of 22
Table 4 Parameters of main feeder conductors for microgrid system. Material
Impedance (ohms/km)
Copper
10.1+j0.149 3.62+j0.138 2.51+j0.130 0.677+j0.361 1.412+j0.388
AAC
0.188+j0.378
AAC
0.375+j0.409
ip t
Size 600V/PVC 2.0mm2 5.5mm2 8.0mm2 30mm2 14mm2 25kV/PEX/200mm2 336 25kV/PEX/100mm2 3/0
cr
Table 5 Technical data of AC meter. Parameters
us
2rated continuous 4rated for 2 seconds 3rated continuous 10rated for 10 seconds 50rated for 1 second
an
Current
16 bits resolution 15 cycle/sec 100 msec
Table 6 Distributions of discrete loads for microgrid system. Rated Capacity S(W)
546 8,000 252 2,600
T(W)
500 2,600
746 180 1,200 2,178
Ac ce
A8 Lamp1 A9 H2 generator A10 Lamp2 A11 Air conditioner A12 PC A13 Air conditioner A14 Ventilator A16 Fan A17 Electric heater Total power consumption
R(W)
ed
Description
pt
Node
M
A/D converter Sampling Rate Response time： Max. input over capability Voltage
11,346
3,100
Table 7 Technical data of PV module. Parameters
Cell per module Cell technology Max. power output per cell Max. power voltage Max. power current Open circuit voltage Short circuit current Rated total power output System output voltage Conversion efficiency
63 Multicrystalline 80W 16.9V 4.73A 21.5V 4.97A 5kW 220Vac 14%
Table 8 Technical data of wind turbine.
Page 14 of 22
Parameters
ip t
Permanent magnet 18 2kW 3kW 30V-240Vac 2.4 m 12.5 m/s 3 m/s 20m/s 13.5 m/s 50 m/s 145-780 rpm 3 45%
cr
Type Number of generator poles Rated power output Peak power output Output voltage Rotor diameter Rated wind speed Cut in wind speed Cut-out wind speed Security wind speed Survival wind speed Rotor speed Number of blades Conversion efficiency
an
PEM 2kW Gaseous hydrogen 99.995% purity 220Vac
M
Parameters Type of fuel cell Rated power Fuel type Fuel purity System output voltage
us
Table 9 Technical data of fuel cell.
Table 10 Distribution of reverse power flows for hybrid energy system.
A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A1 A2 A3 A4 A5 A6 A7 A7 A7 A7 A7 A7 A7
0
Phase S P(W) Q(var) -1598.08 34.5 -1598.08 34.48 -1598.08 34.48 -1598.08 34.48 -1598.15 34.12 -1600.29 32.98
ed
A1 A2 A3 A4 A5 A6 A7 A7 A7 A7 A7 A7 A7 A7 A15 A15 A7 A7 A7 A20 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14
Phase R P(W) Q(var) -3573.36 44.12 -3573.36 44.04 -3573.36 44.03 -3573.37 44.03 -3573.72 42.24 -3584.44 36.52 0 0
pt
To Bus No
0
0
0
0
Phase T P(W) Q(var) -4509 53.06 -4509 52.93 -4509.01 52.93 -4509.01 52.92 -4509.58 50.06 -4526.65 40.96
0
Ac ce
From Bus No.
0 0 0 -1997.06
0 0 0 0.11
-1604.15 -1610.09 3573.36 3573.36 3573.37 3573.72 3584.44 3601.21 0
31.8 2.16 -44.04 -44.03 -44.03 -42.24 -36.52 -31.91 0
0
0
0
0
-1603.65 -1609.64 1598.08 1598.08 1598.08 1598.15 1600.29 1603.65
32.06 2.17 -34.48 -34.48 -34.48 -34.12 -32.98 -32.06
0
0
0
0
0
0 0
0 0
-2948.97 -1604.38 -1610.3 4509 4509.01 4509.01 4509.58 4526.65 4553.34
1.95 31.68 2.15 -52.93 -52.93 -52.92 -50.06 -40.96 -33.63
0 0
0 0
0
Page 15 of 22
0 0 0 0
1610.09 1666.66
-2.16 0
1609.64 1666.66
-2.17 0
3000 1610.3 1666.66
0 -2.15 0
ed
M
an
us
cr
ip t
0 0 0 2000
pt
A7 A15 A15 A7 A7 A7 A20
Ac ce
A15 A16 A17 A18 A19 A20 A21
Page 16 of 22
List of Figures Fig. 1. IEEE Bus benchmark system implemented in MATLAB/Simulink. Fig. 2. Simulation workflow for OPTIMUS software. Fig. 3. Hybrid energy system of Yuan Ze University in Taiwan.
ip t
Fig. 4. Schematic diagram of hybrid energy microgrid system. Fig. 5. One line diagram of hybrid energy microgrid system.
Fig. 9. Statistics of bus power injection at each bus for hybrid energy system.
us
Fig. 8. Voltage duration curve of the whole distribution system in Yuan Ze University
cr
Fig. 6. Power curve of WT 2000 DF wind turbine
Fig. 10. Statistics of power flow at each line segment for hybrid energy system.
an
Fig. 11. Statistics of power loss at each line segment for hybrid energy system.
Fig. 12. Statistics of voltage unbalance factor at Bus A7 for hybrid energy system: (a) with H2 generator; (b) without H2
M
generator.
List of Tables
ed
Table 1 Accuracy comparison for IEEE 13 Bus distribution system.
Table 2 Mismatches of magnitudes of bus voltages between the calculated solutions and IEEE results.
pt
Table 3 Mismatches of angles of bus voltages between the calculated solutions and IEEE results. Table 4 Parameters of main feeder conductors for microgrid system.
Ac ce
Table 5 Technical data of AC meter.
Table 6 Distributions of discrete loads for microgrid system. Table 7 Technical data of PV module.
Table 8 Technical data of wind turbine. Table 9 Technical data of fuel cell.
Table 10 Distribution of reverse power flows for hybrid energy system.
Page 17 of 22
Ia RMS Ib RMS
Power Source
Phasors
Ic RMS 1
A
Ia angle
A
B bB
C
A aA
Ib angle
powergui
Ic angle Pa Qa Pb Qb 2
cC
650
Pa
B
Pc
B
Qc Van angle
P
Vbn angle Vcn angle
Vbn RMS
RG50
Three -Phase Dynamic Load A
cC
1 A
Van RMS
bB
Q
P Q A B C
aA
Qa
C
3 C
Vcn RMS 4 a
a b
5
c
6 c
b
Three- Phase V- I Measurement
cC
RG
bB
Load
aA
Meter 1 A
4 a
Yaa
Yab
-Yab
-Yab1
Yac
-Yac
-Yac1
cC
bB
601 -2000
aA
Yba
2 B
5 b
Ybb
Ybc -Ybc
-Ybc1
Yca 3 C
6 c
Ycc
A
Distribution Feeder
cC
bB
aA
Ycb
632
B
Bb
C
C c
C c
Cc
A
a
Aa
Bb
B
b
Bb
Cc
Cc
C
c
602 -500
633
Cc
cC
603 -500 601 -500
bB
645
aA
603 -300
Aa
Bb
A
DZ-646 646
Aa Bb
B b
ip t
YPQ -645
B b
B C
Cc
XFM -1
YPQ-634
634
cr
A B
cC
bB
aA
C
601 -1500
YPQ-632
B
Distribution Transformer
C
671
652
607 -800
A a
C c
Aa
Aa
Aa
Bb
Bb
Bb
Cc
Cc
Cc
692
606 -500
675
Aa
A
cC
Aa
bB
C c
604 -300 601 -1000
aA
684 A
A
A
cC
bB
680
aA
A
A
C
C
C
605 -300
YPQ-675
B
B
YI-611 611
B C
Cc
an
Aa
cC
A a
bB
A
YZ-652
aA
YPQ-671
us
A
DPQ-671
Capacitor -675
DI -692
M
Capacitor -611
Fig. 1. IEEE Bus benchmark system implemented in MATLAB/Simulink.
ed
MATLAB/Simulink
Pro ba bility Density Functio n
pt
So lutio n Sets
Fig. 2. Simulation workflow for OPTIMUS software.
PV energ y sy stem
H y brid en erg y m o n ito rin g a n d co ntro l sy stem fo r intellig ent m icro g rid
Ac ce
Fuel cell energ y system
Wind po wer sy stem
Fig. 3. Hybrid energy system of Yuan Ze University in Taiwan.
Page 18 of 22
LCD Monitor LCD顯示螢幕
Human Machine Interface HMI人機介面
PC 桌上型電腦
H2 Po wer Co n tro l 氫氣 電力 控制 RS485, MODBUS TCP/IP, RS232
LABVIEW
AC Meter 交流電錶 S olid s tate relay 固態電驛
市電 Power Grid 電力
PLC PLC
太陽光電 PV s ys tem 發電系統 Control panel 中央控制盤
風力發 Wind power s ys tem 電系統
燃料電池 Fuel cell s ys tem 發電系統 高性能直流/ 交流變流器
H 2 generator 水電解系統
Flow meter 流量計 Hous ehold appliances
金屬儲氫罐 H2 s torage
ip t
DC/AC inverter
Flow 流量計 meter
cr
電器負載
Fig. 4. Schematic diagram of hybrid energy microgrid system. 500 MVAsc L1 22.8kV L2 220m 25kV/PEX/200mm 2
us
A1
A2
M-D
A3
A4
5224kW PF = 0.99
25kV/PEX/100mm 2 L3
L4 T1
D-B#3b1f
1000kVA Z% = 4.8% X/R = 5 380Y/220V
A5 L5 60m
B#3b1fB#35f
600V/PVC/30mm2
A6
L8
L9
L10
L11
L12
L13
R
S
R
S
T
T
S
12.5m 15m 12.5m 6.5m 5.5m 10m 12m 2 2 2 2 2 2 22 E: 2 E:8 E: 2 E: 2 E: 2 E: 2 E: 2 A8 Ramp 1
A9
A10 A11
H2 generator
Ramp2
A12
Air conditioner
A13 PC
A14
Air conditioner
B#35f3525R
R T2 L17 L18 L19 L14 5kVA 7.5kVA R T Z% = 3% Z% = 3% T3 110V 10m 80m 220V A15 A20 L15 18m 18m 25.5 25.5 4600V/PVC/5.5mm 2 2 2 E: 2 E: 2 100m E: 2mm 2 E: 2 E: 2 L16 L20 A16 A17 A18 A19 A21
Ventilator
546VA 8000VA 252VA 2600VA 500VA 2600VA 746VA
Fan
Electric heater
180VA 1200VA
ed
L7
600V/PVC/14mm2 380Y/220V A7
M
L6 45m
an
100m
Lumped Load of other feeders
HFC
WT
2kW
3kW
PV
5kW
Ac ce
pt
Fig. 5. One line diagram of hybrid energy microgrid system.
Fig. 6. Power curve of WT 2000 DF wind turbine
Page 19 of 22
Bus Voltages
Bus Voltages 1.05
1.05
1.04
1.04
1.03
1.03
) . u. p( s u b V
1.01
1
1.02
1.01
1
0.99
0.99
0.98
0.98
0.97 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21
ip t
1.02
). u. p( s u b V
0.97 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21
Bus No.
Bus No.
(a)
cr
(b)
us
Fig. 7. Statistics of bus voltage magnitude at each bus for hybrid energy system: (a) with H2 generator; (b) without H2 generator. 1.05 Phase R Phase S Phase T
1.04
an
1.03 1.02 1.01 1 0.99 0.98
0
0.1
0.2
0.3
0.4
0.5 0.6 Time (p.u.)
0.7
0.8
0.9
1
ed
0.97
M
). u. p( V
Fig. 8. Voltage duration curve of the whole distribution system in Yuan Ze University Power Injections 4
0 -2 -4 -6
Ac ce
) W k( P
pt
2
-8 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21
Bus No.
x 10 4
) r a v( Q
2 0 -2
-3
A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21
Bus No.
Fig. 9. Statistics of bus power injection at each bus for hybrid energy system.
Page 20 of 22
Power Flow
) W k( P
13 11 9 7 5 3 1 -1 -3 -5 L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 L16 L17 L18 L19 L20
Line No. 150
100
ip t
) r a v( Q
50
cr
0 L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 L16 L17 L18 L19 L20
Line No.
us
Fig. 10. Statistics of power flow at each line segment for hybrid energy system. Power Loss
an
) W ( P
160 140 120 100 80 60 40 20 0 L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 L16 L17 L18 L19 L20
Line No.
) r a v( Q
40
20
Line No.
ed
0 L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 L16 L17 L18 L19 L20
M
60
Fig. 11. Statistics of power loss at each line segment for hybrid energy system. Voltage Unbalance Ratio
2.5 2
%
1.5 1 0.5 0
Ac ce
3
LVUR%
PVUR(1)%
(a)
PVUR(2)%
d2%
Voltage Unbalance Ratio
3.5
pt
3.5
3 2.5 2
%
1.5 1 0.5 0
LVUR%
PVUR(1)%
PVUR(2)%
d2%
(b)
Page 21 of 22
ip t cr us an M ed pt Ac ce
1 2
Fig. 12. Statistics of voltage unbalance factor at Bus A7 for hybrid energy system: (a) with H2 generator; (b) without H2 generator.
22
Page 22 of 22
S0378-7788(15)00030-4 http://dx.doi.org/doi:10.1016/j.enbuild.2015.01.024 ENB 5629
To appear in:
ENB
Received date: Revised date: Accepted date:
16-5-2014 29-10-2014 5-1-2015
Please cite this article as: N.-C. Yang, W.-C. Tseng, Impact assessment of a hybrid energy generation system on a residential distribution system in Taiwan, Energy and Buildings (2015), http://dx.doi.org/10.1016/j.enbuild.2015.01.024 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Impact assessment of a hybrid energy generation system on a residential distribution system in Taiwan Nien-Che Yang*, Wei-Chih Tseng
ip t
Department of Electrical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 32003, Taoyuan, Taiwan, ROC E-mail: [email protected] Tel: +886-3-4638800 Ext.7114; Fax:+886-3-4639355
Abstract: In this paper, a Monte Carlo based three-phase power flow method is proposed to evaluate the impact of a variety of
cr
a hybrid energy generation system on a residential distribution system. First, the proposed deterministic power flow method is exploited based on graph theory, circuit theory and nonlinear numerical solution methods. The proposed Monte Carlo based power
us
flow method is developed based on the proposed deterministic power flow method and a commercial software package OPTIMUS. Various distributed generations (DGs) are also combined in the proposed method. In this paper, the hybrid energy system in the
an
University of Yuan Ze is used as a sample case. The hybrid energy system consists of a 5kW photovoltaic (PV) system, a 3kW wind power system and a 2kW hydrogen-fuel cell (HFC) system. Then, a comprehensive study of the hybrid energy microgrid system, in
M
terms of steady-state voltage deviations, power flows, power losses, reverse power flows, short-circuit fault currents, maximum allowable DGs capacity and voltage unbalance factor, is performed to evaluate the impacts of the hybrid energy system on the utility
distribution systems.
ed
power grid. The research results are of value to the pre-determination of impacts of the hybrid energy system on the residential
1 Introduction
pt
Keywords: Distributed energy resources, distributed generations, deterministic power flow analysis, graph theory, injection current, microgrids, Monte Carlo power flow analysis.
Ac ce
With the purpose of reducing greenhouse gas emissions and local pollutions, many new developments in power systems have occurred in recent years. Hybrid energy microgrid systems may offer a revolutionary application in power systems [1-3]. In general, there are several distributed generations (DGs) involved in a hybrid energy microgrid system. The DGs may employ the traditional energy sources, such as diesel, gas and coal or the renewable energy resources, such as wind, solar and hydrogen. Due to the advantages of DGs applications, the use of DGs, such as photovoltaic (PV) [4-7], wind turbines [8, 9] and fuel cell systems, is rapidly growing throughout the world. To improve the reliability, flexibility and efficiency of a utility system, some experts and scholars have concentrated on the economic and environmental evaluation of renewable energy use in residential distribution systems. In Ref. [10], the sizing and techno-economical optimization of a stand-alone hybrid PV/wind system with battery storage was presented. In Ref. [11], an electrical energy analysis of a building has been performed to evaluate the power quality of the hybrid energy system. In this hybrid energy system, electrical energy is generated by a 5kW PV system and is stored by a 2.4kW
Page 1 of 22
hydrogen-fuel cell (HFC) system. In Ref. [12], an analysis of the technical and financial viability of grid-only, renewable energy supply (RES) only and grid/RES hybrid power supply configurations for a large-scale grid-connected hotel has been presented. In Ref. [13], two typical micro combined heat and power (CHP) technologies, namely, gas engine and fuel cell for residential buildings, were analyzed. By using an evaluation model for residential micro CHP systems, two different operating modes including minimum-cost operation and minimum-emission operation are taken into account. In other words, the economic and environmental
ip t
potentials of the micro CHP systems are taken into account. In Ref. [14], a study of a grid-connected PV system installed in an institutional building has been proposed. The findings are useful for planning grid-connected PV installations in terms of energy,
cr
environment and financial aspects. In Ref. [15], the impact of roof integrated PV orientation on the residential electricity peak demand has been proposed. The energy efficiency methods in the building with an oriented PV system can reduce the peak energy
us
demand by 62% compared to a code standard building of the same size. In Ref. [16], a feasibility study of small-scale hydro/PV/Wind based hybrid electric supply system was proposed. Each possible combination of the resources is taken into
an
account. The feasible combinations that can meet the required system load demands and constraints are determined. In Ref. [17], the Hybrid Optimization Model for Electric Renewables (HOMER) software was used to perform a comprehensive economic and
M
environmental analysis of hybrid solar and wind energy system based on both solar data and wind data. In Ref. [18], a techno-economic feasibility study of an autonomous hybrid wind/PV/battery power system for a household has been proposed
ed
based on renewable energy resources and load data research. Besides the advantages of DGs applications, several system issues which may be encountered as DGs penetrate into distribution systems were discussed in [19]. The power quality issues may cover
and LTC operations.
pt
the impact of DGs on the system voltage, interaction of DGs and capacitor operations, and interaction of DGs and voltage regulator
Ac ce
This paper differs greatly from the above studies that focused on the economic and environmental evaluation of renewable energy utilization in power systems. The main objective of this paper is to diagnose the power quality and safety of hybrid energy microgrid systems. The proposed Monte Carlo based method can be used to deal with the uncertainty problems of hybrid energy microgrid system operating states. That is, the solution sets obtained by the proposed method can cover all the possible combinations of the critical operating cases of the hybrid energy system. The hybrid energy system of the University of Yuan Ze is used as a case study. The hybrid energy system consists of a 5kW PV system, a 3kW wind power system and a 2kW HFC system. First, a Monte Carlo based three-phase power flow method for microgrid decision-making is proposed. In an easy-to-use manner, the proposed Monte Carlo based power flow method is developed on the MATLAB/Simulink based deterministic power flow method and a commercial software package OPTIMUS. Then, a comprehensive analysis of the hybrid energy system, in terms of steady-state voltage deviations, power flows, power losses, reverse power flows, short-circuit fault currents, maximum allowable DGs capacity and voltage unbalance factor, is performed to evaluate the impact of the hybrid energy system on the residential
Page 2 of 22
distribution system. That is, the proposed method is designed for predetermining the impact of the hybrid energy system on the residential distribution system. The healthy, marginal and at risk system operation conditions can be distinguished. The critical damage situations for systems and equipment can therefore be avoided. This makes the person processing the DG interconnection has confidence in making judgments on the planning and design of DG interconnection. This paper is organized as follows. Section II introduces the proposed Monte Carlo based power flow method for microgrid
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comprehensive analysis of the hybrid energy system. In Section V, a brief conclusion is drawn.
ip t
decision-making. Section III introduces the hybrid energy system of the University of Yuan Ze in Taiwan. Section IV presents a
2 Proposed Algorithm
us
The proposed Monte Carlo based three-phase power flow method for microgrid decision-making is developed based on a deterministic three-phase power flow analysis and a commercial software package OPTIMUS. The proposed method is described
2.1 Deterministic three-phase power flow analysis
an
as follows.
M
The proposed deterministic power flow analysis technique is designed and implemented with the MATLAB/Simulink platform. For a precise power flow simulation, the system components must be represented by exact mathematical models that can describe their physical behaviors. These components [20] include (1) overhead lines and underground cables, (2) capacitors and reactors, (3)
ed
three phase distribution transformers and single phase distribution transformers with mid-tap, (4) automatic voltage regulators, (5) constant power loads, constant current loads and constant impedance loads, and (6) distributed resources.
pt
The IEEE 13 Bus benchmark system was used to demonstrate the validity of the proposed method. The IEEE 13 Bus benchmark
Ac ce
system implemented in MATLAB/Simulink is shown in Fig. 1. The final converged results obtained by the proposed algorithm for the IEEE 13 Bus benchmark system are shown in Table 1. Table 2 and Table 3 show the mismatches of the magnitudes and angles of bus voltages for the final converged voltage solutions obtained by the proposed algorithm and the IEEE results, respectively. The maximum mismatches of the magnitudes and angles of bus voltages between the final converged solutions and the IEEE results are less than 0.0006 p.u. (0.0610%) and 0.02 (0.8032%), respectively. In other words, the final converged voltage results obtained by the proposed algorithm would approximate to the IEEE results. Fig. 1 to be placed here Table 1 to be placed here Table 2 to be placed here
Page 3 of 22
Table 3 to be placed here
2.2 Monte Carlo based three-phase power flow analysis The proposed Monte Carlo based three-phase power flow analysis is implemented by MATLAB/Simulink and OPTIMUS. The most commonly used continuous distributions include the continuous uniform, normal, exponential, gamma and beta distributions.
ip t
The actual operating conditions of the hybrid energy microgrid system may depend on the changes of load demands, power productions of DGs, system configurations and operating statuses of voltage control equipment. To take the uncertainty problems of microgrid operating states, the action probability distribution is represented by normal distribution function with mean and standard
cr
deviation. The simulation workflow for the commerical software package OPTIMUS is shown in Fig. 2.
us
Fig. 2 to be placed here
3 Hybrid Energy Microgrid System
an
Yuan Ze University is the first university in the world passing the Energy Management System ISO 50001 Standards. Therefore, Yuan Ze University was selected by the Taiwan Ministry of Education as a model for implementing the Green University Project
M
with communities and nearby universities to share the experiences of improving energy efficiency, carbon emission reduction and the utilization of green energy and materials. In this paper, the hybrid energy system of the University of Yuan Ze in Taiwan, shown in Fig. 3, is used as a sample system to demonstrate the performance of the proposed Monte Carlo based power flow method. The
ed
scheme of the hybrid energy microgrid system is shown in Fig. 4. The technical data of the AC meter is shown in Table 4. The one-line diagram of the hybrid energy microgrid system is shown in Fig. 5. The parameters of this sample system are listed as
pt
follows.
Ac ce
Table 4 to be placed here Fig. 3 to be placed here Fig. 4 to be placed here Fig. 5 to be placed here
1) The system short-circuit capacity at the primary side of the substation transformer is 500MVA. The voltage level of the primary distribuiton network is 22.8kV.
2) The rated capacity and rated voltage of the distribution transformer T1 are 1000kVA and 22.8kV−0.38kV/0.22kV, respectively. The percent impedance of the distribution transformer T1 is 4.8%. The rated capacity and rated voltage of the distribution transformer T2 are 5kVA and 0.22kV−0.11kV, respectively. The percent impedance of the distribution transformer T2 is 3%. The rated capacity and rated voltage of the distribution transformer T3 are 7.5kVA and 0.38kV/0.22kV−0.22kV, respectively. The percent impedance of the distribution transformer T3 is 3%.
Page 4 of 22
3) The main feeder conductors in the sample system are underground cables. The parameters of main feeder conductors are shown in Table 5. 4) The type and rated capacity of loads for the sample system are shown in Table 6. 5) The rated capacity of the PV system is 5kW. The technical data of the PV module is shown in Table 7. 6) The rated capacity of the wind power system is 3kW. The wind power curve of WT 2000 DF wind turbine is illustrated in Fig.
ip t
6. The technical data of the wind turbine is shown in Table 8.
7) The rated capacity of the HFC system is 2kW. The technical data of the HFC is shown in Table 9.
cr
Table 5 to be placed here
Table 7 to be placed here Fig. 6 to be placed here
an
Table 8 to be placed here
us
Table 6 to be placed here
M
Table 9 to be placed here
4 Results and Discussion
To clarify the applications of the proposed method, a comprehensive analysis of the hybrid energy system in Yuan Ze University
ed
is performed. The steady-state voltage deviations, power flows, power losses and reverse power flows, as well as short-circuit fault currents, maximum allowable DGs capacity, and voltage unbalance factor are examined in this impact analysis study. In the figures
pt
shown below, the output quantities of phase R are drawn by red points (.); the output quantities of phase S are drawn by magenta
Ac ce
crosses (x); and the output quantities of phase T are drawn by blue diamonds (◊).
4.1 Steady-state voltage deviation
To understand the impact of the hybrid energy system on the power grid, the ranges of system parameters, probabilistic characteristics of loads and stochastic changes of generations are all taken into account. In this steady-state voltage deviation study, the normal distribution function is adopted. Both the load demands and power generations are generated randomly. And then, the proposed Monte Carlo based power flow solution is performed. The statistics of bus voltage magnitude at each bus for the hybrid energy system are shown in Fig. 7. In the hybrid energy system, the hydrogen (H2) generator is installed on phase S of Bus A9. When the H2 generator is operated to generate hydrogen gas, the bus voltages on phase S will drop considerably. The minimum bus voltage on phase S is about 0.97 pu (213.4V). Under peak load conditions, the H2 generator must avoid operating for the production of hydrogen gas. The fuel cell energy system is installed on phase R of Bus A18 and the wind power system is installed on phase T of Bus A19. The PV energy system can provide a three-phase power supply and is installed at Bus A21. The maximum bus voltage
Page 5 of 22
is about 1.048 pu (230.56V). Therefore, the bus voltages of the whole hybrid energy system are between 0.97 pu and 1.048 pu. The voltage deviations at the pivot bus (Bus A7) are between 0.38% and 1.01%. The limitation of the steady-state voltage deviation of 2.5% can be met in all cases [21-25]. During the period of September 2013-August 2014, the voltage duration curve of the whole distribution system in Yuan Ze University is shown in Fig. 8. The minimum bus voltage and the maximum bus voltage are 0.97 pu and 1.03 pu, respectively. The critical operation situations of the hybrid energy system were evaded. Therefore, the bus voltage
ip t
magnitudes of the whole distribution system in Yuan Ze University were constrained within the predicted limits. That is, the hybrid energy system was operated at the healthy system operation conditions
cr
Fig. 7 to be placed here
us
Fig. 8 to be placed here
4.2 Power flow
an
The statistics of bus power injection at each bus and power flow at each line segment for the hybrid energy system are shown in Fig. 9 and Fig. 10, respectively. When the hybrid energy system is operated with the minimum load demand and the rated power output of DGs, the reverse power flow will occur around 5kW. According to the Chinese National Standard (CNS), the maximum
M
continuous operation current of 600V/PVC/14mm2 cable is 50A. That is, for a 380V distribution system, the maximum continuous operation power of each cable is about 11kVA.
ed
When the hybrid energy system is operated with the maximum load demand and no power production of DGs, the branch currents on phase S of line segment L6 is close to the maximum continuous operation power 11kVA, where the H2 generator may
pt
consume up to 8kVA. To ensure the distribution system be operated safely, the operation schedules for the H2 generator shall be considered carefully. In other words, the operation period of the H2 generator should be set for the off-peak loading periods.
Ac ce
Therefore, the operation currents of feeders and transformers in the hybrid energy system will not exceed the safety limit. Fig. 9 to be placed here Fig. 10 to be placed here
4.3 Power loss
The power losses of the hybrid energy system can be estimated by a three-phase power flow solution [26, 27] or an annual energy loss evaluation [28, 29]. To evaluate the daily energy loss, weekly energy loss, monthly energy loss and annual energy loss in detail, the daily load curves of each load and hourly power generation curves of each DG should be taken into consideration. The statistics of power loss at each line segment for the hybrid energy system can be determined by using the proposed Monte Carlo based power flow algorithm. The statistics of power losses for the hybrid energy system are shown in Fig. 11. After the DGs are connected to the power grid, the power losses of the hybrid energy system can be reduced up to 103.5W. That is, the annual energy loss of the hybrid
Page 6 of 22
energy system can be decreased up to 894kWh. Fig. 11 to be placed here
4.4 Reverse power flow When the hybrid energy system is operated with the minimum load demand and the rated power output of DGs, the maximum
ip t
reverse power flow will occur on the line segment L6 of feeders. Table 10 shows the distributions of the reverse power flows for the hybrid energy system. According to CNS, the maximum continuous operation current of 600V/PVC/14mm2 cable is 50A. For a 380V distribution system, the maximum continuous operation power of each cable is up to 11kVA. The maximum reverse power
cr
flow is 4553.34W. The operation currents of feeders and transformers in the hybrid energy system will not exceed the security limit.
us
Table 10 to be placed here
4.5 Short-circuit fault current
an
In this short-circuit fault current analysis, the complex short-circuit MVA method [30] is adopted. The short circuit MVA value at Bus A7 is 1.2229MVA. When a three-phase bolted fault occurred at Bus A7, the short circuit fault current is about 1.858kA. All
M
DGs involved in the hybrid energy system are the inverter-based sources. Therefore, the short circuit fault currents contributed by DGs are almost two times the rated currents of DGs. When a three-phase bolted fault occurred at Bus A7, the DGs will contribute a fault current of 0.0608kA. The total short-circuit fault current at Bus A7 is up to 1.9188kA. The fault currents provided by DGs
ed
are much smaller than the fault currents contributed by the power grid. In this case, the effect of the fault currents provided by DGs
pt
on the setting of the protective relays is not obvious.
4.6 Maximum allowable DGs capacity
Ac ce
In practice, (1) is widely adopted to evaluate the maximum allowable installed capacity of DGs corresponding to the steady-state voltage deviations limits [31, 32].
PDG
d% SS.C. cos cos +
(1)
where d% is the steady-state voltage deviation as a percentage of the nominal voltage; SS.C. is the network short circuit capacity at the point of DG interconnection; and and are the phase angle of the system driving-point impedance and the phase angle between the output voltage and current of DG, respectively. The network short-circuit capacity and grid driving-point impedance angle at Bus A7 are 1.23MVA and 23.66 , respectively. The DGs are assumed to be operated at unity factor and the steady-state voltage deviations due to DGs are limited to ±2.5%, as a percentage of the nominal voltage. The maximum possible installed capacity of DGs is 33.57kVA. According to the steady-state voltage deviation and the maximum continuous operation current limitations ruled by local interconnection codes, the remaining installation capacity of DGs for the hybrid energy system is about
Page 7 of 22
20kVA.
4.7 Voltage unbalance factor Voltage unbalance exists in transmission and distribution systems. In three-phase systems, these problems exist due to un-transposed transmission lines, single-phase loads, open delta and open wye connections of transformers, blown fuses on
ip t
three-phase capacitor banks and so on. “Voltage Unbalance” is usually expressed in terms of a voltage unbalance factor (VUF), which can be used to quantify the situation of system voltage unbalance and evaluate some performance of a power system.
application constraints of the different voltage unbalance definition are not the same.
cr
Nowadays, the definitions of voltage unbalance applied by different communities are not consistent. The complexity and
us
In the VUF, the symmetrical components of the phase voltages are used to replace the phase voltages to describe the voltage unbalance in power systems. The negative-sequence voltage unbalance factor d2 and the zero-sequence voltage unbalance factor d0
an
are defined as the ratio of the negative-sequence voltage component to the positive-sequence voltage component and the ratio of the zero-sequence voltage component to the positive-sequence voltage component, respectively. Although the calculation of the VUF is more complicated, both the magnitudes and phase displacements of three-phase voltages are taken into consideration. In other
M
words, the characteristics of the voltage unbalance of the power system can be held accurately. In addition, the VUF is known as the “true definition” of the voltage unbalance. It is usually used for more rigorous requirements. The statistics of voltage unbalance
ed
factor at Bus A7 for the hybrid energy system are shown in Fig. 12. According to IEC 1000 2-1/2 and IEC 1000-2-2, the voltage unbalance factor d2 in low voltage distribution systems shall be less than 2%. The maximum value of the voltage unbalance factor
pt
d2 at Bus A7 is 1%. In all cases, the requirements of IEC standards are satisfied.
Ac ce
Fig. 12 to be placed here
5 Conclusions
A comprehensive analysis has been performed on the impacts of the hybrid energy system on the utility power grid. In this paper, the hybrid energy system of Yuan Ze University was used as a case study. In the hybrid energy system, there are three types of DGs including PV, wind and fuel cell energy systems. In the demonstration field, lamps, computers, electric heater, window type air-conditioners and split air-conditioners are used for testing. In order to ensure the power quality and operation security of the hybrid energy system, the comprehensive analysis was used to diagnose the steady-state voltage deviations, power flows, power losses, reverse power flows, short-circuit fault currents, maximum allowable DGs capacity and voltage unbalance factor for the hybrid energy system. The analysis results are summarized as follows:
Page 8 of 22
1) The steady-state voltage deviations at Bus A7 are between 0.38% and 1.01%. In all cases, the limitation of the steady-state voltage deviation of 2.5% is satisfied. 2) When the hybrid energy system is operated with maximum load demand and no power productions of DGs, the branch currents on phase S of line segment L6 is close to the maximum continuous operation power 11kVA. To ensure the hybrid energy system is operated safely, the operated period of the H2 generator is set for off-peak loading periods.
is, the annual energy loss of the hybrid energy system can be decreased up to 894kWh.
ip t
3) After the DGs are connected to the power grid, the power losses of the hybrid energy system can be reduced up to 103.5W. That
cr
4) When the hybrid energy system is operated with the minimum load demand and the rated power output of DGs, the maximum reverse power flow occurs on the line segment L6 of feeders. According to CNS, the maximum continuous operation current of
us
600V/PVC/14mm2 cable is 50A. For a 380V distribution system, the maximum continuous operation power of each cable is close to 11kVA. The maximum reverse power flow is up to 4553.34W. Therefore, the operation currents of feeders and
an
transformers in the hybrid energy system will not exceed the security limit.
5) All DGs involved in the hybrid energy system are the inverter-based sources. The maximum short circuit fault currents of DGs
M
are two times the rated currents of DGs. Therefore, the fault currents provided by DGs will be much smaller than the fault currents contributed by the power grid. The effect of the fault currents provided by DGs on the setting of the protective relays is
ed
not obvious in this case.
6) According to the steady-state voltage deviation and the maximum continuous operation current limitations ruled by local
pt
interconnection codes, the remaining installation capacity of DGs for the hybrid energy system is about 20kVA. 7) The voltage unbalance factors at Bus A7 are up to 1%. In all cases, the requirements of IEC standards are satisfied.
Ac ce
In terms of the electrical impact assessment, the proposed method based on Monte Carlo simulations can be used for the pre-determination of the critical impact of a variety of a hybrid energy system on a residential distribution system. In the proposed method, the all uncertainties in the actual network configurations and features, as well as load and hybrid energy generation system states can be taken into consideration. That is, all possible combinations of system operation conditions within the feasible regions of the system generations and system loads states are all involved. The healthy, marginal and at risk system operation conditions can be distinguished. This makes the person processing the DG interconnection has confidence in making judgments on the planning and design of DG interconnection.
6 Acknowledgements This work was supported in part by the National Science Council of Taiwan, R.O.C. through grant numbers NSC 102-3113-P-155-001 and NSC 102-2221-E-155-040.
Page 9 of 22
Ac ce
pt
ed
M
an
us
cr
ip t
7 References [1] N. Hatziargyriou, H. Asano, R. Iravani, C. Marnay, Microgrids, IEEE Power and Energy Magazine 5 (4) (2007) 78-94. [2] F. Katiraei, R. Iravani, N. Hatziargyriou, A. Dimeas, Microgrids management, IEEE Power and Energy Magazine 6 (3) (2008) 54-65. [3] B. Kroposki, R. Lasseter, T. Ise, S. Morozumi, S. Papatlianassiou, N. Hatziargyriou, Making microgrids work, IEEE Power and Energy Magazine 6 (3) (2008) 40-53. [4] G.C. Bakos, M. Soursos, N.F. Tsagas, Technoeconomic assessment of a building-integrated PV system for electrical energy saving in residential sector, Energy and Buildings 35 (8) (2003) 757-762. [5] C.D. Jardim, R. Ruther, I.T. Salamoni, T.D.S. Viana, S.H. Rebechi, P.J. Knob, The strategic siting and the roofing area requirements of building-integrated photovoltaic solar energy generators, in urban areas in Brazil, Energy and Buildings 40 (3) (2008) 365-370. [6] R. Sarachitti, C. Chotetanorm, C. Lertsatitthanakorn, M. Rungsiyopas, Thermal performance analysis and economic evaluation of roof-integrated solar concrete collector, Energy and Buildings 43 (6) (2011) 1403-1408. [7] A. Chel, G.N. Tiwari, A. Chandra, Simplified method of sizing and life cycle cost assessment of building integrated photovoltaic system, Energy and Buildings 41 (11) (2009) 1172-1180. [8] A.S. Bahaj, L. Myers, P.A.B. James, Urban energy generation: Influence of micro-wind turbine output on electricity consumption in buildings, Energy and Buildings 39 (2) (2007) 154-165. [9] A.D. Peacock, D. Jenkins, M. Ahadzi, A. Berry, S. Turan, Micro wind turbines in the UK domestic sector, Energy and Buildings 40 (7) (2008) 1324-1333. [10] S. Diaf, M. Belhamel, M. Haddadi, A. Louche, Technical and economic assessment of hybrid photovoltaic/wind system with battery storage in Corsica island, Energy Policy 36 (2) (2008) 743-754. [11]E. Cetin, A. Yilanci, Y. Oner, M. Colak, I. Kasikci, H.K. Ozturk, Electrical analysis of a hybrid photovoltaic-hydrogen/fuel cell energy system in Denizli, Turkey, Energy and Buildings 41 (9) (2009) 975-981. [12] G.J. Dalton, D.A. Lockington, T.E. Baldock, Feasibility analysis of renewable energy supply options for a grid-connected large hotel, Renewable Energy 34 (4) (2009) 955-964. [13] H.B. Ren, W.J. Gao, Economic and environmental evaluation of micro CHP systems with different operating modes for residential buildings in Japan, Energy and Buildings 42 (6) (2010) 853-861. [14] D.H.W. Li, K.L. Cheung, T.N.T. Lam, W.W.H. Chan, A study of grid-connected photovoltaic (PV) system in Hong Kong, Applied Energy 90 (1) (2012) 122-127. [15] S.B. Sadineni, F. Atallah, R.F. Boehm, Impact of roof integrated PV orientation on the residential electricity peak demand, Applied Energy 92 (2012) 204-210. [16] G. Bekele, G. Tadesse, Feasibility study of small Hydro/PV/Wind hybrid system for off-grid rural electrification in Ethiopia, Applied Energy 97 (2012) 5-15. [17] C. Gokcol, B. Dursun, A comprehensive economical and environmental analysis of the renewable power generating systems for Kirklareli University, Turkey, Energy and Buildings 64 (2013) 249-257. [18] C. Li, X.F. Ge, Y. Zheng, C. Xu, Y. Ren, C.G. Song, C.X. Yang, Techno-economic feasibility study of autonomous hybrid wind/PV/battery power system for a household in Urumqi, China, Energy 55 (2013) 263-272. [19] R.A. Walling, R. Saint, R.C. Dugan, J. Burke, L.A. Kojovic, Summary of distributed resources impact on power delivery systems, IEEE Trans. Power Delivery 23 (3) (2008) 1636-1644. [20] N.C. Yang, Three-phase power flow calculations by direct Z(LOOP) method for microgrids with electric vehicle charging demands, IET Generation Transmission & Distribution 7 (9) (2013) 1002-1010. [21] VDEW, Guidelines for the parallel operation of own energy generation systems with the middle voltage grid of the utility company. [22] IEEE, IEEE Standard for Interconnecting Distributed Resources with Electric Power System. In IEEE
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Ac ce
pt
ed
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an
us
cr
ip t
Standard 1547-2003, 2003. [23] VDN, REA generating plants connected to the high and extra-high voltage network, (August 2004). [24] Eltra, Wind turbines connected to grids with voltages below 100 kV, (May 2004). [25] Eltra, Wind turbines connected to grids with voltages above 100 kV, (November 2004). [26] T.H. Chen, N.C. Yang, Three-phase power-flow by direct Z(BR) method for unbalanced radial distribution systems, IET Generation Transmission & Distribution 3 (10) (2009) 903-910. [27] T.H. Chen, N.C. Yang, Loop frame of reference based three-phase power flow for unbalanced radial distribution systems, Electric Power Systems Research 80 (7) (2010) 799-806. [28] T.H. Chen, N.C. Yang, Simplified annual energy loss evaluation method for branch circuits of a home or building, Energy and Buildings 42 (12) (2010) 2281-2288. [29] N.C. Yang, T.H. Chen, Assessment of loss factor approach to energy loss evaluation for branch circuits or feeders of a dwelling unit or building, Energy and Buildings 48 (2012) 91-96. [30] T.-H. Chen, Complex short circuit MVA method for power system studies, IEE Proceedings-Generation, Transmission and Distribution 141 (2) (1994) 81-84. [31] N.C. Yang, T.H. Chen, Dual Genetic Algorithm-Based Approach to Fast Screening Process for Distributed-Generation Interconnections, IEEE Trans. Power Delivery 26 (2) (2011) 850-858. [32] N.C. Yang, T.H. Chen, Evaluation of maximum allowable capacity of distributed generations connected to a distribution grid by dual genetic algorithm, Energy and Buildings 43 (11) (2011) 3044-3052.
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Research Highlights Monte Carlos based three-phase power flow method. Implemented by MATLAB/Simulink and OPTIMUS.
Ac ce
pt
ed
M
an
us
cr
ip t
Hybrid energy system consists of a 5kW PV system, a 3kW wind power system and a 2kW HFC system.
Page 12 of 22
Table 1 Accuracy comparison for IEEE 13 Bus distribution system. Vbn
Vcn
Calculated
IEEE results
Calculated
IEEE results
1.00000.00 1.0625-0.00 1.0211-2.51 1.0181-2.57 0.9940-3.25
1.00000.00 1.06250.00 1.0210-2.49 1.0180-2.56 0.9941-3.23
0.9903-5.34 0.9903-5.34 0.9884-5.36
0.9900-5.30 0.9900-5.30 0.9881-5.32
1.0000-120.00 1.0500-120.00 1.0419-121.68 1.0400-121.73 1.0216-122.19 1.0327-121.87 1.0310-121.95 1.0528-122.26 1.0528-122.26
1.0000-120.00 1.0500-120.00 1.0420-121.72 1.0401-121.72 1.0218-122.22 1.0329-121.90 1.0311-121.98 1.0529-122.34 1.0529-122.34
1.0000120.00 1.0687120.00 1.0176117.76 1.0150117.75 0.9961117.27 1.0157117.78 1.0137117.83 0.9781115.87 0.9781115.87 0.9761115.77 0.9740115.61
1.0000120.00 1.0687120.00 1.0174117.83 1.0148117.82 0.9960117.34 1.0155117.86 1.0134117.90 0.9778116.02 0.9778116.02 0.9758115.92 0.9738115.78
0.9821-5.29 0.9903-5.34 0.9841-5.59
0.9818-5.25 0.9900-5.31 0.9835-5.56
1.0528-122.26 1.0550-122.42
1.0529-122.34 1.0553-122.52
0.9777116.02 0.9758116.03
ip t
IEEE results
cr
Van Calculated
0.9781115.87 0.9759115.88
us
Node ID 650 RG60 632 633 634 645 646 671 680 684 611 652 692 675
Table 2 Mismatches of magnitudes of bus voltages between the calculated solutions and IEEE results.
0.0003 0.0003 0.0003
0.0303 0.0303 0.0304
0.0003 0.0003 0.0006
0.0306 0.0303 0.0610
(%)
p.u.
(%)
0.0000 0.0000 -0.0001 -0.0001 -0.0002 -0.0002 -0.0001 -0.0001 -0.0001
0.0000 0.0000 -0.0096 -0.0096 -0.0196 -0.0194 -0.0097 -0.0095 -0.0095
0.0000 0.0000 0.0002 0.0002 0.0001 0.0002 0.0003 0.0003 0.0003 0.0003 0.0002
0.0000 0.0000 0.0197 0.0197 0.0100 0.0197 0.0296 0.0307 0.0307 0.0307 0.0205
-0.0001 -0.0003
-0.0095 -0.0284
0.0004 0.0001
0.0409 0.0102
M
0.0000 0.0000 0.0000 0.0000 0.0001 0.0098 0.0001 0.0098 -0.0001 -0.0101
Vcn
p.u.
an
Vbn (%)
pt
650 RG60 632 633 634 645 646 671 680 684 611 652 692 675
Van p.u.
ed
Node ID
Node ID 650 RG60 632 633 634 645 646 671 680 684 611 652 692 675
Ac ce
Table 3 Mismatches of angles of bus voltages between the calculated solutions and IEEE results. Van
Vbn
Vcn
Degree
(%)
Degree
(%)
Degree
(%)
0.0000 0.0000 -0.0200 -0.0100 -0.0200
0.0000 0.0000 0.8032 0.3906 0.6192
-0.0400 -0.0400 -0.0400
0.7547 0.7547 0.7519
0.0000 0.0000 0.0400 -0.0100 0.0300 0.0300 0.0300 0.0800 0.0800
0.0000 0.0000 -0.0329 0.0082 -0.0245 -0.0246 -0.0246 -0.0654 -0.0654
0.0000 0.0000 -0.0700 -0.0700 -0.0700 -0.0800 -0.0700 -0.1500 -0.1500 -0.1500 -0.1700
0.0000 0.0000 -0.0594 -0.0594 -0.0597 -0.0679 -0.0594 -0.1293 -0.1293 -0.1294 -0.1468
-0.0400 -0.0300 -0.0300
0.7619 0.5650 0.5396
0.0800 0.1000
-0.0654 -0.1500 -0.0816 -0.1500
-0.1293 -0.1293
Page 13 of 22
Table 4 Parameters of main feeder conductors for microgrid system. Material
Impedance (ohms/km)
Copper
10.1+j0.149 3.62+j0.138 2.51+j0.130 0.677+j0.361 1.412+j0.388
AAC
0.188+j0.378
AAC
0.375+j0.409
ip t
Size 600V/PVC 2.0mm2 5.5mm2 8.0mm2 30mm2 14mm2 25kV/PEX/200mm2 336 25kV/PEX/100mm2 3/0
cr
Table 5 Technical data of AC meter. Parameters
us
2rated continuous 4rated for 2 seconds 3rated continuous 10rated for 10 seconds 50rated for 1 second
an
Current
16 bits resolution 15 cycle/sec 100 msec
Table 6 Distributions of discrete loads for microgrid system. Rated Capacity S(W)
546 8,000 252 2,600
T(W)
500 2,600
746 180 1,200 2,178
Ac ce
A8 Lamp1 A9 H2 generator A10 Lamp2 A11 Air conditioner A12 PC A13 Air conditioner A14 Ventilator A16 Fan A17 Electric heater Total power consumption
R(W)
ed
Description
pt
Node
M
A/D converter Sampling Rate Response time： Max. input over capability Voltage
11,346
3,100
Table 7 Technical data of PV module. Parameters
Cell per module Cell technology Max. power output per cell Max. power voltage Max. power current Open circuit voltage Short circuit current Rated total power output System output voltage Conversion efficiency
63 Multicrystalline 80W 16.9V 4.73A 21.5V 4.97A 5kW 220Vac 14%
Table 8 Technical data of wind turbine.
Page 14 of 22
Parameters
ip t
Permanent magnet 18 2kW 3kW 30V-240Vac 2.4 m 12.5 m/s 3 m/s 20m/s 13.5 m/s 50 m/s 145-780 rpm 3 45%
cr
Type Number of generator poles Rated power output Peak power output Output voltage Rotor diameter Rated wind speed Cut in wind speed Cut-out wind speed Security wind speed Survival wind speed Rotor speed Number of blades Conversion efficiency
an
PEM 2kW Gaseous hydrogen 99.995% purity 220Vac
M
Parameters Type of fuel cell Rated power Fuel type Fuel purity System output voltage
us
Table 9 Technical data of fuel cell.
Table 10 Distribution of reverse power flows for hybrid energy system.
A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A1 A2 A3 A4 A5 A6 A7 A7 A7 A7 A7 A7 A7
0
Phase S P(W) Q(var) -1598.08 34.5 -1598.08 34.48 -1598.08 34.48 -1598.08 34.48 -1598.15 34.12 -1600.29 32.98
ed
A1 A2 A3 A4 A5 A6 A7 A7 A7 A7 A7 A7 A7 A7 A15 A15 A7 A7 A7 A20 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14
Phase R P(W) Q(var) -3573.36 44.12 -3573.36 44.04 -3573.36 44.03 -3573.37 44.03 -3573.72 42.24 -3584.44 36.52 0 0
pt
To Bus No
0
0
0
0
Phase T P(W) Q(var) -4509 53.06 -4509 52.93 -4509.01 52.93 -4509.01 52.92 -4509.58 50.06 -4526.65 40.96
0
Ac ce
From Bus No.
0 0 0 -1997.06
0 0 0 0.11
-1604.15 -1610.09 3573.36 3573.36 3573.37 3573.72 3584.44 3601.21 0
31.8 2.16 -44.04 -44.03 -44.03 -42.24 -36.52 -31.91 0
0
0
0
0
-1603.65 -1609.64 1598.08 1598.08 1598.08 1598.15 1600.29 1603.65
32.06 2.17 -34.48 -34.48 -34.48 -34.12 -32.98 -32.06
0
0
0
0
0
0 0
0 0
-2948.97 -1604.38 -1610.3 4509 4509.01 4509.01 4509.58 4526.65 4553.34
1.95 31.68 2.15 -52.93 -52.93 -52.92 -50.06 -40.96 -33.63
0 0
0 0
0
Page 15 of 22
0 0 0 0
1610.09 1666.66
-2.16 0
1609.64 1666.66
-2.17 0
3000 1610.3 1666.66
0 -2.15 0
ed
M
an
us
cr
ip t
0 0 0 2000
pt
A7 A15 A15 A7 A7 A7 A20
Ac ce
A15 A16 A17 A18 A19 A20 A21
Page 16 of 22
List of Figures Fig. 1. IEEE Bus benchmark system implemented in MATLAB/Simulink. Fig. 2. Simulation workflow for OPTIMUS software. Fig. 3. Hybrid energy system of Yuan Ze University in Taiwan.
ip t
Fig. 4. Schematic diagram of hybrid energy microgrid system. Fig. 5. One line diagram of hybrid energy microgrid system.
Fig. 9. Statistics of bus power injection at each bus for hybrid energy system.
us
Fig. 8. Voltage duration curve of the whole distribution system in Yuan Ze University
cr
Fig. 6. Power curve of WT 2000 DF wind turbine
Fig. 10. Statistics of power flow at each line segment for hybrid energy system.
an
Fig. 11. Statistics of power loss at each line segment for hybrid energy system.
Fig. 12. Statistics of voltage unbalance factor at Bus A7 for hybrid energy system: (a) with H2 generator; (b) without H2
M
generator.
List of Tables
ed
Table 1 Accuracy comparison for IEEE 13 Bus distribution system.
Table 2 Mismatches of magnitudes of bus voltages between the calculated solutions and IEEE results.
pt
Table 3 Mismatches of angles of bus voltages between the calculated solutions and IEEE results. Table 4 Parameters of main feeder conductors for microgrid system.
Ac ce
Table 5 Technical data of AC meter.
Table 6 Distributions of discrete loads for microgrid system. Table 7 Technical data of PV module.
Table 8 Technical data of wind turbine. Table 9 Technical data of fuel cell.
Table 10 Distribution of reverse power flows for hybrid energy system.
Page 17 of 22
Ia RMS Ib RMS
Power Source
Phasors
Ic RMS 1
A
Ia angle
A
B bB
C
A aA
Ib angle
powergui
Ic angle Pa Qa Pb Qb 2
cC
650
Pa
B
Pc
B
Qc Van angle
P
Vbn angle Vcn angle
Vbn RMS
RG50
Three -Phase Dynamic Load A
cC
1 A
Van RMS
bB
Q
P Q A B C
aA
Qa
C
3 C
Vcn RMS 4 a
a b
5
c
6 c
b
Three- Phase V- I Measurement
cC
RG
bB
Load
aA
Meter 1 A
4 a
Yaa
Yab
-Yab
-Yab1
Yac
-Yac
-Yac1
cC
bB
601 -2000
aA
Yba
2 B
5 b
Ybb
Ybc -Ybc
-Ybc1
Yca 3 C
6 c
Ycc
A
Distribution Feeder
cC
bB
aA
Ycb
632
B
Bb
C
C c
C c
Cc
A
a
Aa
Bb
B
b
Bb
Cc
Cc
C
c
602 -500
633
Cc
cC
603 -500 601 -500
bB
645
aA
603 -300
Aa
Bb
A
DZ-646 646
Aa Bb
B b
ip t
YPQ -645
B b
B C
Cc
XFM -1
YPQ-634
634
cr
A B
cC
bB
aA
C
601 -1500
YPQ-632
B
Distribution Transformer
C
671
652
607 -800
A a
C c
Aa
Aa
Aa
Bb
Bb
Bb
Cc
Cc
Cc
692
606 -500
675
Aa
A
cC
Aa
bB
C c
604 -300 601 -1000
aA
684 A
A
A
cC
bB
680
aA
A
A
C
C
C
605 -300
YPQ-675
B
B
YI-611 611
B C
Cc
an
Aa
cC
A a
bB
A
YZ-652
aA
YPQ-671
us
A
DPQ-671
Capacitor -675
DI -692
M
Capacitor -611
Fig. 1. IEEE Bus benchmark system implemented in MATLAB/Simulink.
ed
MATLAB/Simulink
Pro ba bility Density Functio n
pt
So lutio n Sets
Fig. 2. Simulation workflow for OPTIMUS software.
PV energ y sy stem
H y brid en erg y m o n ito rin g a n d co ntro l sy stem fo r intellig ent m icro g rid
Ac ce
Fuel cell energ y system
Wind po wer sy stem
Fig. 3. Hybrid energy system of Yuan Ze University in Taiwan.
Page 18 of 22
LCD Monitor LCD顯示螢幕
Human Machine Interface HMI人機介面
PC 桌上型電腦
H2 Po wer Co n tro l 氫氣 電力 控制 RS485, MODBUS TCP/IP, RS232
LABVIEW
AC Meter 交流電錶 S olid s tate relay 固態電驛
市電 Power Grid 電力
PLC PLC
太陽光電 PV s ys tem 發電系統 Control panel 中央控制盤
風力發 Wind power s ys tem 電系統
燃料電池 Fuel cell s ys tem 發電系統 高性能直流/ 交流變流器
H 2 generator 水電解系統
Flow meter 流量計 Hous ehold appliances
金屬儲氫罐 H2 s torage
ip t
DC/AC inverter
Flow 流量計 meter
cr
電器負載
Fig. 4. Schematic diagram of hybrid energy microgrid system. 500 MVAsc L1 22.8kV L2 220m 25kV/PEX/200mm 2
us
A1
A2
M-D
A3
A4
5224kW PF = 0.99
25kV/PEX/100mm 2 L3
L4 T1
D-B#3b1f
1000kVA Z% = 4.8% X/R = 5 380Y/220V
A5 L5 60m
B#3b1fB#35f
600V/PVC/30mm2
A6
L8
L9
L10
L11
L12
L13
R
S
R
S
T
T
S
12.5m 15m 12.5m 6.5m 5.5m 10m 12m 2 2 2 2 2 2 22 E: 2 E:8 E: 2 E: 2 E: 2 E: 2 E: 2 A8 Ramp 1
A9
A10 A11
H2 generator
Ramp2
A12
Air conditioner
A13 PC
A14
Air conditioner
B#35f3525R
R T2 L17 L18 L19 L14 5kVA 7.5kVA R T Z% = 3% Z% = 3% T3 110V 10m 80m 220V A15 A20 L15 18m 18m 25.5 25.5 4600V/PVC/5.5mm 2 2 2 E: 2 E: 2 100m E: 2mm 2 E: 2 E: 2 L16 L20 A16 A17 A18 A19 A21
Ventilator
546VA 8000VA 252VA 2600VA 500VA 2600VA 746VA
Fan
Electric heater
180VA 1200VA
ed
L7
600V/PVC/14mm2 380Y/220V A7
M
L6 45m
an
100m
Lumped Load of other feeders
HFC
WT
2kW
3kW
PV
5kW
Ac ce
pt
Fig. 5. One line diagram of hybrid energy microgrid system.
Fig. 6. Power curve of WT 2000 DF wind turbine
Page 19 of 22
Bus Voltages
Bus Voltages 1.05
1.05
1.04
1.04
1.03
1.03
) . u. p( s u b V
1.01
1
1.02
1.01
1
0.99
0.99
0.98
0.98
0.97 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21
ip t
1.02
). u. p( s u b V
0.97 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21
Bus No.
Bus No.
(a)
cr
(b)
us
Fig. 7. Statistics of bus voltage magnitude at each bus for hybrid energy system: (a) with H2 generator; (b) without H2 generator. 1.05 Phase R Phase S Phase T
1.04
an
1.03 1.02 1.01 1 0.99 0.98
0
0.1
0.2
0.3
0.4
0.5 0.6 Time (p.u.)
0.7
0.8
0.9
1
ed
0.97
M
). u. p( V
Fig. 8. Voltage duration curve of the whole distribution system in Yuan Ze University Power Injections 4
0 -2 -4 -6
Ac ce
) W k( P
pt
2
-8 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21
Bus No.
x 10 4
) r a v( Q
2 0 -2
-3
A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21
Bus No.
Fig. 9. Statistics of bus power injection at each bus for hybrid energy system.
Page 20 of 22
Power Flow
) W k( P
13 11 9 7 5 3 1 -1 -3 -5 L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 L16 L17 L18 L19 L20
Line No. 150
100
ip t
) r a v( Q
50
cr
0 L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 L16 L17 L18 L19 L20
Line No.
us
Fig. 10. Statistics of power flow at each line segment for hybrid energy system. Power Loss
an
) W ( P
160 140 120 100 80 60 40 20 0 L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 L16 L17 L18 L19 L20
Line No.
) r a v( Q
40
20
Line No.
ed
0 L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 L11 L12 L13 L14 L15 L16 L17 L18 L19 L20
M
60
Fig. 11. Statistics of power loss at each line segment for hybrid energy system. Voltage Unbalance Ratio
2.5 2
%
1.5 1 0.5 0
Ac ce
3
LVUR%
PVUR(1)%
(a)
PVUR(2)%
d2%
Voltage Unbalance Ratio
3.5
pt
3.5
3 2.5 2
%
1.5 1 0.5 0
LVUR%
PVUR(1)%
PVUR(2)%
d2%
(b)
Page 21 of 22
ip t cr us an M ed pt Ac ce
1 2
Fig. 12. Statistics of voltage unbalance factor at Bus A7 for hybrid energy system: (a) with H2 generator; (b) without H2 generator.
22
Page 22 of 22