Automation of a Distributed Generation System - IEEE Xplore

Automation of a Distributed Generation System 1

Lucian Ioan DULĂU PhD Student, Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Romania [email protected]

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Mihail ABRUDEAN Professor, Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Romania [email protected]

Abstract- The paper describes the automatic generation control of a distributed generation (DG) system based on the optimal power flow. The optimization technique based on economical and technical considerations is presented. Also, an automation of the DG system is performed using a SCADA software considering the load, the availability of the system’s generating units. Also, the optimal power dispatch is presented. Index Terms-- distributed generation, automation of a distributed generation system, control of a distributed generation system, optimal power flow, SCADA

I.

INTRODUCTION

Today, due to global concern regarding environmental issues and the problem of climate change, conventional or classical power generating units are intended to be replaced with those having less emission generation. Moreover, the increasing cost of transferring energy causes extra challenge in this area. In this regard, power system planners seek smaller generating units that are installed close to the consumers. For this purpose, distributed generation (DG) technologies have turned to be proper alternatives to replace conventional power plants. Distributed generation is characterized by some features which have not been present in traditional centralized systems: • rather free location in the network area; • relatively small generated power and variation of generated power dependent on the availability and variability of primary energy. One of the main advantages of DG is its close proximity to the consumer loads. DG can play an important role in: • improving the reliability of the grid; • reducing the transmission losses; • providing better voltage support; • improving the power quality. The major obstacle for the distributed generation has been the high cost. However, the costs have decreased significantly over the past years. The distributed generation also reduces green house gas emission addressing pollutant concerns by providing clean and efficient energy. The most commonly used DG technologies and their typical module size are: • Photovoltaic Arrays (PV Arrays): 20 W - 100 kW;

978-1-4799-6557-1/14/$31.00 ©2014 IEEE

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Dorin BICĂ Associate Professor, Department of Electrical and Computer Engineering, Faculty of Engineering, “Petru Maior” University of Tîrgu-Mureú, Romania [email protected]

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Small Hydro: 1 - 100 MW; Micro Hydro: 25 kW - 1 MW; Wind Turbine: 200 W - 3 MW; Biomass Gasification: 100 kW - 20 MW; Geothermal: 5 - 100 MW; Ocean Energy: 100 kW - 5 MW; Battery Storage: 500 kW - 5 MW. DGs can be used in an isolated way, supplying the consumer’s local demand, or integrated into the grid supplying energy to the electric power system, or in a combination of these two. These generating units are generally connected to the power systems at the distribution level (medium voltageMV) or at consumers level (low voltage – LV), like in Fig. 1.

Fig. 1. A unidirectional centralised delivered power generation system (a) and a bidirectional system with distributed generation (b)

There are different types of functions that can be taken in consideration when a optimization is performed: the generation costs, power losses or the reliability of the system components. In this paper is presented the optimization of a system containing distributed generation sources, taking in consideration the power losses and the generation costs. II. MATHEMATICAL FORMULATION OF THE OPTMIAL POWER FLOW PROBLEM

The purpose is to minimize two objective functions, generation cost and total power losses of the system, while

satisfying equality and inequality constraints. The problem can be formulated as follows: min {F= C(P)+ǻS} (1) where: • C(P) – generation costs; • ǻS – total power losses of the system. The generation cost C(P) is expressed as: C(P)= a·PGi2+ b·PGi +c (2) where: • PGi - output of the generating unit; • a, b, and c- are constants. The total power losses of the system can be expressed as: ǻS= PL+jQL= Ȉ Vi·Ii*=UbusT·Ibus* (3) where: • PL, QL- are the active and reactive power losses of the system; • Vbus- the column vector of the bus voltages; • Ibus- the column vector of the injected bus currents. The function is subjected to the following constraints: Pi (V,ș)=PGi- PDi Qi (V, ș)= QGi- QDi PGmin ” PG ”PGmax (4) QGmin ” QG ”QGmax Vimin ” Vi ”Vimax Plmin ” Pl ”Plmax where: • PGi- the real power output of the generator connected to bus i; • QGi- the reactive power output of the generator connected to bus i; • PDi- the real power load connected to bus i; • QDi- the reactive power load connected to bus i; • Pi- the real power injection at bus i; • Qi- the reactive power injection at bus i; • Vi- the voltage magnitude at bus i; • Pl- the power flow at the line l from bus j to bus k. The optimal power flow (OPF) is analyzed for the IEEE 30 bus test system presented in Fig. 2, in which three DG sources are added: • a 3 MW wind turbine WT at bus 30; • a 0.3 MW photovoltaic plant PV at bus 10; • a 4 MW hydro generator HYDRO at bus 7. The OPF is performed using the Neplan software [15], in the cases when the distributed generators are connected to the network (on-grid), and in the cases when the distributed generators are not connected to the network (off-grid). The data for the system are formulated in [13].

Fig. 2. One-line diagram of the IEEE 30 bus test system

The generators data are presented in table I, the loads data are presented in table II, and the cost coefficients for the generating units are presented in table III. The data for the generators, load and cost coefficients are formulated in [13]. TABLE I GENERATORS DATA

Generator

PGimin (MW)

PGimax (MW)

QGimin (MW)

QGimax (MW)

1 2 5 8 11 13 WT PV HYDRO

50 20 15 10 10 12 0.6 0.01 0.1

200 80 50 35 30 40 2.7 0.29 4

-20 -20 -15 -15 -10 -15 0 0 0

250 100 80 60 50 60 0 0 0

TABLE II LOADS DATA

Load

PDimin (MW)

QDimax (MW)

Load

PDimin (MW)

QDimax (MW)

1 2 3 4 5

0 21.7 2.4 7.6 94.2

0 12.7 1.2 1.6 19

16 17 18 19 20

3.5 9 3.2 9.5 2.2

1.8 5.8 0.9 3.4 0.7

6 7 8 9 10 11 12 13 14 15

0 22.8 30 0 5.8 0 11.2 0 6.2 8.2

0 10.9 30 0 2 0 7.5 0 1.6 2.5

21 22 23 24 25 26 27 28 29 30

17.5 0 3.2 8.7 0 3.5 0 0 2.4 10.6

11.2 0 1.6 6.7 0 2.3 0 0 0.9 1.9

TABLE III GENERATING UNITS COST COEFFICIENTS

Generator

a [EUR/MWh2]

b [EUR/MWh]

c [EUR/h]

1 2 5 8 11 13 WT PV HYDRO

0.00375 0.0175 0.0625 0.0083 0.0250 0.0260 0 0 0

2.00 1.75 1.00 3.25 3.00 3.00 4.10 8.00 3.00

0 0 0 0 0 0 0 0 0

The simulation emphasizes that the network MW losses are 10.31, MVAr losses are 3.95 and the network MW generation cost is 857.16 EUR/MWh if the WT and PV are off-grid. If the WT and PV are on-grid the MW losses are 9.11, MVAr losses are 3.25 and the generation cost is 866.04 EUR/MWh. The power losses are lower if the distributed generators are on-grid, but the power price is higher due to the high cost of the distributed generators, especially the PV. III. SIMULATION OF A DG SYSTEM The automation (simulation) is performed using the CitectSCADA software [14] considering the power demand (table IV), with the remark that only the active power is considered in this simulation. Also the power losses are not taken into account. The total load of the IEEE 30 bus test system is 283.4 MW. The corresponding load scaling factor (LSF) is 1.0. The daily load demands of the IEEE 30 bus test system are presented in table IV and graphically represented in Fig. 3. The data for the load demand is formulated in [13]. TABLE IV LOAD DATA

Hour [h]

LSF

Hour [h]

LSF

Hour [h]

LSF

1 2 3 4 5 6 7 8

0.90 0.96 1.00 1.05 1.10 1.15 1.30 1.40

9 10 11 12 13 14 15 16

1.30 1.15 1.10 1.05 1.16 1.30 1.40 1.45

17 18 19 20 21 22 23 24

1.50 1.55 1.40 1.20 1.12 1.03 0.96 0.90

Fig. 3. Load Scale Factor (LSF)

The graphic in Fig. 3 highlights that the maximum demands are in the morning and at night. The maximum load demand is 439.27 MW (1.55 LSF) at 18:00, while the minimum load demand is 255.06 MW (0.9 LSF) between 24:00 and 1:00. The system’s generating units cover demand in order of their generating cost coefficient (b), with the observation that G1 is used as a reserve generator, if the other generators can not cover the load. The order is presented in table V, with the observation that if the distributed energy sources (wind power plant, PV power plant and the hydro generator) are on-grid, they have priority access to the system, so the order changes in the one from table VI. This is because the power provided by the distributed generator is variable in time due to the availability of their primary source. The generators order is also graphically represented in Fig. 4 and Fig. 5. The simulation takes in consideration if the generated power covers the load. If it does, then the excedent power is exported. If the load is higher than the generated power, then the remaining required power is supplied the first generator (generator 1). TABLE V GENERATORS ORDER IN THE SYSTEM

Generator

b [EUR/MWh]

5 2 HYDRO 11 13 8 WT PV 1

1 1.75 3 3 3 3.25 4.1 8 2

TABLE VI GENERATORS ORDER IN THE SYSTEM IF THE DISTRIBUTED GENERATORS ARE ON-GRID

Generator

b [EUR/MWh]

HYDRO WT PV

3 4.1 8

5 2 11 13 8 1

1 1.75 3 3 3.25 2

Fig. 4. Generators order in the system

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2 meters, which illustrates the total generated power and the total load; • 2 charts, which illustrates the total generated power and the total load; • the load (AC consumers), with a slide bar and a numerical object that illustrates the power consumption; • a numerical object that illustrates the hour of the simulation; • 3 buttons and 3 symbols (lights) for each DG, that illustrates if the distributed generator is on-grid or offgrid; • a Cicode Object (f(x)) which controls the system. The most important is the Cicode Object (f(x)) which automatically controls the system. In the Cicode Object the program functioning is written: • if the DGs are off-grid then the dispatch order is the one from table V; • if the DGs are on-grid then the dispatch order is the one from table VI. The only control of the user over the system is to connect or disconnect the DGs. This is done by pressing the corresponding button of the DG from the user interface. The photovoltaic power plant can produce electricity between 06:00 and 18:00 (Fig. 7), while the wind turbine depends on the wind availability and variability.

Fig. 5. Generators order in the system if the distributed generators are ongrid

The SCADA simulation interface is presented in Fig. 6.

Fig. 7. PV generation output

Fig. 6. SCADA interface

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The SCADA interface contains different components: 9 generators, each with a slide bar and a numerical object (####) that illustrates the power output;

The simulation emphasizes that the distributed generators can’t cover the load alone. At the lowest LSF, if the WT and the hydro generator are on-grid, the power produced is not enough to cover the load. In order to cover the load G5, G2, G11, G13, G8 and a part of G1 (13.36 MW) must be used. At the highest LSF, if all DGs are on-grid, the power produced is not enough to cover the load. In order to cover the load G5, G2, G11, G13, G8 and G1 (197.5 MW) must be used. If the DGs are off-grid, then the power produced is lower than the consumption (435