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Microgrid Real-Time Operation, Reactive Power Control, Load Shedding, OPAL-RT, Matlab
MG systems have spurred increasing interest in the electric power industry [1] [2] [3]. MG is a smart small-scale electric power system that consists of a mix of generating units, controllable loads, storage units, low-voltage transmission lines, transformers and a point of common coupling (PCC) to the main grid. PCC represents the main circuit breaker that is used to switch between two operation modes: islanded and grid connected. The successful implementation of these modes in MG has contributed to its widespread deployment, worldwide [4].
In this paper, OPAL-RT real-time analysis of MG is presented. The objective is to optimally allocate solar power to meet power demand in realtime. In an islanded mode, momentary failures in power generation are introduced and recovery of critical loads is simulated in real-time based on a priority scheme. The impact of power balance while varying the amounts of power generation and demand is examined. In the simulation of MG mode transition, reactive power coordination control is used in order to minimize power loss while load shedding is used in order to maintain generation-load balance. GJU microgrid system is used for illustration. The results show that reactive power coordination control not only stabilizes the MG operation in real-time but also reduces power losses.
The remaining sections of the paper are organized as follow: Section 2 gives details of MG system. Section 3 presents GJU MG for real-time simulation. Reactive power control and load shedding mechanisms are presented in Section 4. Section 5 presents simulation results of GJU case study. Finally, the paper is concluded in Section 6.
As shown in Figure 1, MG generally consists of controllable loads and renewable generation resources (e.g. wind and solar) that are complemented by on-site diesel generators and/or storage batteries. MG is managed and operated in real-time either in a grid-connected mode or an islanded mode mainly controlled
through PCC. In the following subsections, formal models of MG components are introduced as a basis of formulation.
Controllable loads consist of load profiles that are generally time varying and are mainly driven by the type of customer behavior. Methods such as linear regression, time series, autoregressive, exponential smoothing, curve fitting, permutation and machine learning, are used for load forecasting [15]. Using curve fitting and given the dominant load profile at MG site under study, a piecewise function in three-time intervals is developed and gives the forecasted total power demand at time t as follows:
where A and B represent constant values that can be calculated using curve fitting of historical data and ω and ϕ denote angular frequency and phase shift of a sinusoidal function that best fits the load profile during 24 hours of a given day, respectively.
Renewable generation consists of solar and wind. Since the output power of solar and wind is intermittent and non-controllable, it is necessary to have storage and/or controllable generation that can replace power shortage and maintain constant local power.
SolarPower: Solar power is produced by large array of photovoltaic cells, formally defined as, [16]:
where G, T, A, η, and α denote solar radiation, ambient temperature, area of panels, system efficiency, and power degradation, respectively.
WindPower: Wind power is produced by converting the kinetic energy of wind turbines, formally defined as, [17]:
where σ, , and ν denote air density, swept area of rotor blade, and wind speed, respectively.
DieselGeneratingPower: The output power of N diesel generators is formally defined as:
where denotes the output power of the ith diesel generator, cost of which at time t is defined as:
and , A, F, and ∆t denote the cost ($), price of diesel ($/Liter), variable cost, and time interval, respectively.
Utilitypower: In a grid-connected mode, the operational cost also includes the cost of power absorbed from utility grid, defined as,
where K is the price of utility grid energy ($/kWh). A negative value represents that energy is being sold from MG to the utility grid.
Storage batteries are considered the fastest element to provide power when it is needed. Charging and discharging of storage batteries depends on the utility grid price of energy when the microgrid is working with grid-connected mode, [18]. At high load peaks, discharge of batteries provides more economic benefits. It avoids the MG from paying high energy prices to the utility grid and in some cases selling can take place When the MG is turning into islanded mode, storage batteries can contribute in stabilizing the system and mentaing generation-load balance.
The single line diagram of the GJU campus microgrid is shown in Figure 2. The main components represented as controllable loads; generating units; and PCC to the main grid. Loads are categorized in buildings A, B, C, D, E and F as essential, nonessential, and air conditioning that are connected through remotely controllable circuit breakers. Details of demonstration have been throughly investegated in the 3DMicrogrid project, [19]. Figure 3 shows a typical load profile for a Winter and a Summer day, where peak loads occur during working hours
8:00-17:00. PV power generation exists at Buildings B, D, E, and F with a total capacity of 1.5 MW. The PV power generation for a Winter and a Summer day is shown in Figure 4. The patterns indicate the intermittent nature of PV and therefore, the six diesel generators that are installed in buildings A to F are used to accommodate the intermittence of PV resources. There is also a PCC substation with 33/11KV transformer which interconnects to the main grid for a two-way power flow.
The transmissions lines interconnect generation resources and building substations with 11/.4KV bus transformers. The PV generation is dependent mainly on weather conditions which vary over the year. Figure 5 shows the energy patterns of PV generation and Load for 12 months. In January, for instance, PV produces 40% less power than load. In May and June, PV produces 32% and 24% more power than load, respectively.
Inverter-based systems have a critical role in controlling the injection and absorption of reactive power in order to perform several functionalities such as voltage variation control, power losses control, and power curtailment. This is mainly achieved through reactive power control which largely reduces power loss in transmission lines. The power loss on a given transmission line is formally defined as, [20]:
where V, R, P and Q represent the voltage, line resistance, active power and reactive power, respectively.
The determination of the new setpoint in the PV block is based on reactive power of load, leading to a minimum reactive power flow via the network feeder. Figure 6 presents an illustration diagram of the proposed coordination control, starting with sensing the reactive power output from the load (Qload). The value of Qload will be fed to Zero Order Hold block. This value will be transformed to a per-unit (pu) value in order to be utilized as a set point of the PV block that is connected with the specified load. PV systems are installed and connected with substations 2, 4, 5 and 6. Q-coordination control has been implemented in substations 2 and 4 for testing. Figure 7 represents the output reactive power real-time readings of substation 2.
The blue line shows the original case without implementing the Q-coordination control. The orange line indicates the impact of implementing the control on reactive power reduction. The disturbance in the signals at t = 5 seconds is due to the transition between islanded and connected modes. In the Simulink model, the conversion between the restoration operation of the system occurs during this interval, resulting in signal disturbance. This allows testing and analyzing the proposed scheme in both operational modes. The results show that power losses at substation 2 were decreased by 9.8%. The losses at substation 4 were decreased by about 6%.
We use RT-LAB, the OPAL-RT''s real-time simulation platform for GJU MG, with Edit, Compile, Execute, and Interact in three subsystems: Master, Slave, and Console as summarized in Table 1. The generic voltage and frequency variables
are represented in discrete time with a step size T. Two factors impact the simulation of RTS, namely: the model complexity and the computational speed of the installed hardware, [15].
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