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most updated information, the proposed approach can be used for determining the network behaviour under any probable changes in PV output, load demand, or even changes in network topology resulting from outage or switching operation. With the capability to assess the solar PV impacts on distribution networks on-line, the proposed approach can also be used for monitoring of dynamic events such as fluctuations induced by passing clouds, etc. Further, this approach may also aid in the decision making (e.g., when to operate a battery energy storage device to mitigate PV output fluctuations and what would be the charging or discharging rates) for compensating the adverse impacts of these dynamic events by proving online assessment of the perturbed parameters. A brief description of these capabilities is provided below.

4.3.1 Dynamic “What-if” Analysis

With an increasing level of solar PV penetration, network operators often need to investigate how the networks would operate under a changed condition of load and PV output. This type of investigation is typically performed using “what-if” type of analysis by first selecting a base case scenario, and then by varying the parameter of interest (for example, the PV output, consumer load, etc), which is similar to a power system security analysis screening process. Conventionally, a base case scenario would typically consist of historical off-line data or synthetic data, and therefore, may not always represent the actual threats of high PV penetration in an operating distribution network. In a real-world scenario, network condition changes with the variations in load demands, PV outputs and network configurations caused by switching operation.

Impacts of high PV penetration would be different under different network conditions.

Therefore, a static base case using off-line data may not be sufficient to assess high PV penetration impacts. Rather a dynamic base case changing with time and reflecting the actual changes in the network would be needed. The real-time distribution network model obtained using the proposed approach can be used as base case for such a dynamic “what-if” analysis.

A simplified block diagram of the dynamic “what-if” analysis is shown in Fig. 4.5.

The proposed online approach compiles time-variant base case scenarios using the real-time load, PV and network data obtained from the actual distribution network using the Advanced Metering Infrastructure. A base case scenario at the k-th time instant will be constructed using the active and reactive load demand, PL(k) and QL(k), the PV output at PPV(k), and the network configuration, each at the k-th time instant. The changed network configuration will affect the network admittance matrix Y(k). The user, e.g., a network operator, would be able to modify the compiled base case scenario to investigate a potential PV penetration scenario to assess the impacts, or, possibility of any threat on the network under that potential scenario. Once the “what-if” analysis for the k-th instant is finished using the load flow engine, results are reported to the operator and the system is ready for analysis for the (k+1)-th instant.

Fig. 4.5. Dynamic “what-if” analysis

4.3.2 Online Monitoring of PV Output Fluctuations

The power output of PV resources depends on the incident sun insolation on PV panels. The PV impacts, therefore, changes throughout the day in accordance with the sun insolation. Any event that creates a sudden change in the sun insolation level, such as passing clouds over the PV installation site [12], eventually produces fluctuations in the PV output and this is reflected in the feeder voltage and power flow. From the collected real-time data, the proposed approach will be able to provide an online assessment of the fluctuating behaviour of network caused by sudden and irregularly variable PV outputs. The ramp rate of the PV output changes during cloud passing can be very high [11] and this will affect the ramp rate of the resulting voltage variation.

Indices are defined to quantify the ramp rate of PV power output variation caused by cloud passing and the resulting voltage variation. A sudden change of the total amount of the PV output in the feeder is considered as a potential impact of PV output fluctuation, which is expressed using Instantaneous Power Ramp Rate (IPRR) with a unit of kW per unit of time, as shown below.

Compilation of the base case scenario at the  k-th time instant

     

   1


IPRR 1 1

 

k t k t

k P k

P k

n i

PV i n

i PV i

(4.10) where, PiPV(k) is the PV power at the i-th node of the feeder at k-th time instant; i = 1,

2, …, n, where, n is the total number of nodes in the feeder. Positive IPRR indicates an increase of feeder PV generation and negative IPRR indicates a decrease of PV generation. The maximum amount of voltage variation observed in a feeder is used for evaluating the voltage ramp rate. The proposed index is defined as Instantaneous Voltage Ramp Rate (IVRR) that indicates the amount of voltage change between the present and the immediate past instant of measurement. The IVRR is expressed in terms of Volts per unit of time and is calculated using the following equation for the kth time instant.

     

  


IVRR max max 1

 

k t k t

k V k

k V (4.11)

In (4.11), Vmax(k) is the voltage at the LV feeder node where the maximum amount of voltage variation is observed and t(k) is the time corresponding to kth time instant. The sign of IVRR indicates the direction of voltage ramp; positive sign means an increase in voltage as compared to previous measurement and a negative sign indicates a decrease in voltage. High resolution time series data (in the scale of seconds) would be more suitable for the measurements of IPRR and IVRR. The same mathematical formulations of IPRR and IVRR given in (4.10) and (4.11), respectively, will be applicable when such high resolution data is available. The moving averages of IPRR and IVRR can be calculated in the same way as shown in (4.6)-(4.9) to obtain long term trends of the fluctuations.

4.3.3 Aiding the Decision Making for PV Impact Mitigation

Online monitoring and measurements of PV impacts can be used for real time decision making for mitigation of PV impacts. For example, if any immediate action is to be taken for mitigating the voltage rise caused by PV, the amounts and the exact time references of occurrence of the voltage rise have to be provided to the controllers of the compensating devices; an on-line impact assessment approach would be helpful in this case. Similarly, if storage devices are used for smoothing out the PV fluctuations caused by passing clouds [13], the voltage and power ramp rates available from on-line

measurements can be used for determining the charge and discharge control parameters of storage devices.

A generic conceptual representation of assisting the PV impact mitigation by the proposed approach is shown in Fig. 4.6(a) using the rate of change of a fictitious Fluctuating Variable (FV). The detection of a sudden fluctuation can be performed from the abrupt change in the ramp rate of the FV. A Compensating Action (CA) designed to mitigate the fluctuating effect has to be triggered at the point of detection. The ramp rate of the compensating action will depend on the desired ramp rate of the FV, which is expressed by (4.12) for the k-th time instant.

     


k desired


k t t



 


(4.12) The ramp rate of the compensating action has to be changed according to the ramp

rate of the fluctuating variable at each instant of time. For example, to mitigate the impacts of PV output fluctuation, a compensating action can be implemented using a Community Energy Storage (CES) [14] that may be used for smoothing out the fluctuations in PV generation in the feeder, as shown in Fig. 4.6(b). The ramp rate of the storage discharge power needs to be adjusted using (4.12) based on the IPRR value obtained by (4.10). The discharge ramp rate has to be adjusted at each instant of time based on the most recent IPRR value. Based on the discharge power ramp rate, the discharge current ramp rate of the storage device can be determined at each instant of time and can be sent to the storage controller for implementation. In this way, the proposed approach can aid in the decision making for a real time mitigation of PV impacts. While the proposed assessment approach could be used to assist in the decision making for mitigation of an ongoing PV impact in real time, it would not in itself be able to mitigate the impact, or control the action of mitigation.

Fig. 4.6. Application of the proposed approach for mitigation of the impact of PV output fluctuations: (a) calculation of ramp rate of a generic compensating action; (b) translation of the idea in terms of a community storage device.