4.2.4 Hosting Capacity Calculation Methods

The main concept of SE is to estimate states of the network (e.g., voltage, active and reactive power) that are not measured. In practice, one estimates the network's state by minimizing the weighted least squares of the difference between the measurement and variable values. If the problem is well formulated, then Gauss-Newton algorithms can find a solution efficiently. To formulate the problem well, accurate models for the electrical distribution networks are required. Distribution system state estimation (DSSE) has been proposed to handle the unbalanced systems recently applied in [22] summarized in Appendix 2.

In Australia, most of the electricity distribution networks are radial (no ‘loops’ in the distribution network). Typically, the nodal power injections and branch power flow in a network are considered as constant real and reactive power over the time interval (typically 5 or 30 minutes) [19]–[21]. Here, the power flow and state estimation should consider two main constraints (i.e., voltage and thermal constraints) and the existing active energy management strategies (i.e., volt/var, volt/watt, etc.).

Based on the reviewed projects, most typically use balanced power flow to calculate hosting capacity, which neglects the phase imbalance that may lead to over- or under-estimating the hosting capacity results and the ability of the network to accommodate DERs. One of the projects developed unbalanced distribution system state estimation (DSSE) technique to calculate the hosting capacity [22]. The state estimation technique needs less input quantities than that required in power flow. However, it is sensitive to the accuracy of the data, with particular high non-linearity in the data increases the risk of numerical instability in the solution finding process. Both power flow and state estimation need explicit models for the network components, including phase imbalance effects to calculate hosting capacity accurately.

Active network management strategies can be defined for both voltage and thermal constraints using centralized, decentralized, and distributed techniques [21]. It is worthy of mentioning that the concepts of hosting capacity and active network management strategies are often profoundly related. An active network management strategy often implicitly or explicitly determines the DER hosting capacity achieved within a given distribution network segment. Active network management strategies focus on providing setpoint control for DER by determining the exact value of the DER output that will best allow some operational objective to be achieved.

4.2.4 Hosting Capacity Calculation Methods

There are several hosting capacity methodologies under development and more likely on the horizon. For ease of discussion, we have focused on four primary methodological categories recommended and discussed by EPRI: stochastic, streamlined, iterative, and hybrid. These methods are briefly summarized in the main body of the report with illustrating their accuracy and comparison for assessing hosting capacity based on distribution feeder and DER evaluation criteria. For more details, the reader can be referred to [14]. Additionally, a capacity constraint-based method can be used to approximate hosting capacity, which can be a useful if only approximate or estimated data is available for many measurements critical for assessing hosting capacity.

Stochastic Methods

This method starts with a model of the existing distribution system, performing a baseline power flow analysis of the existing system and gradually increasing the penetration level of DERs on a feeder for varying sizes and random locations to evaluate any adverse effects arises for different scenarios that results in hosting capacity range. Assumptions such as DERs with similar characteristics, sizes and

locations are considered in this method. It can handle larger, three-phase, and behind-the-meter DER systems and calculate the range of possible impacts by the DER locations and sizes at future penetration levels. This method requires significant executing time and is computationally intensive. Stochastic methods can be an effective approach to develop research tools, but it is not recommended for applications beyond that, such as interconnection studies. This is because of such methods are not effective at capturing full range of distributed DER impacts (e.g., locations), applicable to specific impact factors only and difficult to consider range of possible DER and grid scenarios.

Streamlined Methods

Instead of direct modelling, a set of simplified algorithms are applied for each power system limitation (typically: thermal, safety/reliability, power quality/ voltage, and protection) to approximate the DER capacity limit at nodes across the distribution circuit. Time series data are acquired by leveraging smart meter data to capture daily changes in load, DER, and regulation equipment and observe their impacts on hosting capacity. Some assumptions, such as all existing DER have a fixed output and do not contribute to voltage changes, might overestimate hosting capacity in some cases. This method provides time-based hosting capacity with a faster computation capability that enables analysis of additional scenarios such as DER forecasts, reconfiguration, smart inverter settings, DER mitigation strategies, etc. As a new technique in calculating hosting capacity, many stakeholders are still struggling to understand it. In addition, it uses non-standard distribution modelling data and lacks accuracy.

Iterative Methods

The iterative method essentially increases the DER iteratively at each node on the distribution system until a violation occurs. Power flow simulations are performed to determine the maximum level of DER hosting capacity at different independent locations without exceeding thermal and voltage limits. Besides, a protection analysis is also performed to evaluate the protection criteria and determine the hosting capacity to each node without hindering the protection devices’ ability to detect fault conditions. The iterative method is also sometimes referred to as the detailed method. Systems “as is” and “what-if” scenarios, such as DER forecasts, reconfiguration, smart inverter settings, DER mitigation strategies, etc., are limited due to the computation burden. In this method, vendors implement the same processes, and existing DER is assumed not to contribute to voltage deviation that might lead to overestimating the hosting capacity in some cases. The method resembles the similar concept of executing an interconnection study where the DER impacts are determined using the distribution planning software and requires significant time, data, and computational cycles to complete, which is similar to the stochastic-based approach.

Hybrid Methods

Hybrid methods can be considered an alternative to overcome the computation burden of stochastic and iterative methods. The Distribution Resource Integration and Value Estimation (DRIVE) tool is one example that has been developed by adding new capabilities, improving overall accuracy, and increasing efficiency based on the needs of several DNSPs worldwide. Unlike the iterative method, hybrid methods no longer meticulously iterate through penetration levels to find a hosting capacity solution. Instead, educated penetration increments are used to easily speed up the analysis. These educated penetration increments are a key difference between iterative and hybrid methods. Since the hybrid methods are a newly developed distribution analysis technique, it is not easily understood by all stakeholders.