Volt-VAR Optimization and the Dynamic Electric Distribution Grid

BY JASON LOMBARDO, S&C ELECTRIC COMPANY

Today’s electric distribution grid is becoming more dynamic with the introduction of distributed energy resources (DERs) such as solar photovoltaic systems and the deployment of automated switching schemes.

These applications are modifying the power flow of electricity along the distribution system–something distribution planning and engineering teams traditionally have not considered as they plan for the deployment of a volt/VAR optimization system. Planning and engineering includes placement of capacitor banks and line voltage regulators and control system configuration. If decisions are made without cosidering DER, operational issues with the distribution assets can occur. The devices might not be in optimal locations due to feeder switching events or power being injected into the distribution system because of DER. Volt/VAR optimization systems, therefore, must become aware of and adaptable to the ever changing environment to provide the economic and operational benefits utilities require from them. Several challenges must be overcome to ensure benefits are realized under all dynamic system operating conditions. Some challenges can be overcome with advanced algorithms that take the power flow direction, as well as advanced control monitoring into account. Others will require more cohesive interaction among the systems’ initiating the changes and the volt/VAR optimization system, which works to adjust and control the distribution assets to reduce demand, energy consumption and system losses.

The most fundamental challenge for volt/VAR optimization system designers and engineers is to ensure the system has the most up-to-date feeder topology and connectivity model, including the associated loading on the feeders. These fundamental aspects drive all volt/VAR optimization decisions; they dictate where the distribution assets are located on the feeders, as well as what loads are associated with individual feeders.

Whether volt/VAR optimization is used to reduce demand, energy or distribution losses, topology, connectivity and loading come into play. This challenge comes to light when feeders are reconfigured through automated or manual switching operations.

Some utilities that have deployed volt/VAR optimization systems deal with this challenge by disabling the volt/VAR optimization until the feeders return to their normal configurations. This is necessary when there is no automated mechanism to update the topology, connectivity and loading within the volt/VAR optimization system. Other utilities have addressed this concern by using model-based systems such as those in a distribution management system (DMS), where the system model is continuously updated in near real time through device monitoring or process input from the manual switching events in the field. Other ways to address this challenge using nonmodel-based systems include, monitoring the automated switching devices for status changes, integration with supervisory control and data acquisition (SCADA) or monitoring outage management systems where the switching events are captured, or both. Systems that use advanced heuristic algorithms can be deployed to recalibrate the volt/VAR optimization system to determine how best to control the distribution assets when feeders are reconfigured or loads are added to the system.

These solutions, however, can suffer from a lack of integration if the utility is using different solutions from different vendors for various applications or if the processes that capture changes are not executed in a timely manner. This could result in delays between when the switching events occur and when the topology, connectivity and loading are updated within the volt/VAR optimization system. The best way to deal with this is to ensure the switching events are communicated to and captured by the volt/VAR optimization system in real time. In addition, if a separate system manages the switching events, the two systems must share data before, during and after the event to ensure the switching actions achieve the desired results while the volt/VAR optimization system continues to achieve its objectives.

Even with this level of integration, one objective might not be met because of conflicting needs, which points further to the need to have integrated solutions that can determine how best to meet all operational objectives simultaneously. An example of where an integrated solution could provide additional benefits would be a deployment in which the volt/VAR optimization system and the feeder automation system work together to ensure power is restored to as many customers as possible and acceptable voltage levels are maintained after the feeders are reconfigured. Another example would be in situations where load management is required on the feeders because of a system overload where the feeder automation system and the volt/VAR optimization system would work together to determine the optimal solution for the overload situation (e.g., switching and load reduction).

Another challenge for utilities comes with the introduction of DER into the distribution grid. This creates a much more complex issue that affects volt/VAR optimization systems’ abilities to continue to achieve their objectives. First, based on IEEE 1547, utilities do not have the ability to monitor or control customer-owned DER systems, so their only way to manage the impact of DER on their systems is to try and shock absorb it by changing how they operate and control capacitor banks, line regulators and substation load tap changers. Whether such assets are located close to the customer-owned DER will dictate how well a utility can manage the impact of these systems. All of this has made it difficult for volt/VAR optimization systems to handle situations where there is a significant volume of DER on the distribution feeders they are trying to manage. Some capacitor and line regulator controls, however, can detect reverse power flow from DER resources and make certain operational adjustments to account for the reverse flow. Nevertheless, most operational adjustments these controls make are not available to the volt/VAR optimization system today because they typically are local protection schemes that are not accessible by remote systems.

In cases where the local control changes its operating scheme to account for reverse power flow, the volt/VAR optimization system loses its ability to control those assets and therefore cannot make the proper adjustments to fully optimize the system when this condition occurs on the feeders. This issue could be somewhat alleviated, however, if the local control allowed the volt/VAR optimization system to continue to manage it. They system must, however, recognize the reverse power flow condition and adjust the optimization control algorithm to account for it. Otherwise it might make incorrect decisions when it comes to adjusting the operational status of the asset, such as raising or lowering the voltage on a line regulator or tripping or closing a capacitor bank. This is especially important in situations where the line regulator control can reverse its voltage adjustment.

The other issue that DER can introduce is the ability for the model-based volt/VAR optimization systems to properly model the unbalanced reverse power flow condition. This is a complex modeling exercise that could result in unnecessary or nonoptimal control adjustments being taken by the volt/VAR optimization system if the model does not properly account for the DER impact. Because of this, model-based systems should be analyzed and tested thoroughly when deployed on feeders with DER to ensure they are properly accounting for DER’s impact and making the proper control decisions to meet objectives. This issue is not necessarily unique to model-based systems, however, as rules-based and heuristic-based systems must also properly adjust their algorithms when DER is active on the system, which is not easy.

All volt/VAR optimization systems should be tested thoroughly to ensure they are making the proper control decisions when DER causes abnormal conditions on distribution feeders. More research and development is needed to address the growing concern of DER and the impact it has on the electric distribution system as it relates to volt/VAR optimization. Advancements such as the deployment of more intelligent inverters that can help manage the voltage and VAR flow from the DER systems and the ability to monitor and control these assets, or both could change how volt/VAR optimization systems manage and interact with DER systems. In addition, the deployment of enhanced controls that can detect reverse power flow and then modify their behavior to adjust for this condition would be beneficial when integrating them into the volt/VAR optimization systems. Another solution is to deploy and integrate battery storage technologies with volt/VAR optimization systems. This would allow the volt/VAR optimization system to use these resources when required for voltage control, as well as when enabling demand and energy reduction across the system. With these capabilities and others, volt/VAR optimization systems could better manage the impact DER has on distribution feeders to ensure that power and voltage are delivered within tolerance and as efficiently as possible.

Jason Lombardo is a product manager at S&C Electric Company whose focus is grid network control solutions for electric utilities. He has been working with electric utilities and telecommunication providers more than 10 years to deliver industry-leading products and solutions. Reach him at jason.lombardo@sandc.com

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