Distribution Automation in Action

An ADMS (Advanced Distribution Management System) is an enterprise software solution that can provide utilities a broad range of smart grid applications including the ability to automate fault location, isolation, and system restoration.

Transitioning to an ADMS platform is a major decision for any organization. However, the decision to pursue smart technology to advance a utility company’s functions is an investment that will pay out for years to come in service reliability, customer satisfaction, and reduced costs in the long run.

If there is a power outage on a grid due to a fault in the power lines, many situations will find hundreds, if not thousands, of utility customers out of power regardless of where they are located on the network, with hours-long restoration estimates.

On the other hand, the right ADMS automatically detects the location of the fault, isolates it, and then reroutes the flow of energy to restore power to customers that are without power but are not in the immediate area of the fault. This process happens in less than a minute, keeping costs down and reducing CMI (customer minutes interrupted).

Recently, Suwannee Valley Electric Cooperative in Live Oak, Florida, implemented an ADMS from ACS with the goal of automating their grid and boosting the efficiency and effectiveness of their utility company. Outages are unpredictable and it wasn’t long before SVEC faced their first fault challenge. On March 23, 2018, SVEC achieved their first successful automated restoration and kept energy flowing to hundreds of customers that would otherwise have been without power.

The decision to implement an ADMS system was driven by the organization’s drive for continuous improvement of service reliability and outage restoration times for its members. “It is satisfying to witness the FDIR portion of SVEC’s reliability plan improve service to our member owners,” shared Kurt Miller, Director of Engineering for SVEC. “SVEC is looking forward to completing systemwide FDIR in 2019 with Volt/Var optimization and peak demand management to follow in 2020,” said Miller.

While ADMS can be seen as a technology of the future, implementing it today will put your company on the path to increased efficiency and long-term success. Learn more about how other utilities are using ACS solutions for feeder automation in our white paper, Centralized and Distributed Intelligence Applications for Feeder Automation.

Founded in 1975, Advanced Control Systems™ (ACS) is a leading provider of advanced automation technology and products to the global electric power industry.

Building a Smart Grid Mobile Image

Going Smart at Scale, from the Start

Building a smart grid with the big picture in mind, including functionality and financials

Your smart grid rollout should go live everywhere, right from the start.

Most utilities will conduct a cost-benefit analysis before making any major investment in the grid. Therefore, this analysis should recognize two key strategies for a commitment of this magnitude – a smart grid project, for example.

First, any smart grid rollout will gain the greatest benefits if applied at scale right from the start, to the maximum number of feeders, if not all of them. Second, each smart grid application delivers different benefits, such as cost reduction, improved reliability, greater power quality, or enhanced renewable deployment. The whole exceeds the sum of the parts. Thus, the accumulation of benefits established by layers of multiple smart grid technologies will only enhance the justification analysis. And, the quicker these applications can be implemented, the greater the benefits to be gained.

Yet utilities typically will fail to exploit either of these two strategies. Instead, utilities usually will implement smart grid automation according to a much different, though faulty set of guidelines – much to their disadvantage.

First, utilities typically will seek to minimize complexity, treating a primary smart grid technology as a solo deployment, aimed at meeting a single specific business objective, such as improved reliability through self-healing, cost reduction through loss minimization, or deploying a greater share of renewables. Unfortunately, however, the payback doesn’t keep pace. Nor do the engineers gain the experience of understanding the interaction between the technologies.

Second, to minimize the cost of the evaluation, utilities often will select a limited pilot area to represent all feeders over which the anticipated results can be assessed. However, due to myriad feeder configurations and load types, it may not be possible to find a pilot area diverse enough to fully evaluate all the conditions that could be encountered across the network.

Third, a basic staff often is trained to support and maintain the new automation. Their job is to “own” its implementation and to perform the evaluation, but at the cost of project delay. The support staff’s engagement may be lengthy, since the pilot is deployed on operational feeders. It could take years before meaningful data generated by an extreme fault or weather condition arises where the technology’s performance can be evaluated under stress. Fourth, utilities typically will enable a new automation function only one feeder at a time, such that the benefits will likely fall short of promised effectiveness.

The drawbacks to such a strategy are considerable, not the least of which involve cost and delay. The ability to layer smart grid applications with multiple operational objectives is not just an issue of economic justification, it is critical to delivering quality power.

A better approach would run as follows:

  • Maximize automation across technologies and networks.
  • Expand the scope of deployment beyond a narrow pilot area.
  • Use simulators to minimize involvement of personnel.
  • Go live now, and everywhere.

For example, fully automated, self-healing solutions solve one problem but can sometimes create others. The sudden transfer of unfaulted loads from one feeder to another can result in voltage problems on both feeders. Self-healing applications layered with Volt/VAR control are able to more effectively manage the problems created by network reconfiguration, whether the network reconfiguration was fault driven or planned.

If the layered applications do not coordinate effectively with one another, the system isn’t very smart. The applications must coordinate to compensate for each other, while each achieves their prime objective.

Of all of the considerations, one is often left unanswered: “Is it practical?” We know it will work. It is with prosaic confidence and resolute determination, guided by engineering focus (and supported by state-of-the-art control and communications infrastructure, optimized by advanced closed-loop control algorithms) that an investment in a pilot implementation will perform as expected. But does success mean that it the pilot project is sustainable and affordable for the entire network? Likely, the cost of the needed infrastructure will not prove justifiable for all circuits.

Furthermore, does the time that is required to upgrade the network fit within the short event horizon of the economic payback? These questions must be considered.

A Better Rollout

For utilities with very large feeder networks, deployments of ADMS (Automated Integrated Distribution Management and Outage Management Systems) present the unique problem of adaptation to scale. Outage management systems historically have not faced this problem, since there is no infrastructure to install – just a connectivity model to maintain. But ADMS adds a new level of complexity and cost, beginning with the need to respond to network changes in real time.

In short, ADMS requires a better plan for implementation – one that overcomes the deficiencies of the typical approach. A better process might run as follows:

Maximize Automation. Rather than employ a single technology approach, resources should be focused on understanding the benefits of the synergistic interaction among a suite of advanced applications. So a better approach would embrace a suite of synergistic applications and fully understand their combined potential to improve network operations. Of course, simplification is fine if used to eliminate the time and distraction of building out the supporting infrastructure. But the infrastructure should not define the technology that can be deployed. Rather, the technology that is to be implemented should define the infrastructure.

Moreover, a simulator for the power system network will greatly reduce the cost, time and complexity of building the network infrastructure early on during the pilot phase. The simulated network infrastructure enables the new suite of automation applications to run unhindered. The simulator provides the utility with the ability to gain in-depth understanding of the interaction between integrated technologies with a minimal investment.

Expand Scope. Rather than implement applications over a narrow pilot area, the simulator can model different feeder types very effectively under all network conditions. This way, the simulator’s network model will be the same engineering model that is used in production, which serves to validate the proposed model.

The load data that serves the model is archived data, collected from the substations over each seasonal period. It simulates all-day type conditions at a fraction of the time and cost. The results of the ‘before’ and ‘after’ simulations offer indisputable justification of the business case, under normal and abnormal operational conditions, before the utility commits to an investment in costly field work.

Minimize Personnel. A simulator offers several advantages with respect to personnel. First, the simulator provides a powerful training tool, able to create different scenarios that can be used for operator and engineer training. Second, unlike an operational pilot system, the need for a fully trained 24×7 maintenance staff will become unnecessary during the evaluation and discovery phase under simulation. And third, engineers can evaluate the impact of the new automation technology on the network running under various scenarios, with a simulator that can digest months of historical data in mere hours.

Go Live, Everywhere. The biggest problem with implementing advanced grid automation is the cost and time required to install the supporting infrastructure. This can take years, particularly if a significant portion of the network must be extended with automaton in order to realize the benefits needed for cost justification.

The goal of engaging the maximum number of feeders with smart grid technology is still the key to achieving payback. Full benefit to all feeders with the new technologies can be immediately gained by supplementing traditional telemetry and control with information gained through integrating crews with the application’s solutions. This is the Human Grid. The crews are equipped with a hand-held mobile device, which is directly integrated to the smart grid solution algorithm. The difference between the latter versus full traditional supervisory control and telemetry is simply speed. The quality of the solution is the same.

The automation applications provide a toolset that should enable the control center to engage the crews to receive integrated commands with minimal overhead. The real-time network map, network analysis, and tags are available to the crews overlaid on a Google map in a common browser. Crew input, using the mobile device, is uploaded to the control center operator, who can view the input on his geographical user interface as he would telemetry. The crews are automatically assigned switch plans – such as self-healing fault isolation and restoration automation, outage ticket assignments, work order plans and network survey assessments – as the control center follows the crew’s movements and progress. Outage codes, failure codes, repair codes, and estimated time of restoration are no longer communicated verbally to the operator for manual entry.

Furthermore, the public is likewise engaged in reporting outages, street lights out, complying with demand response, load control, even meter readings. The public gains an unprecedented value proposition by being able to manage their usage, budget, etc. – all things related to utility operations.


PRISM DERMS demonstration project is accepted

The results data is in for ACS’s EPIC distributed energy resources management system

ACS successfully completed, installed and performed the site acceptance test of a full-function Distributed Energy Resource Management System (DERMS) solution on August 25, 2017 as part of the California Public Utilities Commission’s Electric Program Investment Charge (EPIC) program for “applied research and development, technology demonstration and deployment, for clean energy technologies and approaches”.

The demonstration project included two utility substations, selected to substantially improve electrical operations and power quality based on their historical performance. The selected feeders exhibited the following characteristics:

  1. Poor operating performance and reliability
  2. Large installed output of DER, such as PV, fuel cell and micro turbines
  3. Significant energy storage capability
  4. CentrixTM: the Autonomous DERMS Distributed Platform

PRISM DERMS is a suite of renewable, closed-loop automation applications delivered on the ACS Centrix platform, enabling the solution to be distributed anywhere in the utility environment. Centrix is a practical and cost-effective solution for islands of automation that can be deployed incrementally over time. PRISM DMS/ADMS offers an enhanced suite of DERMS supporting applications with operator supervision functions, injection forecast, visualization and mobile operations.

ACS successfully pioneered Centrix as a distributed automation platform. The first installation was deployed at Georgia Power, where Centrix provides autonomous fault detection, isolation and restoration (FDIR) independent of the centralized control center SCADA system. To date, Georgia Power has successfully deployed Centrix on over 750 feeders, saving 39 million customer minutes of interruption in the first two years of operation. During this time, two major ice storms hit Georgia, with the Centrix/FDIR technology effectively proving its mettle at both Georgia Power and Cobb EMC by flawlessly handling progressive outages with multiple outages per circuit.

The PRISM DERMS application suite combines and coordinates the Voltage and VAr control operations with the self-healing capability of FDIR. With DERMS, integrated Volt/VAr control includes the optimization of renewable PV and battery storage on the feeder.

Operator-less Use Cases

The PRISM DERMS application suite on Centrix is the base offering for autonomous operations. Centrix supports the following functions, which are designed to avoid feeder violations under all operating conditions:

  1. Integrated Volt/VAr optimization with renewable DER control (IVVC/r)
  2. Emergency Load Transfer (ELT)
  3. Maximum Injection Capability (MIC) (curtailment)
  4. Protection setting adjustment

When delivered as part of the PRISM Advanced Distribution Management System (ADMS), the DERMS suite offers additional optimization functions that are operator-directed for further “what-if” analysis, including a powerful 24-hour look-ahead feeder violation prognosis.

The primary objective of the DERMS applications is the safe operation of the feeder within acceptable operating limits. The secondary goal is to enable the maximum possible renewable injection. It employs a graduated degree of aggressiveness when voltage violations do occur.

Energy storage devices are operated as either an energy source or as a load (in charge mode). Multiple units on the same feeder may be simultaneously coordinated to operate in both source and load modes, to optimize the feeder’s operating conditions from substation to end of line.


The PRISM DERMS solution provides many benefits. Comparison of the feeder’s normal operation by independent controllers, with the Centrix integrated DERMS suite, has been proven to yield substantial benefits by every measurement:

Compared with typical uncoordinated voltage regulation, IVVC provided lower line drop voltage at lower ‘end of line’ voltage, with 26% fewer regulator control operations.

Capacitor switching was reduced 20% with IVVC/r. IVVC/r controlled the PV inverter to injection or absorb reactive power to provide VAr compensation at maximum real power output.

Unexpected disturbances were tested, such as tripping off-line a large 2500KW PV injection DER. IVVC/r demonstrated the ability to restore the feeder’s operation to an acceptable (green) operating zone three times more quickly than compared to traditional controls. The resulting steady state condition exhibited a flatter overall feeder voltage profile with IVVC/r (see Figure 1, which shows the voltage profile dip at the time of the PV trip—10 minutes after the start of the scenario).

The maximum Line Drop Voltage spread of 0.35 without IVVC/r, compared to 0.05 with IVVC/r, demonstrates superior operation.

Smart Grid DMS & DERMS

Integration Between Smart Grid DMS and DERMS

Managing a multi-player community of renewable consumers and producers successfully

Utility and public interest in renewable integration within distribution networks has rapidly risen in popularity. For the public, general sentiments concerning global warming and the environment have been further stoked by subsidies and economic incentives. Utilities face the technical challenge of anticipating the impact of ever increasing amounts of both utility-owned and private sector-owned renewable generation, while keeping their power grids safe, reliable, and cost effective.

With the continuing focus on renewables technology, it seems a safe bet that the trend of increased renewable penetration throughout the power grid landscape will flourish well into the future. Thus, utilities must leverage advanced algorithms and control technology that originated from the Smart Grid era, to effectively manage both the technical and the commercial challenges of a multi-player community of intermittent consumers and producers.

Preceding the interest in renewables, the initial advancement of the smart grid that emerged with the greatest notoriety was automatic switching functionality, or self-healing, which improved network and individual feeder reliability. Self-healing technology, often called FDIR or FLISR, is the idea of providing tools (automation, software, network modelling) which allows the network to figure out what is wrong and to automatically execute a solution which restores power in real-time. Utilities that adopted these early technologies gained the foundational understanding to use these sophisticated applications to augment and support their manned operational control centers. Those utilities will notably benefit by having laid the automation foundation that is critical to successful management of renewable deployment. The technological progression from self-healing to power quality to Distributed Generation (DG) management is a natural evolution of increasing complexity and as the degree of renewable generation deployments increase, the reliance on these advanced tools to support ongoing safe, reliable, cost-effective operations becomes mandatory.

The greatest challenge regarding renewable deployment is that the amount and variability of generation, along with the various points throughout the grid where the generation is operating (injection sites), is always changing. The power grid was not originally designed to operate in this manner, so in almost every instance today, impact of significant renewable injection within a feeder is unknown and unmeasured. The good news is: the original principles behind transforming our old power delivery networks into Smart Grids was based on the idea of using technology to monitor everything happening with the flow of electricity, and using mathematics and physics to make real-time decisions on how the grid should operate. The complexity of developing the network model and load flow solution algorithms is at the heart of the Smart Grid. Those same tools allow utilities to accept and manage the continuously changing flow of diverse renewable generation sites without risking the safety or reliability of the underlying power delivery network.

Changes in Automation

Smart Grids evolved first with the deployment in the field of automated switches and re-closers, the tools that utilities use to re-route power flows. Next, many of those same utilities took the further step of adding software in the form of Distribution Management Systems (DMS) and advanced algorithm-based applications which allow utility grid operators to assess in real-time what is going on in the network and to take action (remotely operate equipment in the field) to maintain the best and most reliable operation. In an area where new renewable generation sites are being added, these smart grid technologies that were installed to enhance reliability and power quality in the feeder’s operation will initially operate unimpeded with minimal deployment of distributed generation. At low levels of renewables deployment, the application of distributed generation within the feeder resembles a negative load (because instead of consuming electricity, that site is adding net electricity to the grid). In fact, many DMS solutions simplify the network calculations and modeling of injection points within the feeder in this way.

As more and more renewable generation is brought on-line, the over simplification of treating the distributed generation as a negative load is ineffective. Significant injection will soon threaten the feeder’s stability without a feeder load flow analysis that considers the dynamic nature of the injection points. The load flow itself must be capable of handling multiple sources.
To deal with this new challenge, smart grid technologies specifically Voltage Control (such as Integrated Volt/VAR Control, also known as IVVC) must be retooled to include control of the inverters at each renewable site with the objective of increasing the coordination of each feeder’s maximum injection capability. Switch plan optimization applications must likewise establish an objective function, which maximizes the feeder’s ability to accept the maximum injection safely. The solution to successful deployment of significant distributed (renewable) generation injection, builds directly on the classical smart grid automation applications.

Architectural Objectives

The architecture of systems which support distributed generators, distributed energy resources, renewable deployment or energy storage systems must accomplish the following primary objectives:

  • Preserve the viability of the feeder’s operation at all times.
  • Maximize the availability of the DGs/renewable generation, meeting a user-selected business case.
  • Maximize the deployment of energy storage assets, meeting a user-selected business case.

A modular renewable management architecture that is well-suited to accomplish these objectives is performed by two main systems: the DMS and Distributed Energy Resource Management System (DERMS). Since the two primary objectives of a renewable network may work in opposition to one another, an architecture that is dedicated to maximizing the two objectives as their primary mission is required.
The operation is best served if the division of responsibilities of the systems ensure the following:

  • The DMS is eminently positioned to perform the secure, reliable and optimum operation at all assets in the feeder under all operational conditions.
  • The DERMS is dedicated to forecast and maximize the output and availability of the DG / DER resources under the objective established by the use case selected by the operator.

Other architectures may apportion the responsibilities differently, for example they bundle IVVC as a DERMS responsibility. More important than the architecture, or the assigned mission parameters of the DMS/DERMS, is that IVVC or IVVC/r must always be run as a mission critical automation application. IVVC, like FDIR/FLISR, must be protected by high availability with real-time performance, especially under severely stressed network conditions.
This goal is a challenge with some ADMS solutions that have functionally evolved from a non-`real-time origin. In those cases, a distributed automation architecture such as DERMS is preferred in order to meet the mission critical requirements. Some very large models are equally challenged with respect to maintenance where a smaller distributed model is more easily managed within a distributed network of DERMS processors. In these cases, the DMS is a collector of the distributed automation islands for operator oversight purposes, but it is not placed at risk by a low availability centralized system.

In contrast, if the mission critical requirements can be met along with accurate and timely model maintenance, the DMS centric feeder automation architecture that supports IVVC in the DMS, simplifies the analysis and modeling which is always an important consideration, since the modeling of the distribution network feeder is a continuous and arduous effort. In this architecture, the network model is consumed only in the DMS and it is not necessary to distribute it to the DERMS. In short, the architecture can be a combination of central and distributed intelligence, flexible enough to meet the capabilities and requirements of the mission critical functions.

Modules for Renewables

Not all renewables, or distributed generation, are equal. Therefore, the optimum strategy for their deployment is tailored to the application. The ability for the feeder to be able to safely accept the maximum output capability of renewables such as photovoltaics (PV), without resorting to curtailment, is important. Maximizing the potential can only be accomplished if the forecasted capability of the DG is predicted far enough in advance for the DMS to analyze the hourly impact of the scheduled injection.
If the forecast predicts that the schedule will result in time periods that will exceed the network’s stability limits, there are two situations that must be addressed:

  • Can the normal functioning of the Volt/VAR control avoid the violation? If it can, no further action is required. The DMS will handle it.
  • If the DMS cannot compensate for network weakness imposed by the injection a more advanced analysis of the options must be made.

In the latter situation, the DMS has the ability to calculate any network configuration and topology that will support the maximum forecasted schedule or the adjusted forecasted schedule. This may result in switching changes, similar to an enhanced self-healing solution, which will transfer loads and DGs from one feeder to another in order to meet the forecast. An appropriate lead-time to affect change is important since typically few feeder-switching devices are remotely controlled. An optimum integration of sending switch plans from the DMS to crew mobile can improve the efficiency of the switching response.

Activating the Human Grid Through Telemetry

Activating the Human Grid through Telemetry

Tapping your crew and utility customer as a powerful source of network information

The involvement of the crew and the utility customer is a powerful untapped source of telemetry, control and general network information, not available by any other means. Furthermore, it is effective over the entire network. This human grid must be integrated into the control center ADMS systems to receive the input data and to push better visualization of the operation of the network to both crews and customers, for their benefit. Finally a smart grid technology will provide a value proposition. Not only does the human grid enable more informed decisions on the part of the consumer regarding energy usage, but also allows the utility to leverage grid-edge information to further enhance the overall efficiency of the network for all stakeholders impacted by the grid.

Challenges in Maintaining a Real-Time Model

Most smart grid solutions are centralized so application network solutions readily provide an up-to-date view of the entire state of the network. The synergistic integration of various applications relies on a common model, which is usually maintained by the utility control center.
Utilities of different sizes face different complications with respect to managing network changes. Often, smaller utilities either do not operate a control center or they do not have 24/7 support. Their crews are often small, but very experienced, so they can envision their entire network and its normal operations from a crew truck. However, without a centralized model of the network, even minor automation applications, such as peer-to-peer self-healing repair solutions, cannot recover if the feeder’s “normally open” point moves.

Large utilities face a different challenge. The network is so large that their crews cannot possibly assimilate it in real-time. Since widespread down-line automation is rare, it is difficult to maintain an accurate operating model of a large complex network. Multiple crews, working from static maps, are challenged in performing switching changes to repair outages.

In either case, the smart grid requires that an accurate model  be maintained to reflect the true operational state. All automa­tion, analysis and restoration depend on knowing the real-time operating state of all feeders. Smart grid applications often perform network changes in order to meet their objective func­tion. The optimum topology changes may not leave the feeder in its normal configuration, but rather in a non-standard topology. Non-standard configurations neutralize the crew’s experience and operational knowledge of the network while rendering the static maps as inaccurate.

The advantage of automation is that it reflects all network changes immediately in the model with a topology processor. However, the cost and time involved make widespread network automation impractical, particularly in very large networks. Manually switched portions of the network require an alternate method of maintaining an accurate real-time network state. An example of this approach is implemented on a very large utility in India with over three thousand feeders. This beneficial “human grid” application accomplishes near real-time network manage­ment cost-effectively on non-automated networks.

Analysis Tool Sets Replace Automation

Although a smart grid implementation generally automates a small area of the network, utilities who “go-live” can leverage their technology to apply to non-automated feeders. If applied properly, the quality of the switching solutions are the same for automated or non-automated feeders. The only advantage automation offers is the speed of implementation.

A tool set of analysis and switching applications can be applied through an operator’s ad hoc query evoked by selecting any feeder element. The tool set includes solutions to accomplish switching objectives, to generate:

  • restoration plans
  • isolation plans
  • “return to normal” topology plans
  • fault location plans
  •  voltage reduction plans
  • loss minimization plans

Even when applied to self-healing situations, the tool set substitutes the automatic fault location process. Once the fault is located using non-automated methods, such as with an outage management system (OMS) or distribution management system (DMS) allows a fault location (short circuit) analysis to identify the location so the utility operator can request isolation switching with load transfer quickly in a single click.

Mobile Switch Plan Generation

Without supervisory control and data acquisition (SCADA), an alternate method of switch plan execution is vital. In this case a mobile integrated switch plan manager will eliminate extra maintenance and avoid network errors.
Most control centers still manually write switch plans on a paper pad. Few control centers use a centralized switch plan application. A centralized, load flow-capable switch plan manager administers, validates, assigns and archives all switch transac­tions, whether they are manually or automatically generated and executed. The proposed solution extends the centralized switch plan manager further by adopting a tight integration with the crew’s mobile platform. The Switch Plan communicates directly with the crew just as a SCADA system communicates with front-end processors and remote terminal units (RTUs).

Using the switch plan manager, switch steps assigned to crews are sent automatically to each crew’s mobile device. The crew’s execution of each step is recorded with validation of information related to the operation, such as the device state and time of execution. The information uploaded to the DMS updates the centralized network model in a process that is analogous to auto­mated systems. This approach replaces the traditional telemetry.

Due to the inherent delay between creation and execution, the plan may no longer be accurate due to subsequent switching, placement of tags, subsequent faults, etc. The switch plan manager must include a verification feature. Verification enables the opera­tor to validate and confirm the feasibility of a new best practices plan. Any and all changes in this plan should immediately be sent to the utility crew’s mobile device.

Crew Empowerment

Two important crew mobile functions include: the ability to view a dynamic, interactive mobile task list of operational work assign­ments; and the ability for the crew to visualize the real-time state of the network. Previously only the control center operators had access to this detailed information. Displaying it to the crews enables them to validate the information and to immediately correct errors. The improved visualization increases their situational awareness during switching in normal and non-standard configurations.

Mobile visualization displays the real-time colorized network topology overlaid on a Google map. Among other features, the crew is able to view the placement of clearance tags placed by the operator in the control center. The crew can visualize the location of all active switch plans and the extent of the switching steps. Crews also have visibility of the other crew locations, network devices and fault locations. If they have the security authorization, the crews can view DMS displays, reports, or one-line diagrams.

This innovative technology provides several benefits, namely increased real-time information improves crew safety and network reliability, and confirms the network state for the DMS. These advantages benefit all feeders, even those without automation.

Customer Empowerment Improves Utility Customer Satisfaction

The purpose fulfilling the other half of the human grid is to leverage the consumer to provide outage and restoration telemetry, and in some cases, a degree of load control, since the consumer ultimately controls the load.

The recent J.D. Power’s 2016 Electric Utility Business Cus­tomer Satisfaction Study reports an important public perception: “Power quality and reliability satisfaction among business custom­ers…is highest among customers who receive outage information proactively from their utility and lowest among those who did not receive any outage information proactively from their utility.”

The astonishing conclusion is that power quality and reliability satisfaction is perceived to improve, without actu­ally installing automation improvements. When it comes to customers, for all practical purposes, their perception of a utility is in the reality of a power outage. How quickly and efficiently can a utility restore power? Furthermore, the J.D. Power report concludes that compounding “proactive communication, includ­ing using digital and social media, is key to improved business customer satisfaction with electric utility companies” resulting in an approximate thirty-three percent (33%) improvement in customer satisfaction.

The main implication is that utilities must invest in the customer experience to surpass customer satisfaction and create customer cooperation. In order to develop a deeper relationship, the utility must engage the customer with useful information on non-outage days, using the same application. For this purpose, utilities could provide information on a mobile device such as:

  • billing predictions to month end
  • budget alerts
  • bill pay
  • complex billing options
  • energy usage: green footprint reporting
  • outage visualization and updates
  • outage subscription services for special alerts on specific outages
  • street light outage reporting
  • crew scheduling

If the customer’s satisfaction and their perceived reliability of the grid can be greatly improved, with little investment in automation, the utility can transform a satisfied customer into a cooperative and participating consumer. Using direct dynamic and personal communication with the customer’s smart phone, such a consumer will be willing to enroll in new utility programs, such as Passive Load Curtailment, with little to no utility automa­tion investment in the grid.

Maximize the Benefits of Renewables

Maximize the Benefits of Renewables

Preparing your grid for flexible operation as renewable resource advance

The investment in renewable resources, particularly photovoltaic (PV), has the potential to become substantial for certain feeders. Adoption patterns are being studied to understand the influences from natural socioeconomic and spatial neighbor effects to policy-influenced deployment, driven by programs such as “shared solar” or “community-based solar”. Minor levels of adoption will initially provide minimal load reduction or peak shaving; however, when adoption rates reach a critical mass they can offer more than simply load control. PV injection can be applied to substantially improve the power quality of the grid’s operation.

Power quality is a problem faced by utilities worldwide. Problems such as high feeder losses related to reactive power flows and feeder voltage problems are serious operational issues in many locations. Whereas many utilities initially addressed the smart grid by installing self- healing technology to improve feeder reliability, they didn’t consider the quality problems that were imposed on the affected feeders caused by sudden load shifts when unfaulted loads were transferred to backup circuits. These technologies must be considered in synergism with one another.

If these dynamic operational power quality deficits can be improved or resolved through renewable resource investment, its smart deployment will enhance the initial financial justification based on load control alone. Renewable resources can be utilized to shorten the repayment period of the investment through strategic automation of the resource by improving the costly power quality issues related to optimized grid operations.
The question is, are the renewable resources being positioned to maximize their operational effectiveness?

Ownership that Maximizes Effectiveness

Whoever owns the resource may have a large impact on the effectiveness of the resource in grid operations and its significance with regards to financial payback. Typically, government subsidies which provide economic incentives to encourage renewable investment often favor private participation rather than utility-owned deployment. The ability to maximize the operational benefit of the resource is largely affected by ownership. For example:

If the renewable resource is privately owned and installed, the value to the utility may be limited since the resource’s functional objective may be restricted to the load that it serves. Its primary use case will likely be targeted to reduce the owner’s billable load. The added expense of deploying complex controllers that can leverage the resource’s operational advantage may not be recognized as an important feature by the private owner. The public-owned resource’s effectiveness may be of value to the utility only during extreme situations, such as emergency conditions, related to the high cost of energy or microgrid operation. However, under normal operational conditions, its value will be primarily for the benefit of the owner in reducing energy consumption.
If the renewable resource is utility-owned, its value to grid operations can be exploited for broader operational impact, which includes lowering feeder operational costs while improving the feeder’s energy transfer capability. Since these objectives transcend the DER’s benefit to the load alone, the added investment in enabling dynamic control capability would be recognized to be of value only by the utility.

To maximize the advantage of the resource, its operation must be integrated into the operational infrastructure of the DMS/ADMS and DERMS. The overall data collection and control architecture, and the interfaces that are needed, must be established to enable the flexible operation of the renewable resource in its participatory role in a wide range of use cases.

DER Interfaces that Improve Effectiveness

Unfortunately, many of today’s smart inverters are designed for minimum current capability to reduce the inverter cost. This design consideration limits the control usability of the resource. A typical DER, such as a PV inverter, is rated as follows:

  • Wmax: maximum real power output
  • VAmax: maximum apparent power
  • VArmax; maximum reactive power, injection or absorb
  • Amax/rms: maximum AC current

In many cases, the name plate ratings define the operating limits of the DER. However, the ratings do not infer that the inverter offers dynamic set point operational control of the four-quadrant output. It must be understood that control adjustments to some classes of “smart inverters” are intended to be a manual on-site adjustment.
In many cases, the output setting is a voltage setting, and does not allow for the DMS/DERMS to issue a feeder strategic VAR setting to inject or absorb reactive power for the overall benefit of the feeder’s electrical power quality. This shortcoming may limit the DER’s effectiveness towards a strategic control ensuring the feeder’s viability. In this instance, the DER’s function independently of each other at their point of common coupling (PCC).

Strategically, to derive the maximum financial and operational benefit of the DERs, it is necessary to orchestrate their operation to the optimal operational profile for each feeder. The feeder’s operational security and effectiveness for all loads is achieved when maximum renewable injection can be achieved at minimum feeder losses, operating under a flattened, minimum voltage profile. This objective is achieved when all the available resources of the feeder are operated in a single harmonized control scheme, driven by the dynamic feeder automation application.

Role of the DERMS

The real-time application that calculates the optimum settings of each feeder will have different smart grid objective functions. The synergistic operationally critical DERMS functions include:

  • Self-healing
  • Volt/VAR optimization
  • Renewable control

To meet these objectives, the effective real-time control of the feeder’s resources will encompass a three-pass control loop ranging from course to intermediate to fine control of the feeder’s operation. The devices that must be considered and controlled to achieve the optimum operation includes the following:

  • Load tap changer
  • Voltage regulator
  • Capacitor
  • DER
  • Load control