Tag: Christopher Couper

Modeling Distribution Demand Reduction

In the past, distribution demand reduction was a technique used only in emergency situations a few times a year – if that. It was an all-or-nothing capability that you turned on, and hoped for the best until the emergency was over. Few utilities could measure the effectiveness, let alone the potential of any solutions that were devised.

Now, demand reduction is evolving to better support the distribution network during typical peaking events, rather than just emergencies. However, in this mode, it is important not only to understand the solution’s effectiveness, but to be able to treat it like any other dispatchable load-shaping resource. Advanced modeling techniques and capabilities are allowing utilities to do just that. This paper outlines various methods and tools that allow utilities to model distribution demand reduction capabilities within set time periods, or even in near real time.

Electricity demand continues to outpace the ability to build new generation and apply the necessary infrastructure needed to meet the ever-growing, demand-side increases dictated by population growth and smart residences across the globe. In most parts of the world, electrical energy is one of the most important characteristics of a modern civilization. It helps produce our food, keeps us comfortable, and provides lighting, security, information and entertainment. In short, it is a part of almost every facet of life, and without electrical energy, the modern interconnected world as we know it would cease to exist.

Every country has one or more initiatives underway, or in planning, to deal with some aspect of generation and storage, delivery or consumption issues. Additionally, greenhouse gases (GHG) and carbon emissions need to be tightly controlled and monitored. This must be carefully balanced with expectations from financial markets that utilities deliver balanced and secure investment portfolios by demonstrating fiduciary responsibility to sustain revenue projections and measured growth.

The architects of today’s electric grid probably never envisioned the day when electric utility organizations would purposefully take measures to reduce the load on the network, deal with highly variable localized generation and reverse power flows, or anticipate a regulatory climate that impacts the decisions for these measures. They designed the electric transmission and distribution systems to be robust, flexible and resilient.

When first conceived, the electric grid was far from stable and resilient. It took growth, prudence and planning to continue the expansion of the electric distribution system. This grid was made up of a limited number of real power and reactive power devices that responded to occasional changes in power flow and demand. However, it was also designed in a world with far fewer people, with a virtually unlimited source of power, and without much concern or knowledge of the environmental effects that energy production and consumption entail.

To effectively mitigate these complex issues, a new type of electric utility business model must be considered. It must rapidly adapt to ever-changing demands in terms of generation, consumption, environmental and societal benefits. A grid made up of many intelligent and active devices that can manage consumption from both the consumer and utility side of the meter must be developed. This new business model will utilize demand management as a key element to the operation of the utility, while at the same time driving the consumer spending behavior.

To that end, a holistic model is needed that understands all aspects of the energy value chain across generation, delivery and consumption, and can optimize the solution in real time. While a unifying model may still be a number of years away, a lot can be gained today from modeling and visualizing the distribution network to gauge the effect that demand reduction can – and does – play in near real time. To that end, the following solutions are surely well considered.

Advanced Feeder Modeling

First, a utility needs to understand in more detail how its distribution network behaves. When distribution networks were conceived, they were designed primarily with sources (the head of the feeder and substation) and sinks (the consumers or load) spread out along the distribution network. Power flows were assumed to be one direction only, and the feeders were modeled for the largest peak level.

Voltage and volt-ampere reactive power (VAR) management were generally considered for loss optimization and not load reduction. There was never any thought given to limiting power to segments of the network or distributed storage or generation, all of which could dramatically affect the flow of the network, even causing reverse flows at times. Sensors to measure voltage and current were applied at the head of the feeder and at a few critical points (mostly in historical problem areas.)

Planning feeders at most utilities is an exercise performed when large changes are anticipated (i.e., a new subdivision or major customer) or on a periodic basis, usually every three to five years. Loads were traditionally well understood with predictable variability, so this type of approach worked reasonably well. The utility also was in control of all generation sources on the network (i.e., peakers), and when there was a need for demand reduction, it was controlled by the utility, usually only during critical periods.

Today’s feeders are much more complex, and are being significantly influenced by both generation and demand from entities outside the control of the utility. Even within the utility, various seemingly disparate groups will, at times, attempt to alter power flows along the network. The simple model of worst-case peaking on a feeder is not sufficient to understand the modern distribution network.

The following factors must be considered in the planning model:

  • Various demand-reduction techniques, when and where they are applied and the potential load they may affect;
  • Use of voltage reduction as a load-shedding technique, and where it will most likely yield significant results (i.e., resistive load);
  • Location, size and capacity of storage;
  • Location, size and type of renewable generation systems;
  • Use and location of plug-in electrical vehicles;
  • Standby generation that can be fed into the network;
  • Various social ecosystems and their characteristics to influence load; and
  • Location and types of sensors available.

Generally, feeders are modeled as a single unit with their power characteristic derived from the maximum peaking load and connected kilovolt-amperage (KVA) of downstream transformers. A more advanced model treats the feeder as a series of connected segments. The segment definitions can be arbitrary, but are generally chosen where the utility will want to understand and potentially control these segments differently than others. This may be influenced by voltage regulation, load curtailment, stability issues, distributed generation sources, storage, or other unique characteristics that differ from one segment to the next.

The following serves as an advanced means to model the electrical distribution feeder networks. It provides for segmentation and sensor placement in the absence of a complete network and historical usage model. The modeling combines traditional electrical engineering and power-flow modeling with tools such as CYME and non-traditional approaches using geospatial and statistical analysis.

The model builds upon information such as usage data, network diagrams, device characteristics and existing sensors. It then adds elements that could present a discrepancy with the known model such as social behavior, demand-side programs, and future grid operations based on both spatio-temporal and statistical modeling. Finally, suggestions can be made about sensors’ placement and characteristics to the network to support system monitoring once in place.

Generally, a utility would take a more simplistic view of the problem. It would start by directly applying statistical analysis and stochastic modeling across the grid to develop a generic methodology for selecting the number of sensors, and where to place them based on sensor accuracy, cost and risk-of-error introduction from basic modeling assumptions (load allocation, timing of peak demand, and other influences on error.) However, doing so would limit the utility, dealing only with the data it has in an environment that will be changing dramatically.

The recommended and preferred approach performs some analysis to determine what the potential error sources are, which source is material to the sensor question, and which could influence the system’s power flows. Next, an attempt can be made to geographically characterize where on the grid these influences are most significant. Then, a statistical approach can be applied to develop a model for setting the number, type and location of additional sensors. Lastly sensor density and placement can be addressed.

Feeder Modeling Technique

Feeder conditioning is important to minimize the losses, especially when the utility wants to moderate voltage levels as a load modification method. Without proper feeder conditioning and sufficient sensors to monitor the network, the utility is at risk of either violating regulatory voltage levels, or potentially limiting its ability to reduce the optimal load amount from the system during voltage reduction operations.

Traditionally, feeder modeling is a planning activity that is done at periodic (for example, yearly) intervals or during an expected change in usage. Tools such as CYME – CYMDIST provide feeder analysis using:

  • Balanced and unbalanced voltage drop analysis (radial, looped or meshed);
  • Optimal capacitor placement and sizing to minimize losses and/or improve voltage profile;
  • Load balancing to minimize losses;
  • Load allocation/estimation using customer consumption data (kWh), distribution transformer size (connected kVA), real consumption (kVA or kW) or the REA method. The algorithm treats multiple metering units as fixed demands; and large metered customers as fixed load;
  • Flexible load models for uniformly distributed loads and spot loads featuring independent load mix for each section of circuit;
  • Load growth studies for multiple years; and
  • Distributed generation.

However, in many cases, much of the information required to run an accurate model is not available. This is either because the data does not exist, the feeder usage paradigm may be changing, the sampling period does not represent a true usage of the network, the network usage may undergo significant changes, or other non-electrical characteristics.

This represents a bit of a chicken-or-egg problem. A utility needs to condition its feeders to change the operational paradigm, but it also needs operational information to make decisions on where and how to change the network. The solution is a combination of using existing known usage and network data, and combining it with other forms of modeling and approximation to build the best future network model possible.

Therefore, this exercise refines traditional modeling with three additional techniques: geospatial analysis; statistical modeling; and sensor selection and placement for accuracy.

If a distribution management system (DMS) will be deployed, or is being considered, its modeling capability may be used as an additional basis and refinement employing simulated and derived data from the above techniques. Lastly, if high accuracy is required and time allows, a limited number of feeder segments can be deployed and monitored to validate the various modeling theories prior to full deployment.

The overall goals for using this type of technique are:

  • Limit customer over or under voltage;
  • Maximize returned megawatts in the system in load reduction modes;
  • Optimize the effectiveness of the DMS and its models;
  • Minimize cost of additional sensors to only areas that will return the most value;
  • Develop automated operational scenarios, test and validation prior to system-wide implementation; and
  • Provide a foundation for additional network automation capabilities.

The first step starts by setting up a short period of time to thoroughly vet possible influences on the number, spacing and value offered by additional sensors on the distribution grid. This involves understanding and obtaining information that will most influence the model, and therefore, the use of sensors. Information could include historical load data, distribution network characteristics, transformer name plate loading, customer survey data, weather data and other related information.

The second step is the application of geospatial analysis to identify areas of the grid most likely to have influences driving a need for additional sensors. It is important to recognize that within this step is a need to correlate those influential geospatial parameters with load profiles of various residential and commercial customer types. This step represents an improvement over simply applying the same statistical analysis generically over the entirety of the grid, allowing for two or more “grades” of feeder segment characteristics for which different sensor standards would be developed.

The third step is the statistical analysis and stochastic modeling to develop recommended standards and methodology for determining sensor placement based on the characteristic segments developed from the geospatial assessment. Items set aside as not material for sensor placement serve as a necessary input to the coming “predictive model” exercise.

Lastly, a traditional electrical and accuracy- based analysis is used to model the exact number and placement of additional sensors to support the derived models and planned usage of the system for all scenarios depicted in the model – not just summertime peaking.

Conclusion

The modern distribution network built for the smart grid will need to undergo significantly more detailed planning and modeling than a traditional network. No one tool is suited to the task, and it will take multiple disciplines and techniques to derive the most benefit from the modeling exercise. However, if a utility embraces the techniques described within this paper, it will not only have a better understanding of how its networks perform in various smart grid scenarios, but it will be better positioned to fully optimize its networks for load and loss optimization.

Online Transient Stability Controls

For the last few decades the growth of the world’s population and its corresponding increased demand for electrical energy has created a huge increase in the supply of electrical power. However, for logistical, environmental, political and social reasons, this power generation is rarely near its consumers, necessitating the growth of very large and complex transmission networks. The addition of variable wind energy in remote locations is only exacerbating the situation. In addition the transmission grid capacity has not kept pace with either generation capacity or consumption while at the same time being extremely vulnerable to potential large-scale outages due to outdated operational capabilities.

For example, today if a fault is detected in the transmission system, the only course is to shed both load and generation. This is often done without consideration for real-time consequences or alternative analysis. If not done rapidly, it can result in a widespread, cascading power system blackout. While it is necessary to remove factors that might lead to a large-scale blackout, restriction of power flow or other countermeasures against such a failure, may only achieve this by sacrificing economical operation. Thus, the flexible and economical operation of an electric power system may often be in conflict with the requirement for improved supply reliability and system stability.

Limits of Off-line Approaches

One approach to solving this problem involves stabilization systems that have been deployed for preventing generator step-out by controlling the generator acceleration through power shedding, in which some of the generators are shut off at the time of a power system fault.

In 1975, an off-line special protection system (SPS) for power flow monitoring was introduced to achieve the transient stability of the trunk power system and power source system after a network expansion in Japan. This system was initially of the type for which settings were determined in advance by manual calculations using transient stability simulation programs assuming many contingencies on typical power flow patterns.

This type of off-line solution has the following problems:

  • Planning, design, programming, implementation and operational tasks are laborious. A vast number of simulations are required to determine the setting tables and required countermeasures, such as generator shedding, whenever transmission lines are constructed;
  • It is not well suited to variable generations sources such as wind or photovoltaic farms;
  • It is not suitable for reuse and replication, incurring high maintenance costs; and
  • Excessive travel time and related labor expense is required for the engineer and field staff to maintain the units at numerous sites.

By contrast, an online TSC solution employs various sensors that are placed throughout the transmission network, substations and generation sources. These sensors are connected to regional computer systems via high speed communications to monitor, detect and execute contingencies on transients that may affect system stability. These systems in turn are connected to centralized computers which monitor the network of distributed computers, building and distributing contingencies based on historical and recent information. If a transient event occurs, the entire ecosystem responds within 150 ms to detect, analyze, determine the correct course of action, and execute the appropriate set of contingencies in order to preserve the stability of the power network.

In recent years, high performance computational servers have been developed and their costs have been reduced enough to use many of them in parallel and/or in a distributed computing architecture. This results in a system that not only provides a benefit in greatly increasing the availability and reliability of the power system, but in fact, can best optimize the throughput of the grid. Thus not only has system reliability improved or remained stable, but the network efficiency itself has increased without a significant investment in new transmission lines. This has resulted in more throughput within the transmission grid, without building new transmission lines.

Solution and Elements

In 1995, for the first time ever, an online TSC system was developed and introduced in Japan. This solution provided a system stabilization procedure required by the construction of the new 500kV trunk networks of Chubu Electric Power Co. (CEPCO) [1-4]. Figure 1 shows the configuration of the online TSC system. This system introduced a pre-processing online calculation in the TSC-P (parent) besides a fast, post-event control executed by the combination of TSC-C (child) and TSC-T (terminal). This online TSC system can be considered an example of a self-healing solution of a smart grid. As a result of periodic simulations using the online data in TSC-P, operators of energy management systems/supervisory control and data acquisition (EMS/ SCADA) are constantly made aware of stability margins for current power system situations.

Using the same online data, periodic calculations performed in the TSC-P can reflect power network situations and the proper countermeasures to mitigate transient system events. The TSC-P simulates transient stability dynamics on about 100 contingencies of the power systems for 500 kV, 275 kV and 154 kV transmission networks. The setting tables for required countermeasures, such as generator shedding, are periodically sent to the TSC-Cs located at main substations. The TSC-Ts located at generation stations, shed the generators when the actual fault occurs. The actual generator shedding by the combination of TSC-Cs and TSC-Ts is completed within 150 ms after the fault to maintain the system’s stability.

Customer Experiences and Benefits

Figure 2 shows the locations of online TSC systems and their coverage areas in CEPCO’s power network. There are two online TSC systems currently operating; namely, the trunk power TSC system, to protect the 500 kV trunk power system introduced in 1995, and the power source TSC system to protect the 154 kV to 275 kV power source systems around the generation stations.

Actual performance data have shown some significant benefits:

  • Total transfer capability (TTC) is improved through elimination of transient stability limitations. TTC is decided by the minimum value of limitations given by not only thermal limit of transmission lines but transient stability, frequency stability, and voltage stability. Transient stability limits often determines the TTC in the case of long transmission lines from generation plants. CEPCO was able to introduce high-efficiency, combined-cycle power plants without constructing new transmission lines. TTC was increased from 1,500 MW to 3,500 MW by introducing the on-line TSC solution.
  • Power shedding is optimized. Not only is the power flow of the transmission line on which a fault occurs assessed, but the effects of other power flows surrounding the fault point are included in the analysis to decide the precise stability limit. The online TSC system can also reflect the constraints and priorities of each generator to be shed. To ensure a smooth restoration after the fault, restart time of shut off generators, for instance, can also be included.
  • When constructing new transmission lines, numerous off-line studies assuming various power flow patterns are required to support off-line SPS. After introduction of the online TSC system, new construction of transmission lines was more efficient by changing the equipment database for the simulation in the TSC-P.

In 2003, this CEPCO system received the 44th Annual Edison Award from the Edison Electric Institute (EEI), recognizing CEPCO’s achievement with the world’s first application of this type of system, and the contribution of the system to efficient power management.

Today, benefits continue to accrue. A new TSC-P, which adopts the latest high-performance computation servers, is now under construction for operation in 2009 [3]. The new system will shorten the calculation interval from every five minutes to every 30 seconds in order to reflect power system situations as precisely as possible. This interval was determined by the analysis of various stability situations recorded by the current TSC-P over more than 10 years of operation.

Additionally, although the current TSC-P uses the same online data as used by EMS/ SCADA, it can control emergency actions against small signal instability by receiving phasor measurement unit (PMU) data to detect divergences of phasor angles and voltages among the main substations.

Summary

The online TSC system is expected to realize optimum stabilization control of recent complicated power system conditions by obtaining power system information online and carrying out stability calculations at specific intervals. The online TSC will thus help utilities achieve better returns on investment in new or renovated transmission lines, reducing outage time and enabling a more efficient smart grid.

References

  1. Ota, Kitayama, Ito, Fukushima, Omata, Morita and Y. Kokai, “Development of Transient Stability Control System (TSC System) Based on Online Stability Calculation”, IEEE Trans. on Power System, Vol. 11, No. 3, pp. 1463-1472, August 1996.
  2. Koaizawa, Nakane, Omata and Y. Kokai, “Acutual Operating Experience of Online Transient Stability Control System (TSC System), IEEE PES Winter Meeting, 2000, Vol. 1, pp 84-89.
  3. Takeuchi, Niwa, Nakane and T. Miura
    “Performance Evaluation of the Online Transient Stability Control System (Online TSC System)”, IEEE PES General Meeting , June 2006.
  4. Takeuchi, Sato, Nishiiri, Kajihara, Kokai and M. Yatsu, “Development of New Technologies and Functions for the Online TSC System”, IEEE PES General Meeting , June 2006.

The Virtual Generator

Electric utility companies today constantly struggle to find a balance between generating sufficient power to satisfy their customers’ dynamic load requirements and minimizing their capital and operating costs. They spend a great deal of time and effort attempting to optimize every element of their generation, transmission and distribution systems to achieve both their physical and economic goals.

In many cases, “real” generators waste valuable resources – waste that if not managed efficiently can go directly to the bottom line. Energy companies therefore find the concept of a “virtual generator,” or a virtual source of energy that can be turned on when needed, very attractive. Although generally only representing a small percentage of utilities’ overall generation capacity, virtual generators are quick to deploy, affordable, cost-effective and represent a form of “green energy” that can help utilities meet carbon emission standards.

Virtual generators use forms of dynamic voltage and capacitance (Volt/ VAr) adjustments that are controlled through sensing, analytics and automation. The overall process involves first flattening or tightening the voltage profiles by adding additional voltage regulators to the distribution system. Then, by moving the voltage profile up or down within the operational voltage bounds, utilities can achieve significant benefits (Figure 1). It’s important to understand, however, that because voltage adjustments will influence VArs, utilities must also adjust both the placement and control of capacitors (Figure 2).

Various business drivers will influence the use of Volt/VAr. A utility could, for example, use Volt/VAr to:

  • Respond to an external system-wide request for emergency load reduction;
  • Assist in reducing a utility’s internal load – both regional and throughout the entire system;
  • Target specific feeder load reduction through the distribution system;
  • Respond as a peak load relief (a virtual peaker);
  • Optimize Volt/VAr for better reliability and more resiliency;
  • Maximize the efficiency of the system and subsequently reduce energy generation or purchasing needs;
  • Achieve economic benefits, such as generating revenue by selling power on the spot market; and
  • Supply VArs to supplement off-network deficiencies.

Each of the above potential benefits falls into one of four domains: peaking relief, energy conservation, VAr management or reliability enhancement. The peaking relief and energy conservation domains deal with load reduction; VAr management, logically enough, involves management of VArs; and reliability enhancement actually increases load. In this latter domain, the utility will use increased voltage to enable greater voltage tolerances in self-healing grid scenarios or to improve the performance of non-constant power devices to remove them from the system as soon as possible and therefore improve diversity.

Volt/VAr optimization can be applied to all of these scenarios. It is intended to either optimize a utility’s distribution network’s power factor toward unity, or to purposefully make the power factor leading in anticipation of a change in load characteristics.

Each of these potential benefits comes from solving a different business problem. Because of this, at times they can even be at odds with each other. Utilities must therefore create fairly complex business rules supported by automation to resolve any conflicts that arise.

Although the concept of load reduction using Volt/VAr techniques is not new, the ability to automate the capabilities in real time and drive the solutions with various business requirements is a relatively recent phenomenon. Energy produced with a virtual generator is neither free nor unlimited. However, it is real in the sense that it allows the system to use energy more efficiently.

A number of things are driving utilities’ current interest in virtual generators, including the fact that sensors, analytics, simulation, geospatial information, business process logic and other forms of information technology are increasingly affordable and robust. In addition, lower-cost intelligent electrical devices (IEDs) make virtual generators possible and bring them within reach of most electric utility companies.

The ability to innovate an entirely new solution to support the above business scenarios is now within the realm of possibility for the electric utility company. As an added benefit, much of the base IT infrastructure required for virtual generators is the same as that required for other forms of “smart grid” solutions, such as advanced meter infrastructure (AMI), demand side management (DSM), distributed generation (DG) and enhanced fault management. Utilities that implement a well-designed virtual generator solution will ultimately be able to align it with these other power management solutions, thus optimizing all customer offerings that will help reduce load.

HOW THE SOLUTION WORKS

All utilities are required, for regulatory or reliability reasons, to stay within certain high- and low-voltage parameters for all of their customers. In the United States the American Society for Testing and Materials (ATSM) guidelines specify that the nominal voltage for a residential single-phase service should be 120 volts with a plus or minus 6-volt variance (that is, 114 to 126 volts). Other countries around the world have similar guidelines. Whatever the actual values are, all utilities are required to operate within these high- and low-voltage “envelopes.” In some cases, additional requirements may be imposed as to the amount of variance – the number of volts changed or the percent change in the voltage – that can take place over a period of minutes or hours.

Commercial customers may have different high/low values, but the principle remains the same. In fact, it is the mixture of residential, commercial and industrial customers on the same feeder that makes the virtual generation solution almost a requirement if a utility wants to optimize its voltage regulation.

Although it would be ideal for a utility to deliver 120-volt power consistently to all customers, the physical properties of the distribution system as well as dynamic customer loading factors make this difficult. Most utilities are already trying to accomplish this through planning, network and equipment adjustments, and in many cases use of automated voltage control devices. Despite these efforts, however, in most networks utilities are required to run the feeder circuit at higher-than-nominal levels at the head of the circuit in order to provide sufficient voltage for downstream users, especially those at the tails or end points of the circuit.

In a few cases, electric utilities have added manual or automatic voltage regulators to step up voltage at one or more points in a feeder circuit because of nonuniform loading and/or varied circuit impedance characteristics throughout the circuit profile. This stepped-up slope, or curve, allows the utility company to comply with the voltage level requirements for all customers on the circuit. In addition, utilities can satisfy the VAr requirements for operational efficiency of inductive loads using switched capacitor banks, but they must coordinate those capacitor banks with voltage adjustments as well as power demand. Refining voltage profiles through virtual generation usually implies a tight corresponding control of capacitance as well.

The theory behind a robust Volt/ VAr regulated feeder circuit is based on the same principles but applied in an innovative manner. Rather than just using voltage regulators to keep the voltage profile within the regulatory envelope, utilities try to “flatten” the voltage curve or slope. In reality, the overall effect is a stepped/slope profile due to economic limitations on the number of voltage regulators applied per circuit. This flattening has the effect of allowing an overall reduction, or decrease, in nominal voltage. In turn the operator may choose to move the voltage curve up or down within the regulatory voltage envelope. Utilities can derive extra benefit from this solution because all customers within a given section of a feeder circuit could be provided with the same voltage level, which should result in less “problem” customers who may not be in the ideal place on the circuit. It could also minimize the possible power wastage of overdriving the voltage at the head of the feeder in order to satisfy customers at the tails.

THE ROLE OF AUTOMATION IN DELIVERING THE VIRTUAL GENERATOR

Although theoretically simple in concept, executing and maintaining a virtual generator solution is a complex task that requires real-time coordination of many assets and business rules. Electrical distribution networks are dynamic systems with constantly changing demands, parameters and influencers. Without automation, utilities would find it impossible to deliver and support virtual generators, because it’s infeasible to expect a human – or even a number of humans – to operate such systems affordably and reliably. Therefore, utilities must leverage automation to put humans in monitoring rather than controlling roles.

There are many “inputs” to an automated solution that supports a virtual generator. These include both dynamic and static information sources. For example, real-time sensor data monitoring the condition of the networks must be merged with geospatial information, weather data, spot energy pricing and historical data in a moment-by-moment, repeating cycle to optimize the business benefits of the virtual generator. Complicating this, in many cases the team managing the virtual generator will not “own” all of the inputs required to feed the automated system. Frequently, they must share this data with other applications and organizational stakeholders. It’s therefore critical that utilities put into place an open, collaborative and integrated technology infrastructure that supports multiple applications from different parts of the business.

One of the most critical aspects of automating a virtual generator is having the right analytical capabilities to decide where and how the virtual generator solution should be applied to support the organizations’ overall business objectives. For example, utilities should use load predictors and state estimators to determine future states of the network based on load projections given the various Volt/VAr scenarios they’re considering. Additionally, they should use advanced analytic analyses to determine the resiliency of the network or the probability of internal or external events influencing the virtual generator’s application requirements. Still other types of analyses can provide utilities with a current view of the state of the virtual generator and how much energy it’s returning to the system.

While it is important that all these techniques be used in developing a comprehensive load-management strategy, they must be unified into an actionable, business-driven solution. The business solution must incorporate the values achieved by the virtual generator solutions, their availability, and the ability to coordinate all of them at all times. A voltage management solution that is already being used to support customer load requirements throughout the peak day will be of little use to the utility for load management. It becomes imperative that the utility understand the effect of all the voltage management solutions when they are needed to support the energy demands on the system.