Microsoft Helps Utilities Use IT to Create Winning Relationships

The utilities industry worldwide is experiencing growing energy demand in a world with shifting fuel availability, increasing costs, a shrinking workforce and mounting global environmental pressures. Rate case filings and government regulations, especially those regarding environmental health and safety, require utilities to streamline reporting and operate safely enterprise-wide. At the same time, increasing competition and costs drive the need for service reliability and better customer service. Each issue causes utilities to depend more and more on information technology (IT).

The Microsoft Utility team works with industry partners to create and deploy industry-specific solutions that help utilities transform challenges into opportunities and empower utilities workers to thrive in today’s market-driven environment. Solutions are based on the world’s most cost-effective, functionally rich, and secure IT platform. The Microsoft platform is interoperable with a wide variety of systems and proven to improve people’s abilities to access information and work with others across boundaries. Together, they help utilities optimize operations in each line of business.

Customer care. Whether a utility needs to modernize a call center, add customer self-service or respond to new business requirements such as green power, Microsoft and its partners provide solutions for turning the customer experience into a powerful competitive advantage with increased cost efficiencies, enhanced customer service and improved financial performance.

Transmission and distribution. Growing energy demand makes it critical to effectively address safe, reliable and efficient power delivery worldwide. To help utilities meet these needs, Microsoft and its partners offer EMS, DMS and SCADA systems; mobile workforce management solutions; project intelligence; geographic information systems; smart metering/grid; and work/asset/document management tools that streamline business processes and offer connectivity across the enterprise and beyond.

Generation. Microsoft and its partners provide utilities with a view across and into their generation operations that enables them to make better decisions to improve cycle times, output and overall effectiveness while reducing the carbon footprint. With advanced software solutions from Microsoft and its partners, utilities can monitor equipment to catch early failure warnings, measure fleets’ economic performance and reduce operational and environment risk.

Energy trading and risk management. Market conditions require utilities to optimize energy supply performance. Microsoft and its partners’ enterprise risk management and trading solutions help utilities feed the relentless energy demands in a resource-constrained world.

Regulatory compliance. Microsoft and its partners offer solutions to address the compliance requirements of the European Union; Federal Energy Regulatory Commission; North American Reliability Council; Sarbanes-Oxley Act of 2000; Environmental, Health and Safety; and other regional jurisdiction regulations and rate case issues. With solutions from Microsoft partners, utilities have a proactive approach to compliance, the most effective way to manage operational risk across the enterprise.

Enterprise. To optimize their businesses, utility executives need real-time visibility across the enterprise. Microsoft and its partners provide integrated e-business solutions that help utilities optimize their interactions with customers, vendors and partners. These enterprise applications address business intelligence and reporting, customer relationship management, collaborative workspaces, human resources and financial management.

The GridWise Olympic Peninsula Project

The Olympic Peninsula Project consisted of a field demonstration and test of advanced price signal-based control of distributed energy resources (DERs). Sponsored by the U.S. Department of Energy (DOE) and led by the Pacific Northwest National Laboratory, the project was part of the Pacific Northwest Grid- Wise Testbed Demonstration.

Other participating organizations included the Bonneville Power Administration, Public Utility District (PUD) #1 of Clallam County, the City of Port Angeles, Portland General Electric, IBM’s T.J. Watson Research Center, Whirlpool and Invensys Controls. The main objective of the project was to convert normally passive loads and idle distributed generation into actively participating resources optimally coordinated in near real time to reduce stress on the local distribution system.

Planning began in late 2004, and the bulk of the development work took place in 2005. By late 2005, equipment installations had begun, and by spring 2006, the experiment was fully operational, remaining so for one full year.

The motivating theme of the project was based on the GridWise concept that inserting intelligence into electric grid components at every point in the supply chain – from generation through end-use – will significantly improve both the electrical and economic efficiency of the power system. In this case, information technology and communications were used to create a real-time energy market system that could control demand response automation and distributed generation dispatch. Optimal use of the DER assets was achieved through the market, which was designed to manage the flow of power through a constrained distribution feeder circuit.

The project also illustrated the value of interoperability in several ways, as defined by the DOE’s GridWise Architecture Council (GWAC). First, a highly heterogeneous set of energy assets, associated automation controls and business processes was composed into a single solution integrating a purely economic or business function (the market-clearing system) with purely physical or operational functions (thermostatic control of space heating and water heating). This demonstrated interoperability at the technical and informational levels of the GWAC Interoperability Framework (www.gridwiseac.org/about/publications.aspx), providing an ideal example of a cyber-physical-business system. In addition, it represents an important class of solutions that will emerge as part of the transition to smart grids.

Second, the objectives of the various asset owners participating in the market were continuously balanced to maintain the optimal solution at any point in time. This included the residential demand response customers; the commercial and municipal entities with both demand response and distributed generation; and the utilities, which demonstrated interoperability at the organizational level of the framework.

PROJECT RESOURCES

The following energy assets were configured to respond to market price signals:

  • Residential demand response for electric space and water heating in 112 single-family homes using gateways connected by DSL or cable modem to provide two-way communication. The residential demand response system allowed the current market price of electricity to be presented to customers. Consumers could also configure their demand response automation preferences. The residential consumers were evenly divided among three contract types (fixed, time of use and real time) and a fourth control group. All electricity consumption was metered, but only the loads in price-responsive homes were controlled by the project (approximately 75 KW).
  • Two distributed generation units (175 KW and 600 KW) at a commercial site served the facility’s load when the feeder supply was not sufficient. These units were not connected in parallel to the grid, so they were bid into the market as a demand response asset equal to the total load of the facility (approximately 170 KW). When the bid was satisfied, the facility disconnected from the grid and shifted its load to the distributed generation units.
  • One distributed microturbine (30 KW) that was connected in parallel to the grid. This unit was bid into the market as a generation asset based on the actual fixed and variable expenses of running the unit.
  • Five 40-horsepower (HP) water pumps distributed between two municipal water-pumping stations (approximately 150 KW of total nameplate load). The demand response load from these pumps was incrementally bid into the market based on the water level in the pumped storage reservoir, effectively converting the top few feet of the reservoir capacity into a demand response asset on the electrical grid.

Monitoring was performed for all of these resources, and in cases of price-responsive contracts, automated control of demand response was also provided. All consumers who employed automated control were able to temporarily disable or override project control of their loads or generation units. In the residential realtime price demand response homes, consumers were given a simple configuration choice for their space heating and water heating that involved selecting an ideal set point and a degree of trade-off between comfort and price responsiveness.

For real-time price contracts, the space heater demand response involved automated bidding into the market by the space heating system. Since the programmable thermostats deployed in the project didn’t support real-time market bidding, IBM Research implemented virtual thermostats in software using an event-based distributed programming prototype called Internet- Scale Control Systems (iCS). The iCS prototype is designed to support distributed control applications that span virtually any underlying device or business process through the definition of software sensor, actuator and control objects connected by an asynchronous event programming model that can be deployed on a wide range of underlying communication and runtime environments. For this project, virtual thermostats were defined that conceptually wrapped the real thermostats and incorporated all of their functionality while at the same time providing the additional functionality needed to implement the real-time bidding. These virtual thermostats received
the actual temperature of the house as well as information about the real-time market average price and price distribution and the consumer’s preferences for set point and comfort/economy trade-off setting. This allowed the virtual thermostats to calculate the appropriate bid every five minutes based on the changing temperature and market price of energy.

The real-time market in the project was implemented as a shadow market – that is, rather than change the actual utility billing structure, the project implemented a parallel billing system and a real-time market. Consumers still received their normal utility bill each month, but in addition they received an online bill from the shadow market. This additional bill was paid from a debit account that used funds seeded by the project based on historical energy consumption information for the consumer.

The objective was to provide an economic incentive to consumers to be more price responsive. This was accomplished by allowing the consumers to keep the remaining balance in the debit account at the end of each quarter. Those consumers who were most responsive were estimated to receive about $150 at the end of the quarter.

The market in the project cleared every five minutes, having received demand response bids, distributed generation bids and a base supply bid based on the supply capacity and wholesale price of energy in the Mid-Columbia system operated by Bonneville Power Administration. (This was accomplished through a Dow Jones feed of the Mid-Columbia price and other information sources for capacity.) The market operation required project assets to submit bids every five minutes into the market, and then respond to the cleared price at the end of the five-minute market cycle. In the case of residential space heating in real-time price contract homes, the virtual thermostats adjusted the temperature set point every five minutes; however, in most cases the adjustment was negligible (for example, one-tenth of a degree) if the price was stable.

KEY FINDINGS

Distribution constraint management. As one of the primary objectives of the experiment, distribution constraint management was successfully accomplished. The distribution feeder-imported capacity was managed through demand response automation to a cap of 750 KW for all but one five-minute market cycle during the project year. In addition, distributed generation was dispatched as needed during the project, up to a peak of about 350 KW.

During one period of about 40 hours that took place from Oct. 30, 2006, to Nov. 1, 2006, the system successfully constrained the feeder import capacity at its limit and dispatched distributed generation several times, as shown in Figure 1. In this figure, actual demand under real-time price control is shown in red, while the blue line depicts what demand would have been without real-time price control. It should be noted that the red demand line steps up and down above the feeder capacity line several times during the event – this is the result of distributed generation units being dispatched and removed as their bid prices are met or not.

Market-based control demonstrated. The project controlled both heating and cooling loads, which showed a surprisingly significant shift in energy consumption. Space conditioning loads in real-time price contract homes demonstrated a significant shift to early morning hours – a shift that occurred during both constrained and unconstrained feeder conditions but was more pronounced during constrained periods. This is similar to what one would expect in preheating or precooling systems, but neither the real nor the virtual thermostats in the project had any explicit prediction capability. The analysis showed that the diurnal shape of the price curve itself caused the effect.

Peak load reduced. The project’s realtime price control system both deferred and shifted peak load very effectively. Unlike the time-of-use system, the realtime price control system operated at a fine level of precision, responding only when constraints were present and resulting in a precise and proportionally appropriate level of response. The time-of-use system, on the other hand, was much coarser in its response and responded regardless of conditions on the grid, since it was only responding to preconfiured time schedules or manually initiated critical peak price signals.

Internet-based control demonstrated. Bids and control of the distributed energy resources in the project were implemented over Internet connections. As an example, the residential thermostats modified their operation through a combination of local and central control communicated as asynchronous events over the Internet. Even in situations of intermittent communication failure, resources typically performed well in default mode until communications could be re-established. This example of the resilience of a well-designed, loosely coupled distributed control application schema is an important aspect of what the project demonstrated.

Distributed generation served as a valuable resource. The project was highly effective in using the distributed generation units, dispatching them many times over the duration of the experiment. Since the diesel generators were restricted by environmental licensing regulations to operate no more than 100 hours per year, the bid calculation factored in a sliding scale price premium such that bids would become higher as the cumulative runtime for the generators increased toward 100 hours.

CONCLUSION

The Olympic Peninsula Project was unique in many ways. It clearly demonstrated the value of the GridWise concepts of leveraging information technology and incorporating market constructs to manage distributed energy resources. Local marginal price signals as implemented through the market clearing process, and the overall event-based software integration framework successfully managed the bidding and dispatch of loads and balanced the issues of wholesale costs, distribution congestion and customer needs in a very natural fashion.

The final report (as well as background material) on the project is available at www.gridwise.pnl.gov. The report expands on the remarks in this article and provides detailed coverage of a number of important assertions supported by the project, including:

  • Market-based control was shown to be a viable and effective tool for managing price-based responses from single-family premises.
  • Peak load reduction was successfully accomplished.
  • Automation was extremely important in obtaining consistent responses from both supply and demand resources.
  • The project demonstrated that demand response programs could be designed by establishing debit account incentives without changing the actual energy prices offered by energy providers.

Although technological challenges were identified and noted, the project found no fundamental obstacles to implementing similar systems at a much larger scale. Thus, it’s hoped that an opportunity to do so will present itself at some point in the near future.

The Smart Grid: A Balanced View

Energy systems in both mature and developing economies around the world are undergoing fundamental changes. There are early signs of a physical transition from the current centralized energy generation infrastructure toward a distributed generation model, where active network management throughout the system creates a responsive and manageable alignment of supply and demand. At the same time, the desire for market liquidity and transparency is driving the world toward larger trading areas – from national to regional – and providing end-users with new incentives to consume energy more wisely.

CHALLENGES RELATED TO A LOW-CARBON ENERGY MIX

The structure of current energy systems is changing. As load and demand for energy continue to grow, many current-generation assets – particularly coal and nuclear systems – are aging and reaching the end of their useful lives. The increasing public awareness of sustainability is simultaneously driving the international community and national governments alike to accelerate the adoption of low-carbon generation methods. Complicating matters, public acceptance of nuclear energy varies widely from region to region.

Public expectations of what distributed renewable energy sources can deliver – for example, wind, photovoltaic (PV) or micro-combined heat and power (micro-CHP) – are increasing. But unlike conventional sources of generation, the output of many of these sources is not based on electricity load but on weather conditions or heat. From a system perspective, this raises new challenges for balancing supply and demand.

In addition, these new distributed generation technologies require system-dispatching tools to effectively control the low-voltage side of electrical grids. Moreover, they indirectly create a scarcity of “regulating energy” – the energy necessary for transmission operators to maintain the real-time balance of their grids. This forces the industry to try and harness the power of conventional central generation technologies, such as nuclear power, in new ways.

A European Union-funded consortium named Fenix is identifying innovative network and market services that distributed energy resources can potentially deliver, once the grid becomes “smart” enough to integrate all energy resources.

In Figure 1, the Status Quo Future represents how system development would play out under the traditional system operation paradigm characterized by today’s centralized control and passive distribution networks. The alternative, Fenix Future, represents the system capacities with distributed energy resources (DER) and demand-side generation fully integrated into system operation, under a decentralized operating paradigm.

CHALLENGES RELATED TO NETWORK OPERATIONAL SECURITY

The regulatory push toward larger trading areas is increasing the number of market participants. This trend is in turn driving the need for increased network dispatch and control capabilities. Simultaneously, grid operators are expanding their responsibilities across new and complex geographic regions. Combine these factors with an aging workforce (particularly when trying to staff strategic processes such as dispatching), and it’s easy to see why utilities are becoming increasingly dependent on information technology to automate processes that were once performed manually.

Moreover, the stochastic nature of energy sources significantly increases uncertainty regarding supply. Researchers are trying to improve the accuracy of the information captured in substations, but this requires new online dispatching stability tools. Additionally, as grid expansion remains politically controversial, current efforts are mostly focused on optimizing energy flow in existing physical assets, and on trying to feed asset data into systems calculating operational limits in real time.

Last but not least, this enables the extension of generation dispatch and congestion into distribution low-voltage grids. Although these grids were traditionally used to flow energy one way – from generation to transmission to end-users – the increasing penetration of distributed resources creates a new need to coordinate the dispatch of these resources locally, and to minimize transportation costs.

CHALLENGES RELATED TO PARTICIPATING DEMAND

Recent events have shown that decentralized energy markets are vulnerable to price volatility. This poses potentially significant economic threats for some nations because there’s a risk of large industrial companies quitting deregulated countries because they lack visibility into long-term energy price trends.

One potential solution is to improve market liquidity in the shorter term by providing end-users with incentives to conserve energy when demand exceeds supply. The growing public awareness of energy efficiency is already leading end-users to be much more receptive to using sustainable energy; many utilities are adding economic incentives to further motivate end-users.

These trends are expected to create radical shifts in transmission and distribution (T&D) investment activities. After all, traditional centralized system designs, investments and operations are based on the premise that demand is passive and uncontrollable, and that it makes no active contribution to system operations.

However, the extensive rollout of intelligent metering capabilities has the potential to reverse this, and to enable demand to respond to market signals, so that end-users can interact with system operators in real or near real time. The widening availability of smart metering thus has the potential to bring with it unprecedented levels of demand response that will completely change the way power systems are planned, developed and operated.

CHALLENGES RELATED TO REGULATION

Parallel with these changes to the physical system structure, the market and regulatory frameworks supporting energy systems are likewise evolving. Numerous energy directives have established the foundation for a decentralized electricity supply industry that spans formerly disparate markets. This evolution is changing the structure of the industry from vertically integrated, state-owned monopolies into an environment in which unbundled generation, transmission, distribution and retail organizations interact freely via competitive, accessible marketplaces to procure and sell system services and contracts for energy on an ad hoc basis.

Competition and increased market access seem to be working at the transmission level in markets where there are just a handful of large generators. However, this approach has yet to be proven at the distribution level, where it could facilitate thousands and potentially millions of participants offering energy and systems services in a truly competitive marketplace.

MEETING THE CHALLENGES

As a result, despite all the promise of distributed generation, the current decentralized system will become increasingly unstable without the corresponding development of technical, market and regulatory frameworks over the next three to five years.

System management costs are increasing, and threats to system security are a growing concern as installed distributed generating capacity in some areas exceeds local peak demand. The amount of “regulating energy” provisions rises as stress on the system increases; meanwhile, governments continue to push for distributed resource penetration and launch new energy efficiency ideas.

At the same time, most of the large T&D utilities intend to launch new smart grid prototypes that, once stabilized, will be scalable to millions of connection points. The majority of these rollouts are expected to occur between 2010 and 2012.

From a functionality standpoint, the majority of these associated challenges are related to IT system scalability. The process will require applying existing algorithms and processes to generation activities, but in an expanded and more distributed manner.

The following new functions will be required to build a smart grid infrastructure that enables all of this:

New generation dispatch. This will enable utilities to expand their portfolios of current-generation dispatching tools to include schedule-generation assets for transmission and distribution. Utilities could thus better manage the growing number of parameters impacting the decision, including fuel options, maintenance strategies, the generation unit’s physical capability, weather, network constraints, load models, emissions (modeling, rights, trading) and market dynamics (indices, liquidity, volatility).

Renewable and demand-side dispatching systems. By expanding current energy management systems (EMS) capability and architecture, utilities should be able to scale to include millions of active producers and consumers. Resources will be distributed in real time by energy service companies, promoting the most eco-friendly portfolio dispatch methods based on contractual arrangements between the energy service providers and these distributed producers and consumers.

Integrated online asset management systems. new technology tools that help transmission grid operators assess the condition of their overall assets in real time will not only maximize asset usage, but will lead to better leveraging of utilities’ field forces. new standards such as IEC61850 offer opportunities to manage such models more centrally and more consistently.

Online stability and defense plans. The increasing penetration of renewable generation into grids combined with deregulation increases the need for fl ow control into interconnections between several transmission system operators (TSOs). Additionally, the industry requires improved “situation awareness” tools to be installed in the control centers of utilities operating in larger geographical markets. Although conventional transmission security steady state indicators have improved, utilities still need better early warning applications and adaptable defense plan systems.

MOVING TOWARDS A DISTRIBUTED FUTURE

As concerns about energy supply have increased worldwide, the focus on curbing demand has intensified. Regulatory bodies around the world are thus actively investigating smart meter options. But despite the benefits that smart meters promise, they also raise new challenges on the IT infrastructure side. Before each end-user is able to flexibly interact with the market and the distribution network operator, massive infrastructure re-engineering will be required.

nonetheless, energy systems throughout the world are already evolving from a centralized to a decentralized model. But to successfully complete this transition, utilities must implement active network management through their systems to enable a responsive and manageable alignment of supply and demand. By accomplishing this, energy producers and consumers alike can better match supply and demand, and drive the world toward sustainable energy conservation.

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.