The classical school of utility operations prescribes four priorities, ranked in the following descending order: safety, reliability, customer service and profit. Although it’s not hard to engage any number of industry insiders in an argument over whether profit in the classical model has recently switched places with customer service (and/or whether it should), most people accept that safety and reliability still reign supreme when it comes to operating a utility. This is true whether one takes a policy-, economic-, utility- or customer-oriented perspective.
Over many decades the utility industry has established a remarkably consistent pattern of power delivery based on the above-described priorities. Large, centralized generation facilities produce electricity from various sources interconnected via a networked transmission system feeding a predominantly radial distribution system. This classical power distribution system supports a predictable demand pattern that utilities can typically manage by using analytics such as similar day load forecasting. Moreover, future demand is also predictable, since average loads have been growing consistently by just a few percentage points annually, year in and year out.
To support this power delivery model, utilities also employ remarkably consistent system design and operational processes. Although any given utility might employ slightly different processes and procedures at varying degrees of efficiency and effectiveness – or deploy operating assets with slightly different design specifications – the underlying elements are generally consistent from one utility to another. They are engineered to either fail safe (safety) and/or not to fail at all (reliability) based on long-term operating patterns.
So why implement a smart grid? After all, the classical method of managing supply and demand has worked reasonably well over the decades. The system is safe and reliable, and most utilities are very profitable even in economic downtimes. However, a smart grid has three interrelated attributes – transparency, conditionality and kinematics – that together radically improve the “situational awareness” of the real-time state of the grid for both utilities and customers.
With this situational awareness comes the high system-state observability (transparency) that drives conditional management (conditionality) of the grid. All of this will ultimately support future power delivery patterns, which will be much more complex and difficult to predict and manage because demand and supply will fluctuate much more radically than at present (kinematics).
Price transparency is the foundation on which deregulated and competitive markets are built. However, until now price transparency has been limited primarily to wholesale transmission and generation domains. Indeed, the lack of price transparency at the point of distribution (that is, at retail) is a key reason deregulation has stalled in the United States.
Price transparency is of course only one aspect of the issue. Utilities must also synchronize usage transparency with price transparency based on time. That is, the value of knowing real-time pricing is diminished if a customer cannot also see their real-time usage and make energy usage behavior changes in relation to the real-time price signals.
From the utility’s perspective, usage transparency is limited. That’s because the distribution elements of most utility operations are largely opaque to operators. Once beyond the substation, usage disruptions are primarily identified by induction from fault conditions and usage patterns recorded a month after the disruption occurred via meter readings. For example, a distribution circuit may be substantially overloaded, but in most cases the utility won’t know until it fails. And when a failure does occur, utilities still depend on manual processes to determine the precise location and cause of the fault. The customer loads or network conditions that precipitated the failure can only be analyzed well after the event.
A smart grid significantly improves the level of visibility into the distribution grid. Smart meters, line sensors and the embedded processing that takes place within system assets such as switches and reclosers all provide a stream of real-time and near real-time data to the utility about the current operational state of the grid. The result: a dramatic improvement in utilities’ awareness of the state of the distribution grid.
As is the case with transparency, the consumer’s perspective of conditionality is more mature than the utility’s perspective. For example, the idea of the smart building is all about implementing a mini premise-side smart grid within the customer location and installing simple devices such as motion detectors that turn lights on or off in a room. Commercial energy management systems use even more sophisticated ways of optimizing the lighting, heating and other environmental parameters of a work or living space.
From the utility’s perspective, however, conditionality is much less advanced. In today’s operating world, most maintenance or repair activities take place either too late or too soon. When utilities wait until something in the infrastructure fails, it’s too late. If the grid is inspected based on some set time schedule irrespective of its condition, it’s too soon. Utilities thus fall into a pattern of either fault- or usage-based maintenance.
The alternative – condition-based maintenance – is already being used in many industries. The difference in the utilities industry is that outside of energy generation and transmission activities, there’s little data on the ongoing real-time condition of most of the assets a utility utilizes to provide its customers with service.
The chief benefit of conditionality is that it allows utilities to optimize asset utilization in both over- and under-use situations (Figure 1).
Conditionality also opens up opportunities for utilities to fully automate their utility distribution operations. Not only will this enable them to provide more reliable service to customers, it reduces the need for human intervention and thus dramatically cuts labor costs. In addition, automation can be used to mitigate the utilities industry’s looming problem of an aging workforce. For these and other reasons, conditionality is one of the most important contributions the smart grid will make to the industry.
In classical physics, kinematics studies how the position of an object changes with time. In today’s utility operations, neither load nor supply is particularly kinematical because changes to either take a long time and occur slowly (in normal operating conditions) and both can be reliably predicted.
Many industry observers, however, believe that this scenario is about to change dramatically. One thing that’s expected to drive this change is “distributed generation.” Under this scenario, instead of relying on large centralized generation, the industry will see significant growth in distribution-side generation technologies. Unlike today, much of this supply will not be centrally dispatched or under direct central control. The resulting energy supply will be much more complex to predict and manage. To the futurist this may seem like an exciting prospect, but to a grid operator or a utility, this represents a control and management nightmare, because it directly challenges the operational priorities of safety and reliability.
Hybrid and electric automobiles will also substantially alter the pattern of supply and load on the current grid. According to some predictions, electric automobiles will account for upwards of 20 percent of the automobile fleet in the United States in the coming decades. This means that millions of automobiles charging each night could increase customer load profiles over time by upwards of 30 to 50 percent. When coupled with even more futuristic ideas such as “vehicle to grid,” you end up with energy consumption scenarios that no one imagined when the grid was built.
The three attributes of the smart grid – transparency, conditionality and kinematics – are interrelated. Transparency provides situational awareness, which enables conditionality. And conditionality likewise is a requirement for managing the kinematic supply and load patterns of the future. But more importantly, the smart grid is the only way the classical operating priorities of the system can be sustained – or enhanced – given the upcoming expected changes to the industry.