Must retail energy users be mere price takers?

A significant wedge between those seeking to maintain the current regulated prices accompanied by DR and those looking to move to transactive energy for a self-regulating grid is the notion that retail customers are all mere price takers. A price taker watches the market and either buys or does not buy; he takes the prices the market offers. Some see that this “lack of power” can only be addressed by regulating the prices offered. This leads back to today’s model.

Committed positions break this model. A retail customer who commits to buying this much power at this rate for a given time period in the future establishes a committed position. With a committed position, if the customer needs more power at any time than the commitment, then the customer must make up the difference at the spots rates. If the customer needs less power than the commitment, he can only sell back the difference at spots rates, and then only if he finds a buyer. With this assumption of position risk, the customer also gains the ability to interact fully with the market.

In today’s regulated markets, the greatest value of energy storage is as a forward hedge by the energy supplier. The entity that stores the energy on premises cannot make up the economic value required by the storage. This storage is of value as a hedge for the retailer, not as an asset for the customer. This economic imbalance reduces the value of other distributed energy assets, such as distributed generation, as well. By limiting the value of energy storage to only the hedge value for the supplier, distributed energy assets are always undervalued.

Committed forward positions change this equation. A committed forward position in power is a contract that the buyer will purchase this energy whether or not he uses it, and that the supplier will provide the power no matter the market conditions at the time contracted for delivery. (Let’s leave aside for now the issues of true emergencies, liquidated damages, etc.)

When the market allows committed positions, the buyer is rewarded for better understanding his own energy needs. A buyer who is able to plan his energy use could package a series of positions, and take bids from the suppliers. These bids can be considered in the larger context of the business, such as labor planning or the needs of seasonal manufacture. A committed forward position then provides the buyer with choice while limiting risk in price and availability.

Committed purchases enable the buyer to take full advantage of his own distributed energy resources. Energy storage becomes a way to manage purchasing commitments, sometimes using excess energy in the commitment, sometime shaving peak use to stay within the commitment. Distributed generation is managed locally, where the knowledge if value, process, and commitments is greater.

Committed purchases of power move the retail energy buyer beyond the role of a mere price taker, to that of a full market participant. This devolves considerable autonomy to the end nodes of the grid. This increases the rewards of investing in distributed energy resources for those customers that value power surety and economic arbitrage. Because such investments are made by single sites, they will help us move to normal, innovative markets in energy technology.

Commercial Use of Live Energy Models

This is one of a series of posts on how the semantic expression in WS-Calender is beginning to affect buildings and smart energy. WS-Calendar recently completed its third public review and will soon be published as Committee Specification 1.0.

In a previous blog, I discussed new directions in commissioning; including commissioning that incorporates BIM, schedules, and continuous energy models.

Performance Contracting and the new Commissioning

Many building owners are suspicious of energy performance contractors because the performance contractor is both a player and a score keeper. Because a significant effort is required to understand the information in building systems, there are significant start-up costs. These costs, both in money and time, require that each contract include a significant minimum contract lengths over which to amortize the up-front costs. These up-front costs make it uneconomical for energy contracting to use a third party auditor to verify results.If the owner selects a new a new performance contractor, the up-front costs will be incurred again.

Standard semantic tags and ready access to a light-weight BIM can change this.

Imagine a market wherein a cloud-based energy performance contractor could offer same-day initial reports. That same market also supports a number of 3^rd party auditors, cloud-based, each able to independently assess the results of the performance contractor. Each of these parties can hook up to the BSI, read the BIM, read the tags, and begin analyzing right away. A potential energy performance contractor could offer the building owner a selection of third party auditors to report the success of the contract.

This competition between cloud-based services would drive rapid innovation. On one side driving costs down, on the other driving richer models. These models are likely to build upon two significant efforts currently underway. ASHRAE SPC201 would inform the models, and through the linkage of systems and space, become more nuanced. Schedule-based business assertions, as we are beginning to see in the links of WS-Calendar and the IFCs would make these models more business aware.

Continuous commissioning based on such a foundation would support an ecosystem of cloud-based service suppliers, each able to grow to scale.

Retail use of Live Energy Models

As we move in this direction, we move from information models that are tuned to reflect changed operating hours to models that can tied increased energy use to short term activities, including, say those associated with a sale in one portion of a store. That portion of a store with an ongoing sale may have increased HVAC driven by increased traffic or brighter lights to attract shoppers and display the merchandise, and other enhanced amenities. A side effect of the brighter lights may be increased heat load, thus causing still more HVAC requirements than at first expected.

The most respected retailers with superior operations are already using these sorts of models to fine-tune their special Sales.

Non-Energy adaptive re-use of new Energy Components

Because the approaches described above rely on the composition of multiple standards, they create components that building integrators can re-assemble to meet other purposes.

Emergency responders have long wished for a variety of interactive means to acquire situational awareness of the facilities they are entering. The standard light-weight building model described above is a natural basis for situation awareness sharing. During an emergency response, the goal may be closer to raw sensor readings than to energy use. Those sensor readings, like the performance information, cannot be interpreted without a framework that indicates the spaces and the business purposes where those sensors are located.

Common abstractions, business purposes, and frameworks are the foundations for policy-based interactions with any system. The business-purpose-based analysis of space and system and schedule, is a likely target for adaptive reuse for emergency-response based policy. In the simplest (and direst) case, the facility is on fire, every asset is at risk, and so every bit of information about a building might be shared. In a simpler case, if the Spill Response Team is responding to a minor spill in the warehouse, it is inappropriate to share with them acess to, say, a webcam in the executive suite.

The Path to Smart Energy

For the last two years I have been so immersed in smart energy that I sometimes lose track of the big picture myself. This post goes back to basics.

The power industry of North America has provided its customers with the greatest life style that any civilization has ever had. The old service model assumes an ever-present supply of power that is predictable, abundant, and inexpensive. World-wide, our plans are to reduce the power supplied by predictable an inexpensive power sources, to replace them with power sources that are intermittent and less predictable, and that are widely distributed across the grid, including within homes, businesses, and neighborhoods. The old service model will not survive.

None of us wants to face deteriorating life-styles or reduced ability to provide quality services and products as energy supplies become less dependable. Smart Energy is the means we will use to expand both amenities and service quality.

Smart energy looks to each home, business, and industrial site to take responsibility for the management of its own energy in the face of an ever-changing supply. While efficiency is important, it is a small part of the story. Early efforts react to infrequent temporary and perhaps unanticipated shortages by degrading services, i.e., by turning things off. The proactive approach is to pre-consume energy, to take advantage of the more frequent periods of energy surplus in ways that there will be no degradation of service during shortages. As this shifts energy purchase to times of inexpensive supply, smart energy will provide better service for less.

Energy use is more than power use; smart energy is about more than power markets. Smart energy systems use thermal, pressure, chemical, and potential energy to support their purpose. Through balancing a changing portfolio of energy resources to meet the demands placed on them, smart buildings, homes, and facilities will use changing processes to provide consistent and high quality results.

Every node on the power grid, i.e., commercial buildings, homes, and industry, will act as a microgrid. Smart microgrids manage their energy use, generation, storage, recycling, conversion, and rely on market operations (buying and selling) only to make up the difference. Off-grid facilities already act as microgrids; they will become more prevalent as smart energy improves the quality of this choice. Microgrids can be combined into larger microgrids to enhance resilience, to encompass the neighborhood, the office park, the military base, and the campus.

Smart energy is information based. Systems and devices will provide information on their present and anticipated future energy requirements. They will consume information from energy markets and from the predictions of their peers. They will gain situation awareness from weather services and other external sources. They will exchange schedules and requirements with the personal and enterprise systems they support. New energy moves beyond performance to doing the right thing at the right time. Smart energy systems will be autonomous, self-monitoring, and self-managing.

Our homes, commercial buildings, and industry, will share the burden of energy quality, reliability and production with their suppliers. With the new standards ready to use, we have the opportunity for market-driven innovation incented by grid-based economic signals. Today we have the public interest and attention to bring products rapidly to market. The innovators and ventures able to take advantage of the opportunities in these new market realities will reap large rewards.

The end nodes of the grid are consumer-driven, and so are able to support more vibrant technology markets than can any central service. The promise of new energy is to achieve societal benefits by aligning energy supply and use while offering better amenities to buildings, homes, and their occupants while costing less. The challenge of new energy is to bring the digital systems in every system and appliance in our lives into the internet of things, and to have them respond to our needs and wants.

Operational BIM Schedules and Pre-Design Programming

As Chair of WS-Calendar, I receive a number of inquiries about the incorporation of time and schedule into other specifications. In particular, the wider visibility of VAVAILABILITY is attracting some interest. Occasionally these include fragments of xml, and inquiries as to how to apply this information.

WS-Calendar recently completed its third public review and will soon be published as Committee Specification 1.0.

Facility Programming is an important early step in step in the Integrated Design Process. Programming is defined in the Whole Building Design Guidelines (WBDG) as “the research and decision-making process that identifies the scope of work to be designed.” Programming is the first part of the design cycle, during which systems and space requirements are identified by the activities they will support. If the design process is compliant with the formal BIM process (BuildingSmart, NBIMS, etc.), then these systems and spaces are identified as described in the IFCs.

BIM is a collection of information sets and models with identified interfaces / information exchanges between them. A model that is of growing interest is the building’s energy model, which is today derived from a combination of structural and purpose models and [normally] a side questionnaire about the building’s use.

I have recently received early sketches (XML Fragments) of programming documents from Dr. Chris Bogen (Engineering Research and Development Center) in which building services and systems, as expressed in open buildingSMART model format, are included in vavailability to express, for example, the operating schedules of systems supporting dining facilities (and their energy requirements). The ERDC project is aiming toward the development of a format that can be used to compare the expected resource use of a facility during design and express the actual resource use identified through analysis of building sensor systems. With the additional pattern detection algorithms under development at the lab, ERDC expects to have a tool that will compare building use to identify when the use of a building doesn’t match it’s design prediction. The ultimate goal of this work is to create building simulators directly from data provided during traditional design and construction processes.

Over time, many buildings are found to have different energy use profiles then their models predict. Often this is due to changes in operating schedules from that which was predicted. We are beginning to see mandates to update these energy models to match actual results, particularly in government owned or funded facilities.

Lifetime maintenance and updating of these programming documents, including changing the operations schedules, establishes a baseline to compare predicted vs. actual use, and to thereby sooner to detect anomalies due to system degradation or misconfiguration.

An advantage of potential automated modeling within incorporated vavailability, is that schedules can easily be understood and manipulated by building operators/occupants. Once an energy model is in-place, it would be straight-forward to iteratively try out different systems schedules and examine different energy profiles. As we move to dynamic markets, the capability to project different times of use and compare those to projected energy prices might become a new source of value to building operators.