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.

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.

Efficiency, Resilience, and Smart Energy

Far too many of the presentations at Connectivity Week last month touted building efficiency. Efficiency is important to Smart Energy, but can also work to defeat Smart Energy. Resilience is ultimately more important than efficiency for meeting the goals of Smart Energy. What energy efficiency can do, is support energy resilience.

A Smart Grid is one that can work despite a growing volatility of supply. Today’s grid already has a reduced ability to support the ever-changing aggregate consumption by the end nodes. Buildings, houses, and industry, the end nodes of the grid, will be the basis for Smart Energy.

So far, today’s efficiency efforts have wrung the slack from the system. A system without slack becomes brittle because it has a smaller margin for error. The most efficient buildings are limited in how they can trim load when asked. The overall grid has reduced margins for error. An exclusive focus on efficiency drives the impulse to direct load control in the end nodes by the central systems of the energy supplier.

Resiliency is the capacity of a system to absorb disturbance and still retain essentially the same function, structure, identity, and feedbacks. At the local level, resilience is dependent on the ability to adapt and to use diverse resources to achieve the same ends. At the broader level, resilient systems are characterized by diverse participants with non-uniform responses. Homogenous collections of systems respond to a given stimulus in similar ways, resulting in “panics” or “stampedes”. Smart grids will provide many systems with a similar stimulus as power availability changes.

Smart Energy results when the end nodes are able to respond to situations announced by the Smart Grid. It is critical to note that the purposes of the end nodes are not those of the grid. The Smart Grid will present its problems with reliability and balance to the end nodes. The end nodes, whose goal is to deliver divers services to their owner / occupants will use this information to optimize their own service delivery.

Let me present two examples of systems whose proper goal is service resilience rather than energy efficiency.

Cloud computing data centers use immense amounts of power, converting it to business process and to heat. Cloud computing relies on virtual computing machines that can be started and stopped, created and destroyed as needed. Cloud data centers have a growing ability to move these virtual machines between data centers. They are using this capability to provide service resilience whether or not a given data center is operational.

Data center resilience used to be provided through physical security, redundant systems, and back-up generators. The new model provides resilience through an ability to run from the problem, moving a virtual machine from one center to the next. The cost of each data center is reduced as the redundant systems and unnecessary generators are eliminated; construction savings of more than 50% were reported. Each data center is less robust, but together the data centers gain resilience.

Resilient data centers can respond to Smart Grids by moving processes from one site to another. Cloud services are part of smart energy in ways that data centers never could be. This resilience is not built on energy efficiency; six data centers may replace one. They have achieved resilience by focusing on their own missions rather than on support of the grid.

Commercial buildings and homes can achieve resilience by focusing on the times of energy surplus. Many renewable sources on the grid are unable to find adequate markets when they are producing at their maximum. Times of energy surplus may occur every day, while energy shortages may occur a dozen times a year. When the wind is blowing, when the sun is shining, Smart Grids will let the end nodes know with low prices. It is these low prices more than peak price events that will provide the incentives for smart energy.

Periodic low prices will fund resilience in those end nodes that take advantage of them. Capturing and storing the surplus, particularly with in-process storage, makes each building better able to weather shortages. Through storage combined with efficiency, each end node will lessen the urgency to buy power now. A building that is planning around the temporary power surpluses is able to respond to shortages without loss of service. The net effect to the participant is more reliable service at a lower price than competing buildings and properties.

Over time, end-nodes that commit to on-site storage will find that their internal markets change. On-site generation will be the market for site-based energy, in preference to grid-based distribution. The better market is the internal one, wherein storage can enhance service to the building owner and occupant.

As their site-based storage grows, the technology costs will drop. With each progressive step, building resilience grows , and grid dependency is reduced. Because there are many buildings, with many owners, and many motivations, smart energy in buildings better supports the market dynamics of rapid innovation. Because the building owners are inherently diverse, and building systems naturally autonomous, building based smart energy gains resilience as a larger system of systems.

Efficiency supports this developing resilience by reducing the demands. A building that uses half as much energy need store only half as much energy. A building that uses less energy can better weather periods of limited support from grids. To the end node, the advantage of a smart grid is better situation awareness, and an improved ability to broker whatever services are needed locally for the occupants.

The largest Smart Energy opportunities are not in selling to the grid. The real opportunities are in building end-node resilience despite power whose price, quality, and availability will be more volatile. The purpose of this resilience is to better support the owner and the occupants of the end node, not to support smart grids. This focus, on the local decision maker and their needs will lead to faster adoption.

Smart Energy in Industry: Introducing MRP4

Last week, I spoke at the Department of Energy’s Industry to Grid (I2G) Summit, a pre-meeting of the ARC World Industrial Forum. For me, it felt like something of a homecoming. Several careers ago, my biggest customers were manufacturers. In the late 70’s, popular imagination held US manufacturing to be dead, poorly managed and low quality. In a famous Newsweek article, a celebrity athlete boasted of a summer in the UAW, during which he deliberately added rattles to pass the time. As often happens, a renaissance had begun some years before public perception hit bottom.

As a young programmer, I was working with companies trying to improve quality while keeping costs under control. With double digit inflation the norm, the US was beginning its great inventory squeeze. A passing familiarity with the Japanese Kanban system could take you far in industrial consulting. JIT inventory was being supplemented by JIT production. In Toronto, at the world APICS conference, we split MRP (Materials Requirements Planning) into MRP1 and the new MRP2. MRP2 reached beyond the factory floor to incorporate sales budgeting and HR planning. A year later, I first saw AutoCAD, astonishing because it ran on a PC.

Those were the roots of today’s integrated global supply chain management. Eventually MRP2 came to cover all facets of a company, and was re-christened ERP. Time-phased resource acquisition is a critical component of today’s commerce. Executives in every sector now are evaluated based on ratios determined by how lean their inventory is.

Even when it makes no sense, we apply these management principles today. For example, Coal plants used to pride themselves on weeks or even months of supply on hand. Coal is easy to store, and it does not go bad. Still, many utilities today run on same day coal deliveries; any interruption of the supply chain, of the constant stream of trains from mountain to generator, would take a significant portion of US electrical supply off line.

This last week, we saw the effects of a similar lean supply chain in natural gas. The cold snap increased demand and reduced supply, causing affecting electricity supplies in Texas, New Mexico, Colorado, and California. Lean supply chains are brittle. Through ERP, we have made are electricity supplies brittle as well.

Current plans are that we introduce intermittent electricity sources, i.e., solar and wind and tides, throughout the grid. Today, we backstop these with the same natural gas whose supply chain we manage so tightly. Lean supply chains and thin markets demand predictability. When smart grids fail, lean supply chains can make then fail badly, and the effects will be regional.

Pulling this back to my early days in industry, APICS propagated the essential equations for to compute supply chain decisions. In those days before PowerPoint, I used to be able to write these equations, in the style of a grammar school teacher, on the board, behind my back, while facing my clients. Many of them depended upon another, the Cost of Stock-out (COS). The simplest COS was solely lost sales per day. The better ones started with opportunity costs and factory reconfiguration and extended to lost reputation and permanent loss of customers. It is easy to undervalue the COS.

Public Utility Commissions have made affordability their top concern for decades. Utility executives strive to make their financial ratios look like other industries. Volatile energy supplies will increase the likelihood of stock-outs, i.e., shortages of basic supplies. Lean supply chains and renewable energy create a dangerous mix.

The industrial decision-makers in the audience wanted a quick take-away on what smart energy means for them. Many of them generate their own power, and are looking for better ways to bring their excess to market. Others are just beginning to consider the effects volatile prices that swing every day. To me, it was easy, they are already the thought leaders in this area. Industry gave us MRP1 which grew into MRP2. MRP3 is ERP, the dynamic management of resource supply and use that runs our global supply chains and businesses of all kinds. For the end node, smart energy is MRP4, accounting for volatility of supply, and factoring it directly into scheduling on the factory floor.