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Energy Service Requirements for Commercial Demand Response
Primary Author Phil Davis, Senior Manager, Demand Response Solutions, Schneider Electric
OUTLINE In the opinion of many demand side experts, commercial buildings represent the largest untapped potential for smart grid benefits. Traditional building automation systems (“BAS”) provide control loops based on occupancy, temperature, humidity and weather. Often, facility staff override these systems to address tenant complaints. Over time, building occupancy changes, control nodes and sensors deteriorate, and the result is a system poorly in tune with the needs of the owners and occupants. Since this is fairly common, one must conclude in these cases that the BAS did not deliver enough value to justify maintaining it. Now, suppose that same BAS could contribute significantly to economic performance. Is there a configuration strategy that justifies the effort and expense required to keep the BAS in tune? ……………………………………. This potential exists today. By curtailing building demand on a signal from the grid operator, energy can be diverted to more critical needs. The grid will pay for this on a value scale. Reaction time is a critical factor in the value of a curtailment. The faster additional energy can be applied to a critical need, the more value it has. Buildings have an advantage over generators because certain loads can be interrupted instantly, and because individual buildings are likely to be closer to the site of critical need than are the generators. Both will earn premiums in the energy marketplace. There are barriers to these opportunities. First is that building managers are not in the energy business. They do not understand industry customs; nor do they have the sophisticated communications links that integrate generators to the grid. Second, building managers are rightly concerned with the comfort, convenience and safety of their tenants. The value of an investment to enable grid communications can be reduced to a simple business case but not the impact of sporadic energy curtailments on tenants. Emerging techniques will address this with the potential to improve economic performance and tenant satisfaction.
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As individuals, we may have participated in residential demand response programs. Most of these interrupt our electric water heaters or home air conditioning units for a few minutes of each hour during critical times. In well designed programs, we barely notice if at all. The commercial level is a different experience. Usually, curtailments events last several hours; long enough for tenants to notice temperature changes, or to become irritated by reduced lighting or services. These nuisances might be offset by revenue gains and by the sense of “doing the right thing” for the environment…but only up to a point. Market surveys in this area have yielded uniform results. Most facilities staff are willing demand response participants if the number and duration of demand response events is limited, but will would drop these programs if they become frequent. As generation becomes more constrained even in the face of growing energy demand, demand response events certainly will become more frequent, and participation may become mandatory in some markets. Can this impending conflict be resolved? This is exactly the reason for smart grid. For many years, building managers have mitigated the impact of energy demand charges with systems that coordinate the start and run times of energy intensive equipment. In pilot programs, we have learned that intelligent use of these same techniques to interact with smart grid signals enable buildings to respond in subtle ways that often escape tenant notice. The elements required are a smart grid communications system that can forecast hourly conditions for the upcoming 24 hour period and a sophisticated and well tuned BAS that can use this information to create hourly BAS tactics in support of a daylong strategy. The first of three necessary components is a communications standard that can speak to building automation systems. Standards bodies such as OASIS are working hard to a common language of energy. Specifically, the Energy Interop and the Energy Market Information Exchange Technical Committees are developing protocol that will inform energy users about price, carbon content, grid conditions and other information that help users achieve their goals. Ahead of that effort, many grid operators already have market specific techniques to do the same things. Through an inexpensive interface device at each building, the grid can communicate its needs, and the value of supplying those needs. This provides the necessary information for an informed business decision. The second component is the demand side. Here implementation becomes more complex. First, the existing controls system must be optimized. Even new systems sometimes receive less than ideal commissioning. Certainly older systems lose calibration accuracy; and in many cases have not been refreshed to reflect changing space usage. Energy audits routinely reveal that individual systems are working against one another. Sophisticated “retrospective” modeling can reveal expected versus actual performance at the component level, and also illustrate how the operation of one system might be impacting another negatively. These optimization tools are well designed and available today. The optimization of all systems results in a building that is fit and ready to enter the modern era of energy management. This sets the stage for the final critical component: “predictive modeling and management”. Traditionally, building systems react to inputs from weather, occupancy, temperature 2
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and humidity, and they do so over a relatively long thirty day planning horizon. From there, they may be programmed to follow time of use rates, and occupancy patterns. The missing component is interactivity with the grid. This starts by integrating meter data with the BAS, then “teaching” the BAS how to process it. After a period of experimentation, this allows management to select strategies that result in specific demand levels. With this new data and the corresponding BAS controls data, facilities staff can establish scripts which automate BAS functions when staff set external constraints on demand and tenant comfort. The result is a first step in optimizing the building demand curve for a better fit with economic supply. This beginning level of automation manual interaction by the staff as well. Perhaps the biggest benefit is that lowered energy costs help to justify the expense of maintaining the BAS in top shape. What of the building itself? Made of large amounts of concrete and steel, a commercial building also functions as a thermal storage battery. The mass of the building stores heat and cold, then releases it as conditions change throughout the day. Too often, this effect is exaggerated by lighting loads, and HVAC toils to overcome the result. One of the most exciting developments is that we are learning to use this thermal battery to help rather than to hinder building efficiency. The concepts of preheating or precooling are well known. “Start/Stop” models exist that fine tune the times during which these activities are best undertaken. Currently, even the best cases produce broad hours long building responses that leave a great deal of potential untapped. In the EU and US, there have been major efforts to model this behavior and to produce results to fine tune building operation. This modeling does not assume BAS operation. Instead it accounts for thermal mass, hourly energy costs , and the design “sweet spot” of HVAC operation to provide 24 hourly settings for the HVAC system to achieve lowest cost operation. In the absence of such modeling there are many cases where HVAC systems are switched off, or set point changed dramatically during periods of low occupancy in an effort to save energy. When restored during occupancy, the result is an overworked HVAC system trying to regulate a building where enormous amounts of energy are radiating from interior walls and floors. Chillers may operated well outside their design efficiency levels virtually all the time, because there is no coordination between the impact of thermal mass and controls strategy. Individual tenant complaints of discomfort may result from the feel of radiated heat near seating areas even though the air temperature at the thermostat is proper. The thermostat is adjusted to meet the complaint, but the building never achieves equilibrium. Thus starts the cycle of inefficiency and high cost. This can be solved in a well optimized and “grid aware” building by adding a more sophisticated modeling capability able to offer advanced control strategies; but there is an additional benefit. That building now may join a community of buildings, to form a “load portfolio”. The portfolio manager may be a management company, a grid operator, or other entity, but the impact is the same.
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That portfolio of sophisticated buildings presents a valuable and desirable load to the grid because it can adjust rapidly to changing conditions. In exchange, the energy provider may grant price concessions or performance payments. When allowed, the portfolio manager can make very finely tuned adjustments to building operations to keep the portfolio in “energy balance”. In a related effort, certain software companies traditionally providing CAD designware and modeling software have added the ability to create building models of existing structures and to couple this with a growing database of building systems components. Under the general name of Building Information Modeling (“BIM”), the goal of this new class of tool is to provide real world estimates of the impact of new components as part of an integrated building whole rather than savings based strictly on its labeled performance or on static testing. Longer range, the market value of such buildings improves relative to less sophisticated structures because operating costs and carbon emissions are lower, and tenants want to locate there. Such a portfolio can mitigate peaks by coordinating controls strategies across several structures, with the benefit that tenants may not notice that something special is afoot. Such operations lessen the need to use peaking power plants and also create a market friendly to renewable, but intermittent, energy sources. There is no question that retail energy users will face energy price volatility in more immediate ways than ever before. Failure to plan for this will result in dramatically increased energy costs. “Predictive” management coupled with newly sophisticated modeling will allow the introduction of forward energy prices into building control strategies adding the impact of thermal mass to the equation. BIM then will enable efficiency upgrades to systems that will not disrupt the function of the remaining components. The result will be a commercial sector that leads the way in smart grid performance.
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