Energy-saving Tips For Boston’s Industrial Sector: Boosting Profits And Sustainability – Make sure your home is nice and cozy for the cool season! A tune-up for your furnace is the easiest way to ensure it is working optimally and to save money on your heating bill. Most tune-ups confirm that your furnace is properly adjusted, lubricated, and cleaned. Schedule a service in the fall before the cold season.

A cracked or broken window can cause chills in your home just as much as a heavy furnace! Double check the sealing and weather stripping on every door and window. Replace any damaged windows with energy efficient thermal glass to save even more!

Energy-saving Tips For Boston’s Industrial Sector: Boosting Profits And Sustainability

Get an accurate reading of your living space by not placing lights or heat-producing appliances near the thermostat. These can cause the oven to shut down prematurely.

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Reverse the direction of ceiling fans to rotate clockwise to help remove warm air. You can usually find a direction switch on the side of the fan. This will heat the room more evenly and shorten the time the heater has to run, saving you money!

Replace the disposable oven or wash the reusable oven at least 4 times a year to reduce dust and improve the performance of your system.

Large crowds are sure to generate extra heat. Set the thermostat a few degrees lower than usual when entertaining family and friends.

Traveling on vacation? Opening all interior doors, including closets and pantries, will help warm air circulate throughout the home. This will limit the chance of cold spots and pipes freezing while you are away. Another money-saving tip is to lower your furnace and hot water heater temperatures.

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There’s no point in having a warm home if you’re not there to enjoy it! Save money and lower your thermostat while you’re on vacation. Just make sure it’s warm enough to keep the pipes from freezing, as well as the plants and By modeling the conditions of an entire wind farm rather than individual turbines, engineers can squeeze more power out of existing installations.

Caption: Illustration shows the concept of collective flow control of a wind farm. Existing utility-scale wind turbines are used to maximize only their individual power output, generating turbulent wakes (shown in purple) which reduce the wind turbine’s power output. The new collective wind farm control system avoids wind turbine wakes to reduce this effect (shown in orange). This system increased the power output of a three-turbine array in India by 32 percent.

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The illustration shows the concept of collective flow control of the wind farm. Existing utility-scale wind turbines are used to maximize only their individual power output, generating turbulent wakes (shown in purple) which reduce the wind turbine’s power output. The new collective wind farm control system avoids wind turbine wakes to reduce this effect (shown in orange). This system increased the power output of a three-turbine array in India by 32 percent.

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Almost all wind turbines, which produce more than 5 percent of the world’s electricity, are controlled as if they were individual, independent units. In fact, the vast majority are part of larger wind farm installations that include dozens or even hundreds of turbines whose wakes can influence each other.

Now, engineers at MIT and elsewhere have found that, without the need for any new investment in equipment, the power output of such wind farm installations can be increased by modeling the wind flow of the entire collection of turbines and optimizing control of individual units. according to the circumstances.

The increase in energy production from a given installation may seem modest – it is about 1.2 percent overall and 3 percent for optimal wind speed. But the algorithm can be deployed on any wind farm, and the number of wind farms is growing rapidly to meet accelerated climate goals. If this 1.2 percent increase in energy were applied to all of the world’s existing wind farms, it would be equivalent to adding more than 3,600 new wind turbines, or enough to power about 3 million homes, and a total profit for energy producers of almost a billion dollars a year, researchers say. And all this at essentially no cost.

In a study led by the MIT Esther and Harold E. Edgerton Assistant Professor of Civil and Environmental Engineering Michael F. Howland.

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“Essentially all existing utility-scale turbines are ‘greedy’ and independently controlled,” says Howland. The term “greedy,” he explains, refers to the fact that they are controlled to maximize only their power output, as if they were isolated units without detrimental impact on neighboring turbines.

But in the real world, turbines are deliberately placed close together on wind farms to achieve economic benefits related to land use (on or offshore) and infrastructure such as access roads and transmission lines. This proximity means that turbines are often strongly affected by turbulent wakes produced by others upwind of them – a factor that individual turbine control systems currently do not take into account.

“From a flow physics standpoint, putting wind turbines close together in wind farms is often the worst thing you can do,” Howland says. “The ideal approach to maximize total energy production would be to place them as far apart as possible,” but that would increase the associated costs.

This is where the work of Howland and his collaborators comes in. They developed a new flow model which predicts the power output of each turbine on the farm depending on the incident winds in the atmosphere and the control strategy of each turbine. While based on flow physics, the model learns from wind farm operational data to reduce predictive error and uncertainty. Without changing anything about the physical turbine locations and hardware systems of existing wind farms, they used physics-based modeling, aided by data on the flow within the wind farm and the resulting power output from each turbine, given different wind conditions, for find the optimal orientation for each turbine at a given instant. This allows them to maximize output from the entire farm, not just individual turbines.

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Today, each turbine constantly senses the direction and speed of the incoming wind and uses its own internal control software to adjust its pitch angle (vertical axis) position to align as closely as possible with the wind. But in the new system, for example, the team has found that by turning a turbine just slightly away from its maximum output position—perhaps 20 degrees away from its individual peak output angle—the resulting increase in power output from a or more wind. units will more than compensate for the slight reduction in output from the first unit. Using a centralized control system that takes all these interactions into account, the turbine stack was operated at power output levels that were up to 32 percent higher under some conditions.

In a month-long experiment at a real utility-scale wind farm in India, the predictive model was first validated by testing a wide range of deflection orientation strategies, most of which were intentionally suboptimal. By testing many control strategies, including suboptimal ones, on both real and model farms, researchers could identify the true optimal strategy. Importantly, the model was able to predict the farm’s energy production and optimal control strategy for most of the wind conditions tested, giving confidence that the model predictions would track the true optimal operational strategy for the farm. This enables the model to be used to design optimal control strategies for new wind conditions and new wind farms without having to perform new calculations from scratch.

Then, a second month-long experiment on the same farm, which applied only the optimal control predictions from the model, proved that the algorithm’s real-world effects could match the overall energy improvements seen in the simulations. Averaged over the entire test period, the system achieved a 1.2 percent increase in power output at all wind speeds and a 3 percent increase at speeds between 6 and 8 meters per second (about 13 to 18 miles per hour).

While the test was conducted on one wind farm, the researchers say the model and cooperative control strategy can be applied to any existing or future wind farm. Howland estimates that, translated into the world’s existing wind turbine fleet, an overall energy improvement of 1.2 percent would produce more than 31 terawatt-hours of additional electricity per year, roughly equivalent to installing 3,600 additional wind turbines at no cost. That would translate into about $950 million in additional revenue for wind farm operators annually, he says.

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The amount of energy that will be gained will vary greatly from one wind farm to another, depending on a number of factors including the spacing of the units, the geometry of their arrangement, and changes in wind patterns at that location over the course of a

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