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Microgrids – Gaining experience and momentum

The economic value of a microgrid, at least for a single user, is sometimes reduced to two elements: its value in providing power when the rest of the grid is not functioning, and its ability to reduce energy cost. When one doesn’t have power its value is more appreciated, but quantifying the value for an economic analysis can be difficult. When there is loss of product or production it can be quantified. Not so when an outage represents an inconvenience. And, when the loss of power results in a life threatening situation to the user, an economic analysis will seem irrelevant.

The second element, reducing energy cost, could be a simple cost-benefit analysis of grid power and the associated tariff structures versus a single source of generation such as solar or combined heat and power (CHP). Sellers of these technologies have models that provide a payback analysis. However, what if you are considering multiple sources of generation and you’d like to consider storage? What if your objective is to minimize carbon or maximize the time your microgrid can remain isolated from the larger grid? In these cases, an optimization analysis of a portfolio of energy assets is required.

Perhaps the most economic combination of energy assets, including load management, is the desired objective. Or perhaps carbon reduction is important. Knowing how much solar can be included in the microgrid before stability becomes an issue becomes as important as cost. Take the solar question a step further, “How much CHP, storage, and solar in combination might be needed to establish stability and minimize carbon output?” Utilities are becoming more concerned about stability issues regarding their distribution systems as solar installations increase. Stability is as critical to a microgrid, perhaps more so when it is operating in isolation from the grid.

Applying modeling tools to answer the questions is important for project planning and financing purposes. DNV GL has used its Microgrid Optimizer (MGO) tool in several campus scenarios evaluating multiple generation assets to inform clients of the optimal mix of energy assets to deploy and operate in a microgrid. For Universities with carbon reduction commitments, carbon reduction impact may be as important as the return on investment (ROI). Carbon reduction occurs in three ways: the use of no carbon generation, the use of thermal loads to produce electricity resulting in a lower carbon energy mix, and the efficiency gained by controlling the interactions of multiple microgrid assets. A self-optimizing customer (SOC) might opt for load reduction to reduce carbon or opt to reduce cost by both reducing use and selling a demand response to the market. The MGO tool identifies and helps plan the options available based on the assets and operating requirements of the microgrid.

The question that arises from the planning process is, “Can controls achieve the predicted results?” Recently, proof of the efficiencies to be gained in a microgrid came from Phase 2 of the SPIDERS program. A SPIDERS Joint Capability Technology Demonstration was held on April 22, 2014 to share the lessons and results of Phase 2 of the SPIDERS program. One takeaway from the industry day event was the magnitude of efficiencies achieved by the control and optimization of the assets within a microgrid at Fort Carson. Presentations reported that diesel fuel efficiency increased by 30%, due to optimization and controls within the microgrid. The fort has three diesel generators that—when kept to the intended task to power a single building—operate at lower efficiency generating capacities. However, when connected to the recently completed microgrid the load for the three buildings was met with two generators each operating at significantly higher efficiency. Additional loads were connected to the microgrid further increasing the efficiency of the diesel generators. A solar array was also connected to the microgrid as the fort seeks to achieve some of its carbon reduction and zero net energy objectives. There are also electric vehicles (EV) on the base connected to the larger grid but not to the microgrid. The base is evaluating EV to grid impacts, but until the system is linked to the microgrid it won’t be able to evaluate the impact of storage to further improve generation efficiency. The experience at Fort Carson demonstrates the increase in efficiency that might result from a combination of assets. The communication and control necessary to achieve efficiencies have been demonstrated at Fort Carson and reported to the industry.

The industry is gaining momentum and new multi-user microgrid projects will emerge in New York, Connecticut, Massachusetts, and Maryland with a portfolio of energy assets more complex than Fort Carson. Modeling to optimize the portfolio will be a key element of planning and financing these new microgrids. Modeling tools and an understanding of how microgrids participate in energy markets are important to maintain the momentum.

The movement toward microgrids, distributed resources, and markets were key discussion topics at our recent 25th Retail Energy Executive ForumTM and again at our Utility of the Future Leadership Forum. If you have questions about how DNV GL can assist with microgrid planning, communication systems, and market strategies as a participant or as a utility enabling and reacting to these industry changes please contact us at energyadvisory.energy@dnvgl.com.

DNV GL is pleased be a part of the policy discussion and contribute an independent, objective economic methodology to understand how to integrate the increasing deployment of distributed energy resources, including microgrids, into a more resilient and environmentally conscious power distribution grid.


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