Dispatch in Microgrids: Lessons from the Fort Collins ...

ELECTR-5862; No of Pages 13

Mayank Panwar is a graduate student in the Department of Electrical and Computer Engineering at Colorado State University. He holds a Bachelor’s degree in Electronics Engineering and has four years of experience in the electric power industry. Gerald P. Duggan is President of SAV Innovations, Inc., a software consulting business specializing in energy management systems, IT management systems, and device testing. He holds a Master’s degree in Computer Science from the University of Utah, and is enrolled in the Systems Engineering Ph.D. program at Colorado State University. Robert Griffin is a Ph.D. student in the Department of Electrical and Computer Engineering at Colorado State University. He received his Master’s degree from the University of Florida. Siddharth Suryanarayanan is an Assistant Professor in the Department of Electrical and Computer Engineering at Colorado State University, where he also serves as the site director for the Center for Research and Education in Wind (CREW). He holds the Ph.D. in electrical engineering from Arizona State University. Daniel Zimmerle is an Assistant Research Professor in the Department of Mechanical Engineering at Colorado State University, and Scientific Director for the Center for Research and Education in Wind. Marty Pool was until recently employed with the Brendle Group a sustainable and engineering consulting firm located in Fort Collins, CO. Steve Brunner is a senior engineer at Brendle Group, a sustainable and engineering consulting firm located in Fort Collins, Colorado. He received his B.A. from Dartmouth College and his M.S. from the University of Colorado, Boulder. The authors thank the participating entities in the Fort Collins RDSI project for their support and encouragement in the preparation of this manuscript.

Dispatch in Microgrids: Lessons from the Fort Collins Renewable and Distributed Systems Integration Demonstration Project The divergent needs of the various microgrid participants make the dispatch in microgrids more complicated than typical methods. A review of the experience in Fort Collins suggests that regulations should be developed to address the potential financial conflict issues where generation companies may see microgrids as competition, and also that the cost of integrating assets into a microgrid need to be lowered. Mayank Panwar, Gerald P. Duggan, Robert T. Griffin, Siddharth Suryanarayanan, Daniel Zimmerle, Marty Pool and Steve Brunner

I. Introduction Traditionally, electricity has been generated in bulk and transmitted, often over great distance, to consumers. Improved and lower-cost controls, computation, and communications technology has

encouraged distributed and renewable energy integration. Microgrids represent a promising method to manage distributed resources, including distributed renewables. A microgrid is an autonomous self-sustainable subset of the electric power system that can operate in parallel

September 2012, Vol. 25, Issue 8 1040-6190/$–see front matter # 2012 Elsevier Inc. All rights reserved., http://dx.doi.org/10.1016/j.tej.2012.09.011 Please cite this article in press as: Panwar M, et al. Dispatch in Microgrids: Lessons from the Fort Collins Renewable and Distributed Systems Integration Demonstration Project, Electr. J. (2012), http://dx.doi.org/ 10.1016/j.tej.2012.09.011

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with, or in isolation from, the grid.1 Microgrids are currently being developed in both the civilian and military milieus, and are often considered part of the Smart Grid Initiative – the official policy of modernization of the U.S. electricity grid.2 Microgrids possess the potential to: (1) increase supply reliability to local end-user loads, (2) provide ancillary services to the grid; and (3) and provide avenues for increasing renewable penetration in the distribution grid.3 However, there are challenges associated with the operation of a microgrid, especially in planning the dispatch of generation assets. Due to diverse goals and requirements of end users, dispatch in a microgrid may be more challenging than methods commonly deployed in the bulk power system. Additionally, due to the small size and dispersed nature of most microgrids, microgrids cannot generally support the degree of human intervention commonly seen at the bulk power level. efore examining how microgrids function, it is necessary to define what constitutes a microgrid. While the question of ‘‘what features combine to form a microgrid?’’ appears simple at first, there are, in fact, many considerations that make an exact definition challenging. Several definitional issues exist, such as the number of interties between the microgrid and the main grid and the required balance between native

generation and load.3 Differences in definitions of microgrid exist. Some state it to include remote power systems that are isolated from the main grid while others need it to be connected to the main grid at a single point.4 hile not all parties agree on some definitional components, the main features of a microgrid are generally well accepted. 4,5,7,8 The IEEE Standard 1,547.4 provides a well-accepted definition of a microgrid, or a distributed resource (DR) island system as ‘‘DR island systems are parts of electric power systems (EPS) that have DR and load, have the ability to disconnect from and parallel with the EPS, include the local EPS and may include portions of the area EPS, and are intentional and planned.’’8 Additionally, a microgrid will appear as a single manageable entity to the main grid.6

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A conceptual diagram of a microgrid is shown in Figure 1. For the purposes of this article, the ability to be either grid-tied or islanded is important, since the required dispatch method changes between these two modes. For example, a power system on a distant island with no tie to a mainland EPS would likely utilize a dispatch method similar to that of a continental EPS, only on a smaller scale. Similarly, installing solar panels on a group of houses without coordination is not considered a microgrid; rather it is an instance of connection of several DG sources.8,9 In addition, microgrids are usually defined to exclude systems containing high-voltage transmission lines (typically voltages greater than 35 kV or 69 kV depending upon the geographical location)5,8 since high voltage is normally used for

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Figure 1: A Microgrid Connected to the Bulk Electric Power System (EPS) (figure drawn based on information in note 17)

1040-6190/$–see front matter # 2012 Elsevier Inc. All rights reserved., http://dx.doi.org/10.1016/j.tej.2012.09.011

The Electricity Journal

Please cite this article in press as: Panwar M, et al. Dispatch in Microgrids: Lessons from the Fort Collins Renewable and Distributed Systems Integration Demonstration Project, Electr. J. (2012), http://dx.doi.org/ 10.1016/j.tej.2012.09.011


long-distance electricity delivery, while the microgrid concept typically applies to supplying locally generated electricity to support local loads. While there is no intrinsic limit to the geographical size of a microgrid, excluding high-voltage elements naturally constrains the physical area a microgrid will occupy. Conceptually, a microgrid is a flexible and adaptable structure. Some of the reasons microgrids are attractive include the ability to cater to end users with power quality requirements that are different than provided by the main grid, and the ability to trade ancillary services with the main grid.4,7 Microgrids allow distributed generation to be integrated into the main grid. At a large scale, communication and control complexity make it difficult to integrate large numbers of DR facilities directly into the EPS. In these cases, a microgrid can aggregate many DR assets and present them to EPS operator as a single asset, effectively isolating local communication and control issues within the microgrid.10 Microgrids can also provide benefit to the EPS by aggregating assets to provide meaningful demand response during peak load periods.7 By aggregating a larger numbers of assets, the microgrid can bid at contract sizes supported by independent system operator’s (ISO’s) market systems. Aggregation also improves the reliability of the demand response, and reduces communication bottlenecks and

overheads. Revenue for such a system can include incentive payments from utilities or reduced demand charges.

II. Dispatch in a Microgrid versus Dispatch in the Main Grid In the main grid, the goal of economic dispatch is to find the

The solution to this problem is to use chance constrained programming to model the system randomness. lowest-cost grouping of generators that can meet the load without violating any constraints, such as line ratings and bus voltages. In a microgrid, however, additional goals or constraints such as harmonics reduction may influence dispatch decisions. Combined heat and power (CHP) systems are a common type of DR that may be incorporated into a microgrid.5 Dispatch within such a microgrid is complicated by the need to service heat loads, which typically vary unpredictably, and independently, of electricity demands. The presence of renewable resources further complicates dispatch in such a

microgrid due to the stochastic variation in renewable output. The solution to this problem is to use chance constrained programming to model the system randomness, and, based on that model, efficiently dispatch the available resources. It is noted that this process is slow and needs to be made faster to allow for near-real-time applications.11 Since the models used for unit commitment and economic dispatch in a more traditional power system have been developed and refined using historical data for many decades. Since no such historical data exists for microgrids, it is reasonable to expect that models for effectively dispatching resources within them will have to be improved to match real-world conditions as the data for this becomes available. As of 2010, few methods have been presented to deal with the complex problem of efficient dispatch in microgrids. One possible reason is that until recently, competitive markets for electricity were unavailable and most DGs were used only for supplying local loads. In addition, the choice of dispatch model will also impact the reliability of the microgrid by introducing additional complex constraints to the dispatch algorithms.12 n order to fully enjoy the benefits of a microgrid, individual resources within the microgrid must cooperate to achieve a common objective. Such cooperation typically requires a more centralized control scheme,

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such as ‘‘hierarchical control.13 In centralized control, individual generators receive instructions from a master controller that is responsible for optimizing the microgrid operation. However, many generation units may have constraints not fully captured by the master controller – such as maintenance outages, local power requirements, or similar needs. Therefore, constraints on the unit operation must be coordinated between the master controller and the individual unit. Local constraints may limit dispatch flexibility for the microgrid. Similarly, microgrid constraints may limit local operation. This ‘‘exchange of constraints’’ emulates the behavior implemented in most grid systems, where generation asset owners have the option to bid unit services into the market system, or to withhold them for local purposes. However, once bid into the market, the unit must be available for dispatch per market rules, in effect limiting the owner’s short-term flexibility. he value of the microgrid to any participant therefore becomes a question of cost-tobenefit tradeoff between the participation of the assets in the microgrid and the participation of the asset under local control. For example, aggregation of assets by the microgrid may facilitate participation in energy markets that would otherwise be inaccessible to asset owners. The above discussion raises issues of conflicting objectives vis-a`-vis local and global optimizations;

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the optimum dispatch for the microgrid might be sub-optimal to the EPS. To address this problem, the concept of a microgrid being a ‘‘good citizen’’ has been proposed.14 A ‘‘good citizen’’ microgrid must obey the rules of the main grid and must engage in behavior that is significantly-enough different than normal customer operations to justify construction of the microgrid. Without the ‘‘good citizen’’ concept microgrid adoption may be significantly slowed, and in some markets microgrids may be forbidden to fully participate in electricity markets.

III. The Fort Collins RDSI Demonstration Project The Renewable and Distributed System Integration (RDSI) project is a U.S. Department of Energy (DOE) program, started in 2009, with the objective of encouraging the use of distributed resources to reduce peak load demand.15,16

The prime contractor for the Fort Collins RDSI project was the City of Fort Collins Utilities. Project funding consisted of $6.3 million from the DOE, and nearly $5.1 million in state and local investment.17 The purpose of the RDSI project was to demonstrate and develop a system of DG resources that would operate in a coordinated manner to reduce the peak load on two distribution feeders by 20 to 30 percent.18 Another goal of the RDSI project was to provide a strong start to a longer-term goal for Fort Collins: Developing a zero energy district – the ‘‘Fort Collins Zero Energy District’’ or FortZED – encompassing portions of the downtown area and Colorado State University campus, as shown in Figure 2. FortZED represents approximately 10–15 percent of the Fort Collins Utilities distribution system. Plans exist to extend FortZED to a larger, 45–50 MW implementation in the future.19 While the main focus of the RDSI project was on reducing the peak feeder load, R&D was also

Figure 2: FortZED District (Shaded Grey) and Area Covered by the Feeders under the RDSI Study (in Dashes)18





performed on a number of new technologies in conjunction with the microgrid demonstration. A. The goals of RDSI The primary objective of RDSI is to encourage coordinated participation of integrated renewable and distributed energy resources in peak load reduction. The general philosophy of peak load reduction is given in Figure 3 wherein the intended peak load is decreased during the period when the RDSI signal is engaged. he project was started in 2009 and performed in three phases, each with a duration of one year. The site upgrades and new asset installations were completed in first two years and the configured system was demonstrated in a series of test runs between June and early September 2011.18 Figure 4 illustrates the peak load reduction achieved in the RDSI demonstration during the test period between Aug. 15 and Sept. 1, 2012. The reduction in peak load ranged from 6.5 percent to 18.5 percent in that test period, as seen in Figure 4.

Figure 3: Depiction of Peak Load Reduction in RDSI.

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B. Technical overview of the RDSI project Entities which participated in RDSI were classified into different types based on their functional role in the project. Colorado State University (CSU), City of Fort Collins (CFC), Larimer County, New Belgium Brewing Company (NBB), and the Engines and

Figure 4: RDSI Peak Load Reduction for the Test Period of Aug. 15, 2011, to Sept. 1, 2011

Energy Conversion Lab (EECL) – an off-campus laboratory of CSU – were the identified site partners that had deployable load shedding and generation assets. The total capacity was about 5 MW, as shown in Table 1. The capacity during demonstration runs was lower than original, due to unavailability of assets. The technological features of the assets included application of

advanced mixed-fuel technology, advanced generator controls, lowcost grid parallel switchgear, a micro-wind turbine, solar photovoltaic panels, solar thermal systems, solar electric systems, LED and CFL lights, fuel cells, hybrid engines, and plug-in hybrid electric vehicles (PHEV). Spirae Inc., Brendle Group, Woodward Governor Co., Advanced Energy, Eaton, and

Table 1: RDSI Capacity Summary. Location

Site Identifier

Original (kW)

Demonstration (kW)

City of Fort Collins Operation Services CSU Department of Facilities Management

Site 1 Site 2

849 1,201

785 746

CSU EECL and InteGrid Laboratory Larimer County Facilities Department

Site 3, Site 4 Site 5

1,600 29

545 34

New Belgium Brewing Company

Site 6

1,279

1,279

Grand Total

RDSI: all sites

4,958

3,389


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VanDyne were the identified technical partners that provided the technical upgrade, retrofit, and logistics support for various sites and assets. The U.S. DOE provided a grant for the project with matching funding provided by the Woodward Foundation, Fort Collins Downtown Development Authority, Bohemian Foundation, and the Community Foundation of Northern Colorado.18 The site partners also contributed various levels of matching funds. The site partners housed the assets for load dispatch and were categorized as demonstration and R&D sites. onventional sources of generation and load shedding assets formed the largest portion of RDSI capacity during demonstration. Demandside management was performed through other sites which had combinations of load-shedding assets including HVAC, water fountains, and cooling systems which were dispatched to reduce power consumption. All the systems were integrated through communication links working over Distributed Generation Network Operating System (DERNOS), which is a proprietary interface connected to the main control center for the RDSI project demonstration. The control center was located at the facilities of one of the partner institutions. All the control commands were issued through this control center using the Peak Load Management (PLM) program that dispatched assets for load shedding or

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generation depending upon the incident peak load. The algorithm specifically took into consideration the asset types, cost of operation, efficiency, and emissions before issuing command to any asset. The peak load reduction obtained was in range of 6–18.5 percent of total feeder load. As seen in Figure 4, the feeder output reduces on Aug. 29 and stays in range of 7–8 MW until Sept. 1.The reason is that only a single project feeder was engaged for the demonstration period of Aug. 29 to Sept. 1. One of the participating sites had withdrawn from further participation due to economic constraints. So, test feeder serving this site was excluded from test runs on and after Aug. 29, 2011.The participating feeder consisted of about 50 percent of combined loads but around 80 percent of RDSI resource capacity. Thus, the percent feeder peak load reduction was much better than on two feeders combined. C. Load dispatch and asset scheduling in RDSI The time period for which test runs could be conducted was a constraint for asset scheduling and load dispatch. Therefore, asset scheduling was done on a

round-robin basis to maximize asset participation. The target load set point for the control software was entered manually into the SCADA system. PLM calculated and set site priorities for the day and dispatch priority indices for each asset at each site. The site priorities rotated in the order 0 (highest) to 6 (lowest) to allow each site to have highest priority once every rotation. The PLM algorithm assigned asset index ranging from 0 (highest) to 29 (lowest) for any given site depending on nature of asset, i.e., uncontrollable asset (index: 0–9), load shedding (index: 10–19), and generation (index: 20–29). Each site also had the option to set asset priority if needed. Load shed (index: 0–209) and generation priority index (index 210–419) for any given day was calculated based on the following formulas shown in Table 2. s it is clear from the formulas above, loadshedding assets had higher priority than generation assets. Load-shedding assets were given priority over generation assets, as it was an economically better decision to first curtail consumption before engaging additional generation. The control then tracked the combined load on two feeders. When the load

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Table 2: Formulas for Priority Selection in the PLM Algorithm for RDSI Demonstration Runs. Generation Priority = 210 + (Site Index) 30 + (Asset Index) Load Shed Priority = (Site Index) 30 + (Asset Index) Reserve Set Point = (Rating of Asset Adjustment Required)/(Total Spinning Reserve Capacity)





exceeded the set point, available capacity for each asset was summed in order of priority until the projected load matched the set point value. The selected units were then dispatched in priority order. To allow unit sufficient time to respond, the PLM waited for 3 minutes before adjusting any dispatch instructions. An addition delay of 3 minutes was introduced if any of the dispatched assets had a capacity rating above 250 kW. This allowed larger assets sufficient time to ramp and stabilize. No more than 500 kW was dispatched at any one time to maintain stability and avoid sudden feeder fluctuations. Once the PLM set point was reached, the feeder load was monitored and corrections were given every 15 seconds. This was done through spinning reserve assets located at the EECL. The spinning reserve contribution was linearly distributed amongst the running reserve assets as per their rated capacity. This reserve set point is defined in the formula in Table 2. This adjustment from spinning reserves was limited to a maximum of 50 kW on each update cycle to avoid an oscillatory effect on the system due to spinning reserve assets. When the net feeder load reduced below the set point, assets were switched off in reverse order of priority. During this time a lowerlevel dead band was employed to avoid sudden and early release of an asset due to feeder load fluctuations. The run time for

each asset was constrained between a minimum and maximum value, typically 30 minutes and 4 hours, respectively. Once an asset was called and released, it was not called again within that test day. The reason for choosing a round-robin methodology for dispatch can be attributed to various factors, which will be

discussed further in sub-section D. Rule of load shed and generation priority in formulas in Table 2 can be considered as a rule of thumb. Load scheduling and dispatch was controlled as per an algorithm from one of the participating entities. Asset priority was decided for maximum asset participation. The period of test runs which was about 12 days per test phase with an upper cap on allowable runtime for diesel generators. As per EPA norms and CDPHE these are restricted to about 4 hours per day. Thus, the total run time was approximately 48 hours per test phase and during this limited period of

demonstration, maximum asset participation was chosen as a criterion for schedule and dispatch methodology. or peak shaving, the assets to be dispatched were decided using such a method, which made utilization and participation of maximum number of assets based on their nature of being a DR or a generation type. An alternative method could be on basis of random selection of assets and schedule it as per the type of asset and dispatch as per the given set point. So, the process of asset dispatch in a microgrid can be divided into following decision steps: 1. Selection of feeder load set point at which dispatch starts 2. Selection of assets (site-wise or random or as per performance metrics) 3. Selection of scheduling order (queuing in some selected order of priority – either random or as per performance metrics) Emergency first up action includes planning of spinning reserves and rescheduling the resource capacity if available capacity is less than required dispatch capacity as per current feeder load. This builds a case for identifying and designing microgrids with capacity to suffice peak shaving as well as provide appropriate amount of spinning reserves ensuring that the microgrid is not overdesigned to avoid stranding of assets. This method found preference over some of the existing methods of dispatch, which aim at cost reduction or risk limitation. All of

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these require a substantial amount of information about the operational performance of the assets. As the test run periods were short, this historical information could not be obtained for use of dispatch. Also, as the asset base in a microgrid expands, it adds heterogeneity to the system and a simple dispatch philosophy is a better choice for purpose such as peak shaving. D. Operational constraints in RDSI test runs There were several operational constraints during test runs which limited dispatch options for RDSI. The most important of these were air emission restrictions. As indicated above, sites were scheduled on a round-robin basis. This allowed all sites to participate similarly, and also minimized the difference in unit run times between similar units at different sites. However, run time for many backup generators was restricted to a maximum number of run hours per year to satisfy emission standards. Since most sites with backup generation needed to reserve some run time for emergency conditions, emissions restrictions further constrained the dispatch of generators. In other cases, such as research laboratory generators at the EECL, emissions standards were less of an issue, enabling greater participation of generation assets at these sites. sually, emergency power generators with ratings less

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than 1,840 hp and running less than 100 hours annually, rating less than 737 hp and running less than 250 hours annually, or rating less than 260 hp are exempt from Air Pollution Emission Notice (APEN) as required by Colorado Department of Public Health and Emission (CDPHE).20 For backup generators at CSU (two generators of 134 hp and 257 hp) and EECL (one generator of 735 hp), a permission to run for 800 hours per year was obtained. This included routine as well as test runs. Since the generators were used for peak load management during test runs, a provision for a grouped permit was obtained to ensure that their APEN exemption was not lost. A maximum runtime of 800 hours per year amounts to about 66 hours per month for each asset.21 Thus, for a 15-day test schedule, runtime was restricted to about 4 hours per day. It is important to note that certain generation units, specifically biogas and natural gas units were permitted under different rules that supported

substantially longer annual run times, due to the low emissions from these units. The heterogeneous mix of assets resulted in a reduced CO2 emission footprint.22 The average net CO2 emission factor comparison for different asset types is shown in Figure 5. mended APEN approvals were obtained for generation assets at other sites since getting a newer APEN was more difficult than a modifying an existing one. Finally, since load-shedding assets had preference over generation assets, dispatch of demand-response assets was tested more frequently and more extensively than generation assets. The combination of real-world restrictions on unit operation limited the experimental variation in dispatch operation during the test runs, and therefore limited, to some extent, the insights which could be gained. However, these restrictions represent a real-world scenario, which will be encountered in any practical microgrid with a diverse asset base.

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Figure 5: Average Net CO2 Emission Factor Comparison for Different Asset Types (emissions reductions are negative; emissions increases are positive)





E. Operational performance assessment NERC metrics are used for performance quantification of electric power system in North America. Few of the metrics were calculated for the demonstration runs. The median values of availability factor (AF), service factor (SF), weighted availability factor (WAF), and weighted service factor (WSF) for RDSI assets were 100 percent, 25 percent, 78 percent, and 41 percent, respectively. Based on the type, the assets were grouped in categories of generation (GEN) including conventional and biogas generators, demand response (DR) including load shedding and thermal storage and photovoltaic installations (PV). Figure 6 shows the respective values plotted for asset groups. The NERC metrics formulas used are given in Table 3. A more detailed description of metrics

Figure 6: Weighted Service Factor and Weighted Availability Factor for Homogeneous Asset Groups Categorized as Generation Type (GEN), Demand Response (DR), and Photovoltaic Installations (PV)

can be found in NERC Generating Unit Statistical Brochure.23 eneration and DR assets had WAF of 72 percent and 80 percent, respectively, while PV was available 98 percent of the time. The reason was due to nonavailability of generation assets and poor response of loadshedding assets. The higher WSF of the PV installations can be attributed to the non-controllable nature of those assets. Site-wise WSF and WAF are shown in Figure 7.

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IV. Lessons Learned from the Fort Collins RDSI Project Each organization participating in the Fort Collins RDSI project was required to submit a partner narrative, which described the project from the point of view of the partner. These partner narratives were reviewed and are summarized below to illustrate the key lessons learned during the project. Lessons are broken into three sections – technical,

Table 3: NERC Metrics Formulas and Definitions. Metric Name

Abbreviation

Formulas

Availability Factor

AF

(AH/PH) 100 (%)

Service Factor Weighted Service Factor

SF WSF

(SH/PH) 100 (%) [S(SH NMC)/S(PH NMC)] 100 (%)

Weighted Availability Factor

WAF

[S(AH NMC)/S (PH NMC)] 100 (%)

Definitions Available Hours

AH

Sum of all Service Hours, Reserve Shutdown Hours, Pumping Hours, and Synchronous

PH

Condensing Hours. Number of hours a unit was in the active state. A unit generally enters the active state

Service Hours

SH

on its commercial date Total number of hours a unit was electrically connected to the transmission system.

Normal Maximum Capacity

NMC

Capacity a unit can sustain over a specified period when not restricted by ambient conditions

Period Hours

or equipment deratings, minus the losses associated with station service or auxiliary loads.


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Figure 7: Weighted Service Factor and Weighted Availability Factor for RDSI Sites

financial, and program management. A. Technical lessons learned New protection methodology for grouped assets: The EECL integrated several generation units onto one service. The generation size necessitated an upgrade to the facility service. Historically, Fort Collins Utilities has installed protection systems inside distributed generation facilities. This method proved cumbersome for both communications – installing a 900 MHz radio at every generation site – and operations, since access to the generation site is required if generation needs to be disabled for system maintenance. As part of the RDSI program, Fort Collins Utilities designed a new protection method, installing a fault safety interrupter on the primary side of the new service. While the new protection method simplified utility operations, several additional issues were encountered. The system, 10

installed on the medium-voltage equipment, proved expensive – likely too expensive for all but the largest distributed generation locations. As installed, the system isolates the entire facility, dropping local loads along with generation. This creates a possibility that the utility will drop operational loads along with generation in the event of a utilityside issue. Indeed, such a scenario occurred near the end of the RDSI project, when a crane contacted a transmission line miles from the EECL. The utility control systems misidentified the transient as potentially originating at the EECL and disconnected the facility. Equipment upgrade issues: Several organizations reported problems incorporating existing generation assets into the RDSI program. These problems either increased systems integration costs, or decreased system effectiveness, or both. For example, it was found that a 100 kW-rated generator at one CSU building exhibited unstable operation above 60 kW due to a long exhaust stack. After

further testing, the generator was limited to 25 kW. Similarly, another participant reported that a backup generator could not operate at full power, and even after significant time spent tuning the asset, and had to de-rate the asset substantially. In summary, many issues where uncovered when converting backup generators to DG assets. These issues are not often recognized in theoretical discussions of microgrid design. Difficulty in reconciling low-level usage data to feeder-level data: The goal for RDSI was to reduce load at the feeder. The program was able to measure the load at the feeder level, and met the reduction goals. Some larger facilities reported difficulties reconciling feeder-level data with the assets under their control. These facilities had significant aggregated loads, but did not have fine-grained metering on individual assets. Such metering is expensive, and not budgeted within the RDSI project. The difficulties reconciling feederlevel and site data represents a significant challenge for microgrids, since remuneration for asset participation is a significant financial driver for microgrid operations. In short, the metering required to unambiguously identify the quality and quantity of site participation may be too expensive relative to the value of the site’s participation. Granularity of assets under control: Related to the data granularity problem, facilities at the partner





institution were challenged by the number and size of the assets that were needed to meet a particular reduction goal. Given the expense of integrating an asset into the microgrid, only larger assets could be integrated. Unfortunately, because of their size, there was little flexibility scheduling these assets. Decreasing the cost of integration would allow more, smaller assets to be scheduled, increasing the flexibility of the microgrid. Asset dispatch method: The advantages of present dispatch method used were simplicity of selection of assets and an opportunity for maximum asset participation during limited time period of demonstration phase. Although the dispatch prioritization based on the nature of asset (non-controllable generation and load-shedding assets dispatched first) is logical in terms of economic fuel consumption, not to mention a greener option but it does not create a full-fledged scenario for the assets to be chosen randomly for worst-case scenario or based on past performance of the assets. This method could be deployed with ease in real-world microgrids as it has a great degree of human decision-making for selection. For example, site selection and feeder target load set point selection were done manually. It does not however ensure an optimum dispatch method since once a site is selected the choice of asset selection is narrowed down to just that one site in highest priority even though an asset in lower site September 2012,

Vol. 25, Issue 8

priority may be a better choice for dispatch. The main constraint here again was limited time period of test runs. lternatively, a technique based on historical performance of asset can be used for dispatch. For this, the system would need to be run for a sufficient period of time to generate large enough data set of

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attributes which may be used to design a dispatch methodology. For example, the past performance of an asset over a period of a few months can be assessed and based on that performance the scheduling and dispatch can be done. But, this requires a large data set of attributes for a generalized assessment of the performance. The high-performing assets would be given priority over lower-performing assets in the dispatch. Such a method would be slightly complicated to implement but would ensure an optimal dispatch of assets. This again builds a case for appropriate monitors and predictors which can quantify the

asset-level, site-level and systemlevel performance. B. Financial lessons learned Project goals (reduced peak usage) in direct conflict with generation company business model: One reason for customers to invest in DG/microgrids is to reduce operating costs, and one of the simplest ways to do so is to reduce demand and coincident peak charges incurred during peak load periods. Demand and peak charges often represent a significant fraction of utility revenue, and this was certainly the case for the utilities involved in the RDSI project. For many peak-reduction scenarios, the reduction in the utility’s revenue due to peak load reduction exceeds the corresponding reduction in the utility’s costs, due to rate-design and similar business model issues. In such cases, utilities will likely react by adjusting tariff structures, thus reducing the economic value of peak reduction. Due to the short demonstration period of the RDSI project, revenue changes were not a significant issue. However, for a wide-scale adoption of microgrids, such financial model imbalances need to be addressed. Higher than expected costs of interconnect engineering: Resolving the equipment upgrade issues described above led to higher than expected costs for interconnect engineering. In addition, some organizations were surprised by the paperwork required for interconnect

1040-6190/$–see front matter # 2012 Elsevier Inc. All rights reserved., http://dx.doi.org/10.1016/



approval. One organization that had a large number of smaller assets, estimated that $20,000 in costs was directly related to producing the documentation required for its interconnection application – a net cost of $11/kW on a basis of 1.7 MW of rated generation. While this organization had a complicated configuration, this example illustrates that the cost of interconnection engineering is a significant factor impeding widespread adoption of microgrids, particularly when considering large numbers of relatively small assets. Biofuel and thermal storage equipment allowed improved business goals in addition to meeting RDSI project goals: One of the partner institutions installed thermal storage and biofuel equipment to meet the RDSI goals. As a side effect, the partner found that this equipment helped improve the temperature consistency for chilled water, and improved the reliability of hot water production overall. C. Program management lessons learned Difficulties and challenges involved with a centralized management strategy of assets not owned by a single organization: Many organizations expressed difficulties and challenges stemming from the multiple organizations involved in the RDSI effort, and the fact that there was no single (central) owner for all the assets. In addition, since 12

the participating organizations had different levels of expertise, the more knowledgeable organizations were forced to spend time and resources assisting less sophisticated organizations. At the program management level, there was no single organization specifically charged with generator upgrade and maintenance, which led to

this, training and actions to educate stakeholders will stimulate a better understanding and smoother transition through a more robust techno-economic model. These demonstrations should provide a test platform for development of future Smart Grid technologies, concepts, and methodologies.24 Also, the fundamental drivers of change existing in the industry fall short of achieving the present goals and even more so for future.25

V. Conclusion

inefficiencies and unexpected costs. Participation in the RDSI project lead to additional understanding: One partner institution reported that more testing, and over a longer period of time, would be beneficial. There were only three test periods, all performed during peak load, and all during the summer. More test periods, spread over the year, would have led to more varied performance data. Also, performing a test caused all participants to focus on coordination and communication, and much was learned during these times of focused activity. Demonstration projects at such scale require strong cooperation between partners. To enhance

By examining the results from the RDSI project, the following generic recommendations to increase the acceptability of microgrids can be made. Develop regulations to address the potential financial conflict issues where generation companies may see microgrids as competition. Regulations modeled on the 1968 Carterphone decision, which opened the telephone network to end-user equipment, could be used as a model.26 Despite the fact that the Fort Collins RDSI project was implemented in a community with a high level of technical expertise, project experience demonstrated that the level of practical experience in implementing microgrids is lower than expected. Seemingly simple operations, such as converting existing backup generators to act as microgrid dispatchable assets, proved more problematic than expected. This experience





highlights the necessity of advancing theoretical analyses of microgrids into field demonstrations. The bulk of the lessons resulting from the RDSI project originated in the tactical complexity of implementing a real system, rather than in the design and analysis of the potential microgrid. Technology should be developed to lower the cost of integrating assets into a microgrid, particularly for demand side management. & Endnotes: 1. S. Suryanarayanan and J. Mitra, Enabling Technologies for the CustomerDriven Microgrid, PROCEEDINGS OF IEEE POWER & ENERGY SOCIETY GENERAL MEETING, July 2009 at 1–3. 2. See 110th Congress of the United States of America. Energy Independence and Security Act of 2007. ‘‘Title XIII - Smart Grid’’ (Dec. 2007). 3. S. Suryanarayanan, F. MancillaDavid, J. Mitra, et al., Achieving the Smart Grid through Customer-Driven Microgrids Supported by Energy Storage, PROCEEDINGS OF IEEE INT’L. CONFERENCE ON INDUSTRIAL TECHNOLOGY, 2010 at 884– 890. 4. Z. Ye, R. Walling, N. Miller, et al. Facility Microgrids, General Electric Global Research Center, Niskayuna, NY. NREL Report No. NREL/SR-56038019, at www.nrel.gov/docs/ fy05osti/38019.pdf (May 2005). 5. N. Hatziargyriou, H. Asano, R. Iravani, et al., Microgrids in IEEE POWER & ENERGY MAGAZINE, Vol. 5, No. 4, at 78–94 (Aug. 2007). 6. C. Marnay and O.C. Bailey, The CERTS Microgrid and the Future of the Macrogrid, Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA, Report No. LBNL55281, at http://eetd.lbl.gov/ea/ EMS/reports/55281.pdf (Aug. 2004).

September 2012,

Vol. 25, Issue 8

7. S. Suryanarayanan, R.K. Rietz and J. Mitra, A Framework for Energy Management in Customer-Driven Microgrids, in PROCEEDINGS OF IEEE POWER & ENERGY SOCIETY GENERAL MEETING, July 2010 at 1–4. 8. See IEEE Draft Guide for Design, Operation, and Integration of Distributed Resource Island Systems with Electric Power Systems, IEEE P1547.4/D12 (April 2011). 9. M. Smith, Overview of the U.S. Department of Energy’s Research & Development Activities on Microgrid Technologies, 2009 San Diego Symposium on Microgrids, at http:// der.lbl.gov/sites/der.lbl.gov/files/ sandiego_smith.pdf. 10. M. Barnes, J. Kondoh, H. Asano, et al., Real-World Microgrids: An Overview, in PROCEEDINGS OF IEEE INT’L. CONFERENCE ON SYSTEM OF SYSTEMS ENG’G., April 2007 at 1–8. 11. Z. Wu, W. Gu, R. Wang, et al., Economic Optimal Schedule of CHP Microgrid System Using Chance Constrained Programming and Particle Swarm Optimization in PROCEEDINGS OF IEEE POWER & ENERGY SOCIETY GENERAL MEETING, July 2011 at 1–11. 12. M. Meiqin, J. Meihong, D. Wei, et al., Multi-Objective Economic Dispatch Model for a Microgrid Considering Reliability, in PROCEEDINGS OF 2ND IEEE POWER ELECTRONICS FOR DISTRIBUTED GENERATION SYSTEMS (PEDG), June 2010 at 993–998. 13. N. Hatziargyriou, A. Dimeas, A. Tsikalakis, et al., Management of Microgrids in Market Environment, in PROCEEDINGS OF INT’L. CONFERENCE ON FUTURE POWER SYSTEMS, 2005, at http:// www.microgrids.eu/micro2000/ presentations/39.pdf. 14. R. Lasseter, A. Akhil, C. Marnay, et al., White Paper on Integration of Distributed Energy Resources: The CERTS MicroGrid Concept, Consortium for Electric Reliability Technology Solutions (CERTS), CA, Report No. LBNL-50829.certs.lbl.gov/pdf/50829apdf (April 2002). 15. S. Bossart, Renewable and Distributed System Integration Demonstration Projects, U.S. Dept. of Energy, at http://smartgrid.epri.

com/doc/DOE%20Overview%20of% 20RDSI%20projects.pdf. 16. See U.S. Dept. of Energy, Fort Collins RDSI Demonstration Project, at http://smartgrid.epri.com/doc/ Ft%20%20Collins%20RDSI%20Final. pdf. 17. See FortZED Jumpstart, at http:// fortzed.com/img/site_specific/ uploads/RDSI_FlyerFINAL.pdf. 18. D. Zimmerle, A Community-Scale Microgrid Demonstration: FortZED/ RDSI, PROCEEDINGS OF IEEE POWER & ENERGY SOCIETY GENERAL MEETING, July 2012 at 1–2. 19. See City of Fort Collins, CO, RDSI Program Presentation, Oct. 2008, at http://events.energetics. com/rdsi2008/pdfs/presentations/ wednesday-part1/5%20Freeman.pdf. 20. See Colorado Department of Public Health and Emission (CDPHE) Air Quality Control Commission Regulation Number 3 Stationary Source Permitting and Air Pollutant Emission Notice Requirements 5 CCR 1001-5, Sec II.D.1.ttt. 21. See Title V Operating Permit Renewal-Supplemental Information, Colorado State Univ. Permit95OPLR073 FID#0690011. 22. S. Suryanarayanan, The Fort Collins RDSI Demonstration Project and Some Notes on Cyber-Enabled Energy Savings in a University Campus Presentation, Semina´rio de Redes Inteligentes, Foz do Iguac¸u, Brazil, April 26, 2012. 23. See NERC Generating Unit Statistical Brochure, Oct. 2008, at http://www.nerc.com/ page.php?cid=4%7C43%7C47. 24. M. Kolhe, Smart Grid: Charting a New Energy Future: Research, Development and Demonstration, ELEC. J., Mar. 2012, at 88–93. 25. F.P. Sioshansi, So What’s So Smart about the Smart Grid?, ELEC. J., Dec. 2011, at 91–99. 26. T. Wu, A Brief History of American Telecommunications Regulation, Social Science Research Network, at http:// papers.ssrn.com/sol3/ papers.cfm?abstract_id=965860.

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