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Public Interest Energy Research Program: Final Project Report
cover of report Optimal Portfolio Methodology for Assessing Distributed Energy Resources Benefits for the EnergynetTM

Publication Number: CEC 500-2005-096
Publication Date: March 2005
PIER Program Area: Energy Systems Integration

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Executive Summary

The purpose of this project was to demonstrate a methodology that would (a) objectively assess and quantify the benefits of distributed energy resources (DER) to the performance of a power transmission and distribution system (b) determine the location and attributes of beneficial DER projects, and (c) quantify their network benefits. The lack of a systematic method or tool to make these determinations has prevented the full incorporation of DER in system planning. Such a methodology was seen by the Energy Commission as contributing to the use of DER and other non-wires approaches to improve power quality and reliability and relieving congestion in the power system and expanding the deployment of DER as a choice for customers.

We successfully demonstrated using power system models that DER projects in the right locations and with the right characteristics and operating profiles can improve the performance of a given power delivery network. Moreover, we demonstrated an objective method to determine where in the network these projects should be located – whether in the transmission or distribution systems – as well as their sizes, and operating profiles. We were also able to quantify the network benefits these projects would achieve. We refer to these ideally located, sized, and operated projects as the "Optimal DER Portfolio" for a given system.

Approach

We included a variety of DER projects as candidates, including the use of demand response as a measure for network performance improvement rather than simply as an intermittent reduction in energy consumption. In this project we considered as DER the following:

  • Distributed power generation embedded in the network at customer sites (DG),

  • Demand response that could be dispatched by the network operator (DR), and

  • Distributed, switchable reactive sources such as capacitors.

We assessed network benefits of DER using a broad range of measures – measures that would fairly capture the range of network benefits DER could provide, and measures that could also be used to assess network impacts of other types of network upgrades on a comparable basis. We considered the following as indicators of network benefits:

  • Real power loss reduction,

  • Reduced reactive power consumption,

  • Improved voltage profile,

  • Reduction in network "stress,"

  • Increase in the load-serving capability of the network under contingency conditions, and

  • System capacity provided by DER measures.

In this project the subject power delivery network was the Silicon Valley Power (SVP) system, which serves the City of Santa Clara, CA, and lies within the Pacific Gas & Electric (PG&E) regional transmission system, which is part of the Western US transmission grid. The SVP system as configured in 2002 is characterized in regional power system planning models as only two points, with SVP loads split between the points and all embedded SVP generation connected at those two points. SVP characterizes its own system as an approximately 80-bus transmission system, with neither the associated distribution nor the surrounding regional transmission discretely characterized.

Notably, neither of these simulations of the SVP system depicts the specific locations where distribution-connected DER projects would be connected. One key innovation in our approach is the integration of distribution with transmission into a single, combined power delivery network model for use with transmission modeling and analysis tools. In this project we modeled the SVP system as an approximately 850-bus system combining both transmission and about half of the primary distribution feeders, with nodes, components, loads and resources modeled discretely. This part of the system is then wholly integrated into the surrounding Western regional transmission system. We derived individual distribution-level loads from actual SCADA records taken under a range of load conditions and from forecast loads.

As a key feature of this project, we used the AEMPFASTTM power system optimization package developed by Optimal Technologies as our primary tool for the identifying the locations of beneficial resource additions in the network. We established the minimization of real power losses, reactive power consumption, and voltage deviation with a target voltage of 1.05 per unit (PU) as the objective for optimization. AEMPFAST directly calculates the incremental improvement in this objective that would result from real and reactive resource additions at each bus in the network. In doing so, AEMPFAST can rank the hundreds of potential DER locations in the integrated network terms of the value of resource additions at that location, identifying the most valuable locations for DER additions at a bus-by-bus level of detail.

We used AEMPFAST and integrated models for this network to identify resource additions and ultimately specific DER projects that have the location, size, and operating profile needed to enhance the performance of the network. Because resource additions within a network are arguably beneficial right up to where there is no power flow, we placed external limits on additions of both DR and DG projects. We limited DR projects to medium and large customers (over 200 kVA). We also specified DR as ranging from a low of 2% of peak load to a high of 15% of peak load for the largest customers under "1% peak hour" load conditions, while we also assumed DR could be dispatched by location at different levels depending on system conditions. We limited DG projects to 60% of the host customer’s peak load and imposed non-export feeder limits as well. For purposes of this study we also modeled all DG projects as synchronous generators with reactive power output independently dispatchable within limits.

Results and Findings

We found that the value of Optimal DER Portfolio Projects in terms of their contribution to network benefits was driven primarily by their location. At least for this network, we found that smaller projects at more electrically remote locations had more value in terms of network benefits than did large projects at well-supported network locations such as substations or transmission-level customer sites.

We found that the dispatch of at least some distribution-connected DER projects should also vary in response to changing network conditions. However, we also demonstrated that these network-centric operational requirements for DER are commercially practical – they are limited, and, using this methodology we can specify them ahead of time with a modest amount of analysis so they can be incorporated in project specification and commercial arrangements.

The 2002 Optimal DER Portfolio for this network includes DR at essentially all of the 390 eligible (over 200 kVA) customer locations. These projects are ranked according to their value in terms of network benefits under each of the conditions we analyzed. These projects are dispatched or called individually at different levels depending on network conditions. Under the "1% highest hour" Summer Peak conditions these projects represent 10.52 MW, or 2.6% of load, and under more typical summer seasonal conditions these projects represent 3.65 MW or 1.1% load.

Of the DR projects at the 130 large (over 1,000 kVA) customer sites, a portion is dispatchable at two levels under typical conditions (that is, other than the "1% highest hour" summer peak). The locations of the preferred sites for higher levels of dispatch under these conditions are specified. Of these large customer DR projects, only 61 are preferred locations for higher levels of dispatch under both summer and winter seasons and minimum load conditions as well. Accordingly, the remainder of the large customer DR projects could be made available for higher levels of dispatch on a limited seasonal basis only without compromising network performance.

Under just the "1% highest hour" summer peak conditions, a portion of both the medium (200 – 1,000 kVA) customer and large customer DR projects is dispatchable at the highest DR level. Locations of the preferred sites for higher levels of dispatch under these conditions are also specified.

The 2002 Optimal DER Portfolio for this network consists of DG projects at 380 of the 419 eligible customer locations. These projects are also dispatched individually at different levels depending on network conditions, and they are ranked according to their value in terms of network benefits. These projects average 160 kW in size, with the largest 8.9 MW. They total 60.73 MW on a nameplate basis, and dispatched as specified would represent 54.88 MW, or 13.8% of the system’s load, under Summer Peak conditions. We found that the majority (60%) of these projects would not need to vary their real power output in response to changing network conditions to maintain network performance, and could operate on a base load basis for the customer.

The 2005 Optimal DER Portfolio consists of DR projects at all eligible sites and DG projects at 149 customer sites, averaging 450 kW in size with the largest 14.3 MW. Again, these projects are individually identified and ranked by their value in terms of network benefits.

We found that the Optimal DER Portfolio projects for this system as a group yield quantifiable and meaningful network benefits. Real power losses within the SVP system are reduced by 33-40%, and reactive power consumption is reduced by 28-45%. We showed that the reduction in real power losses within the SVP system was due to due to an increase in network efficiency, and not purely due to a reduction in the load being served through the network. There are significant loss reductions in the surrounding regional transmission system as well. We found that these projects also eliminate low- and high- voltage buses, that they improve network voltage profiles, and that they reduce the amount of real power stress in the system. Importantly, we found that these benefits are not limited to peak load conditions. In some cases there are greater benefits under conditions other than the Summer Peak. We found that these projects provide a significant increase in the load-serving capability of the network. We found that the Optimal DER Portfolio projects have the potential to yield network benefits in the same range as those of transmission-level system upgrades using these same measures.

In addition, we found that using detailed, integrated network models yields insight into network conditions, and opportunities for improvement, that would be invisible using models of the transmission system alone and/or models of individual distribution feeders – the local and network-wide impact of incremental distribution-connected DER resources is but one such insight. In particular, we found that localized measures have impacts across the network.

We directly estimated the economic value of network benefits such as reduced losses, reactive capacity, and system capacity, and found that the value of network benefits from these projects might approach $450/kW if system capacity is taken into account. Additional quantifiable network benefits such as increased load-serving capability, improved voltage profile and reduced system stress might have significant value in dollar terms, but are not as readily priced. Conceivably the dollar value of network benefits associated with Optimal DER Portfolio projects could be used to derive value-sharing financial incentives for real projects that yield network benefits.

The Optimal DER Portfolio for this power system contemplates a high penetration of relatively small generation projects to achieve the network benefits described above. We assessed the feasibility of siting the 133 top-ranked 2002 Optimal DER Portfolio generation projects based on their location, size, and operating profile. We found that all of these projects would be located in commercial or industrial districts of Santa Clara, and concluded that they could probably all be sited as either a permitted use or under a conditional use permit. In fact, we found that 18 of these project locations already have power generation units of comparable sizes installed for backup power.

However, we also found siting issues with specific impacts on this particular set of projects. Even if these projects are certified as "ultra clean and low-emission" DG projects by the state Air Resources Board and meet all local noise and visual requirements, they would likely be subject to an individual "Best Available Control Technology" demonstration and issuance of an air permit by the local air quality management district with jurisdiction over these projects. Also, either an Environmental Impact Report or Mitigated Negative Declaration under the California Environmental Quality Act would likely be required for these projects. We also found that local land use ordinances in Santa Clara do not specify requirements for onsite power generation units. This would place an additional burden on the planning staff to familiarize themselves with power generation technologies and exercise judgment to interpret and apply requirements for these projects.

Conclusions and Recommendations

This project demonstrates a way to systematically determine the specific location and operating characteristics of DER projects that benefit a power delivery system. We believe this information would be useful to any grid operator contemplating potential DER development, network upgrades, or simply improved network performance. This project also demonstrates that the grid benefits associated with these projects are readily assessed and quantified. Thus, this methodology could be used to incorporate DER alongside traditional network upgrades in system planning. Further, as real DER projects and network upgrades are implemented, the Optimal DER Portfolio is easily updated to incorporate their network impacts. This project also demonstrates that at least some of these grid benefits can be readily valued in dollar terms. Pricing these benefits permits their exchange among DER stakeholders for improved economic decisionmaking, e.g., through value-sharing incentives. Lastly, this project also demonstrates that characterizing beneficial DER projects individually permits identification of those barriers to project development that have the greatest impact on the most beneficial projects.

We judge the analysis of the network as an integrated whole, including both distribution and transmission and with loads and resources discretely modeled, to be essential to fully assess the impact of distribution-connected DER on the overall performance of the entire power delivery network. In this project we demonstrated that the development and use of such detailed networks model is practical. We also demonstrated the interoperability of such integrated network models with GE PSLF, a commonly-used, legacy network analysis tool. We believe these integrated Energynet datasets could be an important platform for a variety of system planning tasks given the visibility they provide.

An assessment of AEMPFAST as an analytical tool emerged as a key interest in this project. Based on our results and review of our approach by the project Technical Advisory Committee, we are able to conclude that AEMPFAST is both a valid and useful tool for this application.

We judge the barriers noted above to the siting of beneficial generation projects identified for this network to be significant barriers given the small size of most of these projects, especially if these projects are customer-sponsored. We conclude, therefore, that an ordinance establishing an objective set of local requirements for small power generation units, along with exemptions from local air permitting and CEQA review for certified "ultra clean and low-emission" DG projects under a certain size, would facilitate the types of generation projects shown to yield network benefits for this particular power system, providing a meaningful non-financial incentive for projects of this type.

As noted above, network operators and policy makers could use this approach to design financial incentives specifically targeted to DER projects that would improve network performance. However, as we have shown that attributes of projects providing network benefits are highly location-specific, we emphasize that network benefit-driven incentives should also be location specific – not all candidate projects even within a given municipality would be eligible for the same incentive.

An integrated power delivery network, populated by a portfolio of ideally-placed, highly-flexible generation and responsive loads whose operation is can be coordinated for grid performance under varying network conditions is entirely consistent with a distributed, conceivably intelligent energy infrastructure we refer to as the Energynet™ infrastructure. This project presents an opportunity to assess the benefits of migration to such an infrastructure. It also offers the opportunity to develop and/or assess fundamental requirements for enabling Energynet-related technologies. Such technologies include analytical the datasets integrating transmission and distribution in a single power delivery network described above, capabilities for monitoring and control of DER to yield network performance benefits under varying conditions, and measures to make these interoperable with legacy systems.

This project represents an initial demonstration of this methodology, using the transmission and distribution network of SVP, a municipal utility serving a single city. SVP was willing to host this effort and make their system data available, and their relatively compact system made testing the feasibility of this methodology less risky. The Energy Commission has funded a second project that will demonstrate the methodology in a much larger, more complex subject power system of a major California investor-owned utility. The subsequent project will expand this methodology by further demonstrating the adaptation of legacy utility system data into an integrated Energynet dataset. It will consider additional DER devices such as storage and distribution automation, and additional measures of network benefit, such as reliability. The use of the methodology will be demonstrated in a planning setting to identify network problems and expand the set of potential solutions.


Abstract

This project addresses the question of whether distributed generation (DG), demand response (DR), and localized reactive power (VAR) sources, or distributed energy resources (DER), can be rigorously shown to enhance the performance of an electric power transmission and distribution (T&D) system. This report presents a methodology to systematically determine the characteristics of DER projects that enhance the performance of a power delivery network and quantify the potential benefits of these projects. This report also portrays the functioning and potential benefits of an integrated, intelligently managed power delivery network with embedded generation and loads responsive to network conditions, which we refer to as an EnergynetTTM infrastructure.

We conclude that DER projects in the right locations and with the right characteristics and operating profiles can improve the performance of a given network in terms of reduced real power losses, reduced VAR flow and consumption, reduced network voltage variability and eliminated low- and high-voltage buses, reduced network stress, increased load-serving capability, and avoided or deferred network improvements in both the distribution and transmission portions of the network. We demonstrate a methodology to systematically identify these beneficial DER projects and quantify their benefits.

We modeled a T&D system as a single, integrated power delivery network, enabling direct observation of network-wide improvements from changes in the distribution system and the impacts of distribution-connected DER projects. We used AEMPFASTδ software to rank-order locations where real and reactive capacity additions make the greatest contribution to optimal performance of the integrated network.

We identified a portfolio of individual DG and DR projects yielding the greatest enhancement to network performance by location and size and determined their operating profiles for an expected annual range of network conditions.

We quantified the network benefits from this portfolio of DER projects, valued them in economic terms, and compared to the network benefits from specific traditional network improvements. We showed how this portfolio could be used to target DER initiatives and incentives for the greatest impact on those DER projects yielding the most benefits is demonstrated.

KEYWORDS: Project, Network, DER, Condition, Benefit, Power



Table of Contents

Abstract

Executive Summary

1.0 Introduction

1.1. Background and Overview

1.2. Project Objectives

1.2.1. Overall Project Goals

1.2.2. Technical and Economic Performance Objectives

1.3. Report Organization

2.0 Project Approach, or Methods

2.1. Development of Integrated Datasets

2.1.1. Approach

2.1.2. Analytical Results

2.1.3. Conclusions

2.2. Development of Recommended DER Capacity Additions

2.2.1. Method for Identification of Recommended Real and Reactive Capacity Additions

2.2.2. Analytical Results

2.2.3. Conclusions

2.2.4. AEMPFAST Evaluation

2.3. Characterization of DER Capacity Additions as DER Projects

2.3.1. Approach

2.3.2. Analytical Results

2.3.3. Conclusions

2.4. Quantification of Network Benefits

2.4.1. Approach

2.4.2. Analytical Results

2.4.3. Economic Benefits

2.4.4. Conclusions

2.5. Identification of Barriers to Siting of Optimal DER Portfolio Projects

2.5.1. Approach

2.5.2. Analytical Results

2.5.3. Conclusions

2.6. Incentives

2.6.1. Approach

2.6.2. Analytical Results

2.6.3. Recommendations for Incentives

2.6.4. Policy Implications

2.6.5. Conclusions

3.0 Project Results

3.1. Integration of T&D into a Single Model

3.2. Characterization of Subject System Prior to DER Additions

3.2.1. "As Found" Conditions

3.2.2. "Recontrols"

3.2.3. "P Stress"

3.3. Identification of DER Additions to Improve Network Performance

3.4. Establish Optimal DER Portfolios

3.4.1. 2002 Optimal DER Portfolio

3.4.2. 2005 Optimal DER Portfolio

3.5. Quantifiable Improvement in Network Performance

3.5.1. Network Performance Improvement

3.5.2. Value of Network Improvement

3.6. Guided Policies and Targeted Incentives based on Optimal DER Portfolios

3.6.1. Nonfinancial Incentives

3.6.2. DR Financial Incentives

3.6.3. DG Financial Incentives

4.0 Conclusions and Recommendations

4.1. Conclusions

4.1.1. Optimal DER Portfolios

4.1.2. Quantifiable Improvement in Network Performance

4.1.3. Integration of T&D Into a Single Model

4.1.4. Guided Policies and Targeted Incentives based on Optimal DER Portfolios

4.1.5. Characterization of Subject System Prior to DER Additions

4.1.6. Identification of DER Additions to Improve Network Performance

4.2. Recommendations

4.2.1. Optimal DER Portfolios

4.2.2. Quantifiable Improvement in Network Performance

4.2.3. Integration of T&D Into a Single Model

4.2.4. Guided Policies and Targeted Incentives based on Optimal DER Portfolios

4.2.5. Characterization of Subject System Prior to DER Additions

4.2.6. Identification of DER Additions to Improve Network Performance

4.3. Commercialization Potential

4.4. Benefits to California

References

Glossary

Appendices


Appendices

DER Capacity Additions Appendix

Cumulative Change in Objective per Cumulative DG Capacity Addition Appendix

Barriers Appendix

DER Best Practices Questionnaire Appendix

Suggestions of Best Practices in Permitting DG Appendix

Model Resolution Appendix


List of Figures

Figure 1 Summer Peak 2002 Transmission Voltage Profile - Base Case

Figure 2 Summer Peak 2002 Energynet Voltage Profile - Base Case

Figure 3 "As Found" Energynet Voltage Profiles

Figure 4 Summer Peak 2005 Energynet Voltage Profile - Base Case

Figure 5 Summer Peak Engerynet Voltage Profile - Recontrolled Case

Figure 6 Summer Peak 2002 Initial P Indices (Recontrolled Case)

Figure 7 Core1 Feeder305 Initial P Index and DR Rank Summer Peak 2002 Case

Figure 8 Change in Objective with DG Capacity Additions Summer Peak 2002 Case

Figure 9 Summer Peak 2005 Energynet Voltage Profile - Recontrolled Case

Figure 10 Summer Peak 2005 Initial P Index

Figure 11 Change in Objective with DG Capacity Additions Summer Peak 2005 Case

Figure 12 "As Found" Seasonal Voltage Profiles

Figure 13 Seasonal Voltage Profiles with Recontrols

Figure 14 Seasonal Voltage Profiles with Optimal DER Portfolio Projects

Figure 15 Summer Peak 2005 Voltage Profiles

Figure 16 Summer Peak 2002 Voltage Profiles

Figure 17 Summer Peak 2002 P Indices

Figure 18 Knee Peak 2002 Voltage Profiles

Figure 19 Knee Peak 2002 P Indices

Figure 20 Winter Peak 2002 Voltage Profiles

Figure 21 Winter Peak 2002 P Indices

Figure 22 Minimum Load 2002 Voltage Profiles

Figure 23 Minimum Load Peak 2002 P Indices

Figure 24 Summer Peak 2005 Voltage Profiles

Figure 25 Summer Peak 2005 P Indices

Figure 26 "As Found" Energynet Voltage Profiles

Figure 27 Summer Peak 2005 Energynet Voltage Profile – Base Case

Figure 28 Summer Peak 2002 Transmission Voltage Profile – Base Case

Figure 29 Summer Peak 2002 Energynet Voltage Profile - Recontrolled Case

Figure 30 Summer Peak 2005 Energynet Voltage Profile - Recontrolled Case

Figure 31 Summer Peak 2002 Initial P Indices (Recontrolled Case)

Figure 32 Summer Peak 2005 Initial P Index

Figure 33 "As Found" Seasonal Voltage Profiles

Figure 34 Seasonal Voltage Profiles with Recontrols

Figure 35 Seasonal Voltage Profiles with Optimal DER Portfolio Projects

Figure 36 Summer Peak 2005 Voltage Profiles


List of Tables

Table 1 Base Case Load Flow Results

Table 2 Loss Rates

Table 3 Comparison of Transmission only to Energynet Voltage Profiles

Table 4 Potential Optimal DER Locations Based on Load Flow Analysis Summer Peak 2002 Case

Table 5 Summer Peak 2002 Top 133 DR Locations by Feeder

Table 6 Summer Peak 2002 Top 133 DG Locations by Feeder (Light Load limited)

Table 7 Summer 2005 Top 99 DR Locations by Feeder

Table 8 Summer 2005 Top 100 DG Locations by Feeder (Light Load limited)

Table 9 Large Customer DR Projects Preferred for 5% DR Capability under Knee Peak, Winter Peak, and Minimum Load Conditions

Table 10 Large Customer DR Projects Preferred for 5% DR Capability under Summer Seasonal Conditions

Table 11 Large Customer DR Projects Preferred for 5% DR Capability under Winter Seasonal Conditions

Table 12 Large Customer DR Projects Preferred for 5% DR Capability under Minimum Load Conditions

Table 13 Large Customer DR Projects Preferred for 15% DR Capability under Summer Peak Conditions

Table 14 Medium Customer DR Projects Preferred for 15% DR Capability under Summer Peak Conditions

Table 15 2002 DG Projects

Table 16 2005 Large Customer DR Sites with 15% DR Capability

Table 17 2005 Medium Customer DR Sites with 15% DR Capability

Table 18 2005 DG Projects by Feeder

Table 19 DER Portfolio Load Flow Results

Table 20 DER Portfolio Load Flow Results

Table 21 DER Portfolio Load Flow Results

Table 22 Summer Peak 2002 Results Summary

Table 23 Summer 2005 Results Summary

Table 24 Knee Peak 2002 Results Summary

Table 25 Winter Peak 2001-2 Results Summary

Table 26 Minimum Load 2002 Results Summary

Table 27 2002 DER Portfolio Real Power Loss Benefit (MW)

Table 28 2002 DER Portfolio SVP System Percentage Loss Reduction

Table 29 2002 DER Portfolio Reactive Power Consumption Benefit (MVAR)

Table 30 2002 DER Portfolio SVP System Percentage Reactive Power Consumption Reduction

Table 31 Summer Peak 2002 Voltage Profile and System Stress Results

Table 32 Knee Peak 2002 Voltage Profile and System Stress Results

Table 33 Winter Peak 2002 Voltage Profile and System Stress Results

Table 34 Minimum Load 2002 Voltage Profile and System Stress Results

Table 35 Capacity (MW) – DG Projects

Table 36 Capacity (MW) – DR Projects

Table 37 2005 DER Portfolio Real Power Loss Benefit (MW)

Table 38 2005 DER Portfolio SVP System Percentage Loss Reduction

Table 39 2005 DER Portfolio Reactive Power Consumption Benefit (MVAR)

Table 40 2005 DER Portfolio SVP System Percentage Reactive Power Consumption Reduction

Table 41 Summer 2005 Voltage Profile and System Stress Results

Table 42 Capacity Value (MW) – DG Projects

Table 43 Capacity Value (MW) – DR Projects

Table 44 Summer 2005 System With SVP Capital Additions - Results

Table 45 Comparison of SVP Network Benefits of Optimal DER Portfolio and SVP Capital Projects

Table 46 Summer 2005 System without NRS 115 kV DR and DG Additions Results

Table 47 Loss Reduction Value ($ per year) – DG Projects

Table 48 New York ISO Monthly UCAP auction results 2001-2

Table 49 Capacity Value ($/year) – DG Projects

Table 50 Capacity Value ($/year) – DR Projects

Table 51 Loss Reduction Value ($ per year) – DG Projects

Table 52 The New York ISO Monthly UCAP Auction Results 2004 Summer And 2004-5 Winter

Table 53 Capacity Value ($/year) – DG Projects

Table 54 Capacity Value ($/year) – DR Projects

Table 55 DER Projects

Table 56 Specific Requirements for DER Projects

Table 57 Potential Barriers And Potential Approaches

Table 58 Base Case Load Flow Results

Table 59 Voltage Profile Comparison

Table 60 Loss Reduction (MWh per hour) – DG Projects

Table 61 Loss Reduction (MWh per hour when called) – DR Projects

Table 62 2002 DER Portfolio SVP System Percentage Loss Reduction

Table 63 Reduced Reactive Power Consumption (MVAR) – DG Projects

Table 64 Reduced Reactive Power Consumption (MVAR) – DR Projects (when called)

Table 65 2002 DER Portfolio SVP System Percentage Reactive Power Consumption Reduction

Table 66 Voltage Profile and P Stress with DR and DG Projects

Table 67 Capacity Value (MW) – DG Projects

Table 68 Capacity Value (MW) – DR Projects

Table 69 Loss Reduction (MWh per hour) – DG Projects

Table 70 Loss Reduction (MWh per hour when called) – DR Projects

Table 71 2005 DER Portfolio SVP System Percentage Loss Reduction

Table 72 Reduced Reactive Power Consumption (MVAR) – DG Projects

Table 73 Reduced Reactive Power Consumption (MVAR) – DR Projects (when called)

Table 74 2005 DER Portfolio SVP System Percentage Reactive Power Consumption Reduction (reduction relative to "recontrols" only)

Table 75 Voltage Profile and P Stress with DR and DG Projects

Table 76 Capacity Value (MW) – DG Projects

Table 77 Capacity Value (MW) – DR Projects

Table 78 Summer 2005 System With SVP Capital Additions - Results

Table 79 Comparison of SVP Network Benefits of Optimal DER Portfolio and SVP Capital Additions

Table 80 Summer 2005 System without NRS 115 kV

Table 81 Loss Reduction Value

Table 82 DG Projects ($/year)

Table 83 DR Projects ($/year)

Table 84 Loss Reduction Value ($ per year) – DG Projects

Table 85 Capacity Value ($/year) – DG Projects

Table 86 Capacity Value ($/year) – DR Projects

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