The overall objective of this project was to demonstrate a method whereby a predictable kilowatt (kW) capacity reduction can be achieved to support enrolling Department of Defense (DoD) installations in Demand Response (DR) programs. The demand reduction mechanism was a central control of building energy consuming assets using existing capabilities at the demonstration sites. This was to be accomplished by using a central control center as a  depository of data collections, coupled to various Building Management Systems (BMS), an Advanced Metering Infrastructure (AMI) including metering and metering data systems, and a uniform Industrial Control System (ICS) to monitor and control building systems. This was to occur in 15 separate buildings of various DoD use types.

Since DR programs have proven very useful in the commercial sector and are part of many energy management programs this project was to develop and demonstrate a mechanism that could easily, safely, and effectively allow DoD installations to participate in various DR programs. In many situations, participating in a DR program can bring an installation resiliency, energy use reductions, and economic incentives. In many locations, where an installation might be the largest consumer of electrical energy, it can become problematic. When the utility grid is stressed, and an installation is unable to curtail its load efficiently it can contribute to grid instability and fragility. This can lead to delivery failures and if the installation is not properly prepared adversely impact its ability to accomplish the mission effectively.

Technology Description

The project was to use a mix of DoD building end-use types, consisting of up to 15 buildings, on three Naval District Washington (NDW) region bases (Washington Navy Yard [WNY], Naval Support Facility Dahlgren, and Joint Base Anacostia-Bolling). This project would collect and perform an analysis of data collected from the secure side of the network of the various building energy usages when controlled temperature set points were changed to determine the effects of electrical usage and peak demand under a variety of environmental conditions. This was to show that through the deliberate control of building energy consuming assets, a predictable kW capacity reduction can be achieved to support enrolling DoD installations in DR programs.

As envisioned, the project required only enabling, configuring, and interconnecting the existing capabilities and functional controls between the systems. The project data were to be gathered using the Shore Operations Center (SHoC), located at the WNY, which was designed as the central control center and depository of data collections. The various buildings BMS’s would gather the needed data from the selected buildings. The AMIs included metering and metering data systems, and a uniform ICS to monitor and control building systems. The AMI system deployed for NDW was a Commercial Off the Shelf solution currently being used commercially for DR and Demand Peak Reduction, energy monitoring, analysis, and commissioning applications. The SHoC would integrate signals from the AMI and National Oceanic and Atmospheric Administration weather to monitor performance, forecast demand curves, and establish baselines. As a request for demand reduction comes from the power supplier, SHoC could then send out appropriate signals to the respective building ICS. The targeted heating, ventilation, and air-conditioning (HVAC) and Lighting loads could be adjusted, and generators brought online as needed to meet the mission requirements while still meeting negotiated load reduction commitments.

Demonstration Results

The demonstration effort included the participation (virtual/modeled) of the grid operator, PJM Interconnection (PJM), and simulated participating in an Extended Summer DR Program. This was to be a pilot program within PJM and the Department of the Navy that would develop a theoretical financial model to show economic benefits should DoD wish to sign contracts in the future, to enter the program and receive actual financial rewards.

A DR estimation framework to predict the load reduction possible (kW capacity) from building HVAC systems was developed. An interesting development of the study is that DR participation as an economic resource (price-based DR) is extremely low with PJM. Most customers received greater, more consistent bill savings via other contract methods translating load reductions into cost savings. Additionally, capacity market participation does not preclude additional compensation in the energy market for the actual energy reduced.

However, because back-up generation can be used in place of the load reduced it provides a basis for DR participation if an installation has extra generation capacity available. This would require significant planning and evaluation of the pros and cons. Although the need for DR events are rare at the present time, should one occur, back-up generation and any renewable resources not currently being used could supplement the capability to participate in the “Capacity Performance” DR market.

Implementation Issues

Controlling energy-consuming assets (e.g., HVAC, lighting, and motors) based on “signals” from the grid allows on-demand electrical capacity reduction, which frees up capacity to be used elsewhere on the grid. This in some cases can be very beneficial to the stability and reliability of the grid. However, the overarching DOD concern with DR is the cyber and operational security threat that is potentially involved by participating in any of the many varied DR programs.

One of the key lessons learned while preparing for this demonstration was that there were limitations as to how much and when data could be made available for the purposes of generating the reports to verify the approach and integrate that data into the functional design development.

Although all the necessary security measures and authority to operate documents were initially in place, the actual information gathering, and controls were not and delayed the data acquisition required. During this significant time delay a new risk management framework cybersecurity process superseded the initial security measures put in place for the demonstration that was more restrictive. Unfortunately, the data to integrate into the analysis and reports never became available.