Advanced peak demand forcast and battery dispatch algorithms to integrate storage-based demand response with BAS

Abstract:

Large scale applications of cyber physical systems (CPS) such as commercial buildings with Building Automation System (BAS)-based demand response (DR) can play a key role in alleviating demand peaks and associated grid stress, increased electricity unit cost, and carbon emissions. However, benefits of BAS alone are often limited because their demand peak reduction cannot be maintained long enough without unduly affecting occupant comfort. This project seeks to develop control algorithms to closely integrate battery storage-based DR with existing BAS capabilities. The overarching objective is to expand the building's DR capabilities, providing crucial benefits towards smarter grids, while maintaining appropriate occupant comfort and reducing levelized building ownership cost (i.e. electricity cost savings of the equipment that exceed its CapEx). The research follows a 2-phase approach towards more effective integration. First, building peak demand forecasting will be added to existing battery dispatch methods. Under electricity tariffs geared towards daily [monthly] peaks, such forecasting could result in the same batteryenabled demand charge ($ per kW) savings as previously demonstrated storage dispatch algorithms. However, supply charges ($ per kWh) and associated emissions would be reduced because battery dispatch would be geared towards reducing only the biggest daily [monthly] peaks while not incurring roundtrip charging losses on more moderate peaks. Phase 2 builds on phase 1, adding closer integration and systematic optimization to the algorithms for forecasting, BAS, and battery dispatch. The expected transformative capabilities of these algorithms are derived from an integrative Systems-of-Systems approach by formulating a new hierarchical optimization. This integration will allow the integrated CPS to manipulate the BAS process itself, thereby optimizing e.g., light dimming, temperature setpoints, and precooling in unison with battery-based DR. Feasibility and future promise of such experimental control methodology will be measured by a multi-objective cost function which includes demand and energy charges, savings from DR participation, storage equipment CapEx (required size, achievable lifetime), and occupant comfort. Integrating BAS- with battery-based DR is nascent, mostly because the peak demand forecast, BAS, and storage dispatch algorithms that such a CPS requires have yet to be developed. This NSF EAGER seeks to lay important methodological groundwork for such applications, thus furthering commercial buildings' role in the Internet of Things. The PI's participation in the NIST/US-Ignite Global City Team Challenge (with partners Urban Electric Power, Siemens Corporate Technology, City University of New York, and NY-Best) furthers public engagement with such technology and will help catalyze its translation into the commercial space.