Battery Capacity of Deferrable Energy Demand

We investigate the ability of a homogeneous collection of deferrable energy loads to behave as a battery; that is, to absorb and release energy in a controllable fashion up to fixed and predetermined limits on volume, charge rate and discharge rate. We derive bounds on the battery capacity that can be realized and show that there are fundamental trade-offs between battery parameters. By characterizing the state trajectories under scheduling policies that emulate two illustrative batteries, we show that the trade-offs occur because the states that allow the loads to absorb and release energy at high aggregate rates are conflicting

Assessment of Optimal Flexibility in Ensemble of Frequency Responsive Loads

Potential of electrical loads in providing grid ancillary services is often limited due to the uncertainties associated with the load behavior. A knowledge of the expected uncertainties with a load control program would invariably yield to better informed control policies, opening up the possibility of extracting the maximal load control potential without affecting grid operations. In the context of frequency responsive load control, a probabilistic uncertainty analysis framework is presented to quantify the expected error between the target and actual load response, under uncertainties in the load dynamics. A closed-form expression of an optimal demand flexibility, minimizing the expected error in actual and committed flexibility, is provided. Analytical results are validated through Monte Carlo simulations of ensembles of electric water heaters.Comment: IEEE International Conference on Smart Grid Communication

Demand side participation for frequency containment in the web of cells architecture

A large number of demand side management schemes have been proposed in literature for provision of frequency control ancillary services to the network. However, it is assumed that all the flexible devices within the network are managed and controlled under one demand side management (DSM) scheme. In this paper, two independent demand side management schemes control the portfolio of flexible devices within a web of cells architecture. A methodology and scenarios for analysis of the performance of more than one DSM scheme within the same network have been realized using a real-time power hardware-in-the-loop co-simulation platform, and the paper presents this as a basis for investigations of such arrangements

Individual risk in mean-field control models for decentralized control, with application to automated demand response

Flexibility of energy consumption can be harnessed for the purposes of ancillary services in a large power grid. In prior work by the authors a randomized control architecture is introduced for individual loads for this purpose. In examples it is shown that the control architecture can be designed so that control of the loads is easy at the grid level: Tracking of a balancing authority reference signal is possible, while ensuring that the quality of service (QoS) for each load is acceptable on average. The analysis was based on a mean field limit (as the number of loads approaches infinity), combined with an LTI-system approximation of the aggregate nonlinear model. This paper examines in depth the issue of individual risk in these systems. The main contributions of the paper are of two kinds: Risk is modeled and quantified: (i) The average performance is not an adequate measure of success. It is found empirically that a histogram of QoS is approximately Gaussian, and consequently each load will eventually receive poor service. (ii) The variance can be estimated from a refinement of the LTI model that includes a white-noise disturbance; variance is a function of the randomized policy, as well as the power spectral density of the reference signal. Additional local control can eliminate risk: (iii) The histogram of QoS is truncated through this local control, so that strict bounds on service quality are guaranteed. (iv) This has insignificant impact on the grid-level performance, beyond a modest reduction in capacity of ancillary service.Comment: Publication without appendix to appear in the 53rd IEEE Conf. on Decision and Control, December, 201

Online Convex Optimization with Binary Constraints

We consider online optimization with binary decision variables and convex loss functions. We design a new algorithm, binary online gradient descent (bOGD) and bound its expected dynamic regret. We provide a regret bound that holds for any time horizon and a specialized bound for finite time horizons. First, we present the regret as the sum of the relaxed, continuous round optimum tracking error and the rounding error of our update in which the former asymptomatically decreases with time under certain conditions. Then, we derive a finite-time bound that is sublinear in time and linear in the cumulative variation of the relaxed, continuous round optima. We apply bOGD to demand response with thermostatically controlled loads, in which binary constraints model discrete on/off settings. We also model uncertainty and varying load availability, which depend on temperature deadbands, lockout of cooling units and manual overrides. We test the performance of bOGD in several simulations based on demand response. The simulations corroborate that the use of randomization in bOGD does not significantly degrade performance while making the problem more tractable

Building Flexibility Estimation and Control for Grid Ancillary Services

The increased adoption of intermittent renewable energy, such as wind and solar, onto the electrical grid is increasing the need for greater demand flexibility and the development of more advanced demand management solutions. For example, in March 2017 solar and wind set record highs in California, contributing over 49% of its power supply. Furthermore, Hawaii has committed to meeting 100% of its electrical demand from renewables by 2045. This transformation requires solutions to robustly and cost-effectively manage dynamic changes on the grid while ensuring quality of service. Advanced demand response approaches are a key way of enabling this required grid flexibility. Advances in direct digital control of building systems, combined with the increased connectivity of end devices now enable greater participation. To achieve this, end devices will need to estimate the amount of grid services (flexibility) they can offer, and then automatically fulfil that commitment when called upon without noticeable loss in quality of service (e.g. indoor comfort). This paper presents data-driven methods for estimating the demand flexibility of commercial buildings and the control architecture to enable the execution of committed reserves while ensuring quality of service. In particular, we describe the methodology for 1) qualifying the HVAC system to provide three power grid ancillary services (frequency response, frequency regulation and ramping services) based on defined metrics for response and ramp time, 2) quantifying the magnitude and frequency bandwidth of the service it can provide, and 3) controlling the building’s cooling and heating demand within the specified flexibility limits to provide grid service. UTRC’s high performance building test-bed, a medium-sized commercial office building was used for the experimental study. The building testing was focused on the air-side electricity consumer - the supply air fans in the AHU. The resulting data verifies that air-side HVAC loads (ventilation fans) are sufficiently responsive to meet the requirements of frequency regulation (\u3c5 seconds response time) and ramping services (\u3c10 minutes response time) with ON/OFF control command, direct fan speed control, and indirect control through static pressure set-point adjustment. The proposed frequency regulation control changes the command to the AHU fan motor speed (and hence power consumption) by indirectly modifying the duct static pressure set-point to track a given regulation reference signal. This architecture was selected for equipment reliability and ease of implementation. The experimental frequency response data from static pressure set-point to AHU fan power consumption shows that each ventilation fan can provide up to 1.5 kW for frequency regulation (16.7% of its rated power) during operational hours without impacting the indoor climate or baseline controls, and the acceptable frequency range was identified as 0.0055 - 0.022 Hz based on the grid response metrics and controls requirement. The accuracy of the flexibility estimation and the performance of the frequency regulation controller were verified through closed-loop active response experiment. Moreover, we describe how a population of commercial buildings with different flexibilities can be engaged and coordinated to provide adequate and reliable frequency regulation service to the grid

Ancillary service provision by demand side management : a real-time power hardware-in-the-loop co-simulation demonstration

The role of demand side management in providing ancillary services to the network is an active topic of research. However, their implementation is limited due to lack of practical demonstrations and tests that can rigorously quantify their ability to support the grid’s integrity. In this paper, provision of time critical frequency control ancillary service is demonstrated by means of integrating PowerMatcher, a well discussed demand side management mechanism in literature, with real-time power hardware. The co-simulation platform enables testing of demand side management techniques to provide ancillary services