Unlocking the Potential of Flexible Energy Resources to Help Balance the Power Grid

Flexible energy resources can help balance the power grid by providing different types of ancillary services. However, the balancing potential of most types of resources is restricted by physical constraints such as the size of their energy buffer, limits on power-ramp rates, or control delays. Using the example of Secondary Frequency Regulation, this paper shows how the flexibility of various resources can be exploited more efficiently by considering multiple resources with complementary physical properties and controlling them in a coordinated way. To this end, optimal adjustable control policies are computed based on robust optimization. Our problem formulation takes into account power ramp-rate constraints explicitly, and accurately models the different timescales and lead times of the energy and reserve markets. Simulations demonstrate that aggregations of select resources can offer significantly more regulation capacity than the resources could provide individually.Comment: arXiv admin note: text overlap with arXiv:1804.0389

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

Enhanced frequency response from industrial heating loads for electric power systems

Increasing penetration of renewable generation results in lower inertia of electric power systems. To maintain the system frequency, system operators have been designing innovative frequency response products. Enhanced Frequency Response (EFR) newly introduced in the UK is an example with higher technical requirements and customized specifications for assets with energy storage capability. In this paper, a method was proposed to estimate the EFR capacity of a population of industrial heating loads, bitumen tanks, and a decentralized control scheme was devised to enable them to deliver EFR. Case study was conducted using real UK frequency data and practical tank parameters. Results showed that bitumen tanks delivered high-quality service when providing service-1-type EFR, but underperformed for service-2-type EFR with much narrower deadband. Bitumen tanks performed well in both high and low frequency scenarios, and had better performance with significantly larger numbers of tanks or in months with higher power system inertia

Machine learning and robust MPC for frequency regulation with heat pumps

With the increased amount of volatile renewable energy sources connected to the electricity grid, there is an increased need for frequency regulation. On the demand side, frequency regulation services can be offered by buildings that are equipped with electric heating or cooling systems, by exploiting the thermal inertia of the building. Existing approaches for tapping into this potential typically rely on a first-principles building model, which in practice can be expensive to obtain and maintain. Here, we use the thermal inertia of a buffer storage instead, reducing the model of the building to a demand forecast. By combining a control scheme based on robust Model Predictive Control, with heating demand forecasting based on Artificial Neural Networks and online correction methods, we offer frequency regulation reserves and maintain user comfort with a system comprising a heat pump and a storage tank. We improve the exploitation of the small thermal capacity of buffer storage by using affine policies on uncertain variables. These are chosen optimally in advance, and modify the planned control sequence as the values of uncertain variables are discovered. In a three day experiment with a real multi-use building we show that the scheme is able to offer reserves and track a regulation signal while meeting the heating demand of the building. In additional numerical studies, we demonstrate that using affine policies significantly decreases the cost function and increases the amount of offered reserves and we investigate the suboptimality in comparison to an omniscient control system.Comment: 13 pages, 12 figures, 1 table, submitted to IEEE Transactions on Control Systems Technolog

Modeling and control of complex building energy systems

Building energy sector is one of the important sources of energy consumption and especially in the United States, it accounts for approximately 40% of the total energy consumption. Besides energy consumption, it also contributes to CO2 emissions due to the combustion of fossil fuels for building operation. Preventive measures have to be taken in order to limit the greenhouse gas emission and meet the increasing load demand, energy efficiency and savings have been the primary objective globally. Heating, Ventilation, and air-conditioning (HVAC) system is a major source of energy consumption in buildings and is the principal building system of interest. These energy systems comprising of many subsystems with local information and heterogeneous preferences demand the need for coordination in order to perform optimally. The performance required by a typical airside HVAC system involving a large number of zones are multifaceted, involves attainment of various objectives (such as optimal supply air temperature) which requires coordination among zones. The required performance demands the need for accurate models (especially zones), control design at the individual (local-VAV (Variable Air Volume)) subsystems and a supervisory control (AHU (Air Handling Unit) level) to coordinate the individual controllers. In this thesis, an airside HVAC system is studied and the following considerations are addressed: a) A comparative evaluation among representative methods of different classes of models, such as physics-based (e.g., lumped parameter autoregressive models using simple physical relationships), data-driven (e.g., artificial neural networks, Gaussian processes) and hybrid (e.g., semi-parametric) methods for different physical zone locations; b) A framework for control of building HVAC systems using a methodology based on power shaping paradigm that exploits the passivity property of a system. The system dynamics are expressed in the Brayton-Moser (BM) form which exhibits a gradient structure with the mixed-potential function, which has the units of power. The power shaping technique is used to synthesize the controller by assigning a desired power function to the closed loop dynamics so as to make the equilibrium point asymptotically stable, and c) The BM framework and the passivity tool are further utilized for stability analysis of constrained optimization dynamics using the compositional property of passivity, illustrated with energy management problem in buildings. Also, distributed optimization (such as subgradient) techniques are used to generate the optimal setpoints for the individual local controllers and this framework is realized on a distributed control platform VOLTTRON, developed by the Pacific Northwest National Laboratory (PNNL)