Alternative methods for mitigating natural photovoltaic variability: dynamic HVAC load compensation and curtailed PV power

Continued integration of renewable energy resources onto the electric grid increases variability and decreases grid stability. Energy storage can help mitigate some of these effects, but conventional energy storage such as batteries is typically expensive and has other disadvantages such as round trip inefficiency and limited lifetime. Real, high-speed solar panel data is used to characterize the stochastic energy output of PV sources, and the numerous challenges faced and methods used when manipulating this real-life data set are detailed. Two alternative methods are then presented to absorb or reduce the variability imposed upon the grid by PV or other generation. (1) Dynamic HVAC load compensation is shown to absorb or "filter" short-term PV variability and act as effective grid inertia. A proposed Butterworth filter power target technique balances energy storage demands with decreased uncertainty. A small-scale model of a variable speed blower and fan is used to provide a conversion between fan speed and power consumed and to estimate filtering limitations imposed by undesirable acoustic effects. Considering the acoustic, physical, and thermal limitations simultaneously, the variation absorption or filtering capability of dynamic HVAC load compensation is analyzed for various building sizes and on-site PV penetrations. The resulting reduction in battery storage capacity and utilization is briefly investigated. (2) PV operating reserve curtailment is introduced. The same Butterworth filter power set-point is used, its implementation is shown as feasible through simulation, and the variability reduction is quantified in two different ways. The claim is made that PV should be treated and priced like conventional grid generation, which is responsible for both energy and regulation capabilities. PV operating reserve curtailment is then shown to be economically favorable for at least some level of reserve. Finally, a proposed metric of optimality is presented that balances energy production with decreased variability

Power electronics implementation of dynamic thermal storage as effective inertia in large energy systems

Modern large energy systems such as electricity grids and electrified transportation encounter increasing processed power in multi-physics domains, such as electrical, mechanical, thermal, and chemical. Although many systems are becoming predominantly electrical dependent, an integrated multi-physics energy approach creates additional avenues to higher power density, system efficiency, and reliability. Power electronics, serving as power conversion mechanisms, are key linking subsystems consisting of electronic devices, electro-mechanical units, energy storage, etc. This dissertation first studies the use of power electronic drives to implement dynamic thermal storage as effective inertia in solar-interfaced grid-connected low-energy buildings, as an example of a stationary large energy system. Dynamic management of energy components is used to offset variability of stochastic solar resources. Emphasis is on power electronic HVAC (heating, ventilation, and air-conditioning) drives, which can act as an effective electric swing bus to mitigate solar power variability. In doing so, grid power flows become substantially more constant, reducing the need for fast grid resources or dedicated energy storage such as batteries. The work defines a bandwidth over which such HVAC drives can operate. A practical band-pass filter is realized with a lower frequency bound such that the building maintains consistent temperature, and an upper frequency bound to ensure that commanded HVAC fan speeds do not update arbitrarily fast, avoid acoustic discomfort to occupants, and prevent undue hardware wear and tear. The dissertation then moves onto investigation of a mobile energy system, specifically more electric aircraft (MEA), with the purpose of evaluating thermal inertia’s efficacy in a microgrid-like inertia-lacking electrical system. Thermal energy inherent in the cabin air and aircraft fuel serves as a dynamic management solution to offset stochastic load power in the MEA power system. Power electronic controlled environmental control system (ECS) drives, emulating dynamic thermal inertia, showcase a more constant generator output power, allowing potential to downsize required generator ratings. An operating bandwidth is proposed similar to that of building HVAC systems, subject to additional degrees of constraints unique on MEA. A more sensitive virtual synchronous machine control boosts desirable inertia in sub-seconds scales in the MEA power system. To validate the thermal storage as effective inertia in both stationary and mobile energy systems, comprehensive simulation studies and experimental work are conducted at multiple levels. For the energy-efficient building research platform, building electrical and thermal energy systems modeling is addressed, including solar and HVAC systems as well as batteries and large-scale thermal storage. A lab-scale power system features various update rates of a variable frequency fan drive over stochastic solar data. A full-scale multiple-day case study provides insight on potential grid-side and storage-related benefits. The simulation and experimental studies are supported by 18 months of solar data collected on sub-millisecond time scales as a basis to evaluate efficacy, determine solar frequency-domain content, and analyze mitigation of variability. For the MEA research platform, steady-state and dynamic behaviors of electrical components in the Boeing 787 power systems, including electric machines, power converters, batteries, transformers, and loads, are modeled. In particular, in-depth discussions cover a multi-timescale parametric electrical battery model for use in dynamic electric transportation simulations. An integrated thermal model within electrical components and electrical systems captures temperature variations and ECS thermal dynamics. Simulation studies based on realistic load power demand over a 5-hour mission profile show mitigation of generator power transients while maintaining relatively comfortable cabin temperature bounds. Finally a scaled-down lab power system is implemented on a microcontroller-tied industrial drive to demonstrate feasibility in a potential commercial system

Implementation and assessment of demand response and voltage/var control with distributed generators

The main topic of this research is the efficient operation of a modernized distribution grid from both the customer side and utility side. For the customer side, this dissertation discusses the planning and operation of a customer with multiple demand response programs, energy storage systems and distributed generators; for the utility side, this dissertation addresses the implementation and assessment of voltage/VAR control and conservation voltage reduction in a distribution grid with distributed generators. The objectives of this research are as follows: (1) to develop methods to assist customers to select appropriate demand response programs considering the integration of energy storage systems and DGs, and perform corresponding energy management including dispatches of loads, energy storage systems, and DGs; (2) to develop stochastic voltage/VAR control techniques for distribution grids with renewable DGs; (3) to develop optimization and validation methods for the planning of integration of renewable DGs to assist the implementation of voltage/VAR control; and (4) to develop techniques to assess load-reduction effects of voltage/VAR control and conservation voltage reduction. In this dissertation, a two-stage co-optimization method for the planning and energy management of a customer with demand response programs is proposed. The first level is to optimally select suitable demand response programs to join and integrate batteries, and the second level is to schedule the dispatches of loads, batteries and fossil-fired backup generators. The proposed method considers various demand response programs, demand scenarios and customer types. It can provide guidance to a customer to make the most beneficial decisions in an electricity market with multiple demand response programs. For the implementation of voltage/VAR control, this dissertation proposes a stochastic rolling horizon optimization-based method to conduct optimal dispatches of voltage/VAR control devices such as on-load tap changers and capacitor banks. The uncertainties of renewable DG output are taken into account by the stochastic formulation and the generated scenarios. The exponential load models are applied to capture the load behaviors of various types of customers. A new method to simultaneously consider the integration of DGs and the implementation of voltage/VAR control is also developed. The proposed method includes both solution and validation stages. The planning problem is formulated as a bi-level stochastic program. The solution stage is based on sample average approximation (SAA), and the validation stage is based on multiple replication procedure (MRP) to test the robustness of the sample average approximation solutions of the stochastic program. This research applies big data-driven analytics and load modeling techniques to propose two novel methodologies to assess the load-reduction effects of conservation voltage reduction. The proposed methods can be used to assist utilities to select preferable feeders to implement conservation voltage reduction.Ph.D