Arbitrage with Power Factor Correction using Energy Storage

The importance of reactive power compensation for power factor (PF) correction will significantly increase with the large-scale integration of distributed generation interfaced via inverters producing only active power. In this work, we focus on co-optimizing energy storage for performing energy arbitrage as well as local power factor correction. The joint optimization problem is non-convex, but can be solved efficiently using a McCormick relaxation along with penalty-based schemes. Using numerical simulations on real data and realistic storage profiles, we show that energy storage can correct PF locally without reducing arbitrage profit. It is observed that active and reactive power control is largely decoupled in nature for performing arbitrage and PF correction (PFC). Furthermore, we consider a real-time implementation of the problem with uncertain load, renewable and pricing profiles. We develop a model predictive control based storage control policy using auto-regressive forecast for the uncertainty. We observe that PFC is primarily governed by the size of the converter and therefore, look-ahead in time in the online setting does not affect PFC noticeably. However, arbitrage profit are more sensitive to uncertainty for batteries with faster ramp rates compared to slow ramping batteries.Comment: 10 pages, 8 figure

Control of Electric Load Aggregations for Power System Services

In electrical power systems, when the supply from wind or solar-powered generation fluctuates, other resources adjust their power to maintain the system’s balance between demand and supply. Traditionally, gas, coal, and hydro-powered generators have provided this balancing service. In the future, as the proportion of renewable power generation increases, additional balancing resources will be needed. In this work, we develop methods that enable a new resource—aggregations of flexible loads—to provide energy balancing. Load aggregations are a promising resource for transmission-level energy balancing, but this service should not come at the expense of lower-level services and requirements. Specifically, an aggregator’s control should not compromise the loads’ service to the end-user and should not cause operational issues on the distribution network. Thermostatically controlled loads (TCLs), such as air conditioners and water heaters, have user-set temperature limits and cycling constraints that must be satisfied. Distribution networks have loading and voltage constraints to ensure reliable operation. When providing balancing services, aggregators partially synchronize loads, which can cause constraint violations on the distribution network. Third-party aggregators are unaware of conditions on the network and must coordinate with the distribution operator to ensure network reliability. The objective of this dissertation is to develop control methods by which a third-party aggregator can provide energy balancing without disrupting consumers and without causing unsafe conditions on the distribution network. Multiple methods are proposed for identifying and protecting distribution constraints that are at risk of violation due to load control. We conduct a simulation study of realistic distribution networks and find only a small subset of network constraints is at risk of violation. This result implies that network-safe control strategies may need to account for only a subset of network constraints, enhancing computational efficiency. We propose using a ``mode-count algorithm’’ to control a group of TCLs to minimize their impact on an at-risk network constraint. Results show that the mode-count algorithm can effectively reduce the variability of voltage at a constrained distribution node. Developing an online method to identify the set of at-risk constraints is non-trivial; towards this end, we propose an optimization-based method that identifies the network’s most at-risk individual constraint and provides a conservative, global safety constraint on power deviations caused by the aggregator. Because the method is computationally intensive, we develop techniques based on power-flow analysis to reduce the problem size; we are able to reduce the problem size by more than 60% for a test network. Two network-safe control strategies for energy balancing are proposed. Both strategies are hierarchical: the aggregator controls loads to track an energy-balancing signal, and the operator removes particular TCLs from the aggregator’s control when necessary for network safety. The strategies differ in terms of modeling and communication requirements. In a case study, the more complex strategy achieves a root-mean-square tracking error of 0.10% of the TCLs’ baseline power consumption while removing fewer than 1% of TCLs from the aggregator’s control; the other strategy achieves a 0.70% tracking error while removing approximately 15% of TCLs. The two strategies provide options — one better performing, one less costly — for operators and aggregators with different capabilities and preferences. Overall, these strategies enable third-party aggregators to control larger proportions of distribution-network load, enhancing competition in wholesale markets and providing the greater balancing capacities that will be needed by future, low-carbon power systems.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155144/1/sjcrock_1.pd

Risk Hedging Strategies in New Energy Markets

In recent years, two typical developments have been witnessed in the energy market. On the one hand, the penetration of renewable generations has gradually replaced parts of the traditional ways to generate energy. The intermittent nature of renewable generation can lead to energy supply uncertainty, which might exacerbate the imbalance between energy supply and demand. As a result, the problem of energy price risks might occur. On the other hand, with the introduction of distributed energy resources (DERs), new categories of markets besides traditional wholesale and retail markets are emerging. The main benefits of the penetration of DERs are threefold. First, DERs can increase power system reliability. Second, the cost of transmission can be reduced. Third, end users can directly participate in some of these new types of markets according to their energy demand, excess energy, and cost function without third-party intervention. However, energy market participants might encounter various types of uncertainties. Therefore, it is necessary to develop proper risk-hedging strategies for different energy market participants in emerging new markets. Thus, we propose risk-hedging strategies that can be used to guide various market participants to hedge risks and enhance utilities in the new energy market. These participants can be categorized into the supply side and demand side. Regarding the wide range of hedging tools analyzed in this thesis, four main types of hedging strategies are developed, including the application of ESS, financial tools, DR management, and pricing strategy. Several benchmark test systems have been applied to demonstrate the effectiveness of the proposed risk-hedging strategies. Comparative studies of existing risk hedging approaches in the literature, where applicable, have also been conducted. The real applicability of the proposed approach has been verified by simulation results