Duncan S. Callaway

Achieving Controllability of Electric Loads

This paper discusses conceptual frameworks for actively involving highly distributed loads in power system control actions. The context for load control is established by providing an overview of system control objectives, including economic dispatch, automatic generation control, and spinning reserve. The paper then reviews existing initiatives that seek to develop load control programs for the provision of power system services. We then discuss some of the challenges to achieving a load control scheme that balances device-level objectives with power system-level objectives. One of the central premises of the paper is that, in order to achieve full responsiveness, direct load control (as opposed to price response) is required to enable fast time scale, predictable control opportunities, especially for the provision of ancillary services such as regulation and contingency reserves. Centralized, hierarchical, and distributed control architectures are discussed along with benefits and disadvantages, especially in relation to integration with the legacy power system control architecture. Implications for the supporting communications infrastructure are also considered. Fully responsive load control is illustrated in the context of thermostatically controlled loads and plug-in electric vehicles.

A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

This paper examines the problem of optimally splitting driver power demand among the different actuators (i.e., the engine and electric machines) in a plug-in hybrid electric vehicle (PHEV). Existing studies focus mostly on optimizing PHEV power management for fuel economy, subject to charge sustenance constraints, over individual drive cycles. This paper adds three original contributions to this literature. First, it uses stochastic dynamic programming to optimize PHEV power management over a distribution of drive cycles, rather than a single cycle. Second, it explicitly trades off fuel and electricity usage in a PHEV, thereby systematically exploring the potential benefits of controlled charge depletion over aggressive charge depletion followed by charge sustenance. Finally, it examines the impact of variations in relative fuel-to-electricity pricing on optimal PHEV power management. The paper focuses on a single-mode power-split PHEV configuration for mid-size sedans, but its approach is extendible to other configurations and sizes as well.

Decentralized charging control for large populations of plug-in electric vehicles

The paper develops a novel decentralized charging control strategy for large populations of plug-in electric vehicles (PEVs). We consider the situation where PEV agents are rational and weakly coupled via their operation costs. At an established Nash equilibrium, each of the PEV agents reacts optimally with respect to the average charging strategy of all the PEV agents. Each of the average charging strategies can be approximated by an infinite population limit which is the solution of a fixed point problem. The control objective is to minimize electricity generation costs by establishing a PEV charging schedule that fills the overnight demand valley. The paper shows that under certain mild conditions, there exists a unique Nash equilibrium that almost satisfies that goal. Moreover, the paper establishes a sufficient condition under which the system converges to the unique Nash equilibrium. The theoretical results are illustrated through various numerical examples.

Real-time scheduling of deferrable electric loads

We consider a collection of distributed energy resources [DERs] such as electric vehicles and thermostatically controlled loads. These resources are flexible: they require delivery of a certain total energy over a specified service interval. This flexibility can facilitate the integration of renewable generation by absorbing variability, and reducing the reserve capacity and reserve energy requirements. We first model the energy needs of these resources as tasks, parameterized by arrival time, departure time, energy requirement, and maximum allowable servicing power. We consider the problem of servicing these resources by allocating available power using real-time scheduling policies. The available generation consists of a mix of renewable energy [from utility-scale wind-farms or distributed rooftop photovoltaics], and load-following reserves. Reserve capacity is purchased in advance, but reserve energy use must be scheduled in real-time to meet the energy requirements of the resources. We show that there does not exist a causal optimal scheduling policy that respects servicing power constraints. We then present three heuristic causal scheduling policies: Earliest Deadline First [EDF], Least Laxity First [LLF], and Receding Horizon Control [RHC]. We show that EDF is optimal in the absence of power constraints. We explore, via simulation studies, the performance of these three scheduling policies in the metrics of required reserve energy and reserve capacity.

State Estimation and Control of Heterogeneous Thermostatically Controlled Loads for Load Following

Thermostatically controlled loads (TCLs), such as refrigerators, air conditioners, and electric water heaters, can be aggregated and used to deliver power systems services. The effectiveness of control strategies depends on the level of infrastructure and communications. This paper explores the use of TCLs for load following when measured state information is not available in real time. We use Markov Chain models to describe the temperature state evolution of populations of TCLs, and Kalman filtering techniques for both state estimation and joint parameter/state estimation. We find power tracking RMS errors in the range of 2-16% of the aggregate steady state power consumption of the TCL population. Results depend upon the information available for system identification, state estimation, and control. If high precision tracking is not required, TCLs may not need to be metered to provide state information to the central controller in real time or at all.

Arbitraging Intraday Wholesale Energy Market Prices With Aggregations of Thermostatic Loads

We investigate the potential for aggregations of residential thermostatically controlled loads (TCLs), such as air conditioners, to arbitrage intraday wholesale electricity market prices via non-disruptive load control. We present two arbitrage approaches: 1) a benchmark that gives us an optimal policy but requires local computation or real-time communication and 2) an alternative based on a thermal energy storage model, which relies on less computation/communication infrastructure, but is suboptimal. We find that the alternative approach achieves around 60%-80% of the optimal wholesale energy cost savings. We use this approach to compute practical upper bounds for savings via arbitrage with air conditioners in Californias intraday energy market. We investigate six sites over four years and find that the savings range from

Real-Time Scheduling of Distributed Resources

2-

Model predictive control approach to online computation of demand-side flexibility of commercial buildings HVAC systems for Supply Following

37 per TCL per year, and depend upon outdoor temperature statistics and price volatility.

Examining uncertainty in demand response baseline models and variability in automated responses to dynamic pricing

We develop and analyze real-time scheduling algorithms for coordinated aggregation of deferrable loads and storage. These distributed resources offer flexibility that can enable the integration of renewable generation by reducing reserve costs. We present three scheduling policies: earliest deadline first (EDF), least laxity first (LLF), and receding horizon control (RHC). We offer a novel cost metric for RHC-based scheduling that explicitly accounts for reserve costs. We study the performance of these algorithms in the metrics of reserve energy and capacity through simulation studies. We conclude that the benefits of coordinated aggregation can be realized from modest levels of both deferrable load participation and flexibility.

Using ICT-Controlled Plug-in Electric Vehicles to Supply Grid Regulation in California at Different Renewable Integration Levels

Commercial buildings have inherent flexibility in how their HVAC systems consume electricity. We investigate how to take advantage of this flexibility. We first propose a means to define and quantify the flexibility of a commercial building. We then propose a contractual framework that could be used by the building operator and the utility to declare flexibility on the one side and reward structure on the other side. We then design a control mechanism for the building to decide its flexibility for the next contractual period to maximize the reward, given the contractual framework. Finally, we perform at-scale experiments to demonstrate the feasibility of the proposed algorithm.