Price-responsive electricity consumption can facilitate the efficient operations of power systems. In this paper, we study the impact of real-time pricing (RTP) and the automation of a major residential load – Heating, Ventilation, and Air Conditioning (HVAC) systems – on residential distribution systems. First, we deploy a simulation-based case study of 437 houses in Austin, Texas, which we calibrate based on real-world data. Second, we study the implications of switching from a fixed retail rate to RTP. We estimate aggregate welfare gains of 39 USD per customer and year. All customers contribute to the welfare gain, but not equally. Moreover, all customers benefit and benefits are higher for customers with electric heating, lower comfort preference, and lower thermal insulation. However, RTP and load automation can critically increase peak system load by more than 30%. Third, we study the introduction of a market-based demand management system under which the local RTP increases if congestion is present. In this case, for reasonable specifications, another 12% of welfare gains can be realized during operations. Moreover, optimal grid investment can be reduced. Our work informs policy makers by illustrating under which conditions load automation under RTP can be particularly beneficial.

This paper presents a limit order book (LOB) market mechanism design for transactive energy systems. The proposed design is planned for deployment in New Hampshire and Maine under a US Department of Energy Connected Communities project. The new LOB mechanism is intended to replace or work in conjunction with the conventional transactive energy double auction mechanisms designed for retail real-time electricity price discovery, and will facilitate significant scaling of transactive energy systems. The paper provides LOB market rules, clearing algorithm, and illustrative examples and discusses clearing algorithm performance and reliability. The proposed LOB design includes support for discovering prices arising from wholesale electricity markets, distribution system asset constraints, distributed energy resource constraints, and consumer willingness to consume or produce at a reservation price.

Transactive energy systems (TS) use automated device bidding to access (residential) demand flexibility and coordinate supply and demand on the distribution system level through market processes. In this work, we present TESS, a modularized platform for the implementation of TS, which enables the deployment of adjusted market mechanisms, economic bidding, and the potential entry of third parties. TESS thereby opens up current integrated closed-system TS, allows for the better adaptation of TS to power systems with high shares of renewable energies, and lays the foundations for a smart grid with a variety of stakeholders. Furthermore, despite positive experiences in various pilot projects, one hurdle in introducing TS is their integration with existing tariff structures and (legal) requirements. In this paper, we therefore describe TESS as we have modified it for a field implementation within the service territory of Holy Cross Energy in Colorado. Importantly, our specification addresses challenges of implementing TS in existing electric retail systems, for instance, the design of bidding strategies when a (non-transactive) tariff system is already in place. We conclude with a general discussion of the challenges associated with "brownfield" implementation of TS, such as incentive problems of baseline approaches or long-term efficiency.

Protection equipment is used to prevent damage to induction motor loads by isolating them from power systems in the event of severe faults. Modeling the response of induction motor loads and their protection is vital for power system planning and operation, especially in understanding system's dynamic performance and stability after a fault occurs. Induction motors are usually equipped with several types of protection with different operation mechanisms, making it challenging to develop adequate yet not overly complex protection models and determine their parameters for aggregate induction motor models. This paper proposes an optimization-based nonlinear regression framework to determine protection model parameters for aggregate induction motor loads in commercial buildings. Using a mathematical abstraction, the task of determining a suitable set of parameters for the protection model in composite load models is formulated as a nonlinear regression problem. Numerical examples are provided to illustrate the application of the framework. Sensitivity studies are presented to demonstrate the impact of lack of available motor load information on the accuracy of the protection models.

Power systems solvers are vital tools in planning, operating, and optimizing electrical distribution networks. The current generation of solvers employ computationally expensive iterative methods to compute sequential solutions. To accelerate these simulations, this paper proposes a novel method that replaces the physics-based solvers with data-driven models for many steps of the simulation. In this method, computationally inexpensive data-driven models learn from training data generated by the power flow solver and are used to predict system solutions. Clustering is used to build a separate model for each operating mode of the system. Heuristic methods are developed to choose between the model and solver at each step, managing the trade-off between error and speed. For the IEEE 123 bus test system this methodology is shown to reduce simulation time for a typical quasi-steady state time-series simulation by avoiding the solver for 86.7% of test samples, achieving a median prediction error of 0.049%.

Electrification of transport and the deployment of plug-in electric vehicles (PEVs) shift emissions from tail-pipes to bulk power systems (BPSs). Coordinated distributed energy resources and PEV charging can mitigate the impact of this shift. This study presents an analysis of photovoltaic (PV) solar parking lots that address this benefit. Real-world charging data, solar data, and electricity tariffs are used to determine the microgrid system that minimises the cost of retrofitting an existing parking lot with PV and PEV infrastructure coupled. The result is a load scheduling algorithm that takes into account tariffs and insolation to reduce costs while ensuring customer satisfaction. The techno-economic feasibility of PV infrastructure in the microgrid is determined by minimising the net present cost (NPC) in two case studies: Victoria, BC, and Los Angeles, CA. Relatively low solar irradiation and electricity prices make it economically infeasible to install solar panels in Victoria even though the operational costs are reduced by 11%. In Los Angeles, high time-of-use prices, together with abundant solar radiation, make PV retrofitting economically feasible with any array capacity. At the current solar infrastructure price, coordinated charging in this region yields 8-16% savings on NPC and smaller feeder size requirements with greater load growth opportunities.

The deployment of new sensors and devices on electric distribution systems is increasing the awareness of phenomena characterized by intermittent periods of highly dynamic activity that occur within extended periods of relatively static behavior. The deployment of new devices has enabled the observation of these phenomena; however, the currently available simulation methods cannot accurately reproduce the entire system behavior. Existing simulation methods, and their associated models, are able to capture portions of these phenomena, but there is not a method for efficiently modeling the entire event in a single simulation. This paper presents a novel method of adaptive simulation that enables automated transitions between quasi-static time-series and electromechancial simulation modes, as necessary to capture relevant system dynamics. The transitions between the simulation modes are triggered automatically during the running simulation based on the evolution of the system variables, utilizing multistate modes for generators and motors. This method allows for a single simulation that spans the entire time-frame, has the ability to capture dynamic events, and includes all relevant power system controls. The method of adaptive simulation can support the direct analysis of dynamic power system events, co-simulation of transmission and distribution systems, the development of control systems, and the development of reduced-order models.

The human operator is an integral part of a stable and safe power system. While there is increasing attention paid to automation improvements, the importance of understanding and training human operators may be understated. This paper discusses a project to enhance operator training programs by evaluating human performance relative to a reference operator model identified using optimal control theory. Along with establishing a simple computer-based operator workstation for future training purpose, this paper describes the optimal control response design methodology for a human-in-the-loop power system experiment. The overall system model is presented. An optimal controller synthesis methodology is applied to the model system and the optimal controller is designed. The performance of the optimal controller is then compared to human subject performance.

An optimal coordination of residential motor protection has been considered a potential remedy for many sustained low voltage problems caused by faults in the transmission system. This paper presents the modeling of residential end-use motor loads equipped with dedicated protection devices in a dynamic-phasor-based simulation program. Common types of mechanical torque are implemented for different functional motors in typical single-family and multi-family homes. The point-on-wave study is performed to examine the responses of air-conditioner (A/C) compressor to the instant of voltage depression. Voltage depressions are imposed at the head of the feeder supplying multiple residential homes to investigate the behaviors of A/C compressor stalling, actions of motor tripping and reconnection, and system-level responses to motor stalling and motor protection.

Human operators interact with the power control system as "in-the-loop" control elements to ensure the system stability and safety. The role of human operators becomes more critical with the increasing usage of renewable energy resources. This research seeks to support operator training by developing a technique to quantitatively model human operators' performance in the context of a simple power dispatch task. In the designed compensatory tracking task, the operator acted on the system output error and was part of a feedback control loop. The primary metric developed to evaluate and model the operator's performance is the normalized deviation, which is defined as the difference between the individual quadratic error and the averaged performance. Twenty-three human subjects participated in the experimental study. The data collected was then used to examine the proposed modeling approach and to obtain insights into possible effects of human factors. The proposed modeling technique will enable us to evaluate and compare the operator's performance to the optimal controller designed for the same task, and to design the training program that aims to align the operator's performance closer to the optimal controller.

New sources for ancillary services are needed, yet the requirements for service provision in most countries are explicitly formulated for traditional generators. This leads to waste of the potential for new technologies to deliver ancillary services. In order to harness this potential, we propose to parameterize the requirements of ancillary services so that reserves can be built by combining the advantageous properties of different technologies. The proposal is exemplified through a laboratory test where it shown that the system needs can be covered through cheaper and smaller reserves.

Motor loads are equipped with protective mechanisms to prevent any damage to the loads in the event of a fault. Accurate representation of dynamic motor loads and their protection schemes is vital for power system planning and operation, especially in understanding system's response moments after a fault has occurred. Existing load models are inadequate to capture the behavior of protection mechanisms which can vary widely between different end-use appliances. This article proposes a methodology to generate composite protection profiles for commercial building motor loads. Combining knowledge of various protection schemes available in various end-use appliances, with the commercial buildings survey data (from U.S. Energy Information Administration) and typical end-use profiles generated using EnergyPlusTM), composite load protection profiles were generated at one-hour interval over a year, for different commercial buildings in representative cities from different climate zones.