Abstract: The present disclosure provides a distributed and anonymous approach to demand response of an electricity system. The approach conceptualizes energy consumption and production of distributed-energy resources (DERs) via discrete energy packets that are coordinated by a cyber computing entity that grants or denies energy packet requests from the DERs. The approach leverages a condition of a DER, which is particularly useful for (1) thermostatically-controlled loads, (2) non-thermostatic conditionally-controlled loads, and (3) bi-directional distributed energy storage systems. In a first aspect of the present approach, each DER independently requests the authority to switch on for a fixed amount of time (i.e., packet duration). The coordinator determines whether to grant or deny each request based electric grid and/or energy or power market conditions. In a second aspect, bi-directional DERs, such as distributed-energy storage systems (DESSs) are further able to request to supply energy to the grid.
Abstract: Transmitting a large file across the internet requires breaking up the file into smaller packets of data. Packetized energy management (PEM) leverages similar concepts from communication theory to coordinate distributed energy resources by breaking up deferrable residential consumer demands into smaller fixed-duration/fixed-power packets of energy. Each individual load is managed by a probabilistic automaton that stochastically requests energy packets as a function of its local dynamic state (e.g., temperature or state-of-charge). Based on the aggregate request rate from packetized loads and grid conditions, the PEM coordinator will modulate the rate of accepting requests, which permits tight tracking of a reference (load-shaping or market) signal. This paper presents a state bin transition (macro) model suitable for characterizing a diverse population of electric water heaters (EWHs) and energy storage systems (ESSs) under a single PEM coordinator that is validated against an agent-based simulation of the diverse loads. The resulting model illustrates how diversity of packetized load types enhances the level of flexibility offered by the coordinator.
Abstract: Because of their internal energy storage, electrically powered, distributed thermostatically controlled loads (TCLs) have the potential to be dynamically managed to match their aggregate load to the available supply. However, in order to facilitate consumer acceptance of this type of load management, TCLs need to be managed in a way that avoids degrading perceived quality of service (QoS), autonomy, and privacy. This paper presents a real-time, adaptable approach to managing TCLs that both meets the requirements of the grid and does not require explicit knowledge of a specific TCL's state. The method leverages a packetized, probabilistic approach to energy delivery that draws inspiration from digital communications. We demonstrate the packetized approach using a case-study of 1000 simulated water heaters and show that the method can closely track a time-varying reference signal without noticeably degrading the QoS. In addition, we illustrate how placing a simple ramp-rate limit on the aggregate response overcomes synchronization effects that arise under prolonged peak curtailment scenarios.
Abstract: This paper presents a state bin transition (macro)model for a large homogeneous population of thermostatically controlled loads (TCLs). The energy use of these TCLs is coordinated with a novel bottom-up asynchronous, anonymous, and randomizing control paradigm called Packetized Energy Management (PEM). A macro-model for a population of TCLs is developed and then augmented with a timer to capture the duration and consumption of energy packets and with exit-ON/OFF dynamics to ensure consumer quality of service. PEM permits a virtual power plant (VPP) operator to interact with TCLs through a packet request mechanism. The VPP regulates the proportion of accepted packet requests to allow tight tracking of balancing signals. The developed macro-model compares well with (agent-based) micro-simulations of TCLs under PEM and can be represented by a controlled Markov chain.
Abstract: Systems and methods for distributing electric energy in discrete power packets of finite duration are presented. Systems may include an aggregator for providing power packets to one or more nodes. An aggregator may receive requests for power packets from nodes. In other embodiments, an aggregator may transmit status broadcasts and nodes may receive power packets based on the status broadcasts.
Abstract: Sales of privately-owned plug-in electric vehicles (PEVs) are projected to increase dramatically in coming years and their charging will impact residential service transformer loads. Transformer life expectancy is strongly related to the cumulative effects of internal winding temperatures, which are a function of loading. Thermal models exist (e.g., IEEE Standard C57.91) for predicting these internal temperatures, the most sophisticated being the Annex G model. While this model has been validated with measurements from large power transformers, small residential service transformers have been given less attention. Given increasing PEV loads, a better understanding of service transformer aging could be useful in replacement planning processes. Empirical data from this paper indicate that the Annex G model over-estimates internal temperatures in small 25 kVA 65 °C rise mineral-oil-immersed transformers. This paper presents an alternative model to Annex G by using a genetic program. Empirical results using a thermally-instrumented transformer suggest that this model is both simpler and more accurate at tracking empirical transformer data. We conclude that one can use a simple thermal model in combination with data from advanced metering infrastructure to more accurately estimate service transformer lifetimes, and thus better plan for transformer replacement.
Abstract: Plug-in electric vehicle (PEV) charging could cause significant strain on residential distribution systems, unless technologies and incentives are created to mitigate charging during times of peak residential consumption. This paper describes and evaluates a decentralized and ‘packetized’ approach to PEV charge management, in which PEV charging is requested and approved for time-limited periods. This method, which is adapted from approaches for bandwidth sharing in communication networks, simultaneously ensures that constraints in the distribution network are satisfied, that communication bandwidth requirements are relatively small, and that each vehicle has fair access to the available power capacity. This paper compares the performance of the packetized approach to an optimization method and a first-come, first- served (FCFS) charging scheme in a test case with a constrained 500 kVA distribution feeder and time-of-use residential electricity pricing. The results show substantial advantages for the packetized approach. The algorithm provides all vehicles with equal access to constrained resources and attains near optimal travel cost performance, with low complexity and communication requirements. The proposed method does not require that vehicles report or record driving patterns, and thus provides benefits over optimization approaches by preserving privacy and reducing computation and bandwidth requirements.