As both the generation resources and load types have changed and grown over the past few decades, there is a growing need for analysis that spans traditional simulation boundaries; for example, evaluating the impact of distribution-level assets (e.g. rooftop solar, EV chargers) on bulk-power system operation. Co-simulation is a technique that allows simulators to trade information during run-time, effectively creating larger and more complex models. HELICS is a co-simulation platform that has been developed to enable these kinds of power system analysis, incorporating tools from a variety of domains including the electrical power grid, natural gas, transportation, and communications. This paper summarizes the technical design of HELICS, describes how tools can be integrated into the platform, and reviews a number of analyses that have been performed using HELICS. A short video summary of this paper can be found at https://youtu.be/BIUiR_K87Wc.

PowerSimulations.jl is a Julia-based BSD-licensed power system operations simulation tool developed as a flexible and open source software for quasi-static power systems simulations including Production Cost Models. PowerSimulations.jl tackles the issues of developing a simulation model in a modular way providing tools for the formulation of decision models and emulation models that can be solved independently or in an interconnected fashion. This paper discusses the software implementation of PowerSimulations.jl as a template for the development and implementation of operation simulators, providing solutions to commonly encountered issues like time series read/write and results sharing between models. The paper includes a publicly-available validation of classical operations simulations as well as examples of the advanced features of the software.

PowerSystems.jl is a package to organize and manipulate data for the study of energy systems with diverse modeling requirements. This software serves two main purposes: to reduce the burden of large power system data set development, and to promote reproducible research and simulation. PowerSystems.jl implements an abstract hierarchy to represent and customize power systems data and includes data containers for quasi-static and dynamic simulation applications. Key features include efficient management of large quantities of time series data, optimized serialization, and comprehensive validation capabilities. The paper includes detailed discussion and examples for reference.

The evolving nature of electricity production, transmission, and consumption necessitates an update to the IEEE's Reliability Test System (RTS), which was last modernized in 1996. The update presented here introduces a generation mix more representative of modern power systems, with the removal of several nuclear and oil-generating units and the addition of natural gas, wind, solar photovoltaics, concentrating solar power, and energy storage. The update includes assigning the test system a geographic location in the southwestern United States to enable the integration of spatio-temporally consistent wind, solar, and load data with forecasts. Additional updates include common RTS transmission modifications in published literature, definitions for reserve product requirements, and market simulation descriptions to enable benchmarking of multi-period power system scheduling problems. The final section presents example results from a production cost modeling simulation on the updated RTS system data.

The rapid growth of distributed energy resources (DERs) has prompted increasing interest in the monitoring and control of DERs through hybrid smart grid communications resulting in the typical smart grid cyber-physical system. To fully understand the interdependency between them, we propose to integrate the Network Simulator 3 (NS3) into the High Engine for Large-Scale Infrastructure Co-Simulation (HELICS), a new open-source, cyber-physical-energy co-simulation platform. This paper aims to the development and case study of the HELICS-based high performance distribution-communication co-simulation framework for the DER coordination. The novel co-simulation framework for the NS3 integrating into the HELICS is developed. The DER monitoring application about hybrid smart grid communication network design is simulated and validated on this proposed HELICS-based cyber-physical co-simulation platform.

This presentation provides an overview of full-scale, high-quality, synthetic distribution system data set(s) for testing distribution automation algorithms, distributed control approaches, ADMS capabilities, and other emerging distribution technologies.

This paper explores the differences between simulating price-responsive load (PRL) interactions with power systems using integrated transmission and distribution (T&D) models and transmission-only (T-only) models. This analysis uses the Integrated Grid Modeling System (IGMS) software to capture "ISO-to-appliance" simulations using a synthetic T&D model built on the PJM 5-Bus transmission network with multiple full-scale taxonomy feeders that include physics-based models of thousands of customers and PRLs. The results show important differences in the impacts of PRLs between integrated T&D and T-only models. Experiments with the synthetic integrated T&D dataset demonstrated that integrated T&D simulation revealed notably larger differences between the PRL and no-PRL cases for load, and prices compared to T-only simulation. Similarly, differences are observed between the price response of individual buildings and distribution feeders and the corresponding transmission bus in the integrated T&D simulations, which are difficult to capture in traditional T-only simulations.

Increasing penetration levels of distributed energy resources are making the deployment of microgrids more feasible. Controllers that operate such microgrids are key to realizing the objectives of the microgrid owner or operator and there is a need to evaluate microgrid controller performance prior to field deployment. This paper describes a controller hardware-in-the-loop and power hardware-in-the-loop microgrid controller test bed that was designed and constructed to evaluate the capabilities of a microgrid controller for a proposed campus microgrid. This paper also presents a test methodology to evaluate microgrid controller functionality, and it describes how the controller was assessed through the application of different test scenarios. Results from the testing are presented to provide insight into the capabilities of the test bed.

Rapid growth of distributed energy resources has prompted increasing interest in integrated Transmission (T) and Distribution (D) modeling. This paper presents the results of a distributed generation from solar photovoltaics (DGPV) impact assessment study that was performed using a synthetic T&D model. The primary objective of the study was to present a new approach for DGPV impact assessment, where along with detailed models of transmission and distribution networks, consumer loads were modeled using the physics of end-use equipment, and DGPV was geographically dispersed and connected to the secondary distribution networks. The study highlights (i) how a lack of DGPV forecasting can increase the Area Control Error (ACE) at the transmission level for high penetration levels; and (ii) how capturing transmission voltage changes using integrated T&D can change simulated distribution voltage profiles and voltage regulator operations between integrated T&D and distribution-only simulations.

We explore the potential for using dynamic programming (DP) and approximate dynamic programming (ADP) techniques to optimize the set points of distributed photovoltaic (DGPV) inverters under uncertainty about future DGPV deployment. We consider a case where a large ( ≥ 1MW) system is installed first, and growth in deployment of small rooftop systems is anticipated, but uncertain. We find that for a real feeder (EPRI's J1 feeder), a significant reduction in the number and severity of voltage violations can be expected when DP or ADP is used compared to selecting set points based on the current conditions (the traditional myopic approach). Additionally, we find that using a simple ADP algorithm, sampled backward induction, is more than twice as fast as DP with similar outcomes.

Hardware-in-the-loop (HIL) simulations are increasingly employed in power engineering as more inverter-based generation and smart appliances are connected to the electric grid. HIL techniques allow for the co-simulation of analytical models with actual devices whose complex behavior is computationally inefficient or difficult to model. They also allow for testing the behavior of these devices under adverse conditions that rarely occur in the field but are important to evaluate. This paper provides an overview of how HIL simulations have been used to date and proposes that HIL simulations should play an important role in evaluating new control strategies, especially at the distribution level, that are being proposed to allow for continued affordable and reliable operation of the electric grid. We present a new capability that was developed to evaluate the interactions between residential loads and the smart grid: smart home hardware-in-the-loop. The paper includes results from an HIL experiment that incorporates multiple technologies and controls.

Electricity markets must match real-time supply and demand of electricity. With increasing penetration of renewable resources, it is important that this balancing is done effectively, considering the high uncertainty of wind and solar energy. Storing electrical energy can make the grid more reliable and efficient and energy storage is proposed as a complement to highly variable renewable energy sources. However, for investments in energy storage to increase, participating in the market must become economically viable for owners. This paper proposes a stochastic formulation of a storage owner's arbitrage profit maximization problem under uncertainty in day-ahead and real-time market prices. The proposed model helps storage owners in market bidding and operational decisions and in estimation of the economic viability of energy storage. Case study results on realistic market price data show that the novel stochastic bidding approach does significantly better than the deterministic benchmark.

This paper explores Home Energy Management System (HEMS) algorithms to minimize household cost while maintaining comfort when faced with uncertain weather, and demand. Specifically, we consider a HEMS that optimizes forward looking schedules for a home’s heating, ventilation, and air conditioning (HVAC); water heater (WH); and electric vehicle (EV) charging while considering uncertainty in outside temperature, hot water usage, and non-controllable load (NCL). We adopt a Dynamic Programming (DP) formulation and utilize the Dynamic programming for Adaptive Modeling and Optimization (DYNAMO) toolkit to implement DP and approximate dynamic programming (ADP) algorithms. Simulation results under a single tariff plan compare the quality of the solution generated by ADP to that of DP, and show significant improvement in computation time while maintaining acceptable solution accuracy.

This paper explores the applicability of using dynamic programing (DP) and approximate dynamic programming (ADP) based methods for optimal dispatch of utility scale energy storage systems (ESS). In this study, the effectiveness of these approaches have been tested using the IEEE 13 node test feeder with distributed photovoltaics (PVs) and a utility scale storage system. In this work, a co-simulation based approach has been used to setup the experiment to be able to implement detailed ESS and network models. The results obtained from DP/ADP runs have been compared with three other control strategies both myopic and intelligent. Simulations results show that DP/ADP algorithms are a good candidate for optimal EES dispatch in terms of both solution quality and execution time.