End-use Loads Research Articles

A Data-Driven Algorithm for Enabling Delay Tolerance in Resilient Microgrid Controls Using Dynamic Mode Decomposition

The increased implementation of smart grid technologies in the power distribution grid presents unique opportunities that enable resiliency, but also brings challenges motivating needs for novel solutions and mitigation techniques. The bi-directional power and data flow allow for the grid to operate with increased resiliency, which is the ability to avoid discontinuity of service to end-use loads during extreme events. However, in applications where control of the distribution grid or microgrid relies on communication networks, the degradation of communication systems in the form of loss or high latency can cause maloperation and result in loss of end-use loads. This paper presents a novel framework to enable delay tolerance of centralized microgrid control schemes to mitigate communication system latency impacts and guarantee successful control action. We demonstrate the delay tolerance on a control scheme that operates a battery energy storage system (BESS) to offset the sudden loss of generation and maintain system frequency. During periods of severely degraded communication system performance, the proposed delay-tolerant algorithm compensates for the latency by utilizing a data-driven model generated at the device level using dynamic mode decomposition (DMD) to determine the performance of the communications. The DMD technique predicts the system’s frequency using device-level terminal measurements and provides updated control signals. The HELICS cosimulation platform evaluates the cyber-physical interaction of the power system model in GridLAB-D, the centralized control agent in Python, and the discrete network model in NS-3. The framework is tested and validated on the IEEE-123 node system modified to represent a networked remote microgrid model, and the results show an improvement in the dynamic performance of the control scheme when subject to communication delays.

Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology

The disparate nature of data for electric power utilities complicates the emergency recovery and response process. The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads, and in extreme events, leave the public without power for extended periods. This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology, population, and electric distribution line reliability indices into a unified, cloud-based, serverless framework that supports power system operations in response to extreme events. An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency, which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events. This technology was developed initially for Puerto Rico, following extreme hurricane and earthquake events in 2017 and 2020, but is applicable to utilities around the world. Given the highly abstract and modular design approach, this technology is equally applicable to any geographic region and similar natural hazard events. In addition to a review of the requirements, development, and deployment of this framework, technical aspects related to application performance and response time are highlighted.

Fifty shades of grey: Automated stochastic model identification of building heat dynamics

To reach the carbon emission reduction targets set by the European Union, the building sector has embraced multiple strategies such as building retrofit, demand side management, model predictive control and building load forecasting. All of which require knowledge of the building dynamics in order to effectively perform. However, the scaling-up of building modelling approaches is still, as of today, a recurrent challenge in the field. The heterogeneous building stock makes it tedious to tailor interpretable approaches in a scalable way. This work puts forward an automated and scalable method for stochastic model identification of building heat dynamics, implemented on a set of 247 Dutch residential buildings. From established models and selection approach, automation extensions were proposed along with a novel residual auto-correlation indicator, i.e., normalized Cumulated Periodogram Boundary Excess Sum (nCPBES), to classify obtained model fits. Out of the available building stock, 93 building heat dynamics models were identified as good fits, 95 were classified as close and 59 were designed as poor. The identified model parameters were leveraged to estimate thermal characteristics of the buildings to support building energy benchmarking, in particular, building envelope insulation performance. To encourage the dissimination of the work and assure reproducibility, the entire code base can be found on Github along with an example data set of 3 anonymized buildings. The presented method takes an important step towards the automation of building modeling approaches in the sector. It allows the development of applications at large-scale, enhancing building performance benchmarks, boosting city-scale building stock scenario modeling and assisting end-use load identifications as well as building energy flexibility potential estimation.

A Framework for Coordinated Self-Assembly of Networked Microgrids Using Consensus Algorithms

With the rapidly increasing deployment of distributed energy resources (DERs), electric power systems across the world are moving towards more distributed designs and operations. While not every distributed device will be part of a microgrid, microgrids do offer solutions for scalability and interoperability, and for coordination with centralized utility control systems. The challenge is that the microgrids will be heterogenous with respect to ownership, operational objectives, resource mix, and control systems. Due to these variations within the population of microgrids, there will be significant challenges with coordinating their operations to achieve global objectives using existing centralized controls and optimizations. This paper presents a framework for the coordination of heterogeneous groups of microgrids using consensus algorithms. The framework enables the active engagement of utility and non-utility microgrids using a distributed architecture to support end-use loads during the loss of the bulk power system. Results from laboratory testing and preliminary implementations, as well as details of an ongoing operational deployment at the Electric Power Board of Chattanooga are presented in this paper.

Standardizing Performance Metrics for Building-Level Electrical Distribution Systems

Building-level electrical distribution systems comprise a myriad of current-carrying equipment, conversion devices, and protection devices that deliver power from the utility or local distributed energy resources to end-use building loads. Electric power has traditionally been generated, transmitted, and distributed in alternating current (AC). However, the last decade has seen a significant increase in the integration of native direct current (DC) equipment that has elevated the importance of DC distribution systems. Numerous studies have comparatively examined the performance of various electrical distribution systems in buildings but have failed to achieve uniform conclusions, primarily because of a lack of consistent and analogous performance evaluation methods. This paper aims to fill this gap by providing a standard set of metrics and measurement boundaries to consistently evaluate the performance of AC, DC, or hybrid AC/DC electrical distribution systems. The efficacy of the proposed approach is evaluated on a representative medium-sized commercial office building model with AC distribution and an equivalent hybrid AC/DC and DC distribution model, wherein the AC distribution model is concluded to be the most efficient. The simulation results show variation in computed metrics with different selected boundaries that verify the effectiveness of the proposed approach in ensuring consistent computation of the performance of building-level electrical distribution systems. This paper provides an initial set of guidelines for building energy system stakeholders to adopt appropriate solutions, thus leading to more efficient energy systems.

Control algorithms to mitigate the effect of uncertainties in residential demand management

Uncertainties at end-user and aggregator levels can be highly detrimental to the practical implementation of residential load control schemes for electricity market applications. Uncertainty factors such as end-user non-compliance, comfort violations and load set-point changes associated with the demand response aggregator are unavoidable in practice. This paper proposes a novel two-stage control algorithm for robust centralised management of aggregate residential loads which guarantee precise load set-point tracking in the presence of uncertainties occurring in real-time while ensuring that end-user thermal comfort is not compromised. The approach is underpinned by optimal selection of appliances based on an emulated supply curve followed by solving a one-step-ahead optimisation problem. Using air conditioners and water heaters as the controllable loads, the paper illustrates the effectiveness of the proposed approach in load management whilst mitigating the effects of unknown uncertainties. Further, the developed control scheme is compared with an existing industry approach. The results yield that the proposed control scheme is robust to uncertainties, preserves thermal comfort and is applicable for practical implementation under existing demand response standards.

Research on Model Predictive Control Strategy of Converter in Building DC Power Supply System

The popularization of distributed renewable energy, the change of end-user load charACteristics, and the requirements of energy conservation and environmental protection have brought great challenges to the traditional AC power supply. Compared with AC power supply, DC power supply is widely concerned because of its powerful energy-saving advantages. Building electricity demand fluctuates greatly, and various power loads are frequently switched, which requires a higher demanding for the dynamic response capability. Based on switched system theory, this paper proposes a switched control method for building dc microgrid converter. The outer voltage loop adopts PI control and the inner current loop adopts predictive current control. The switched control model of the converter is established and the optimal switched rate is designed. An experimental platform based on SiC MOSFET is built for verification. The switched frequency is 50KHz, which verifies the effectiveness of the control strategy.

Off-grid solar photovoltaic/hydrogen fuel cell system for renewable energy generation: An investigation based on techno-economic feasibility assessment for the application of end-user load demand in North-East India

In this paper, an optimal off-grid solar photovoltaic (PV)/hydrogen fuel cell (FC) (HFC) based energy system is proposed for renewable energy generation to supply electricity to the end-user load demand in north-east (NE) Indian states. The energy system is modeled and simulated in the HOMER software. The monthly average global horizontal solar radiation (GHSR) and ambient temperature of the NE states are found to be 3.95–4.79 kWh/m2 and 12.9–25.3 °C, respectively. The synthesized typical AC electrical load profile of 10 kWp is assumed to have a scaled daily annual mean value equal to 138 kWh/d and an average base demand value equal to 5.75 kW. With the considered connected load, the available GHSR and ambient temperature, the optimal sizes of renewable energy components for the considered eight geographical locations (latitude: 23° 30′-27° 3′ North, longitude: 88° 37' – 94° 7′ East) are determined based on the minimum levelized cost of energy (COE) and lowest net present cost (NPC). The optimal system architecture range obtained is 110–120 kW PV array, 10–15 kW FC, 30–60 kW electrolyzer, 40–60 kg hydrogen tank capacity and 15 kW converter. Based on optimization results, the levelized COE and NPC range obtained are 0.509–0.689 $/kWh and $ 3,27,557–4,43,004, respectively, which confirm the cost-effectiveness of the proposed optimal energy system. Besides, with a renewable fraction of 100%, the electricity generated by the PV/HFC system satisfied the load demand and produced excess electricity with capacity factor found to be 16.4–31.2% in all the NE Indian states. Moreover, the optimized electrical profile, PV profile, inverter profile, and hydrogen production from an FC are compared and analyzed in terms of monthly average and annualized values for all the NE states. Also, the range of net hydrogen produced by the electrolyzer over the year obtained is 1538–1856 kg/yr.

A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles

End-use specific residential electricity load profiles are of interest for energy system modelling that requires future load curves or demand-side management. We present a model that is applicable across countries to predict consumption on a regional and national scale, using openly available data. The model uses neural networks (NNs) to correlate measured consumption from one country (United Kingdom) with weather data and daily profiles of a mix of human activity and device specific power profiles. We then use region-specific weather data and time-use surveys as input for the trained NNs to predict unscaled electric load profiles. The total power profile consists of the end-use household load profiles scaled with real consumption. We compare the model’s results with measured and independently simulated profiles of various European countries. The NNs achieve a mean absolute error compared with the average load of 6.5 to 33% for the test set. For Germany, the standard deviation between the simulation, the standard load profile H0, and measurements from the University of Applied Sciences Berlin is 26.5%. Our approach reduces the amount of input data required compared with existing models for modelling region-specific electricity load profiles considering end-uses and seasonality based on weather parameters. Hourly load profiles for 29 European countries based on four historical weather years are distributed under an open license.

OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies

Electrification and the growth of distributed energy resources (DERs), including flexible loads, are changing the energy landscape of electric distribution systems and creating new challenges and opportunities for electric utilities. Changes in demand profiles require improvements in distribution system load models, which have not historically accounted for device controllability or impacts on customer comfort. Although building modeling research has focused on these features, there is a need to incorporate them into distribution load models that include DERs and can be used to study grid-interactive buildings. In this paper, we present the Object-oriented, Controllable, High-resolution Residential Energy (OCHRE) model. OCHRE is a controllable thermal-electric residential energy model that captures building thermal dynamics, integrates grid-dependent electrical behavior, contains models for common DERs and end-use loads, and simulates at a time resolution down to 1 minute. It includes models for space heaters, air conditioners, water heaters, electric vehicles, photovoltaics, and batteries that are externally controllable and integrated in a co-simulation framework. Using a proposed zero energy ready community in Colorado, we co-simulate a distribution grid and 498 all-electric homes with a diverse set of efficiency levels and equipment properties. We show that controllable devices can reduce peak demand within a neighborhood by up to 73% during a critical peak period without sacrificing occupant comfort. We also demonstrate the importance of modeling load diversity at a high time resolution when quantifying power and voltage fluctuations across a distribution system.

Data-Driven Engineering: The Reliability and Resilience of the North American Bulk Power System [Technology Leaders

Electricity is an essential need for modern society. Nearly everything we do relies on safe and affordable electric energy. The constant demand for reliable energy delivery exists during a time of rapid changes to and evolutions of the bulk power system (BPS) in North America. Inverter-based resources, such as wind, solar photovoltaic (PV), battery energy storage systems, and hybrid plants, continue to transform the mix of BPS-connected generating resources. Sustainability and climate change initiatives are driving innovations in end-use loads, such as the electrification of the transportation sector. Distributed energy resources (DERs), aggregators, and the Internet of Things are pushing consumers of electric energy toward becoming prosumers in our electrical ecosystem. The persistent threat of cyberattacks on the electrical infrastructure by cybercriminals and nation-states is bringing attention to securing the BPS. These challenges are significant, yet the common themes toward how to overcome them may be more straightforward: Collaboration is key, information sharing is critical, technology innovation is inevitable, and engineering decisions require adequate data to develop appropriate solutions.

A Case Study to Quantify Variability in Building Load Profiles

Recent technology development and penetration of advanced metering infrastructure (AMI), advanced building control systems, and the internet-of-things (IoT) in the built environment are providing detailed information on building operation, performance, and user’s comfort and behavior. Building owners can obtain a wide range of energy consumption details at various levels of time granularity to augment their decisions as they manage the building operation and interact with the grid. AMI data are providing a new level of detail and visibility that may enhance building services and assets in the smart grid domain and make buildings inch closer to becoming a grid-interactive energy efficient buildings (GEB). While utility-installed AMI typically records energy consumption at a 15, 30, or 60-minute resolution, building-owner-installed metering can record energy consumption at one-minute or sub-minute time scales, providing information about how much the energy consumption varies from one sub-minute to the other (i.e. variability) at a finer time resolutions than typically available from AMI. This paper examines one-minute building load profile data sets and presents a framework to study, define, extract, quantify and analyze variability in buildings’ load profiles. The discussion of variability and its analysis is based on a case study of an actual sub-minute time-resolution data set, collected in 2019, for two buildings in a Midwest state in the USA. The result shows that for the case studies, the level of variability in an end-use category is not simply proportional to its consumption. Furthermore, distinct and predictable daily variability patterns emerge in end-use load categories. This information is useful for a host of applications including prediction, forecasting, and modeling.

ELECTRICITY LOAD PROFILING FOR COASTAL HOUSING COMPLEXES BASED ON THE MEASUREMENTS OF FLATS ACTUAL LOAD

In this paper, a methodology for estimating end-use load shapes using the hourly whole-house metered load data, the household demographic survey data and the weather data (temperature) is presented. End use load shapes presents a method of generating realistic electricity load profile data for some of city of Tripoli domestic buildings. This method could help in predicting the daily load profile from individual flats to community. The results obtained show that the overall methodology provides an effective means for end-use load shape modeling and estimation.

Power Quality Phenomena in Electric Railway Power Supply Systems: An Exhaustive Framework and Classification

Electric railway power systems (ERPS) as one of the most critical and high-power end-user loads of utility grids are characterized by outlandish power quality (PQ) problems all over the world. The extension and evolution of different supply topologies for these systems has resulted in significant and various forms of distortions in network voltage and current in all ERPS, the connected power system, and adjacent consumers. During the last years, numerous studies have been offered to investigate various aspects of PQs in a specific supplying topology. Variation in the supply structure of the ERPS and different types of locomotives has propelled the observation of different PQ phenomena. This versatility and development have led to confront considerable types of two-way interactive interfaces as well as reliability and PQ problems in ERPS. In addition, the lack of standards explicitly dedicated to ERPS has added to the ambiguity and complexity of this issue. In this paper, an extensive review of PQ distortions and phenomena in different configurations of ERPS is proposed and a systematic classification is presented. More than 140 scientific papers and publications are studied and categorized which can provide a fast review and a perfect perspective on the status of PQ indexes for researchers and experts.

Simulating dispatchable grid services provided by flexible building loads: State of the art and needed building energy modeling improvements

End-use electrical loads in residential and commercial buildings are evolving into flexible and cost-effective resources to improve electric grid reliability, reduce costs, and support increased hosting of distributed renewable generation. This article reviews the simulation of utility services delivered by buildings for the purpose of electric grid operational modeling. We consider services delivered to (1) the high-voltage bulk power system through the coordinated action of many, distributed building loads working together, and (2) targeted support provided to the operation of low-voltage electric distribution grids. Although an exhaustive exploration is not possible, we emphasize the ancillary services and voltage management buildings can provide and summarize the gaps in our ability to simulate them with traditional building energy modeling (BEM) tools, suggesting pathways for future research and development.

Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings

The U.S. power grid is transforming to become “smarter,” cleaner, and more efficient. This is leading to the addition of significant distributed variable renewable generation. Due to the variable nature of renewable generation, the short- and long-term supply-demand imbalances are less predictable, and conventional approaches to mitigating the imbalance will not be efficient or cost-effective. To address this challenge, transactive control technologies have been proposed. The transactive control approach requires individual end-use loads to express flexibility as a function of price. To model flexibility while maintaining robustness to any non-linear behavior exhibited by end-use loads, machine learning approaches for load forecasting are being explored. However, certain aspects, such as how much training data is required and how deep models for load forecasting should be structured and trained are not well understood. This work explores how to apply sequence-to-sequence recurrent neural networks to short-term electrical load forecasting with a case study of four commercial office buildings. We find that it is best to start the training in the middle of a heating or cooling season with at least six months of data. We further show that models perform best when predictions are conditioned on three to 12 h of prior data, with a decrease in performance for shorter contexts. We identify recommended ranges for common hyperparameters that could be used by practitioners applying similar models to their own tasks. Finally, we find that transferability of models across buildings is highly dependent on the building pairs, but in the best case, models are highly transferable.

Distribution system segmented model simplification with independent dynamically changing end-use loads

With more emerging technologies in power distribution systems, accurately modeling the dynamic behavior of end-use load under various conditions has become important. One approach has been quasi-static time-series (QSTS) power flow simulation over short time frames. However, time and cost can be a burden for the QSTS simulation of thousands of houses with dynamic loads on large distribution feeders. This may limit sensitivity analysis, design optimization or exploration of alternatives on the distribution system. There are two main contributions of this paper in order to reduce the QSTS simulation complexity. First, the paper presents a method to stochastically model the dynamic behavior of end-use load for QSTS simulations. Second, using the stochastically modeled load behavior as part of a distribution system, and building on our previous QSTS simulation simplification/acceleration method for uniform load behavior, this paper presents a segment substitution approach for the simplification of distribution system models with multiple independent dynamically changing end-use loads. Our numerical results show that compared to GridLAB-D, a software tool that simulates end-use load behavior through physical models, the proposed stochastic modeling approach achieved an 89.3% simulation time reduction for end-use loads, while accurately capturing the temporally changing total load behavior. Moreover, the QSTS simulation of a large test distribution system through OpenDSS showed that the proposed simplification method decreased the feeder simulation time by a factor of three at less than 0.3% voltage error. Compared to our previous method, the voltage error with independent dynamic loads is reduced by a factor of four. This makes it usable for transactive system studies, including battery integration, compared to earlier methods that were tested on photovoltaic data.

Improving primary frequency response in networked microgrid operations using multilayer perceptron‐driven reinforcement learning

Individual microgrids can improve the reliability of power systems during extreme events, and networked microgrids can further improve efficiency through resource sharing and increase the resilience of critical end-use loads. However, networked microgrid operations can be subject to large transients due to switching and end-use loads, which can cause dynamic instability and lead to system collapse. These transients are especially prevalent in microgrids with high penetrations of grid-following inverter-connected renewable energy resources, which do not provide the system inertia or fast frequency response needed to mitigate the transients. One potential mitigation is to engage the existing generator controls to reduce system voltage in response to a frequency deviation, thereby reducing load and improving primary frequency response. This study investigates the use of a reinforcement-learning-based controller trained over several switching transient scenarios to modify generator controls during large frequency deviations. Compared to previously used proportional–integral controllers, the proposed controller can improve primary frequency response while adapting to changes in system topologies and conditions.

Learning‐based load control to support resilient networked microgrid operations

Networked and interconnected microgrids can improve resilience of critical end-use loads during extreme events. However, the frequency deviations in microgrids during transient events are significantly larger than those typically seen in bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. Grid Friendly ApplianceTM (GFA) controllers can mitigate the transient event effects by engaging end-use loads. This paper presents a method to select set-points for end-use loads equipped with GFA controllers, while minimizing the interruptions to end-use customers. An online (i.e. real-time), device-level algorithm adjusts individual GFA controller frequency setpoints based on the operational characteristics of each end-use load and on the changing grid dynamic characteristics to selectively engage the load for mitigating the switching transients. The adaptive gradient-descent-based algorithm does not require control or coordination amongst end-use devices for adapting frequency set-points. The method is validated using dynamic simulations on a modified version of the IEEE 123-node test system with three microgrids using the GridLAB-DTM simulation environment. The improved dynamic stability achieved through the engagement of GFAs support the switching operations necessary for networked microgrid operations.

Demand Side Management: Optimal Demand Response in Distribution Networks With Several Energy Retail Companies

AbstractIn this work, a social welfare maximization problem is solved to determine the optimal scheduling of end-user controllable loads, smart appliances, and energy storage. The framework considers multiple retail energy suppliers as well as the AC power flow constraints of the distribution system. The demand side management program is focus on residential and commercial end-users. We have formulated a day-ahead residential bidding/buyback scheme modeled as an optimal power flow problem. This demand side program schedules end-user’s controllable loads or smart appliances and takes advantage of the flexibility of an energy storage system. The demand side management scheme minimizes retail company’s operating costs in the wholesale market, and it also considers distribution network constraints, assuring the appropriate quality of service. We have used a dual decomposition method to decouple some constraints while maximizing social welfare. We have also introduced a demand response call event with the main objective to take into consideration the system operational constraints. Through the coordination via local marginal prices, we have obtained a decentralized and distributed bidding/buyback scheme proposing a demand side management program that preserves the integrity of the private information of the different participants.