Secure Scalable Control and Communications for Distributed PV (Final Technical Report)

The ESS is considered as an effective tool for enhancing the flexibility and controllability of a wind farm, and the optimal control scheme of a wind farm with distributed ESSs is vital to the stable operation of wind power generation. In this paper, a coordinated active and reactive power control strategy based on model predictive control (MPC) is proposed for doubly fed induction generator (DFIG)-based wind farm (WF) with distributed energy storage systems (ESSs). The proposed control scheme coordinates the active and reactive power output among DFIG wind turbines (WTs), grid-side converters (GSCs), and distributed ESSs inside the WF, and the aim is to decrease fatigue loads of WTs, make the WT terminal voltage inside the extent practicable, and take the WF economic operation into consideration. Moreover, the best reactive power references of DFIG stator and GSC are produced independently based on their dynamics. At last, the control scheme generates optimal power references for all ESS to make the SOC of each ESS converge to their average state. With the distributed ESSs, the WF controller regulates the WTs inside WF more flexibly. A WF composed of 10 DFIG WTs was utilized to verify the control performance of the proposed coordinated active and reactive power control strategy.

This paper proposes the droop control algorithm for multiple distributed Battery Energy Storage Systems (ESS) with their state of charge (SOC) feedback, shown to be effective in providing grid services while managing the SOC of the ESS. By extending the mathematical links between the ESS SOC and power dynamics for frequency regulation, this paper elaborates on how the proposed scheme integrates multiple ESS into the load frequency control and the ESS effectively augments the functional roles of the incumbent generators. The control performance for a group of ESS in grid operations is evaluated and characterized by using metrics such as the cut-off frequency and settling time, and the initial and final value theorems. Theoretical insights and practical issues are discussed. Various numerical examples and case studies for a simplified Australian network with high penetration of renewables demonstrate the validity and efficacy of the proposed method.

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks.

This paper considers a single-machine scheduling problem with sequence-dependent setup times and energy-generation and storage systems. Each job requires a sequence-dependent setup to be processed on the machine, and both setup and processing of the job require job-dependent amounts of energy. The energy consumed by the machine can be bought from an Electric Power Company (EPC) or generated by own Distributed Energy Resource (DER), such as solar photovoltaic or wind, and the energy can be stored in an Energy Storage System (ESS). The objective is to minimise the total cost, the sum of production cost depending on makespan and energy cost by considering energy usage from the EPC, DER and ESS. For the problem, a mathematical programming model is first derived by using period-based indexes. Then, a hybrid genetic algorithm, which adds appropriate idle times between jobs and determines an efficient energy schedule by storing some energy during less expensive periods into the ESS for later use in high-price periods, is developed. Finally, computational experiments show that the proposed algorithm provides effective solutions, and which component of the total cost affects the performance the most, how effective adding idle times between jobs is, and how much cost can be saved by having a DER and ESS.

This study explores the management of a system of multiple interconnected microgrids in a loop configuration. To facilitate renewable integration and reduce the cost of operation, an economic dispatch model of the distributed energy resources (DER) with dynamic update of the states of the energy storage systems (ESS) to handle the forecasting error in the photovoltaic (PV) power output is developed. By simulating the economic dispatch of DER for various PV output profiles, the minimum size of ESS required in a microgrid can be determined. The sizing and placement of ESS in a loop configuration, be it one ESS for each individual microgrid or one ESS for the whole interconnected system, are also explored. The case study conducted found that the total capacity of the ESS for the interconnected microgrids in a loop configuration with a single shared ESS is smaller than that for the loop configuration with individual ESS, which is in turn smaller than that for the islanded mode.

From April 2021 through March 2024, the Interstate Renewable Energy Council (IREC) led a collaborative project to develop a free online clearinghouse of educational resources about solar photovoltaics (PV), energy storage systems (ESS), electric vehicle supply equipment (EVSE), and grid-interactive efficient building (GEB) technologies. Two websites—the Clean Energy Clearinghouse and CleanEnergyTraining.org—housed over 70 educational resources. Over the course of the three-year project, 154,272 unique visitors accessed the learning materials. Learner feedback was overwhelmingly positive. Even through the end of the project, there was sustained demand for education and communication. A primary innovation of the project was to drive multiple complementary audiences to the same place. Building owners, designers, installation contractors and developers, authorities having jurisdiction (AHJs), and fire service personnel all benefit from a shared understanding of clean energy technologies, including safety and code-related requirements. When considering the impact on the target audience, the project team worked with partners and advisors to inform resource creation and delivery in such a way as to address key motivational factors of the target audience and compel each user to seek additional information on the topic and return to the Clean Energy Clearinghouse website as their central location for more information. Resources were intentionally developed to be concise—five to 15 minutes—and accessible, meaning not overly technical. Providing basic information demystified the technologies and invited the professional to explore additional learning opportunities. Awardee and partner collaboration was key to project success. IREC facilitated collaboration among the other Topic 2 awardees, Southface and New Buildings Institute (NBI). The three awardees shared relevant information gained through discovery and validation questionnaires that informed product development and reduced duplication of effort by coordinating the development of complementary, and not competing, educational resources. Inspired by this collaboration, IREC brought on additional partners even in the final year of the project. Five regional energy efficiency organizations were part of the project, which expanded the connection between efficiency and distributed energy resources. We also included resources on the Clearinghouse that were developed through other federally funded projects, such as the Buildings Energy Efficiency Frontiers & Innovation Technologies (BENEFIT) program. The website was developed with the learner in mind, and not solely the funding source. Feedback from stakeholders throughout the project, and especially in its final year, indicated the need for continued education and facilitated communication among stakeholders to further the safe and widespread adoption of clean energy.

A microgrid (MG) is a discrete energy system consisting of an interconnection of distributed energy sources and loads capable of operating in parallel with or independently from the main power grid. The microgrid concept integrated with renewable energy generation and energy storage systems has gained significant interest recently, triggered by increasing demand for clean, efficient, secure, reliable and sustainable heat and electricity. However, the concept of efficient integration of energy storage systems faces many challenges (e.g., charging, discharging, safety, size, cost, reliability and overall management). Additionally, proper implementation and justification of these technologies in MGs cannot be done without energy management systems, which control various aspects of power management and operation of energy storage systems in microgrids. This review discusses different energy storage technologies that can have high penetration and integration in microgrids. Moreover, their working operations and characteristics are discussed. An overview of the controls of energy management systems for microgrids with distributed energy storage systems is also included in the scope of this review.

The increasing penetration of distributed energy resources (DERs) into power systems has transformed the landscape of energy distribution, bringing both opportunities and significant challenges. This study examines the impact of DER integration on power system stability, focusing on voltage stability, frequency stability, and transient stability. A comprehensive review of 50 high-quality studies from peer-reviewed journals and reputable conference proceedings was conducted, utilizing advanced modeling, simulation, and empirical analysis methods. The findings reveal that the intermittent nature of renewable DERs, such as solar and wind, leads to voltage fluctuations, necessitating advanced control strategies to maintain stability. Additionally, the displacement of traditional synchronous generators by inverter-based DERs reduces system inertia, posing severe frequency stability challenges that require innovative solutions like synthetic inertia and fast frequency response mechanisms. Transient stability issues are also exacerbated by DER integration, highlighting the need for advanced inverter controls and enhanced fault ride-through capabilities. Energy storage systems (ESS) are identified as crucial for buffering the variability of renewable DERs, providing essential services such as frequency regulation and voltage support. However, high costs and scalability issues remain barriers to widespread ESS adoption. The study underscores the importance of supportive regulatory and policy frameworks in facilitating the seamless integration of DERs while maintaining grid stability. Effective policies that promote smart grid technologies and DER-friendly regulations are essential for ensuring a stable, resilient, and sustainable power grid. This research contributes to a deeper understanding of the complex dynamics introduced by DERs and offers insights into developing robust strategies to address stability challenges in modern power systems.

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs).In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation(SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

A smart grid (SG) is an emerging technology that provides electricity in a cost-efficient and eco-friendly way. SG combined with distributed energy resources (DERs) plays a crucial role in extending the existing grid’s capacity while mitigating carbon emissions. The potential sources of DERs include solar, wind, and tidal energy. Usually, these DERs are located far away from the grid and not necessarily tied to the grid system. However, the energy trading capabilities of a grid-tied DERs are getting attention, both from academia and industry. This bonding of grid-tied DERs helps to decrease the loss of surplus energy, build an energy storage capacity, and other operational charges. Energy-consuming flexible home tasks can be optimized coordinately with the operations of DERs to minimize the economic cost and $CO_{2}$ emissions. In this work, our problem is multi-objective and we aim to reduce both electricity price and $CO_{2}$ emission. We proposed a multi-objective self-adaptive multi-population based Jaya algorithm (PMO-SAMP-Jaya) to schedule the operations of flexible home tasks. Different pricing schemes have been applied to uncover the correlation between $CO_{2}$ emission, economic cost, and pricing schemes. We assume a smart building, including 30 smart homes with PV and energy storage system (ESS) as DERs. Promising results have shown the effectiveness of our proposed scheme.

Distributed energy resources (DER) such as photovoltaic (PV) systems, when deployed in a large scale, are capable of influencing significantly the operation of power systems. Looking to the future, stakeholders are working on standards to make it possible to manage the potentially complex interactions between DER and the power system. In 2009, the Electric Power Research Institute (EPRI), Sandia National Laboratories (SNL) with the U.S. Department of Energy (DOE), and the Solar Electric Power Association (SEPA) initiated a large industry collaborative to identify and standardize definitions for a set of DER grid support functions. While the initial effort concentrated on grid-tied PV inverters and energy storage systems, the concepts have applicability to all DER. A partial product of this on-going effort is a reference definitions document (IEC TR 61850-90-7, Object models for power converters in distributed energy resources (DER) systems) that has become a basis for expansion of related International Electrotechnical Commission (IEC) standards, and is supported by US National Institute of Standards and Technology (NIST) Smart Grid Interoperability Panel (SGIP). Some industry-led organizations advancing communications protocols have also embraced this work. As standards continue to evolve, it is necessary to develop test protocols to independently verify that the inverters are properly executing the advanced functions. Interoperability is assured by establishing common definitions for the functions and a method to test compliance with operational requirements. This document describes test protocols developed by SNL to evaluate the electrical performance and operational capabilities of PV inverters and energy storage, as described in IEC TR 61850-90-7. While many of these functions are not currently required by existing grid codes or may not be widely available commercially, the industry is rapidly moving in that direction. Interoperability issues are already apparent as some of these inverter capabilities are being incorporated in large demonstration and commercial projects. The test protocols are intended to be used to verify acceptable performance of inverters within the standard framework described in IEC TR 61850-90-7. These test protocols, as they are refined and validated over time, can become precursors for future certification test procedures for DER advanced grid support functions.

This paper proposes a hierarchical look-ahead framework to conduct conservation voltage reduction (CVR) when distributed energy resources such as solar photovoltaic (PV) systems and energy storage systems (ESSs), and demand response programs are integrated into distribution systems. With the increasing deployment of PV systems in distribution systems, their frequently varying power output due to cloud movements could have a detrimental impact on the consumer’s voltage quality, consequently leading to degraded CVR performance. A two-level CVR framework for the coordination of an on-load tap changer (OLTC), capacitor banks (CBs), and the smart inverters of PV systems/ESSs is presented, in which these elements operate to reduce the voltage profile along the distribution feeder at different temporal scales. At the global level, the operations of the OLTC and the CBs are scheduled every hour to achieve the best CVR performance in an optimization problem using mixed-integer linear programming. When voltage violations occur rapidly, the smart inverters of PV systems and ESSs help to maintain a lower voltage profile every second based on the proposed piecewise droop control functions at the local level. A simulation study is carried out in an IEEE 33-bus distribution system with an OLTC, CBs, PV systems, and ESSs, and our results demonstrate the advantages of the proposed approach in terms of voltage level and energy savings. Furthermore, the impact of demand reduction on the proposed approach is quantified, and we verify that a higher demand reduction yields more energy savings in the proposed framework.

In order to solve the problems of increased voltage fluctuation and increased peak load caused by the high proportion of distributed photovoltaic (PV) and electric vehicles (EVs) connected to the distribution network, it is helpful to increase the storage efficiency and reduce the incremental cost of energy storage system (ESS) to carry out the energy storage supplement planning based on the aggregation of the EVs. Firstly, the “distributed energy storage +” resource partition hierarchical control topology and the economic incentive model of on-board energy storage aggregation are established, including on-board energy storage and distributed energy storage system (DESS). The mathematical models of on-board energy storage, DESS and the calculation model of the potential of on-board energy storage are established. Then, a DESS supplement planning model is built, which is including on-board energy storage participating in system peak shaving and valley filling. And the solution flow is given. Finally, an example is given based on IEEE-33 bus distribution network. It is verified that the supplementary planning based on the aggregation of existing resource is effective for cost reduction and efficiency increase of energy storage resource.

Distributed Energy Resources and their increased penetration within the power grid are a suitable method to provide essential reliability services. In order to do so, they must operate in aggregate. Aggregation of a wide variety of systems with varying standards and protocols is a complex task, which could benefit from rules and standards to support interoperability. To this end, the Portland State University Power Lab has developed the Energy Grid of Things project, which includes a Distributed Energy Resource Management System to provide aggregation and demonstrate the usage of an Energy Services Interface: a set of rules governing the interface between resource owners and aggregator businesses. This thesis presents a Modeling Environment that provides a simulation and test platform reflecting the complex needs of an aggregator. A grid simulator is required that is able to reflect the effects of thousands of Distributed Energy Resources operating in tandem; furthermore, these effects must be communicated to external actors such as the Grid Operator, which provides grid service requests to the aggregator and requires feedback. The Modeling Environment provides all of this functionality by leveraging the GridAPPS-D platform to develop an interoperable class-based system that provides configurable simulations with modular input and output APIs. The Modeling Environment uses abstractions to allow any feasible type of input to be reflected within the model as a Distributed Energy Resource, and serves as a proof-of-concept for the development of aggregation simulation and testing within GridAPPS-D.