Distributed Energy Resources

With the proliferation of distributed energy resources (DERs) like solar PV and other clean energy generation, battery energy storage systems (BESS), emergency generator arrays etc., the entire landscape of electrical distribution is undergoing a radical transformation. “Microgrids” – as defined by the U.S. Department of Energy, “a group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid” – are becoming an integral part of the modern electrical distribution domain. The connection of local microgrids to larger capacity utility grids brings a whole host of design challenges to engineering and operations, both at individual facilities and at utilities. These parameters can be broadly classified in two categories:

• Electrical: The rise of “prosumers” (proactive consumers) means that protection and control, grid stability etc. must now be designed with a distributed microgrid infrastructure in mind.

• Data (Digitization): Design of a modern digital system architecture warrants a thorough understanding of requirements in terms of data models, software interfaces and interchange of data between various sub-systems.

Three factors are responsible for accelerating the adoption of microgrids. Firstly, the economics of deploying distributed energy resources, especially renewables like solar photovoltaic (PV) and battery energy storage systems (BESS), has changed dramatically over the last decade. The installed cost of solar PV has fallen significantly, and many vendors now offer packaged BESS solutions. Many states and local governments offer incentives for “net zero” energy consumption or other economic incentives to export generation capacity back to the utility grid. Facilities can get significant savings by optimizing their local generation to take advantage of rate tariffs or reduce peak demand charges.

Secondly, many companies now embrace sustainability directives to evolve to “green” consumption. ESG (Environmental, Social, and Governance) metrics for many companies now include sustainability and renewable energy targets, driving microgrid projects.

Finally, the importance of facility resilience and dependable power has magnified enormously. Costs of power interruptions are astronomical at mission-critical facilities, driving microgrid projects to increase local generation to ride out natural disasters like the California wildfire related power shutoffs of 2019 and the Texas winter storm disruptions of 2021. Additionally, there is a strong impetus for all backup generation (both existing and new) to switch to low carbon sources like solar PV.

The scale and size of microgrids varies widely, ranging from commercial and industrial microgrids (very large) serving smart cities, municipalities, manufacturing plants, large military bases. all the way to the small and medium microgrids serving individual buildings, gas stations, supermarkets, schools, supermarkets. This scale/size factor influences design criteria such as the complexity of controls, resilience, or the need to operate in islanded mode, and the interconnection with utilities.

Industry standards related to microgrids (both ANSI and IEC) are evolving rapidly and can be classified roughly as operating at three different levels.

• Individual DER, or component level: These include microgrid related language within component-level switchboard or panelboard standards, solar PV inverter standards.

• System level: These include explicit microgrid system standards relating to energy management or controls that involve multiple DERs operating as a synchronized unit. For example, the IEEE 2030.7 standard includes control functions for microgrid as a system that can manage itself, operate autonomously or grid connected, and seamlessly connect to and disconnect from the main distribution grid.

• Interconnection level: These focus specifically on the interconnection and interoperability between local microgrids or DERs with utility electric power systems (EPSs). These standards provide requirements relevant to the performance, operation, testing, safety considerations, and maintenance of the interconnection. For example, California Rule 21 has specific requirements on the types of data to be shared between microgrids or local smart DERs and the utility energy management system. Architectural details about the interconnection such as protocols of data interchange (IEEE2030.5), frequency of data updates, are also specified in California Rule 21.

Microgrid Standards classifies the various standards pertinent to the deployment of microgrids but is not intended to be comprehensive. Many local, state, and regional jurisdictions may also be relevant.

Typical microgrid functions can be classified into two main categories – microgrid operation, and microgrid optimization - as described below.

Microgrid Operation

• Monitoring:

  Many microgrids require monitoring from remote network operation centers (NOCs). Dependable monitoring of microgrids require the measurement and display of energy, power, and other metrics for individual DERs and loads. If microgrid sites have on-site operators, factor into the design a local HMI to display microgrid loads, generation and status information.

• Alarming and notification:

  Many microgrids require monitoring from remote network operation centers (NOCs). Dependable monitoring of microgrids require the measurement and display of energy, power, and other metrics for individual DERs and loads. If microgrid sites have on-site operators, factor into the design a local HMI to display microgrid loads, generation and status information.

  Alerting on abnormal operating conditions or malfunctions of microgrid components is an essential component of microgrid design.

• Export management: In some microgrid deployments, utilities may prohibit or limit export of active power to the grid. In these cases, use excess PV production to charge BESS or curtailed to minimize exceeding export thresholds. In addition to the basic control-limiting functions, export management can be extended to include optimization functions. For example, BESS may be preemptively discharged in preparation to absorb the expected excess PV based on weather forecasts.

• Grid connection management: When the microgrid is islanded (off-grid mode), many islanding sequences of operation must be safely managed. Balancing various generation sources optimally while islanded is another control function. For example, PV production and BESS charging or discharging may need to be orchestrated precisely to avoid imbalanced conditions. Conversely, when the utility grid is restored, the restoration sequences may need to be tuned to safely manage state transitions. These grid connection and disconnection sequences must be designed into the microgrid control algorithms.

• Load management: Both when islanded or when connected to utility grids, loads must be monitored and either shed or reconnected automatically based on the generation-consumption balance. For example, lower priority non-critical loads may be disconnected when islanded and only restored last after utility restoration. Even when connected to the utility grid, some local loads may need to be disconnected for certain operating conditions. Prioritization and control capabilities for loads is usually an important design criterion for microgrid operation.

Microgrid Organization

• Forecasting: Forecasting of both generation and loading is a fast-evolving R&D area with many applications. For example, weather forecasts can be used effectively to optimize microgrids. When stormy conditions or natural disasters like wildfires are forecasted, microgrids can be directed towards charging BESS in preparation, to improve system resilience during outages.

• Energy optimization: By controlling DERs optimally, significant savings can be achieved, especially at locations where rate tariffs vary substantially. For example, using local DER production (such as previously charged BESS or solar PV) during expensive on-peak tariff periods and by charging BESS during less expensive off-peak periods. More advanced applications can also optimize local generation to respond to real-time rate changes.

• Demand charge reduction: By controlling DERs appropriately, sites can avoid expensive peak demand charges. Using trends and analytics, software may also predict approaching peak demand values and make operational decisions in advance. For example, BESS may be charged in anticipation of predicted peaks and discharged to avoid them.

• Net zero management: By intelligently controlling energy storage and solar PV, utility consumption may be partially offset to facilitate “net zero” initiatives. For example, during sunny periods when local PV production is higher than site consumption, energy storage systems may be charged. Later in the day or at night, when solar PV production is low to zero, then the energy storage system can be discharged to meet local site loads.

• Demand response programs: Many utilities offer financial incentives to reduce or shift electricity usage during peak periods. Demand response programs are used as dynamic options for balancing supply and demand through mechanisms like real time pricing, and critical peak rebates. When utilities issue demand response signals to customers with microgrids, controls can increase local PV or BESS production to shift loads away from utility. Such participation in demand response programs can be financially attractive to customers.

Typical components of a microgrid can be categorized into three layers:

• Connected devices layer: These comprise “smart” devices and associated communications such as generator controls, battery energy storage systems, inverters, EV chargers, communicating circuit breakers.

• Edge layer: This layer comprises of controllers and software associated with orchestrating the coherent operation of the microgrid as a system.

• Analytics layer: This layer typically comprises of optimizing and analytics software that sends decision data to the edge layer. For example, predictive software may send down control signals to control individual DERs.

Cybersecure communications are an integral transverse function of data flow between the components of these three layers.

Geographies – differing tariffs, incentives, government initiatives

Clearly, microgrids provide energy users with a variety of benefits, ranging from providing energy resilience, to offering cost savings and carbon emission reductions. However, different entities may value these benefits very differently, depending on the mission and objective of the specific project. To determine an optimal system design, the local market and resource conditions must be carefully considered.

Geographical and regional considerations influence microgrid project development and design in the following ways:

• Availability or local suitability of energy resources: Some areas have a variety of energy resources readily accessible, including strong solar (such as, southern U.S.), consistent wind (such as, regions of the Midwestern U.S.), or built-out natural gas infrastructure. Other regions may be limited in infrastructure or natural resources that can be harnessed. Another dimension of geographic evaluation is urban versus. rural. For instance, urban locations may have space limitations for outdoor equipment because of high real-estate costs or zoning requirements. Urban locations may also be subject to solar PV shading from surrounding buildings. In contrast, rural customers may seemingly have ample land to locate resources like ground-mounted solar PV but may be in flood plains or be situated across public rights-of-way. Resource availability is highly site-specific and therefore requires consideration on a local level for optimal microgrid design.

• Energy rates: Energy customers are served by hundreds of electric utilities across the US and are charged for that energy using very different tariff structures. A simple tariff may use only the total energy used (quantity of kWh) a site consumes. Other tariffs may include a peak demand per kW charge, reactive power, power factor penalties and other billing determinants. Time-of-Use (TOU) rates where the cost per kWh of energy varies between off-peak, mid-peak and on-peak rates, depending on the time-period of use. Given that factors such as the competitive environment at the local level, the degree of renewables penetration, local politics. all impact the cost for utilities to produce and deliver electricity, customers face a variety of prices and mechanisms for how they are charged. These mechanisms greatly impact microgrid project development and ROI calculations.

• Federal, state, and local incentive programs: Federal incentives like the Investment Tax Credit (ITC) are key enablers of microgrids and apply to all U.S. geographies, reducing solar PV and BESS capital costs. Additional local incentive programs at the state, local or municipal level may further subsidize the upfront expense of onsite generation. For example, state-wide incentives like the Self Generating Incentive Program (SGIP) in California and the DEEP Microgrid Grant Program in Connecticut have proven to be highly effective mechanisms for deploying more distributed, clean, and resilient energy systems. Successful program references and an uptick of large policy measures such as the 2021 Federal Infrastructure Bill have prompted more state and local emphasis and funding for microgrids. The commercial and technical requirements that are applicable to take advantage of these incentives must be evaluated and applied for on a project-by-project basis.

• Market participation: Additional programs applicable to specific regions may also be available, enabling microgrids to offer unique benefits to the local Utility or Regional Transmission Organizations (RTOs) via participation in market programs like Demand Response or Frequency Regulation. This type of market participation is highly location-specific and is typically oriented around specific outcomes that DER technology or microgrid operational mode can drive.

The space footprint of the electrical distribution equipment and controls cabinets is a key consideration in microgrid design. Some of the design parameters impact the space footprint are the main bus amperage rating, the number of sections needed to fit all the required breakers and devices, and whether the electrical distribution equipment is going to be located indoors or outdoors. Physical space is also a key factor when determining potential options for the size, type and mix of DERs (solar PV, BESS, generator sets) to be installed on site.

For solar PV systems, the type of available space (rooftop, land, or parking lot space) is an important criterion. This dictates whether rooftop PV, ground mount PV, carport PV, or some combination of the three can be designed into the system. Once PV type is determined, the amount of space dictates the size of the solar system that can be installed. Typically, physical space is a restricting factor with PV - meaning the system capacity that can fit within the available space is often insufficient to meet site demand. The design challenge is that of maximizing the size of the PV system based on the available space. A rough estimate of space for one MW of ground-mount PV is about four acres. Non-ground mount systems have additional considerations. Rooftop PV systems must factor in existing rooftop equipment such as HVAC and sprinklers and incorporate row spacing and rooftop setbacks for maintenance and safety. Carport systems must factor in the parking lot set up and incorporate fire lane widths. Additionally, local zoning and safety codes also impact the placement of PV systems.

For a BESS, the physical space requirements and performance varies widely depending on the type of system and battery chemistry selected. A common battery chemistry integrated into microgrid systems is Lithium-Ion Iron Phosphate (LFP). Depending on the specific BESS, the power blocks can vary in size which impacts the overall power and energy density of a system. Therefore, the physical space for a BESS is dependent upon the power and energy capacity needed. For example, a typical 300 kW/745 kWh BESS has an energy density of 12.4 kWh/sq. ft. and a system power density of 5 kW/sq. ft. A typical footprint for this BESS is 90 inches H x 52 inches D x 155 inches L. In addition to required system size of a BESS, the depth increases if the system needs to be outdoor rated. For other battery chemistries, footprint and space design constraints vary widely.

Physical space requirements for generator sets vary depending on the fuel and particular system selected. Two common generator fuel types are natural gas and diesel. For example, a two MW Natural Gas Generator set has dimensions of 291 inches L x 84 inches W x 95 inches H, or a power density of 11.8 kW/sq ft. Comparatively, an example two MW Diesel Generator Set maximum dimensions are 404 inches L x 99.62 inches W x 156.6 inches” H, or a power density of 10.6 kW/sq ft. On-site fuel storage must be factored into the design. A typical fuel oil storage tank with a capacity of 1000 gallons has a diameter of 48 inches and a length of 130 inches. There may be also local safety requirements for on-site fuel storage. Additionally, certain mission-critical sites (like hospitals) may have minimum on-site fuel storage capacities specified in corporate guidelines and safety codes.

In addition to PV, Lithium-Ion BESS and generators, there are many other technologies that can be implemented for energy generation and storage in a microgrid system. A few of these other technologies include fuel cell, flow battery, Uninterruptable Power Supply (UPS), and wind turbines. Each of these technologies vary widely in footprint and density, and physical space availability need to be considered when designing the microgrid system.

Building Information Modeling (BIM) and other automated layout software tools are typically used during the early design phase of microgrids to optimize the asset locations and positions appropriately.

Economic analysis and project return-on-investment (ROI) tools

One of the main influences driving the adoption of Behind-The-Meter (BTM) microgrid systems in Commercial and Industrial (C&I) facilities is lowering the net cost of energy they purchase from utilities. Through the appropriate use of on-site Distributed Energy Resources (DERs) to at least partially offset consumption and optimizing the remaining utility usage, microgrid can significantly lower facility bills. Several key components of data are needed during the early project assessment phase to complete a complete economic analysis to estimate the potential return-on-investment (ROI) of microgrid projects.

First, the site geographical location is very critical, and helps identify the size of DERs that can be installed, their types, and potential locations. Estimates of the solar irradiance (power per unit area of energy from the Sun) at a given location helps determine the potential for solar PV deployment. As labor rates vary regionally, location also helps determine the expected installation and operations and maintenance (O&M) costs, which impacts ROI calculations.

Next, a comprehensive analysis of past (or projected, in the case of greenfield projects) utility bills are needed to understand site energy usage and current tariffs. A minimum of twelve months of utility bills is typically needed to establish a site load baseline. Additional years of data helps minimize projection errors due to any year-to- year variability. Using utility bill kWh rates, the impact of kWh’s delivered from on-site DERs can be financially quantified. Time-of-use tariffs with on-peak, mid-peak and off-peak rates can also be factored into the analysis. Other energy management strategies such as demand-shifting, demand compensation, power factor penalty compensation, can also be analyzed within the economic analysis.

Fifteen-minute interval data helps to size dispatchable DERs by modeling the daily load profile of the site, rather than monthly averages. For example, a Battery Energy Storage System (BESS) can be sized to avoid costly demand peaks and reduce energy usage during expensive on-peak periods. If a Combined Heat and Power (CHP) system is also within scope, usage data from the gas utility can help with proper sizing and operation relative to thermal load. Generally, CHP tends to be attractive when there are high and coincident peaks in electrical and thermal demand, and when the price of electricity ($/kWh) is roughly three times or more than the unit price of natural gas ($/therm). This unit price disparity is commonly referred to as “Spark Spread.”

Fortunately, there are many open-source and commercial software tools available to optimize microgrid deployment and provide recommendations for DER types and sizing. These tools typically require the input data discussed above (such as site location, utility tariff structures, load profile, installation costs). In addition to DER sizing, these software tools may also produce a Discounted Cash Flow (DCF) model to simulate incentive programs like the Investment Tax Credit (ITC), Modified Accelerated Cost Recovery System (MACRS) for accelerated depreciation, and any local or regional incentives. This DCF model outputs important metrics like the Net Present Value (NPV) and payback period, which are critical values in determining if the project is a worthy investment. Running these ROI calculations is typically an iterative process – making best-guess estimates, examining the outputs, adjusting the inputs, and repeating the exercise. By leveraging these analysis tools, iterative changes to design and cost inputs can be run quickly and reliably, accelerating the critical timeline between project development, and securing financing.

The requirement that is applied to virtually every utility installation, is that the service overcurrent device must coordinate with the upstream utility overcurrent device, typically a recloser or utility substation circuit breaker. If there is standby power on the premises, the utility typically requires that paralleling the alternate power source with the utility source not be possible unless stipulated in the rate agreement for the service in question.

Requirements for restricted access to service cable termination and service disconnect compartments in the service switchgear are another common. In some cases, these must be in a dedicated switchgear or switchboard section, increasing the service equipment footprint. In many cases grounding means must be provided with the equipment to allow the utility’s preferred safety grounding equipment to be installed. In some cases, requirements may be imposed on the entire service switchgear, such as electrical racking for circuit breakers or barriers that are not standard for the equipment type used.

In some cases, the control power for the service switchgear, such as a battery, must be designed to the utility’s specifications. Additional protective relaying may be required to minimize abnormal conditions which, although not harmful to the system being served, affect the reliability of the utility system. In some cases, the makes and models of protective relays for the service overcurrent protection are restricted to those the utility has approved.

Metering considerations

Measurement of load and system performance are critical to the functionality of the microgrid, both while grid connected and while islanded from the grid. This section does not address specific meter hardware but does address the information that meters may need to collect, and their interaction with other devices, including edge control systems and local or remote energy management systems.

Metering considerations must cover both functional and financial operation of microgrids. Grid-connected metering and grid-independent metering requirements must reflect the nature of the overall system, including possibly divided responsibilities between owner and operator. Some microgrid systems are components within larger power distribution systems, while others are self-contained, so the architecture of the system influences the metering choices and options.

The following measurements are typical for most microgrid systems:

• Metering variables like energy, voltage, frequency, active power, reactive power. at the Point of Common Coupling (POCC) that is, interconnection point with utility.

• State/status for each DER.

• Instantaneous and historical load profiles for the full system as well as secondary and tertiary loads.

Functional considerations for grid-connected operations typically includes metering and monitoring the grid reference, and individual DERs. The financial opportunities and constraints of the system dictate the location and type of metering. For example, revenue grade metering may be required for PV and BESS systems to comply with Investment Tax Credit verification, utility billing/credit for renewable energy delivered to the customer and the grid, and for ancillary services performance, such as participation in demand response or frequency response programs. Measurement of loads typically does not require revenue grade metering unless there is a specific need for sub-billing or tenant metering functions.

Metering sample rates and the volume of historical data collected from metering (through various devices such as dedicated meters, LV trip units, relays, or revenue grade meters) influence the performance of the edge layer and analytics layer software. There is an implicit tradeoff between volume of data captured and the performance of these software packages. Optimization software processes multiple streams of data such as BESS state of charge, metered PV production, CHP utilization, metered grid data, to output control signals that meets the system and customer goals. Production versus consumption decisions utilize meter data that is optimal for the algorithm being processed. Typically, cloud optimization software samples averaged metering values to send DER optimization signals to the site microgrid.

To meet resilience goals, accurately monitoring the state of the grid reference is crucial. Edge layer software monitors the POCC, establish and stabilize local grid forming resources, and execute load management (shed/add) decisions based on the collected metering data. The methodology and decision making related to sequence of operations related to grid transitions relies on appropriate metering of loads and sources.

Many sites and customers are likely not be mindful of the importance of power quality in microgrids; however, this is an important design criterion. Initial microgrid assessments and power system studies dictate the necessity to include power quality metering or even power quality correction equipment. Appropriate power quality metering help maintain system uptime (resilience) through awareness of changes of DER or other system component performance prior to system malfunction.

In the configuration above, there is typically no way to control individual loads. The source simply supports loads on its own circuit and loading that exceeds source capacity simply trips the source offline.

In contrast, in a behind-the-meter microgrid, multiple sources (Solar PV, Battery Storage, Combined Heat and Power, Fuel Cells, Generators to name common assets) may be combined in parallel, where the microgrid controller manages loading and power stability between sources. In addition to active source management, microgrids may also manage site loads directly and indirectly. The ability to manage both sources and loads introduces a range of economic and functional project design options.

• Direct load management:

  Example 1: the microgrid controller communicates with a site building management system (BMS), which has several predefined energy profiles. The BMS may adjust HVAC, chiller, and boiler setpoints to reduce load.

  Example 2: the microgrid controller communicates with one or more Motor Control Centers (MCCs), to run high horsepower pumps at reduced output.

• Indirect load management: Example: the microgrid controller operates breakers that serve the loads, in order of increasing load criticality. In general, this tier of load management occurs after direct management has been attempted, as it is more abrupt in suddenly cutting power.

Managing loads actively has both economic and functional benefits. When sizing certain resources, such as battery energy storage, the ability to manage loads may reduce the need to oversize resources. Consider a site with a 500 kW peak that occurs once in a 12-month data set, and where the next highest peak is 300 kW. In that case, the ability to shed non-critical loads during an edge case may allow for a reduced size of expensive backup power that would see low annual utilization. By actively managing load usage and schedules, consumption can be optimized for specific tariffs by avoiding peak price loading for example.

Functionally, managing loads actively has several benefits. The ability to manage loads at multiple levels of load criticality allow maximum utilization of energy sources at a site. In a fixed system, a degraded condition of source capacity (below the site load) would simply trip the site offline. However, with the capability to shed loads selectively, a microgrid controller maintain loads to the maximum extent possible. During a utility outage, managing loads provides extra resilience (sometimes referred to as “load preservation systems”). Non-critical loads may be shed up to level of source capacity (this is a typically a configurable sequence, that may be modified as site needs change over time). With controllable breakers, and with some foresight at the design stage, the microgrid load management sequence may be modified as site needs change over time (a “Future Proof” design). For example, if a new source is added at a future date under similar loading conditions, the load shedding scheme may be modified to optimize the new total source capacity. If site load increases with no new sources, the load shedding scheme may be modified to reduce additional load during an outage, to continue to maximally leverage available source capacity.

Resilience and response time

A key benefit of microgrids is providing resilience - powering a site even when the main electrical grid has suffered an outage. The cost of full or even partial outages has risen astronomically. In addition to directly measurable business costs, intangible “brand damage” is a major factor driving decisions by business and government entities to implement resilience through microgrids. The capability of a microgrid to isolate from the grid and provide power to a site through local DERs — “islanding” — is especially crucial for critical infrastructure like hospitals, military bases, datacenters, and college campuses.

The architectural details of the islanding process, balancing of power flow amongst DERs and loads when islanded, and sequence of reconnection back with the grid greatly impact system complexity in relation to the microgrid’s overall configuration, architecture, and operational intent. A full assessment of loads with particular attention to arranging them into tranches or tiers of critical, essential, and normal, is an important design step. For example, critical loads or life support systems likely needs UPS coverage so this decision has implications on UPS sizing, ATS configuration, generator capacity. In contrast, normal (or non-essential) loads may be able to withstand extend outages, reducing DER sizing.

Response time, that is, how quickly power is restored to loads when power is lost, is typically a significant determining factor in the complexity and cost of a microgrid project. During an unplanned outage, the microgrid controller can transition from grid-connected to islanding mode in many ways. The simplest option, a basic open transition, results in a momentary loss of power and blackout of the site. A more complex option is to engineer a seamless closed transition or fast transition microgrid, with the advantage of very minimal disruption upon outages or restoration. Particularly sensitive loads (mission-critical IT loads, semiconductor processes) require routing into the critical loads tier to help with adequate UPS coverage. Microgrid control code must be designed to provide all the sequences of operations associated with islanding, restoration, load and source management.

Microgrids must be carefully designed with load characteristics, as well as generation capacity in mind to provide a solution that best accomplishes the site’s specific response time needs and requirements.

As more utilities either embrace or are forced to contend with distributed generation, many standards and regulations are evolving to specify interoperability requirements. These interconnection agreements and standards typically involve requirements for both electrical connectivity and data connectivity. Electrical interconnectivity requirements may include more traditional topics such as revenue metering, relay settings, no-export conditions, demand response clauses. Data connectivity requirements are a fast-evolving area and prescribe both the data interchange and communications between utilities and microgrid systems.

IEEE1547.1-2018 covers guidance for microgrids and individual DERs connected to typical primary or secondary distribution voltage levels for voltage/power control, islanding, power quality. IEEE2030.5 covers specific guidance for interconnectivity communications including data model, messaging model, communication protocol and security. There are a wide variety of architectural choices covered in the standard and allied documents such as the Sunspec CSIP Implementation Guide. For example, direct DER communications through either an embedded Smart Inverter Control Unit (SMCU) or DER with Generating Facility Energy Management System (GFEMS). Potentially, DER aggregator software at the edge or cloud-based may be an intermediary node for communications between the utility and the local microgrid. For design and architectural details on these topics, see IEEE Std. 1547-2018 IEEE Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces. Revision of IEEE Std 1547-2003.*. Common Smart Inverter Profile (CSIP) IEEE 2030.5 Implementation Guide for Smart Inverters March 2018 (Version 2.1).* and IEEE Std. 2030.5 IEEE Standard for Smart Energy Profile Application Protocol.*.

A detailed discussion of these interconnection requirements is beyond the scope of this section. Please refer to the additional references provided below:

• IEEE Std. 1547-2018, IEEE Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces. Revision of IEEE Std 1547-2003.

• Common Smart Inverter Profile (CSIP) IEEE 2030.5 Implementation Guide for Smart Inverters, March 2018 (Version 2.1).

• IEEE Std. 2030.5, IEEE Standard for Smart Energy Profile Application Protocol.