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This planning guide provides information and resources on how distributed generation, with a focus on combined heat and power, can help communities meet resilience goals and ensure critical infrastructure remains operational regardless of external events.
(DG)2 is an impact analysis tool for electrical utilities to perform spatio-temporal analysis with solar and battery energy storage systems. The purpose of the tool is to help distribution utilities with identifying impacts and costs associated with locating a MW-scale solar farm and energy storage system at a certain location over other choices. This tool also assists utilities to assess the amount of reverse power flow at the transmission lines.
The Pacific Northwest National Laboratory (PNNL) has launched a project funded by the Bonneville Power Association (BPA) to identify strategies for increasing industrial energy efficiency and reducing energy costs of Northwest Food Processors Association (NWFPA) plants through deployment of novel combinations and designs of variable-output combined heat and power (CHP) distributed generation (DG), combined cooling, heating and electric power (CCHP) DG and energy storage systems. Detailed evaluations and recommendations of CHP and CCHP DG systems will be performed for several Northwest (NW) food processing sites. The objective is to reduce the overall energy use intensity of NW food processors by 25% by 2020 and by 50% by 2030, as well as reducing emissions and understanding potential congestion reduction impacts on the transmission system in the Pacific Northwest.
Efficient and well‐timed investment in distributed generation (DG) in distribution networks in the electricity market environment presents a big challenge for distribution companies. In this study, a real option valuation framework is proposed to determine the optimal investment strategies for DG including the investment location, size, and timing. Within the proposed framework, the profit from investing in DG is modelled, where the benefits include the operation cost savings and capacity update deferral benefit compared with a no‐DG‐investment scenario over the study period. Future power demands and electricity prices are modelled as stochastic variables. The candidate DG investment plans are considered as multiple mutually exclusive options, and the corresponding managerial flexibility for seizing opportunities and mitigating risk of loss upon an unfavourable unfolding of future uncertainties is assessed with real option analysis using the extended least square Monte Carlo method. The distribution of future investment strategies and optimal initial investment threshold levels under various scenarios are analysed in a case study to demonstrate the characteristics of the proposed framework and methodology.
Interphase-microgrids formed by three islanded single-phase feeders in distribution systems are proposed in this paper. Loss of utility caused by natural and human-made disasters may isolate each residential subdivision from the utility. In this case, the household loads are supplied by distributed generation (DG) units which may not be enough to meet the load demand. This can lead to deviations in voltage and frequency, and the deviations may vary for each phase. The proposed solution is to form an interphase-microgrid by seamlessly interconnecting the islanded single-phase feeders. This interphase-microgrid can effectively balance the load demands and DG generation in distribution systems after losing the utility supply. This paper presents different case studies to demonstrate the viability of forming interphase-microgrids for residential distribution systems.
The rise in frequency and severity of extreme weather events highlights the need for resilient power distribution networks. Microgrids can help improve the resilience of distribution grids by providing continuous power supply using local distribution generation (DG) when the distribution grid fails. In this paper, we propose an approach for optimal placement and sizing of DG to form multiple microgrids throughout the distribution network by restoration actions such as switching operations in case of distribution grid outages caused by extreme weather events. Considering the randomness of damaged distribution lines, the DG placement and sizing problem is formulated as a two-stage stochastic mixed-integer program, with the first stage determining the placement and size of DG, and the second stage focusing on minimizing the amount of load shedding through network restoration and microgrid formations for each scenario. Due to the large number of scenarios, the sample average approximation (SAA) method is employed to solve the problem. The results of case studies on a modified IEEE 33 bus distribution grid demonstrate the effectiveness of the proposed DG placement and sizing strategy in improving the resilience of distribution grids by allowing the formation of multiple microgrids. In addition, the robustness and accuracy of the SAA method are validated through various case studies.
With increasing penetration of distributed generation (DG) in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative mixed integer quadratic constrained quadratic programming model to optimize the operation of a three phase unbalanced distribution system with high penetration of Photovoltaic (PV) panels, DG and energy storage (ES) is developed. The proposed model minimizes not only the operating cost, including fuel cost and purchasing cost, but also voltage deviations and power loss. The optimization model is based on the linearized sensitivity coefficients between state variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DG and ES). To avoid slow convergence when close to the optimum, a golden search method is introduced to control the step size and accelerate the convergence. The proposed algorithm is demonstrated on modified IEEE 13 nodes test feeders with multiple PV panels, DG and ES. Numerical simulation results validate the proposed algorithm. Various scenarios of system configuration are studied and some critical findings are concluded.
In this paper, a fuel-based distributed generator (DG) allocation strategy is proposed to enhance the distribution system resilience against extreme weather. The long-term planning problem is formulated as a two-stage stochastic mixed-integer programming (SMIP). The first stage is to make decisions of DG siting and sizing under the given budget constraint. In the second stage, a post-extreme-event-restoration (PEER) is employed to minimize the operating cost in an uncertain fault scenario. In particular, this study proposes a method to select the most representative scenarios for the SMIP. First, a Monte Carlo Simulation (MCS) is introduced to generate sufficient scenarios considering random fault locations and load profiles. Then, the number of scenarios is reduced by the K-means clustering algorithm. The advantage of scenario reduction is to make a trade-off between accuracy and computational efficiency. Finally, the SMIP is solved by the progressive hedging algorithm. Here, the case studies of the IEEE 33-bus and 123-bus test systems demonstrate the effectiveness of the proposed algorithm in reducing the expected energy not served (EENS), which is a critical criterion of resilience.
The increasing integration of renewable-based distributed generation (DG) brings growing challenges to distribution network (DN) voltage control. To address this issue, a two-stage distributed online voltage control framework (TDO-VC) is proposed in this paper. In the proposed method, legacy voltage control devices are controlled in the upper stage on an hourly timescale, while DGs operate autonomously online in the lower stage to remove instantaneous voltage violations. The idea of receding horizon control is applied in the upper stage to comprehensively consider the current and future renewable generation, and the generalized fast dual ascent (Gf-DA) is employed in the lower stage to effectively manage DGs through near real-time optimization. The effectiveness of the proposed TDO-VC is demonstrated by rigorous theoretical analysis and case studies on IEEE-123 bus system.
A New Mexico statute allows investor-owned utilities to recover costs of “standby and ancillary services” resulting from distributed generation (DG). In attempting to do so, it is important to recognize both the physical and economic impacts, positive and negative, that distributed generation creates. Distributed generation has different impacts on ancillary and standby services depending on the location, capacity, and production patterns of the systems. Low penetrations of small distributed generation systems will not create significant contingencies for utilities. System impact of fluctuations in DG production may be no larger than normal fluctuations caused by customer demand, such as an air conditioner cycling. Higher penetrations and large DG system may produce noticeable impacts; so magnitude is critical when determining potential additional recoverable utility costs. Understanding DG impact on ancillary services is a relatively new and emerging area of study and as such there are not well-established formulas or protocols. Discretion is required. A hosting capacity analysis can help determine how much DG can be implemented before the impacts become substantive. Standby rates are traditionally used for large industrial generators or combined heat and power systems and threshold system size levels are often used in determining applicability of standby rates. System size thresholds may also be appropriate for standby rates and DG. Relatively small, geographically diverse DG systems are unlikely to create a noticeable coincident outage, so developing a standby charge based on assumed coincidence may not be appropriate. Individual lines and feeders may have considerable excess capacity during coincident outages, or they may be fully utilized and facing upgrades in the near future, which could vary over time. Understanding specific events that create concern for utilities is critical to developing an appropriate rate mechanism (e.g., single residential or community system outage, cascading event, clouds, eclipse). Any modeling or studies of the grid should also recognize the unique situation created when DG is coupled with a smart inverter or storage. These devices change the value proposition and can help mitigate some of the voltage and ramping issues DG can pose for a utility. Utilities can recover their fixed costs in a variety of ways, and some are better suited for DG than others. Beyond charging for standby and ancillary services, other options include fixed customer charges, demand charges, minimum bills, time-of-use rates and updated net energy-metering policies. Studies and analyses of the specific impacts that DG has on the grid should inform utilities’ decision-making process because distributed generation creates both costs and benefits for the grid. Any charge specific to DG customers should be backed up with analysis. At the heart of this conversation is utilities attempting to recover fixed costs. Allowing utilities to indiscriminately recover all stranded fixed costs can negatively affect long-term market efficiency by blunting utility incentives to lower costs and acting as a barrier to entry and exit. The current status of New Mexico’s grid, impending infrastructure updates, and likely customer adoption patterns for DG (backed up by transparent analysis) should remain at the forefront when considering charges for standby and ancillary services. Recognizing that utility regulation is largely a reactive process, it is recommended that applications for DG related standby or ancillary services cost recovery mechanisms be thoroughly reviewed by an experienced economist. If the commission has no such skill set on staff, one might be hired.
Compensation mechanisms for electricity generation systems installed behind the meter are under scrutiny in several jurisdictions in the United States. Legislators in 29 states introduced bills to amend net metering provisions in 2014, and in 33 states in the 2015 legislative session as of August 20, 2015. Some utilities have also sought to increase the revenue they receive from net-metered customers through rate redesign. The circumstances that have triggered the recent push for change include a growing percentage of net-metered customers, potential effects of distributed generation on cost allocation, decreasing photovoltaic (PV) system costs, the challenges of integrating high levels of solar generation in the distribution network, and increasing pressure on utility business models. This report presents a survey of options to charge and compensate PV customers, as well as options that may preserve utility revenues in scenarios with increased DG. These options could be used as building blocks to create a distributed generation (DG) compensation policy that may preserve the value of the different stakeholders and balance their interests.
Active network management (ANM) aims to increase the capacity of variable distributed generation (DG), which can be connected to existing distribution networks. In this study, it is proposed to simultaneously consider the efficient use of energy resources when high shares of DG are procured through the ANM approach. To that end, a multi-period and multiobjective optimisation algorithm, based on the linearised optimal power flow, is formulated. The algorithm seeks to maximise the installed capacity of DG while minimising the energy losses and consumption of voltage-dependent loads. The objectives are optimised considering the coordinated operation of voltage regulators and on-load tap changers, and the management of DG generation curtailment and reactive power compensation from DG. Additionally, the effects of load and generation uncertainties are addressed through a two-stage stochastic programming formulation of the multiobjective problem. The result is a set of non-inferior solutions, which allows exploring the degree of conflict among the objectives. The proposed approach was tested on two IEEE test feeders and the solutions show a significant improvement in the system's energy efficiency with a low impact on the amount of connected DG.
Integration and operation of distributed generation (DG) and energy storage (ES) in power distribution systems are enabled by communication networks and embedded sensor and control devices that increase the vulnerability of the systems to cyber-threats, broadening the attack surface and making adversary actions more unpredictable. This paper proposes a methodology that uses ellipsoidal approximations to quantify the potential damage caused by successful attacks that affect, directly or indirectly, the desired operation setpoints and may drive the power distribution operation to unsafe states by violating the limits of voltage or line flows. More specifically, a new methodology is introduced to find the optimal non-symmetric operating constraints that can be imposed to each DG and ES in order to guarantee that the power distribution system is resilient to any malicious setpoints. The proposed method takes as inputs the system topology, DG and ES capabilities, and load limits to solve a convex optimization problem formulated using linear matrix inequalities (LMIs) and the power flow equations. The proposed solution is agnostic to the attacker's action or load profile and it does not require any assumption about the location or means of the attack. The numerical results on a test distribution feeder with several DG and ES illustrate how the proposed resilient operating constraints guarantee the security of the power distribution system under setpoint attacks.
With the influx of distributed generation (DG) penetration, power utilities have adopted active anti-islanding protection methods to prevent an unintentional islanding condition. One commonly used protection scheme is implemented through the use of a transfer trip from the upline connected substation to the DG facility and is required when the capacity of connected DGs exceed a certain threshold of local minimum load. The threshold established by standard IEEE 1547.2-2009 states that a 3:1 minimum load to generation ratio is acceptable to ensure the DG will not sustain an unintentional island. However, the term “minimum load” is not precisely defined in any standard, and in practice, a light load value is chosen by engineers through manual methods that can be inconsistent, not representative and change drastically based on various cases. To solve this problem, this paper proposes a new light load calculation method creating consistency and better accuracy; the method has been implemented in. NET C# application and is used within Dominion Energy.
Conservation voltage reduction (CVR) is a common technique used by utilities to strategically reduce demand during peak periods. As penetration levels of distributed generation (DG) continue to rise and advanced inverter capabilities become more common, it is unclear how the effectiveness of CVR will be impacted and how CVR interacts with advanced inverter functions. In this work, we investigated the mutual impacts of CVR and DG from photovoltaic (PV) systems (with and without autonomous Volt-VAR enabled). The analysis was conducted on an actual utility dataset, including a feeder model, measurement data from smart meters and intelligent reclosers, and metadata for more than 30 CVR events triggered by the utility over the year. The installed capacity of the modeled PV systems represented 66% of peak load, but reached instantaneous penetrations reached up to 2.5x the load consumption over the year. While the objectives of CVR and autonomous Volt-VAR are opposed to one another, this study found that their interactions were mostly inconsequential since the CVR events occurred when total PV output was low.
Distributed generation (DG) sources like photovoltaic (PV) systems with advanced inverters are able to perform grid-support functions, like autonomous Volt-VAR that attempts to mitigate voltage issues by injecting or consuming reactive power. However, the Volt-VAR function operates with VAR priority, meaning real power may be curtailed to provide additional reactive power support. Since some locations on the grid may be more prone to higher voltages than others, PV systems installed at those locations may be forced to curtail more power, adversely impacting the value of that PV system. Adaptive Volt-VAR (AVV) could be implemented as an alternative, whereby the Volt-VAR reference voltage changes over time, but this functionality has not been well-explored in the literature. In this work, the potential benefits and grid impacts of AVV were investigated using yearlong quasi-static time-series (QSTS) simulations. After testing a variety of allowable AVV settings, we found that even with aggressive settings AVV resulted in <0.01% real power curtailment and significantly reduced the reactive power support required from the PV inverter compared to conventional Volt-VAR but did not provide much mitigation for extreme voltage conditions. The reactive power support provided by AVV was injected to oppose large deviations in voltage (in either direction), indicating that it could be useful for other applications like reducing voltage flicker or minimizing interactions with other voltage regulating devices.
Reliability and resilience are critical concerns for distributed generation (DG) at the rural electric level. The integration of renewable energy sources, such as small-scale hydroelectric distributed generators (hydro DGs), introduces operational challenges, particularly regarding aging infrastructure and grid stability. Artificial Intelligence (AI)-driven Machine Learning (ML) models and applications of Large Language Models (LLMs) offer promising solutions for optimizing DG operations and enhancing resilience. This paper explores AI-based models for improving efficiency, fault resolution, and outage mitigation in small-scale hydro DGs. Furthermore, it highlights the development of a centralized, AI-powered information portal for rural electric cooperatives and municipalities. The research evaluates hydro DG plant models and discusses the applicability of AI-powered question-answering tools for real-time operations, focusing on statistical data, load flow, voltage regulation, and generation power. The findings demonstrate AI’s potential to transform DG management to ensure greater stability and resilience in rural electric grids.
Mexico's energy reform will have far-reaching effects on how people produce and consume electricity in the country. Market liberalization will open the door to an increasing number of options for Mexican residential, commercial, and industrial consumers, and distributed generation (DG), which for Mexico includes generators of less than 500 kilowatts (kW) of capacity connected to the distribution network. Distributed generation is an option for consumers who want to produce their own electricity and provide electricity services to others. This report seeks to provide guidance to Mexican officials on designing DG economic and regulatory policies.