Browsing by Subject "Distributed energy resources"

Now showing 1 - 6 of 6

A bilevel modeling methodology to optimize the value of distributed energy resources in electric transmission and distribution systems
(2023-12) Laws, Nicholas D.; Chen, Dongmei, Ph.D.; Webber, Michael E., 1971-; Beaman, Joseph; Warren, Adam; Djurdjanovic, Dragan
The transition of electricity generation from a centralized structure to a more distributed framework in grids across the globe calls for new methods to appropriately value the services that distributed energy resources (DER) can provide. Current methods for valuing DER services account for the grid operator perspective but typically ignore DER owner objectives and constraints. The goal of this dissertation is to develop new methods for valuing distributed energy resources in electricity transmission and distribution systems, with a particular focus on accounting for multiple perspectives. This goal is achieved by developing a new linearization technique for bilevel optimization problems that allows modeling energy system optimization problems at scales that matter. The linearization technique is leveraged to develop a new framework for valuing distributed energy resources in transmission and distribution systems. The proposed framework allows for competing perspectives to be modeled. Furthermore, a new method for creating synthetic electricity price scenarios is developed and its value demonstrated in a stochastic optimization framework. The current model for valuing electricity generation in deregulated energy markets determines prices from an optimal power flow problem whose objective is to maximize the social welfare. This work develops a general framework for determining the spatiotemporal value of DER that includes DER owner objectives in concert with maximizing the social welfare. The framework is built in a bilevel program that allows for incorporation of any optimal power flow model as well as replacing the social welfare objective with any value function, such as the objective of a profit-oriented DER aggregator. Special attention is placed on linearizing bilinear products of dispatch and price variables such that the framework can scale to large network models. The general framework is leveraged to develop a method to assess the techno-economic potential of DER for distribution system upgrade deferrals. The state-of-the-art for valuing DER for distribution system upgrade deferrals is advanced by accounting for DER owner objectives and constraints in concert with system operator goals and constraints. A use-case shows how the framework can be leveraged to value DER for non-wires alternatives. Comparing life cycle costs over 20 years for the system planner, the results show that by valuing DER for non-wires alternatives the DSO can avoid upgrading most of the overloaded components as well as achieve a net present value of nearly $3M relative to the cost of the traditional upgrades. The results also show that the DSO can achieve an additional $1M in net present value when valuing privately owned DER relative to a scenario with utility owned batteries. Finally, recognizing the need for better representation of the uncertainty in electricity market prices in energy system models, a novel method for generating realistic, synthetic electricity prices is developed. Several weaknesses in the state-of-the-art for stochastic price generation methods are addressed: (1) better characterization of daily and weekly trends is achieved by replacing the mean-reversion component of the stochastic differential equation with an autoregressive integrated moving average process; (2) the conditional probability of consecutive price-spikes, or “jumps”, is captured for the first time by replacing the traditional Poisson process with a generalized point process inspired by brain neuron models; and (3) a more realistic model variance is achieved by replacing the static empirical variance with a Markov process. The new methodology allows researchers and practitioners to evaluate bidding strategies for DER in electricity markets. In addition to accurately modeling historical trends, market behavior that has not been observed can be created by tuning model parameters. The method is exercised with electricity prices from the US ERCOT market and a use-case example is provided for bidding an energy storage unit into the ERCOT market. The results show that accounting for price uncertainty via the synthetic time-series can increase market profits by as much as 47% over a bidding strategy that relies on a deterministic price forecast.
A spatio-temporal load recovery framework for enhancing the observability of distributed energy resources
(2020-09-11) Lin, Shanny; Zhu, Hao (Ph. D. in electrical and computer engineering)
This thesis presents algorithms that enhances the spatio-temporal observability of residential loads. This increased visibility in the load profiles is crucial for achieving secure and efficient operations in distribution systems, especially with the increasing penetration of distributed energy resources. The recovery formulation utilizes a joint inference framework by leveraging the spatial and temporal strengths of multiple data sources. More specifically, smart meter data is available for almost all residential households with low temporal resolution while distribution synchrophasor data is available at limited locations with very high temporal resolution. By combining the respective strengths of the two types of data, the load recovery problem is cast as a matrix recovery problem. Regularization terms are introduced to promote the underlying low-rank plus sparse structure characteristics of the load matrix to improve the recovery performance. As a result, the matrix recovery problem can be formulated as a convex optimization problem which can be solved using standard convex solvers. Numerical studies using real residential load data demonstrate the effectiveness of the proposed algorithms capability in identifying large appliance activities and recovering the irradiance pattern of rooftop solar output profiles. Furthermore, our numerical studies have suggested that the presence of periodic loading can degrade the recovery performance. To address this issue, we have explored the introduction of an additional sinusoidal wave component. Last, online implementations of the recovery algorithms are discussed to accelerate the computational speed and process the data streams in real-time, while a rectangular waveform model is considered to better represent the presence of periodic loads. The proposed methods discussed in this thesis serve to enhance the observability of residential distributed energy resources.
Assessing power disruption distribution, social vulnerabilities, and energy security in Texas counties (2016-2020)
(2022-05-10) Wingo, Kelsey; Bixler, R. Patrick (Richard Patrick)
Severe weather events, aging infrastructure, and pressure to decarbonize have shed light on the reliability of the U.S. electricity supply. In this analysis, I considered the colocation and relationship between power supply disturbances and socially vulnerable populations from the years 2016-2020 in Texas counties. Through spatial analysis, two trends were observed: counties with large populations and counties near the southern border of Texas showed the greatest frequency of power disturbances and concentrations of socially vulnerable populations. Statistical analysis did not indicate significant relationships between counties with high social vulnerability scores and those with high frequency of power disturbances events. Statistical regression findings did indicate a relationship between counties with large populations and increased frequency of power disturbances, likely due to more populated counties having more expansive electricity infrastructure and demand and thus more pathways for errors. This analysis was limited by the availability of power outage data publicly available. This study demonstrates both a need to standardize and make accessible power outage incident data, continue observing and analyzing power disruption trends, and to engage with the technological, political, and regulatory options available to safeguard socially vulnerable populations against future power disruptions
Battery energy storage control : modeling, uncertainty, and applications
(2020-05) Rosewater, David Martin; Santoso, Surya; Baldick, Ross; Zhu, Hao; Huang, Qin (Alex); Hanasusanto, Grani; Byrne, Raymond
Battery energy storage systems (BESS) can restore flexibility to power systems that grow increasingly constrained from proliferation of variable renewable power and retirement of fossil-fuel based generation. BESS are often controlled through an energy management system (EMS), which may not have access to detailed, physical models developed by battery manufacturers. The research outlined in this dissertation makes substantive contributions to quantify, reduce, and ultimately account for the effects of battery system modeling uncertainty on energy management and control. Battery models for optimal control are reviewed in detail and metrics for their accuracy and uncertainty are derived. Optimal parameter identification and adaptive modeling methods are developed to reduce model uncertainty as much as possible. As some uncertainty always remains, a method for risk-averse model predictive control for BESS is developed to account for uncertainty and hedge control decisions to reduce optimistic shortfall. Lastly, the potential for controller performance improvement to bolster the value of BESS on the grid is demonstrated. Together, more accurate, adaptive models working in conjunction with risk-adverse control algorithms dismantle the impacts of model uncertainty on energy storage grid integration. These contributions represent an advance to the state-of-the-art in the engineering methods for addressing molding uncertainty in BESS control.
Optimal sizing of distribution-scale energy storage
(2023-04-21) Pretorius, Leah Naomi; Webber, Michael E., 1971-; Rhodes, Joshua D
Global warming and reliability concerns have intensified the interest in developing energy storage technologies. Energy storage has the potential to reduce carbon emissions by decreasing electricity demand from power plants, increasing the reliability of electricity supplied to customers, and saving costs by deferring upgrades of overloaded systems. Current efforts to implement storage are limited because existing power systems have a large generation capacity that can ordinarily match system demand; thus, it is difficult to justify the economic gains of storage technologies in an already interconnected network. However, with the rise in electrification and variable renewable energy sources such as wind and solar power, the value proposition of storage might be changing, especially in local distribution systems. As such, more advanced modeling techniques are needed to assess the technical and economic feasibility of integrating batteries into electric grids. This study uses REopt (Renewable Energy Integration and Optimization), a comprehensive and accurate optimization tool developed by the National Renewable Energy Lab, to quantify the energetic, environmental and economic impacts of distributed energy resources in Austin Energy’s power portfolio. Austin Energy is a municipal electric utility owned and operated by the City of Austin, which is the state capital of Texas. This study’s findings suggest that the value of utility-scale energy storage is expected to increase with future load growth on the grid and as extreme weather events become more catastrophic. We show that batteries as small as 1MW can increase reliability of the grid; however, a larger battery is more profitable by providing higher savings from energy arbitrage and 4 Coincident-Peak (4CP) avoidance. Our results demonstrate that a battery is only economical if it can perform 4CP avoidance and although perfect foresight of 4CP events is useful, it is not that important when considering the value of storage. In summary, there are promising opportunities for energy storage as small as 1MW in providing economic savings and resilience benefits to overloaded feeders in Austin Energy’s distribution network.
Zero to sixty hertz : electrifying the transportation sector and enhancing the reliability of the bulk power system
(2015-08) Legatt, Michael Elazar; Baldick, Ross; Webber, Michael E
A revolution is underway in the energy sector. Traditional approaches for managing a bulk power system are beginning to give way to a "smart grid" world, in which controllers may have bidirectional communications, with engaged users. At the same time a second transformation has been underway and growing in strength, namely the transition from petroleum as a transportation fuel source towards natural gas for large fleet vehicles, and electricity for consumer vehicles. This thesis focuses primarily on the synergy between the "smart grid" and vehicle electrification transitions. Moving the transportation sector to electricity as a fuel source, at least in Texas, has a myriad of benefits: Charging an electric vehicle without significant growth in renewable or lower-emitting SOFC technologies leads to very significant (80% per mile, 58% per neighborhood) reductions in CO₂ emissions, as well as significant reductions in NO[subscript X] (41% per mile, 17% per neighborhood), PM₁₀ (73% / 62%), PM₂.₅ and UFPM (62% / 55%). SO[subscript X] levels rose by 37%, but could be mitigated with controlled EV charging strategies. Vehicle charging strategies also significantly improved the neighborhood's total emissions profile. Adding in distributed energy resources, microgrid generation and intelligent charging, when optimally allocated, can further reduce these emissions. Vehicle charging schemes that respond dynamically to distributed renewable generation can even be thought of as having zero emissions due to the continual balance of PV generation and EV load on the low side of the distribution transformer. This thesis argues that there may be additionally significant societal benefits by shifting vehicle transportation to electricity, likely far in excess of what could be achieved by controlling power plant emissions alone. Based on an analysis of the ERCOT region, this shift would be expected to produce significant cost reductions for overall energy, improve health (due primarily to the relocation of UFPM far away from major population centers), and lower societal costs. Further gains can be considered as electric vehicles are significantly more energy efficient than their ICE counterparts. Also, on a larger scale, it’s generally easier to reduce emissions from hundreds of fixed power plants than millions of moving ICE vehicles.