Substantial decreases in the cost of solar and energy storage systems create suitable conditions for implementing microgrids that operate independently from the main transmission/distribution grids. Such microgrids concept is particularly of interest for islanded and remote communities, which oftentimes rely on expensive energy resources to supply their demand. This paper presents the design of a microgrid for an island community, in which transmission infrastructure (an aging subsea cable that connects to the mainland grid) is replaced by solar and energy storage systems. Based on historical demand data and solar generation forecasts, an optimization framework is proposed to determine sizes of the microgrid components such that the local generation resources are self-sufficient and reliable. Results of this analysis show that, indeed, solar and energy storage systems are viable choices for implementing a microgrid and replacing transmission infrastructure.
We consider the problem of decentralized control of reactive power provisioned by distributed energy resources for voltage support in the distribution grid. We assume that the reactance matrix of the grid is unknown and potentially time-varying. We present conditions for stability of the system when the reactive power at each inverter is set using a potentially heterogeneous droop curve. These conditions utilize energy dissipation requirements and can be naturally satisfied even when the reactance matrix is unknown by using an adaptive controller and when the reactance matrix is time-varying.
This paper presents Energy Storage-based Packetized Delivery of Electricity (ES-PDE) that is radically different from the operation of today's grid. Under ES-PDE, loads are powered by energy storage systems (ESS) most of the time and only receive packets of electricity periodically to power themselves and charge their ESSs. Therefore, grid operators can schedule the delivery of electricity in a manner that utilizes existing grid infrastructure. Since customers are powered by the co-located ESSs, when grid outages occur, they can be self-powered for some time before the grid is fully restored.In this paper, two operating schemes for ES-PDE are proposed. A Mixed-Integer-Linear-Programming (MILP) optimization is developed to find the optimal packet delivery schedule for each operating scheme. A case study is conducted to demonstrate the operation of ES-PDE.
Microgrid parameter estimation is essential to enable optimal voltage and frequency control using distributed energy resources (DER). Microgrid parameters vary through time, e.g., when generation is re-dispatched/committed, during microgrid reconfiguration. Furthermore, sensor measurements are noisy and preservation of the fast dynamics measurements is required, which is difficult to achieve with a lowpass filter. In this paper, a moving horizon estimation (MHE) approach is applied to estimate microgrid parameters for voltage and frequency support. The proposed approach estimates the states i.e., frequency, rate of change of frequency, grid voltage and current, and system parameters i.e., inertia, damping, and equivalent impedance. The MHE is formulated as an optimization problem using data over a fixed past horizon and solved online such that the sum of the square of measurement noise and process noise is minimized. Results showed that the proposed approach was able to estimate microgrid states, parameters, and disturbances within 5% for most values, which is sufficient to use in microgrid voltage and frequency control.
Energy storage systems (ESS) can provide multiple services to the electric grid, each with a unique charge/discharge profile. One category of such services comprises power quality applications, where ESS is deployed to protect downstream customers from events or disturbances that might result in poor power quality. This paper analyzes ESS usage to simultaneously mitigate two power quality issues: harmonic distortion and low power factor. Techniques for solving each one of these issues are already known by utilities; however, the main contribution of this paper is the utilization of a single asset to mitigate both power quality issues simultaneously. An optimization model was developed to determine the ESS dispatch that would satisfy the requirements for these stacked applications. Through case studies of a medium-size commercial customer, it was demonstrated that ESS can, indeed, correct and/or mitigate poor power quality issues.
In this work, a model predictive dispatch framework is proposed to utilize Energy Storage Systems (ESSs) for voltage regulation in distribution systems. The objective is to utilize ESS resources to assist with voltage regulation while reducing the utilization of legacy devices such as on-load tap changers (OLTCs), capacitor banks, etc. The proposed framework is part of a two-stage solution where a secondary layer computes the ESS dispatch every 5-min based on 1-hr generation and load forecasts while a primary layer would handle the real-time uncertainties. In this paper, the secondary layer to dispatch the ESS is formulated. Simulation results show that dispatching ESSs by providing active and reactive support can minimize the OLTC movement in distribution networks thus increasing the lifetime of legacy mechanical devices.
For the optimal performance of a microgrid where line resistance to reactance (R/X) ratio is high, one must consider the coupling between voltage and frequency. Furthermore, microgrid operational costs are time-varying. Thus, the voltage and frequency support controller must be flexible enough to handle technical and economic constraints. In this paper, the model predictive control (MPC) approach is proposed for voltage and frequency support considering the coupling between voltage and frequency dynamics. With the predictive model of the system, the finite horizon optimization problem is solved online and a control signal is calculated such that defined cost is minimized. By proper choice of MPC parameters, desired performance based on the availability of resources and market incentives can be achieved.
In this work, we introduce the concept of virtual transmission using large-scale energy storage systems. We also develop an optimization framework to maximize the monetized benefits of energy storage providing virtual transmission in wholesale markets. These benefits often come from relieving congestion for a transmission line, including both reduction in energy cost for the downstream loads and increase in production revenue for the upstream generators of the congested line. A case study is conducted using ISO-New England data to demonstrate the framework.
In a power distribution network with energy storage systems (ESS) and advanced controls, traditional monitoring and protection schemes are not well suited for detecting anomalies such as malfunction of controllable devices. In this work, we propose a data-driven technique for the detection of incidents relevant to the operation of ESS in distribution grids. This approach leverages the causal relationship observed among sensor data streams, and does not require prior knowledge of the system model or parameters. Our methodology includes a data augmentation step which allows for the detection of incidents even when sensing is scarce. The effectiveness of our technique is illustrated through case studies which consider active power dispatch and reactive power control of ESS.
Energy storage systems (ESSs) are being deployed widely due to numerous benefits including operational flexibility, high ramping capability, and decreasing costs. This study investigates the economic benefits provided by battery ESSs when they are deployed for market-related applications, considering the battery degradation cost. A comprehensive investment planning framework is presented, which estimates the maximum revenue that the ESS can generate over its lifetime and provides the necessary tools to investors for aiding the decision making process regarding an ESS project. The applications chosen for this study are energy arbitrage and frequency regulation. Lithium-ion batteries are considered due to their wide popularity arising from high efficiency, high energy density, and declining costs. A new degradation cost model based on energy throughput and cycle count is developed for Lithium-ion batteries participating in electricity markets. The lifetime revenue of ESS is calculated considering battery degradation and a cost-benefit analysis is performed to provide investors with an estimate of the net present value, return on investment and payback period. The effect of considering the degradation cost on the estimated revenue is also studied. The proposed approach is demonstrated on the IEEE Reliability Test System and historical data from PJM Interconnection.
In 2017, Hurricane Maria damaged a large portion of the Puerto Rico (PR) electrical infrastructure, leading to longterm outages across many regions. Therefore, transformation and re-development of the island's grid system is of high interest. A key part of this re-development is the passing of an updated renewable portfolio standard (RPS), originally enacted in 2010, which states that by 2050 the energy needs in PR will be met entirely by renewable resources. The purpose of this paper is to determine the amount of renewable generation and energy storage necessary to meet these energy goals. An increase in renewable generation within any system poses several difficulties such as meeting night-time demand and handling the operation of the existing thermal fleet. A mixed-integer linear program optimization is formulated to identify the required investments in photovoltaic solar and energy storage to meet the RPS and to minimize the investment and operational costs. Within this RPS case study a sensitivity analysis is performed with different energy storage investment costs scenarios.
The state of California is leading the nation with respect to solar energy and storage. The California Energy Commission has mandated that starting in 2020 all new homes must be solar powered. In 2010 the California state legislature adopted an energy storage mandate AB 2514. This required California's three largest utilities to contract for an additiona11.3 GW of energy storage by 2020, coming online by 2024. Therefore, there is keen interest in the potential advantages of deploying solar combined with energy storage. This paper formulates the optimization problem to identify the maximum potential revenue from pairing storage with solar and participating in the California Independent System Operator (CAISO) day ahead market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes were analyzed to identify trends and opportunities for the deployment of solar plus storage.
2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2020
Byrne, Raymond H.; Nguyen, Tu A.; Headley, Alexander; Trevizan, Rodrigo D.
Increased deployment of rooftop solar in California has resulted in the "duck curve", where there is a decrease in the midday net load followed by a very fast increase in late afternoon caused by declining solar output and a simultaneous increase in load. The large observed and even larger predicted ramp rates present a reliability concern. Therefore, there is keen interest in the potential advantages of deploying solar combined with energy storage. This paper formulates the optimization problem to identify the maximum potential revenue from pairing storage with solar and participating in the California Independent System Operator (CAISO) hour-ahead scheduling process (HASP) real-time market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes was analyzed to identify trends and opportunities for the deployment of solar plus storage. In addition, a comparison of the opportunities in the day ahead and HASP real-time energy markets is presented.
Forced oscillations in power systems are of particular interest when they interact and reinforce inter-area oscillations. This paper determines how a previously proposed inter-area damping controller mitigates forced oscillations. The damping controller modulates active power on the Pacific DC Intertie (PDCI) based on phasor measurement units (PMU) frequency measurements. The primary goal of the controller is to improve the small signal stability of the north south B mode in the North American Western Interconnection (WI). The paper presents small signal stability analysis in a reduced order system, time-domain simulations of a detailed representation of the WI and actual system test results to demonstrate that the PDCI damping controller provides effective damping to forced oscillations in the frequency range below 1 Hz.
This paper describes the design and implementation of a proof-of-concept Pacific dc Intertie (PDCI) wide area damping controller and includes system test results on the North American Western Interconnection (WI). To damp inter-area oscillations, the controller modulates the power transfer of the PDCI, a ±500 kV dc transmission line in the WI. The control system utilizes real-time phasor measurement unit (PMU) feedback to construct a commanded power signal which is added to the scheduled power flow for the PDCI. After years of design, simulations, and development, this controller has been implemented in hardware and successfully tested in both open and closed-loop operation. The most important design specifications were safe, reliable performance, no degradation of any system modes in any circumstances, and improve damping to the controllable modes in the WI. The main finding is that the controller adds significant damping to the modes of the WI and does not adversely affect the system response in any of the test cases. The primary contribution of this paper, to the state of the art research, is the design methods and test results of the first North American real-time control system that uses wide area PMU feedback.
A generic constant-efficiency energy flow model is commonly used in techno-economic analyses of grid energy storage systems. In practice, charge and discharge efficiencies of energy storage systems depend on state of charge, temperature, and charge/discharge powers. Furthermore, the operating characteristics of energy storage devices are technology specific. Therefore, generic constant-efficiency energy flow models do not accurately capture the system performance. In this work, we propose to use technology-specific nonlinear energy flow models based on nonlinear operating characteristics of the storage devices. These models are incorporated into an optimization problem to find the optimal market participation of energy storage systems. We develop a dynamic programming method to solve the optimization problem and perform two case studies for maximizing the revenue of a vanadium redox flow battery (VRFB) and a Li-ion battery system in Pennsylvania New Jersey Maryland (PJM) interconnection's energy and frequency regulation markets.
This paper explores the revenue potential for electric storage resources (ESRs), also referred to as electrical energy storage, in the Southwest Power Pool Integrated Marketplace. In particular, opportunities in the day-ahead market with the energy and frequency regulation products are considered. The revenue maximization problem is formulated as a linear program model, where an ESR seeks to maximize its revenue through the available revenue streams. The ESR has perfect foresight of historical prices and determines the optimal policy accordingly. A case study using FY2018 data shows that frequency regulation services are the most lucrative for revenue potential. This paper also explores different methods of using area control error data to infer the regulation control signal and the consequent effect on the optimization. Finally, the paper conducts a sensitivity analysis of ESR payback period to energy capacity and power rating.
Energy storage systems are flexible and controllable resources that can provide a number of services for the electric power grid. Many technologies are available, and corresponding models vary greatly in level of detail and tractability. In this work, we propose an adaptive optimal control and estimation approach for real-time dispatch of energy storage systems that neither requires accurate state-of-energy measurements nor knowledge of an accurate state-of-energy model. Specifically, we formulate an online optimization problem that simultaneously solves moving horizon estimation and model predictive control problems, which results in estimates of the state-of-energy, estimates of the charging and discharging efficiencies, and future dispatch signals. We present a numerical example in which the plant is a nonlinear, time-varying Lithium-ion battery model and show that our approach effectively estimates the state-of-energy and dispatches the system without accurate knowledge of the dynamics and in the presence of significant measurement noise.
This paper focuses on a transmission system with a high penetration of converter-interfaced generators participating in its primary frequency regulation. In particular, the effects on system stability of widespread misconfiguration of frequency regulation schemes are considered. Failures in three separate primary frequency control schemes are analyzed by means of time domain simulations where control action was inverted by, for example, negating controller gain. The results indicate that in all cases the frequency response of the system is greatly deteriorated and, in multiple scenarios, the system loses synchronism. It is also shown that including limits to the control action can mitigate the deleterious effects of inverted control configurations.
Energy storage systems are flexible resources that accommodate and mitigate variability and uncertainty in the load and generation of modern power systems. We present a stochastic optimization approach for sizing and scheduling an energy storage system (ESS) for behind-the-meter use. Specifi-cally, we investigate the use of an ESS with a solar photovoltaic (PV) system and a generator in islanded operation tasked with balancing a critical load. The load and PV generation are uncertain and variable, so forecasts of these variables are used to determine the required energy capacity of the ESS as well as the schedule for operating the ESS and the generator. When the forecasting uncertainties can be fit to normal distributions, the probabilistic load balancing constraint can be reformulated as a linear inequality constraint, and the resulting optimization problem can be solved as a linear program. Finally, we present results from a case study considering the balancing of the critical load of a water treatment plant in islanded operation.
Energy storage is a unique grid asset in that it is capable of providing a number of grid services. In market areas, these grid services are only as valuable as the market prices for the services provided. This paper formulates the optimization problem for maximizing energy storage revenue from arbitrage and frequency regulation in the CAISO market. The optimization algorithm was then applied to three years of historical market data (2014-2016) at 2200 nodes to quantify the locational and time-varying nature of potential revenue. The optimization assumed perfect foresight, so it provides an upper bound on the maximum expected revenue. Since California is starting to experience negative locational marginal prices (LMPs) because of increased renewable generation, the optimization includes a duty cycle constraint to handle negative LMPs. The results show that participating in frequency regulation provides approximately 3.4 times the revenue of arbitrage. In addition, arbitrage potential revenue is highly location-specific. Since there are only a handful of zones for frequency regulation, the distribution of potential revenue from frequency regulation is much tighter.
In this work, we provide an economic analysis of using behind-the-meter (BTM) energy storage systems (ESS) for time-of-use (TOU) bill management together with power factor correction. A nonlinear optimization problem is formulated to find the optimal ESS's charge/discharge operating scheme that minimizes the energy and demand charges while correcting the power factor of the utility customers. The energy storage's state of charge (SOC) and inverter's power factor (PF) are considered in the constraints of the optimization. The problem is then transformed to a Linear Programming (LP) problem and formulated using Pyomo optimization modeling language. Case studies are conducted for a waste water treatment plant (WWTP) in New Mexico.
Reliable engineering quality, safety, and performance are essential for a successful energy-storage project. The commercial energy-storage industry is entering its most formative period, which will impact the arc of the industry's development for years to come. Project announcements are increasing in both frequency and scale. Energy-storage systems (ESSs) are establishing themselves as a viable option for deployment across the entire electricity infrastructure as grid-connected energy-storage assets or in combination with other grid assets, such as hybrid generators. In conclusion, how the industry will evolve-in direction and degree-will depend largely on building a firm foundation of sound engineering requirements into project expectations.
Energy storage is a unique grid asset in that it is capable of providing a number of grid services. In market areas, these grid services are only as valuable as the market prices for the services provided. This paper formulates the optimization problem for maximizing energy storage revenue from arbitrage (day-ahead and real-time markets) in the California Independent System Operator (CAISO) market. The optimization algorithm was then applied to three years of historical market data (2014-2016) at 2200 nodes to quantify the locational and time-varying nature of potential revenue. The optimization assumed perfect foresight, so it provides an upper bound on the maximum expected revenue. Since California is starting to experience negative locational marginal prices (LMPs) because of increased renewable generation, the optimization includes a duty cycle constraint to handle negative LMPs. Two additional trading algorithms were tested that do not require perfect foresight. The first sets a buy price threshold and a sell price threshold (e.g., limit orders) for participation in the real time market, subject to the constraints of the energy storage system. The second uses the day-ahead prices as an estimate for the real time prices and performs an optimization on a rolling time horizon. The simple threshold algorithm performed the best, but both fell well short of the potential revenue identified by the optimization with perfect foresight.
Techno-economic analyses of energy storage currently use constant-efficiency energy flow models. In practice, charge/discharge efficiency of energy storage varies as a function of state-of-charge, temperature, charge/discharge power. Therefore, using the constant-efficiency energy flow models will cause suboptimal results. This work focuses on incorporating nonlinear energy flow models based on nonlinear efficiency models in the revenue maximization problem of energy storage. Dynamic programming is used to solve the optimization problem. A case studies is conducted to maximize the revenue of a Vanadium Redox Flow Battery (VRFB) system in PJM's energy and frequency regulation market.
The increased penetration of renewable resources has made frequency regulation and generation control a growing concern. This has created an opportunity for Energy Storage Resource to participate in the frequency regulation market. This paper investigates the potential of Battery Energy Storage systems to participate in the German secondary frequency regulation market. A simulation model is developed to investigate the revenue opportunity of a 48 MWh Battery System participating in the secondary frequency regulation market.
The Hawaiian Electric Company intends to procure grid-scale Battery Energy Storage System (“BESS”) capacity. The purpose of this study is to determine whether providing contingency reserve or time-of-day shifting is of more benefit to the Oahu grid, and to better understand the relationship between BESS size and level of benefit. This is an independent study by Sandia, and is not being used to support the regulatory case for BESS capacity by Hawaiian Electric. The study team created a production cost model of the Oahu grid using data primarily from the Hawaiian Electric Company. The proposed BESS supplied contingency reserve in one set of runs and time-of-day shifting in another. Supplying contingency reserve led to larger savings than time-of-day energy shifting. Assuming a renewable reserve and a quick-start reserve, and $15/MMBtu for Low-Sulphur Fuel Oil, the 50-MW/25-MWh, 100-MW/50-MWh, and 150-MW/75-MWh systems supplying contingency reserve provided, respectively, savings of 9.6, 15.6, and 18.3 million USD over system year 2018. Over the range of fuel prices tested, these cost savings were found to be directly proportional to the cost of fuel. As the focus is the operational benefit of BESS capacity, the capacity value of the BESS was not included in benefit calculations.
This paper presents simulation results of a control scheme for damping inter-area oscillations using high-voltage DC (HVDC) power modulation. The control system utilizes realtime synchrophasor feedback to construct a supplemental commanded power signal for the Pacific DC Intertie (PDCI) in the North American Western Interconnection (WI). A prototype of this controller has been implemented in hardware and, after multiple years of development, successfully tested in both open and closed-loop operation. This paper presents simulation results of the WI during multiple severe contingencies with the damping controller in both open and closed-loop. The primary results are that the controller adds significant damping to the controllable modes of the WI and that it does not adversely affect the system response in any of the simulated cases. Furthermore, the simulations show that a feedback signal composed of the frequency difference between points of measurement near the Washington-Oregon border and the California-Oregon border can be employed with similar results to a feedback signal constructed from measurements taken near the Washington-Oregon border and southern California. This is an important consideration because it allowed the control system to be designed without relying upon cross-system measurements, which would have introduced significant additional delay.
FERC Order 755 requires RTO/ISOs to compensate the frequency regulation resources based on the actual regulation service provided. Based on this rule, a resource is compensated by a performance-based payment including a capacity payment which accounts for its provided regulation capacity and a performance payment which reflects the quantity and accuracy of its regulation service. The RTO/ISOs have been implementing different market rules to comply with FERC Order 755. This paper focuses on the MISO's implementation and presents the calculations to maximize the potential revenue of electrical energy storage (EES) from participation in arbitrage and frequency regulation in the day-ahead market using linear programming. A case study was conducted for the Indianapolis Power & Light's 20MW/20MWh EES at Harding Street Generation Station based on MISO historical data from 2014 and 2015. The results showed the maximum revenue was primarily produced by frequency regulation.
Distributed control compensation based on local and remote sensor feedback can improve small-signal stability in large distributed systems, such as electric power systems. Long distance remote measurements, however, are potentially subject to relatively long and uncertain network latencies. In this work, the issue of asymmetrical network latencies is considered for an active damping application in a two-area electric power system. The combined effects of latency and gain are evaluated in time domain simulation and in analysis using root-locus and the maximum singular value of the input sensitivity function. The results aid in quantifying the effects of network latencies and gain on system stability and disturbance rejection.
Lightly damped electromechanical oscillations are a source of concern in the western interconnect. Recent development of a reliable real-time wide-area measurement system (WaMS) has enabled the potential for large-scale damping control approaches for stabilizing critical oscillation modes. a recent research project has focused on the development of a prototype feedback modulation controller for the Pacific DC Intertie (PDCI) aimed at stabilizing such modes. The damping controller utilizes real-time WaMS signals to form a modulation command for the DC power on the PDCI. This paper summarizes results from the first actual-system closed-loop tests. Results demonstrate desirable performance and improved modal damping consistent with previous model studies.
The Sterling Municipal Light Department (SMLD) is a progressive public power utility located 10 miles NNE of Worcester, Massachusetts in the Town of Sterling. SMLD has a long history of investment in renewable generation, with approximately 35% of generation coming from renewable sources. The goal of this report is to identify potential benefits and value streams from electrical energy storage. Benefits considered in this analysis include: energy arbitrage, frequency regulation, reduction in monthly network load, reduction in capacity payments to ISO New England, and grid resiliency.
The transformation of today's grid toward smart grid has given the energy storage systems (ESSs) the opportunity to provide more services to the electric grid as well as the end customers. On the grid's side, ESSs can generate revenue streams participating in electricity markets by providing services such as energy arbitrage, frequency regulation or spinning reserves. On the customers' side, ESSs can provide a wide range of applications from on-site back-up power, storage for off-grid renewable systems to solutions for load shifting and peak shaving for commercial/industrial businesses. In this work, we provide an economic analysis of behind-the-meter (BTM) ESSs. A nonlinear optimization problem is formulated to find the optimal operating scheme for ESSs to minimize the energy and demand charges of time-of-use (TOU) customers, or to minimize the energy charge of net-metering (NEM) customers. The problem is then transformed to Linear Programming (LP) problems and formulated using Pyomo optimization modeling language. Case studies are conducted for PG&E's residential and commercial customers in San Francisco.
This paper proposes a method to modulate the power output of converter interfaced generators (CIGs) according to frequency variations. With the proposed approach, CIGs can successfully engage in the primary frequency regulation of a power system. The approach is a variation on the traditional droop-like proportional controller where the feedback signal is a global frequency measurement instead of a local one. Obtaining the global measurement requires transferring data using communications. This paper analyzes the performance of the proposed approach with respect to communications issues such as latencies and data dropouts. The approach implemented and tested in a simulation environment is compared against a method entirely based on local information. The results show that using global information in droop control provides benefits to the system as it improves its frequency regulation. The results also indicate that the proposed approach is robust to latencies and communication failures.
This paper proposes a method of enabling photovoltaic (PV) power plants to participate in primary frequency response by providing synthetic inertia (SI). This variation, referred to as communication enabled synthetic inertia (CE-SI), utilizes communication capabilities to provide global system frequency information to PV plants to emulate the inertial response of synchronous generators. The performance of CE-SI is analyzed with respect to the challenges associated with communication, such as latency and availability. Results indicate improvements in frequency response over SI using local frequency measurements when communication latency is sufficiently small.
A hierarchical control algorithm was developed to utilize photovoltaic system advanced inverter volt-VAr functions to provide distribution system voltage regulation and to mitigate 10-minute average voltages outside of ANSI Range A (0.95-1.05 pu). As with any hierarchical control strategy, the success of the control requires a sufficiently fast and reliable communication infrastructure. The communication requirements for voltage regulation were tested by varying the interval at which the controller monitors and dispatches commands and evaluating the effectiveness to mitigate distribution system over-voltages. The control strategy was demonstrated to perform well for communication intervals equal to the 10-minute ANSI metric definition or faster. The communication reliability impacted the controller performance at levels of 99% and below, depending on the communication interval, where an 8-minute communication interval could be unsuccessful with an 80% reliability. The communication delay, up to 20 seconds, was too small to have an impact on the effectiveness of the communication-based hierarchical voltage control.
Today, the stability of the electric power grid is maintained through real time balancing of generation and demand. Grid scale energy storage systems are increasingly being deployed to provide grid operators the flexibility needed to maintain this balance. Energy storage also imparts resiliency and robustness to the grid infrastructure. Over the last few years, there has been a significant increase in the deployment of large scale energy storage systems. This growth has been driven by improvements in the cost and performance of energy storage technologies and the need to accommodate distributed generation, as well as incentives and government mandates. Energy management systems (EMSs) and optimization methods are required to effectively and safely utilize energy storage as a flexible grid asset that can provide multiple grid services. The EMS needs to be able to accommodate a variety of use cases and regulatory environments. In this paper, we provide a brief history of grid-scale energy storage, an overview of EMS architectures, and a summary of the leading applications for storage. These serve as a foundation for a discussion of EMS optimization methods and design.
This paper describes the initial open-loop operation of a prototype control system aimed at mitigating inter-area oscillations through active DC power modulation. The control system uses real-time synchrophasor feedback to construct a commanded power signal added to the scheduled power on the Pacific DC Intertie (PDCI) within the western North American power system (wNAPS). The control strategy is based upon nearly a decade of simulation, linear analysis, and actual system tests. The control system must add damping to all modes which are controllable and 'do no harm' to the AC grid. Tests were conducted in which the damping controller injected live probing signals into the PDCI controls to change the power flow on the PDCI by up to ±125 MW. While the probing tests are taking place, the damping controller recorded what it would have done if it were providing active damping. The tests demonstrate that the dynamic response of the DC system is highly desirable with a response time of 11 ms which is well within the desired range. The tests also verify that the overall transfer functions are consistent with past studies and tests. Finally, the tests show that the prototype controller behaves as expected and will improve damping in closed-loop operation.
To demonstrate and validate the performance of the wide-are a damping control system, the project plans to conduct closed-loop tests on the PDCI in summer/fall 2016. A test plan details the open and closed loop tests to be conducted on the P DCI using the wide-area damping control system. To ensure the appropriate level of preparedness, simulations were performed in order to predict and evaluate any possible unsafe operations before hardware experiments are attempted. This report contains the result s from these simulations using the power system dynamics software PSLF (Power System Load Flow, trademark of GE). The simulations use the WECC (Western Electricity Coordinating Council) 2016 light summer and heavy summer base cases.
The goal of this effort was to assess the effect of high penetration solar deployment on the small signal stability of the western North American power system (wNAPS). Small signal stability is concerned with the system response to small disturbances, where the system is operating in a linear region. The study area consisted of the region governed by the Western Electricity Coordinating Council (WECC). General Electric's Positive Sequence Load Flow software (PSLF®) was employed to simulate the power system. A resistive brake insertion was employed to stimulate the system. The data was then analyzed in MATLAB® using subspace methods (Eigensystem Realization Algorithm). Two different WECC base cases were analyzed: 2022 light spring and 2016 heavy summer. Each base case was also modified to increase the percentage of wind and solar. In order to keep power flows the same, the modified cases replaced conventional generation with renewable generation. The replacements were performed on a regional basis so that solar and wind were placed in suitable locations. The main finding was that increased renewable penetration increases the frequency of inter-area modes, with minimal impact on damping. The slight increase in mode frequency was consistent with the loss of inertia as conventional generation is replaced with wind and solar. Then, distributed control of renewable generation was assessed as a potential mitigation, along with an analysis of the impact of communications latency on the distributed control algorithms.
A central control algorithm was developed to utilize photovoltaic system advanced inverter functions, specifically fixed power factor and constant reactive power, to provide distribution system voltage regulation and to mitigate voltage regulator tap operations by using voltage measurements at the regulator. As with any centralized control strategy, the capabilities of the control require a reliable and fast communication infrastructure. These communication requirements were evaluated by varying the interval at which the controller sends dispatch commands and evaluating the effectiveness to mitigate tap operations. The control strategy was demonstrated to perform well for communication intervals faster than the delay on the voltage regulator (30 seconds). The communication reliability, latency, and bandwidth requirements were also evaluated.
FERC order 755 and FERC order 784 provide pay-for-performance requirements and direct utilities and independent system operators to consider speed and accuracy when purchasing frequency regulation. Independent System Operators (ISOs) have differing implementations of pay-for-performance. This paper focuses on the PJM implementation. PJM is a regional transmission organization in the northeastern United States that serves 13 states and the District of Columbia. PJM's implementation employs a two part payment based on the Regulation Market Capability Clearing price (RMCCP) and the Regulation Market Performance Clearing Price (RMPCP). The performance credit includes a mileage ratio. Both the RMCCP and RMPCP employ an actual performance score. Using the PJM remuneration model, this paper outlines the calculations required to estimate the maximum potential revenue from participation in arbitrage and regulation in day-ahead markets using linear programming. Historical PJM data from 2014 and 2015 was then used to evaluate the maximum potential revenue from a 5 MWh, 20 MW system based on the Beacon Power Hazle Township flywheel plant. Finally, a heuristic trading algorithm that does not require perfect foresight was evaluated against the results of the optimization algorithm.
This paper describes a control scheme to mitigate inter-area oscillations through active damping. The control system uses real-time phasor measurement unit (PMU) feedback to construct a commanded power signal to modulate the flow of real power over the Pacific DC Intertie (PDCI) located in the western North American Power System (wNAPS). A hardware prototype was constructed to implement the control scheme. To ensure safe and reliable performance, the project integrates a supervisory system to ensure the controller is operating as expected at all times. A suite of supervisory functions are implemented across three hardware platforms. If any controller mal-function is detected, the supervisory system promptly disables the controller through a bumpless transfer method. This paper presents a detailed description of the control scheme, simulation results, the bumpless transfer method, and a redundancy and diversity method in the selection of PMU signals for feedback. This paper also describes in detail the supervisory system implemented to ensure safe and reliable damping performance of the real-time wide area damping controller.
This paper presents a control design methodology that begins to address high penetration of renewable energy sources into networked AC microgrid systems. To bring about high performing microgrid systems that contain large amounts of stochastic sources and loads is a major goal for the future of electric power systems. Alternative methods for controlling and analyzing AC microgrid systems will provide understanding into tradeoffs that can be made during the design phase. This paper utilizes a control design methodology, based on Hamiltonian Surface Shaping and Power Flow Control (HSSPFC) [1] that regulates renewable energy sources, loads and identifies energy storage requirements for an AC microgrid system. Both static and dynamic stability conditions are derived for the AC microgrid system. Numerical simulations are performed to demonstrate stability and performance. Two scenarios are considered; i) simple random stochastic renewable source and load AC Microgrid example and ii) a random variable pulse load application for Navy ship AC microgrid systems.
With increasing renewable penetrations and advancements in power electronics associated with smart grid technologies, distributed control of the power grid is quickly becoming a necessity. Once communications are introduced into a control system, the impacts of latency and unreliable communications quickly become a priority. Vector Lyapunov techniques are well suited for the analysis of control systems with structured perturbations. These perturbations can be employed to model uncertainty in communications as well as parameter uncertainty. In this paper, we present results for small signal stability of a simplified two area power system model for several scenarios: bandwidth limited local communications and tie line uncertainty; local communications and bandwidth limited global communications combined with tie line uncertainty; and uncertainty in global communications. These results are intended to be a starting point for the analysis of the impact of communications uncertainty on the stability of power systems.
The goal of this effort was to apply four potential control analysis/design approaches to the design of distributed grid control systems to address the impact of latency and communications uncertainty with high penetrations of photovoltaic (PV) generation. The four techniques considered were: optimal fixed structure control; Nyquist stability criterion; vector Lyapunov analysis; and Hamiltonian design methods. A reduced order model of the Western Electricity Coordinating Council (WECC) developed for the Matlab Power Systems Toolbox (PST) was employed for the study, as well as representative smaller systems (e.g., a two-area, three-area, and four-area power system). Excellent results were obtained with the optimal fixed structure approach, and the methodology we developed was published in a journal article. This approach is promising because it offers a method for designing optimal control systems with the feedback signals available from Phasor Measurement Unit (PMU) data as opposed to full state feedback or the design of an observer. The Nyquist approach inherently handles time delay and incorporates performance guarantees (e.g., gain and phase margin). We developed a technique that works for moderate sized systems, but the approach does not scale well to extremely large system because of computational complexity. The vector Lyapunov approach was applied to a two area model to demonstrate the utility for modeling communications uncertainty. Application to large power systems requires a method to automatically expand/contract the state space and partition the system so that communications uncertainty can be considered. The Hamiltonian Surface Shaping and Power Flow Control (HSSPFC) design methodology was selected to investigate grid systems for energy storage requirements to support high penetration of variable or stochastic generation (such as wind and PV) and loads. This method was applied to several small system models.
This paper outlines the calculations required to estimate the maximum potential revenue from participation in arbitrage and regulation in day-ahead markets using linear programming. Then, we use historical Electricity Reliability Council of Texas (ERCOT) data from 2011-2013 to evaluate the maximum potential revenue from a hypothetical 32 MWh, 8 MW system. We investigate the maximum potential revenue from two different scenarios: arbitrage only and arbitrage combined with regulation. This analysis was performed for each load zone over the same period to show the impact of location and to identify trends in the opportunities for energy storage. Our analysis shows that, with perfect foresight, participation in the regulation market would have produced more than twice the revenue compared to arbitrage in the ERCOT market in 2011-2013. Over the last three years, there has been a significant decrease in the potential revenue for an energy storage system. We also quantify the impact of location on potential revenue.
Low signal-to-noise data processing algorithms for improved detection, tracking, discrimination and situational threat assessment are a key research challenge. As sensor technologies progress, the number of pixels will increase significantly. This will result in increased resolution, which could improve object discrimination, but unfortunately, will also result in a significant increase in the number of potential targets to track. Many tracking techniques, like multi-hypothesis trackers, suffer from a combinatorial explosion as the number of potential targets increase. As the resolution increases, the phenomenology applied towards detection algorithms also changes. For low resolution sensors, blob tracking is the norm. For higher resolution data, additional information may be employed in the detection and classification steps. The most challenging scenarios are those where the targets cannot be fully resolved, yet must be tracked and distinguished for neighboring closely spaced objects. Tracking vehicles in an urban environment is an example of such a challenging scenario. This report evaluates several potential tracking algorithms for large-scale tracking in an urban environment. The algorithms considered are: random sample consensus (RANSAC), Markov chain Monte Carlo data association (MCMCDA), tracklet inference from factor graphs, and a proximity tracker. Each algorithm was tested on a combination of real and simulated data and evaluated against a common set of metrics.
Low signal-to-noise data processing algorithms for improved detection, tracking, discrimination and situational threat assessment are a key research challenge. As sensor technologies progress, the number of pixels will increase signi cantly. This will result in increased resolution, which could improve object discrimination, but unfortunately, will also result in a significant increase in the number of potential targets to track. Many tracking techniques, like multi-hypothesis trackers, suffer from a combinatorial explosion as the number of potential targets increase. As the resolution increases, the phenomenology applied towards detection algorithms also changes. For low resolution sensors, "blob" tracking is the norm. For higher resolution data, additional information may be employed in the detection and classfication steps. The most challenging scenarios are those where the targets cannot be fully resolved, yet must be tracked and distinguished for neighboring closely spaced objects. Tracking vehicles in an urban environment is an example of such a challenging scenario. This report evaluates several potential tracking algorithms for large-scale tracking in an urban environment.
Electromechanical oscillations often limit transmission capacity in the western North American Power System (termed the wNAPS). Recent research and development has focused on employing large-scale damping controls via wide-area feedback. Such an approach is made possible by the recent installation of a wide-area real-time measurement system based upon Phasor Measurement Unit (PMU) technology. One potential large-scale damping approach is based on energy storage devices. Such an approach has considerable promise for damping oscillations. This paper considers the placement of such devices within the wNAPS system. We explore combining energy storage devices with HVDC modulation of the Pacific DC Intertie (PDCI). We include eigenanalysis of a reduced-order wNAPS system, detailed analysis of a basic two-area dynamic system, and full-order transient simulations. We conclude that the optimal energy storage location is in the area with the lower inertia.