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.