Renewable Energy Sources (RESs) particularly photovoltaic (PV) and wind are becoming important sources for power generation. Frequently varying output of PV and wind caused by clouds movement, weather condition and wind speed make them an intermittent and unreliable sources when connected to grid. Connecting intermittent sources to grid introduces challenges in various technical aspects such as power quality, protection, generation dispatch control and reliability. In this context, leveling intermittent source׳s output is necessary inorder to maintain grid׳s stability. This paper is aimed at bringing out the latest comprehensive literature review on problems associated when the intermittent PV is connected to grid and the methods of smoothing the output power fluctuation from PV. This paper also briefly discusses control strategy built for battery energy storage pertaining to this issue.

Mitigating methods of power fluctuation of photovoltaic (PV) sources – A review

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.

Energy Management and Optimization Methods for Grid Energy Storage Systems

Optimal integration of a hybrid solar-battery power source into smart home nanogrid with plug-in electric vehicle

As we transition toward a power grid that is increasingly based on renewable resources like solar and wind, the intelligent control of distributed energy resources (DERs) including photovoltaic (PV) arrays, controllable loads, energy storage, and plug-in electric vehicles (EVs) will be critical to realizing a power grid that can handle both the variability and unpredictability of renewable energy sources as well as increasing system complexity. Realizing such a decentralized and dynamic infrastructure will require the ability to solve large scale problems in real-time with hundreds of thousands of DERs simultaneously online. Because of the scale of the optimization problem, we use an iterative distributed algorithm previously developed in our group to operate each DER independently and autonomously within this environment. The algorithm is deployed within a framework that allows the microgrid to dynamically adapt to changes in the operating environment. Specifically, we consider a commercial site equipped with on-site PV generation, partially curtailable load, EV charge stations and a battery electric storage unit. The site operates as a small microgrid that can participate in the wholesale market on the power grid. We report results for simulations using real-data that demonstrate the ability of the optimization framework to respond dynamically in real-time to external conditions while maintaining the functional requirements of all DERs.

Dynamic Control and Optimization of Distributed Energy Resources in a Microgrid

The rapid development of photovoltaic (PV) systems in electrical grids brings new challenges in the control and operation of power systems. A considerable share of already installed PV units is small-scale units, usually connected to low-voltage (LV) distribution systems that were not designed to handle a high share of PV power. This study provides an in-depth review of methods and strategies proposed to prevent overvoltage in LV grids with PV and discusses the effectiveness, advantages, and disadvantages of them in detail. On the basis of the mathematical framework presented in this study, the overvoltage caused by high PV penetration is described, solutions to facilitate higher PV penetration are classified, and their effectiveness, advantages, and disadvantages are illustrated. The investigated solutions include the grid reinforcement, electrical energy storage application, reactive power absorption by PV inverters, application of active medium-voltage to LV transformers, active power curtailment, and demand response. Coordination between voltage control units by localised, distributed, and centralised voltage control methods is compared using the voltage sensitivity analysis. On the basis of the analysis, a combination of overvoltage prevention methods and coordination between voltage control units can provide an efficient solution to increase the PV hosting capacity of LV grids.

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Methods and strategies for overvoltage prevention in low voltage distribution systems with PV

In this paper, we develop optimization and control methods for a grid-tied photovoltaic (PV) storage system. The storage component consists of two separate units, a large slower moving unit for energy shifting and arbitrage and a small rapid charging unit for smoothing. We use a Model Predictive Control (MPC) framework to allow the units to automatically and dynamically adapt to changes in PV output while responding to external system operator requests or price signals. At each time step, the system is modeled using convex objectives and constraints and solved to obtain a control schedule for the storage units across the MPC horizon. For each subsequent time step, the first step of the schedule is executed before repeating the optimization process to account for changes in the operating environment and predictions due to availability of additional information. We present simulation results that demonstrate the ability of this optimization framework to respond dynamically in real time to external price signals and provide increased system benefits including smoother power output while respecting and maintaining the functional requirements of the storage units and power converters.

Steve Willard

Papers

Control and Optimization of Grid-Tied Photovoltaic Storage Systems Using Model Predictive Control