Publications

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IEA Implementing Agreement for International Smart Grid Action Network, a Cooperative Program on Smart Grids (ISGAN) discussion papers are meant as input documents to the global discussion about smart grids. Each is a statement by the author(s) regarding a topic of international interest. They reflect works in progress in the development of smart grids in the different regions of the world. Their aim is not to communicate a final outcome or to advise decision-makers, but rather to lay the ground work for further research and analysis. In this report, SIRFN laboratories (Sandia, AIT, RSE and FREA) establish a harmonized Battery Energy Storage System (BESS) evaluation/certification protocol for advanced energy storage functions. The authors present this standardized protocol as an adoption or revision option for jurisdictions when creating or modifying certification testing requirements. To complete this process, each laboratory shared information on national, international, and jurisdictional grid codes and standards for BESS. Based on these requirements, and BESS testing and certification literature, a broad list of interoperability functions, use cases, storage capabilities, and requirements were compiled. This list was then consolidated to a unique set of BESS functions for inclusion in the certification procedure. Draft certification protocols for five initial functions were created by the SIRFN group in order to harmonize the international effort to establish a unified set of procedures for interoperability testing of BESS.

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We have examined ground faults in PhotoVoltaic (PV) arrays and the efficacy of fuse, current detection (RCD), current sense monitoring/relays (CSM), isolation/insulation (Riso) monitoring, and Ground Fault Detection and Isolation (GFID) using simulations based on a Simulation Program with Integrated Circuit Emphasis SPICE ground fault circuit model, experimental ground faults installed on real arrays, and theoretical equations.

The increasing pressure for network operators to meet distribution network power quality standards with increasing peak loads, renewable energy targets, and advances in automated distributed power electronics and communications is forcing policy-makers to understand new means to distribute costs and benefits within electricity markets. Discussions surrounding how distributed generation (DG) exhibits active voltage regulation and power factor/reactive power control and other power quality capabilities are complicated by uncertainties of baseline local distribution network power quality and to whom and how costs and benefits of improved electricity infrastructure will be allocated. DG providing ancillary services that dynamically respond to the network characteristics could lead to major network improvements. With proper market structures renewable energy systems could greatly improve power quality on distribution systems with nearly no additional cost to the grid operators. Renewable DG does have variability challenges, though this issue can be overcome with energy storage, forecasting, and advanced inverter functionality. This paper presents real data from a large-scale grid-connected PV array with large-scale storage and explores effective mitigation measures for PV system variability. As a result, we discuss useful inverter technical knowledge for policy-makers to mitigate ongoing inflation of electricity network tariff components by new DG interconnection requirements or electricity markets which value power quality and control.

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Increasing the penetration of distributed renewable sources, including photovoltaic (PV) sources, poses technical challenges for grid management. The grid has been optimized over decades to rely upon large centralized power plants with well-established feedback controls, but now non-dispatchable, renewable sources are displacing these controllable generators. This one-year study was funded by the Department of Energy (DOE) SunShot program and is intended to better utilize those variable resources by providing electric utilities with the tools to implement frequency regulation and primary frequency reserves using aggregated renewable resources, known as a virtual power plant. The goal is to eventually enable the integration of 100s of Gigawatts into US power systems.

Increasing the penetration of distributed renewable sources, including photovoltaic (PV) generators, poses technical challenges for grid management. The grid has been optimized over decades to rely on large centralized power plants with well-established feedback controls. Conventional generators provide relatively constant dispatchable power and help to regulate both voltage and frequency. In contrast, photovoltaic (PV) power is variable, is only as predictable as the weather, and provides no control action. Thus, as conventional generation is displaced by PV power, utility operation stake holders are concerned about managing fluctuations in grid voltage and frequency. Furthermore, since the operation of these distributed resources are bound by certain rules that require they stop delivering power when measured voltage or frequency deviate from the nominal operating point, there are also concerns that a single grid event may cause a large fraction of generation to turn off, triggering a black out or break-up of an electric power system.

The continued exponential growth of photovoltaic technologies paves a path to a solar-powered world, but requires continued progress toward low-cost, high-reliability, high-performance photovoltaic (PV) systems. High reliability is an essential element in achieving low-cost solar electricity by reducing operation and maintenance (O&M) costs and extending system lifetime and availability, but these attributes are difficult to verify at the time of installation. Utilities, financiers, homeowners, and planners are demanding this information in order to evaluate their financial risk as a prerequisite to large investments. Reliability research and development (R&D) is needed to build market confidence by improving product reliability and by improving predictions of system availability, O&M cost, and lifetime. This project is focused on understanding, predicting, and improving the reliability of PV systems. The two areas being pursued include PV arc-fault and ground fault issues, and inverter reliability.

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