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Opportunistic Hybrid Communications Systems for Distributed PV Coordination

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Opportunistic Hybrid Communications Systems for Distributed PV Coordination 1 Opportunistic Hybrid Communications Systems for Distributed PV Coordination Bri-Mathias Hodge National Renewable Energy Laboratory NREL/PR-5D00-71282 2 Motivation Sensor PV panel Current Status Future Needs • Lack of sensors in distribution grids • Monitoring and control of • Limited communication distributed PV infrastructure in generation distribution grids • Distributed PV state • No access to high- estimation/forecasting resolution PV • Distribution system state generation data for estimation DSO/TSO • Ancillary services • No access to high • Active distribution resolution grid state switching and islanding data 3 Project Innovation and Objective Innovation: To highly use the diverse communication infrastructure existed in the power system, we aim to develop the hybrid communication system at the least cost to coordinate distributed PV generation. Objective: Communicate the state of the grid from the inverter to the system operator by full- scale, operational implementation of the opportunistic hybrid communication system: – Hybrid: various communications pathways, e.g. LoWPAN, PLC, WiFi, WiMAX etc. – Opportunistic: route messages through each of these systems based on recent data about latency and availability to ensure reliable message passing. 4 The Smart Grid Communication LEGEND Home Area Network LoWPAN (e.g., ZigBee, IEEE 802.15.4) Wireless LAN (e.g., IEEE 802.11) NSP Broadband Wireless (e.g., LTE, IEEE 802.16) PLC (e.g., Broad band (IEEE 1901, HomePlug); Narrow band (IEEE 1901.2)) Ethernet (e.g., IEEE 802.3) Network Service Provider Smart 88 Meter Inverter Edge Inverter Router Database Wide Area Network Control Center Data 88 Router Concentrator 88 Smart Smart Meter Meter Neighborhood Area Network 5 Potential Use Cases and Applications • Three design metrics: Latency, Throughput, and Packet Loss Rate for different hybrid communication architecture designs; • Monitoring Functions: Distributed state estimation algorithms for distributed PV systems and distribution power systems; • Control Functions: Distributed PV dispatch commands from DSO or TSO; • Ancillary Services from utility point of view. 6 Hybrid Communication Design Stages Reference Test Case A (RTC-A) • Taxonomy feeder R2-25.00-1 from DOE’s Modern Grid Initiative representing moderate urban environment • System of 1080 nodes on a distribution feeder 7 Data Concentrator Placement Ethernet WiFi WiMAX 8 Opportunistic Hybrid Communication Models Home Area Network (HAN): BPLC, NPLC, LoWPAN Neighborhood Area Network (NAN): Ethernet, WiFi, WiMAX Total 9 hybrid designs 9 NS-3 Hybrid Communication Simulation Performance [1] [300ms-2sec] <1% [9.6-56Kbps] WiFi > 1% Data Rate = 24 Kbps Data Rate = 56 Kbps 10 Optimal Performance and Parameters Variables: Date rate and Packet size 11 Distributed State Estimation Techniques Information Static/Dynamic Exchange Estimation of Power System States PV State Information Dynamic Estimation of Distributed PV States PV Inverter Irradiance Sensor • Improve PV generation efficiency by using dynamic estimation of distributed PV states • Enable monitoring and control of distribution PV generation using distribution state 12 Distributed PV System State Estimation Multi Rate and Event DRIven Kalman Kriging (MREDRIKK) Filter [2]. 0.16 0.2 0.14 0.195 0.12 0.19 Kriging MMSE Estimation Persistence Persistence 0.1 0.185 D = 15; θ = 0.01 D = 15; θ = 1 0.08 0.18 Global MAE Kriging Average in Global Estimation MAE Average in 0.175 0.06 Subsampling at 5 min Interval 3 Estimated at Location 3 True Value at Location 3 0.17 Kriged at Location 10 0.04 2.5 2 4 6 8 10 12 14 16 14 12 10 8 6 4 2 Kriging at 17 Locations using Number of Active Sensors Number of Sensors Kriged 1 Sensor (6%) Measurements. Estimation and Kriging Performs Better Than Persistence. 2 1.5 Solar Power Index Power Solar 1 0.5 0 7 8 9 10 11 12 13 14 15 16 17 Time (Hours) 13 Distributed State Estimation for Power Distribution System Spectral clustering based automatic regionalization [3]. Di stributed Ladder Iterative State Estimation (DiLISE) [4]. Performance of DiLISE for Different Test Cases Impact of Bad Data on DiLISE Algorithm 14 Communication and Power HIL Test Power System Model Power Hardware in the Communication Hardware (Feeder RS-25.00-1) Loop (PHIL) in the Loop (CHIL) 6LoWPAN WiFi Mesh Ethernet Smart meter 1 PV inverter 1 Smart meter 3 Smart meter 2 Data Concentrator 1 PV inverter 2 PHI L Power Amplifier Smart meter N-1 Data Concentrator 2 Smart meter N Ethernet Core Network Edge Router Control Workstation 15 In Progress… • Integration of NS-3 simulation testbed with communication and power hardware-in-the-loop (HIL) testbed. • Integration of distributed state estimation algorithms for power distribution system and distributed PV systems. Reference [1] Zhang et al., “Hybrid Communication Architectures for Distributed Smart Grid Applications”, 1st revision submitted to MDPI Energies. [2] Alam et al., “Multi-Rate and Event-DRIven Kalman Kriging Filter for Distributed PV System State Estimation,” 1st revision submitted to IEEE Trans. Smart Grid. [3] Wang et al., “Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems”, IEEE GlobalSIP 2016. [4] Wang et al., “Distributed Ladder Iterative State Estimation and Automatic Regionalization for Distribution Systems”, 1st revision submitted to IEEE Trans. Smart Grid. 16 Thank You!! [email protected].
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