I-EMS Group Ltd. and EdgeTunePower Inc. are proud to announce their collaboration on delivering the very first AI-based, complementary, energy and power management solutions for optimal, reliable, and resilient operation of a portion of Energy+ power distribution system in Cambridge, Ontario, as a three-microgrid network. The project overall value is more than a million dollar with roughly half a million dollar in-cash support from IESO Grid Innovation Fund program.

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The Energy+ legacy system composed of three radial feeders and is feeding a commercial customer in Cambridge, Ontario. The feeders are equipped with one 0.5 MW solar-PV unit, one 1.5 MW backup generator, and are enhanced with two Tesla Battery Energy Storage Systems (BESS) with aggregated capacity of roughly 2.7 MWh. The two AI-based technologies are developed and realized over industrial-grade hardware platforms via the following methodology to be comprehensively verified for field implementation.

• I-EMS AI-based Energy Management System (EMS) conducts predictive analytic algorithms (in the order of hours to days) to forecast three-microgrid system load and solar generation levels with the optimal objectives of i) minimizing the system’s electrical generation cost and loss, ii) conducting optimal peak shaving and load leveling internally in each microgrid, and iii) responding to energy arbitrage signals exchanged between the three microgrids and the Energy+ acting as the system’s host grid.

• I-EMS technology also reacts in (in the order of seconds to minutes) to contingency conditions that may happen in the system, e.g., islanding of three-, two-, and/or single-microgrid systems, to enable optimal operation of the islanded system in long run.

• I-EMS technology runs on a Software-as-a-Service (SaaS) and communicates optimal setpoints over cyber-secured OPEN-ADR platform from its server located in Waterloo to EdgeTunePower’s server located in Toronto.

 

EdgeTunePower comprehensively conducts real-time Hardware-In-the-Loop (HIL) control performance testing and verification over industrial-grade hardware platforms of Fig. 1, as follows.

• The Energy+ three-microgrid system is simulated in real-time over 6 RTDS fiber-enhanced-interconnected real-time simulators, as depicted in Fig. 2, including RTDS latest NovaCor technology.

• The local control systems for system components, e.g., BESS and solar-PV units, are implemented over RTDS FPGA platform and National Instrument (NI) Compact-RIO technology.

• The EdgeTunePower’s AI-based PMS is implemented in real-time over PLCnext controller, provided by its industrial partner, Phoenix Contact Canada.

• The three-microgrid system, running in real-time on RTDS, and the EdgeTunePower’s AI-based technology, running on PLCnext, exchange information over IEEE-61850-compliance MODBUS-TCP-IP protocol, passing through RAPTOR-iMX950 cyber-secured gateway, provided by iS5 COMMUNICATIONS. The gateway is then exchanging data with I-EMS server, in Waterloo, over cyber-secured OPEN-ADR platform.

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Wide range of test cases and scenarios are verified over the HIL platform to demonstrate functionality of the hardware-implemented AI-based energy and power management technologies and to comply with interconnection requirements set out by IESO. The same software-hardware platforms and communication protocols are targeted to be retrofitted in Energy+ power distribution system.