Electric Vehicles (EVs) are emerging to play a key role in mitigating fossil fuel reliance and the environmental impacts brought on by the transportation sector. In parallel, it is essential that this growing electricity demand be met mostly with renewable sources. This study, as part of the Irvine Smart Grid Demonstration (ISGD) project, analyzes the effects of a renewable solar photovoltaic (PV) nanogrid — a small-scale, autonomous power system, that ultimately ties into the central electrical distribution grid — for EV charging associated with a controllable battery energy storage system, deployed in a primary circuit on the UCI (University of California, Irvine) Microgrid. The system’s dynamic behavior is characterized for different control algorithms that govern battery dispatch, creating four different energy management strategies.

Power quality aspects of EV charging are also investigated, with a focus on harmonic distortion. A robust system design using delta-wye grounded transformers was shown to prevent any excessive harmonic currents from flowing upstream into the microgrid. Additionally, a diversity of EV charging loads promotes favorable harmonic cancellation.

A power flow model of the nanogrid was developed in a commercial power flow software (ETAP) and verified against smart meter data, which were used as inputs for the ETAP Real-Time module.

Lastly, a linear program was developed for optimally sizing nanogrid assets in the context of minimizing operation costs for the microgrid operator. The combination of an 80 kW PV array and a 95 kWh/45 kW battery energy storage system was shown to be the best option to power 20 Level-2 (7.2 kVA) EV chargers deployed at one of the university campus parking structures.

In response to climate change and sustainability challenges, various incentive programs have promoted solar photovoltaic (PV) economic feasibility and interconnection into the low voltage electrical distribution system. However, the existing power system infrastructure can only accommodate a limited amount of PV generation before reverse power flow (PV generation flowing back into the distribution network) becomes an issue. This limits the ability to achieve Zero Net Energy (ZNE) behind individual meters and in whole communities since ZNE communities typically require large PV deployments. In parallel, district-level energy systems, such as Advanced Energy Communities (AEC) microgrids— electrically contiguous areas that leverage the clustering of load and generation by integrating multiple utility customer-owned Distributed Energy Resources (DER) — that include battery storage, offer a great prospect for integrating high levels of solar PV into the built environment.

Designing the least-cost and technically feasible system to serve a district load has been a challenge to utilities and city planners. Battery energy storage and smart-inverter technologies emerge in this context to enable higher penetration of solar PV by locally regulating voltage and controlling active and reactive power flows. However, there is no straight forward way nor a practical rule or consensus on how to size such assets optimally.

This work proposes a Mixed Integer Linear Program (MILP) optimization to determine the least-cost DER portfolio consisting of inverter-connected solar PV and battery storage. It also allocates it in a multi-node electrical grid and dispatches it considering the existing electric infrastructure limits (transformer capacities and nodal voltage magnitudes). Novel linearization techniques such as polygon relaxations are used to limit otherwise non-linear apparent power flows at transformers. A novel Alternating Current (AC) decoupled linearized power flow is also integrated into the MILP optimization. Moreover, for the first time, smart-inverter droop-control functions are included in the DER optimal allocation problem. Results show that such comprehensive MILP is achievable and tractable for a 115-node AEC.