Penetration of solar photovoltaic (PV) generation into power distribution grids is happening around the globe. Recent research has indicated that the coordination of these technologies' functions with the existing grid infrastructure can help mitigate the adverse effects on voltage stability that has been observed with decentralized generation. The volt-var control functionality of smart inverters is often considered for mitigation of both short and long term, small disturbances to voltage. In many studies, smart inverter volt-var control functionality is primarily considered to mitigate overvoltage and reverse power flow. This research demonstrates the use of smart inverter volt-var control to mitigate long term, highly variable voltage deviations from the nominal voltage on a distribution feeder in Kathmandu, Nepal. The work considers data collected from an existing, grid-tied, PV plus battery system to show the feasibility of voltage stabilization in a region with an inconsistent power supply from the local utility.

Climate change mitigation motivates the transformation of the electric power sector towards a low carbon future. A successful and timely transformation depends on robust and independent methods for understanding benefits and impacts of the integration of renewable energy resources and energy storage technologies into the electric power system. Therefore the objectives of this dissertation are to investigate how energy storage technologies should be allocated and operated to mitigate the impacts of variable solar photovoltaic (PV) resources in distribution systems while providing economic incentives for storage owners, and to assess the indirect regional environmental impacts of economic energy storage operation.

The allocation of energy storage systems (ESSs) in distribution systems for voltage support under high penetration solar PV is investigated. A genetic algorithm based bi-level optimization method is developed that reduces the voltage fluctuations caused by PV penetration through deploying ESS among permitted nodes of a distribution system while accounting for their capital, land-of-use, and installation costs using a qualitative cost model. 

A convex optimization based charge/discharge scheduling algorithm for distributed ESSs co-located with solar PV systems is developed. The daily charge/discharge schedules reduce (1) peak net demand (that is, load minus PV generation) of the customer, (2) power fluctuations in the customer net demand profile, and (3) the reliance of the customer on the grid by way of promoting self-consumption of local solar PV generation. Moreover, a novel idea of a "supply charge" tariff that incentivizes ESS customers to store excess solar PV generation that may otherwise result in reverse power flow in the distribution grid is investigated. Introduction of a supply charge successfully reduces the maximum solar PV power supply to the grid and does not financially impact ESS owners.

Finally, the economic and emissions effects of residential ESS operation for cost minimization in each of the eight regional electric reliability entities of the contiguous U.S. is investigated. It is observed that the overall economic performance and environmental impact of ESS varies considerably from region to region and is driven most by regional emissions and utility tariff structure. Results indicate that policy makers seeking emission reductions should carefully consider the interaction between emissions and rate structure in future orders for net metering and residential rate reform.

Battery Energy Storage Systems (BESS) are often used for demand charge reduction through monthly peak shaving. The effect of two temporal resolutions, 15-min and 1-hour, on peak shaving is compared across a battery ratings space defined by power capacity and energy capacity of the battery. Based on the 15-min load of a particular day, a critical power and critical energy can be defined, yielding a critical point in the power- energy space. A linear program of the system optimizes the peak of the net load and the associated demand charge assuming perfect forecasts. Based on the difference of demand charge (DODC) across the two load profiles at high and low temporal resolution for a real building, the battery rating space is divided into three different regions: optimal region, power-constrained region, and energy-constrained region, which can be identified by the critical power (CP) and critical energy (CE) derived from the load profile. As the conclusion shows, the DODC in the power-constrained and energy-constrained regions is explained by the averaging operation and the load sequence at high resolution. In the power-constrained region of the battery rating space, the difference between the original 15 min peak and the 1 hour average peak persists in the optimized net load until the battery power capacity is sufficiently large. In the energy-constrained region, averaging may change the peak period duration, which depends on the sub-hourly sequence of the original load data. Through artificial load data and reordering of real load data, we demonstrate that the sequence effect causes energy-constrained batteries to underestimate peak shaving and demand charge reduction.