In 2008, the State of Hawaii initiated a clean energy initiative that set an ultimate goal of 70% clean energy by 2030 (40% from renewable energy and 30% from energy efficiency). A controllable Battery Energy Storage Systems (BESSs) can be used to manage intermittent renewable resources on a power system to address both circuit and system level issues. Simulation and experimental results of applying a novel algorithm for the charging and discharging of a BESS are presented, using actual grid data for controlling a BESS for the purpose of peak load shaving, power curve smoothing, and voltage regulation of a distribution transformer. Two optimization objectives for peak shaving are presented in which proposed load forecasting methods are used. The application of a BESS for voltage regulation is examined and analyzed with different tests, and the observed results are discussed.
The addition of renewable energy resources to power grids in the U.S. has grown rapidly in recent years. Photovoltaic (PV) devices are the fastest growing renewable category with a 60% growth rate, followed by wind power at 27% and biofuels at 18% . The inherent intermittent nature of renewables poses some challenges to the continued expansion of their use due to limitations of existing conventional generation facilities that are designed more for efficiency than flexibility and existing transmission and distribution systems that are designed for one-way power flows and load connection rather than generation interconnections.
Energy storage is one of the ways to deal with the variability of renewable resources. Energy storage devices can harvest excess energy during periods of low demand and inject the stored energy when needed during peak usage periods. The storage devices can also play the role of reserve power plants, providing extra energy in case of power system contingencies or a rapid change in demand. A popular use of energy storage is for system peak demand shaving, which involves absorbing energy when there is excess energy, generated either by renewables or base power plants, during off-peak times and injecting the stored energy back into the distribution system during system peak load times. As a result, renewable generation curtailment is reduced, and expensive fast generating units can be avoided. Energy storage can also be used for peak demand shaving on a particular distribution feeder transformer, with the objective to reduce the peak power demand on the transformer and extend its useful life. The Battery Energy Storage System (BESS) is a battery equipped with bidirectional converters which can absorb or inject active and reactive power at the designated set points. In this paper, an algorithm is developed to manage stored energy and storage capacity effectively for peak shaving and load leveling purposes and which considers estimates of future hourly pricing and renewable generation output.
There is a growing number of research works which employ different storage technologies for dealing with the intermittency of renewables. In , different technologies used in battery energy storage systems deployed at the grid level are introduced. The optimal power and size of a hybrid energy storage system consisting of BESS and a high-speed superconducting flywheel energy storage system are investigated in  for the purpose of stabilizing the power system. In , a real-time State of Charge (SOC) based control method is proposed to reduce the fluctuations in the power system in response to a high level of integration of variable energy sources such as PV and wind. The sizing of energy storage for micro-grids is examined in , where a neural network is used to forecast the PV and wind power generation levels, and the optimal size of BESS is determined with and without connection to the main grid. In [6,7], a scheme consisting of wind generation in combination with a BESS is proposed for scheduling short-term power dispatch to maximize the energy harvested from wind generation.Different methods have been proposed for battery operation optimization and leveling the load profile.
In [8,9], dynamic programming techniques are used to find the optimal battery energy storage and power levels for peak load shaving applications. Battery storage is examined in  for reducing transmission and distribution losses, and a set of normalized charts are provided to quantify the benefit of BESS for leveling the utility load. Finally, in , BESS is used to regulate active and reactive power according to SOC limits, and the control signals are fed into the switches using a current control loop.
Here, a grid scale BESS (1 MW, 1 MWH) is connected to a distribution feeder via a 1 MVA step-up transformer and is used for peak shaving of the distribution grid circuit shown in Fig. 1.
A 69 kV transmission grid provides the energy balancing needs of the distribution circuit and BESS collectively via a 69/12.47 kV distribution transformer. The goal of peak shaving is to optimally control the BESS to reduce the peak load of the circuit.
The BESS consists of twelve Li-ion battery racks and a master control rack. A single battery rack contains 22 trays (2 columns of 11) each populated with 38 prismatic flat pack cells and one Battery Management System (BMS) tray at the top. Together, these components form a 1 MW, 1 MW h energy storage system. The BESS is connected to a 1 MW bidirectional three phase inverter with 12,470 V AC output. The battery management system has a SOC estimation algorithm, which estimates the amount of usable electrical energy stored in the battery pack . The SOC is limited to an operating range of 0.2–0.8 in which the battery is neither fully depleted nor fully charged [13,14], in order to avoid adversely impacting the battery life. Control modes, set points, and active and reactive power commands are sent from the dispatch room to the BESS controller using the Maui Electric supervisory control and data acquisition (SCADA) system utilizing the DNP3 protocol.
In the context of a deregulated energy market system, a Distribution System Company (DISCO) can offer peak load shaving and load smoothing services with optimal operation of a BESS under its control at a market based price to the Independent System Operator (ISO). The ISO can in turn then utilize this DISCO provided resource to meet its system operational objectives, such as peak demand shaving and operational reserves.