This section selects some of the most applicable and, ideally, open source energy storage-capable valuation tools currently in use. These tools range in their scope, approach, purpose, and implementation, all of which impact their applicability and usability. The tools described below are also selected to be applicable in the United States and developed recently to account for contemporary rules and use cases.
The Distributed Energy Resources – Customer Adoption Model (DER-CAM) was developed by Lawrence Berkeley National Laboratory (LBNL). It is a DER size optimization tool that has a broad range of modeling capabilities across DER technologies and stacked service combinations. (LBNL, n.d.) The tool focuses on microgrids or behind the meter DER mixes and includes thermal resources/loads as well as electric.
DER-CAM adopts a mixed integer linear programming approach, which selects the optimal combination of DER technologies to include in a DER mix. Every constraint and objective fed into the optimization problem is linearized to maintain fast runtimes. Additionally, while a user may have access to a large amount of data, DER-CAM focuses its optimization on a few days of data per month to reduce the complexity of the problem when designing a DER mix. This means that some information about the benefits of the DER mix are lost, increasing the model’s reliance on the assumptions around a few days of data. Additionally, this approach lacks the ability to value long-duration storage, when the day-to-day or week-to -week changes in the state of the storage system are important.
The model does not capture the complexities that long-duration reliability and resilience entail. It represents a few days of outage and a cost of lost load that may force the optimization to deploy DERs to guarantee power availability. This leaves aside the notion of outage probability and probability to serve load, which is critical to the design of grid infrastructure.
In its most recent versions, DER-CAM can consider internal power flow constraints of the DER mix/microgrid being modeled. This means that, unlike single-node models, DER-CAM can determine the optimal placement for each technology in addition to the optimal size.
Environmental considerations can be included in the analysis. These are either valued in the same dimension as the economic considerations or will be the only objective.
DER-CAM has been extensively used and reviewed.
The Hybrid Optimization of Multiple Electric Renewables (HOMER) tool originated with the National Renewable Energy Laboratory (NREL) but is a separate, commercial tool today. HOMER is a microgrid design tool that uses rules-based dispatch for a wide range of behind the meter DERs. HOMER runs many rules-based simulations to search for the optimal DER mix in a discrete set of design options. This rules-based approach may not arrive at the optimal solution when energy storage is included, especially when market services are involved.
This tool is mainly oriented to calculating the size and costs of an islanded system with solar generation technologies, batteries, and fuel-based generation. In this context, the simple rule-based dispatch was near optimal. The same is not true for a grid-connected scenario, especially where a storage system provides grid services. The ability to model grid services in HOMER was released in 2018. especially where a storage system provides grid services. HOMER can consider both electric and thermal loads and resources.
Demand response is modeled in HOMER as deferrable load, where the load requires a specified amount of energy in a time period, but exactly when that energy is delivered can be decided as part of the simulation.
The System Advisor Model (SAM) is a solar modeling tool developed by NREL and includes energy storage. SAM employs rules-based dispatch for energy storage and does not optimize the size of storage – its purpose is to estimate the cost of energy from a solar plus storage system and does not recommend ideal sizing or technology combinations.
SAM can handle either energy arbitrage or monthly demand reduction from storage, but these services are not co-optimized. (DiOrio, 2017) Recently, SAM was given a connection to the REopt API, which can optimally size and operate the battery outside of SAM.
The focus of the tool is to model a high level of detail in a solar system. It also facilitates the access to data for the models, supporting quick imports from the TMY database and other repositories but does not allow for standalone storage to be modeled.
REopt Lite is designed to identify the economic best size and dispatch strategy for a combination of grid-connected wind, solar, and battery storage and can consider existing diesel generation. The tool allows the user to constrain the design based on critical load requirements that must be met during user-specified grid outages.
REopt Lite adopts the perfect-foresight approach with a mixed integer linear program to determine optimal size and dispatch. (National Renewable Energy Laboratory)
The Battery Storage Evaluation Tool (BSET) was developed by the Pacific Northwest National Laboratory (PNNL) and is made publicly available for free on their website. (Pacific Northwest National Laboratories, n.d.) The tool can model energy time shift, frequency regulation, resource adequacy, distribution deferral, or outage coverage with or without perfect foresight.
BSET calculates the optimal dispatch and benefits from a battery energy storage system based on input load, prices, etc. It outputs the amount of time the storage system spends doing each selected service, the value of each service, and plots of the resulting power and SOC profile. PNNL also published an optimal sizing tool for battery storage. (Pacific Northwest National Laboratories, n.d.) This tool is very similar to the BSET but includes size optimization and some other features.
Published in 2012, developed by Navigant for the US Department of Energy. ESCT is capable of modeling batteries, flywheels, compressed air energy storage, and supercapacitors in a wide range of large-scale, front of the meter applications. ESCT includes default value numbers for many locations around the US. range of large-scale, front of the meter applications. ESCT includes default value numbers for many locations around the US.
Instead of simulating the operation of the storage in time, ESCT uses information like the average on and off-peak energy price to calculate energy time shift value and limits the number of stacked services in one case. This means that the results do not represent the optimum case and do not consider any of the constraints one service applies to another. However, this is mitigated because only certain service combinations are allowed. (US Department of Energy, n.d.)
OSESMO is an open-source MATLAB or Python based dispatch optimization for storage systems paired with PV at California customer sites. It is specifically directed toward California Self-Generation Incentive Program use cases but could be used outside of this setting. OSESMO can consider and optimize for carbon emissions outcomes in addition to customer energy bill savings. (Mann, 2018)
QuESt is an open-source perfect foresight dispatch optimization model that includes a data manager for downloading ISO data and OpenEI tariffs. (Sandia National Laboratories, 2018) This tool contains the ability to perform sensitivity analysis by sweeping model parameters between a minimum and maximum value. (Sandia National Laboratories, 2018) This tool can handle a couple of use cases, including customer energy bill reduction, wholesale energy market participation, and wholesale regulation market participation.