Energy Storage Sizing Research Articles

Sizing of energy storage for hybrid power plants aiming for turbine stress reduction under FCR-N requirements

This paper examines the storage sizing for hybrid power plants providing the Frequency Containment Reserve (FCR) for power grid balancing, where the hybridization of a turbine with energy storage system aims to decrease turbine stress under the most recent Nordic FCR requirements. This objective is addressed via the formulation of a hybrid plant optimal control problem, focusing on the achievable stress reduction as a function of storage energy capacity. The analysis of the optimization problem provided an analytical representation of the least upper bound for the best attainable turbine stress reduction, which was used to derive sizing recommendations for energy capacity. The obtained results imply that the hybrid plant’s control policy has a significant impact on the achievable stress reduction, as relatively small energy capacity can provide a high-stress reduction with “well–designed” control.

Effect of temperature on seasonal wind power and energy potential estimates in Nordic climates

AbstractA major obstacle standing in the way of full‐scale adoption of renewable energy sources is their intermittency and seasonal variability. To better understand the power generation dynamics, the effect of air density due to temperature on power and energy generation figures was modelled. The model uses historical ERA5 data and considers changes in weather patterns in a subarctic climate where seasonal changes are most pronounced. The power generation figures of using a mean and a dynamic air density value were compared and the results show that power generation estimates may be under‐ and overestimated by on average 5% up to 10% in winter and summer, respectively. This can have implications for the sizing of power transmission infrastructure and energy storage in both on‐grid and off‐grid applications as well as power availability. The topic is highly relevant in the Nordic countries where roughly 10% of new global added wind capacity is installed annually.

Simulation-Based Hybrid Energy Storage Composite-Target Planning with Power Quality Improvements for Integrated Energy Systems in Large-Building Microgrids

In this paper, we present an optimization planning method for enhancing power quality in integrated energy systems in large-building microgrids by adjusting the sizing and deployment of hybrid energy storage systems. These integrated energy systems incorporate wind and solar power, natural gas supply, and interactions with electric vehicles and the main power grid. In the optimization planning method developed, the objectives of cost-effective and low-carbon operation, the lifecycle cost of hybrid energy storage, power quality improvements, and renewable energy utilization are targeted and coordinated by using utility fusion theory. Our planning method addresses multiple energy forms—cooling, heating, electricity, natural gas, and renewable energies—which are integrated through a combined cooling, heating, and power system and a natural gas turbine. The hybrid energy storage system incorporates batteries and compressed-air energy storage systems to handle fast and slow variations in power demand, respectively. A sensitivity matrix between the output power of the energy sources and the voltage is modeled by using the power flow method in DistFlow, reflecting the improvements in power quality and the respective constraints. The method proposed is validated by simulating various typical scenarios on the modified IEEE 13-node distribution network topology. The novelty of this paper lies in its focus on the application of integrated energy systems within large buildings and its approach to hybrid energy storage system planning in multiple dimensions, including making co-location and capacity sizing decisions. Other innovative aspects include the coordination of hybrid energy storage combinations, simultaneous siting and sizing decisions, lifecycle cost calculations, and optimization for power quality enhancement. As part of these design considerations, microgrid-related technologies are integrated with cutting-edge nearly zero-energy building designs, representing a pioneering attempt within this field. Our results indicate that this multi-objective, multi-dimensional, utility fusion-based optimization method for hybrid energy storage significantly enhances the economic efficiency and quality of the operation of integrated energy systems in large-building microgrids in building-level energy distribution planning.

Optimal sitting, sizing and control of battery energy storage to enhance dynamic stability of low‐inertia grids

AbstractAs inverter‐based resources like wind turbines increase, grid inertia and stability decrease. Optimal placement and control of energy storage systems can stablise low‐inertia grids. This paper investigates how optimal battery energy storage systems (BESS) enhance stability in low‐inertia grids after sudden generation loss. The sitting, sizing and control of BESS are determined simultaneously in each genetic algorithm (GA) population, then voltage and frequency stability is evaluated based on the network simulation. This continues until the optimal solution is found. A network based on Kundur's four‐machine system is modelled for the first study and two of the four synchronous generators (SGs) have been replaced with wind farms. Then, the production of the third SG has been decreased by 13%. According to the results, addition of wind farms causes the frequency drop below 49.6 Hz for more than 5 min, indicating instability. It is also demonstrated that with optimal control parameters and placement, a 60 MW BESS can alleviate the voltage and frequency fluctuations, leading to enhanced stability. This method has also been tested on the IEEE 39‐bus network, where the installation of a BESS with a capacity of 9 MVA could restore the frequency stability.

Multi-Objective Energy Management in Microgrids: Improved Honey Badger Algorithm with Fuzzy Decision-Making and Battery Aging Considerations

A multi-objective energy management and scheduling strategy for a microgrid comprising wind turbines, solar cells, fuel cells, microturbines, batteries, and loads is proposed in this work. The plan uses a fuzzy decision-making technique to reduce pollution emissions, battery storage aging costs, and operating expenses. To be more precise, we applied an improved honey badger algorithm (IHBA) to find the best choice variables, such as the size of energy resources and storage, by combining fuzzy decision-making with the Pareto solution set and a chaotic sequence. We used the IHBA to perform single- and multi-objective optimization simulations for the microgrid’s energy management, and we compared the results with those of the conventional HBA and particle swarm optimization (PSO). The results showed that the multi-objective method improved both goals by resulting in a compromise between them. On the other hand, the single-objective strategy makes one goal stronger and the other weaker. Apart from that, the IHBA performed better than the conventional HBA and PSO, which also lowers the cost. The suggested approach beat the alternative tactics in terms of savings and effectively reached the ideal solution based on the Pareto set by utilizing fuzzy decision-making and the IHBA. Furthermore, compared with the scenario without this cost, the results indicated that integrating battery aging costs resulted in an increase of 7.44% in operational expenses and 3.57% in pollution emissions costs.

A robust bi‐level programming approach for optimal planning an off‐grid zero‐energy complex

AbstractElectric power provision for all the customers is not always possible for distribution companies. Some customers are interested in serving their load through renewable resources regarding the climate situation. Electrically off‐grid zero‐energy building is an applicable concept, in which the electrical energy provision of the buildings is isolated from the power supply infrastructures. This paper introduces a robust bi‐level programming model to create a cost‐effective off‐grid zero energy complex in Kish Island under risk management. The upper level of the planning is composed of two components, the passive design of buildings within the complex and the design of a stand‐alone energy system. The passive design as an energy‐saving tool includes the selection of insulation material for building external walls and finding the optimum thickness. Also, the stand‐alone energy system design denotes the sizing of diesel generator, photovoltaic, and battery energy storage as the distributed energy resources. The lower‐level problem optimally handles the annual scheduling of these resources to meet the complex demand under the impact of passive cooling. The Karush–Kuhn–Tucker condition method is used to solve this bi‐level planning problem. Furthermore, the battery degradation is concerned via the throughput model to consider the replacement cost of the problem.

Modelling and optimization of concentrated solar power using response surface methodology: A comparative study of air, water, and hybrid cooling techniques

This research introduces a novel approach specifically designed to improve the design of Concentrated Solar Power plants utilizing the Response Surface Methodology. The objective of the suggested methodology is to enhance energy production efficiency by simultaneously minimizing the levelized cost of electricity and the land footprint associated with the power plant while comparing three different cooling techniques: air, water, and hybrid. Two software tools, System Advisor Model and Design-Expert, are employed to validate the primary model, evaluate the responses, generate the predictive models, and verify the results. The configuration of a Concentrated Solar Power plant is influenced by four main factors: the size of the solar field (solar multiple), row spacing, number of solar assemblies per loop, and size of thermal energy storage. In this study, these factors are varied within the following ranges: solar multiple from 1 to 5, row spacing from 10 to 30 m, number of solar assemblies from 4 to 10 per loop, and thermal energy storage from 5 to 15 h. The generated predictive models demonstrated very high accuracy, particularly for the annual energy production, with an error ranging between 0.2% and 1.5%. The findings showed that the hybrid cooling system is the most cost-effective cooling technique and has the highest energy output compared to the evaporative and air-cooling methods. When optimizing the required area of the hybrid cooled plant with a reduction of 47.44%, the analysis indicated a minimal decrease in energy output of 3.61% and a slight increase in the levelized cost of electricity by 0.95%. According to the results, the effect of area on the annual energy production and levelized cost of electricity is significant below the optimal area, while this effect becomes minor at higher values.

Assessing residential sustainable energy autonomous buildings for hot climate applications

This paper introduces an innovative solution for clean energy autonomous building (AB) sustainability, offering a 5Z concept—zero-carbon, zero-energy, zero grid connections, zero energy bills, and zero emission mobility. This paper focuses on fundamental research to design sustainable, energy-ABs striving for self-sufficiency in arid climates, using Kuwait as a case study. The study highlights the importance of stringent engineering AB modeling, renewable technology integration, and clean energy storage. The technical approaches include characterizing non-thermal and thermal demands, achieving net-zero energy generation, and custom sizing renewable energy and energy storage systems (ESS) for electric vehicle (EV) charging points. The research methodology involves local construction with yearly weather and energy profile data to simulate the actual system using ESP-r building model. The study's findings reveal the need for a large-scale energy system, energy demand reduction (EDR) inclusive of heating, cooling, appliances, and EV, with the corresponding changes in local energy production system size, battery capacity, and the number of photovoltaics (PVs). The results show varying EDR levels ranging from base case to 10% and to 50% leading to proportional changes in energy system size, size of battery from 3415 kWh to 1710 kWh and the number of PVs from 362 to 181, which means EDR not only optimizes space but also proves cost-effective. The significant implication of this study, not only bridging the knowledge gap and the lack of how-know, but also making a significant advancement and forward thinking in sustainable green electric home modernization and environmentally friendly transportation. This approach transforms energy management practices for more sustainable cities and societies, reducing emissions in both urban infrastructure and transportation.

Seasonal hydrogen energy storage sizing: Two-stage economic-safety optimization for integrated energy systems in northwest China

The evident seasonal variations in photovoltaic output as well as electric and thermal loads will result in significant energy wastage and carbon emissions. In order to address the problem, a two-stage sizing cooptimization method considering economy-safety characteristics is proposed for the integrated energy system combined power-hydrogen-heat cogeneration (CPHH-IES), with seasonal hydrogen storage. Subsequently, an economic-durability-safety optimized objective is introduced, assessing the total cost throughout the sizing cycle, equipment degradation during operation, and safety indicators of the hydrogen energy system. Finally, a two-stage sizing framework based on heat-determined hydrogen is established, and a combined configuration-scheduling double-layer strategy is put forward within the framework to accommodate seasonal hydrogen storage and multi-energy coupling. The feasibility of the method was validated using data from a site in northwest China, demonstrating its capacity to ensure the safety of the hydrogen energy system and enable seasonal hydrogen storage.

Effects of energy sharing and electricity tariffs on optimal sizing of PV-battery systems for grid-connected houses

This paper investigates the optimal sizing of solar photovoltaic (PV) and battery energy storage (BES) for grid-connected houses based on mutually-agreed energy sharing prices by considering the flat and time-of-use (TOU) tariffs. The grid-tied house with PV-BES, referred to house 1 (H1) in the paper shares electricity with house 2 (H2) whenever needed with mutually-agreed electricity tariffs. The main objective function of the study is to minimize the cost of electricity (COE) for the H1 while decreasing COE for H2. Eight different schemes are investigated with the combination of flat and TOU tariffs for buying, selling, and mutually-agreed rates, respectively. For each scheme, optimal sizing of components and COE for both houses are evaluated. Realistic hourly-arranged annual data of temperature, solar irradiation, and load consumption of two houses are used as the input data. For each scheme, the results are compared with the situation when H1 does not have PV-BES and there is no electricity sharing. Sensitivity, operational, and uncertainty analyses are provided for the scheme with the lowest COE.

Sizing energy storage in electricity grids containing flexible loads

Flexible loads such as EV chargers and heat pumps have the potential to reduce the amount of energy storage required to handle local power imbalances. However, when sizing energy storage these loads are often neglected. Thereby it is unclear how intelligently controlled flexible loads influence energy storage sizing. Therefore, this paper presents an empirical study into the effect of controlling four different classes of flexible loads on energy storage sizing, under which fall existing technologies such as EV chargers, heat pumps and white-goods. We use a Profile Steering control strategy to minimize the power imbalance over the considered period. Furthermore, we use parameter sweeps to investigate the influences of load availability and load demand on the energy storage sizing, and visualize the energy storage reduction with heat maps. The results show that significant energy storage sizing reductions are possible if flexible loads are considered, including a reduction in capacity of more than 60%. Furthermore, if flexible loads can also supply their buffered power to the local microgrid (such as with V2G applications), the need for energy storage is almost completely removed. We conclude that it is important to consider local flexible devices when sizing storage in order to avoid over-dimensioning.

Operation Scheduling of Distribution Network with Photovoltaic/Wind/Battery Multi-Microgrids and Reconfiguration considering Reliability and Self-Healing

In this paper, a new simultaneous framework of distribution network operation is proposed based on the scheduling of photovoltaic/wind/battery multi-microgrids with network reconfiguration considering self-healing. The objective function is considered to minimize the energy losses, and multi-microgrids cost and improve the reliability indices including minimization of energy not-supplied (ENS) and minimizing system average interruption duration (SAIDI) and system average interruption frequency (SAIFI) indices. The optimization variables are defined as the situation of the distribution network switches to find the network’s optimal configuration with the installation location and size of renewable resources and battery energy storage during 24 hours. An improved beluga whale optimization (IBWO) based on a nonlinearly diminishing inertia weight (NDIW) approach is used to find the optimal variable set of the problem. The recommended methodology is implemented on 33-bus and real 59-bus distribution networks. The results demonstrated that by obtaining an optimal state of the network switches in the event of a fault, as well as the optimal scheduling of the two microgrids, the energy losses have decreased and the reliability indices have improved. The outcomes of the proposed methodology based on the hybrid multi-microgrid allocation and network reconfiguration are stated that the losses, ENS, SAIDI, and SAIFI are reduced by 66.39%, 54.00%, 50.24%, and 33.61%, respectively, for 33-bus network and are declined by 65.29%, 57.44%, 48.63%, and 62.33%, respectively, for the real 59-bus Ahvaz network in comparison with the base network. The obtained results illustrated that in the condition of the network line outage, by simultaneously implementing the reconfiguration with the change of network switches and the optimal allocation and scheduling of HMGs in the network, self-healing is provided to prevent a significant weakening of the network performance so that the resilience is improved in addition to minimizing the energy losses and the cost of HMG energy injection compared to the base network. The findings revealed that the objectives including losses, ENS, SAIFI, and SAIDI are increased by 78.29%, 33.00%, 83.33%, and 35.11%, respectively, due to outage of line 22 of the 33-bus network and these objectives are increased by 52.32%, 10.30%, 32.29%, and 79.66%, respectively, due to outage of line 12 of the 59-bus network compared with not considering the line outage. Moreover, the superior capability of the recommended NDIW-based IBWO has been confirmed in comparison with the well-known particle swarm optimizer (PSO) and ant lion optimizer (ALO) in solving the problem to achieve better objective value.

Sizing Energy Storage Systems to Dispatch Wind Power Plants

Integrating wind power plants into the electricity grid poses challenges due to the intermittent nature of wind energy generation. Energy storage systems (ESSs) have shown promise in mitigating the intermittent variability associated with wind power. This paper presents a distributionally robust optimization (DRO) model for sizing energy storage systems to dispatch wind power plants. The variable wind power is formulated as a moment-based ambiguity set. Dispatchability is described by the expected value of the insufficient power of wind power relative to the dispatch command, which is a sum of nonlinear functions and is taken as the optimal index. A deterministic semi-definite positive model is derived to solve the problem effectively. Numerical studies are conducted to demonstrate the effectiveness and advantages of the proposed method.

An optimal expansion planning of power systems considering cycle-based AC optimal power flow

This paper presents a novel mixed-integer linear optimization formulation of the AC network-constrained, cost-based, integrated expansion planning problem. The formulation is used to determine the investment needs per technology including the location and sizing of new generation, energy storage, and transmission network assets in a future low-carbon power system. To reduce the size of the resulting problem, the AC optimal power flow (AC-OPF) model is represented in a compact way using cycle constraints. A bound tightening procedure is also considered to reduce the search space and improve the solver performance by adjusting the voltage bounds within the AC-OPF. Contrary to typically used formulations of the integrated expansion planning problem, the constraints considered here include all main aspects of system operation, namely unit commitment, energy storage system management, AC-OPF, and reactive power compensation. Thus, in this paper, we examine how both the proposed transmission expansion modeling developments and the interrelation of the integrated planning constraints affect the computation of the solution to the expansion planning problem. The performance of this formulation is assessed on the RTS-GMLC test system by computing the expansion plan and comparing it with the results of three other expansion planning formulations most frequently employed in the recent literature to address the integrated expansion planning problem for medium to large-scale systems. Expansion plans are computed and compared for different case studies and multiple scenarios. According to the comparative analysis, neglecting the AC-OPF or the unit commitment constraints can increase the total system costs by 7.10%–9.57% or 6.29%–8.39%, respectively. Unlike other modeling approaches, the proposed approach does not rely on simplifications that impact the quality of the solution. Thanks to the incorporated cycle-based AC-OPF constraints and the consideration of a bound tightening procedure, the computation time is reduced by 17.67%–27.21%.

Hybrid energy storage sizing in energy hubs: A continuous spectrum splitting approach

Renewable powered energy hub is a promising way to realize efficient use of renewable resources through multi-energy integration. To cope with the fluctuation of renewable power at different timescales, long-term and short-term energy storage devices are essential. This paper proposes a frequency-domain approach to determine the appropriate capacities of hydrogen and battery energy storage units in an electricity–hydrogen–heat integrated energy hub. The net demand is mapped to the frequency domain via discrete Fourier transformation (DFT). A continuous spectrum splitting method is developed to allocate the frequency components among generator, hydrogen storage and battery storage. Compared with the time-domain method, it makes better use of the spectral characteristics of the year-round data, leading to robust sizing results without complex modeling of uncertainty. In contrast to the existing cut-off frequency method which assigns high-frequency components to battery storage and low-frequency components to generators according to a cut-off frequency, the proposed framework allows a single spectrum component to be allocated to multiple system devices, which not only makes the optimization model convex but also improves optimality. In addition, the spectrum clustering process further reduces the number of core variables and the computational efficiency is high. The rationality of the sizing results is verified through online operation, where the proposed method achieves an average of 1.93% total load shedding rate compared with 4.42% of the time-domain method, verifying the operation reliability under uncertainty. While fully utilizing the spectrum characteristics, the investment cost is about 22% less than the cut-off frequency result.

Optimization of Electrical and Thermal Storage in a High School Building in Central Greece

Nearly zero-emission buildings (nZEBs) are increasingly being constructed in Europe. There are also incentives to refurbish older buildings and transform them into nZEBs. However, permission is not always granted for their connection to the grid to infuse surplus photovoltaic electricity due to the grid being overloaded with a large number of renewables. In this study, the case of a refurbished school building in Central Greece is examined. After refurbishing it, a significant amount of photovoltaic electricity surplus is observed during the summer and neutral months, which cannot be exported to the grid. The absence of an adequate battery storage capacity resulted in the rejection of an application for exporting the school’s surplus to the network and the photovoltaic installation staying idle. An alternative approach is proposed in this work, involving a shift in the export of the photovoltaic electricity surplus to the evening hours, in order for the school to be granted permission to export it to the network. To this end, an optimal battery storage size is sought by employing a building energy system simulation. The mode of operation of the battery designed for this application is set to discharge daily, in order to export the electricity surplus in the afternoon hours to the evening hours, when it is favorable for the network. Additionally, the optimal size of the thermal energy storage of the heating system is studied to further improve its energy efficiency. Our battery and storage tank size optimization study shows that a significant battery capacity is required, with 12 kWh/kWp photovoltaic panels being recommended for installation. The ever-decreasing cost of battery installations results in the net present value (NPV) of the additional investment for the battery installation becoming positive. The solution proposed forms an alternative path to further increase the penetration of renewables in saturated networks in Greece by optimizing battery storage capacity.

Energy community with shared photovoltaic and storage systems: influence of power demand in cost optimization

AbstractEnergy management of distributed energy resources has gradually become a complex problem because of the intermittent nature of renewable energy sources, such as photovoltaic power, and the large use of energy storage systems. A way to deal with these issues is to operate within an energy community. However, the efficient management of the community in terms of costs is particularly relevant. Specifically, the minimization of the energy community costs, which consists of properly utilizing shared energy storage and renewable energy sources, becomes an important objective. In this context, a fundamental role is played by demand power characteristics which strongly influence the benefits brought by this energy management scheme. This work investigates the influence of the variability of power demand on the minimization of the operating cost problem of an energy community while determining the optimal capacity of the energy storage system that increases the self‐consumption potential of the photovoltaic source. Two main scenarios are implemented where the effects of considering the community photovoltaic capacity as a variable or a parameter on costs and energy storage system size are investigated. This analysis consists of a multi‐objective optimization coupled with a Monte Carlo framework. The community management is conducted by considering random power demand profiles of each unit belonging to the same community, and different sizes, categories of users and users' aggregations. A comparison is led among different users' categories in terms of costs, photovoltaic unit and energy storage system size. The results provide an overview of how each category benefits from taking part in an energy community both in terms of cost and energy storage and photovoltaic sizes and show how these aspects change within a multi‐category aggregation where each category makes a different contribution to the community. In particular, we find evidence of the “synergy effect” brought by multi‐category aggregations capable of exploiting differences in consumption profiles. Each building category, with its numerosity, has a different effect on the energy community, resulting in a different impact on total costs and cost savings. We also investigate how the energy storage system capacity is affected by both the available photovoltaic capacity and the consumption profiles of the categories within the energy community.

Dual-level design for cost-effective sizing and power management of hybrid energy storage in photovoltaic systems

Integration of hybrid energy storage systems (HESS) into photovoltaic (PV) applications has been a hot topic due to their versatility. However, the proper allocation and power management schemes of HESS are challenges under diverse mission profiles. In this paper, a cost-effectiveness-oriented two-level scheme is proposed as a guideline for the PV-HESS system (i.e., PV, Li-ion battery and supercapacitor), to size the system configuration and extend battery lifespan while considering the power ramp-rate constraint. On the first level, a sizing methodology is proposed to balance the self-sufficiency and the energy throughput between the PV system and the grid to achieve the most cost-effectiveness. On the second level, an improved adaptive ramp-rate control strategy is implemented that dynamically distributes the power between the battery and supercapacitor to reduce the battery cycles. The case study presents the whole two-level design process in detail, and verifies the effectiveness of the proposed strategy, where the results show that the battery cycles are reduced by up to 13% over one year without affecting the self-sufficiency of the PV system.

Techno-Economic Comparison of Electricity Storage Options in a Fully Renewable Energy System

To support increasing renewable capacity for a net-zero future, energy storage will play a key role in maintaining grid stability. In this paper, all current and near-future energy storage technologies are compared for three different scenarios: (1) fixed electricity buy-in price, (2) market-based electricity buy-in price, and (3) energy storage integrated into a fully renewable electricity system. In the first part of this study, an algorithm is devised to simulate strategic buy-in of electricity for energy storage. This analysis yields a qualitative decision-making tool for a given energy storage duration and size. Building upon the first part’s findings, an integration study gives insight into expected power prices and expected storage size in a typical northwestern European fully renewable energy system. The integration study shows significant need for electricity storage with durations spanning from one to several days, typically around 40 h. Pumped Hydro Storage and Pumped Thermal storage surface as the best options. The overall levelized costs of storage are expected to be in the USD 200–500/MWh range. Integration of storage with renewables can yield a system-levelized cost of electricity of about USD 150/MWh. Allowing flexibility in demand may lower the overall system-levelized cost of electricity to USD 100/MWh.

A deep learning-based battery sizing optimization tool for hybridizing generation plants

Hybrid generation and energy storage systems can enhance asset flexibility, enabling various services and optimizing financial performance. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary or too complex for most asset owners to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run).This work presents a deep learning-based battery sizing optimization tool for hybridizing generation facilities. The tool uses deep learning technique to predict revenue over a broad search space of potential battery sizes, estimate capital and operations costs (including accounting for battery degradation), and calculate financial performance of each potential battery system investment; an output is a recommendation of battery that maximizes financial performance. The tool is tested and validated for hydropower generation and is publicly available on Idaho National Laboratory's GitHub page (https://github.com/idaholab/Hydro_Hybrids), documented in Zenodo (https://zenodo.org/record/7562692#.Y9Q7anbMKUm), and accessible through an intuitive web app (hydrohybrids.inl.gov). This tool will help a greater cross-section of generation owners consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.