Storage Assets Research Articles

Deep-learning-based multi-timescale load forecasting in buildings: opportunities and challenges from research to deployment

Electricity load forecasting for buildings and campuses is becoming increasingly important as the penetration of distributed energy resources (DERs) grows. Efficient operation and dispatch of DERs require reasonably accurate predictions of future energy consumption in order to conduct near-real-time optimized dispatch of on-site generation and storage assets. Electric utilities have traditionally performed load forecasting for load pockets spanning large geographic areas, and therefore, forecasting has not been a common practice by buildings and campus operators. Given the growing trends of research and prototyping in the grid-interactive efficient buildings domain, characteristics beyond simple algorithm forecast accuracy are important in determining the algorithm’s true utility for smart buildings. Other characteristics include the overall design of the deployed architecture and the operational efficiency of the forecasting system. In this work, we present a deep-learning-based load forecasting system that predicts the building load at 1-hour intervals for 18 hours in the future. We also discuss challenges associated with the real-time deployment of such systems as well as the research opportunities presented by a fully functional forecasting system that has been developed within the National Renewable Energy Laboratory’s Intelligent Campus program.

Multi-stage planning of clean resources and energy storage assets with hybrid uncertainty modeling for low-carbon resilient distribution systems

Climate change drives the urgent need for low-carbon and resilient energy system transitions. However, current planning methods ignore the inherent conflicts between carbon emission reduction and resilience enhancement, failing to optimally balance asset allocation for both aspects. They also neglect the long-term dynamic and stochastic nature of transitions, exposing systems to hybrid uncertainties. This paper presents a multi-stage dynamic planning method for clean resources and energy storage assets in power distribution networks. First, to facilitate low-carbon and resilient transitions, adaptive, stage-wise planning decisions are optimally determined under various planning strategies to mitigate risks stemming from hybrid uncertainties. Second, to precisely quantify the impact of hybrid uncertainties on decision-making, a systematic hybrid-uncertainty modeling approach is designed to capture short- and long-term uncertainties arising from both exogenous and endogenous factors. Third, to alleviate computational complexity, a decomposition-based stochastic dynamic dual integer programming method with augmented cut generation is introduced, which decomposes the original problem into stage-wise subproblems coordinated through forward and backward steps. Numerical results show that the proposed method generates cost-effective planning schemes, yielding $60 K in savings and reducing carbon emissions by 3853 tons. The method also demonstrates strong scalability, with a 52% reduction in solution time for large-scale planning problems.

Robust day-ahead scheduling of cooperative energy communities considering multiple aggregators

Future cities must play a vital role in reducing energy consumption and decarbonizing the electricity sector, thus evolving from passive structures towards more efficient smart cities. This transition can be facilitated by energy communities. This emerging paradigm consists of collectivizing a set of residential installations equipped with onsite renewable generators and storage assets (i.e., prosumers), which can eventually share resources to pursue collective welfare. This paper focuses on cooperative communities, where prosumers share resources without seeking selfish monetary counterparts. Despite their apparent advantages, energy management and scheduling of energy communities suppose a challenge for conventional tools due to the high level of uncertainty (especially due to intermittent renewable generation and random demand), and privacy concerns among prosumers. This paper addresses these issues. Specifically, a novel management structure based on multiple aggregators is proposed. This paradigm preserves users' confidential features while allowing them to extract the full potential of their assets. To efficiently manage the variety of assets available under uncertainty, an adaptive robust day-ahead scheduling model is developed, which casts as a solvable and portable Mixed Integer Linear Programming framework, which eases its implementation in real-world cases. The new proposal concerns uncertain generation and demand using a polyhedral representation of the uncertainty set. A case study is conducted to validate the developed model, showing promising results. Moreover, different results are obtained and analysed. Finally, it is worth remarking on how the level of robustness impacts the collective bill, incrementing it by 75 % when risk-averse conditions are assumed. In addition, the role of storage assets under pessimistic conditions is remarked, pointing out that these assets rule the scheduling plan of the community instead of renewable generators.

EXPRESS: On the Valuation and Monetization Roles of the Rolling Intrinsic Policy for Merchant Commodity Storage

Merchants use the rolling intrinsic policy to value commodity storage assets and monetize the values they attribute to these assets. A recent study ascribes to informational inconsistency two discrepancies between its partial monetization findings and the known excellent valuation performance of this method: (i) The rolling intrinsic policy can perform worse than the intrinsic policy and (ii) it appears to be far from optimal. This paper contends that this claim generally confounds informational inconsistency and the risk adjustment that underpins the common no arbitrage valuation of this policy. In particular, lack of risk adjustment in that investigation explains at least the first difference and informational inconsistency cannot be associated with the second dissimilarity in about four percent of its instances. Further, this work establishes that equipping the rolling intrinsic policy with forward trading rules out the former disparity, whereas the latter one in general can persist. Finally, it uses known results to argue that in realistic settings this extended policy incurs small monetization losses on average for storage assets acquired at a fair price and outlines alternative monetization methods with potentially improved effectiveness.

Optimizing Injection Well Trajectory to Maximize CO2 Storage Security and Minimize Geomechanical Risk

Summary The work demonstrates an optimal well design for a potential geological carbon storage (GCS) project in Kern County, California (USA). Carbon dioxide (CO2) plume shape, size, and pressure response history in the subsurface are outcomes. We created a toolbox (pyCCUS) to standardize the well design optimization process and it is applicable to different carbon storage assets. This toolbox is helpful to maximize storage security and minimize geomechanical risk. The numerical model of the storage formation features two-way coupled transport and geomechanical deformation. It honors a predefined injection scheme with injection rates that ramp up and then decline for a total of 12.3 MtCO2 injection in 18 years. The peak injection rate is greater than 1 MtCO2/yr, whereas the post-injection monitoring period is 100 years. We propose to develop a long, deviated injection well to best address the injectivity and plume migration challenges for this complex, heterogeneous, dipping formation. The chosen well trajectory improves injectivity while minimizing formation pressure buildup. The well design optimization successfully reduces the pressure buildup by 54% over the base design while only increasing the areal extent of the plume by 21%. We quantify the CO2 plume shape and size at the land surface. The plume grows rapidly during injection, but it increases only slightly after shut-in due to slow updip migration driven by buoyancy. The plume becomes stationary within the post-injection monitoring period. The optimal injector design balances the optimization goals of CO2 plume size, pressure increase, and pressure buildup at geological faults. The optimal injection well design is robust under uncertainties from injection schemes and geological model realizations. Rock deformation due to the pressure buildup is also computed. The model estimates 2.1 cm of uplift that occurs during the year of the peak annual injection rate. Land surface uplift strongly correlates with the subsurface pressure response.

AI-Driven Optimization of V2G Systems for Electric School Buses

This paper explores the transformative potential of AI-driven optimization in Vehicle-to-Grid (V2G) systems, with a focus on electric school buses. V2G technology enables bidirectional energy flow, allowing electric vehicles to not only draw power from the grid but also discharge electricity back to it. This capability is particularly beneficial for electric school buses, which can serve as valuable energy storage assets during their idle periods. By integrating AI, these systems can enhance operational efficiency, reduce costs, and support environmental sustainability through optimized energy management. Despite these advantages, challenges such as technological complexities and infrastructure limitations persist. Addressing these challenges through targeted research and supportive policy frameworks is crucial for realizing the full potential of AI-driven V2G systems in advancing sustainable transportation and energy solutions.

Energy storage as a transmission asset: Definitions and use cases

There is growing interest in deploying energy storage as a transmission asset (SATA), as evidenced by an evolving body of supportive policies and regulations and an expanding body of literature on the topic. Despite nearly two decades of evolution, however, transmission planning processes in the United States rarely consider storage alternatives and only a handful of projects have been selected. Regulators have assigned the responsibility of proposing non-transmission alternatives to non-utility participants in the planning process, so the sparse record of SATA projects may therefore result from a lack of specificity about the use cases for SATA and uncertainty about when it should be proposed. Extensive literature has been devoted to the topic of SATA, but much of it relies on definitions that are inconsistent with regulatory precedent and that exacerbate confusion. This paper reviews regulatory proceedings to define three types of energy storage assets than can interact with the transmission system: storage as a transmission asset, storage in place of a transmission asset (SIPTA), and dual-use energy storage. It then provides an inventory of use cases for SATA and SIPTA projects, using brief case studies to illustrate each use case. By clarifying the definition of SATA and its roles in transmission system planning, this work may be of use to transmission system planners and other participants in transmission planning processes to identify situations in which SATA alternatives merit consideration.

Hybrid power plants: An effective way of decreasing loss-of-load expectation

Diversifying variable renewable resources by combining wind, solar photovoltaic, and battery assets in a hybrid power plant can increase renewable energy usage efficiency and improve system flexibility, particularly in distributed energy systems. However, the resilience impact of these systems, particularly outage mitigation, can be difficult to quantify due to uncertainty in resource, energy demand, and outage occurrence. This study outlines a framework to quantify the incremental benefit of hybrid power plant assets for reducing loss-of-load expectation during random outage events. Hybrid power plant performance during outages (considering varying duration and severity) is simulated using a Monte Carlo methodology to reflect uncertainty associated with renewable resource, load demand, and outage timing. Results demonstrate the additional incremental value from increasingly hybrid designs, in which relative capacities of wind, solar photovoltaic, and storage assets contribute to lower loss-of-load expectation than the constituent technologies would alone. The value of added wind or solar capacity increases as the plant composition approaches an equal split. The value of added battery capacity depends on the outage duration and severity, but the first 50 MWh of added storage capacity is the most valuable for reducing the loss-of-load expectation for all plant designs.

Optimal resource allocation and operation for smart energy hubs considering hydrogen storage systems and electric vehicles

Energy hubs (EHs) have substantially paved the way for the coordinated operation of various energy carriers, converters, and storage. However, the establishment of optimal planning and operation of the EH include several challenges, e.g., the stochastic nature of non-dispatchable generation assets, obtaining a satisfactory performance from the storage assets with reasonable energy and power density costs, and long-term load growth. Accordingly, this paper proposes a two-stage stochastic optimal resource allocation and operation approach for multiple smart energy hubs-based microgrids. The first stage optimizes the location of each EH and different assets' capacities. On the other hand, the second stage optimizes the charging/discharging rates of each asset and parking lot power. These microgrids combine renewable energy sources. Moreover, for research gap and unlike existing research, the cooperative operation of hybrid storage systems (i.e., solar-powered compressed air energy storage, hydrogen storage systems, battery energy storage systems, thermal energy storage systems) along with plug-in electric vehicles (PEV) has been investigated. Furthermore, annual load growth and price-based demand response (PBDR) are employed in this study. The simulation results demonstrate that the controlled charging/discharging of the PEVs reduces the total annualized cost by 5.49%, while the operation and emission costs are reduced by 21.3% and 19.86%, respectively. Furthermore, employing PBDR has led to a reduction of 10.1% in capital and operational costs.

Physics-Aware Degradation Model of Lithium-ion Battery Energy Storage for Techno-Economic Studies in Power Systems

Power system operation and planning decisions for lithium-ion battery energy storage systems are mainly derived using their simplified linear models. While these models are computationally simple, they have limitations in how they estimate battery degradation, either using the energy throughput or the Rainflow method. This paper proposes a hybrid approach for lithium-ion battery system modeling suitable for use in power system studies that enhances representation of battery degradation at a reasonable computational cost. The proposed hybrid model combines a physics-based model for improved degradation estimates with a simple and linear energy reservoir model commonly used to represent a battery storage system. The advantage of constructing the battery model with this vision is that it allows its smooth integration into the mixed-integer optimization frameworks. The proposed model is evaluated using both power- and energy-based use cases from the electrical grid. The simulations demonstrate that degradation could be reduced by 45% compared to other modeling strategies while generating the same level of operation profits. The net result of using the proposed hybrid model could be an extended lifespan through better informed planning and operating decisions for energy storage assets.

On different collective storage schemes in energy communities with internal market

Energy communities are recognised as a valuable framework to promote penetration of renewable sources at the residential level, as well as increment the efficiency and self-sufficiency of domestic users. In such paradigms, energy storage may bring notable benefits due to its energy arbitrage and capacity to efficiently manage renewable sources. However, at the residential level, storage systems are not widely deployed and, in this regard, collective storage options may suppose an attractive alternative. From a market perspective, energy communities can be cooperative or competitive. This paper focuses on the second case when internal community markets are launched to promote competition among prosumers. In such a framework, two possible collective storage schemes are analysed. On the one hand, the so-called competitive storage paradigm, in which storage assets partake in internal markets as an independent agent. On the other hand, the so-called cooperative storage scheme, by which storage assets are centrally managed to pursue collective welfare. To this end, the day-ahead scheduling tool for the community is extended to include both collective storage arrangements, thus resulting in different complementarity models that can be efficiently solved using off-the-shelf solvers. Such complementarity models are applied to a benchmark three-prosumers community, extracting various relevant results and conclusions. Generally, it is concluded that the cooperative storage scheme is more attractive for prosumers, allowing them to reduce their bill notably. However, this scheme seems very sensitive to the storage and community size and can discourage installing high-power photovoltaic units. In this sense, the so-called competitive storage scheme, although less beneficial for prosumers, seems a more easily implementable option in communities with internal market structure.

Modelling the formation of peer-to-peer trading coalitions and prosumer participation incentives in transactive energy communities

Peer-to-peer (P2P) energy trading and energy communities have garnered much attention over in recent years due to increasing investments in local energy generation and storage assets. Much research has been performed on the mechanisms and methodologies behind their implementation and realisation. However, the efficiency to be gained from P2P trading, and the structure of local energy markets raise many important challenges. To analyse the efficiency of P2P energy markets, in this work, we consider two different popular approaches to peer-to-peer trading: centralised (through a central market maker/clearing entity) vs. fully decentralised (P2P), and explore the comparative economic benefits of these models. We focus on the metric of Gains from Trade (GT), given optimal P2P trading schedule computed by a schedule optimiser. In both local market models, benefits from trading are realised mainly due to the diversity in consumption behaviour and renewable energy generation between prosumers in an energy community. Both market models will lead to the most promising P2P contracts (the ones with the highest Gains from Trade) to be established first. Yet, we find diversity decreases quickly as more peer-to-peer energy contracts are established and more prosumers join the market, leading to significantly diminishing returns. In this work, we aim to quantify this effect using real-world data from two large-scale smart energy trials in the UK, i.e. the Low Carbon London project and the Thames Valley Vision project. Our experimental study shows that, for both market models, only a small number of P2P contracts i.e. less than 10% of the possible P2P contracts are required to achieve the majority of the maximal potential Gains from Trade. Similarly, only a fraction of prosumers are required to participate in energy trading to realise significant GT; namely we found that 60% of the maximal GT can be realised with only 30% of prosumers’ participation, with the percentage of maximal GT reaching 80% when participation increases to 50% of prosumers. Finally, we study the effect that diversity in consumption profiles has on overall trading potential and dynamics in an energy community. We show that in a community with a DF(load diversity factor) = 1, 80% of potential maximal GT can be achieved by 10% of prosumers engaging in P2P trading, while in a community with DF = 1.5, it is beneficial for 40% of the prosumers to trade.

Strategic Guidelines for Battery Energy Storage System Deployment: Regulatory Framework, Incentives, and Market Planning

This research addresses strategic recommendations regarding the applications of battery energy storage systems (BESS) in the context of the deregulated electricity market. The main emphasis is on regulatory dimensions, incentive mechanisms, and the provision of marketable storage services. The study’s findings demonstrate that battery energy storage systems (BESS) have distinct characteristics that challenge their conventional classification as a load or generator within power systems. The study additionally emphasizes the insights, lessons learned, and good practices gained from early adopter countries in implementing energy storage systems (ESS). These insights include the importance of establishing a precise definition of ESS, promoting collaborative engagement with relevant stakeholders, and developing a series of incentive strategies. The results show that nations that pioneered BESS’s application in their electricity matrices have effectively promoted storage services in deregulated markets, employing storage assets for various purposes such as peak reduction, frequency regulation, renewable energy support, and energy arbitrage applications. These applications underline the potential of BESS to increase grid stability and minimize exposure to risk and volatility in the revenues of storage agents in deregulated markets.

Monitoring the Italian transposition of the EU regulation concerning renewable energy communities and the relevant policies for battery storage

This paper deals with the transposition of the European Union (EU) Directive concerning Renewable Energy Communities (RECs) into the Italian legislation. The methodology aims at identifying those national provisions which do not comply with the EU legislation. Consequently, long and onerous infringement procedures may ultimately limit the development of RECs. Moreover, the proposed methodology quantifies the economic value of national policies concerning the participation of storage assets in RECs. Three policy options are compared against the case without flexible assets. To do so, ad-hoc mixed-integer linear programming optimization problems are developed. The assessment is based on the formulation of three key performance indicators, concerning the revenues and costs of the REC. The main data sources in the work are the actual Italian Wholesale Market prices and the price incentives introduced by relevant Italian authorities to support the operation of the RECs. Results indicate an incorrect transposition of the fundamental rights of the EU law. These concern a significant alteration of the membership criteria for prospective RECs and the introduction of external third-parties, which may bypass fundamental requirements set out in the EU law. Numerical results call for a profound revision of the policy options for battery storage in RECs since the envisaged configurations introduce structural limits to the REC's revenues This work can support researchers and policy makers at both national and EU-level in developing policy options and techno-economic measures that foster the development of new RECs.

Electric Vehicles as Electric Energy Storage for a Zero Emission Grid

Electric Vehicles, especially electric vehicles with bidirectional Vehicle-to-Grid (V2G) connectivity, can serve as electric energy storage assets. Following the dramatic increase in electric vehicle registrations in 2022 there can be little doubt that electric vehicles will become a major portion of the entire light duty personal transportation fleet. This paper endeavours to project the growing electric energy storage capability of this asset. It shows that the storage available from the EV fleet may grow to match the daily output of projected solar PV generation in the 2025-30 time period, and greatly exceed the requirements of the EV fleet itself. This presents the possibility of a massive renewable generation capability, levelled and controlled by an even more massive electric vehicle fleet energy storage capability which can offset its intermittent and uncontrolled nature, as a future zero emission energy grid.

Market-based asset valuation of hydrogen geological storage

Because of hydrogen's low energy density, hydrogen storage is a critical component of the hydrogen economy, particularly when large-scale and flexible hydrogen utilization is required. There is a sense of urgency to develop hydrogen geological storage projects to support large-scale yet flexible hydrogen utilization. This study aims to answer questions not yet resolved in the research literature discussing the valuation of hydrogen geological storage options for commercial development. This study establishes a net present value (NPV) evaluation framework for geological hydrogen storage that integrates the updated techno-economic analysis and market-based operations. The capital asset pricing model (CAPM) and the related finance theories are applied to determine the risk-adjusted discount rate in building the NPV evaluation framework. The NPV framework has been applied to two geological hydrogen storage projects, a single-turn storage serving downstream transportation seasonal demand versus a multiturn storage as part of an integrated renewables-based hydrogen energy system providing peak electric load. From the NPV framework, both projects have positive NPVs, $46, 560, 632 and $12, 457, 546, respectively, and International Rate of Return (IRR) values, which are higher than the costs of capital. The NPV framework is also applied to the sensitivity analysis and shows that the hydrogen price spread between withdrawal and injection prices, site development, and well costs are the top three factors that impact both NPV and IRR the most for both projects. The established NPV framework can be used for project risk management by discovering the key cost drivers for the storage assets.

Deep reinforcement learning for wind and energy storage coordination in wholesale energy and ancillary service markets

Wind energy has been increasingly adopted to mitigate climate change. However, the variability of wind energy causes wind curtailment, resulting in considerable economic losses for wind farm owners. Wind curtailment can be reduced using battery energy storage systems (BESS) as onsite backup sources. Yet, this auxiliary role may significantly weaken the economic potential of BESS in energy trading. Ideal BESS scheduling should balance onsite wind curtailment reduction and market bidding, but practical implementation is challenging due to coordination complexity and the stochastic nature of energy prices and wind generation. We investigate the joint-market bidding strategy of a co-located wind-battery system in the spot and Regulation Frequency Control Ancillary Service markets. We propose a novel deep reinforcement learning-based approach that decouples the system’s market participation into two related Markov decision processes for each facility, enabling the BESS to absorb onsite wind curtailment while performing joint-market bidding to maximize overall operational revenues. Using realistic wind farm data, we validated the coordinated bidding strategy, with outcomes surpassing the optimization-based benchmark in terms of higher revenue by approximately 25% and more wind curtailment reduction by 2.3 times. Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to participating in each market separately. Simulations also show that using curtailed wind generation as a power source for charging the BESS can lead to additional financial gains. The successful implementation of our algorithm would encourage co-location of generation and storage assets to unlock wider system benefits.

A multi-agent model of urban microgrids: Assessing the effects of energy-market shocks using real-world data

The shift towards renewable energy sources (RES) in energy systems is becoming increasingly important. Residential energy generation and storage assets, smart home energy management systems, and peer-to-peer (P2P) electricity trading in microgrids can help integrate and balance decentralized renewable electricity supply with an increasingly electrified power, heat, and transport demand, reducing costs and CO2 emissions. However, these microgrids are difficult to model because they consist of autonomous and interacting entities, leading to emergent phenomena and a high degree of complexity. Agent-based modeling is an established technique to simulate the complexity of microgrids. However, the existing literature still lacks real-world implementation studies and, as a first step, models capable of validating the existing results with real-world data. To this end, we present an agent-based model and analyze the corresponding microgrid performance with real-world data. The model quantifies economic, technical, and environmental metrics to simulate microgrid performance holistically and, in line with state-of-the-art research, consists of self-interested, autonomous agents with specific load profiles, RES generation, and demand-response potential. The model can simulate a P2P marketplace where electricity is traded between agents. In the second part of the paper, we validate the model with data from a medium-sized German city. In this case study, we also compare microgrid performance in 2022, during the energy market crisis in Europe, with historical data from 2019 to assess the effects of energy market shocks. Our results show how microgrids with P2P trading can reduce electricity costs and CO2 emissions. However, our trading mechanism illustrates that the benefits of energy-community trading are almost exclusively shared among prosumers, highlighting the need to consider distributional issues when implementing P2P trading.

The Co-development of Offshore Wind and Hydrogen in the UK – A Case Study of Milford Haven South Wales

Green hydrogen produced from renewable energy resources can not only contribute to the decarbonisation of different energy sectors, but also serve as a carrier for long-distance delivery of renewable generation, offering a cost-effective way to exploit the renewables far from electrical grids. To facilitate the co-development of offshore wind and hydrogen, the paper develops a modelling framework to dispatch power and hydrogen flows across dedicated offshore wind hydrogen production systems to meet onshore hydrogen demands while keeping similar state of charge levels between multiple systems. Then the hydrogen supply to shore and the system investments and ongoing costs are discounted to their present values to calculate the levelised cost of hydrogen, which is minimised by the particle swarm optimsiation algorithm to suggest the best capacities of hydrogen system components including converters, desalination devices, electrolysers, compressors and storage assets. The proposed modelling framework is tested based on a case study at Milford Haven South Wales which is evaluated to have massive offshore wind resources in the Celtic Sea and comparable demands for hydrogen by 2040. The optimisation results are presented based on the techno-economic input parameters projected for 2030 and 2050 scenarios and discussed alongside the influences of technology advances on the system optimisation and resulting metrics including the levelised costs of hydrogen, net present values and potential levels of green hydrogen supply to Milford Haven.

Carbon-Oriented Planning of Distributed Generation and Energy Storage Assets in Power Distribution Network With Hydrogen-Based Microgrids

The pressure of climate change has been driving the transition of power distribution networks (PDNs) to low-carbon energy systems. Hydrogen-based microgrids (HMGs), as emerging urban energy subsystems in PDNs with significant carbon emissions reduction potentials, are valuable assets in smoothing the economic transition to low-carbon energy systems. However, it remains a challenging issue to make the HMGs perceive their carbon emissions in the planning and operation process, so that they can effectively change energy consumption patterns to reduce carbon emissions. To achieve the overall carbon emission reduction target, a carbon-oriented planning method for PDN and HMGs is proposed. Firstly, an integrated planning model of distributed generation and energy storage assets is formulated with embedded carbon emission constraints. Secondly, a chronological carbon emission flow model for electrical storage systems is introduced to accurately capture its impact on the chronological carbon emission flow distribution, which is incorporated into the planning problem to quantify carbon emissions of different HMGs. Finally, the proposed planning problem is formulated as a mixed-integer nonconvex quadratically constrained programming (MINCQCP) problem, and solved by the tailored penalty-based proximal distance algorithm to derive the local optimum. Numerical results indicate that integrated planning of PDN and HMGs could avoid overinvestment and meet the given carbon emission target in a cost-effective way.