Energy Arbitrage Research Articles

Quantifying the emissions impact of repurposed electric vehicle battery packs in residential settings

The market share of plug-in electric vehicles (PEVs) is growing, thanks to improvements in battery efficiency, declining production costs, and sustained policy support. Concurrently, concerns are growing over the supply of decommissioned PEV batteries. This has attracted a growing interest among scientists to determine the utility of repurposing PEV batteries for energy storage. Previous work has optimized behind-the-meter (BTM) battery storage systems (BSSs) for self-sufficiency and energy arbitrage, but few have used the system to lessen a home's electricity-related carbon footprint. This study uses high-resolution 2018 electricity demand from energy-efficient homes in Austin, Texas and grid generation data to simulate the daily operations of a 6 kWh BTM-BSS to minimize daily CO2e emissions. Results suggest that such homes with rooftop solar can reduce electricity-related household CO2eemissions by 21%, on average (about 1 ton of CO2e annually), while homes without solar could reduce their CO2e emissions by just 2% (about 0.12 tons per year). Pairing a BSS with rooftop solar increases average annual carbon savings by greater energy retention and lessening demand from the grid during carbon-intense generation periods. To make BTM-BSSs cost-effective for Austin homeowners, the price of repurposed PEV batteries must fall to $5.71/kWh or per-ton carbon pricing must rise to $70.34 for homeowners to reach break-even over an estimated 10-year BSS lifespan.

Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles

Smart charging of Electric Vehicles (EVs) reduces operating cost, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional charging and improved connectivity enable efficient power grid support. Today, however, uncoordinated charging, e.g., governed by users’ habits, is still the norm. Thus, the impact of upcoming smart charging applications is mostly unexplored. We aim to estimate the expenses inherent with smart charging, e.g., battery aging costs, and give suggestions for further research. Using typical onboard sensor data we concisely model and validate an EV battery. We then integrate the battery model into a realistic smart charging use case and compare it with measurements of real EV charging. The results show that i) the temperature dependence of battery aging calls for precise thermal models for charging power greater than 7 kW, ii) disregarding battery aging underestimates EVs’ operating cost by approx. 30%, and iii) the profitability of Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">energy arbitrage</i> , depends on battery aging costs and the electricity price spread.

Design and validation of synthetic duty cycles for grid energy storage dispatch using lithium-ion batteries

• New algorithm to generate synthetic duty cycles for grid-scale batteries. • Principal component analysis and k-means clustering form the basis of the algorithm. • Validation of the synthetic duty cycles using an electrochemical-aging model. Energy storage systems (ESSs) are a critical component of the electric grid, dispatching (charging and discharging) to performing grid applications such as frequency regulation, energy arbitrage, and peak shaving. Today, lithium-ion batteries are considered the best electrochemical ESSs in grid applications, and various cathode chemistries have been developed. On the electric grid, batteries can provide up to 13 ESS services, and different combinations of grid services and chemistries produce different battery aging and life performance under the given dispatch. Therefore, the characterization of each grid application dispatch can give an insight into optimal participation strategies for lithium-ion chemistries for each grid service. In this paper, an efficient algorithm is presented which uses a dispatch interval matrix to capture metrics in the ESS dispatch relevant to lithium-ion battery aging and performance, and implements unsupervised learning and dimensionality reduction on this matrix to produce characteristic duty cycles of the dispatch, from which synthetic duty cycles are produced that are suitable for laboratory testing and fast simulation. The algorithm is demonstrated for the dispatch under the grid application of peak shaving. Finally, an electrochemical-aging model is used to simulate a lithium-ion battery under both the original power dispatch and the synthetic duty cycle to validate the effectiveness of the method proposed in this paper to retain the operating stress factors.

Complex Power Flexibility Evaluation using Energy Arbitrage between Transmission and Distribution

The high-level integration of distributed energy resources has given rise to system security challenges at both transmission and distribution grid levels. A major share of these distributed energy resources is integrated at the distribution end. A constructive solution for system security challenges is to provide flexibility services by the distribution grid itself. The methodology presented in this manuscript evaluates complex power capability and flexibility from these distributed energy resources at primary substation node that connects transmission and distribution at physical levels using probabilistic optimal power flow based on the Monte Carlo method. Monte Carlo is used to generate various scenarios of load and generator flexibilities. This novel research work provides an extensive evaluation of both capability and flexibility regions to quantify various active power ramp services to achieve frequency control ancillary services indicating actual flexibility at transmission-distribution node. Energy arbitrage between transmission and distribution is simulated as an alternating current optimal power flow problem for different scenarios of flexible loads and generators. The analysis is validated using IEEE 5, 9 and 33 bus systems. Effects of active-reactive power dependency, distribution of load and generator flexibility and size of the test system have been highlighted in results. The results highlight the applications of the flexibility region as a subset of the capability graph.

Optimisation and economic feasibility of Battery Energy Storage Systems in electricity markets: The Iberian market case study

This study identifies the optimal operating strategy of storage systems in the electricity markets, from the perspective of a market participant with a renewables' portfolio. The energy storage system provides a balancing service for renewable sources, while also performing energy arbitrage at the considered three short-term markets. A Long Short-Term Memory (LSTM) model is developed to forecast spot price and renewable generation which are used to guide the bidding decision-making process to maximise the agents’ economic profit. Additionally, the designed decision support models consider market rules and the technical constraints in the operation of the storage system. The influence of storage systems in the optimal operation, regulation costs and revenues are analysed on a daily and yearly basis. The Iberian electricity market has been utilised as a case study. It was shown that the use of forecasting techniques and battery implementation reduce daily and yearly regulation costs up to 100% and 53%, respectively and that acting in the balancing market increases the expected profit of the storage system between 21% and 36%. Additionally, the economic viability evaluation in 2018 and 2025, for several battery types, shows that only the lithium-ion battery is a profitable investment.

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning

As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy. In this paper, the model-free deep reinforcement learning algorithm Rainbow Deep Q-Networks is used to control a battery in a microgrid to perform energy arbitrage and more efficiently utilise solar and wind energy sources. The grid operates with its own demand and renewable generation, as well as dynamic energy pricing from a real wholesale energy market. Four scenarios are tested including using demand and price forecasting produced with local weather data. The algorithm and its subcomponents are evaluated against an actor-critic method and a linear programming model with Rainbow able to outperform all other methods. This research shows the importance of using the distributional approach for reinforcement learning for complex environments, as well as how it can visualise and contextualise the agents’ behaviour for real-world applications.

Grid-Scale Battery Energy Storage for Arbitrage Purposes: A Colombian Case

This study seeks to determine a suitable arbitrage strategy that allows a battery energy storage system (BESS) owner to obtain the maximum economic benefits when participating in the Colombian electricity market. A comparison of different arbitration strategies from the literature, such as seasonal, statistical, and neural networks-based models, is performed. To determine BESS’s optimal operation, a Mixed Integer Linear Programming (MILP) optimization problem is formulated, including a battery degradation model based on an upper piecewise linear approximation method. A financial evaluation of the different arbitrage strategies is carried out, resulting, for all the analyzed cases, in a negative net present value (NPV); thus, the results show that the income obtained from BESS when only performing energy arbitrage in the Colombian market do not compensate the investment costs. Results have also shown that strategies based on statistical and prediction models have a better performance than seasonal strategies, especially in atypical circumstances such as COVID-19.

Assessing the societal benefits of energy storage in electricity systems with large shares of renewables

Energy storage systems (ESS) can offer significant benefits to electricity systems and hence to society. Some of them include avoiding the costs of expensive centralized electricity generation and its environmental impact. However, existing electricity market arrangements aiming at rewarding these benefits present large externalities and therefore fail to reward well the true societal benefits of energy storage. In particular, ESS could be operated to avoid the emissions of polluting centralized generation. Thus, ESS could also avoid the damage cost of those emissions such as the social cost of climate change and the damage on population health. These large externalities are likely much higher than typically seen carbon taxes and prices. In this paper, we present an empirical assessment of the locational societal benefits of energy storage in a real electricity system that has a significant presence of solar and hydro power generation, and important transmission constraints. The assessment is performed via a linear programming model that is applicable for small scale ESS. Major conflicts were found between energy and emission arbitrage in zones with hydrothermal generation, yet strong synergies in zones with high solar generation. Transmission congestion has a significant impact on this. Energy storage benefits associated with the provision of reserve services are the highest source of societal benefit. Finally, it is shown that a battery system may be an attractive investment from a societal perspective.

Assessment of Energy Arbitrage Using Energy Storage Systems: A Wind Park’s Perspective

With the growing application of green energy, the importance of effectively handling the volatile nature of these energy sources is also growing in order to ensure economic and operational viability. Accordingly, the main contribution of this work is to evaluate the revenue potential for wind parks with integrated storage systems in the day-ahead electricity markets using genetic algorithm. It is achieved by the concept of flexible charging–discharging of the Energy Storage System (ESS), taking advantage of the widespread electricity prices that are predicted using a feedforward-neural-network-based forecasting algorithm. In addition, the reactive power restrictions posed by grid code that are to be followed by the wind park are also considered as one of the constraints. Moreover, the profit obtained with a Battery Energy Storage System (BESS) is compared with that of a Thermal Energy Storage System (TESS). The proposed method gave more profitable results when utilizing BESS for energy arbitrage in day-ahead electricity markets than with TESS. Moreover, the availability of ESS at wind park has reduced the wind power curtailment.

Evaluation of benefits through coordinated control of numerous thermal energy storage in highly electrified heat systems

This paper proposes a novel decentralised control approach dedicated to coordinating the operation of a large population of residential thermal energy storage characterised by diversified specifications, while innovatively considering the operational constraints on both the national and the local level. The presented iterative algorithm sequentially updates the storage operational schedule under the real-time electricity price scheme to achieve cost savings for each individual participant and the total system, while effectively avoiding the violation of local distribution network restrictions. The transmission topology is explicitly considered to investigate the impacts of transmission congestion on the electricity marginal prices. The results of a series of case studies demonstrate that the proposed coordination approach can effectively enable individual energy arbitrage and achieve 22.53 % system cost savings compared to the 10.34 % savings when coordination is absent. Moreover, the simulation results manifest that the coordinated control approach can cost-effectively perform distribution congestion management to ensure no local network is overloaded, which will cause 12.3 % increase in system operational costs. Overall, through coordinating the operation of numerous residential thermal energy storage to perform both energy arbitrage and distribution network congestion management, the proposed control approach can benefit the system operation at both the national and local levels.

Liquid metal battery storage in an offshore wind turbine: Concept and economic analysis

As wind energy increases its global share of the electrical grid, the intermittency of wind becomes more problematic. To address the resulting mismatch between wind generation and grid demand, long-duration (day-long) low-cost energy storage is offered as a potential solution. Lithium-ion (Li-ion) storage is an obvious, well-developed candidate, but it is currently too expensive for such long-duration applications. Liquid metal battery (LMB) storage offers large cost reductions and recent technology developments indicate it may be viable for MW-scale storage. Accordingly, we investigate co-locating and integrating LMB and Li-ion storage within the substructure of an offshore wind turbine. Integration allows the substructure to cost-effectively double as a storage container and allows for costly electrical farm-to-shore connections to be reduced to near the average power size (by reducing peak power). These benefits are compared to the costs for battery integration. Simulations show that line size can be reduced by 20% with 4 h of storage or by 40% with 12 h of storage, with negligible capacity factor losses. However, with 24 h of average power storage using LMB, no line size reduction provided the best overall net value of the turbine-storage system due to the ability to capture all available wind energy and profit from energy arbitrage and full capacity credit. In general, LMB integrated storage results in an increased relative value with current system costs. Projected technology trends indicate that these benefits will significantly improve and that integrated Li-ion storage will also become cost-effective.

Value of deterministic day-ahead forecasts of PV generation in PV + Storage operation for the Australian electricity market

During the last decade, numerous solar forecasting tools have been developed to predict the energy generation of photovoltaic (PV) farms. The quality of solar forecasts is assessed by comparing predictions with measured solar data. However, this methodology does not consider the added value of the forecasts for their applications. As a consequence, what value could be given to the improvement of forecasts considering this evaluation framework?To answer this question, this work compares the value of different operational solar forecasts for a specific application. The aim is to look for relationships between the economic value and the error metrics defined to evaluate the forecast quality.A new generation of large-scale PV plants integrates ESS. The aim is to add flexibility to the injection of the production into the grid and thus to maximize the profit by taking advantage of the possibilities offered by the electricity market, such as energy arbitrage. To optimize the operation of these specific ESS, forecasting of the solar production is of paramount importance. The study case considered in this work is a large-scale PV farm of several megawatts associated with Li-ion batteries in the Australian energy market context.For this specific case study, the results show that the metrics used to evaluate the forecast quality based on the mean absolute error (MAE) have an almost linear relationship with the economic gain brought by applying the forecast. More precisely, an improvement of 1% point in MAE results approximately in an increase of 2% points in economical gain.

Life-cycle assessment of gravity energy storage systems for large-scale application

Interest in energy storage systems has been increased with the growing penetration of variable renewable energy sources. This paper discusses a detailed economic analysis of an attractive gravitational potential energy storage option, known as gravity energy storage (GES). The economic performance of this energy storage system is compared to other alternative energy storage technologies such as pumped hydro energy storage (PHES) and compressed air energy storage (CAES). Moreover, a life cycle costs and levelized cost of electricity delivered by this energy storage are analyzed to provide expert, power producers, and grid operators insight about the economic implications of this grid-scale gravitational energy storage technology. Depending on the considered scenarios and assumptions, the levelized cost of storage of GES varies between 7.5 €ct/kWh and 15 €ct/kWh, while it is between 3.8 €ct/kWh and 7.3 €ct/kWh for gravity energy storage with wire hoisting system (GESH). The LCOS of GES and GESH were then compared to other energy storage systems. The obtained results show that GESH is very cost-competitive with pumped hydro and Compressed Air Energy Storage technologies; while GES is competitive with PHES and may be cost-competitive with CAES depending on the operation cycles. The performed analysis has also shown that both GES and GESH offer interesting economic advantages for the provision of energy arbitrage service.

Driving to the future of energy storage: Techno-economic analysis of a novel method to recondition second life electric vehicle batteries

The transportation sector is trending towards electrification which means a dramatic change to the availability of used Lithium-ion (Li-ion) batteries which can be reused for grid energy storage systems (ESS). However, second life battery modules can have an imbalanced state of health (SOH) between cells which can reduce battery safety, life, and depth of discharge. This work evaluates the economics of a novel Heterogeneous Unifying Battery (HUB) reconditioning system that cycles battery modules to unify cells’ SOH to improve their second life battery performance. The HUB reconditioning cycles can be performed in one of two ways: recondition with grid services or recondition through energy shuffle. The results from this work demonstrate that a simple repurposing process will likely have a lower second life resale price (56 $/kWh) than the HUB system (62 $/kWh) in our baseline scenario; however, in our target scenario the HUB system (34 $/kWh) has a lower resale price than the repurposing system (38 $/kWh). This work also includes an economic analysis for using reconditioned batteries in a grid ESS that was compared to an ESS that is assembled with new Li-ion batteries. Results show that HUB reconditioned ESS require less grid revenue (194 $/kW-year) than new Li-ion ESS (253 $/kW-year). Finally, the HUB reconditioned ESS is shown to be economically feasible in 63% of frequency regulation, 18% of transmission congestion relief, and 16% of demand charge reduction markets but not economically feasible in spin/non-spin reserve, voltage support, and energy arbitrage markets.

Energy Storage Planning for Profitability Maximization by Power Trading and Ancillary Services Participation

One of the main applications of energy storage systems (ESSs) is transmission and distribution systems cost deferral. Further, ESSs are efficient tools for localized reactive power support, peak shaving, and energy arbitrage. This article proposes an ESSs planning algorithm that includes all previous services. The proposed algorithm increases the distribution company profit and minimizes its future system upgrade cost. For a comprehensive planning algorithm, other options, such as including static VAR compensators (SVCs), feeders upgrade, or adding distributed generators, are considered along with ESSs. Different scenarios are utilized to model the load variation, the renewable resources’ intermittency, and the market price fluctuation. The problem constraints include an ESS dynamic model that reflects the capacity and lifetime limitations. Different battery technologies with different costs and dynamic characteristics are compared. The network power flow model is added to account for the voltage level and feeders’ capacity. Power limits constraints are included for the DGs and SVCs as well. The optimization problem is formulated as a mixed-integer quadratic programming problem. To validate the proposed technique, a case study is conducted on a real 41-bus radial feeder in Ontario, Canada using real data for renewable sources, loads, and market prices.

Thermo-Economic Modeling and Evaluation of Physical Energy Storage in Power System

In order to assess the electrical energy storage technologies, the thermo-economy for both capacity-type and power-type energy storage are comprehensively investigated with consideration of political, environmental and social influence. And for the first time, the Exergy Economy Benefit Ratio (EEBR) is proposed with thermo-economic model and applied to three different storage systems in various scenarios, including pumped storage, compressed air energy storage and flywheel energy storage. The impact of the total system efficiency, annual utilization hour, life time, and other key factors are also analyzed. The results show that the EEBRs of pumped storage and compressed air energy storage under peak load shaving condition and flywheel energy storage under frequency modulation service condition are all larger than zero, which means they are all thermo-economically feasible. With extra consideration of political, environmental and social impact, the exergy cost could reduce by about 25% and the EEBR doubles. The sensitivity analysis indicates the similarity and diversity of influence to EEBR between capacity-type and power-type energy storage systems. The former is that energy efficiency is the dominated factor for all three storage systems. The latter is that the difference of exergy benefit mode causes variety in other major factors. For energy-type storage system, like pumped storage and compressed air storage, the peak-to-valley price ratio is very sensitive in energy arbitrage. For power-type storage system, like flywheel storage, the mileage ratio is in leading position in auxiliary service benefit by mileage. In the three cases studied, the pumped storage has the best thermo-economy; the compressed air energy storage is the second, and the flywheel energy storage is the third. The main reason is that the pumped storage has the least non-exergy cost, and flywheel has the most.

Flexible block offers and a three-stage market clearing method for distribution-level electricity markets with grid limits

This paper proposes a new flexibility management method which can offer incentives to flexibility service providers so that they are encouraged to actively participate into the congestion management of distribution grids. The proposed method consists of a three-stage market clearing method and three types of flexible block offers: flexible demand bids, flexibility service offers, and energy arbitrage blocks. These cover most of the needs of flexibility providers. These flexible block offers are more efficient in terms of computation burden in comparison with the other flexibility offers in the existing literature. Flexibility prices are specified in these flexible block offers, which can guarantee a certain level of rewards for the customers who can help in the congestion management process. Case studies on a modified Roy Billinton Test System demonstrated the efficacy of the proposed flexibility management method for managing congestion and encouraging customers to provider flexibility services.

Optimal planning and operational strategy of energy storage systems in power transmission networks: An analysis of wind farms

This study formulated a bi-level mixed integer non-linear optimization planning and operation model for the optimal configuration (location, capacity, and power ratings) of energy storage systems (ESSs) in power transmission networks. The model was formulated with consideration for independent and correlated wind farms. The single objective function in the inner layer of the bi-level model includes the difference between the total daily expected operational cost of conventional generators and the energy arbitrage benefits derived when considering the operational strategies of ESSs. The outer layer is a multi-objective function composed of three objective functions to be minimized. The objective functions encompass the total daily expected planning and operational cost, total daily expected emission, and the maximum expected voltage deviation. Wind power uncertainties in independent and correlated wind farms were also examined. Multivariate model-based Clayton copulas, which represent joint power distribution amongst correlated wind farms, were discretized using a developed five-point estimation method based on the discretization. A hybrid non-dominating sorted genetic algorithm and multi-objective particle swarm optimization were used to minimize the outer layer objective function, whilst fast Tabu search that considers the probabilistic load flow represented by wind power uncertainties and the operational strategies of ESSs was adopted to minimize the inner layer objective function. An IEEE 57-bus system was subjected to a case study using the proposed two-stage model. The simulation results confirmed the advantage of considering the benefits of a peak shaving operational strategy from economic, technical, and environmental points of view.

Optimal scheduling for profit maximization of energy storage merchants considering market impact based on dynamic programming

This paper analyzes how electricity merchants' market impact affects merchants' profit. Energy storage has long been studied for its role in maximizing profit, and merchant decisions are assumed to have no impact on market prices. However, the trading decisions of large-scale energy storage merchants (e.g., pumped storage hydro) will affect the market prices. This paper employs dynamic programming theory to investigate merchants' optimal economic dispatch considering the market impact and physical characteristics of storage systems. Our findings show that the State-of-Charge (SOC) based analytical solution significantly facilitates energy storage merchants' decision-making. The SOC range is segmented into three regions by two optimal SOC reference points, which depend on the available energy in storage, given prices, and market impact. By comparing the current storage SOC with the reference points, the merchant can get the corresponding optimal actions. We analytically show that if the merchant neglects the market impact on the power market, she will exaggerate her expected profit when the price-taker and price-maker merchants have the same generating and pumping upper limits offered to Independent System Operators (ISOs). The profit-maximizing merchant must, therefore, assay to balance the trade-off correctly between the intensity of market impact and the dispatched power. Our findings are verified by numerical simulation, and results demonstrate the ramifications for electricity merchants in energy arbitrage decisions.