Energy Storage System Energy Research Articles

Analyzing EV User Behavior in Aggregator Smart Charging with ESS and Real-Time Pricing

Amidst increasing global electricity consumption, governments prioritize energy efficiency and electric vehicle (EV) integration into energy markets. This study examines EV aggregator strategies employing smart charging methods, adjusting charging rates based on user preferences. Through simulations in Quito's distribution system, it analyzes actions' effects on aggregator costs and technical conditions, with a focus on demand response (DR) strategies, particularly in residential areas. Exploring EVs' potential as energy storage via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options, the study introduces a collaborative evaluation of dynamic pricing and peak power limiting-based DR strategies, integrating bi-directional EV and energy storage system (ESS) use. It also proposes a novel mixed-integer linear programming (MILP) model for home energy management (HEM), incorporating distributed renewable energy, V2H/V2G capabilities, two-way ESS energy trading, and diverse DR strategies. This comprehensive approach assesses the impact of EV owner preferences and ESS availability on reducing total electricity costs through case studies.

Dynamic Demand Modeling Incorporating Renewable Energy Sources Using a Population-Based Optimization Method

Due to the inclusion of distributed generation (DG) in microgrids (MGs), the accelerated growth in demand, and environmental concerns, suitable management and operational strategies are imperative. The utilization of wind and solar energy has rapidly increased in MGs. However, due to the uncertainties these systems present, accurately predicting energy generation remains challenging. This necessitates modeling the system’s random variables (such as renewable resource output and possibly load demand) using appropriate and feasible methods. The primary objective of this article is to determine the optimal setpoints for renewable energy sources (RES) and all elements involved in the MG, minimizing the total operation cost. The system comprises wind turbines (WT), photovoltaic panels (PV), energy storage systems (ESS), and electric vehicles (EVs). Weibull distribution and the Hottel and Liu Jordan equations are employed to determine the potential available capacity of wind and solar energy generation, respectively. ESS is utilized to enhance MG performance. For optimal management, a comprehensive mathematical model with practical constraints for each MG element is extracted. An efficient Population-Based Incremental Learning (PBIL) metaheuristic method is proposed to solve the optimization objective in an MG, demonstrating that this energy management system optimizes and effectively coordinates DG and ESS energy generation considering economic considerations. Finally, PBIL is compared with a commonly used model, Particle Swarm Optimization (PSO), across various scenarios, analyzing and evaluating their outcomes, showcasing a reduction in operation costs.

Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling

Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO2 emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO2 was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively.

Evaluating EV User Behavior on Aggregator Smart Charging with ESS and Real-Time Pricing-Based Demand Response

With rising global electricity consumption, governments prioritize energy efficiency and the integration of electric vehicles (EVs) into energy markets. This study evaluates EV aggregator strategies using a smart charging method that modulates charging power rates based on user preferences. Simulations in Quito's distribution system assess various actions' impacts on aggregator costs and technical conditions. The study focuses on demand response (DR) strategies, particularly for residential areas, exploring EVs' potential as energy storage via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options. It introduces a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies, incorporating bi-directional EV and energy storage system (ESS) use. A novel mixed-integer linear programming (MILP) model for home energy management (HEM) integrates distributed renewable energy, V2H/V2G capabilities, and two-way ESS energy trading and diverse DR strategies. This comprehensive approach assesses the impact of EV owner preferences and ESS availability on reducing total electricity costs through case studies.

Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins.

The Internet of Things (IoT) has brought about significant transformations in multiple sectors, including healthcare and navigation systems, by offering essential functionalities crucial for their operations. Nevertheless, there is ongoing debate surrounding the unexplored possibilities of the IoT within the energy industry. The requirement to better the performance of distributed energy systems necessitates transitioning from traditional mission-critical electric smart grid systems to digital twin-based IoT frameworks. Energy storage systems (ESSs) used within nano-grids have the potential to enhance energy utilization, fortify resilience, and promote sustainable practices by effectively storing surplus energy. The present study introduces a conceptual framework consisting of two fundamental modules: (1) Power optimization of energy storage systems (ESSs) in peer-to-peer (P2P) energy trading. (2) Task orchestration in IoT-enabled environments using digital twin technology. The optimization of energy storage systems (ESSs) aims to effectively manage surplus ESS energy by employing particle swarm optimization (PSO) techniques. This approach is designed to fulfill the energy needs of the ESS itself as well as meet the specific requirements of participating nano-grids. The primary objective of the IoT task orchestration system, which is based on the concept of digital twins, is to enhance the process of peer-to-peer nano-grid energy trading. This is achieved by integrating virtual control mechanisms through orchestration technology combining task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. The nano-grid energy trading system's architecture utilizes IoT sensors and Raspberry Pi-based edge technology to enable virtual operation. The evaluation of the proposed study is carried out through the examination of a simulated dataset derived from nano-grid dwellings. This research analyzes the efficacy of optimization approaches in mitigating energy trading costs and optimizing power utilization in energy storage systems (ESSs). The coordination of IoT devices is crucial in improving the system's overall efficiency.

Sizing of stationary energy storage systems for electric vehicle charging plazas

Increasing numbers of electric vehicles (EV) and their fast charging stations might cause problems for electrical grids. These problems can be prevented by energy storage systems (ESS). Levelling the power demand of an EV charging plaza by an ESS decreases the required connection power of the plaza and smooths variations in the power it draws from the grid. In this article, a study of sizing of stationary ESSs for EV charging plazas is presented based on one year of data compiled from four direct current fast charging (DCFC) stations. Effects of the charging plaza size, grid connection power, and temporal resolution of input data on ESS requirements were studied. The ESS was controlled to reduce the connection power below certain limits which were altered from 5% to 100% with respect to the nominal power of the charging plaza. The charging plaza size ranged from 1 to 40 DCFC stations. The results show that the relative ESS power and energy requirements and the utilization rate of the ESS decrease, as the connection power and charging plaza size increase. The required connection power of an EV charging plaza can be decreased considerably by a relatively small ESS capacity. The effects of temporal resolution were studied, for the first time, by averaging the charging power time series of the charging plaza with averaging time intervals ranging from 1 s to 1 h. The temporal resolution of EV charging data had a notable effect on the results of ESS sizing: too sparse data distorts the results leading to an underestimation of ESS specifications and utilization.

Sizing and operation co‐optimization strategy for flexible traction power supply system

AbstractDispatchable energy storage system (ESS) plays a critical role in the smart grid through energy shift and power support. However, it exhibits different operational strategies and economic benefits in different application scenarios due to its inherent degradation behaviour. This paper aims to explore the technical and economic feasibility of the flexible traction power supply system (FTPSS) integrating ESS and renewable energy sources (RES) based on the traction load characteristics. First, a battery degradation model applicable in its frequent charging and discharging operating conditions is derived. Then this paper develops an operational‐sizing co‐optimization framework for the ESS in the FTPSS, where the operation decisions are made considering the degradation costs varying with the sizes and energy throughput. To solve this large‐scale non‐linear intertemporal decision‐making problem, an iterative method with a linear programming (LP) core is proposed to achieve the trade‐off between computational efficiency and accuracy. The results of the extensive comparative cases show that the proposed approach can achieve approximately 10% higher economic benefits than the existing bi‐level sizing strategies for FTPSS.

Coordinated emergency control strategy of high‐voltage direct current transmission and energy storage system based on Pontryagin minimum principle for enhancing power system frequency stability

AbstractWith the increase of the proportion of renewable energy sources, the rotational inertia of the power system decreases, which results in the risk of frequency instability increasing. Based on Pontryagin minimum principle, this paper presents a systematic emergency control strategy by coordinating the active power of voltage source converter based high‐voltage direct current transmission (VSC‐HVDC) and energy storage system (ESS) to improve the system frequency stability, reduce the operation cost of VSC‐HVDC and ESS and implement energy management of ESS. In the stage of frequency fall, a hierarchical control strategy is proposed to prevent the frequency from dropping below the preset threshold value and achieve the optimal distribution of support power between VSC‐HVDC and ESS. For the system level, the total optimal power support trajectory of VSC‐HVDC and ESS is optimized by the Gaussian pseudospectral method with the minimum total control energy, considering the constraints of frequency. For the station level, the power references of VSC‐HVDC/ESS are determined by the model predictive controller (MPC), with the minimum frequency deviation of the non‐disturbed system connected to VSC‐HVDC, control cost of VSC‐HVDC and loss cost of ESS. In the stage of frequency recovery, an energy recovery control strategy is designed to aim at quickly recovering energy of ESS while considering the minimum frequency limit. Finally, the correctness and effectiveness of the proposed strategy are verified in the improved IEEE 39 bus system and a real‐power system.

Energy management in Converter-Interfaced Renewable Energy Sources through ultracapacitors for provision of ancillary services

The ever-growing penetration of Converter-Interfaced Renewable Energy Sources (CI-RES) and the gradual decommission of synchronous generators (SGs) are posing several challenges to guarantee the stability and robustness of the electric power system. A possible solution to overcome the foreseen problems is to enable the provision of ancillary services (AS) by advanced CI-RES emulating the SG performance. However, given the fact that most of the CI-RES operate at maximum power point, it will be required to rely on energy storage systems (ESS) to deal with active power related ASs. The ESS integration can be done in a straightforward manner within the CI-RES DC bus by means of a DC/DC converter. It is required, however, an adequate DC energy management to properly control the DC bus voltage, the ESS energy and the power delivered to the grid by the primary power source. This is a major challenge which must be solved to unlock the AS provision by means of CI-RES. With this regard, the main contribution of the paper is the design of an energy management system to simultaneously regulate the CI-RES DC bus voltage using the UC and also maintain the UC voltage within their safety limits while a given AS is provided. The control strategy is validated experimentally using a prototype with results that reveal a reliable and stable operation.

A probabilistic risk-averse approach for energy storage sizing in all-electric ship

In recent years, energy storage systems (ESS) are becoming an integral part of modern all-electric ships (AES). The topic of optimal ESS sizing is important as it determines the cost and effectiveness of the vessel operation. Conventional ESS sizing only considers the investment stage and ignores the operation stage and uncertainties. This research work adopts a risk-averse approach by coordinating investment and operation stages. The proposed two-stage method ensures optimal operation by reducing cost and emission while addressing the stochasticity present in voyage planning. Firstly, multi-objective ESS sizing and energy management scheduling are jointly optimized by considering the operation and sizing objectives with the probabilistic hydrodynamic data-oriented constraints from the voyage scenarios. In the second stage, the information-gap decision (IGD) decides the number of modules and configuration of the ESS to achieve power and energy requirements. Various ESS technologies and numerous scenarios are used to verify the effectiveness of the proposed strategy. The results indicate the validity and requirement of the proposed method.

Impact of Dynamic Thermal Rating on optimal siting and sizing of energy storage systems under Renewable Portfolio Standards requirements

Renewable Portfolio Standards (RPS), enforced as part of environmental regulations, are taking precedence worldwide to promote investments in renewable energy sources (RES). From the grid perspective, one of the bottle-neck factors that limits large-scale RES integration is the transmission network congestion. Dynamic Thermal Rating (DTR) is a smart grid technology that utilizes line condition and real-time weather conditions to alleviate network congestion by exploiting the transmission network inherent flexibility. Due to the intermittency of the RES, energy storage systems (ESS) have emerged as an essential component of power system that stores the excess generated energy by RES and pumps it back into the network at peak hours. However, despite the benefits of these technologies, the coordination of these two technologies with RPS regulations are not studied together. This paper presents a co-optimized model to evaluate the role of DTR in optimal siting and sizing of ESS units in connection with RPS requirements. The modeling framework co-optimizes bulk ESS expansion and DTR investment planning subject to constraints on RPS regulations and is formulated as a two-stage stochastic mixed integer linear programming (MILP) optimization problem. The model utilizes DTR to overcome the problem of network congestion and ESS to mitigate RES curtailment by saving the extra generated energy in non-peak hours. The efficacy of the proposed model in realizing impact of the DTR on ESS (location, energy capacity and power rating) in achieving high RPS targets is investigated on the modified IEEE 24-Bus system. Results demonstrate that DTR complements ESS under different RPS constraints by reducing the total system cost and ESS size significantly.

Sizing of energy storage systems for ramp rate control of photovoltaic strings

In this article, a comprehensive study on the sizing of energy storage systems (ESS) for ramp rate (RR) control of photovoltaic (PV) strings is presented. The effects of RR limit and inverter sizing, including their combined effect, on the sizing of the ESS are herein studied systematically for the first time. The study is based on 38 days of current–voltage curve measurements of a PV string of 23 PV modules. The results show that the daily irradiance profile has a crucial effect on the ESS power and energy requirements. It was found that an ESS power rating of 60% of the PV string nominal power is adequate to smooth almost all detected PV power ramps even with strict RR limits. With a typical DC/AC power ratio of 1.5, about 1.0 h of energy storage capacity is needed at the nominal power of the PV string to smooth all PV power ramps. The results illustrate that the set RR limit and the inverter sizing are important factors for sizing the ESS for PV RR control. Moreover, the results indicate that the needed ESS power capacity can be considerably reduced by smoothing the fastest upward power ramps by power curtailment.

Scheduled Power Control and Autonomous Energy Control of Grid-Connected Energy Storage System (ESS) With Virtual Synchronous Generator and Primary Frequency Regulation Capabilities

This paper presents a combined control scheme for the grid-connected energy storage system (ESS). There are two control modes: the power control mode for the charging or discharging condition and the energy control mode for the stand-by condition. In the power control, a derivative element is added in the inertial power-frequency droop as a simple power system stabilizer to enhance the system damping. The energy control is developed from the power control by considering the energy storage dynamics. During system disturbances, both control modes are able to provide autonomous grid support. The small-signal model and the large-signal transient model of the ESS with the proposed control scheme are derived. As a result, the output power and/or the stored energy of ESS is in proportional to the frequency deviation and will be restored to the pre-disturbance condition. By selecting appropriate control parameters, the stored energy is controlled within a safe range constrained by the frequency limits without manual interventions. The primary frequency regulation with both power-frequency and energy-frequency proportional relationships is realized by the energy control, which coordinates with the grid secondary frequency regulation. The control performance and the derived energy model are validated by detailed time-domain simulation studies.

An optimal home energy management system with integration of renewable energy and energy storage with home to grid capability

Residential building consumes a significant amount of energy. To address the issue, these structures have been supplied with renewable energy sources (RES), an energy storage system (ESS), and an electric vehicle (EV). In a home, a home energy management system (HEMS) has been implemented to schedule and regulate domestic appliances. Many studies in HEMS have been conducted in order to reduce the cost of power and the peak to average ratio (PAR). However, there is insufficient use of RES, ESS, EV, and excess domestic energy. As a result, this research presents a HEMS architecture that is integrated with RES and ESS and includes home-to-grid (H2G) power flow functionality. The RES electricity is initially utilized to power domestic appliances and charge ESS before being transferred back into the main grid to reap economic rewards. During the low-price period, the ESS and EV are charged and discharged during the high-price period. The paper built a multi-objective optimization problem using the provided approach that integrates electricity cost and system PAR. The grey wolf optimization algorithm is used to tackle the multi-objective optimization problem. The results clearly show that the proposed technique decreases expenditures by 45.80% and PAR by 28.44%, compared to the baseline timetable in 24 h. The H2G power flow feature allows the home to send back excess energy to the grid, cutting electricity expenditures by 70% and achieving additional economic benefits.

Hybrid energy storage system topology approaches for use in transport vehicles: A review

AbstractHigh peak current for vehicle starting, recuperation of regenerative braking energy, longer battery lifespan, and more significant acceleration among others in modern transport vehicles (TVs) require increased battery size. Moreover, batteries have high energy density and low power density. Therefore, a big battery pack can weigh more, shorten its lifespan, increase vehicle total mass, and increase battery degradation costs. On the one hand, higher power energy storage systems (ESSs) such as supercapacitors, lithium‐ion capacitors, and superconducting magnetic ESSs have a lower energy density, higher power density, and greater lifespan. Thus, to satisfy the requirements of modern TVs, the combination of higher energy and higher power density can provide enhanced performance and a longer battery lifespan for these vehicles. Available research publications in the literature have addressed a similar problem. However, these publications have reported the findings separately, providing various research and conclusions. Currently, no available literature has compiled an intelligible and combined analysis for addressing hybrid ESS configurations, sizing methods, and energy management strategies to create further knowledge in this domain. There is a need to consolidate a compact and insightful knowledge toward this research direction for a more significant societal and industrial impact. This paper critically reviews the hybrid higher energy density batteries and higher power density ESSs used in TVs. It discusses the integration configurations, applications, and provides sizing methods to achieve the best hybrid energy storage systems (HESSs). Also, applied control methods are described for these HESSs such that the overall system performance matches the vehicle requirements. Lastly, it provides insights and future research direction for HESS configuration, sizing, and control.

WEC Array Optimization with Multi-Resonance and Phase Control of Electrical Power Take-Off

This new research provides transformative marine energy technology to effectively power the blue economy. Harmonizing the energy capture and power from Wave Energy Converter (WEC) arrays require innovative designs for the buoy, electric machines, energy storage systems (ESS), and coordinated onshore electric power grid (EPG) integration. This paper introduces two innovative elements that are co-designed to extract the maximum power from; i) individual WEC buoys with a multi-resonance controller design and ii) synchronized with power packet network phase control through the physical placement of the WEC arrays reducing ESS requirements. MATLAB/Simulink models were created for the WEC array dynamics and control systems with Bretschneider irregular wave spectrum as inputs. The numerical simulation results show that for ideal physical WEC buoy array phasing of 60 degrees the ESS peak power and energy capacity requirements are minimized while the multi-resonant controllers optimize EPG power output for each WEC buoy.

Optimal Stochastic Control of Energy Storage System Based on Pontryagin Minimum Principle for Flattening PEV Fast Charging in a Service Area

This letter discusses stochastic optimal control of an energy storage system (ESS) for reducing the impact on the grid of fast charging of electric vehicles in a charging area. A trade off is achieved between the objectives of limiting the charging power exchanged with the grid, and the one of limiting the fluctuation, around a given reference, of the ESS energy. We show that the solution of the problem can be derived from the one of a related deterministic problem, requiring the realistic assumption that the charging area operator knows an estimate of the aggregated charging power demand over the day. In addition, two alternative configurations of the charging area are discussed, and it is shown that, while they share the same solution, one better mitigates the demand uncertainty. Numeric simulations are provided to validate the proposed approach.

마이크로그리드의 수용성 향상을 위한 ESS의 전력제어

Microgrid(MG) pursues its economic power distribution in order to maximize its efficiency and also business expansion. To achieve it, an energy management system(EMS) is key technology. The ultimate pursuit of the MG is to minimize the use of diesel fuel by making the most of renewable energy. This paper proposes a power control algorithm of energy storage system(ESS) in stand-alone MG that considers the hosting capacity as an index to minimize the size of diesel generator(DG) capacity. For this, the ESS is installed at the end of the line of the MG and uses a bi-directional power flow between the DG and the ESS to increase the hosting capacity of the load and renewable energy. In addition, by using ESS, customers and renewable energy exceeding the capacity of DG are stably introduced without adding facilities to the grid. The simulation results using Matlab/Simulk show the effectiveness of the proposed algorithm.

Two-Stage Energy Management for Energy Storage System by Using Stochastic Model Predictive Control Approach

There is a growing tendency for industrial consumers to invest in both photovoltaic (PV) and energy storage systems (ESSs) to meet their electricity requirements. However, the uncertainty of load demand and PV output brings great challenges for ESS operation. In this paper, a stochastic model predictive control (MPC) approach-based energy management strategy for ESSs is proposed. A non-parametric probabilistic prediction method embedded in time series correlation is adopted to describe the uncertainty of load demand and PV output. Then, a two-stage energy management model is proposed aiming at minimizing the total operation cost. The upper stage can generate an hourly operation strategy for ESSs, while the lower stage focuses on a more detailed minute-level operation strategy. The hourly operation strategy is also used as a basis to guide the ESS operation in the lower stage. Besides, a chance constraint was introduced to achieve a win–win solution between PV power consumption and electricity tariff, while the terminal value constraint of the capacity of ESSs to better cope with the uncertainty beyond the prediction time window. Finally, the numerical results showed that the proposed method can achieve an effective ESS energy management strategy.

Impact of battery storage on residential energy consumption: An Australian case study based on smart meter data

Advanced metering infrastructure has been widely recognized as a key enabling factor for delivering a range of benefits to the electricity industry as well as to energy consumers. The data is commonly used for billing and to educate consumers so they can make economically optimal decisions on choosing between electricity retail plans. More importantly, an in-depth analysis of this data can drive the development and implementation of new energy technologies and to enable evidence-based policy decisions to be made. In this paper, an Australian case study is presented on how residential battery installations lead to behavioral changes in the way energy is consumed. Furthermore, data-informed recommendations to both distribution network operators and policy-makers are also provided. The data comprises more than 5000 energy consumers with either distributed generation systems such as Photovoltaics (PV) and Energy Storage Systems (ESS), or without. The methodology focuses on the analysis of energy consumption of consumers with PV and ESS energy consumers and compares them against consumers without ESS. In addition, an economic analysis on the benefit of installing ESS is presented using payback period and internal rate of return. The main finding is that residential energy storage systems provide a range of benefits to the distribution network. It is recommended that Governments should continue supporting the rollout of such systems by subsidy programs and implementing necessary policy directives.