Multiple Energy Storage Systems Research Articles

Hybrid energy storage system for electric motorcycles: Technical and economic analysis

This paper presents the multiple energy storage system usability for electric motorcycle focused on hybrid topology. This study focuses on evaluating the cost-effectiveness of a hybrid energy storage system (HESS) for e-motorcycles over a 10-year period by considering the number of battery replacements instead of solely the initial system price. The motorcycle studied in this manuscript, is a recreational electric motorcycle utilized as an experimental test bench in EV-SCHS laboratory at the OJEN company. The 10-year equivalent price of the energy storage system includes both the initial purchase cost and the cost of battery replacements over the system's lifespan. A battery life model is essential for estimating the frequency of replacements over the 10-year period. Incorporating ultracapacitors (UC) in the HESS enhances power specifications, leading to improved acceleration and regenerative braking capabilities. This reduces battery current and extends battery life, resulting in fewer required replacements and lower overall system costs over the 10-year period. The study findings demonstrate that optimally-sized HESS configurations offer favorable power specifications, with the 10-year equivalent price of HESS being $3466 lower than that of conventional energy storage systems, despite the initial price of HESS being $833 higher.

Novel Current Source Converter for Integrating Multiple Energy Storage Systems

The increasing penetration of renewable energy sources (RESs) in transmission and distribution systems presents several challenges for grid operators. In particular, the unpredictable behavior of RESs can disrupt the balance between energy production and load demand, potentially affecting the stability of the entire system. Grid-connected energy storage systems (ESSs) offer a possible solution to manage the uncertainty associated with RESs. In fact, ESSs exchange power with the grid through the adoption of suitable energy management strategies, which are typically implemented by power electronics-based grid interfaces. Unlike other current source converter (CSC) solutions described in the literature, which only interface with a single energy storage device, this paper introduces a novel topology for a three-phase delta-type current source converter (D-CSC), which is capable of integrating three independent ESSs using the same number of semiconductors as traditional CSC solutions. Thus, it considerably enhances the flexibility of a power conversion system (PCS) without increasing the number of converter components. In addition, an innovative energy management control strategy is also introduced. This strategy enables the D-CSC to compensate for energy imbalances arising between the three ESSs, which might be caused by several factors, such as different aging characteristics, converter component tolerances, operating conditions, and temperature drifts. Hence, the D-CSC-based interface is capable of proper grid operation even if the three ESSs have different characteristics, thus opening the possibility of employing this converter to integrate both first and second-life devices. First, the topology of the proposed D-CSC is introduced, followed by a detailed mathematical description of its control strategy. The proper grid operation of the D-CSC was tested under different scenarios, considering the grid integration of three independent superconducting magnetic energy storage systems in a marine vessel. The proposed D-CSC is compared to traditional CSC solutions, highlighting the superior performances of the novel converter topology in terms of efficiency, total harmonic distortion of the output currents, and overall cost reduction for the PCS.

Knowledge-network-embedded deep reinforcement learning: An innovative way to high-efficiently develop an energy management strategy for the integrated energy system with renewable energy sources and multiple energy storage systems

To achieve efficient energy management in complex integrated energy systems (IESs) with renewable energy sources (RESs) and multiple energy storage systems (ESSs), the study aims to propose a novel approach. Evolutionary-based methods are difficult to find the optimal scheme, while deep reinforcement learning (DRL)-based methods face problems with low training efficiency. To address the limitations, a knowledge-network-embedded DRL method is proposed, which combines the fast-searching capability of genetic algorithm (GA) with the environment-aware decision-making capability of DRL, with the aim of developing a highly efficient energy management strategy in improving user comfort and reducing operating costs. GA is used to solve the IES optimization model for obtaining a solution set that serves as domain knowledge, and a knowledge network is constructed using deep learning (DL). Then, DRL uses this network as an actor network to explore the optimal strategy. The use of GA helps the DRL to enhance training efficiency and find an optimal strategy. Simulations show significant improvements in cost reduction (5.7 %) and user comfort (4.2 %), along with improved training efficiency (58.3 %) compared to baseline DRLs.

Thermal performance enhancement of multiple tubes latent heat thermal energy storage system using sinusoidal wavy fins and tubes geometry modification

In this paper, performance enhancement of a shell and multiple tube latent heat thermal energy storage system (LHTESS) is numerically investigated by implementing sinusoidal wavy fins and different geometrical designs of heat transfer tubes. The annular space of the shell is filled with the paraffin wax RT35 as phase change material (PCM) and absorbs heat from hot water through the multiple tubes. With the constant total cross-section area of all tubes and fins as the constraint condition, the effect of tube number and tube shape incorporated with the proposed fins on the heat transfer properties of PCM during the melting process is evaluated. The computational results demonstrate that the sinusoidal wavy fins can improve the phase change efficiency compared to the conventional straight fins. The double-pitch sinusoidal wavy fins decrease the total melting time by 9.5% compared to the straight fins for the single-tube case. As the number of HTF tubes increased from one to four, the complete PCM melting time decreased by 38.3%. Geometrical design alterations of heat transfer tubes from circular to petal-shaped are proposed to further improve the thermal performance of multiple tubes LHTESS. The melting rate of PCM is accelerated with an increase in the number of petals. The petal tubes with nine petals can save the charging time up to 24 % in comparison with the circular tubes in a four-tube case with double-pitch sinusoidal wavy fins, and up to 57% compared to the base case with a single circular HTF tube and straight fins.

Deep Reinforcement Learning-Based Security-Constrained Battery Scheduling in Home Energy System

The home energy system today involves multiple renewable energy sources and battery energy storage systems, which can be considered as a microgrid. The battery energy storage system is a key component in the home energy system for the sake of filling the gap between the user demand and volatile energy supplies to maximize the techno and economic performances. However, the battery scheduling must suffer the stochastic nature of renewable energy resources and loads, which results in an intractable multi-period stochastic optimization problem with security constraints. An improved actor-critic-based reinforcement learning is proposed for this issue, where a distributional critic net is applied for faster and more accurate reward estimation under uncertainties, and a policy net incorporating protective secondary control is adopted to satisfy security constraints, preventing the unsafe state of batteries during the trial-and-error process. Numerical tests show that the proposed approach outperforms conventional reinforcement learning algorithms, as well as the rule-based battery scheduling approach while guaranteeing safe operation. The robustness and adaptability of the proposed method are also verified in case studies with different optimization tasks.

CuEMS: Deep reinforcement learning for community control of energy management systems in microgrids

Nowadays, energy management assumes a pivotal role in ensuring the stable operation of microgrids, facilitating enhanced monitoring, analysis, and optimization of energy utilization for end-users. The implementation of an energy management system (EMS) offers invaluable assistance to microgrid users, enabling them to cut electricity costs, ensure reliability, and provide an elevated level of comfort. However, as the scope of microgrid management proliferates, balancing between the comfort of community microgrid users with more devices and their profitability becomes increasingly challenging. To address this issue, this paper proposes a novel community control approach for EMS, called CuEMS, that utilizes deep reinforcement learning (DRL) to manage multiple appliances and energy storage systems (ESSs). The proposed approach optimizes energy profitability for operators while taking into account user comfort. It reformulates energy scheduling as a Markov decision process and learns the optimal scheduling strategy through deep Q-networks. The presented CuEMS approach is evaluated using real world data from Finland, and the results demonstrate that it can optimize the operation of multiple appliances and improve the profitability and user comfort compared to other methods.

Nearly-zero carbon optimal operation model of hybrid renewable power stations comprising multiple energy storage systems using the improved CSO algorithm

In light of the abundant renewable energy resources in Northwestern China, this study introduces a novel hybrid power plant structure known as the (Renewable energy-concentrating solar power-combined heat and power) RCC system. The RCC system integrates various energy sources, including the photovoltaic, the wind power plant, the concentrating solar power (CSP) plant, and the combined heat and power (CHP) plant. It also incorporates power-to-gas (P2G) technology to convert wind and solar power surpluses into methane. Moreover, carbon capture and storage (CCS) technology is applied to capture carbon dioxide emissions from the CHP plants, which serves as a raw material for the P2G process. To address the energy trilemma, we develop a nearly-zero carbon emission optimization model for the RCC system, considering different renewable energy source (RES) endowments. The fuzzy membership function method is employed to identify the optimum satisfaction target across multiple attributes. An enhanced CSO algorithm is introduced and validated using a simulation study on a CSP plant in Dunhuang. The results show that compared with traditional power stations, the proposed RCC system can reduce the investment cost by 34.45 %, increase the operating income by 17.7 %, and reduce carbon emission by 3.2 %. At the same time, the methane stock in the system increased by 53.7 % compared with the traditional power station containing P2G equipment, which helps the hybrid power station to participate in the methane market and obtain more profits. In addition, the direct energy supply ratio of RES decreased by 83.16 %, reducing the risk of the system caused by the uncertainty of RES. To sum up, the proposed RCC system has a good development prospect.

Multiple Grid-Connected Microgrids with Distributed Generators Energy Sources Voltage Control in Radial Distribution Network Using ANFIS to Enhance Energy Management

Voltage conditions and power quality for customers and utility equipment are significantly impacted by the addition of microgrid-generating sources within distribution networks. Designing the right control for distributed generators for the various generating units of a Microgrid is important in enabling the synchronization of renewable energy generation sources, energy storage unity, and integration of Microgrids into a radial distribution network. This research provides control mechanisms based on an adaptive technique employing ANFIS, to reduce fluctuation of voltage and current difficulties faced when multiple renewable energy sources and storage systems are incorporated into a distribution network. A step-by-step Voltage Source Converter (VSC) Controller was designed for controlling the DC voltage power sources used. The ANFIS training, test system modeling, and the distributed energy source were modeled in MATLAB/SIMULINK 2021a Software. Four microgrids were developed each consisting of a Photovoltaic plant, Wind Turbine, and Battery Storage System. Non-critical and critical loads were considered during the system testing. The simulated result reveals that the proposed control system works effectively in maintaining a constant system voltage of 340VAC which significantly mitigates system voltage and current fluctuation without using any static synchronous compensator (STATCOM) and power system stabilizers.

A Transaction Model and Profit Allocation Method of Multiple Energy Storage Oriented to Versatile Regulation Demand

This study proposes a day-ahead transaction model that combines multiple energy storage systems (ESS), including a hydrogen storage system (HSS), battery energy storage system (BESS), and compressed air energy storage (CAES). It is catering to the trend of a diversified power market to respond to the constraints from the insufficient flexibility of a high-proportion renewable energy system (RES). The model is a double-layer game based on the Nash–Stackelberg–cooperative (N–S–C) game. Multiple users in the upper layer form the Nash game with the goal of maximizing their own benefits, while the multiple ESSs in the lower layer form a cooperative game with the goal of maximizing the overall benefits; the two layers form a Stackelberg game. Moreover, an allocation mechanism is proposed to balance the overall and individual rationality and promote the sustainable development of multiple ESSs, considering the operational characteristics. A numerical simulation is carried out using the rationality and effectiveness of the proposed model, which is based on data from the renewable energy gathering area in northwest China. The results show that this strategy shortens the energy storage payback period and improves the energy storage utilization. The simulation results indicate that small-scale energy storage with a rated power of less than 18 MWh does not have a price advantage, indicating the need to improve the configuration capacity of energy storage in the future from decentralized energy storage to independent/shared energy storage.

Prediction of Dynamic Temperature and Thermal Front in a Multi-Aquifer Thermal Energy Storage System with Reinjection

The accurate temperature and thermal front prediction in aquifer thermal energy storage systems during reinjection are crucial for optimal management and sustainable utilization. In this paper, a novel two-way fully coupled thermo–hydro model was developed to investigate the dynamic thermal performance and fronts for multiple aquifer thermal energy storage systems. The model was validated using a typical model, and the evolution characteristics of wellbore temperature before and after the breakthrough of the hydraulic front and thermal front were deeply studied. Sensitivity analysis was conducted to delineate the influence of various reservoir and reinjection factors on the thermal extraction temperature (TET). The results revealed that thermal conductivity significantly impacts the thermal extraction rate among the various reservoir factors. In contrast, volumetric heat capacity has the weakest influence and negatively correlates with the TET. Concerning the reinjection factors, the effect of the reinjection volume rate on the TET was significantly more significant than the reinjection temperature. Furthermore, the correlation between the TET and different properties was observed to be seriously affected by the exploitation period. The coupled model presented in this study offers insight into designing the exploitation scheme in deep reservoirs and geothermal resources.

Distributed control for multiple hybrid energy storage systems using consensus algorithm in direct current power supply system

Multiple hybrid energy storage systems (multi-HESSs) consisting of batteries and supercapacitors (SCs) is widely used to share the requirement of system pulsating power, reducing the dependence on a single hybrid energy storage system (HESS) to improve the system reliability. The filter composed of virtual resistance droop control (VRDC) and virtual capacitance droop control (VCDC) in a HESS can achieve frequency distribution of pulsating power between a battery and SC. However, it suffers from the output voltage deviation and terminal voltage variation. Mismatched line impedances and different system parameters can cause different output voltages and inaccurate current sharing in the multi-HESSs. Meanwhile, the differences in the terminal voltages of SCs can affect the transient output capability. Therefore, this paper proposes a consensus-based distributed control strategy (CDCS) for multi-HESSs, which not only achieves global average output voltage and current sharing for batteries but also realizes restoration and consistency of SCs terminal voltage. The state variable information of adjacent converters for multi-HESSs is exchanged by the sparse communication network, and the steady-state analysis of state variables is proved. The direct current (DC) power supply system with four HESSs is established to verify the effectiveness of the proposed control strategy.

SoC-Adaptive Power Sharing Strategy Applied to DC Microgrids Supplied by Multiple Energy Storage Systems

A non-linear <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$SoC$</tex-math></inline-formula> -adaptive management and control scheme is applied to DC microgrids (MGs) supplied by energy storage systems (ESSs) and connected to the grid. The management and control strategy are performed via decentralized methodologies, allowing power sharing between the battery banks with or without a low-speed communication link, while their limits of operation for charging and discharging are not exceeded. Additionally, the aforementioned technique is based on the sigmoidal function where the solution shows two degrees of freedom as the DC-link voltage error and the state-of-charge <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(SoC)$</tex-math></inline-formula> in the outer loop (DC-link voltage control), and classical proportional-integral <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(PI)$</tex-math></inline-formula> controllers regulating the current flow through the DC/DC power converters in the inner loop. To analyze the system stability, the DC MG model was calculated to evaluate the poles movement, frequency and step responses at different scenarios. Finally, the authors rely on a set of results in the Typhoon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\text{\text{®}}}$</tex-math></inline-formula> 602+ hardware to demonstrate the effectiveness of the proposed solution as well as, a comparison between it and the classical droop controller.

Probabilistic optimal planning of multiple photovoltaics and battery energy storage systems in distribution networks: A boosted equilibrium optimizer with time-variant load models

In recent years, there has been a rapid increase in distributed generation (DG) technologies incorporated into distribution networks (DNs) to meet the challenge of load growth. However, the stochastic nature of renewable energy, such as photovoltaic (PV), makes the amount of energy produced uncertain. This uncertainty, along with changes in generation and load demand, can increase energy losses and voltage instability. To address this issue, energy storage systems can be integrated to decrease the effects of the intermittency associated with renewable technologies. This paper proposes a new variant of an equilibrium optimizer (EO) based on reinforced learning, named RLEO, for optimal incorporation of multiple battery energy storage (BES) units integrated synchronously with solar PVs into distribution systems while minimizing energy loss. The RLEO algorithm employs reinforced learning mechanisms to prevent premature convergence of the EO and improve its exploration and exploitation capabilities. The performance of the RLEO algorithm is assessed using standard CEC 2017 benchmark functions and compared with the original EO and other popular algorithms using various statistical criteria. The RLEO algorithm is also applied to determine the optimal size and position of multiple PV units in IEEE 69-bus and 118-bus DNs with single and multi-objective optimization problems. Using the developed algorithm, the optimal arrangement of three non-dispatchable PVs results in a slight increase in the percentage reduction of energy loss across various load profiles: 53.0035 % for commercial, 19.6372 % for industrial, and 28.1783 % for residential. In contrast, by employing the optimal configuration of three PV + BES units, the reduction in energy loss percentage experiences a remarkable surge to 68.3466 % for commercial, 68.0917 % for industrial, and 68.1779 % for residential load scenarios using the proposed algorithm. This clearly indicates that the proposed RLEO algorithm significantly surpasses recent optimization methods documented in the literature when it comes to addressing the challenge of optimal allocation for multiple DGs. Furthermore, its applicability extends to more intricate optimization problems.

Equilibrium Allocation of ESSs in Multiple UIESs-Accessed Distribution Networks Considering the Resilience and Economic Benefits

Recently, extreme disasters considered as high-impact low-probability (HILP) events have caused severe power outages. User-level integrated energy systems (UIESs) is being widely formed and accessed to distribution networks (DNs), which has the potential to enhance the resilience of DNs. Therefore, multiple UIESs-accessed DNs against such HILP events are required in order to enhance the resilience of the distribution network. In this paper, from a resilience-oriented perspective, an equilibrium allocation method of multiple energy storage systems (ESSs) is proposed considering the planning of distribution network with multiple UIES for improving the resilience of distribution network. Firstly, the topology and operation modes of multiple UIESs-accessed DNs are analyzed. Secondly, a quantitative evaluation method of UIES power support capability is formulated, in which the differences in type, quantity and scale of coupling equipment in different scenarios are considered. An allocation model of multiple ESSs is then established using the Nash equilibrium method to balance the distribution network resilience and economics. Finally, simulation tests verify the superiority of the proposed equilibrium allocation model of multiple ESSs compared with other resilience enhancement strategies.

Research On Multi-source Collaborative Distribution Network Fault Recovery Technology

When a power outage occurs in the power system, multiple distributed power sources and energy storage systems connected to the distribution network can be coordinated to restore power to important lost loads in a priority and fast manner. However, the generation output of intermittent power sources such as wind and photovoltaic is uncertain, which makes it difficult to provide a continuous and stable power supply to the loads in isolated islands. In this paper, we first explore the improvement effect of multi-source cooperative fault recovery on distribution system resilience, consider the distribution system with distributed power sources, energy storage systems, and controllable loads, and propose a distribution system recovery model with the goal of maximizing the power consumption of the restored loads for a relatively short period of time after a power outage incident. Then, the power output characteristics and power supply capacity of various distributed power sources are analyzed, and the intermittent power output fluctuations are compensated using controllable loads. Finally, the proposed model and method are validated using a modified IEEE 33-node distribution network as an example.

COST-WINNERS: COST reduction WIth Neural NEtworks-based augmented Random Search for simultaneous thermal and electrical energy storage control

The combination of local renewable energy production, dynamic loads, and multiple energy storage systems with different dynamics requires sophisticated control systems to maximize the energy cost efficiency of the combined energy system. Battery and thermal energy storage systems can be combined to increase the local use of on-site renewable energy, reduce peak power demand, and exploit time-of-use energy pricing. In this paper, we focus on how the augmented random search algorithm and artificial neural networks can be used together to solve an energy cost optimization problem involving the control of a battery energy storage system and a thermal energy storage system at the same time in a smart warehouse. As part of this work, a simulated training environment made using the data from the smart warehouse’s operations. In addition to the energy storage systems, the warehouse energy system has integrated a large roof mounted photovoltaic power plant and an industrial-scale cooling system.The developed solution is able to minimize the energy costs by modulating both energy systems, depending on the situation. Additionally, when it is tested against the state-of-the-art solutions, our developed solution at worst matches performance when the alternative algorithm is allowed to increase training time by a factor of nearly three. On average, our presented solution doubles the performance of the benchmark algorithm with much less computational resource expenditure.

Integrated Renewable Photovoltaic/Thermal and Non-renewable Combined Heat and Power Unit Commitment Considering Multiple Energy Storage Systems

Integrated Renewable Photovoltaic/Thermal and Non-renewable Combined Heat and Power Unit Commitment Considering Multiple Energy Storage Systems

A Decentralized Power Allocation Strategy for Dynamically Forming Multiple Hybrid Energy Storage Systems Aided With Power Buffer

Multiple hybrid energy storage systems (HESSs) consisting of batteries and super-capacitors (SCs) are widely used in DC microgrids to compensate for the power mismatch. According to their specific energy and power characteristics, batteries and SCs are used to compensate low-frequency and high-frequency power mismatches, respectively. This paper proposes a decentralized power allocation strategy for dynamically forming multiple HESSs aided with a novel power buffer. The power buffer is a device combing a capacitor and a bidirectional DC-DC converter, it is used as an interface between the batteries and DC bus, allowing easy Plug-and-Play of different energy storage units and effective and efficient power allocation. First, the power buffer and SCs split the power mismatch into a low-frequency and high-frequency part with a modified I-V droop control. Then the power buffer transfers the low-frequency mismatch to the batteries for compensation based on their respective state-of-charges (SoCs), while the high-frequency part is dealt by the SCs directly. This new scheme further allows elimination of the DC bus voltage deviations. Finally, the real-time hardware-in-loop (HIL) tests of three case studies confirm the effectiveness of the proposed control strategy.

From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company

Digitalization and sector coupling enable companies to turn into flexumers. By using the flexibility of their multi-energy system (MES), they reduce costs and carbon emissions while balancing the electricity grid. However, to identify the necessary investments in energy conversion and storage technologies to leverage demand response (DR) potentials, companies need to assess the value of flexibility. Therefore, this study quantifies the flexibility value of a production company’s MES by optimizing the synthesis, design, and operation of a decarbonizing MES considering self-consumption optimization, peak shaving, and integrated DR based on hourly prices and carbon emission factors (CEFs). The detailed case study of a beverage company in northern Germany considers vehicle-to-X of electrical industrial forklifts, power-to-heat on multiple temperatures, wind turbines, photovoltaic systems, and energy storage systems (thermal, electrical, and hydrogen). We propose and apply novel data-driven metrics to evaluate the intensity of price-based and CEF-based DR. The results reveal that flexibility usage reduces decarbonization costs (by 19%–80% depending on electricity and carbon removal prices), total annual costs, operational carbon footprint, energy-weighted average prices and CEFs, and fossil energy dependency. The results also suggest that a net-zero operational carbon emission MES requires flexibility, which, in an economic case, is provided by a combination of different flexible technologies and storage systems that complement each other. While the value of flexibility depends on various market and consumer-specific factors such as electricity or carbon removal prices, this study highlights the importance of demand flexibility for the decarbonization of MESs.

An Economic Dispatch for a Shared Energy Storage System Using MILP Optimization: A Case Study of a Moroccan Microgrid

Energy storage systems are an effective solution to manage the intermittency of renewable energies, balance supply, and demand. Numerous studies recommend adopting a shared energy storage system (ESS) as opposed to multiple single ESSs because of their high prices and inefficiency. Thus, this study examines a shared storage system in a grid-connected microgrid. By modifying the power outputs of the energy resources, this work intends to implement an economic dispatch of a shared ESS in order to satisfy the power balance and lower the overall cost of electricity. In this context, an optimization problem was formulated and developed using a mixed-integer linear programming (MILP) model. Furthermore, a pilot project (the Solar Decathlon Africa Village) in the Green &amp; Smart Building Park (GSBP), Benguerir, Morocco, was employed to evaluate and verify the proposed approach. Some comparable scenarios were therefore run in a MATLAB environment. The collected findings demonstrate the efficacy of the developed algorithm in terms of optimizing energy cost reductions and enhancing the integration of renewable resources into the Moroccan energy mix.