Sizing Of Energy Storage System Research Articles

Optimal power system design and energy management for more electric aircrafts

Recent developments in fuel cell (FC) and battery energy storage technologies bring a promising perspective for improving the economy and endurance of electric aircraft. However, aircraft power system configuration and power distribution strategies should be reasonably designed to enable this benefit. This paper is the first attempt to investigate the optimal energy storage system sizing and power distribution strategies for electric aircraft with hybrid FC and battery propulsion systems. First, a novel integrated energy management and parameter sizing (IEMPS) framework is established to co-design aircraft hardware and control algorithms. Under the IEMPS framework, a new real-time power distribution algorithm with a flexible ratio is established to facilitate integrated parameter optimization, which can adapt to different power system configurations. Based on the comprehensive analysis of hydrogen economy, FC aging cost, and aircraft stability, a multi-objective parameter optimization model is established to decide the size of aircraft energy storage systems and hyper-parameters in the power controller. The X-57 Maxwell, an experimental electric aircraft designed by NASA, is employed to verify the developed methods. This work provides a novel power system configuration, sizing, and power management method for future commercial aircraft design, and it can further promote the aviation electrification process.

Optimal sizing and deployment of gravity energy storage system in hybrid PV-Wind power plant

The world today is continuously tending toward clean energy technologies. Renewable energy sources are receiving more and more attention. Furthermore, there is an increasing interest in the development of energy storage systems which meet some specific design requirements such as structural rigidity, cost effectiveness, life-cycle impact, and increased energy capacity. Gravity energy storage (GES) is one of those innovative storage technologies that is still under development. Hence, this study proposes a new methodology which aims to optimally design and deploy a large-scale GES system in a hybrid PV-Wind plant to make it more competitive technically and economically. The objective of this study is to minimize GES construction cost restricted by handling mechanical load applied on the system's structure. The sizing methodology is based on genetic optimization algorithm which aims to determine the optimum dimensions of GES components. A case study has been used to verify the effectiveness of the proposed model. The findings of this study have shown that the optimal system's height is about 48 m with a diameter of 24 m and a wall thickness of 3m. The construction cost of the system reaches 6.7 M€ with the piston representing the largest cost share. The cost of electricity was found equals to 0.19 €/kWh. In addition, this work investigates the integration of GES with a hybrid wind-solar grid-connected power plant to properly dispatch renewable power by incorporating an efficient storage strategy preventing overcharging/discharging of GES. The findings demonstrate the ability of this new storage system to balance the energy supply and demand. A comparison between the obtained results with that of a battery energy storage has shown that GES performs better due to its high DOD and lifetime, as well as its good efficiency.

Hybrid Energy Storage Management Strategy for Electric Propulsion Aircraft Based on Three-Step Power Distribution

To solve the problem of severe DC bus voltage fluctuations caused by frequent changes in the distributed electric propulsion aircraft load, and to further optimize the size and life of the hybrid energy storage system (HESS), this paper proposes a method based on three-step power distribution (TSPD). This strategy realizes the reasonable distribution of battery and supercapacitor power by using a low pass filter. Through the introduction of a supercapacitor state-of-charge (SOC) automatic recovery control and maximum power value dynamic limit strategy, the size of the HESS is optimized, and the service life of the energy storage device is extended. Finally, simulation and experiment platform are built to prove the effectiveness of the proposed strategy.

Optimal Sizing and Management of Battery Energy Storage Systems in Microgrids for Operating Cost Minimization

Microgrids are one of the best solutions to present energy crisis, where the expansions of distributed networks are not feasible. This is achieved with penetration of renewable energy sources along with suitable selection of Energy Storage Systems (ESS).The load demand can be supported with microgrid in the grid connected mode or autonomous mode by suitable sizing of DGs and BESS. Sizing of suitable ESS in terms of its capacity or discharge helps in shaving the peak demand and also maximizes the benefits related to their use. Various advantages to end-users can be provided by microgrid with ESS, like improvement in energy efficiency, minimizing overall energy consumption through distributed generations (DGs) and increasing reliability of power supply to the customers. All these advantages can be achieved by obtaining the optimal microgrid operating cost with ESS. One of the challenging tasks in Microgrids is obtaining optimal energy utilization of DGs to meet the load demand. This can be achieved through proper energy management in the microgrid by obtaining optimal power allocations of DGs and ESS. This paper presents an algorithm for microgrid operating cost minimization for 24 hrs time horizon with optimal power allocation of DGs considering randomness in DGs, with and without BESS. Accelerated particle swarm optimization (APSO), Jaya optimization (JAYA) technique and Linear programming based interior point algorithm (LP-IP) have been applied to minimize the operating cost of microgrid with optimal Energy management while selecting proper size of BESS. Simulation results obtained by PSO, APSO, JAYA and LP-IP solver based algorithms are compared for BESS size and microgrid operating cost for 24 hrs. Results of LP-IP solver based optimization algorithm are proved to be the most effective of all other algorithms in terms of cost reduction. Optimal sizing of BESS with energy management of DGs and BESS reduces the operating cost of microgrid meeting the load criteria.

Storage size determination for grid-connected wind power system based on power spectral density analysis

Energy storage system (ESS) is essential for wind power integration, and it has become more and more important to optimize the wind-energy storage system (WESS) for keeping power grid safe and stable. In this paper, a novel energy storage sizing approach which can improve the performance of WESS is put forward. Firstly, the hinges model is introduced into the power spectral density (PSD) analysis to obtain the low-pass decomposition frequency (LPDF), which is then used for energy storage sizing optimization based on the single-day energy balance. Secondly, a comprehensive evaluation criteria system based on cloud model is proposed to assess various characteristics of WESS. Thirdly, a case study based on a 30 MW wind farm indicates that the power curve of the proposed method meets the conditions for wind power integration, and the size and stability of ESS are optimized. Compared with the traditional filtering algorithms, the WESS performance of the proposed algorithm is improved by 77.55% than wavelet packet decomposition (WPD) and 65.84% than empirical mode decomposition (EMD), and its applicability in different time spans is also verified.

Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids

Networked microgrids are considered an effective way to enhance resilience of localized energy systems. Recently, research efforts across the world have been focusing on the optimal sizing and pre-positioning problems of distributed energy resources for networked microgrids. However, existing literature on mobile energy storage systems mainly focused on single pre-positioning or operational problems rather than a comprehensive resilience-driven planning model capturing both optimal sizing and pre-positioning, especially in the presence of several MGs operating in a networked fashion. Additionally, centralized control is the method typically used to model networked microgrids that may be perceived as unrealistic in presence of high-impact extreme events. Therefore, this paper focuses on developing a three-level defender–attacker–defender model to solve resilience-driven optimal sizing and pre-positioning problems of mobile energy storage systems in networked microgrids with decentralized control. The upper level problem is formulated to obtain optimization results against a certain contingency, while the middle level problem and the lower level problem are merged as a subproblem to select a contingency that can cause the most severe damage. An adaptive genetic algorithm has been employed to search for sizing and positioning decisions and capture various potential attack plans, while a decentralized control approach based on consensus algorithm and linearized AC optimal power flow are utilized to model microgrid operations and capture technical constraints relating to voltage and power loss. Uncertainties relating to renewable energy sources and load profiles are incorporated into the model via stochastic programming. Extensive case studies considering meshed networks and load discrimination into essential/non-essential are developed to demonstrate the effectiveness of the proposed model on accurate decision making of capacities and initial locations.

Emergency reserve constrained optimal allocation of energy storage in a novel honeycomb‐like microgrid cluster with volatile renewable energy resources

With the increasing penetration of large-scale distributed renewable energies, the electricity distribution network is growing more complex with challenges brought by the intermittency and uncertainty of renewable energies. Microgrid (MG), one of feasible solutions to integrating renewable energies, has shortages because of limited capacity. Therefore, MG cluster (MGC) was proposed as an improved solution. To efficiently and friendly integrate renewable energies, a novel honeycomb-like MG cluster (H-MGC) is proposed in this paper. Considering the uncertainty of renewable energies, a robust optimisation method of the siting and sizing of energy storage system (ESS) constrained by emergency reserve is proposed. Combining the column-and-constraint generation algorithm and big-M method, the proposed optimisation model is decomposed into mixed integer second-order cone programming master problem and sub-problems, which are alternately solved. Based on the optimal results of study case, the siting and sizing results of ESS are analysed, as well as the operating characteristics of H-MGC. Compared to existing MGC system, the proposed H-MGC has a more flexible operation mode. The related costs of ESS are reduced, and the operational life is relatively extended, under the condition that the emergency reserve of system is satisfied.

Optimal sizing of a high‐speed flywheel in an electric vehicle using the optimal control theory approach

This study proposes an approach for finding the optimal size of a high-speed flywheel in an energy storage system based on battery-flywheel cooperation, called battery-flywheel energy storage system (BFESS), supplying an electric vehicle. Considering an energy-based model for the flywheel and battery, including the losses, the optimal control theory is utilised as a two-dimensional Pontryagin's minimum principle for solving the problem. The objective function of the flywheel sizing problem includes two terms, in which its maximum interaction with the system from the energy and filtering aspects are taken into consideration by a desirable weighting. While energy interaction is a common task, filtering the fast dynamics of the system such as the harsh conditions for the battery is the key role of the considered lightweight and high-speed flywheel. A trade-off between the energy contribution and filtering is made, targetted for the long-term and short-term dynamics in the drive cycles, respectively. The proposed methodology is presented in detail using some simple dynamics. Also, the sizing is carried out for the ECE-15 urban drive cycle, the WLTC class-3 and FTP-75 drive cycles. The results confirm that by using the determined optimal size of the flywheel energy storage system (FESS), most of the dynamics could be handled from the filtering and energy interaction aspects.

A Vision for Energy Decarbonization: Planning Sustainable Tertiary Sites as Net-Zero Energy Systems

The power system is changing towards a decarbonized one. The Kyoto protocol and the Paris climate agreement have prompted many nations to approve energy policies based on volatile renewable energy sources (RESs). However, the integration into the grid of the power generated by RESs as well as the electrification of the heating, gas and transportation sectors is becoming a huge challenge. Planning industrial and tertiary sites as net-zero energy systems (NZESs) might contribute to advance the solutions of fully integrating volatile RESs into the power system. This study aims to point out the importance of planning large energy consumer sites such as NZESs, and to depict a holistic modeling approach for this. The methodology is based on a multi-layer approach, which focuses on on-site power generation by RESs, on the improvement of energy efficiency, and on the increase of system flexibility. A qualitative case study has been conducted. It considers the planning of a Net-Zero Energy Data Center located in Germany. Results point out that new interdisciplinary and in particular social analysis methods are necessary. They might be used for accelerating the decision making process during the planning of RES-based on-site power generation systems. Besides, for computation and cooling systems, new technologies that are continuously emerging in the market should be taken into account. If well designed, they contribute to significantly decrease the whole energy demand of data center. Finally, optimal sizing of energy storage systems (electric and thermal) as well as an expedient choice of performance indicators to evaluate technology options are identified as the key factor for decreasing the external energy demand of tertiary sites, such as data center.

Optimal Sizing of Energy Storage System for Operation of Wind Farms Considering Grid-Code Constraints

Transmission system operators impose several grid-code constraints on large-scale wind farms to ensure power system stability. These constraints may reduce the net profit of the wind farm operators due to their inability to sell all the power. The violation of these constraints also results in an imposition of penalties on the wind farm operators. Therefore, an operation strategy is developed in this study for optimizing the operation of wind farms using an energy storage system. This facilitates wind farms in fulfilling all the grid-code constraints imposed by the transmission system operators. Specifically, the limited power constraint and the reserve power constraint are considered in this study. In addition, an optimization algorithm is developed for optimal sizing of the energy storage system, which reduces the total operation and investment costs of wind farms. All parameters affecting the size of the energy storage systems are also analyzed in detail. This analysis allows the wind farm operators to find out the optimal size of the energy storage systems considering grid-code constraints and the local information of wind farms.

Optimal Location and Sizing of Energy Storage Systems in DC-Electrified Railway Lines Using a Coral Reefs Optimization Algorithm with Substrate Layers

This paper deals with the problem of finding the optimal location and sizing of Energy Storage Systems in DC-electrified railway lines. These devices increment the use of the regenerated energy produced by the trains in the braking phases, as they store the energy to later provide to the catenary the excess of regenerated energy, that otherwise would be lost in the rheostats. However, these infrastructures require a high initial investment that, in some cases, may question their profitability. We propose a multi-method ensemble meta-heuristic to obtain the optimal solution to the problem, with a high level of accuracy. Specifically, the Coral Reefs Optimization with Substrate Layers (CRO-SL) is proposed, an evolutionary-type approach able to run different search procedures within the same population. We will evaluate the performance of the CRO-SL in the problem, and we will show that it performs better than the best known existing meta-heuristics for this problem.

Machine learning-based utilization of renewable power curtailments under uncertainty by planning of hydrogen systems and battery storages

Increasing wind and solar generation in power grids leads to more renewable power curtailments in some periods of time due to the fast and unpredictable variations of their outputs. The utilization of these sources for energy storage can unlock huge potential benefits. Therefore, aiming at minimizing the curtailments of renewable power from the viewpoint of an independent system operator (ISO), in this paper, we propose deep learning-driven optimal sizing and operation of alkaline water electrolyzers (AWE) and battery energy storage systems (BESS). For this purpose, a set of actual renewable power curtailment data of California ISO was fully investigated, and deep learning forecast methods were employed to determine the prediction error and its probability distribution function (PDF). Using the fitted PDF, a set of scenarios was generated and reduced to some accurate and probable ones. Consequently, a two-stage scenario-based stochastic model was proposed to determine the optimal planning of this system, and a penalty variable was defined in the second stage to maximize the utilization of curtailed renewable energy sources (RESs). The learning results showed that the prediction errors were minimized using the gated recurrent unit (GRU) method. It was also shown that 97% of curtailments were utilized using AWEs with annual costs of $233.55 million, which had 63.5% fewer costs than using BESSs. Furthermore, using AWEs reduced operational expenses by 89.1% compared with using BESSs, owing to their operational benefits.

A Predictive Control Strategy for Energy Management in Micro-Grid Systems

The integration of renewable energy sources (RES) was amplified, during the past decades, in order to tackle the challenges related to energy demands and CO2 increases. Recently, many initiatives have been taken by promoting the deployment and the usage of micro-grids (MG) in buildings, as decentralized systems, for energy production. However, the variable nature of RESs and the limited size of energy storage systems require the deployment of adaptive control strategies for efficient energy balance. In this paper, a generalized predictive control (GPC) strategy is introduced for energy management (EM) in MG systems. Its main objective is to efficiently connect the electricity generators and consumers in order to predict the most suitable actions for energy flow management. In fact, based on energy production and consumption profiles as well as the availability of energy storage systems, the proposed EM will be able to select the best suitable energy source for supplying the building’s loads. It will efficiently manage the usage of energy storage and the utility grid while maximizing RESs power generation. Simulations have been conducted, using real-sitting scenarios, and results are presented to validate the proposed predictive control approach by showing its effectiveness for MG systems control.

Corrigendum: Sizing of Hybrid Energy Storage Systems for Inertial and Primary Frequency Control

Corrigendum: Sizing of Hybrid Energy Storage Systems for Inertial and Primary Frequency Control

Optimal Placement and Sizing of an Energy Storage System Using a Power Sensitivity Analysis in a Practical Stand-Alone Microgrid

The energy storage system (ESS) is developing into a very important element for the stable operation of power systems. An ESS is characterized by rapid control, free charging, and discharging. Because of these characteristics, it can efficiently respond to sudden events that affect the power system and can help to resolve congested lines caused by the excessive output of distributed generators (DGs) using renewable energy sources (RESs). In order to efficiently and economically install new ESSs in the power system, the following two factors must be considered: the optimal installation placements and the optimal sizes of ESSs. Many studies have explored the optimal installation placement and the sizing of ESSs by using analytical approaches, mathematical optimization techniques, and artificial intelligence. This paper presents an algorithm to determine the optimal installation placement and sizing of ESSs for a virtual multi-slack (VMS) operation based on a power sensitivity analysis in a stand-alone microgrid. Through the proposed algorithm, the optimal installation placement can be determined by a simple calculation based on a power sensitivity matrix, and the optimal sizing of the ESS for the determined placement can be obtained at the same time. The algorithm is verified through several case studies in a stand-alone microgrid based on practical power system data. The results of the proposed algorithm show that installing ESSs in the optimal placement could improve the voltage stability of the microgrid. The sizing of the newly installed ESS was also properly determined.

Energy storage sizing in plug-in Electric Vehicles: Driving cycle uncertainty effect analysis and machine learning based sizing framework

Transportation electrification has been considered as one of the promising solutions towards clean environment. On-board Energy Storage System (ESS) has significant influence on the cost and reliability of Electric Vehicles (EVs). Furthermore, the choice of the ESS in terms of type and size, i.e., its capacity heavily depends on the driving patterns which, usually, vary significantly depending on factors like driver characteristics, geographical locations and traffic congestion. Hence, it is crucial to understand the effect of driving patterns/cycles on the sizing of ESS in order to avoid over/under-sizing. In this context, we investigate the effect of driving cycle uncertainties on the optimal sizing of ESS in EVs. We specifically focus on the uncertainty arising from the unpredictability of the future state of speed and acceleration and model the same as a scaling factor on velocity-magnitudes and time-windows of the velocity profile. Subsequently, (i) we derive the quantitative relationship between uncertainty and corresponding change in ESS size based on four thousand test driving cycles; (ii) we develop a framework which provides a guideline towards the choice of appropriate sizing of ESS in EVs subject to uncertainty in driving cycle; and (iii) we propose a machine-learning based framework that can enable a data-driven approach for ESS sizing with lower computational requirement. We choose a city bus application to illustrate our approach and consider three different ESS configurations: (i) Li-ion battery alone, (ii) Supercapacitor alone, and (iii) Battery–Supercapacitor hybrid ESS.

Optimal sizing of thermal energy storage systems for CHP plants considering specific investment costs: A case study

In the next decades, energy storage technologies will play a major role in decarbonizing the European heating and cooling sector. In this regard, the deployment of complementary technologies is of paramount importance for the energy transformation of the Polish heating sector. This work addresses the challenge of sizing large-scale thermal energy storage (TES) systems for combined heat and power (CHP) plants connected to district heating networks and participating in day-ahead electricity markets. In this paper, a method based on a mixed-integer linear programming approach is proposed to find the optimal capacity of TES units connected to coal-fired CHP systems. The model considers the specific investment costs of the storage technology and optimizes the annual operation scheduling of the CHP-TES system. The model is applied to the case study of a coal-fired CHP system. Four scenarios are used to investigate the performance of the CHP system and evaluate the effects of rising carbon prices on the optimal capacity of the TES unit. The results reveal that the integration of the TES leads to a significant drop in the utilization of the heat-only boilers, helping mitigate fuel and environmental costs.

Optimal Sizing of Rooftop PV and Battery Storage for Grid-Connected Houses Considering Flat and Time-of-Use Electricity Rates

This paper investigates a comparative study for practical optimal sizing of rooftop solar photovoltaic (PV) and battery energy storage systems (BESSs) for grid-connected houses (GCHs) by considering flat and time-of-use (TOU) electricity rate options. Two system configurations, PV only and PV-BESS, were optimally sized by minimizing the net present cost of electricity for four options of electricity rates. A practical model was developed by considering grid constraints, daily supply of charge of electricity, salvation value and degradation of PV and BESS, actual annual data of load and solar, and current market price of components. A rule-based energy management system was examined for GCHs to control the power flow among PV, BESS, load, and grid. Various sensitivity analyses are presented to examine the impacts of grid constraint and electricity rates on the cost of electricity and the sizes of the components. Although the capacity optimization model is generally developed for any case study, a grid-connected house in Australia is considered as the case system in this paper. It is found that the TOU-Flat option for the PV-BESS configuration achieved the lowest NPC compared to other configuration and options. The optimal capacities of rooftop PV and BESS were obtained as 9 kW and 6 kWh, respectively, for the PV-BESS configuration with TOU-Flat according to two performance metrices: net present cost and cost of electricity.

Sizing and Management of Energy Storage Systems in Large-Scale Power Plants Using Price Control and Artificial Intelligence

Energy storage systems are expected to play a fundamental part in the integration of increasing renewable energy sources into the electric system. They are already used in power plants for different purposes, such as absorbing the effect of intermittent energy sources or providing ancillary services. For this reason, it is imperative to research managing and sizing methods that make power plants with storage viable and profitable projects. In this paper, a managing method is presented, where particle swarm optimisation is used to reach maximum profits. This method is compared to expert systems, proving that the former achieves better results, while respecting similar rules. The paper further presents a sizing method which uses the previous one to make the power plant as profitable as possible. Finally, both methods are tested through simulations to show their potential.

Sizing of Hybrid Energy Storage Systems for Inertial and Primary Frequency Control

The exponential rise of renewable energy sources and microgrids brings about the challenge of guaranteeing frequency stability in low-inertia grids through the use of energy storage systems. This paper reviews the frequency response of an ac power system, highlighting its different time scales and control actions. Moreover, it pinpoints main distinctions among high-inertia interconnected systems relying on synchronous machines and low-inertia systems with high penetration of converter-interfaced generation. Grounded on these concepts and with a set of assumptions, it derives algebraic equations to rate an energy storage system providing inertial and primary control. The equations are independent of the energy storage technology, robust to system nonlinearities, and rely on parameters that are typically defined by system operators, industry standards, or network codes. Using these results, the authors provide a step-by-step procedure to size the main components of a converter-interfaced hybrid energy storage system. Finally, a case study of a wind-powered oil and gas platform in the North Sea demonstrates with numerical examples how the proposed methodology 1) can be applied in a practical problem and 2) allows the system designer to take advantage of different technologies and set specific requirements for each storage device and converter according to the type of frequency control provided.