Sizing Of Energy Storage System Research Articles

Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm

The deployment of utility-scale energy storage systems (ESSs) can be a significant avenue for improving the performance of distribution networks. An optimally placed ESS can reduce power losses and line loading, mitigate peak network demand, improve voltage profile, and in some cases contribute to the network fault level diagnosis. This paper proposes a strategy for optimal placement of distributed ESSs in distribution networks to minimize voltage deviation, line loading, and power losses. The optimal placement of distributed ESSs is investigated in a medium voltage IEEE-33 bus distribution system, which is influenced by a high penetration of renewable (solar and wind) distributed generation, for two scenarios: (1) with a uniform ESS size and (2) with non-uniform ESS sizes. System models for the proposed implementations are developed, analyzed, and tested using DIgSILENT PowerFactory. The artificial bee colony optimization approach is employed to optimize the objective function parameters through a Python script automating simulation events in PowerFactory. The optimization results, obtained from the artificial bee colony approach, are also compared with the use of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS placement approach can successfully achieve the objectives of voltage profile improvement, line loading minimization, and power loss reduction, and thereby significantly improve distribution network performance.

Optimal Sizing of Energy Storage Systems Using Frequency-Separation-Based Energy Management for Fuel Cell Hybrid Electric Vehicles

The current development of fuel cell hybrid electric vehicles is facing many technical challenges, evolving the power sources, the power electronic configuration, the energy management strategy, and the control techniques. Among these challenges rises the optimal sizing issue as fuel cell hybrid electric vehicle efficiency is highly dependent on the on-board energy storage system. Thus, this paper focuses on the optimal sizing of the hybrid energy storage systems by considering the energy management strategy based on frequency separation. Best solutions are computed for different load profiles through a multi-objective grey wolf optimizer. Then, the obtained results are presented and discussed.

Optimal sizing of PV and battery-based energy storage in an off-grid nanogrid supplying batteries to a battery swapping station

Nanogrids are expected to play a significant role in managing the ever-increasing distributed renewable energy sources. If an off-grid nanogrid can supply fully-charged batteries to a battery swapping station (BSS) serving regional electric vehicles (EVs), it will help establish a structure for implementing renewable-energy-to-vehicle systems. A capacity planning problem is formulated to determine the optimal sizing of photovoltaic (PV) generation and battery-based energy storage system (BESS) in such a nanogrid. The problem is formulated based on the mixed-integer linear programming (MILP) and then solved by a robust optimization approach. Flexible uncertainty sets are employed to adjust the conservativeness of the robust optimization, and Monte Carlo simulations are carried out to compare the performance of the solutions. Case studies demonstrate the merits of the proposed applications and verify our approach.

A thermo-economic methodology to select sCO2 power cycles for CSP applications

The interest in Supercritical Carbon Dioxide (sCO2) power cycles has grown exponentially in the last decade, thanks to distinctive features like the possibility to achieve high thermal efficiencies at intermediate temperature, small footprint and adaptability to a wide variety of energy sources. In the present work, the potential of this technology is studied for Concentrated Solar Power applications, in particular Solar Tower systems with Thermal Energy Storage.Further to a previous thorough sensitivity analysis of twelve sCO2 cycles assessing the impact of turbine inlet temperature and pressure ratio on thermal efficiency, specific work, solar share and temperature rise across the solar receiver, the present paper investigates the features of two of these cycles in more detail. The most important conclusions of this section are that: a) the peak values of these thermodynamic figures of merit are obtained at different pressure ratios; b) specific work and temperature rise across the receiver seem to follow parallel trends whilst this is not the case for thermal efficiency; c) for a given turbine inlet temperature, higher pressure ratios increase the temperature rise across the receiver strongly, but the effect on thermal efficiency is uncertain as this can either increase or decrease, depending on the cycle considered.A deeper analysis of thermal efficiency and receiver temperature rise is therefore mandatory, given that these parameters have a very strong effect on the capital cost of CSP power plants. On one hand, a higher thermal efficiency implies a smaller solar field, the largest contributor to the plant capital cost; on the other, the temperature rise across the receiver is inversely proportional to the size of the thermal energy storage systems, as it is also the case for state of the art steam turbine based CSP plants. In order to quantify these trends, an economic analysis is developed using an in-house code and the open-source software System Advisor Model to evaluate the trade-offs between these two effects. As a result, the Overnight Capital Cost is estimated at some 5000 $/kW, with the individual contributions of solar field, thermal energy storage and power block being given in the paper.

Optimized Sizing and Scheduling of Hybrid Energy Storage Systems for High-Speed Railway Traction Substations

The integration of hybrid energy storage systems (HESS) in alternating current (AC) electrified railway systems is attracting widespread interest. However, little attention has been paid to the interaction of optimal size and daily dispatch of HESS within the entire project period. Therefore, a novel bi-level model of railway traction substation energy management (RTSEM) system is developed, which includes a slave level of diurnal HESS dispatch and a master level of HESS sizing. The slave level is formulated as a mixed integer linear programming (MILP) model by coordinating HESS, traction load, regenerative braking energy and renewable energy. As for the master level model, comprehensive cost study within the project period is conducted, with batteries degradation and replacement cost taken into account. Grey wolf optimization technique with embedded CPLEX solver is utilized to solve this RTSEM problem. The proposed model is tested with a real high-speed railway line case in China. The simulation results of several cases with different system elements are presented, and the sensitivity analyses of several parameters are also performed. The obtained results reveal that it shows significant economic-saving potentials with the integration of HESS and renewable energy.

Optimal Component Sizing for Peak Shaving in Battery Energy Storage System for Industrial Applications

Recent attention to industrial peak shaving applications sparked an increased interest in battery energy storage. Batteries provide a fast and high power capability, making them an ideal solution for this task. This work proposes a general framework for sizing of battery energy storage system (BESS) in peak shaving applications. A cost-optimal sizing of the battery and power electronics is derived using linear programming based on local demand and billing scheme. A case study conducted with real-world industrial profiles shows the applicability of the approach as well as the return on investment dependence on the load profile. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, peak-power tariff, and battery aging. This underlines the need for a general mathematical optimization approach to efficiently tackle the challenge of peak shaving using an energy storage system. The case study also compares the applicability of yearly and monthly billing schemes, where the highest load of the year/month is the base for the price per kW. The results demonstrate that batteries in peak shaving applications can shorten the payback period when used for large industrial loads. They also show the impacts of peak shaving variation on the return of investment and battery aging of the system.

Optimal Sizing of Hybrid Energy Storage System Taking into Account Economic Factors and Power Allocation

The sizing of hybrid energy storage system (HESS) for smoothing wind power fluctuations was studied in this paper. The goal of the proposed method is to find the optimal sizing of the battery and supercapacitor, and its meet the constraints of the power ramp rate constraint and the economic cost. Therefore, a power allocation strategy which based on the operating period of battery and the cutoff frequency of battery absorption power was proposed. The sizing optimization model, which considered the cost structure of HESS, the ESS characteristics and cycle life in different operating modes, is established by using minimum daily cost as objective function. The results of numerical example shows that the cycle life of ESS can be effectively extended with reasonable operating period of battery and the cutoff frequency of battery absorption power, the cost of HESS can be reduced.

Li-Ion Battery Lifetime Model’s Influence on the Economic Assessment of a Hybrid Electric Bus’s Operation

The present paper is focused on the evaluation of the economic influence of a battery lifetime model upon the optimal sizing and energy management strategy of a dual energy storage system (ESS) composed of Lithium-ion batteries and supercapacitors. The operation of a Hybrid Bus is taken as a case study in order to evaluate the effects of battery lifetime models’ accuracy on ESS sizing and operation in a heavy-duty application. For this purpose, two different lifetime models (a Wöhler-curve-based model and a semi-empirical model) were applied in the multi-objective optimisation of a hybrid electric urban bus. Differences up to ca. 8% on the daily operation costs and ca. 25% on the dual ESS costs were estimated depending on the lifetime model considered for the optimisation.

Sizing of a recuperative supercritical CO2 Brayton cycle as power conversion system for DEMO fusion reactor based on Dual Coolant Lithium Lead blanket

A modified recuperative supercritical CO2 cycle for DEMO fusion reactor is analysed. A layout with just one recuperator is used, because of the relatively low temperature of the breeding blanket coolants. The proposed cycle is able to integrate several thermal sources with different temperature ranges and includes two kinds of thermal energy storage to overcome the pulse operation: a two-tank molten salt storage and a concrete storage. The design point is obtained using the Engineering Equation Solver (EES) environment, which allows to use pure substance models for CO2, helium and water. Once the design point is calculated, the sizing of pipes, heat exchangers, turbomachines and energy storage systems has been performed in order to obtain a preliminary estimation of the layout and its complexity. So, the power conversion system has been split into four equal parts in order to use commercial pipe diameters. Even though Printed Circuit Heat Exchangers (PCHE) have been used, extremely large sizes have been obtained, especially for the HTR recuperator. These sizes contrast with the small values obtained for the turbomachines, whose preliminary design has been carried out, using the AXIAL™ meanline design software. In addition, the two largest heat exchangers are divided into serial and parallel configurations when assessing the allocation of the components of the power conversion system to reduce the footprint. An average electric efficiency (including pulse and dwell period) of 28.25% has been reached. The required mass of molten salt per each power conversion subsystem is 2568 t, while the mass of concrete is 833 t. Regarding the footprint, 85 × 170 m2 are required for each power conversion subsystem, including the storage systems.

Optimal sizing and control of hybrid energy storage system for wind power using hybrid Parallel PSO-GA algorithm

This paper proposes a frequency-based method for sizing the hybrid energy storage system in order to smoothen wind power fluctuations. The main goal of the proposed method is to find the power and energy capacities of the hybrid energy storage system that minimizes the total cost per day of all the systems. The energy management strategy used in this paper is designed as a two-level energy distribution scheme: the first level is responsible for setting the output power of hybrid energy storage system, the second level manages the power flow between the battery and supercapacitor. The hybrid parallel particle swarm optimization-genetic algorithm (PSO-GA) optimization algorithm is proposed to solve the control parameters of energy management strategy. In addition, the proposed method uses the piecewise fitting function to describe the lifetime of battery. Obtained results show that the hybrid energy storage system with the proposed energy management strategy is able to offer the best performances for the wind power system in terms of cost and lifetime.

Stochastic Sizing of Energy Storage Systems for Wind Integration

Stochastic Sizing of Energy Storage Systems for Wind Integration

Stochastic Sizing of Energy Storage Systems for Wind Integration

In this paper, we present an optimal capacity decision model for energy storage systems (ESSs) in combined operation with wind energy in power systems. We use a two-stage stochastic programming approach to take into account both wind and load uncertainties. The planning problem is formulated as an AC optimal power flow (OPF) model with the objective of minimizing ESS installation cost and system operation cost. Stochastic wind and load inputs for the model are generated from historical data using clustering technique. The model is tested on the IEEE 39-bus system.

Optimal sizing of energy storage systems under uncertain demand and generation

Energy storage systems have been recently recognized as an effective solution to tackle power imbalances and voltage violations faced by distribution system operators due to the increasing penetration of low carbon technologies. To fully exploit their benefits, optimal sizing of these devices is a key problem at the planning stage. This paper considers the sizing problem of the energy storage systems installed in a distribution network with the aim, e.g., of preventing over- and undervoltages. In order to accommodate uncertainty on future realizations of demand and generation, the optimal sizing problem is formulated in a two-stage stochastic framework, where the first stage decision involves the storage sizes, while the second stage problem provides the optimal storage control policy for given demand and generation profiles. By taking a scenario-based approach, the two-stage problem is approximated in the form of a single multi-scenario, multi-period optimal power flow, whose size, however, becomes computationally intractable as the number of scenarios grows. To overcome this issue, the paper presents a procedure to compute upper- and lower bounds to the optimal cost of the approximate problem. Moreover, when the objective is to minimize the total installed storage capacity, an iterative algorithm based on scenario reduction is proposed, which converges to the optimal solution of the approximate problem. The whole procedure is tested on the topology of the IEEE 37-bus test network, considering scenarios of demand and generation which feature over- and undervoltages in the absence of storage devices.

Sizing Hydrogen Energy Storage in Consideration of Demand Response in Highly Renewable Generation Power Systems

From an environment perspective, the increased penetration of wind and solar generation in power systems is remarkable. However, as the intermittent renewable generation briskly grows, electrical grids are experiencing significant discrepancies between supply and demand as a result of limited system flexibility. This paper investigates the optimal sizing and control of the hydrogen energy storage system for increased utilization of renewable generation. Using a Finnish case study, a mathematical model is presented to investigate the optimal storage capacity in a renewable power system. In addition, the impact of demand response for domestic storage space heating in terms of the optimal sizing of energy storage is discussed. Finally, sensitivity analyses are conducted to observe the impact of a small share of controllable baseload production as well as the oversizing of renewable generation in terms of required hydrogen storage size.

Modeling and Sizing of an Undersea Energy Storage System

This paper presents modeling and sizing of an undersea energy storage system (USS). The USS, which is placed at the seabed, consists of a concrete sphere, a reversible pump-turbine unit, a permanent magnet synchronous machine, and a steel pipe through which water flows into/out of the sphere from/to the deep ocean. A novel mathematical model is derived to describe the governing equations of the USS operation. It is assumed that the USS operates in parallel with a direct-drive wave energy converter (DDWEC) and a novel sizing algorithm is developed to obtain the required USS capacity/size for regulating the DDWEC output power fluctuations during a designated period of time. In order to determine the DDWEC output power profile, wave spectrums are required. Real significant wave height and dominant wave period data, which are measured and collected by a buoy station in the Gulf of Maine, are used to generate wave profiles based on the modified two-parameter Pierson-Moskowitz spectrum. Eventually, Monte Carlo simulations with a thousand generated wave profiles are carried out to determine the optimal size of the USS for all possible scenarios. Furthermore, through promising results obtained from hardware-in-the-loop studies, the effective role of the USS in regulating the wave energy converter output power fluctuations is demonstrated.

Direct sizing and characterization of Energy Storage Systems in the Energy-Power plane

This paper presents an original sizing method for Energy Storage Systems (ESS) based on directly matching their capabilities – as specified by their energy-power Safe Operation Area (SOA) in the Energy-Power (EP) plane – with the energy and power demand required to accomplish their missions. Starting from the system requirements and from an energy management strategy, the power demanded by a set of representative operating scenarios and its associated energy are calculated and represented as trajectories in the EP plane. The objective is to size the ESS such as its SOA contains these trajectories. Comparison between different technologies of Energy Storage Devices (ESDs) is possible using this SOA characterization. Special attention should be paid to compare specific SOAs across devices. Diverse energy management strategies can be synthesized in the EP plane where they can be compared and analyzed. The sizing method converges extremely fast and is suitable for its integration in an optimization loop. The method allows to determine directly and efficiently the technology and the size most appropriate (in terms of indicators such as mass or cost) to a given EP demand. In the paper, three different technologies (SuperCapacitor, Li-Ion and H2∕O2 batteries) are characterized and compared in terms of sizing synthesis.

Optimal Sizing of Energy Storage System for Wind Farms Combining Stochastic Programming and Sequential Monte Carlo Simulation

Optimal Sizing of Energy Storage System for Wind Farms Combining Stochastic Programming and Sequential Monte Carlo Simulation

Incorporating variable lifetime and self‐discharge into optimal sizing and technology selection of energy storage systems

Technology selection and sizing are key aspects of the design procedure for energy storage systems (ESSs) for power system applications. Here, the authors extended existing methodologies for optimal sizing and technology selection by introducing self-discharge effects, and variable ESS lifetime as a function of energy throughput, which results in a non-convex optimisation problem. Simulation results confirmed that making operational lifetime a variable has a significant impact on the results of the optimal sizing and technology selection problem. More specifically, considering the variable ESS lifetime as a function of energy throughput showed that ESSs of various technologies tend to operate such that their operational lifetimes would far exceed their calendar lifetimes. This has confirmed the importance of considering operational lifetime as a variable rather than a fixed value, as without doing this could result to underutilised and/or oversized systems. Taking into account, the self-discharge effect showed that the electrochemical technologies considered here, with the exception of supercapacitors, have low levels of self-discharge, which are largely obscured by the significant impact of the roundtrip efficiency characteristic.

Inaugural Editorial

Inaugural Editorial

Economic Value Assessment and Optimal Sizing of an Energy Storage System in a Grid-Connected Wind Farm

This study identifies the optimal management policy of a given energy storage system (ESS) installed in a grid-connected wind farm in terms of maximizing the monetary benefits and provides guidelines for defining the economic value of the ESS under optimal management policy and selecting the optimal size of the ESS based on economic value. Considering stochastic models for wind power and electricity price, we develop a finite-horizon periodic-review Markov decision process (MDP) model to seek the optimal management policy. We also use a simple optimization model to find the optimal storage capacity and charging/discharging capacity of the ESS. By applying our analytic approach to a real-world grid-connected wind farm located in South Korea, we verify the usefulness of this study. Our numerical study shows that the economic value of the ESS is highly dependent on management policy, wind electricity variability, and electricity price variability. Thus, the optimal size of ESS should be carefully determined based on the locational characteristics and management policy even with limited investments. Furthermore, this study provides a meaningful policy implication regarding how much of a subsidy the government should provide for installing ESS in a wind farm.