1. Introduction
The European Green Deal is expected to have zero net emission of greenhouse gases by 2050, when economic growth will be decoupled from resource use. Besides, the grid-related global storage systems deployment was about 10 GWh in 2019 and is projected to increase 15 times to almost 160 GWh with the deployment of 31.2 GWh in the EU and 34.3 GWh in the USA by 2030 [
1]. A study in [
2] also indicates that 150 to 900 million electric vehicles are expected to be on the market in 2040 and that stationary storage may reach up to 1300 GWh at that time. These projections point toward the potentially significant market growth of Li-ion batteries and a view of action against climate change by decarbonizing. However, the cost reduction of a lithium-ion battery (LIB) pack is slower in the stationary storage sector than in electric vehicles. In 2017, the benchmark costs of Li-ion stationary storage systems were approximately 500 EUR/kWh for energy-designed systems, 800 EUR/kWh for power-designed systems, and 750 EUR/kWh for residential batteries [
2]. By 2040, stationary storage system costs are anticipated to range from 165–240 EUR/kWh for the energy-focused grid-connected system, while power-focused and residential systems are expected to cost 280–410 EUR/kWh and 250–365 EUR/kWh, respectively [
2,
3].
It is also found that for a battery-based system with more than 90% efficiency and a lifetime of thousands of cycles, the system cost is expected to be below 150 EUR/kWh (for a 100 kW reference system) in 2030 [
4].
The different types of battery energy storage system (BESS) technologies and their suitability to grid-relieving applications have been presented in several research works with respect to efficiency, power, energy density, response time, cost, and other performance indicators [
5,
6,
7]. Despite recent price reductions in Li-ion cells, the battery is one of the primary cost drivers of BESSs [
6].
The standard stationary energy storage systems that use a single type of battery pack technology are able to supply services to the grid; however, a trade-off must often be made between power and energy performances [
8]. It is found that most types of BESS and power electronics (PE) interfaces either have significant energy or power needs [
9,
10,
11,
12]; sizing one type of battery pack to multiple use cases is challenging. For example, by connecting additional cells in series/parallel, a single battery pack technology can provide the power and energy demands. However, the cells cannot be connected arbitrarily because of the voltage and current limits imposed by the PE interfaces. These current and voltage constraints increase complexity in design and contribute to oversizing the battery pack, resulting in either a higher power-to-energy ratio or a higher energy-to-power ratio. It should be noted that in most commercial battery systems, a greater energy-to-power battery pack is preferred since more energy in the battery pack increases longevity while reducing the strain on the cells. However, because of oversizing, this method is not cost-effective.
In contrast to this single-pack approach, a hybrid battery unit uses at least two different types of cells, which enhances the sizing and design possibilities. For example, combining a high-energy (HE) pack and a high-power (HP) pack enables to cover more applications for an interoperable service portfolio. Such a strategy increases the flexibility in sizing and thus the possibility of the economy of scales, which may help reduce the TCO.
There are different types of LIB technologies on the market, including the increasingly large number of second-life modules coming from the electric vehicle (EV) sector [
13]. The LIB cells are mainly identified by name as lithium cobalt oxide (LCO), lithium manganese oxide (LMO), lithium iron phosphate (LFP), lithium nickel cobalt aluminum oxide (NCA), lithium nickel manganese cobalt oxide (NMC), and lithium titanate oxide (LTO). They have distinct cell voltage, energy density, cycle life, and cost because of their internal structure and composition [
14]. Four types of lithium-ion cells are considered in this paper to cope with the high power and high energy demand in grid services.
The design optimization of BESSs is usually carried out regarding sizing, grid integration, technical performance, and economic perspective. In ref. [
15], a single type of battery energy storage system was created from sodium sulfur (NaS), lead acid (LA), and vanadium redox (VR) and its related power converters to maximize profit for an EV park owner, using linear programming to identify the ideal storage size. The objective function includes cycle life and cost. In ref. [
16], authors optimized and evaluated the BESS size for wind and solar energy fluctuations using genetic algorithm (GA). Optimizing the BESS installation capacity helps reduce power outage costs, fuel costs, and committed power plant costs. According to ref. [
17], BESSs have issues with investment costs and operating lifetimes; therefore, adequate BESS sizing is crucial for microgrid design and administration. This article proposed a problem structure and solution approach for calculating the optimal size of BESSs in a microgrid using the binary particle swarm optimization (BPSO) combined with a quadratic programming algorithm for two objectives—investment cost and operating cost. Furthermore, to solve other multi-objective problems, such as minimization of annual net profits, energy consumptive rate, annual energy exchanged with the grid, and battery degradation, the NSGA-II algorithm has been utilized in [
18,
19]. To deal with BESS sizing, other optimization algorithms such as ant colony, particle swarm, and simulated annealing have been reviewed for multi-objective problems with different constraints based on different applications [
20]. Among the other used algorithms, the NSGA-II and its derivative algorithms are found to be the popular ones in multi-objective optimization problem solving [
21].
However, a multi-objective genetic algorithm (MOGA) can be used for nonlinear, nonconvex, discrete, continuous, mixed-integer, and multiple objective optimization problems [
20,
22]. For certain rule-based energy management methods, previous research optimized HBESS size. However, many studies [
23,
24] have focused on developing control strategies for the BESS sizing to achieve certain economic objectives, such as maximizing profit and minimizing power loss or battery degradation. In contrast, a system-level optimization technique is realized through the design of integrated subcomponents of HBESS and advanced control strategies, as it is necessary to know not only the revenue to maximize but also the upfront cost of investments, taking into account potential replacements over the system time horizon. In terms of assessment, the total ownership cost model has been used in [
25,
26] for BESS.
At the time of writing, to the authors’ best knowledge, a generalized co-design optimization framework (COF) for a lithium-ion HBESS design in a grid application has not been developed yet. In this work, each battery pack is sized from cell to pack level along with the sizing of the power converter system (PCS) for interfacing the battery system to the grid. In this paper, the sizing of the battery pack is referred to as cell configuration of series-parallel to the battery pack rating in power and energy. The sizing of PCS is referred to the required rating and value for different PCS topologies.
The main contribution of this work is three-fold: (a) first, instead of a single type of battery pack technology, two distinct battery packs have been sized with the optimal combination of series-parallel cells out of four lithium-ion battery technologies for the HBESS design; (b) second, a novel and interoperable multi-objective optimization methodology with a co-design approach is developed for the optimal design, selection, and sizing of the HBESS subcomponents (battery and PCS) with respect to system-level techno-economic performance (i.e., cost, roundtrip efficiency, and lifetime); and (c) third, two different PCS architectures with modularity have been incorporated to interface the battery to the grid instead of a single PCS architecture.
This paper is organized as follows. The methodology for sizing and designing an optimal HBESS applicable for COF is introduced in
Section 2. The detailed modeling required for the coupled HBESS simulation in the COF is described in
Section 3. Besides, the processes for implementing the optimization routine of multi-objective COF are discussed in
Section 4 to find the optimal storage system sizing and selection; a real-field grid service (i.e., UK grid service) power profile for enhanced frequency support is used in the framework to find the optimal sizing and selection of technology for HBESS from the selected four types of cells and two distinct PCSs. The optimization results and global best selection are presented in
Section 5. Finally, the conclusions are outlined in
Section 6.
2. System Description
This section presents the system considered for the framework shown in
Figure 1. A synergic design goal with nominal power of 100 kW and nominal capacity of 100 kWh HBESS with two different PCS topologies (defined in an optimization routine,
Section 4) is set in the COF to define its variables presented in
Table 1. The COF is applied to the DC side of the system for an optimal hybridization of cell technologies for two independent battery packs, namely battery pack-1 and battery pack-2, and to the PE interface with two distinct architectures, PCS-1 and PCS-2.
The first architecture (PCS-1) is comprised of a DC/DC stage and a DC/AC stage as a single module to connect the grid with different battery modules for bidirectional charging and discharging functionalities, as highlighted in
Figure 1. A modular PCS has been considered in the COF to find the optimal number of modules to be used for the HBESS.
In the second PCS architecture (PCS-2), the modular DC/DC stage is connected with the modular DC/AC stage through a single DC-link capacitor, as highlighted in
Figure 1, which also enables bidirectional power flow between the hybrid BESS and the grid.
The optimal voltage rating of the DC-link capacitor and the number of modules of the DC/DC and DC/AC stages for PCS-2 will be the outcome of the COF for the designed system. The details of the PCS architecture specification and constraints defined for HBESS are presented in
Section 4.
The battery pack is sized by optimizing the number of cells in series and parallel. In the battery storage unit, two independent battery packs are sized where one acts as a high-power pack and the other one acts as a high-energy pack, considering different cell technologies. To this extent, this paper will propose an optimized battery storage unit based on the combination of battery pack-1 (high-energy) and battery pack-2 (high-power) to obtain the optimum level of power and energy of the battery for the considered mission power profile.
The design simulation framework, the COF, is prepared with a MATLAB/Simulink® environment and incorporates design optimization-oriented battery models and PCS models coupled with an operational control plant, namely an energy management system (EMS) for optimal sizing and selection.
2.1. Connection Topology (TO) of the HBESS
The specific connection configuration of the BESS affects the system performances, such as efficiency and reliability [
27]. Considering both conceivable PCS architectures and their characteristics, four different connection topologies have been incorporated into the COF for individual selection or all at once while sizing the HBESS. The configurations are mentioned as follows in the remainder of the paper:
- ▪
TO-1: Both battery packs are connected to the grid using PCS-1, as shown in
Figure 2.
- ▪
TO-2: Both battery packs are connected to the grid using PCS-2, as shown in
Figure 3.
- ▪
TO-3: Battery pack-1 is connected to the grid using PCS-2, while battery pack-2 is connected using PCS-1, as shown in
Figure 4.
- ▪
TO-4: Battery pack-1 is connected to the grid using PCS-1, while battery pack-2 is connected using PCS-2, as shown in
Figure 5.
2.2. Real-Field Grid Load Profile for Case Study
The HBESS is subjected to multiple service scenarios. The framework considers a real-field grid-related service power profile [
28]. The power profile dynamics from the UK grid services in enhanced frequency support have been considered in the COF. The power-to-frequency characteristic enables the conversion of recorded frequency variations to the required output/input power on a second-by-second basis, as shown in
Figure 6.
5. Optimization Results
In this part, the results of the co-design optimization are presented for HBESS. The COF can be applied to each combination of battery packs and PE interfaces, totaling 16 combinations as indicated in
Table 5.
The framework is capable of selecting any defined connection topology selection (
Section 2.1) toward the global optimal HBESS sizing and selection. In the framework, the selection of batteries for HBESS combinations is based on the categorization of cell characteristics, particularly C-rate. LTO and NMC cells are regarded for HP applications because of their high C-rate and low energy capacity, while LFP (1st- and 2nd-life) cells are chosen for energy-centric applications because of their large energy capacity and relatively low C-rate restrictions. Despite the fact that the LFP 2nd-life battery has a limited energy capacity, it has been classified as an energy-focused application to increase its remaining usable lifetime by restricting the C-rate.
For each combination within the selected connection topology, the NSGA(II) algorithm finds the Pareto fonts that size the design components toward lower cost and higher efficiency and lifetime. Then, the programming layer stores these fronts in the database for further post-processing and reiterates another combination within the same selected connection topology. The Pareto fronts produced from the framework for connection topology-1 (
Figure 2) are illustrated in
Figure 10. Then applying the defined selection criteria, for example, here in Equation (32), the best HBESS solutions and their sizes are generated in terms of the defined objective functions and stored in the COF result database as per the combinational name such as C1, C2, C3, and C4 for the connection topology, TO-1. The best combinational HBESS solutions obtained from the framework result database for the TO-1 are presented in
Table 6. Please note that a similar approach is followed if different topologies are selected in the framework at once.
To find the global optimal solution the same selection criteria is applied to the saved best optimal solution set by normalizing the multi-objective functions of the solution set. The normalized values for C1, C2, C3, and C4 are illustrated in
Figure 11. The framework then finds the lowest value in the normalized solution set. The lowest value in the connection topology-1 indicates that solution C3, consisting in the use of LTO and second-life battery technologies, is the global optimum solution among the four possible hybridizations considered in the framework. It should be noted that different selection criteria with different weights can lead to different best/global optimum solutions; also, since in the used selection criteria the system cost carries the highest share of the weights, the system components’ cost will have an influence toward the best optimal solution.
However, a baseline comparison is carried out to assess the optimality of the obtained solution from the framework in terms of TCO and system efficiency for TO-1. For the baseline, each battery pack is sized with a nominal power of 50 kW as an equally balanced system to reach the previously mentioned system target of 100 kW/100 kWh. In the baseline, the sizing of the battery pack is first made based on the voltage operation defined by the PCS-1 (
Table 3) and then on the maximum power and energy required for the designed system. The results of the sizing of each battery pack for the baseline comparison are presented in
Table 7.
The baseline comparison in terms of TCO and system roundtrip efficiency with the obtained optimized solution is illustrated in
Figure 12. Please note that during the assessment of TCO and system efficiency, the sizing from baseline and COF is evaluated under similar conditions by using the framework simulation models and power-sharing principle and by respecting all the PCS-1 constraints. The optimal sizing from the COF for C1, C2, C3, and C4 reduces the TCO by 29.3%, 12.3%, 29.6%, and 16.2%, respectively, compared to the baseline solutions; also, the system round trip efficiency is improved by 4.2%, 4.4%, 4.7%, and 5% against the baseline sizing for B1, B2, B3, and B4, respectively, which confirms the relevance of the co-design optimization framework.