Energy Consumption of Transfer Points in Passive and Plus-Energy Warehouses—A Systemic Approach to Internal Transport

Abstract

Sustainable logistics increasingly requires energy-efficient solutions for warehousing systems. However, current energy consumption models often neglect the role of pallet transfer points that act as interfaces between various internal transport subsystems, despite their measurable impact on overall energy demand. This study addresses the energy implications of such transfer points in passive and plus-energy warehouse environments. Using the results of an operational analysis and empirical observations, we propose a dual classification of transfer nodes based on their technological characteristics (manual, semi-automated, automated, integrated) and energy profile (low, medium, high consumption). A novel Energy Performance Index (EPI) is introduced to quantify the energy efficiency of transfer nodes by combining both classification dimensions with weighted coefficients. Practical data indicate that overlooking these interfaces can lead to underestimating total energy use by up to 30%. Furthermore, the results emphasise the importance of technical integration and synchronisation between subsystems to reduce idle consumption and transfer losses. The approach presented in this paper provides a system-level modelling framework for energy assessment and supports the design of more sustainable and energy-conscious warehouse operations. The findings are relevant for logistics planners and system designers aiming to meet passive or plus-energy standards in intralogistics.

1. Introduction

The growing environmental requirements, along with the drive towards passive and plus-energy standards [1,2,3] in warehouse facilities, necessitate precise modelling of energy consumption in intralogistics [4].

In both planning and research practice, the dominant approach focuses on the movements of equipment (horizontal and vertical) [5,6], while transfer points (TP) are often left outside the modelling scope, despite the fact that they generate measurable losses due to micro-positioning, control synchronisation, buffering, and idle states (standby/restart). As a result, total energy consumption at the operational cycle level is systematically underestimated; our operational observations indicate that this underestimation may reach approximately 20–30% (cf. [7,8,9,10]). At the same time, there is a lack of a simple, replicable quantitative tool that would allow for comparison of TPs across different technological configurations and levels of automation.

In this study, TP is defined as the location and procedure for handing over a pallet/load carrier between two internal transport subsystems (e.g., forklift truck → roller conveyor, AGV → stacker crane, overhead crane → storage area), along with the associated infrastructural operations (detection, positioning, signalling, safety interlocks) and energy states (activation/standby). To capture the impact of TPs on the overall energy balance, we introduce the Energy Performance Index (EPI)—a first-level indicator that combines the technological class and energy class of a transfer point into a single diagnostic metric, aimed at both design and operational applications.

The objective of this paper is to propose a method for assessing the energy intensity of TPs in warehouse systems and to demonstrate that neglecting them in modelling leads to significant errors in estimating total energy consumption.

The following research questions were formulated:

RQ1. To what extent does the omission of TPs lead to an underestimation of the total energy intensity of warehouse operations?

RQ2. How can technological classes of TPs be linked with their energy profiles into a single assessment indicator (EPI)?

RQ3. What are the design and operational implications of including TPs in energy modelling for facilities aiming at passive or plus-energy standards?

This study contributes to the existing body of knowledge by (i) conceptualising TPs as active energy-consuming nodes within internal pallet handling systems; (ii) introducing a dual classification of TPs—both technological and energy-based; (iii) formulating, defining and applying the EPI as a simple, comparable indicator of energy intensity for TPs; and (iv) illustrating the consequences of neglecting TP-related energy use based on operational data from real facilities (PL/LT/HR), demonstrating a potential underestimation of at least 20% (typically ranging from around 20% to 30%).

The structure of the paper consists of six sections. Section 2 presents the background and related work. Section 3 describes the classification of TPs and the definition of the EPI. Section 4 provides the data and computational assumptions. Section 5 presents the results and discussion. Section 6 formulates the conclusions and design implications.

2. Intralogistics and Short-Range Transport in Global Supply Chain Management

The potential for short-range transport to make it a component of the global SCM infrastructure, impacting the economy’s ability to achieve specified levels of energy intensity and pursue “green” strategies.

What is important is that it was not proven that “cloning” of a warehouse that achieved, e.g., level passive, to other SCM components brings the assumed (expected) reduction in energy intensity levels (as confirmed by ongoing implementation projects). On the contrary, the analysis points to seeking indices for the entire SCM to achieve similar results in the whole SCM, for example, in terms of energy intensity. Logistic engineering uses the term “systemic warehouse”. There is a belief that the systemic warehouse, due to its verified in-practice and operational structure, materials, techniques, and technology, ensures the achievement of contracted warehouse parameters in the adjoining SCM points. A digital warehouse 3D model in ACAD (a bill of materials and equipment) is a formal record of that approach.

Figure 1 illustrates expanding the functions of short-range transport from local to global in SCM. It is noted that the operation of the warehouse in (extensive, long) SCMs is characterised by varying energy intensity, sufficiently large to warrant scientific research that may contribute to finding its underlying causes. A centrally managed SCM using ERP computer systems reacts fast to the changing business environment, despite its large size. The use of tools for optimising transport and logistics processes on a global scale introduces consistency to short-range transport operations, also in terms of energy intensity. ERP systems support the capture of anomalies in the energy intensity of individual processes of short-range transport and trigger remedy actions in the entire SCM.

3. Theoretical Background and Literature Review

By way of introduction, the concepts of internal transport, energy intensity, and efficiency are briefly outlined.

Short-range transport (in the literature used alternately with industrial and internal transport) covers a number of areas, the knowledge, structuring, and understanding of the functioning of which aims at optimising and reducing energy consumption in the entire supply chain. Key terms of energy intensity and system effectiveness are clearly distinguished [11].

Energy intensity of pallet warehouses [12] refers to the amount of energy consumed by a given warehouse system, process, or device. It measures the energy needed and required to perform specific operations. Energy is expressed as kilowatt-hours [kWh] or joules [J]. Energy intensity focuses on energy consumption itself, without considering effectiveness in the use of that energy to achieve assumed objectives. Higher energy intensity means that the system consumes more energy to perform specific tasks, and this may be ineffective from the point of view of sustainable and effective operation of the warehouse system.

The effectiveness of a pallet warehouse represents the ratio of energy consumed to the obtained results or benefits. It measures the effectiveness of the transport and warehousing system in the use of available energy to achieve objectives. It can be expressed as a percentage input-to-output ratio for the system/subsystem, where the effectiveness increases as the system achieves more with less energy consumed. Effectiveness is a value showing how conscientiously the system uses available energy to achieve its objectives. It may consider aspects such as quantities of goods transported per unit of energy consumed. Higher effectiveness means that the system achieves more with the same or less energy consumed, and this is desirable from the point of sustainable and environmentally friendly functioning.

Optimisation of transport and warehousing systems consists of minimising energy intensity with simultaneous high effectiveness in the use of consumed energy for execution of logistic processes [4].

Table 1. The essence of individual energy intensity indices. Own elaboration.

1	2	3	4	5
2	Study area	Pallet warehouse (without air conditioning)	Pallet warehouse (with air conditioning)	Pallet warehouse (cold store)
3	Lighting	24.0%	7.0%	9.0%
4	Air Conditioning and Ventilation	15.0%	55.0%	0.0%
5	Process Automation	5.0%	4.0%	1.0%
6	Cargo Technologies	2.0%	1.0%	1.0%
7	Transport Systems in a Pallet Warehouse	50.0%	29.0%	23.0%
9	Cold Store	0.0%	0.0%	62.0%
11	Warehouse Management Systems (WMS)	2.0%	1.0%	1.0%
12	Monitoring and Data Analysis	2.0%	3.0%	3.0%
		100.00%	100.00%	100.00%

presents data on energy intensity; such data are extremely valuable for validating theoretical considerations or computational models. As part of the ERASMUS project carried out in Croatia and Lithuania, the data were obtained with conditional permission for publication in aggregated form only (cf. [7,8]).

Energy efficiency of short-range transport becomes one of the main areas for research in the context of sustainable development. The analyses focus on aspects associated with the total energy consumption by transport equipment, as well as the influence of design and organisational solutions on losses at interfaces between warehouse subsystems [13].

(1)

Energy consumption in pallet warehousing. Transport of pallets within a warehouse significantly contributes to the total energy balance. The studies show that it is necessary to design closed loops of cargo flow, taking into account the energy intensity of its transfer [14]. The use of alternative vehicles and drives sufficiently influences the achieved savings [15]. The selection of pallet material (e.g., wood vs. plastic) also contributes to the energy characteristics of operations [16]. Other studies emphasise the importance of short-range transport in the entire supply chain [17] and the environmental impact of the load carrier management [18].

(2)

Energy modelling for transfer points. The TP issue holds a prominent place among model analyses. TPs are locations where a pallet is transferred between devices of different operating characteristics, such as trucks, gantries, stacking cranes, or conveyors. It should be noted that if such locations are omitted in models, this results in a significant underestimation of energy consumption [19]. The importance of the modular warehouse structure [20] and the effects of delays between subsystems [21] are also emphasised. Furthermore, appropriate technological integration may reduce losses resulting from uncoordinated operation of equipment [13], and the geometry of racks contributes to energy consumption in the system [22].

Pallet warehouses differ in the level of automation and storage conditions in determined temperature environments (HVAC) and in meeting regulations concerning the storage of hazardous goods or foodstuffs (HACCP). Table 1 illustrates the concept of energy intensity of TPs against the background of general problems in the area of a passive or plus-energy warehouse. Item 7 in Table 1 confirms an important role of short-range transport and an analysis of its energy intensity, taking TPs into account, in various scenarios. This subject is discussed to a greater extent in Section 4 and its subsections.

(1)

Classification of energy flows and losses. Analyses point to a need to create systematic classifications of energy losses and flows in warehouses. The literature presents frameworks for the evaluation of environmental operations in logistics, in which energy holds a prominent place [23]. Diversified drives that are implemented at random without previous analyses lead to losses of many types, including mechanical, heat, or conversion ones [15]. Options for using the approach known from the commercial power industry, i.e., classification of losses by their source and place of origination, were also identified [24]. Further works expand this approach with intralogistic systems [25] and call for the standardising of energy data [26].

(2)

Organisational and technical solutions supporting the reduction in losses. The proposed solutions focus on optimising entire intralogistic systems. Reference [13] discusses green warehousing practices, including automation and coordination of transport and measuring systems. References [13,27,28], in turn, analyse the use of AGVs together with management systems and energy recovery. Ref. [13] points to an option for reducing energy consumption by using adaptive lighting [29]. In their paper, Ref. [28] presents algorithms for scheduling handling tasks from the point of view of their energy intensity [27]. A model developed in paper [30] enables forecasting of energy consumption in AVS/RS systems [30].

(3)

Technologies supporting energy efficiency of internal transport. In the analysed sources, the technology was named the essential factor enabling the energy deficiency improvement in warehouse operations. Practices such as the integration of AGV systems, implementation of Industry 4.0 solutions, or the use of smart systems for lighting and monitoring work cycles were considered activities that effectively reduce energy consumption [28,31]. The works also show that optimisation of load transfer points influences not only the cycle time but also energy losses resulting from delays and equipment standby [32,33,34].

3.1. Packaging Influence on Energy Intensity

When the knowledge on packaging in logistics (warehousing) is analysed, on the basis of [35], it can be concluded that the packaging system is diversified, with many dividing lines that cross areas not only of the packaging materials [27,36], but also of technique [37,38] and technologies for placing information [39,40] on packaging, its transfer and recycling [41], and LCA [42,43], etc.

These aspects were not considered in the paper: the effect of packaging development, from the initial design to achieving the TRL-9 level, as well as on energy intensity of packaging transfer by handling equipment. Individual design features of packaging are of particular importance. These features are reflected in the selection of handling equipment, e.g., conveyors, forklift trucks, stacking cranes, AUTO-ID, and its mechanical and drive characteristics. Analysing this problem further, the physical and mechanical properties of a pallet are of significance for passive and plus-energy warehouses because they influence their efficient transfer and automation of transport and warehouse processes. These issues are extensively discussed in [44]. Sustainable development requires planning of packaging reuse [45], directly after its use (e.g., reusable packaging) or through material recycling [46].

3.2. Design and Mechanical Aspects of Transfer Points

Apart from the factors discussed above, the energy intensity of short-range transport is influenced by auxiliary works, e.g., set and strictly determined conditions of storage, pallet parcelling/dis-parcelling, or securing a load on a pallet. All these operations are characterised by their individual energy intensities, while they indirectly contribute to the energy intensity of load unit handling.

Pallet handling consists of cyclic, repeatable operations, which include picking, transferring, lifting, lowering, or horizontal or vertical transferring in relation to the warehouse floor. It is proven from experience that within a warehouse, a pallet is transferred by internal transport, including by people, between 11 and 30 times. The analysis shows that when the weight, transfer trajectory, and operating characteristics of machines are known, the energy intensity of these processes can be determined [7].

The previously used approach assumes an analysis of energy intensity for handling equipment—e.g., a stacking crane or a forklift truck—as separate energy-consuming units. However, modern logistics systems increasingly often involve integrated transfer of loads, in which TP is a central link for pallet transfer from one subsystem to another.

4. Concept of Energy Intensity of Transfer Points

The study used the conceptual model approach to evaluate the energy intensity of pallet TPs in various warehousing systems. The proposed classification of TPs considers both technological (a type of equipment transferring the pallet) and energy (average energy consumption per unit of operation) aspects. Assumptions based on operational data from observations and analyses of warehouse equipment manufacturers were adopted. These were used as a basis for developing a formula for the Energy Performance Index (EPI) of a transfer point, which takes into account the energy class and the technological class of that point, with significance weights assigned to them. The aim was to create a universal tool supporting design and modernisation decision-making (Figure 2).

A review of the SCOPUS database indicates that the proposed Energy Performance Index (EPI) for transfer points (TPs) represents a novel approach not yet described in intralogistics (MHE) literature. It combines an energy class, determined by the measurement of $P\left(t\right)$ with a technological integration class, enabling a comparable assessment of TP energy intensity across equipment types and automation levels in two modes: “coarse” and “precise”.

4.1. Warehouse Energy Intensity Model Considering TPS

TP is defined as a physically separate place in a warehouse forming an interface between components of a transport system and/or warehousing system [32]. At TP, a transported load is transferred between transport devices; the devices can be configured. These relations include human–machine ones, e.g., transfer at TP, designated as “HS” (Handover Station), which can be performed by a human. Its design and organisation determine the smoothness of material flow and optimisation of energy consumption in the warehouse system. Its optimisation may contribute to the total energy intensity of the entire process. It can be modelled as an energy buffer, and the accompanying energy required to transfer a pallet between devices is defined as transfer energy $E_{TP}$. Energy should be minimised through optimised synchronisation of work (reducing waiting time), minimising the idle time (the faster the load is transferred, the lower the energy consumption needed to maintain the system in the active mode), and optimal equipment layout to minimise unnecessary movements, including handling equipment with a mechatronic arm, which includes, e.g., a stacking crane, mini-loader or AGV. The standards, firmly anchored in SCM, are of importance here, such as [32,33] or [34] (the author is an energy auditor).

To reflect the actual conditions of system operation, it is proposed to consider the warehouse energy balance as an interconnected system, in accordance with Formula (1), where $E_{net}$ is a net energy balance, positive if the system recovers energy or operates in an energy-efficient way.

$$E_{net}=E_{in}-E_{con\mathrm{s}\mathrm{u}med}-E_{lost}$$

(1)

$E_{in}$ means energy supplied to the system (e.g., from the grid, renewable sources, or batteries), $E_{consumed}$ is energy consumed in logistic operations (transport, storage, picking, etc.), $E_{lost}$ is energy lost due to movement resistance, idle time, technological mismatches, heat dissipation, and similar. This formula reflects the systemic approach to energy efficiency analysis in passive warehouses. Instead of focusing on the total energy consumption of individual devices, such as stacking cranes or forklift trucks, the formula considers complex interactions between them, especially those occurring at TPs. The analysis and optimisation of TP energy intensity may help to achieve the required energy intensity level. It can either be passive, at warehouses that are characterised by low energy intensity and limited environmental impact, or plus-energy, at warehouses in which energy generation exceeds its consumption. These challenges require establishing strategic objectives, which include the following: shortening idle time for devices waiting for load transfer, reducing the number of unnecessary operations (e.g., double positioning), integrating energy recovery mechanisms (e.g., recovery during pallet lowering), or intelligently managing a task pipeline. Considering TP energy efficiency in the energy balance of a warehouse as a separate link in the system enables precise specification of the TP influence on the total energy efficiency and determination of real options for warehouse modernisation with sustainable development in mind. In such a case, TP is an energy interface: it not only participates in the pallet transfer but also generates additional operations (e.g., lifting, positioning, stopping, and adjusting height), which directly contribute to the total energy intensity. At the same time, the analysis of TP energy intensity reveals aspects of losses that have previously been overlooked, which include organisational and technological losses (AUTO-ID, EDI, RFID, colliding sequences, unsynchronised tasks, or buffer overload). The total energy of the transfer $E_{TP}$ is expressed by an equation for the energy balance (2).

$$E_{TP}=\sum _{i=1}^n{E}_{SD}+E_{TD}-E_{\eta }-E_{rec}$$

(2)

SD (Source Device) means a device delivering a pallet, while TD (Target Device) is a device collecting a pallet. $E_{\eta }$ describes energy losses associated with a lack of transfer efficiency (e.g., friction, vibrations, or electrical resistance in drives), and $E_{rec}$ represents energy recovered in the transfer process (e.g., recovery of braking energy). Structural and operational characteristics of SDs and TDs directly contribute to achieving energy passiveness and the plus-energy status of a warehouse. Therefore, concepts minimising the need for additional operations (e.g., precise matching of a height) are of importance, and optimised control of equipment operation may shorten adjustment times and reduce energy consumption.

In automated and robotised warehouses, TP is a crucial link integrating various transport subsystems (e.g., stacking cranes, forklift trucks, and robots). At this point, additional technical operations are frequently performed—such as load lifting and adjustment to the working height—which may significantly contribute to the energy intensity of the entire system. Today, data sheets of the devices of this type may and should include information on options for their mechanical or mechatronic integration. Operations performed at TP include barcode reading (or RFID), starting an arm of a handling robot, pallet verification, placing a barcode or radio frequency code on a pallet (AUTO-ID, WMS, ERP), and many others.

4.2. Verification of a Research Problem—An Experimental Study

Studies were performed to verify the presented problems. These studies are described in [47], and they were performed in logistics centres in Poland, Croatia, and Lithuania. A TP subsystem consisting of a forklift truck and a stacking crane was isolated for the study. The pallet was transferred from the forklift truck to the stacking crane, which placed it in a rack. During its transfer in a horizontal plane, the pallet is at a “transport height”. This height, depending on the forklift truck characteristics, is within the adjustment range from 0.3 to 0.7 m. The stacking crane picks the pallet at a minimum height of 0.7 m. This results from the design of the stacking crane carriage system. Assuming the TP value at a height of 0.7 m results from the analysis of the studied facilities [47]. A different TP height in passive and plus-energy warehouses can be specified, depending on technical conditions. It should be remembered that TP works in both directions, e.g., receiving-issuing, as when a pallet is picked from a rack and transferred to a forklift truck. Adjusting the height (including the automated one) for pallet transfer between devices is termed “technical integration”.

The conducted studies on energy intensity [47] show that it can be reduced by 10 to 20%. Taking into account the fact that processes of short-range transport and warehousing are, as a general rule, cyclic, their energy intensity should not be omitted (Figure 3).

The aim of the study shown in the chart is to evaluate the effect of additional operations (listed above) on energy consumption at TP if they take place/occur, depending on the TP parameters and characteristics of technical operations (lifting and adjusting).

The chart in Figure 3 presents a three-dimensional analysis of the influence of idle time at TP (axis X, [minutes]), total energy of lifting and adjustment (axis Y, [kWh]), and total energy consumption of the system (axis Z, [kWh]). The studies were conducted at the logistics centre (as in [27]), for the same forklift truck and stacking crane. Nine operational variants were analysed in total, covering three levels of lifting energy: 0.5/1.0/1.5 kWh, and three levels of adjustment energy: 0.3/0.6/0.9 kWh. Each of these variants was measured with the idle time gradually extended from 0 to 10 min.

The results show a clear increase in the energy intensity of the system with the increasing idle time at TP and intensification of the lifting and adjustment operations. Variants with the lowest energy burden (0.3–0.5 kWh) are characterised by relatively low and slow dynamics of the energy consumption increase. On the other hand, configurations introducing the greatest burden (0.9–1.5 kWh) demonstrate a significant and linear growth in energy consumption with the increase in idle time at TP.

It was also observed that the effect of adjustment energy is non-linear, and with the higher values of base energy (lifting), its effect is scaled faster, and this implies an interaction between these two operations. This proves that the idle time at TP is one of the crucial factors increasing the energy intensity of the system, even for moderate operating parameters. The cumulation of lifting and adjustment operations significantly contributes to the total energy consumption. Both the reduction in idle time and the limiting of unnecessary technical operations improve the energy efficiency of the entire warehouse. Considering the passive and plus-energy warehouses, minimising energy intensity at TP should be perceived as one of the priority strategic activities.

Measurements were conducted using measurement systems that ensured reliable and repeatable data recording. For each measurement point, data collection was initiated only after the system had stabilised, and each measurement was repeated 30 times. The resulting dataset supports evidence-based decision-making in projects aimed at the revitalisation and modernisation of warehouses (logistics centres) towards achieving passive or plus-energy performance levels.

5. Modelling Energy Intensity of Short-Range Transport, with TPs Considered

In ref. [47], the energy intensity of TPs was studied in pallet warehouses at logistics centres. This data was used for the analyses presented in this paper. The studies considered the accuracy of energy intensity measurements at the TP level. In energy modelling for pallet warehouses, TPs are an important yet frequently omitted component. On the basis of the discussed operational data and simulation analyses, the average contribution of these points to energy intensity ranges from 10 to 20% of energy consumption per pallet. The detailed analysis of TPs identifies main sources of consumption as standby, positioning micromovements, buffering, sensor operation, and idle movements. Movement modelling in the MS ADAMS environment, described in [47] (and other works of the author), was used in the calculations.

The EPI analyses presented in this section provide a foundation for further research in the field of internal transport. The case study discussed here is used to demonstrate how the modelling of discovered physical relationships in the context of energy intensity in internal transport can be approached. In this case, the use of weights (or expert-assigned values) in the EPI model is sufficient. In cases where energy recovery occurs at the TP, additional operations such as pallet weighing, temperature control requirements, or the level of process automation can be formally incorporated into the EPI model.

5.1. Research Methodology

The aim of the studies was to determine the TP influence on the total energy intensity of a warehouse and an analysis of this problem, especially in the context of additional technical operations performed at TP and the idle time. The studies were performed as numerical simulations, using assumptions representing typical operational conditions at automated warehouses and proprietary research results at warehouses of logistics centres in Poland, Croatia, and Lithuania [47] as a reference for verification of theoretical considerations.

The structure of the analysed system is typical for short-range transport. The analysis concerned a system consisting of two subsystems: vertical (a rack-stacking crane) and horizontal (a forklift or an automated truck). Both subsystems share a TP, at which a pallet is physically transferred within the short-range transport system, e.g., pallet picking, placing in, and collecting from a rack. In the analysis, TP is an energy node that introduces additional energy consumption resulting from operations of lifting a load unit to the required working height, adjustment of the load position (vertical) to maintain compatibility of devices, and delays and idle time resulting from unsynchronised operation of the systems. TP can also be a location for energy recovery, or energy can be balanced at TP—this is the case, for example, when a gravitational rack is installed, in which a pallet moves due to the effect of a gravitational force on its mass.

The results in

Table 2. TP energy intensity for a pallet with various equipment configurations. Own elaboration.

1	2	3	4	5
2	Equipment configuration	TP energy (Wh/unit)	Cycle time (s)	Operational notes
3	Forklift truck → roller conveyor	40–80	8–15	Manual positioning, limit sensors
4	Forklift truck → stacking crane	70–120	12–20	Active buffer, precise centring
5	Conveyor → stacking crane	50–90	8–12	Full automation, low energy consumption on rollers
6	Conveyor → gantry	80–140	12–25	Synchronisation of drives, blocking signals
7	AGV → conveyor	90–150	15–25	Standby, wireless communication
8	Gantry → stacking crane	100–160	18–30	Precise stopping, dynamic buffer
9	Truck → AGV	60–100	10–20	Operator supported by sensors

prove that the technology selection and subsystem integration directly influence the TP energy balance. Energy models should treat them as separate nodes with specific parameters of cycle time, peak power, losses and recovery potential.

The obtained results are consistent with the author’s R&D works, in which the influence on SCM energy intensity of reading and recording 1D and 2D barcode symbols and the correct placement of an AUTO-ID element on a pallet was studied in a real SCM. Furthermore, errors of barcode and RFID readout at TP were analysed [48,49], with their effect on SCM energy intensity. The conclusions from this analysis were implemented into a real SCM, in which SCM energy intensity was visibly reduced. The R&D works and their results are described in [50].

5.2. A Balance Between the Analysis Level and the Underestimation of Energy Intensity

Analyses of—frequently very complex—problems, of which the results are to be implemented in the industry, aim at a certain balance between the level of the analysis extent and the results obtained in practical applications. Additionally, their extent is determined by the computing power of computers and the virtual modelling environment (this poses a certain problem for companies).

In many simplified energy analyses, it is assumed that the pallet transport cycle is limited to energy used for the logistic process defined by start and end points of the transport process, e.g., from the “receiving area” to the “storage area” (Figure 4) employing a single device (a forklift truck, a stacking crane, or AGV). This approach does not include energy consumption at TP, i.e., where the pallet is physically transferred between devices or systems (e.g., from a conveyor to a stacking crane). It is understandable that the stacking crane will not collect a pallet from a floor or from the forks of a forklift truck. The stacking crane requires precise positioning at TP, at a height of ca. 0.7 [m] [47].

The analysis of actual data measured at TP for real handling devices at logistics centre facilities [47] proves that the energy intensity of an average complete pallet handling cycle at TP is at the level of 600 ÷ 1000 Wh per pallet. When TP is considered (standby, adjustments, sensors, idle movements), energy intensity will increase by 20 ÷ 30% (in numbers, it is 800 ÷ 1250 Wh).

In practice this means that models that do not consider TPs underestimate the energy intensity of the infrastructure. The lack of reliable operational data leads to wrong design decisions (e.g., selection of too small PV or recovery systems),

Table 3. Analysis of scenarios for energy intensity modelling. Own elaboration.

1	2	3	4
2	Modelling scenario	Cycle energy (Wh/pallet)	Notes
3	Simplified model	700	Only main movement of the device
4	Model considering TPs	980	Transfers, idle time, adjustments, standby

.

The significance of the technical integration of devices in TP lies at the interface of handling equipment. This issue is well known to specialists but is disregarded due to many associated problems. At TP, not only does a physical transfer of a load occur, but also controllers, drives, and sensors of two independent systems (e.g., a conveyor and a stacking crane) interact. If these devices are not technically synchronised, this may lead to unnecessary standby, manifesting as a long total cycle and an increase in energy consumption. A very interesting and important issue of positioning errors will be the subject of the next publication, as the research on this issue is in progress. This concerns the implementation of additional mechatronic systems, causing positioning errors through necessary adjustments.

These identified problems are casually termed “high energy intensity”, without seeking their causes. Different devices and different pallets can “meet” at TP (Figure 1)—TP energy intensity will result from the operation time. However, it mainly results from an ineffective cooperation between the handling devices, especially in mixed (manual and automated) systems. Therefore, the technical integration of systems (signal, logical, and optical interfaces) is a crucial direction for further studies. Before optimisation, a sequence and a moment of starting subsystems (e.g., the start of a conveyor precisely when an AGV reaches it) should be analysed. Possibly the greatest technological barrier that needs to be overcome is installing smart control schedules that minimise idle time and improve the cooperation of devices characterised by different dynamics. It is described as “the greatest” on the basis of the experience when it is necessary to go into ERP systems. The practice shows that frequently it is very difficult.

The interface between transport devices is not only a physical location of pallet transfer but also an area where the control and energy responsibility of two different systems are integrated. The quality of that integration directly translates into energy consumption. The better synchronised the systems, the lower their energy intensity.

5.3. Energy Intensity Assessment, EPI Usefulness

To implement EPI, a reference scale was adopted to determine the extent to which a specific EPI value is still acceptable, or whether repair activities should be initiated. The energy intensity is mainly assessed in two cases: first—when no operational data is available, the assessment is performed at the initial stage, or data is not collected; or second—when data is regularly collected and recorded for optimising analyses. The use of new, proprietary indices will be presented in a real-life example.

Details of an example TP from a typical warehouse in which energy intensity studies were conducted [47] were used in the calculations. Therefore, this study does not include the data query methodology. The warehouse operating details were as follows: storage height: 14 [m], which corresponds to 6 levels of pallets in a rack (4), Figure 4. The short-range transport consists of a forklift truck—1, a roller conveyor—2, a stacking crane—3, and a rack—4. The average number of pallet cycles: 800 [pallets]/24 [hours], conveyor length: 28 [m], number of TPs: 2 (conveyor 2 in Figure 4 is a part of TP).

Figure 4. Pallet energy cycle in a high-bay warehouse—an analysis of losses and model simplifications. Own elaboration.

Figure 4. Pallet energy cycle in a high-bay warehouse—an analysis of losses and model simplifications. Own elaboration.

Energy intensity was calculated for TP_A and TP_B and analysed in two variants: first—for a full model (considering local losses) and second—for a simplified model, without intermediate points, from

Table 4. Pallet cycle stages. Own elaboration.

Cycle Stage	Stage Name	Average Consumption [Wh/Pallet]	Operational Notes
1	Horizontal transport on the conveyor	200	Speed of 0.4 m/s, average time of 70 s
2	Transfer: conveyor → stacking crane	90	Positioning, stopping, sensors, standby
3	Vertical transport—stacking crane	420	Elevator with a partial recovery
4	Transfer: stacking crane → rack	70	Moving out, centring, active rollers
5	Total with TPs	780 Wh	-
6	Total without TPs	490 Wh	With intermediate operations omitted

. A visible difference in energy intensity implies a significant influence of TP on the energy intensity of the short-distance transport process

When energy intensity at TPs is omitted, the total energy intensity is underestimated by ca. 290 Wh per cycle, i.e., about 37%. In the analysed case, a significant repeatability of transport cycles, both in a single warehouse and in the entire SCM, translates into 800 cycles a day, so the loss in the energy balance will amount to approximately 232 [kWh/day] and to ca. 84,680 kWh in a balance for the whole year. In the analysed example, the omission of TP energy intensity leads to an underestimation of energy intensity. An incorrect estimation of the warehouse energy intensity may have long-reaching consequences, e.g., through incorrectly selected PV systems, incorrect assessment of the warehouse performance in LCA, wrong investment decisions concerning digitalisation and automation, and failure to perform assumed strategies to achieve the passive or plus-energy warehouse level.

5.4. Design or Quantitative Energy Intensity Ratio

Following the identification and analysis of results of other studies [51,52,53,54,55], a classification of TPs was developed for short-range transport in warehouses. A functional and energy classification of TPs was introduced, based on two main criteria: the first—“the technological criterion” (equipment and integration type),

Table 5. Technological criterion (equipment and integration type). Own elaboration.

1	2	3	4	5
2	Point type	Description	Relationship	Integration
3	Manual	Manual transfer of load	Forklift truck → operator	None or basic
4	Semi-automated	One device is controlled, the other is passive	Truck → conveyor	Signalisation or sensors
5	Automated simple	Both devices are automated, no full synchronisation	Conveyor → gantry	Transmitters, blockades, limiting sensors
6	Integrated smart	Full two-directional exchange of signals and synchronisation	AGV → stacking crane	Logical interface, scheduled cycles

, and the second—“the energy criterion” (energy consumption profile),

Table 6. Energy criterion (energy consumption profile). Own elaboration.

1	2	3	4	5	6
2	Class	Consumption level	Energy features	Consumption [Wh/pallet]	Potential for improvement
3	E1	Low consumption	Short cycle time, no standby	<60 Wh	limited
4	E2	Average consumption	Buffering or position adjustments	60–120 Wh	moderate
	E3	High consumption	Idle time, unsynchronised drives	>120 Wh	high

. This approach can be used at the concept design or optimisation stage, before precise data on energy intensity becomes available. The article proposes two supplementary approaches to calculating the TP energy performance indicator, $EP$I (Energy Performance Indicator, [kWh/m²/year] or [kWh/production unit])—using terms from [33,56,57].

Furthermore, the indicator (so-called expert) ${EPI}_{design}$ is also required to assess TP energy intensity, to reliably evaluate energy intensity in terms of achieving the objectives of a passive or plus-energy warehouse. The concept ${EPI}_{design}$ was expressed with Formula (3), which is used to calculate the indicator for assessment of the pallet transfer point. The higher the value ${EPI}_{design}$ for the selected TP, the less effective that TP is, and it does not support achieving the passive level by the warehouse. This implies a change in the strategy of activities.

$${EPI}_{design}=C_E·W_E+C_T·W_T$$

(3)

where $C_E$ means an energy class, assuming the following values: 1 for E1, 2 for E2, and 3 for E3. While $C_T$ describes the TP technology class, which adopts the following values: 1—manual warehouse, 2—semi-automated warehouse, 3—simple automated warehouse, 4—smart integrated, level Industry 4.0. Formula (3) includes weights $W_{E}i{W}_T$, their values were selected accordingly $W_E=0.7 W_E=0.3$. The value of weights was adopted using the expert method, taking into account the energy consumption influence on the warehouse energy balance.

The warehouses perform energy audits of existing systems. In such cases, it is recommended to calibrate the weights individually, on the basis of empirical data or simulation analyses.

The energy class weight has a higher significance because the real energy consumption (e.g., standby, idle time) directly influences the energy balance of a warehouse. The weight of the technological class is of a lower significance, because the level of automation and integration may change in time, and its impact on the energy intensity is indirect (e.g., through a cycle time, synchronisation, etc.) (

Table 7. An example of assessment for two points. Own elaboration.

1	2	3	4	5	6	7	8	9	10
2	Point	$C_E$	$C_T$	$E_{TP}$	N operations	EPI quantitative	EPI design	Interpretation/decision	Discrepancy
3	A (Truck → conveyor)	2	2	40	20	2.0	2.0	Moderate effectiveness	None
4	B (AGV → stacking crane)	3	4	108	30	3.6	3.6	High losses—optimisation is required	None
5	C (conveyor → gantry)	2	3	52	20	2.6	2.6	Technological improvement is required	None

).

The indicator ${EPI}_{design}$ may take into account a component related to the cycle time (t), or standby, and/or other weights may be adopted, depending on whether the project concerns modernisation or a new investment.

In the initial assessment process for technological solutions at high-bay warehouses, we need a fast and reliable analysis of TP energy intensity. Full modelling of the system, taking into account all aspects of movements and losses, may be time-consuming and difficult to perform at the conceptual stage. Therefore, a qualitative indicator ${EPI}_{quantitative}$ was proposed for fast comparison and initial selection of variants in terms of their conformance with requirements for passive or plus-energy warehouse criteria.

${EPI}_{quantitative}$ (4) is used in conditions of optimisation, mainly in a situation when there is data from measurements or energy simulations available in the system. In that case, the assessment is based on measured, actual TP energy intensity.

$${EPI}_{quanti\mathrm{ś}tative}={\displaystyle \frac{E_{TP}}{N_{operations}}} \text{[kWh/pallet]},$$

(4)

$E_{TP}$—total energy consumed at TP, covering lifting and adjusting operations and idle time (as kWh), $N_{operations}$—number of transfer operations in a given cycle or time interval.

This ratio enables determining the individual energy intensity associated with pallet handling at TP. Its value can be treated as a measure of the energy performance of a given design variant.

Variants with low ${EPI}_{design}$ (e.g., <0.9 [kWh/pallet]) are qualified as consistent with the requirements for passive warehouses. Variants characterised by an $\mathrm{E}\mathrm{P}\mathrm{I}$ value close to net zero (reduced by energy recuperation or integration with RES) can be considered as potentially plus-energy.

The described EPI is used as a tool for comparisons and design, facilitating an initial assessment of TP energy intensity in areas of short-range transport at unit or pallet warehouses. $\mathrm{E}\mathrm{P}\mathrm{I}$ does not replace full analyses of energy intensity but is an engineering tool—termed “first estimation”—which is particularly useful at the stage of investment planning or optimising of short-range transport in terms of achieving determined energy intensity levels [56,57,58].

Its use enables early detection of configurations of potentially high energy intensity and supports the decision-making process for further detailed simulation studies. The consistency of both variants implies that $\mathrm{E}\mathrm{P}\mathrm{I}$ can be used as a uniform tool supporting engineering decisions in short-range transport and energy intensity monitoring (in the context of passive and plus-energy levels)—especially as a data query during R&D works at warehouses, disclosing very significant shortages in data on energy intensity. Industrial devices and systems are not adopted for collecting operational data.

In the article, EPI values were verified by measurements in the TP of a stacking crane—forklift truck. The article is planned as a part of a series of three to five articles representing a scientific achievement in the promotional proceedings, where this article presents directions for further studies, in which EPI is to be expanded or calibrated on the basis of data from test cycles of specific devices.

6. Conclusions

Transfer points (TPs) significantly influence the energy intensity of short-range transport and the entire pallet warehouse, and in consequence, the energy intensity of SCM. Failure to consider TP energy intensity in models results in an underestimation of the total energy consumption, even by 30%. These are not only physical locations at which loads are transferred but also decision and technological nodes, generating real energy losses (AUTO-ID, standby, micromovements, synchronisation times, etc.).

Modelling the energy intensity of a passive or plus-energy warehouse requires a systemic approach covering all subsystems of the internal transport and their interactions. In the implementation of logistic strategies, particular attention should be paid to the selection and integration of technologies at points of transfer, because their configuration affects cycle time, energy losses, and energy recovery possibilities, especially when a warehouse achieves a passive or plus-energy level.

The proposed classification of TPs and specific values for energy consumption for various equipment configurations form a basis for parametric consideration of those nodes in simulation models and LCA for a warehouse. They enable better estimation of energy demand and adopt the technical solutions to passive standard requirements.

Achieving a standard of the passive or plus-energy warehouse requires taking transfer points into account as crucial components of warehouse energy intensity, both at the design and operation stages.

Further development of R&D works is planned towards DT (digital twins), interoperable control systems, and standardisation of energy intensity data.