Systems In Commercial Buildings Research Articles

Optimization and Comparison of Photovoltaic Parameters of Zinc Oxide (ZnO)/Graphene Oxide (GO) and Zinc Oxide (ZnO)/Carbon Quantum Dots (CQDs) Hybrid solar cell using Firefly Algorithm for application in Solar Trigeneration System in Commercial Buildings

Solar cells formed by the combination of organic and inorganic nanoparticle semiconductors are gaining interest in today’s era because of their major features, such as being suitable for scalable solar power conversion and being of low cost to produce desirable photovoltaic devices. This work is an attempt to develop two forms of hybrid solar cells, one by the amalgamation of zinc oxide and carbon quantum dots and second, by incorporating graphene oxide into zinc oxide. In this study, optimization, validation, and comparison of the photovoltaic parameters of the two nanostructured solar cells were attempted using the Artificial Neural Network technique. The ANN was instructed using the Firefly Algorithm. The input parameters are the spectral power density and temperature. The output parameters are the short circuit current (ISC), open-circuit voltage (VOC), fill factor of the cell (FF), and maximum voltage point prediction (VMPP). The results obtained for both the hybrid solar cells were compared and it was noted that the addition of CQDs to ZnO resulted in a considerable increase in the values of ISC, VOC and FF. The output obtained by the ANN trained model was compared with the results obtained from the experimental tests. It is observed that there is considerable agreement between the results obtained from the ANN and experimental values. The optimized values of VOC, ISC, FF, VMPP, Incident Photon to Current Efficiency (IPCE), Power Conversion Efficiency (PCE) for ZnO/GO are 0.623 V, 0.546 mA, 62%, 0.509 V, 36.95%, 9.12% respectively. Similarly values of VOC, ISC, FF, VMPP, IPCE, PCE for ZnO/CQDs are 0.636 V, 0.597 mA, 68%, 0.531 V, 40.72%, 10.35% respectively. Therefore, the precise values of all the stated parameters were obtained for both the cells after optimization and hybrid cells made of CQDs proves to be a better candidate. Also, the desirable value of the coefficient of correlation (R) is obtained, approximately close to 1, which means that the fabricated samples are efficient for useful applications. Proper execution of this algorithm on any model of the hybrid solar cell can lead to an evolution in the field of solar cells, resulting in the improvement of efficiency. These optimized cells have been utilized to propose two models of solar trigeneration system used in a commercial building North Service Centre (Olefin building) of Haldia Petrochemicals Limited, Haldia, West Bengal, India. The trigeneration systems were based on photovoltaic modules, heat pump and photovoltaic-thermal collectors. The objective is to provide enough electricity, domestic hot water, heating and cooling power to meet the typical demand of a single office building. System performance has been predicted and evaluated in the work. Considerations should be made regarding the physical constraints imposed by the environment where the installation has to be performed. The location should be carefully selected to achieve maximum efficiency.

Multi-label load disaggregation in presence of non-targeted loads

Non Intrusive Load Monitoring (NILM), also called, load disaggregation aims to infer load level electrical energy consumption from the aggregate power signal. Several challenges are limiting the deployment of NILM systems in residential and commercial buildings. In this paper, we treat the case of energy disaggregation in the presence of non-targeted loads. Non-targeted loads in our context stand for electrical loads for which we do not have labels during the training phase of the NILM algorithm. However, those loads may exist in a real-world scenario, and their power consumption adds to the aggregate power signal. In this work, we present our load disaggregation method based on a multi-label classification approach and study the impact of non-targeted loads on the NILM disaggregation performance. We show that the non-targeted loads can negatively affect the disaggregation performance of NILM systems and found a significant correlation between the disaggregation performance impact and the overlapping coefficient of the targeted and non targeted loads’ power distributions. Results are obtained using a publicly available dataset of power measurements.

Evaluation of cooling setpoint setback savings in commercial buildings using electricity and exterior temperature time series data

Commercial buildings account for a significant amount of total energy produced in the US, and the Heating Ventilation and Cooling (HVAC) systems are one of the most significant components of their overall consumption. In this study, we proposed a new data-driven approach to evaluate HVAC cooling systems in commercial buildings and identify savings opportunities. The focus is an investigation of the impact of thermostat setpoint setback but using only whole building, electricity data taken at 15-min intervals for the analysis. We conducted a comparative study of setpoint setback characteristics on 432 commercial buildings with 5 building usage types across the United States. To accomplish this, both piecewise and Random Forest regression algorithms were employed using electricity and exterior temperature datasets to identify operational characteristics and the effective setpoints in the building to determine the corresponding savings opportunities. Both occupied and unoccupied time periods were studied across cooling degree days (CDD), when air conditioning is typically operational. The results show that in commercial buildings, on average, cooling systems account for 9.5% of total consumption. When a one degree setback during the cooling season is applied, an average of approximately 1.1% of annual consumption is achieved; retail and office buildings demonstrate the highest potential for savings. Additionally, we identified that the number of cooling degree days and base to peak ratio (BPR) are the most important variables for predicting the magnitude of the consumption of cooling systems.

A New Approach for Characterizing Pile Heat Exchangers Using Thermal Response Tests

Pile heat exchangers offer a cost effective route to implementation of ground-source heat pump systems for many large commercial buildings compared with traditional boreholes. Such projects typically use thermal response tests to determine the key input parameters for system design, namely soil thermal conductivity and heat exchanger thermal resistance. However, this brings challenges for pile heat exchanger based systems, where in situ thermal response tests are known to be less reliable due to the large thermal capacity of the pile. This paper presents a new “black box” resistance capacitive model for applications to pile thermal response tests. The approach is tested against case study data and shown to perform well. Additional test duration savings are shown to be possible if a novel combination of borehole and pile thermal response tests is applied together to determine design parameters.

Thermodynamic performance analyses for CCHP system coupled with organic Rankine cycle and solar thermal utilization under a novel operation strategy

The combination of an organic Rankine cycle (ORC) and solar collector (ST) with combined cooling heating and power (CCHP) system has the potential to improve its thermodynamic performances. Therefore, in this paper, fully considering thermal cascade utilization, a novel CCHP system combining ORC and ST is proposed, which uses solar energy and exhaust heat as ORC driving heat sources. Compared with traditional CCHP and CCHP-ST systems, the advantages of the CCHP-ORC-ST system are demonstrated. A collaborative optimization method considering both system configuration optimization and operation strategy is proposed, which realizes the improvement of system energy efficiency and economy. Based on the collaborative optimization method, the CCHP system with different integrated configurations is applied to commercial and office buildings. Through parameter analysis, the effects of parameters (such as the rated capacity of internal combustion engine and ORC evaporation temperature, etc.) are discussed. The findings portrays that the cost per unit supply area of the CCHP-ORC-ST system in typical years of office and commercial buildings is about 19.8 $/m2 and 25.9 $/m2. Compared with the CCHP system, the cost-saving rates of the CCHP-ORC-ST system in commercial and office buildings increase by 15.0% and 27.0%, respectively. The CCHP-ORC-ST system can flexibly utilize ORC to generate more electricity, improving energy efficiency and reducing wasted heat. Consequently, the proposed CCHP-ORC-ST system based on the collaborative optimization method can determine the optimal system configuration and operation design for energy savings and consumption reduction.

What are the impacts on the HVAC system when it provides frequency regulation? – A comprehensive case study with a Multi-Zone variable air volume (VAV) system

Heating, ventilation, and air-conditioning (HVAC) systems in commercial buildings have proven to be a great asset for providing secondary frequency regulation (FR) service to the power grid. However, how the provision of FR service affects the HVAC system performance is still largely unknown. This paper investigates how a HVAC system performance and its associated controls are impacted during and after a FR service. We exploit a dynamic Modelica-based medium office building model as a virtual testbed with the Air Handling Unit (AHU) fans providing the FR service for the assessment. The virtual testbed adopts state-of-the-art HVAC control sequences and an experimentally validated frequency regulation control scheme. The comprehensive study of 832 case studies considers several scenarios, consisting of different regulation capacities (demand levels), standardized FR test signals, and building load profiles in 16 U.S, climate zones. The holistic assessment includes the interaction between the FR and the HVAC baseline controls, the impact of FR on the occupant’s thermal comfort, and, more importantly, the energy efficiency.The results show that FR has little or no impact on the HVAC operation and occupant thermal comfort while the building is providing the service if an appropriate regulation capacity is pre-determined. However, if the regulation capacity is overestimated, the quality of the FR service provided by the HVAC system and the HVAC energy efficiency will be affected due to the equipment operating constraints. In addition, the AHU supply air temperature fluctuates, and the controller cannot maintain the desired setpoint as the FR magnitude is increased. After the FR service time, the system takes a certain period to return to normal operation. The rebound period extends with the increase in the FR magnitude. For this case study, there exists a control conflict between the FR control and the existing HVAC control (from ASHRAE Guideline 36-2018) during high load conditions, causing the fan speed to saturate. The increased fan electricity consumption caused by this control conflict will offset the potential revenue a building can earn from offering the FR service. In addition, the rebound power usage could have a negative effect on the grid.

Aggregated impact of coordinated commercial-scale battery energy storage systems on network peak demand, and financial outcomes

The penetration of distributed battery energy storage systems in electricity grids globally is forecast to grow rapidly over the next 10 years. These systems can provide benefits for the customers that install them, but also introduce new opportunities and challenges for distribution network operators. This paper presents an algorithm that optimises the sizing and operation of a cluster of battery energy storage systems in commercial buildings. It aims to either optimise the battery energy storage system independently to maximise the financial benefits for each building or coordinate them to maximise the value of reducing demand peaks for both the customer and the local zone substation. To achieve the latter, the algorithm assumes a certain percentage of the network deferment value associated with reducing the peak demand for the zone substation is passed through to customers. Scenario analysis show that the peak demand reduction and financial outcomes for both the building owners and the network result from a complex interaction between the customer and network load profiles, the tariff structure, the size and cost of the BESS, and the percentage of the network deferment value that is passed through.

Novel Nd–Mo co-doped SnO2/α-WO3 electrochromic materials (ECs) for enhanced smart window performance

In an urbanized city, about a third of total electrical consumption is allocated for indoor lighting and air conditioning system in residential and commercial buildings. The majority of the worldwide energy generation comes from burning of non-renewable fossil fuel which is not sustainable in the long run. The use of smart windows technology may catalyze the effort to reduce energy consumption of building and houses. More than 50% of heat entering a building through windows originate from the solar radiation in the near infrared (NIR) region. This candidate smart window material must exhibit dual-band (visible and NIR) modulation that allows selective modulation of NIR heat without affecting visible light transmission. A good electrochromic material in this respect should possess high visible light transmission, high NIR modulation, fast switching between colored and bleached state, and good stability over prolonged usage. In this work, we propose a novel Nd–Mo co-doped SnO2/α-WO3 electrochromic materials (ECs). As compared to the traditional SnO2/α-WO3 ECs, our Nd–Mo co-doped SnO2/α-WO3 ECs exhibits up to 90% visible light transparency (at λ = 600 nm), 62% NIR modulation (at wavelength 1200 nm), high coloration efficiency (~200 cm2 C−1), fast switching time with only 31% electrochromic performance drop (vs 59% of undoped sample) after up to 1000 reversible cyclic test. The enhanced electrochromic performance comes from the presence of Nd–Mo co-dopants that limit the trapping of Li + ion within α-WO3 framework, reduce the extent of crystallization of α-WO3 layer and enhancement of the electronic conductivity by transferring their excess electron to the conduction band of the SnO2. To the best of the authors’ knowledge, the present composition of ECs offers one of the better candidate materials for electrochromic to be used as thermal management layers on smart windows application.

The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings

Cooling systems play a key role in maintaining human comfort inside buildings. The key challenges that are facing conventional cooling systems are the rapid growth of total cooling energy and annual electricity consumption in commercial buildings. This is even more significant in countries with an arid climate, where the cooling systems are typically working 80% of the year. Thus, there has been growing interest in developing smart control models to assign optimal cooling setpoints in recent years. In the present work, we propose an occupancy-based control model that is based on a non-linear optimization algorithm to efficiently reduce energy consumption and costs. The model utilizes a Monte-Carlo method to determine the approximate occupancy schedule for building thermal zones. We compare our proposed model to three different strategies, namely: always-on thermostat, schedule-based model, and a rule-based occupancy-driven model. Unlike these three rule-based algorithms, the proposed optimization approach is a white-box model that considers the thermodynamic relationships in the cooling system to find the optimal cooling setpoints. For comparison, different case studies in five cities around the world were investigated. Our findings illustrate that the proposed optimization algorithm is able to noticeably reduce the cooling energy consumption of the buildings. Significantly, in cities that were located in severe hot regions, such as Doha and Phoenix, cooling energy consumption can be reduced by 14.71% and 15.19%, respectively.

ANP model for evaluating the performance of adaptive façade systems in complex commercial buildings

PurposeDecisions taken during the early design of adaptive façades involving kinetic, active and responsive envelope for complex commercial buildings have a substantial effect on inclusive building functioning and the comfort level of inhabitants. This study aims to present the application of an analytic network process (ANP) model indicating the order of priority for high performance criteria that must be taken into account in the assessment of the performance of adaptive façade systems for complex commercial buildings.Design/methodology/approachThe nominal group technique (NGT) stimulating and refining group judgments are used to find and categorize relevant high performance attributes of the adaptive façade systems and their relative pair-wise significance scores. An ANP model is applied to prioritize these high performance objectives and criteria for the adaptive façade systems.FindingsEmbodied energy and CO2 emission, sustainability, energy saving, daylight and operation maintenance were as the most likely and crucial high performance criteria. The criteria and the weights presented in this study could be used as guidelines for evaluating the performance of adaptive façade systems for commercial buildings in planning and design phases.Practical implicationsThis research primarily provides the required actions and evaluations for design managers in accomplishing a high performance adaptive façade system, with the support of an ANP method. Before beginning the adaptive façade system of a building design process, the design manager must determine the significance of each of these attributes as high performance primacies will affect the results all through the entire design process.Originality/valueIn this research, a relatively innovative, systematic and practical approach is proposed to sustain the decision-making procedure for evaluation of the high performance criteria of adaptive façade systems in complex commercial buildings.

Risk management strategy for a renewable power supply system in commercial buildings considering thermal comfort and stochastic electric vehicle behaviors

Reduction of greenhouse gas emissions in the building sector has become a global concern in terms of ensuring sustainable development. Thus it is imperative to investigate the utilization of clean electricity in buildings. This study proposes a novel, cost-effective commercial building-oriented power supply system for providing a comfortable environment in a shopping mall. An energy management strategy is also proposed for this system. Two types of electric vehicles (EVs) are considered, and their models are developed separately based on different stochastic behaviors. Furthermore, the integration of retired electric vehicle batteries (REVBs) in commercial buildings is one of the possible methods for achieving echelon utilization. To address the uncertainties in the system, the scenario-based stochastic optimization method is applied, which formulates the energy management problem as a mixed-integer linear programming model. Results show that by integrating only photovoltaic (PV) energy and both PV and REVBs into commercial buildings, reductions of 82.2% and 83.3%, respectively, in the expected operational cost can be achieved. This proves that the application of PV and REVBs is beneficial for commercial buildings. Moreover, the robustness of the employed method is proven through comparison with other methods.

Energy Efficiency Opportunities for Lighting Systems and Miscellaneous Electrical Loads in Commercial Buildings – A Review

Energy Efficiency Opportunities for Lighting Systems and Miscellaneous Electrical Loads in Commercial Buildings – A Review

Empirical Modeling of Direct Expansion (DX) Cooling System for Multiple Research Use Cases

This study provides a general procedure to generate a direct expansion (DX) cooling coil system for a roof top unit (RTU), which is a typical heating ventilation and air-conditioning (HVAC) system for commercial buildings in the United States. Experimental data from a full-scale unoccupied 2-story commercial building is used for the HVAC modeling. The regression for identifying the model coefficients was carried out with multiple stages, and the results were validated with measured data. The model’s applicability was evaluated with multiple case studies, including a building energy simulation (BES) program validation, model-based predictive control (MPC), and fault diagnostics and detection (FDD).

Genetic algorithm-based operation strategy optimization and multi-criteria evaluation of distributed energy system for commercial buildings

Distributed energy systems (DESs) are supposed to help alleviate energy crisis and environmental issues. However, the effects of operation strategy on their design and performance have not been fully understood yet. Based on the genetic algorithm, this research aims to conduct the design optimization and performance analysis of the DES in shopping malls, hotels, and office buildings under seven operation strategies, that is, following thermal/electric energy load (FTL and FEL), following hybrid load with no/maximal surplus energy, and with no thermal startup threshold (FHL-ns, FHL-ms, and FHL-nst), and following monthly/seasonal electric–thermal load ratio (FMLR and FSLR). Results show that: (1) The optimal capacities of power generation unit are 623-1 782 kW, while the optimal electric cooling ratios are 0.5-0.9. (2) The optimal primary energy saving ratio, exergy difference, equivalent CO2 and PM2.5 emission reduction ratios, total cost saving ratio, and operation and maintenance cost saving ratio are respectively up to 0.298, 0.114, 0.495, 0.912, -0.166, and 0.221. (3) The optimal strategy for the above commercial buildings is respectively FMLR, FMLR, and FHL-nst, and the comprehensive performance is negatively related to the coal-to-gas consumption ratio. Overall, results can offer a reference for selecting the optimal strategy for DESs in commercial buildings.

Operation and performance of VRF systems: Mining a large-scale dataset

The energy consumption of air-conditioning systems has gained increasing attention as it contributes significantly to the global building energy use. The variable refrigerant flow (VRF) system is a common air-conditioning system applied widely in residential and office buildings in China. Understanding the actual operation and performance of VRF systems is fundamental for the energy-efficient design and operation of VRF systems. Previous research on VRF system operation used either limited field data covering certain building types and climate zones or used a questionnaire to obtain a larger dataset. However, they did not capture the wide applications of VRF systems quantitatively across all building types, climate zones, and operating conditions. To fill this gap, statistical and clustering analysis was conducted on the newly proposed key performance indicators of approximately 287,000 VRF systems for residential and commercial buildings in all five climate zones in China. The main findings are: (1) VRF systems are mainly used for cooling in all climate zones in China; (2) among all building types, the duration of use is lowest in residential buildings and highest in hotels and medical buildings; (3) the distribution of the ideal VRF cooling coefficient of performance (COP) is similar across all climate zones and building types; whereas the COPs of ideal VRF heating in the Severe Cold region and Cold regions are lower than those in other climate zones; and (4) partial load operations for VRF systems are common in residential buildings and office buildings due to the part-time-part-space operation mode. These findings can inform the actual application of VRF systems in China, supporting the design, operation, industry standard development, and performance optimization of VRF systems.

The Potential of Solar Energy for Domestic and Commercial Buildings in Malaysia

The rise in electricity consumption can affect global warming and climate change. Exploitation of solar energy is amongst the most sustainable and relatively cheap solution in Malaysia. It is imperative to study the potential of its application for domestic and commercial buildings. In this paper, financial viability of grid-connected solar photovoltaic (PV) system for domestic and commercial buildings is analysed using payback period and return on investment (ROI). The payback period and ROI are determined and compared based on Feed-in Tariff (FiT) and revised Net Energy Metering (NEM) schemes. Various scenarios and case studies were conducted using factors such as availability, installation, maintenance, and depreciation. It was found that domestic consumers i.e. homes with monthly electricity bills of RM2500 and RM5000 had the shortest payback period at 8.7 years for both schemes. For the commercial buildings with average bill of RM5003.43 the shortest payback period was 8.2 years for both schemes. The ROI of solar PV system installed for this commercial building were 155.03 % (FiT) and 203.65 % (revised NEM). Thus, solar PV system under FiT and revised NEM schemes would be financially viable for commercial buildings with monthly electricity bills of RM2500 and RM5000.

Peak load reduction and load shaping in HVAC and refrigeration systems in commercial buildings by using a novel lightweight dynamic priority-based control strategy

Reducing peak power demand in a building can reduce electricity expenses for the building owner and contribute to the efficiency and reliability of the electrical power grid. For the building owner, reduced expenses come from the reduction or elimination of peak power charges on electricity bills. For the power system operator, reducing peak power demand leads to a more predictable load profile and reduces stress on the electric grid system. We present a computationally inexpensive, dynamic, and retrofit-deployable control strategy to effect peak load reduction and load shaping. The effectiveness of the control strategy is examined in a simulation with 80 air-conditioning units and 40 refrigeration units. The results show that a peak demand reduction of 60 kW can be achieved relative to peak demand in a typical set point–based approach. The proposed strategy was deployed in a gymnasium building with four rooftop HVAC units, where it showed over 15% peak demand (kW) reduction savings while maintaining or lowering energy consumption (in kilowatt-hours) relative to the set point–based thermostat controls.

Quantifying the flexibility of lighting systems by optimal control in commercial buildings: Insight from a case study

Lighting systems in commercial buildings can supply flexible services, lower grid stress caused by large peak-valley differences of electricity loads, and use a large share of fluctuating renewable energy. A flexibility quantification framework is necessary to gain a more comprehensive insight into these systems. This study proposes a methodology to quantify lighting demand flexibility and the calculation process of flexibility indicators. The flexibility indicators consist of characteristic parameters that can identify the properties of lighting power adjustments in multiple dimensions as well as performance indicators that can evaluate the capability of modifying a building’s demand profile according to the grid requirements. The proposed method is applied to quantify flexibility under the different occupancy and lighting control systems in an office building. Seasonal differences are also examined by an annual analysis. The results indicate that the average lighting power curtailment is 32.6% (4.8 kW) of peak lighting demand and the duration is about 3800 h annually. When the flexibility is managed to modify a building’s demand profile, the standard variation may decrease by 10% in heating or cooling seasons, and by around half in intermediate seasons. The consistency between flexibility and energy saving is also demonstrated.

Optimal energy management of Ice thermal energy storage-based air conditioning system for commercial buildings in real-time – A review based on POET framework

This paper applied the POET framework to analyze and identify possible energy efficiency activities that may reduce energy costs in HVAC cooling systems with Ice Thermal Energy Storage (ITES) in order to achieve maximum potential cost-savings, particularly for cooling loads in commercial buildings. Significant cost savings may be realized by the optimal shifting of the energy usage profile to the least costly regions of the Time-of-Use (TOU) using the ITES system. Moreover, a further reduction of energy costs may be achieved by introducing renewable energy (RE) sourced systems. A potential reduction of approximately 50% in energy cost savings could be achieved with the application of the POET framework. Improvement in operational efficiency may be achieved if systems were optimally sized and controlled. Studies conducted on control methods have shown that Model Predictive Control (MPC) offers significant benefits of achieving reasonable operational efficiency compared to other methods, particularly on HVAC systems. Combined ITES and hybrid energy systems allow an additional advantage in demand-side management, per the TOU tariff and peak demand. This may ultimately result in minimal energy usage during the costly regions of the TOU tariff, which would minimize energy costs and technology and equipment efficiency may be improved.

Fuel cells as combined heat and power systems in commercial buildings: A case study in the food-retail sector

Abstract This work investigates the viability of fuel cells (FC) as combined heat and power (CHP) prime movers in commercial buildings with a specific focus on supermarkets. Up-to-date technical data from a FC manufacturing company was obtained and applied to evaluate their viability in an existing food-retail building. A detailed optimisation model for enhancing distributed energy system management described in previous work is expanded upon to optimise the techno-economic performance of FC-CHP systems. The optimisations employ comprehensive techno-economic datasets that reflect current market trends. Outputs highlight the key factors influencing the economics of FC-CHP projects. Furthermore, a comparative analysis against a competing internal combustion engine (ICE) CHP system is performed to understand the relative techno-economic characterisitcs of each system. Results indicate that FCs are becoming financially competitive although ICEs are still a more attractive option. For supermarkets, the payback period for installing a FC system is 4.7–5.9 years vs. 4.0–5.6 years for ICEs when policies are considered. If incentives are removed, FC-CHP systems have paybacks in the range 6–10 years vs. 5–8.5 years for ICE-based systems. A sensitivity analysis under different market and policy scenarios is performed, offering insights into the performance gap fuel cells face before becoming more competitive.